THE ROLE OF ANTI-MÜLLERIAN HORMONE IN ASSISTED REPRODUCTION IN WOMEN A thesis submitted to the University of Manchester for the degree of MD in the Faculty of Medical and Human Sciences 2014 OYBEK RUSTAMOV SCHOOL OF MEDICINE
THE ROLE OF ANTI-MUumlLLERIAN HORMONE
IN ASSISTED REPRODUCTION IN WOMEN
A thesis submitted to the University of Manchester
for the degree of MD in the Faculty of Medical and Human
Sciences
2014
OYBEK RUSTAMOV
SCHOOL OF MEDICINE
2
TABLE OF CONTENTS Abstracthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip3 Publications arising from the thesishelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip5 Chapter 1 General Introduction amp Literature reviewhelliphelliphelliphelliphelliphelliphelliphellip8 Chapter 2 Evaluation of the Gen II AMH Assay between-sample variability
and assay- method comparabilityhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip43 21 Anti-Muumlllerian hormone serum levels and reproducibility in a large cohort of subjects suggest sample instabilityhelliphelliphelliphellip44 22 AMH Gen II assay A validation study of observed variability between repeated AMH measurementshelliphelliphelliphellip65
Chapter 3 The measurement of anti-Muumlllerian hormone a critical appraisalhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip78
Chapter 4 Extraction preparation and collation of datasets for the
assessment of the role of the markers of ovarian reserve in female reproduction and IVF treatmenthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip106
Chapter 5 Assessment of determinants of anti-Muumlllerian hormone in infertile womenhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip135
51 The effect of ethnicity BMI endometriosis and the causes of infertility on ovarian reservehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip136 52 The effect of salpingectomy ovarian cystectomy and unilateral salpingoopherectomy on ovarian reservehelliphelliphelliphellip167
Chapter 6 Assessment of determinants of oocyte number using large
retrospective data on IVF cycles and explorative study of the potential for optimization of AMH-tailored stratification of controlled ovarian hyperstimulationhellip187
Chapter 7 General Summaryhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip229 Authors and affiliationshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip245 Acknowledgmentshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip246
3
ABSTRACT The University of Manchester Dr Oybek Rustamov Degre MD Title The role of anti-Muumlllerian hormone in assisted reproduction in women Date 30 March 2014
Anti-Muumlllerian hormone appears to play central role in regulation of oocyte recruitment and folliculogenesis Serum AMH concentration was found to be one of the best predictors of ovarian performance in IVF treatment Consequently many fertility centres have introduced AMH for the assessment of ovarian reserve and as a tool for formulation of ovarian stimulation strategies in IVF However published evidence on reliability of AMH assay methods and the role of AMH-tailored individualisation of ovarian stimulation in IVF appear to be weak Consequently I decided to conduct a series of studies that directed towards an improvement of the scientific evidence in these areas of research
The studies on performance of Gen II AMH assay revealed the assay suffers from significant instability and provides erroneous results Consequently the manufacturer introduced a modification on assay method
In view of the observed issues with Gen II assay I conducted a critical appraisal of all published research on the previous and current assay methods that reported AMH variability assay method comparison and sample stability The literature indicated clinically important variability between AMH measurements in repeated samples which was reported to be more significant with Gen II assay The studies on between-assay conversion factors derived conflicting conclusions Correspondingly the review of studies on sample stability revealed conflicting reports on the stability of AMH under normal storage and processing conditions which was reported to be more significant issue in Gen II assay In view of above findings we concluded that AMH in serum may exhibit pre-analytical instability which may vary with assay method Therefore robust international standards for the development and validation of AMH assays are required In the analysis of determinants of ovarian reserve I evaluated the effect of ethnicity BMI endometriosis causes of infertility and reproductive surgery on AMH AFC and FSH measurements using data on a large cohort of infertile patients
Using robust multivariable regression analysis in a large cohort of IVF cycles I established the effect of age AMH AFC diagnosis attempt COS protocol changes gonadotrophin type USOR operator regime and initial dose of gonadotrophins on oocyte yield Then I examined effect of gonadotrophin dose and regime on total and mature oocyte numbers The study found that after adjustment for all above variables there was no increase in oocyte yield with increasing gonadotrophin dose categories beyond the very lowest doses This suggests that there may not be significant direct dose-response effect and consequently strict protocols for tailoring the initial dose of gonadotrophins may not necessarily optimize ovarian performance in IVF treatment
In summary studies described in this thesis have revealed instability of Gen II assay samples and raised awareness of the pitfalls of AMH measurements These studies have also demonstrated the effect of clinically measurable factors on ovarian reserve and provided data on the effect of AMH other patient characteristics and treatment interventions on oocyte yield in cycles of IVF Furthermore a robust database and statistical models have been developed which can be used in future studies on ovarian reserve and IVF treatment interventions
4
DECLARATION
No portion of the work referred to in the thesis has been submitted in support
of an application for another degree or qualification of this or any other
university or other institute of learning
COPYRIGHT STATEMENT
i The author of this thesis (including any appendices andor schedules to this
thesis) owns certain copyright or related rights in it (the ldquoCopyrightrdquo) and she
has given The University of Manchester certain rights to use such Copyright
including for administrative purposes
ii Copies of this thesis either in full or in extracts and whether in hard or
electronic copy may be made only in accordance with the Copyright Designs
and Patents Act 1988 (as amended) and regulations issued under it or where
appropriate in accordance with licensing agreements which the University has
from time to time This page must form part of any such copies made
iii The ownership of certain Copyright patents designs trade marks and
other intellectual property (the ldquoIntellectual Propertyrdquo) and any reproductions
of copyright works in the thesis for example graphs and tables
(ldquoReproductionsrdquo) which may be described in this thesis may not be owned
by the author and may be owned by third parties Such Intellectual Property
and Reproductions cannot and must not be made available for use without the
prior written permission of the owner(s) of the relevant Intellectual Property
andor Reproductions
iv Further information on the conditions under which disclosure publication
and commercialisation of this thesis the Copyright and any Intellectual
Property andor Reproductions described in it may take place is available in
the University IP Policy (see
httpdocumentsmanchesteracukDocuInfoaspxDocID=487) in any
relevant Thesis restriction declarations deposited in the University Library The
University Libraryrsquos regulations (see
httpwwwmanchesteracuklibraryaboutusregulations) and in The
Universityrsquos policy on Presentation of Theses
5
PUBLICATIONS ARISING FROM THE THESIS
Journal Articles
1 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton
The measurement of Anti-Muumlllerian hormone a critical appraisal
The Journal of Clinical Endocrinology amp Metabolism 2014 Mar99(3)723-32
2A Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large
cohort of subjects suggests sample instability Human Reproduction 2012 Oct
27(10) 3085-91
2B Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Human Reproduction Dec2012 Vol 27 Issue 12 p3641
6
Conference presentations
1 O Rustamov S Roberts C Fitzgerald
Ovarian endometrioma is associated with increased AMH levels
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2014 Munich
Poster Presentation
2 O Rustamov M Krishnan R Mathur S Roberts C Fitzgerald
The effect of BMI to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral presentation Dr O Rustamov
3 M Krishnan O Rustamov R Mathur S Roberts C Fitzgerald
The effect of the ethnicity to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral Presentation Dr M Krishnan
4 O Rustamov M Krishnan S Roberts C Fitzgerald
Reproductive surgery and ovarian reserve
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr O Rustamov
5 C Fitzgerald O Rustamov P Pemberton A Smith A Yates M Krishnan
R Russell L Nardo SRoberts
AMH assays A review of the literature on assay method comparability
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr C Fitzgerald
6 M Krishnan O Rustamov R Russell C Fitzgerald S Roberts
The role of the ethnicity and the body weight in determination of AMH levels
in infertile women
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
7
Poster presentation
7 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
AMH Gen II assay - can we believe the measurements
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
8 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Old and new AMH assays Can we rely on current conversion factor
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
9 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Random AMH measurement is not reproducible
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
10 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
The reproducibility of serum Anti-Muumlllerian hormone AMH Gen II assay
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2012 Istanbul
Oral Presentation Dr O Rustamov
8
GENERAL INTRODUCTION
AND LITERATURE REVIEW
1
9
CONTENTS I LITERATURE REVIEWhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10 GENERAL BACKGROUNDhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10
1 OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip12 11 Primordial Follicle Assemblyhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip13 12 Oocyte recruitmenthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip14 13 Theory of neo-oogenesishelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip15 2 MARKERS OF OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 21 Ovarian reserve markers with limited clinical valuehelliphelliphelliphelliphellip16 213 Inhibin Bhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 214 Basal oestradiolhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 215 Dynamic testshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 216 Ovarian volumehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 22 Ovarian reserve markers in routine clinical usehelliphelliphelliphelliphelliphelliphellip18 221 Chronological agehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 222 Basal FSHhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 223 Antral follicle counthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip19 3 ANTI-MUumlLLERIAN HORMONEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 31 Biology of anti-Muumlllerian hormonehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 311 The role of AMH in the ovaryhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip21 312 AMH in women with polycystic ovary syndromehelliphelliphelliphelliphellip22 32 AMH Assayhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip23 33 Variability of AMH measurementshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip24 34 Role of AMH in assessment of ovarian reservehelliphelliphelliphelliphelliphellip25 341 Prediction of poor and excessive ovarian response in IVFhelliphellip25 342 Prediction of live birth in cycles of IVFhelliphelliphelliphelliphelliphelliphelliphelliphellip26
3 5 Role of AMH in ovarian stimulation for cycles of IVFhelliphelliphelliphellip26
4 MULTIVARIATE TESTShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip27
5 SUMMARYhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip28 II GENERAL INTRODUCTIONhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip29 REFERENCEShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip31
10
I LITERATURE REVIEW GENERAL BACKGROUND
Infertility is a disease of the reproductive system defined by the failure to
achieve a pregnancy after 12 months of regular unprotected sexual intercourse
although the criteria for the duration vary between different countries (NICE
2013) Worldwide prevalence of infertility estimated to be around 724 million
couples and around 40 million of those seek medical care (Hull et al 1985) In
the UK 15 couples present with infertility with an annual incidence of 12
couples per 1000 general population (Scott et al 2009) The main causes of
infertility are tubal disease ovulatory disorders male factor and poor ovarian
reserve In a third of couples the cause of failure to achieve pregnancy is not
established which is known as unexplained infertility (NICE 2013) Effective
treatment options include improving lifestyle factors medical andor surgical
treatment of underlying pathology induction of ovulation and Assisted
Reproductive Technology (ART) Assisted Reproduction consist of
intrauterine insemination (IUI) and in vitro fertilisation (IVF) cycles with or
without introcytoplasmic sperm injection (ICSI) as well as treatment involving
donated gametes It is estimated that 75 of infertile couples presenting at
primary care centres in the UK are referred to fertility specialists based at
secondary or tertiary care centres and nearly 50 of those are subsequently
offered IVFICSI treatment (Scott et al 2009) This is supported by figures of
Human Fertility and Embryology Authority (HFEA) which indicates more
than 50000 IVF treatment cycles are performed in the UK annually (HFEA
2008)
An IVF treatment cycle involves a) pituitary down regulation b)
controlled ovarian stimulation c) oocyte recovery c) in vitro fertilisation of eggs
with sperm d) transfer of resulting embryo(s) back to uterus and c) luteal
phase support (NICE 2013) Prevention of premature surge of luteinising
hormone during controlled ovarian stimulation (COS) is achieved by pituitary
down regulation using either preparations of gonadotrophin releasing hormone
agonist which is widely known as ldquoAgonist cyclerdquo or gonadotrophin releasing
hormone antagonist which is known ldquoAntagonist cyclerdquo (Figure 1 and 2)
Controlled ovarian stimulation involves administration of gonadotrophins to
encourage the development of supernumerary preovulatory follicles followed
by administration of exogenous human chorionic gonadotropin (hCG) or
11
recombinant luteinising hormone (rLH) to assist in maturation of oocytes 34-
36 hours prior to egg collection which is usually conducted with guidance of
transvaginal ultrasound scanning Subject to sperm parameters the fertilisation
of oocytes is conducted by in vitro insemination or intracytoplasmic sperm
injection The resulting embryo(s) are cultured under strict laboratory
conditions and undergo regular qualitative and quantitative assessments before
transferring the best quality embryo(s) back into uterus during its cleavage
(Day 2 or Day 3) or blastocyst (Day 5 or Day 6) stage of development In
natural menstrual cycles under the influence of HCG progesterone secreted
by the ovarian corpus luteum ensures proliferative changes in the endometrium
providing the optimal environment for implantation of embryo(s) (van der
Linden et al 2011) However in IVF treatment cycles owing to pituitary down
regulation and lack of HCG progesterone levels are not in sufficiently high
concentration to ensure an adequate endometrial receptivity and therefore
exogenous analogues of this hormone is administered following transfer of
embryo(s) This is called ldquoluteal phase supportrdquo and in patients with viable
pregnancy usually lasts till 12th week of gestation when placenta starts
producing progesterone in sufficient quantities (van der Linden et al 2011)
In IVF programmes the ldquosuccessrdquo of the treatment often defined as
achieving a live birth following IVF cycle and expressed using Live Birth Rate
(LBR) In general success in IVF predominantly determined by womanrsquos age
cause(s) of infertility ovarian reserve previous reproductive history and
lifestyle factors (NICE 2013 Taylor 2003 Lintsen et al 2005) However
effectiveness of medical interventions as well as the quality of care play
important role in determining the outcome of IVF treatment This is evident
from significant variation in live birth rates among fertility clinics given for
instance in the UK LBR for women younger than 35 years of age after IVF
cycles varies from 15 to 61 (HFEA 2008 HFEA 2007) The provision of
effective interventions in both clinical and laboratory aspects of the care
appears to be the key in achieving high success rates Identification of patients
with sufficient ovarian reserve who benefit from IVF cycles followed by
providing optimal ovarian stimulation regimens may be useful in improving the
outcomes of IVF programmes According to HFEA data around 12 of IVF
cycles are cancelled due to poor or excessive ovarian response (Kurinczuk et al
2010) Availability of reliable markers for assessment of ovarian reserve and
tailoring ovarian stimulation regimens to the need of each individual patient
12
may improve selection of patients with sufficient ovarian reserve and reduce
the rate of cycle cancellation consequently improving the success of IVF
cycles (Yates et al 2011)
Assessment of ovarian reserve can be achieved using various biomarkers
and four of those are currently used by most clinics womanrsquos chronological
age (Age) serum follicle stimulating hormone (FSH) antral follicle count
(AFC) and serum anti-Muumlllerian hormone (AMH) More recently AMH has
been a focus of interest given it is the only available endocrine marker that is
suitable for direct assessment of the activity of ovarian follicles in their non-
cyclical stage development providing a window to FSH independent phase of
follicular recruitment Furthermore it appears to be reliable biomarker for a)
both the assessment of ovarian reserve and the optimisation of ovarian
stimulation regimens (Yates et al 2011 La Marca et al 2009) b) screening and
diagnosis of polycystic ovarian syndrome (PCOS) (Cook et al 2002) c)
monitoring of disease activity in women with a history of granulosa cell
tumours (Lane et al 1999) d) prediction of the age of diminished fertility and
the menopause (van Disseldorp et al 2008 Broer et al 2011) and finally (e)
assessment of the long term effect of chemotherapy on ovarian reserve
(Anderson 2011)
In this review I first discuss current knowledge on factors that
determine ovarian reserve including the formation and loss of oocyte pool
Then characteristics of the markers of ovarian reserve are reviewed Finally I
examine current understanding of biology of anti-Muumlllerian hormone and its
role in management of infertility
1 OVARIAN RESERVE
It is important to recognize that there is no universal definition for the
term ldquoovarian reserverdquo and the term can have various meanings depending on
the context in which it is used For instance the scientific literature describing
the biology of ovarian reserve usually refers to ldquothe total number of remaining
oocytes in the ovaries which consists of the number of resting primordial
follicles and growing primary pre-antral and antral folliclesrdquo (Gleicher et al
2011) In contrast the use of the term in the context of clinical studies may
refer to ldquoclinically measurable ovarian reserve established using available
biomarkers of ovarian reserverdquo For the purpose of clarity in this thesis the
13
term ldquoovarian reserverdquo refers to clinically measurable ovarian reserve whilst
true biological ovarian reserve will be termed ldquobiological ovarian reserverdquo
Recent studies have demonstrated that ovarian reserve is highly variable
between women due to the variation in the size of initial ovarian reserve at
birth as well as the rate of loss of ovarian reserve thereafter (Wallace et al
2010) Interestingly the rate of oocyte loss appears to be mainly determined by
the initial ovarian reserve which is believed to be facilitated by most potent
ovarian growth factor anti-Muumlllerian hormone Similarly the size of the initial
ovarian reserve is mainly underpinned by the rate of primordial follicle
assembly in the embryo which is also regulated by AMH Both primordial
follicle assembly and the rate of oocyte loss appear to be primarily under the
influence of genetic factors although developmental and environmental factors
are also believed to play a role (Nilsson et al 2010 Shuh-Huerta et al 2012)
11 Primordial follicle assembly
The process of assembly of primordial follicles in the female embryo
spans from the early embryonic to the early postnatal period and formation of
primordial follicles consists of following stages 1) primordial germ cell (PGC)
2) oogonia 3) primary oocyte and 4) primordial follicle In the human female
fetus around a hundred cells that differentiated from extra-embryonic
ectoderm form early PGCs on the yolk sac and migrate via hindgut to gonadal
ridges during 4th - 6th weeks of gestation (MC et al 1953 Donovan 1998) Once
arrived to the gonadal ridges these cells are called primary oogonia which
consequently undergo several rounds of mitotic division during 6th - 28th weeks
of gestation Interestingly the numbers of oogonia reach as high as six million
during its highest rate of mitotic division at around 20 weeks of gestation
Following the last round of mitotic division oogonia enter meiosis which
marks their new stage of development-primary oocyte Formation of
primordial follicles starts as early as at 8th week of gestation and is characterised
by meiosis of primary oocyte that arrest in diplotyne stage and surrounding of
the oocyte by somatic granulosa cell (Baker et al 1963 Maheshwari and Fowler
2010) Indeed the primordial follicle is the cardinal unit of the biological
ovarian reserve and therefore the rate of formation of primordial follicles is the
main determinant of initial biological ovarian reserve at birth
Interestingly the process of loss of oogonia and oocytes which is also
one of the main determinants of the initial ovarian reserve takes place
14
throughout the period of follicle assembly The formation of the granulosa cell
layer around the oocyte prevents the oocyte from subsequent atresia The
oocyte enveloped in a single layer of granulosa cells which is also known as
primordial follicle remains quiescent until recruitment of the follicle for
growth which may not take place for a number of decades after the formation
of a particular primordial follicle (Skinner 2005 Maheshwari and Fowler 2010)
12 Oocyte recruitment
Follicle growth in women consists of two stages a) the initial non-cyclical
recruitment of primordial follicles and the formation of a primary and a pre-
antral follicles and b) cyclical development of antral follicles with subsequent
selection of usually a single dominant follicle The initial recruitment of
primordial follicles is continuous non-cyclical process that starts as early as
from 18-20 weeks of gestation and lasts till the depletion of follicle pool which
later results in the menopause (McGee and Hsueh 2000) Transformation of
flat granulosa cells into cuboidal cells increases the diameter of the oocyte and
the formation of zona pellicuda completes the stage of formation of a primary
follicle During pre-antral stage oocytes increase in diameter and mitotic
division of granulose cells create a new layer of cells-theca cells The
mechanism of initial recruitment of oocytes is not well understood but it is
clear that the process is independent of influence of pituitary gonadotrophins
and appears to be governed by the genetically pre-programmed interaction of
the oocyte with local growth factors the most important of which appears to
be anti-Muumlllerian hormone and cytokines (McGee and Hsueh 2000)
The cyclical phase of development of oocytes is characterised by the
transformation of secondary follicle into antral follicle and subsequent growth
of antral follicles into pre-ovulatory stages In general the process of cyclic
recruitment starts from puberty under the influence of rising levels of pituitary
follicular stimulating hormone (FSH) During the antral stage oocyte increases
in size even further and the formation of a fluid filled space in follicle is
observed Under the influence of FSH luteinising hormone (LH) and local
growth factorsselection of a single dominant follicle occurs which followsby an
ovulation (McGee and Hsueh 2000)
Oocyte loss is a continuous process and occurs due to atresia of oocytes
during primary secondary and antral stages of development The rate of
oocyte loss appears to increase until the age of around 14 and declines
15
thereafter until the age of the menopause when around 1000 primordial
follicles remain (Hansen et al 2008 Oktem and Oktayl 2008) Furthermore by
the age of 30 years the average age at which women of western societies plan
to start a family around 90 of initial primordial follicles are lost which
illustrates that formation and maintenance of ovarian reserve is wasteful
process in humans (ONS 2012 Wallace and Kelsey 2010) As mentioned
above there is a wide individual variation in both sizes of initial primordial
follicular pool and the rate of oocyte loss which explains variation in the
reproductive lifespan in women Evidently the number of primordial follicles
at birth ranges between around 35000 to 25 million per ovary and similarly
the rate of oocyte loss during its peak at 14 years of age may range between
100 to 7500 primordial follicles per month which is believed to be inversely
proportional to initial size of primordial follicle pool (Wallace and Kelsey
2010)
13 Theory of neo-oogenesis
The traditional view of oogenesis states that the process of the creation
and the mitotic division of oogonia with subsequent formation of primordial
follicles takes place only during embryonic and foetal life (Zuckerman 1951)
According to this central theory of mammalian reproductive biology females
are born with a certain number of germ cells that is gradually lost but not
renewed during postnatal period However Johnson et al have recently
challenged this view and reported that adult mammalian ovary may possesses
mitotically active germ cells that continuously replenish the primordial follicle
pool (Johnson et al 2004) The group reported that ovaries of juvenile and
young adult mice contained large ovoid cells which resemble germ cells of
foetal mouse ovaries Interestingly immunohistochemical staining for a gene
which is expressed exclusively in germ cells have been reported to have
confirmed that these large ovoid cells were of germline lineage Furthermore
application of a mitotic germ cell toxicant busulphan appeared to have
eliminated primordial follicle reserve by early adulthood but did not induce
atresia suggesting the presence of proliferative germ cells in postnatal mouse
ovary (Johnson et al 2004 Bazer 2004) The study has generated enormous
amount of interest as well as debate among reproductive biologists (Notarianni
2011) Some other groups have also reported an evidence of postnatal
oogenesis (Pacchiarott et al 2010 Zou et al 2009 Bukovsky et al 2004)) while
16
others do not support the theory (Bristol-Gould et al 2006 Byskov et al 2005
Begum et al 2008) Furthermore some authors argued that adult mouse
germline stem cells exist and remain quiescent in physiologic conditions and
neo-oogenesis occurs only in response to ovotoxic damage (Tilly et al 2007 De
Felici 2010) Although consensus has yet to emerge to date there is no
conclusive evidence on validity of theory of neo-oogenesis
2 MARKERS FOR ASSESMENT OF OVARIAN RESERVE
Biological ovarian reserve is defined as the number of primordial and
growing follicles left in the ovary at any given time and therefore only
counting the number of primordial follicles by histological assessment can
accurately determine ovarian reserve which is clearly not feasible in clinical
setting However ovarian reserve can be estimated using various biomarkers
dynamic clinical tests and implied from the outcomes of ART cycles
Although a wide range of clinical (age ovarian response in previous IVF
cycles) biochemical (basal FSH Inhibin B basal oestradiol AMH) ultrasound
(ovarian volume antral follicle count (AFC)) and dynamic (clomiphene
challenge test exogenous FSH ovarian reserve test GnRH analogue
stimulating test) tests of ovarian reserve exist only a few of the markers are
reliable and practical enough to be of use in routine clinical practice In this
chapter first I discuss the research evidence on the assessment of the markers
andor tests of ovarian reserve that have limited clinical value Then I
evaluated more reliable markers that are in routine clinical use Age FSH
AFC and combination of these markers in multivariable tests Finally I
conducted detailed review of biology of AMH and the role AMH measurement
in the management of infertility
21 Ovarian reserve markers with limited clinical value
211 Inhibin B
Inhibins are members of TGFβ family and expressed in granulosa cells
of growing follicles Principal role of inhibins is thought to be the negative
feedback regulation of pituitary FSH secretion and therefore the serum level of
circulating hormone is believed to reflect the state of folliculogenesis
17
Consequently several groups have studied the role of serum Inhibin β in the
assessment of ovarian reserve Although initial reports were encouraging
(Seifer et al 1997) more robust studies demonstrated that serum Inhibin β was
less reliable than chronological age or basal FSH (Creus et al 2000 Urbancsek
2005) The systematic review of nine studies demonstrated that accuracy of the
Inhibin β test for predicting poor ovarian response and non-pregnancy in IVF
cycles was modest even at a very low threshold level (Broekmans et al 2006)
Therefore it is recommended that inhibin β at best can be used as only
screening test in the fertility centers where other more reliable markers are not
available (Broekmans et al 2006)
212 Basal oestradiol
Some studies suggested that elevated basal oestradiol levels indicate low
ovarian reserve and are associated with poor fertility prognosis (Johannes et al
1998 Licciardi and Rosenwaks 1995) Johannes et al demonstrated basal
oestradiol in conjunction with serum FSH is more reliable than serum FSH
alone in prediction of cycle cancellation due to the poor response in IVF cycles
(Johannes et al 1998) However there are no published data on the comparison
of basal oestradiol to more reliable markers such as AMH or antral follicle
count (AFC) Moreover a recent systematic review has demonstrated that
basal oestradiol has very low predictive value for poor response and has no
discriminatory power for accuracy of non-pregnancy prediction (Broekmans et
al 2006)
213 Dynamic tests of ovarian reserve
The dynamic tests of ovarian reserve are based on assessment of ovarian
response by measuring serum FSH and oestradiol levels following
administration of exogenous stimulation The following tests are reported in
literature Clomiphene Citrate Challenge Test (CCCT) Exogenous FSH
Ovarian Reserve Test (EFORT) and GnRH agonist stimulation test A recent
systematic review and meta-analysis on the accuracy of these tests showed that
none of them can adequately predict poor response or non-pregnancy in IVF
cycles and therefore are not recommended for use in routine clinical practice
(Maheshwari et al 2009)
18
214 Ovarian volume
There is some evidence that increased age is associated with decreased
ovarian volume and women with smaller ovaries are more likely to have
cancellation of their IVF cycles due to poor ovarian response (Syrop et al 1995
Syrop et al 1999 Templeton 1995) However a meta-analysis of the published
studies on the accuracy of ovarian volume as a predictor of poor response and
non-pregnancy in IVF cycles failed to demonstrate clinical usefulness of the
test and suggested the test is not reliable enough for use in a routine clinical
practice (Broekmans et al 2006)
22 Ovarian reserve markers in routine clinical use
221 Chronological age
Owing to the biological age-related decline of the quantity and arguably
the quality of oocytes the chronological age can be used as a marker of ovarian
reserve Studies have demonstrated that ovarian reserve (Wallace and Kelsey
2010 Kelsey 2011) natural fecundity (Islam et al 1989 and outcomes of ART
(Templeton et al 1996 van Kooij et al 1996) decline significantly from age of
35 when it is believed the ovarian reserve undergoes accelerated decline
Although there is a strong association between chronological age and reduction
in fertility evidently there is a significant variation in age-related ovarian
reserve indicating chronological age alone may not be sufficient to estimate the
individual womanrsquos ovarian reserve reliably (Broekmans et al 2006)
222 Basal FSH
Basal FSH was one of the first endocrine markers introduced in ART
programs and is still utilized in many fertility clinics albeit in conjunction with
other markers which are considered more reliable (Creus et al 2000) Secretion
of FSH is largely governed by the negative feedback effect of steroid
hormones primarily oestradiol and inhibins which are expressed in granulosa
cells of growing ovarian follicles Consequently decreased or diminished
recruitment of ovarian follicles is associated increased serum FSH
measurements and high particularly very high basal FSH reading is considered
as a good marker of very low or diminished ovarian reserve (Abdalla et al
2006) However unlike some other markers FSH measurements do not
appear to have discriminatory power for categorisation of patients to various
19
bands of ovarian reserve Given between-patient variability FSH measurement
(CV 30) is similar to its within-patient variability (27) stratification of
patients to various ranges of ovarian reserve does not appear to be feasible
(Rustamov et al 2011) Indeed a recent systematic review of 37 studies on the
prediction of poor response and non-pregnancy in IVF cycle has concluded
that basal FSH is an adequate test at very high threshold levels and therefore
has limited value in modern ART programs (Broekmans et al 2006)
223 Antral follicle count
Antral follicle count estimation involves ultrasound assessment of
ovaries between 2nd and 4th day of menstrual period and counting ldquofolliclesrdquo
which corresponds to antral stage of folliculogenesis (Broekmans et al 2010)
The test provides direct quantitative assessment of growing follicles and is
known as one of the most reliable markers of ovarian reserve (Broekmans et al
2006) AFC measurement has been reported as having a similar sensitivity and
specificity to AMH in prediction of poor and excessive ovarian response in
IVF cycles (Broekmans et al 2006 Broer et al 2010 Jayaprakasan et al 2010)
Given AFC measurement is available instantly and allows patients to be
counseled immediately the test eliminates the need for an additional patient
visit prior to IVF cycle However AFC is normally performed only in the early
follicular phase of the menstrual cycle given most published data on
measurement of AFC are based on studies that assessed antral follicles during
this stage of the cycle (Broekmans et al 2010a) Interestingly more recent
studies suggest that variability of AFC during menstrual cycle is small
particularly when follicles between 2-6mm are counted and therefore
assessment of AFC without account for the day of menstrual cycle may be
feasible (Deb et al 2013)
One of the main drawbacks of AFC is that the cut off levels for size of
counted follicles remains to be standardised (Broekmans 2010b) Initially
follicles of 2-10mm were introduced as the range for AFC and many studies
were based on this cut off Later counting follicles of 2-6mm was reported to
provide most accurate assessment of ovarian reserve (Jayaprakasan et al 2010b
Haadsma et al 2007) and therefore some newer studies are based on AFC
measurements that used this criterion Consequently direct comparison of the
outcomes of various studies on assessment of AFC requires careful analysis
20
3 ANTI-MUumlLLERIAN HORMONE
31 Biology of Anti-Muumlllerian hormone
AMH is a member of transforming growth factor β superfamily which
was discovered by Jost et al in 1947 and was initially known for its is role in
regression of Muumlllerian ducts in sex differentiation of the male embryo In
women AMH is believed to be solely produced by ovaries and expressed in
granulosa cells of growing follicles of 2-6 mm in size which corresponds to
primary pre-antral and early antral stage of follicular development Although
there has been a report of expression of AMH in endometrial cells to date
there is no other published evidence that supports this finding (Wang et al
2009) Indeed studies that evaluated half-life of AMH in serum have
demonstrated that in women who had bilateral salpingo-oopherectomy AMH
becomes undetectable within 3-5 days of following surgery suggesting ovaries
are the only source of secretion of AMH in appreciable quantity (La Marca et
al 2005b) Anti-Muumlllerian hormone is a dimeric glycoprotein which is
composed of a long N-terminus and short C-terminus and was believed to be
secreted in serum only in this dimeric form (AMH-N C)
Like other members of TGF-β family which includes inhibins activins
bone morphogenic proteins (BMPs) and growth and differentiation factors
(Massague et al 1990) AMH binds to two type of serinethreonine kinase
receptors referred to as type I and type II In order to activate AMH signaling
pathway both receptors have to form a heteromeric complex When AMH
binds to the type II (AMHR-II) receptor (Massague et al 2000) this will
phosphorylate and activate a type I receptor (ALK2 -3 andor -6) which
subsequently activates the SMAD pathway through phosphorylation of
SMAD 1 5 andor 8 These activated SMADs interact with SMAD4 and
translocate to the nucleus regulating the expression of different genes
inhibiting the recruitment of primordial follicles and reducing FSH sensitivity
in growing follicles In addition AMH receptors as well as the other members
of TGF-β family can activate MAPK and PI3KAKT pathways
Studies on AMHR II-deficient male mice demonstrated lack of
regression of Muumlllerian ducts suggesting that type II receptor is essential in
AMH signaling (Mishina et al 1996) Similarly Type I receptors which includes
three members of activin receptor-like kinase (ALK2 ALK3 and ALK6) also
appear to play an important role in the regression of Muumlllerian ducts although
21
the role of ALK 6 in AMH signaling appears not to be crucial (Visser 2003
Clarke et al 2001) The signal transduction pathway of AMH in the ovary is
largely not understood In postnatal mice ovary AMHR-II receptor was
expressed in both granulosa and theca cells of pre-antral and antral follicles
(Visser 2003) AMH type I receptors ALK 2 and ALK 3 is expressed in foetal
as well as adult mouse ovary while ALK 6 is expressed in only adult ovary
(Visser 2003)
311 The role of AMH in the ovary
In the mammalian ovary the role of AMH appears to be one of a
regulation of size of the primordial follicle pool by its inhibitory effect on the
formation as well as the growth of primordial follicles (Nilsson et al 2011) In
the embryonic mouse ovary AMH inhibits the initiation of the assembly of
follicles when the process of apoptosis of the majority of oocytes is observed
(Nilsson et al 2011) Consequently AMH reduces the rate of oocyte loss
which plays an important role in the determination of the size of initial follicle
pool Similarly in the adult mouse ovary AMH plays a central role in
maintaining the follicle pool AMH inhibits both the processes of the initial
(non-cyclical) recruitment of primordial follicles and subsequent FSH-
dependent cyclical growth of antral follicles (Figure 3) Inhibition of the initial
recruitment of a new cohort of follicles is believed to be achieved by a
paracrine negative feedback effect of the rising levels of AMH secreted from
already recruited growing follicles (Durlinger et al 1999) Durlinger et al
compared the complete follicle population of AMHnull mice and wild type
mice of different ages of 25 days 4 months old and 13 months old and found
that the ovaries of 25 day and 4 months old AMHnull females contained
significantly higher number of growing pre-antral and antral follicles but
significantly fewer primordial follicles compared to wild-type females
(Durlinger et al 1999) Interestingly almost no primordial follicles were
detected in 13 months old AMHnull mice ovaries suggesting AMH is a potent
inhibitor of the recruitment of primordial follicles and in the absence of AMH
ovaries undergo premature depletion of primordial follicles due to an
accelerated recruitment Subsequent study conducted by the group
demonstrated that in addition to its inhibitory effect to the resting follicles
AMH also suppresses the development of the growing follicles (Durlinger et al
2001 Durlinger et al 2002 Themmen 2005) It appears that AMH inhibits
22
FSH-induced follicle growth by reducing the sensitivity of growing follicles to
FSH which has been confirmed by in vivo as well as in vitro studies (Durlinger
et al 1999 Durlinger et al 2001) In the initial study the group observed that
despite lower levels of serum FSH concentration ovaries of AMHnull mice
contained more growing follicles than that of their wild-type littermates which
has been supported by the findings of subsequent in vitro study (Durlinger et al
1999) Addition of AMH to the culture inhibited FSH-induced follicle growth
of pre-antral mouse follicles due to reduction in granulosa cell proliferation
(Durlinger et al 2001)
In the human embryo the expression of AMH commences in the late
foetal life and can be detected only from 36 weeks of gestation (Rajpert-De et
al 1999 Lee et al 1996) Following a small decline in first two years of life
AMH levels gradually increase to peak at (mean 5 ngml) around age of 24
years In line with the pattern of oocyte loss serum hormone levels gradually
decline with increasing age and become undetectable around 5 years prior to
menopause (Kelsey et al 2011 Nelson et al 2011)
It has been suggested that anti-Muumlllerian hormone plays a central role in
determining the pace of recruitment of primordial follicles hence maintaining
the primordial follicle pool of postnatal mammalian ovary Consequently a
reduction in the concentration of circulating AMH signals the exhaustion of
the primordial follicle pool and the decline of ovarian function
312 AMH in women with polycystic ovary syndrome
Polycystic ovary syndrome (PCOS) endocrine abnormality characterised
by increased ovarian androgen secretion infrequent ovulation and the
appearance of ldquopolycysticrdquo ovaries on ultrasound scan (Dunaif 1997 Homburg
et al 1993) It is the commonest endocrine abnormality in women of
reproductive age and affects around 15-20 of women PCOS is also one of
the main causes of anovulation and subsequent sub-fertility (Webber et al
2003) Although the role of anti-Muumlllerian hormone in the development of
PCOS is not fully understood it is becoming increasingly evident that the
hormone plays an important role in its pathogenesis (Pehlivanov et al 2011)
There is a strong association between serum AMH levels and PCOS and it
appears that women diagnosed with PCOS have two to three fold higher
serum AMH concentration compared to normo-ovulatory women (Cook et al
2002 Pigny et al 2003) Similarly women with PCOS are found to have
23
significantly higher number antral follicles Interestingly the expression of
AMH in granulosa cells of follicles were found to be 75 times higher in women
with PCOS compared to those without a the disease suggesting increased
serum AMH in PCOS may be due to increased secretion of hormone per
follicle rather than due to an increased number of antral follicles (Pellat et al
2007) High AMH concentrations may act as the main facilitator of abnormal
folliculogenesis in PCOS given the follicles appear to arrest when they reach
an antral stage (2-6mm) of development (Rajpert-De et al 1999) Indeed the
studies of Durlinger et al have demonstrated that AMH inhibits selection of
dominant follicle when follicles reach antral stage of development (Durlinger et
al 2001) Serum AMH levels appear to decrease with treatment of PCOS
which may play important role in restoration of ovulatory cycles Studies have
reported a significant reduction in serum concentration of AMH following
treatment of PCOS with metformin and laparoscopic ovarian diathermy (Falbo
et al 2010 Amer et al 2009 Elmashad 2011) Similarly reduction of BMI
following intensified endurance exercise training for treatment of PCOS may
also lead to a significant reduction in serum AMH levels (Moran et al 2011)
This suggests that there is strong association between serum concentration of
AMH and abnormal folliculogenesis in PCOS and therefore understanding the
molecular mechanisms of this interaction should be one of the priorities of
future research
32 AMH Assays
Enzyme-linked immunosorbent assay specific for measurement of anti-
Muumlllerian hormone was first developed in 1990 and was recognised as a
significant step in the assessment of ovarian reserve (Hudson et al 1990)
Subsequently a number of non-commercial immunoassays were developed
which were mainly used in research settings (Lee et al 1996) Later Diagnostic
Systems Ltd (DSL) and Immunotech Beckman Coulter Ltd (IOT) introduced
two commercial immunoassays for the routine clinical assessment of ovarian
reserve which are known as ldquofirst generation AMH assaysrdquo (Nelson and La
Marca 2011) These assays employed two different antibodies against AMH
and used different standards for calibration providing non-comparable
measurements (Nelson and La Marca 2011) Consequently several studies
attempted to develop a reliable between-assay conversion factor which
interestingly revealed from five-fold higher with the IOT assay to assay
24
equivalence causing significant impact to reliability of AMH measurements and
interpretation of research findings (Hehenkamp et al 2006 Freour et al 2007
Bersinger et al 2007 Taieb et al 2008 Lee et al 2011)
Later the manufacturer of IOT assay (Beckmann Coulter Ltd)
consolidated the manufacturer of the DSL assay (Diagnostic Systems
Laboratories Inc) and introduced a new assay ldquoGen II AMH assayrdquo which is
only available commercial immunoassay in most countries including the UK
AMH Gen II assay was developed using the antibodies derived from first
generation DSL assay and calibrated using the standards used for IOT assay
and was believed to be considerably more stable compared to the first
generation immunoassays providing more reliable measurements (Kumar et al
2010 Nelson and La Marca 2011) The manufacturer as well as initial external
validation study recommended when compared to old DSL assay AMH Gen
II assay provides around 40 higher measurements and therefore previously
reported DSL-based clinical cut-off levels for estimation of ovarian reserve
should be increased by 40 in order to use Gen II-based AMH results (Kumar
et al 2010 Wallace et al 2011 Nelson and La Marca 2011)
33 Variability of AMH measurements
It is generally believed that AMH values do not change throughout the
menstrual cycle and early studies reported that variation in AMH
measurements between repeated measurements of same patient was negligible
(van Disseldorp et al 2010 La Marca 2010) On the basis of these studies
sampling at a random time in the menstrual cycle was introduced as a method
for measurement of AMH in routine clinical practice However the
methodologies of some of these studies do not appear to be robust enough to
reliably estimate sample-to-sample variability of AMH which is mainly due to
small sample sizes (Rustamov et al 2011) Consequently in a recent study we
assessed sample-to-sample variability of AMH using DSL assay and found that
within-subject coefficient of variation (CV) of AMH between samples were as
high as 28 which cannot be attributed to any patient or cycle characteristics
(Rustamov et al 2011) Although there is no consensus in the causes of this
observed variability in AMH measurements we believe it is largely attributable
to instability of AMH samples given initial recruitment of primordial follicles
and growth of AMH producing pre-antral and antral follicles are continuous
process and therefore the true biological variation between samples is unlikely
25
to be high However given the importance of establishing true variability of
AMH in both understanding of the biology of hormone and clinical
application of the test future studies should be conducted to establish the
source of variability in the clinical samples
3 4 The role of AMH in the assessment of ovarian reserve
341 Prediction of poor and excessive ovarian response in cycles of
IVF
A number of studies have assessed the role of AMH in the prediction of
poor ovarian response in IVF cycles using first generation AMH assays and
found that AMH and AFC were the best predictors of poor ovarian response
compared to other markers of ovarian reserve Nardo et al showed that the
predictive value of AMH in receiver operating characteristic curve (ROC)
analysis was similar to (AUC 088) that of AFC (AUC 081) and found that
AMH cut offs of gt375 ngmL and lt10 ngmL would have modest
sensitivity and specificity in predicting the extremes of response (Nardo et al
2009) These findings were largely supported by subsequent prospective studies
and a systematic review (Nelson et al 2007 Jayaprakasan et al 2010 Broer et al
2011) Similarly comparison of chronological age basal FSH ovarian volume
AFC and AMH found that only AMH (AUC 090) and AFC (AUC 093) were
reliable predictors of poor ovarian response in cycles of IVF Subsequent
combination of the effect of AMH and AFC using multivariable regression
analysis did not improve the level of prediction of poor ovarian response
significantly (AUC 094) suggesting both AMH and AFC can be used as
independent markers (Jayaprakasan et al 2010)
Similarly most studies agree that AMH and AFC are the best predictors
of excessive ovarian response and ovarian hyperstimulation syndrome (OHSS)
compared to other clinical endocrine and ultrasound markers (Nardo et al
2009 Nelson et al 2007) Broer et al compared these two tests in systematic
review of 14 studies and reported that the summary estimates of the sensitivity
and the specificity for AMH were 82 and 76 respectively and for AFC 82
and 80 respectively (Broer et al 2011) Consequently the study concluded
that AMH and AFC were equally predictive and the difference in the predictive
value between the tests was not statistically significant
26
342 Prediction of live birth rate (LBR) in cycles of IVF
Lee at al reported that AMH and chronological age were more accurate
than basal FSH AFC BMI and causes of infertility in the prediction of live
birth rate (Lee et al 2009) Similarly La Marca et al suggested that odds of live
birth could be reliably predicted using AMH (La Marca et al 2010b) although
subsequent review of the study questioned strength of the evidence (Loh and
Maheshwari 2011)
A study conducted by Nelson et al found that higher AMH levels had
stronger association with increased live birth rate compared to age and FSH
(Nelson et al 2007) However the study also suggested that this association
was mainly confined in the women with low AMH levels and there was no
additional increase in live birth in women with AMH levels of higher than 710
pmolL This may suggest that achieving a live birth may be under the
influence of number of other factors and that markers of ovarian reserve alone
may not be able predict this outcome reliably
35 The role of AMH in individualisation of ovarian stimulation in
IVF cycles
Prediction of ovarian response to the stimulation of ovaries in cycles of
IVF plays an important role in the counseling of couples undergoing treatment
programmes and hence many clinical studies on AMH have focused on the
prognostic value of AMH measurements However data on using AMH as a
tool for improving the clinical outcomes in IVF cycles appear to be lacking
considering AMH may be useful tool in tailoring treatment strategies to an
individual patientrsquos ovarian reserve Unlike most other markers AMH has
discriminatory power in determining various degrees of ovarian reserve due to
significantly higher between patient (CV 94) variability compared to its
within-patient (CV 28) variation (Rustamov et al 2011) which allows
stratification of patients into various degrees of (eg low normal high) ovarian
reserve Subsequently most optimal ovarian stimulation protocol may be
established for each band of ovarian reserve Consequently reference ranges
on the basis of distribution of AMH in infertile women were developed which
were subsequently adopted by fertility clinics for a tailoring the mode of
27
ovarian stimulation and daily dose of gonadotrophins in IVF (The Doctors
Laboratory 2008 However currently available clinical reference ranges are
based on the first generation DSL assay and may not be reliably convertible to
currently available Gen II assay measurements (Wallace et al 2011) Indeed the
findings of the studies on comparability of the first generation AMH assays
suggest that establishing a reliable between assay conversion factor between
AMH assays may not be straightforward Furthermore the reference ranges
appear to reflect the distribution of AMH measurements within a specific
population and may therefore not be directly applicable for the prediction of
response to ovarian stimulation in IVF patients (The Doctors Laboratory
2008)
More importantly despite lack of good quality evidence on the
effectiveness of AMH-tailored ovarian stimulation protocols a number of
fertility clinics appear to have introduced various AMH-based COH protocols
in their IVF programs At present research evidence on AMH-tailored
ovarian stimulation in IVF is largely based on two retrospective studies
(Nelson et al 2009 Yates et al 2012) Both of these studies display considerable
methodological limitations including small sample size and centre-related or
period-related selection of their cohorts In this context AMH is used as a tool
for therapeutic intervention and therefore the research evidence should ideally
be derived from randomised controlled trials However recruitment of large
enough patients in IVF setting may take considerable time and resources In
the meantime given AMH-tailored ovarian stimulation has already been
introduced in clinical practice and there is urgent need for more reliable data
the studies with a larger cohorts and robust methodology should assess the role
of AMH in individualisation of ovarian stimulation in IVF treatment cycles
4 Multivariate models of assessment of ovarian reserve
In view of the fact there is not a single marker of ovarian reserve that
can accurately predict ovarian response various models for combination of
multiple ovarian markers have been developed (Verhagen et al 2008) A
number of studies reported that multivariate models are better predictors of
poor ovarian response in IVF compared to a single marker (Bancsi et al 2002
Balasch et al 1996 Creus et al 2000 Durmusoglu et al 2004) However a meta-
analysis showed that when compared to a single marker (AFC) multivariate
28
model has a similar accuracy in terms of prediction of poor ovarian response
(Verhagen et al 2008) In contrast a more recent study demonstrated that
multivariate score was superior to chronological age basal FSH or AFC alone
in predicting likelihood of poor ovarian response and clinical pregnancy
(Younis et al 2010) However the study did not include one of the most
reliable markers AMH in either arm necessitating further assessment of the
role of combined tests which include all reliable biomarkers
4 SUMMARY
During the last two decades a significant leap has been taken towards
understanding the biology of anti-Muumlllerian hormone and its role in female
reproduction (Durlinger et al 2002 Themmen et al 2005) Availability of
commercial AMH assays has resulted in significant increase in interest in the
role of the measurement of serum AMH in the assessment of ovarian reserve
which has been followed by the introduction of the test into routine clinical
practice (Nelson et al 2011) However more recent studies suggest that current
methodologies for the measurement of AMH may provide significant sampling
variability (Rustamov et al 2011) Furthermore the studies that compared first
generation commercial assay methods appear to provide non-reproducible
results suggesting there may be underlying issues with assay methodologies
(Lee et al 2011) Similarly despite lack of sufficient evidence in the role of
AMH in individualisation of ovarian stimulation protocols in IVF AMH-
tailored IVF protocols have been introduced in routine clinical practice of
many fertility clinics around the world
Consequently it appears that clinical application of AMH test has
surpassed the research evidence in some aspects of fertility treatment and
therefore future projects should be directed toward areas where gaps in
research evidence exist On the basis of the review of literature we believe that
evaluation of the performance of assay methods understanding the role of
AMH in assessment ovarian reserve and establishing its role in
individualisation of ovarian stimulation protocols should be research priority
29
II GENERAL INTRODUCTION
On the basis of the review of published literature I have identified that
the following areas of research on the clinical application of AMH in the
management of infertility requires further investigation 1) Within-patient
variability of measurement of AMH using Gen II assay method 2)
Establishment of clinically measurable determinants of AMH levels and 3) The
role of AMH in individualisation of ovarian stimulation in IVF treatment
cycles
In our previous study we estimated that there was significant sample-to-
sample variation (CV 28) in AMH measurements when the first generation
DSL assay was used (Rustamov et al 2011) The source of variability is likely to
be related to the assay method given that biological within-cycle variation of
AMH is believed to be small (La Marca et al 2006) Therefore assessment of
sample-to-sample variability of AMH using the newly introduced Gen II assay
which is believed to be significantly more stable and sensitive compared to that
of DSL assay should enable us to establish the measurement related variability
of AMH Furthermore given I am planning to use data from both DSL and
Gen II assays I need to establish between-assay conversion factor for these
assays using data on clinical samples
There appears to be a lack of good quality data on the effect of
ethnicity BMI causes of infertility reproductive history and reproductive
surgery on ovarian reserve Therefore I am planning to ascertain the role of
above factors on determination of ovarian reserve by analysing AMH
measurements of a large cohort of patients
There is a strong correlation between AMH and ovarian performance
in IVF treatment when conventional ovarian stimulation using GnRH agonist
regimens with a standard daily dose of gonadotrophins are used (Nelson et al
2007 Nardo et al 2007) Furthermore studies suggest tailoring the ovarian
stimulation protocols to AMH measurement may improve ovarian
performance and subsequently the success of IVF treatment (Nelson et al
2011 Yates et al 2012) However given methodologies of the published
studies the effectiveness of currently proposed AMH-tailored ovarian
stimulation protocols remains unknown Therefore I am planning to develop
individualised ovarian stimulation protocols by establishing the most optimal
mode of pituitary down regulation and starting dose of gonadotrophins for
30
each AMH cut-off bands using a robust research methodology However
development of individualised ovarian stimulation protocols on the basis of
retrospective data requires a reliable and validated database containing a large
number of observations In the IVF Department of St Maryrsquos Hospital we
have data on a large number of patients who underwent ovarian stimulation
following the introduction of AMH However the data on various aspects of
investigation and treatment of patients is stored in different clinical data
management systems and may not be easily linkable In addition it appears that
data on certain important variables (eg causes of infertility AFC) are available
only in the hospital records necessitating searching for data from the hospital
records of each patient Consequently I designed a project for building a
research database which will have comprehensive and validated datasets that
are necessary for investigation of the research questions of the MD
programme
In conclusion I am planning to conduct a series of studies to improve
the understanding of the role of AMH in the management of women with
infertility Specifically I am intending to evaluate 1) sample-to-sample variability
of Gen II AMH measurements 2) conversion factor between DSL and Gen II
assays in clinical samples 3) the effect of ethnicity BMI causes of infertility
endometriosis reproductive history and reproductive surgery to ovarian
reserve and explore AMH-tailored individualisation of ovarian stimulation in
IVF cycles
31
References
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Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175 Bristol-Gould SK et al Fate of the initial follicle pool empirical and mathematical evidence supporting its sufficiency for adult fertility Dev Biol 2006 298(1) p 149-54 Broekmans FJ et al A systematic review of tests predicting ovarian reserve and IVF outcome Hum Reprod Update 2006 12(6) p 685-718
32
Broekmans Frank J M de Ziegler Dominique Howles Colin M Gougeon Alain Trew Geoffrey and Olivennes Francois The antral follicle count practical recommendations for better standardization Fertility and Sterility 2010 94 p 1044-51 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011 Aug96(8)2532-9
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Bukovsky A et al Origin of germ cells and formation of new primary follicles in adult human ovaries Reprod Biol Endocrinol 2004 2 p 20 Byskov AG et al Eggs forever Differentiation 2005 73(9-10) p 438-46 Clarke TR et al Mullerian inhibiting substance signaling uses a bone morphogenetic protein (BMP)-like pathway mediated by ALK2 and induces SMAD6 expression Mol Endocrinol 2001 15(6) p 946-59
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146 Creus M PJ Faacutebregues F Vidal E Carmona F Casamitjana R and BJ Vanrell JA Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-2346 Creus M PaJ Fabregues F Vidal E Carmona F Casamitjana R et al Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-6 Cook CL SY Brenner AG et al Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertility and Sterility 2002 77 p 141-6 Deb S Campbell B K Clewis JS Pincott-Allen C and Raine-Fenning NJ Intracycle variation in number of antral follicles stratified by size and in endocrine markers of ovarian reserve in women with normal ovulatory menstrual cycles Ultrasound Obstet Gynecol 2013 41 216ndash222 De Felici M Germ stem cells in the mammalian adult ovary considerations by a fan of the primordial germ cells 2010 Mol Hum Reprod 16(9) p 632-6 Donovan PJ (1998) The germ cell ndash the mother of all stem cells Int J Dev Biol 42 1043ndash50 Dunaif A Insulin resistance and the polycystic ovary syndrome mechanism adn implications for pathogenesis Endocr Rev 1997 18 p 774-800
33
Durlinger AL et al Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 1999 140(12) p 5789-96 Durlinger AL et al Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 2001 142(11) p 4891-9 Durlinger AL JA Visser and AP Themmen Regulation of ovarian function the role of anti-Mullerian hormone Reproduction 2002 124(5) p 601-9 Durmusoglu F EK Yoruk P Erenus M Combining day 7 follicle count with the basal antral follicle count improves the prediction of ovarian response Fertility and Sterility 2004 81 p 1073-78 Ebner T et al Basal level of anti-Mullerian hormone is associated with oocyte quality in stimulated cycles Hum Reprod 2006 21(8) p 2022-6 Elmashad AI Impact of laparoscopic ovarian drilling on anti-Muumlllerian hormone levels and ovarian stromal blood flow using three-dimensional power Doppler in women with anovulatory polycystic ovary syndrome Fertility and Sterility 2011 95 p 2342-6 Falbo A RM Russo T DEttore A Tolino A Zullo F Orio F Palomba S Serum and follicular anti-Mullerian hormone levels in women with polycystic ovary syndrome (PCOS) under metformin J Ovarian Resere 2010 Jul p 16 Fanchin R et al Anti-Mullerian hormone concentrations in the follicular fluid of the preovulatory follicle are predictive of the implantation potential of the ensuing embryo obtained by in vitro fertilization J Clin Endocrinol Metab 2007 92(5) p 1796-802 Fasouliotis SJ A Simon and N Laufer Evaluation and treatment of low responders in assisted reproductive technology a challenge to meet J Assist Reprod Genet 2000 17(7) p 357-73 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164 Gleicher N A Weghofer and DH Barad Defining ovarian reserve to better understand ovarian aging Reprod Biol Endocrinol 9 p 23 Haadsma ML BA Groen H Roeloffzen EM Groenewoud ER Heineman MJ et al The number of small antral follicles (2ndash6 mm) determines the outcome of endocrine ovarian reserve tests in a subfertile population Human reproduction 2007 22 p 1925-31 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699ndash708
34
Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hazout A et al Serum antimullerian hormonemullerian-inhibiting substance appears to be a more discriminatory marker of assisted reproductive technology outcome than follicle-stimulating hormone inhibin B or estradiol Fertil Steril 2004 82(5) p 1323-9
Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 HFEA Fertility Figures 2005 2007 HFEA HFEA Fertility Facts and Figures 2008 HFEA 2010 Homburg R BD Levy T Feldberg D Ashkenazi J Ben-Rafael Z In vitro fertilisation and embryo transfer for the treatment of infertility associated with polycystic ovary syndrome Fertility and Sterility 1993 60 p 858-863 Hudson PL et al An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 1990 70(1) p 16-22 Hull MG GC Kelly NJ et al Population study of causes treatment and outcome of infertility Br Med J Clin Res Ed 1985 291 p 1693-1697 Islam MN and MM Islam Biological and behavioural determinants of fertility in Bangladesh 1975-1989 Asia Pac Popul J 1993 8(1) p 3-18 Jayaprakasan K et al A prospective comparative analysis of anti-Mullerian hormone inhibin-B and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation(2010a) Fertil Steril 2010 93(3) p 855-64 Jayaprakasan et al (2010b) The cohort of antral follicles measuring 2ndash6 mmreflects the quantitative status of ovarian reserve as assessed by serum levels of anti-Mullerian hormone and response to controlled ovarian stimulation Fertil Steril_ 2010941775ndash81 Johannes L H Evers MD Peronneke Slaats MS Jolande A Land MD John C M Dumoulin PhD and Gerard A J Dunselman MD Elevated Levels of Basal Estradiol-17β Predict Poor Response in Patients with Normal Basal Levels of Follicle-Stimulating Hormone Undergoing In Vitro Fertilization Fertility and Sterility 1998(69) p 1010-4 Johnson J et al Germline stem cells and follicular renewal in the postnatal mammalian ovary Nature 2004 428(6979) p 145-50 Kelsey TW et al A validated model of serum anti-mullerian hormone from conception to menopause PLoS One 2011 6(7) p e22024
35
Kumar A et al Development of a second generation anti-Mullerian hormone (AMH) ELISA J Immunol Methods 362(1-2) p 51-9 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A De Leo V Giulini S Orvieto R Malmusi S Giannella L Volpe A Anti-Mullerian hormone in premenopausal women and after spontaneous or surgically induced menopause J Soc Gynecol Investig 2005b12545-548 La Marca A et al Normal serum concentrations of anti-Mullerian hormone in women with regular menstrual cycles (2010a) Reprod Biomed Online 2010 21(4) p 463-9 La Marca A et al Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction (2010b) Reprod Biomed Online 2010 22(4) p 341-9 La Marca A et al Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART) Hum Reprod Update 16(2) p 113-30 La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351 Lee MM et al Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 1996 81(2) p 571-6 Lee TH et al Impact of female age and male infertility on ovarian reserve markers to predict outcome of assisted reproduction technology cycles Reprod Biol Endocrinol 2009 7 p 100
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604 Licciardi FL LH Rosenwaks Z Day 3 estradiol serum concentrations as prognosticators of ovarian stimulation response and pregnancy outcome in patients undergoing in vitro fertilization Fertility and Sterility 1995 64 p 991-4 Lie Fong S et al Anti-Mullerian hormone a marker for oocyte quantity oocyte quality and embryo quality Reprod Biomed Online 2008 16(5) p 664-70 Lintsen AM et al Effects of subfertility cause smoking and body weight on the success rate of IVF Hum Reprod 2005 20(7) p 1867-75 Maheshwari A and PA Fowler Primordial follicular assembly in humans--
36
revisited Zygote 2008 16(4) p 285-96 Maheshwari A et al Dynamic tests of ovarian reserve a systematic review of diagnostic accuracy Reprod Biomed Online 2009 18(5) p 717-34 Massague J et al TGF-beta receptors and TGF-beta binding proteoglycans recent progress in identifying their functional properties Ann N Y Acad Sci 1990 593 p 59-72 Massague J and YG Chen Controlling TGF-beta signaling Genes Dev 2000 14(6) p 627-44 Mc KD HA Adams EC Danziger S Histochemical observations on the germ cells of human embryos Anat Rec 1953 2 p 201-219 McGee EA and AJ Hsueh Initial and cyclic recruitment of ovarian follicles Endocr Rev 2000 21(2) p 200-14 Mishina Y et al Genetic analysis of the Mullerian-inhibiting substance signal transduction pathway in mammalian sexual differentiation Genes Dev 1996 10(20) p 2577-87 Moran LJ HC Hutchinson SK Stepto NK Strauss BJ Teede HJ Exercise decreases anti-Mullerian horomone in anovulatory overweight women with polycystic ovary syndrome-A pilot study Horm Metab Res 2011 October Nardo LG et al Circulating basal anti-Mullerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 92(5) p 1586-93 Nelson SM RW Yates and R Fleming Serum anti-Mullerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007 22(9) p 2414-21 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867 Nelson SM and A La Marca The journey from the old to the new AMH assay how to avoid getting lost in the values 2011 Reprod Biomed Online Nelson SM et al External validation of nomogram for the decline in serum anti-Mullerian hormone in women a population study of 15834 infertility patients Reprod Biomed Online 2011 23(2) p 204-6 NICE Assessment and treatment for people with fertility problems NICE Guidelines 2013 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS ONE 5(7) e11637 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS
37
ONE 2010 5(7) 11637 Nilsson EE et al Inhibitory actions of Anti-Mullerian Hormone (AMH) on ovarian primordial follicle assembly PLoS One 2011 6(5) p e20087 Notarianni E Reinterpretation of evidence advanced for neo-oogenesis in mammals in terms of a finite oocyte reserve 2011 J Ovarian Res 4(1) p 1 Office of National Statistics 2012 1 2011 Live Births in England and Wales by Characteristics of Mother Oktem O and B Urman Understanding follicle growth in vivo Hum Reprod 25(12) p 2944-54 Oktem O and K Oktay The ovary anatomy and function throughout human life Ann N Y Acad Sci 2008 1127 p 1-9 Ottosen LD et al Pregnancy prediction models and eSET criteria for IVF patients--do we need more information J Assist Reprod Genet 2007 24(1) p 29-36 Pacchiarotti J et al Differentiation potential of germ line stem cells derived from the postnatal mouse ovary Differentiation 2010 79(3) p 159-70 Paternot G WA Thonon F Vansteenbrugge A Willemen D Devroe J Debrock S DHooghe TM Spiessens C Intra- and interobserver analysis in the morphological assessment of early stage embryos during an IVF procedure a multicentre study Reprod Biol Endocrinol 2011 9 p 127 Pehlivanov B OM Anti-Muumlllerian hormone in women with polycystic ovary syndrome Folia Medica 2011 53 p 5-10 Pellat L HL Brincat M et al Granulosa cell production of anti-Muumlllerian hormone is increased in polycystic ovaries J Clin Endocrinol Metab 2007 92 p 240-5 Pigny P ME Robert Y et al Elevated serum level of anti-Mullerian hormone in patients with polycystic ovary syndrome relationship to the ovarian follicle excess and the follicular arrest J Clin Endocrinol Metab 2003 88 p 5957-62 Porter RN et al Induction of ovulation for in-vitro fertilisation using buserelin and gonadotropins Lancet 1984 2(8414) p 1284-5 Rajpert-De Meyts E et al Expression of anti-Mullerian hormone during normal and pathological gonadal development association with differentiation of Sertoli and granulosa cells J Clin Endocrinol Metab 1999 84(10) p 3836-44
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-
38
Scott Wilkes Murdoch Alison DC and Greg Rubin Epidimiology and management of infertility a poppulation-based study in UK primary care Family Practice 2009 26 p 269-274 Seifer DB L-MG Hogan JW Gardiner AC Blaza AS Berk CA Day 3 serum inhibin-B is predictive of assisted reproductive technologies outcome Fertility and Sterility 1997 67 p 110-4 Skinner MK (2005) Regulation of primordial follicle assembly and development Hum Reprod Update 11 461ndash71 Syrop CH et al Ovarian volume may predict assisted reproductive outcomes better than follicle stimulating hormone concentration on day 3 Hum Reprod 1999 14(7) p 1752-6 Syrop CH A Willhoite and BJ Van Voorhis Ovarian volume a novel outcome predictor for assisted reproduction Fertil Steril 1995 64(6) p 1167-71 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Taylor A ABC of subfertility Making a diagnosis Br Med J Clin Res Ed 2003 327 p 799-801 Templeton A JK Morris and W Parslow Factors that affect outcome of in-vitro fertilisation treatment Lancet 1996 348(9039) p 1402-6 Templeton A Infertility-epidemiology aetiology and effective management Health Bull (Edinb) 1995 53(5) p 294-8 TDL test update AMH Stability Hormones and OCPs The Doctors Laboratory Guide 2008 page 29 Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34) p 18-21 Tilly JL and J Johnson Recent arguments against germ cell renewal in the adult human ovary is an absence of marker gene expression really acceptable evidence of an absence of oogenesis Cell Cycle 2007 6(8) p 879-83 Urbancsek J Use of serum inhibin B levels at the start of ovarian stimulation and at oocyte pickup in the prediction of assisted reproduction treatment outcome Fertility and Sterility 2005 83(2) p 341-348 van der Linden M BK Farquhar C Kremer JAM Metwally M Luteal phase support for assisted reproduction cycles (Review) Cochrane Library 2011 October
39
van Disseldorp J et al Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2011 25(1) p 221-7 van Kooij RJ et al Age-dependent decrease in embryo implantation rate after in vitro fertilization Fertil Steril 1996 66(5) p 769-75 van Rooij IA et al Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002 17(12) p 3065-71 Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539 van Disseldorp J Kwee CBL J Looman CWN Eijkemans MJC and FJ Broekmans Comparison of inter- and intra-cycle variability of anti-Muuml llerian hormone and antral follicle counts Human reproduction 2010 25 p 221-227 Verberg MF et al Predictors of low response to mild ovarian stimulation initiated on cycle day 5 for IVF Hum Reprod 2007 22(7) p 1919-24 Verhagen TE et al The accuracy of multivariate models predicting ovarian reserve and pregnancy after in vitro fertilization a meta-analysis Hum Reprod Update 2008 14(2) p 95-100 Visser JA AMH signaling from receptor to target gene Mol Cell Endocrinol 2003 211(1-2) p 65-73 Wallace WH and TW Kelsey Human ovarian reserve from conception to the menopause PLoS One 5(1) p e8772 Wallace AM et al A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 2011 48(Pt 4) p 370-3 Webber L J SS Stark J Trew G H Margara R Hardy K Franks S Formation and early development of follicles in the polycystic ovary Lancet 2003 362(September) p 1017-1021
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362 Younis JS et al A simple multivariate score could predict ovarian reserve as well as pregnancy rate in infertile women Fertil Steril 2010 94(2) p 655-61 Zou K et al Production of offspring from a germline stem cell line derived from neonatal ovaries Nat Cell Biol 2009 11(5) p 631-6 Zuckerman The number of oocytes in the mature ovary Recent Prog Horm Res 1951 6(63-108)
Figure 1 Schematic representation of a long GnRH agonist cycle
In a long agonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH agonist preparations starting from mid-luteal phase of the preceding menstrual cycle till the day of administration of HCG
Cycle Started
Menstrual Period
Daily GnRH agonist
From mid-luteal phase
Daily GnRH agonist
Menstrual
Period
Daily GnRH agonist
amp
Daily hMG
Day 2-10
HCG
USOR
amp
ET
41
Figure 2 Schematic representation of GnRH antagonist cycle
In an antagonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH antagonist preparations starting from the 5th day of IVF cycle till the day of administration of HCG Therefore an ldquoAntagonistrdquo cycle is significantly shorter than an ldquoAgonistrdquo cycle
Cycle Started
Menstrual Period
Daily GnRH antagonist
(Day 5-10)
amp
Daily hMG
(Day 2-10)
HCG
USOR
amp
ET
42
Figure 3 The role of AMH in regulation of oocyte recruitment and folliculogenesis
It appears that AMH plays an important role in a) the recruitment of primordial follicles and b) the selection of a dominant follicle from a cohort of antral follicles AMH is believed to be the main regulator of ovarian reserve which is achieved by its paracrine negative feedback effect to resting primordial follicles (Durlinger et al 1999) AMH was found to play an important role
in the regulation of the selection of a dominant follicle by inhibition of the FSH-induced follicle growth (Durlinger et al 2001)
EVALUATION OF THE GEN II AMH ASSAY BETWEEN-SAMPLE VARIABILITY AND
ASSAY-METHOD COMPARABILITY
2
44
ANTI-MUumlLLERIAN HORMONE SERUM LEVELS AND REPRODUCIBILITY
IN A LARGE COHORT OF SUBJECTS SUGGEST
SAMPLE INSTABILITY
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G
Nardo Philip W Pemberton
Human Reproduction 2012a 273085-3091
21
45
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large
cohort of subjects suggest sample instability
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester Foundation Trust
Manchester M13 9WL UK
c Health Sciences - Methodology Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
d School of Medicine University of Manchester Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Corresponding author
Oybek Rustamov MRCOG
Research Fellow in Reproductive Medicine
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
E-mail oybekrustamovcmftnhsuk oybek_rustamovyahoocouk
Word count 3909
Conflicts of Interest There are no potential conflicts of interest
Acknowledgement of financial support
Dr Steve Roberts is supported by the NIHR Manchester Biomedical Research Centre
46
Declaration of authorsrsquo roles
OR led on clinical aspects of this study with responsibility for collation of the
clinical database and the analysis of the clinical data OR prepared the first
draft of the clinical work and was involved in preparation of the whole paper
and submission of the final manuscript CF and LGN contributed to clinical
data analysis draft preparation and approval of the final manuscript MK was
involved in clinical data collation and approval of the final draft PWP was the
laboratory lead responsible for all of the laboratory based experiments and for
the routine analysis of clinical samples PWP prepared the first draft of the
laboratory work and was involved in the preparation of the whole paper and
submission of the final manuscript AS suggested the sample stability studies
and was involved in discussion draft preparation and approval of the final
manuscript APY was involved in some of the routine clinical analyses and
progression of drafts to approval of the final manuscript SAR was involved in
clinical study design oversaw the statistical analysis and progression of drafts
through to approval of the final manuscript OR and PWP should be
considered as joint first authors
47
ABSTRACT
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large cohort of
subjects suggest sample instability
Study question
What is the variability of anti-muumlllerian hormone (AMH) concentration in
repeat samples from the same individual when using the Gen II assay and how
do values compare to Gen I (DSL) assay results
Summary answer
Both AMH assays displayed appreciable variability which can be explained by
sample instability
What is known already
AMH is the primary predictor of ovarian performance and is used to tailor
gonadatrophin dosage in cycles of IVFICSI and in other routine clinical
settings A robust reproducible and sensitive method for AMH analysis is of
paramount importance The Beckman Coulter Gen II ELISA for AMH was
introduced to replace earlier DSL and Immunotech assays The performance
of the Gen II assay has not previously been studied in a clinical setting
Study design size and duration
For AMH concentration study we studied an unselected group of 5007
women referred for fertility problems between 1st September 2008 to 25th
October 2011 AMH was measured initially using the DSL AMH ELISA and
subsequently using the Gen II assay AMH values in the two populations were
compared using a regression model in log(AMH) with a quadratic adjustment
for age Additionally women (n=330) in whom AMH had been determined in
different samples using both the DSL and Gen II assays (paired samples)
identified and the difference in AMH levels between the DSL and Gen II
assays was estimated using the age adjusted regression analysis
In AMH variability study 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay and 87 women had repeated AMH
determinations using the Gen II assay (n=177 samples) were identified A
mixed effects model in log (AMH) was utilised to estimate the sample-to-
48
sample (within-subject) coefficients of variation of AMH adjusting for age
Laboratory experiments including sample stability at room temperature
linearity of dilution and storage conditions used anonymised samples
Main results and the role of chance
In clinical practice Gen II AMH values were ~20 lower than those
generated using the DSL assay instead of the 40 increase predicted by the kit
manufacturer Both assays displayed high within-subject variability (Gen II
assay CV=59 DSL assay CV=32) In the laboratory AMH levels in serum
from 48 subjects incubated at RT for up to 7 days increased progressively in
the majority of samples (58 increase overall) Pre dilution of serum prior to
assay gave AMH levels up to twice that found in the corresponding neat
sample Pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave higher readings (72 overall) compared to sequential
addition Storage at -20ordmC for 5 days increased AMH levels by 23 compared
to fresh samples The statistical significance of results was assessed where
appropriate
Limitations reasons for caution
The analysis of AMH levels is a retrospective study and therefore we cannot
entirely rule out the existence of differences in referral practices or changes in
the two populations
Wider implications of the findings
Our data suggests that AMH may not be stable under some storage or assay
conditions and that this may be more pronounced with the Gen II assay The
published conversion factors between the Gen II and DSL assays appear to be
inappropriate for routine clinical practice Further studies are urgently required
to confirm our observations and to determine the cause of the apparent
instability In the meantime caution should be exercised in the interpretation
of AMH levels in the clinical setting
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA DSL Active MIS AMH ELISA sample stability
49
INTRODUCTION
AMH in women is secreted by the granulosa cells of pre-antral and small
antral follicles (Vigier et al 1984 Themmen 2005) and circulating levels reflect
the ovarian pool from which follicles can be recruited (Loh amp Maheshwari
2011) Measurement of AMH has become of paramount significance in clinical
practice in IVF units to assign candidates to the most suitable controlled
ovarian hyperstimulation protocol and its level is used to predict poor or
excessive ovarian response (Nelson et al 2007 Nardo et al 2009 Yates et al
2011) It is also of increasing importance in (a) prediction of live birth rate in
IVF cycles (La Marca et al 2011) (b) screeningdiagnosis of polycystic ovarian
syndrome (Cook et al 2002) (c) follow up of women with a history of
granulosa cell tumours (Lane et al 1999) (d) prediction of the age of onset of
infertility due to the menopause (van Disseldorp et al 2008 Broer et al 2011)
and finally (e) assessment of the long term effect of chemotherapy on fertility
(Anderson 2011)
Following development of the first laboratory AMH assay in 1990
(Hudson et al 1990 Lee et al 1996) first generation commercially available
immunoassays were introduced by Diagnostic Systems Ltd (DSL) and
Immunotech Ltd (IOT) These assays used different antibodies and standards
(Nelson amp La Marca 2011) and the resulting AMH concentrations obtained
using the IOT assay were found to be higher than those produced using the
DSL assay by most but not all authors (Freour et al 2007Taieb et al 2008 Lee
et al 2011) The AMH Gen II Assay (Beckman-Coulter Ltd) replaced both of
these assays using the DSL Gen I antibody with the IOT standards AMH
values obtained using this kit were predicted to correlate with but be higher
than those using the old DSL kit (Kumar et al 2010 Nelson amp La Marca
2011) This was confirmed (Wallace et al 2011) with the AMH Gen II assay
giving values approximately 40 higher than the DSL assay The
recommended conversion factor of 14 (AMH Gen II = DSL x 14) was also
applied to the DSL reference ranges but this recommendation does not appear
to have been independently validated
It is generally accepted that serum AMH concentrations are highly
reproducible within and across several menstrual cycles and therefore a single
blood sampling for AMH measurement has been accepted as routine practice
50
(Hehenkamp et al 2006 La Marca et al 2006 Tsepelidis et al 2007) However
we recently challenged this view and reported significant sample-to-sample
variation in AMH levels using the DSL assay in women who had repeated
measurements 28 difference between samples taken from the same patient
with a median time between sampling of 26 months and taking no account of
menstrual cycle (Rustamov et al 2011) Although we could not explain the
cause of this variability we speculated that it might be due to true biological
variation in secretion of AMH or due to post-sampling pre-analytical
instability of the specimen
Given the widespread adoption of AMH in Clinical Units it is critical
that the sources of variability in any AMH assay are understood and quantified
This paper presents the results of clinical and laboratory studies on routine
clinical samples using the new AMH Gen II assay specifically comparing assay
values with the older DSL assay assessing between sample variability and
investigating analytical and pre-analytical factors affecting AMH measurement
METHODS
Study population
Samples were obtained from women of 20-46 years of age attending for
investigation of infertility requiring AMH assessment at the secondary
(Gynecology Department) and tertiary (Reproductive Medicine Department)
care divisions of St Maryrsquos Hospital Manchester from 1st September 2008 to
25th October 2011 Samples which were lipaemic or haemolysed and samples
not frozen within 2 hours of venepuncture were excluded from the study
Anonymised samples from this pool of patients were used for stability studies
after routine AMH measurements had been completed The full dataset
comprised AMH results on 5868 samples from 5007 women meeting the
inclusion criteria Additionally we identified women in whom AMH had been
determined in different samples using both the DSL and Gen II assays (paired
samples from 330 women)
51
Sample processing
Collection and handling of all AMH samples was conducted according
to the standards set out by the manufacturers and did not vary between the
different assays Serum samples were transported immediately to the
Department of Clinical Biochemistry based in the same hospital and
separated within 2 hours of venepuncture using the Modular Pre-Analytics
Evo (Roche Diagnostics Burgess Hill West Sussex UK) Samples were frozen
in aliquots at -20C until analysis normally within one week of receipt The
laboratory participates in the pilot National external quality assessment scheme
(UKNEQAS) for AMH in Edinburgh and performance has been satisfactory
AMH analysis
All AMH assays were carried out strictly according to the protocols
provided by the manufacturer and sample collection and storage also
conformed to these recommendations All AMH samples were analysed in
duplicate and the mean of the two replicates was reported as the final result
1) The DSL AMH assay The enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) was used for measurement of AMH prior to 17th
November 2010 The working range of the assay was up to 100pmolL with a
minimum detection limit of 063pmolL The intra-assay coefficient of
variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at 56pmoll) The
inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at 56pmoll)
2) The Beckman Coulter Gen II assay After 17th November 2010
AMH was measured using the enzymatically amplified two-site immunoassay
(AMH Gen II ELISA Beckman Coulter Inc Brea CA USA) The working
range of the assay is up to 150pmolL with a minimum detection limit of
057pmolL The intra-assay CV (n=16) is 292 (at 18pmoll) and 203 (at
60pmoll) The inter-assay coefficient of variation (n=28) is 357 (at
18pmoll) and 364 (at 60pmoll)
Sample Stability Studies
(1) Stability of AMH in serum at room temperature (RT) serum samples
(n = 48) were allowed to thaw and then left at RT for one week At 0 1 2 4
and 7 days 100microl aliquots were removed and immediately stored at -80 ordmC in
52
2ml screw-capped polypropylene tubes (Alpha Laboratories Eastleigh UK)
Two freezethaw cycles had no effect on AMH concentration (results not
shown) Samples from individual subjects were analysed for AMH on the same
GenII microtitre plate to eliminate inter-assay variability Results were
expressed as a percentage of the day 0 value
(2) Linearity of Dilution 100microl fresh serum (n = 9) was added to 100microl
AMH Gen II sample diluent incubated for 30min at RT and the mixture
analysed using the standard GenII assay procedure
(3) Comparison between the Standard Assay method and an equivalent
procedure in the standard GenII ELISA assay method the first steps involve
the addition of calibrators controls or serum samples to microtitration wells
coated with anti-AMH antibody Assay buffer is then added to each well As a
comparison serum and assay buffer were mixed in a separate tube incubated
for 10min at RT and then added in exactly the same volume and proportions
to the microtitre plate Thereafter the assay was performed using the standard
protocol
(4) Stability of AMH during storage fresh serum samples (n = 8)
analysed on the day of reception were compared with aliquots from the same
samples that had been frozen for 5 days either in polystyrene tubes at -20degC or
polypropylene tubes at -80degC
Statistical Analysis
Data analysis was performed using the Stata 12 analytical package
(StataCorp Texas USA) Data management and analysis of clinical data was
conducted by one of the researchers (OR) and verified independently by
another member of the research team (SR) using different statistical software
(R statistical environment) Approval for the use of the data was obtained from
the Local Research Ethics Committee (UK-NHS 10H101522) The age-
related relationship of the DSL and Gen II assays to AMH was visualised using
scatter plots and quadratic fit on a logarithmic scale (Nelson et al 2011) The
age adjusted regression analysis of paired samples was used to estimate the
difference in AMH levels between the DSL and Gen II assays A mixed effects
model in log (AMH) was utilised to estimate the sample-to-sample (within-
subject) coefficients of variation of AMH levels in women who had repeated
53
measurements within a 1 year period from the patientrsquos first AMH sample
adjusting for age as above In the sample stability studies percentage changes
are expressed as mean plusmn SEM In the stability of AMH in serum at RT study a
paired t-test determined the level of significance between baseline and
subsequent days
RESULTS
Population studies and variability
AMH concentration
Table 1 summarizes the results of AMH determinations in our
population of women attending the IVF Clinic prior to the 17th November
2010 (using the DSL assay) and after that date (using the Gen II assay) A
second analysis compares AMH levels in women who had AMH measured
using both assays at different times Results were consistent with lower serum
levels of AMH observed when samples were analysed using the Gen II assay
compared to the DSL assay Figure 1 shows the correlation of AMH with age
for the unselected groups After adjustment for age the total cohorts showed
Gen II giving AMH values 34 lower than those for DSL Analysis restricted
to patients with AMH determinations using both assays gave an age-adjusted
difference of 21
AMH variability
During the study period 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay with 295 patients having two samples 17
three samples and one five samples The median time between samples was 51
months Eighty seven women had repeated AMH determinations using the
Gen II assay (n=177 samples) with 84 women having two samples and 3
having three samples The median interval between repeat samples was 32
months Both assays exhibit high sample-to-sample variability (CV) this was
32 in the DSL assay group (our previous finding (Rustamov et al 2011) in a
smaller group was 28) variability in the Gen II assay group was much higher
(59)
54
Table 1 Median and inter-quartile range for the two assays in the
different datasets along with the mean difference from an age-
adjusted regression model expressed as a percentage
DSL Gen II
difference ()
n age AMH (pmoll
)
n Age
AMH (pmoll
)
all data
3934
33 (29 36)
147 (78250
)
1934 33 (29 36)
112 (45 216)
-335 (-395 to -
275)
paired sample
s
330 32 (29 36)
149 (74 247)
330 34 (30 37)
110 (56 209)
-214 (-362 to -64)
Figure 1 Unselected AMH values from DSL (circles) and Gen II
(triangles) assays as a function of age Lines show the regression
fits of log(AMH) against a quadratic function of age solid lines
Gen II broken lined DSL
20 25 30 35 40 45
Age
AM
H [p
mo
lL
]
DSLGen II
11
01
00
55
Sample stability studies
(1) Stability of AMH in serum at room temperature
AMH levels in 11 of the 48 individuals remained relatively unchanged
giving values within plusmn10 of the original activity over the period of a week
and one patient had an undetectable AMH at all time points The remaining 36
serum samples had AMH values that increased progressively with time In the
47 samples with detectable AMH levels increased significantly (plt0001) for
each time interval compared to baseline the increase at day 7 being 1584 plusmn 76
(Figure 2)
Figure 2 Stability of AMH in serum at RT
Results at each time interval are expressed as a percentage of the patientrsquos AMH concentration at day 0 Means plusmn SEM are indicated
56
(2) Linearity of Dilution
In a group of nine anonymised samples proportionality with two-fold
sample dilution does not hold and on average there is a 574 plusmn 123 increase
in the apparent AMH concentration on dilution compared to neat sample (see
table 2a) Two samples which gave the highest increases were diluted further It
was apparent that after the anomalous doubling of AMH concentration on
initial two-fold dilution subsequent dilutions gave a much more proportional
result (see Table 2b) Linearity of dilution was maintained only in samples that
showed no initial increase on two-fold dilution
Table 2a Proportionality with two-fold dilution of serum
AMH (pmoll)
sample no neat serum x2 dilution recovery
1 1105 2294 2076 2 4941 9900 2004 3 415 483 1164 4 923 1122 1216 5 2801 3066 1091 6 362 628 1734 7 2739 3962 1447 8 553 1034 1870 9 1849 2892 1564
Table 2b Linearity with multiple dilution of serum
AMH (pmoll)
sample no dilution Measured expected recovery ()
1 x1 1105 1105 100 x2 1147 5525 2076 x4 5532 2763 2002 x7 3072 1579 1946 x10 2145 1105 1941
2 x1 4941 4941 100
x2 4950 2471 2003 x4 2286 1235 1851 x7 1228 706 1739 x10 857 494 1735
57
(3) Comparison between the Standard Assay method and an equivalent
procedure Serum samples that had been pre-mixed with buffer prior to
addition gave on average 718 plusmn 48 higher readings than those added
sequentially using the standard procedure (see table 3)
Table 3 Comparison between equivalent ELISA procedures
AMH (pmoll)
sample no A B BA ()
1 1466 2284 1558 2 839 1642 1957 3 3151 6446 2046 4 1244 2014 1619 5 1393 2276 1634 6 701 1246 1777 7 778 1358 1746 8 1693 3298 1948 9 955 1793 1877 10 2849 5437 1908
11 1365 2062 1511 12 1773 2868 1617 13 1468 2429 1655 14 1499 2115 1411 15 249 357 1434 16 1284 2289 1783
A = 20microl serum added directly to the plate followed by 100microl assay buffer
B = 60microl serum + 300microl assay buffer mixed amp incubated at RT for 10min 120microl mixture added to the plate
(4) Stability of AMH during storage AMH levels in samples stored at -20degC
showed an average increase of 225 plusmn 111 over 5 days compared with fresh
values while those samples stored at -80degC showed no change (18 plusmn 31)
(see Table 4)
Table 4 Stability of AMH in serum on storage
AMH (pmoll)
sample no
fresh -20ordmC PS -80ordmC PP
1 1241 1551 1312 2 4217 7542 4508 3 1193 1712 1239 4 1042 1282 1228 5 956 905 879 6 1902 2601 1884 7 2402 2016 2362 8 145 137 132
PS = polystyrene LP4 tube PP = polypropylene 2ml tube
58
DISCUSSION
This publication arose from two initially separate pieces of work in the
Clinical IVF Unit at St Maryrsquos Hospital and in the Specialist Assay Laboratory
at Central Manchester Foundation Trust The IVF Unit had become
concerned with their observed increase in variation in AMH values and
consequently with the reliability of their AMH-tailored treatment guidance
The Laboratory wished to establish whether the practice of sending samples in
the post (which has been adopted by many laboratories rather than frozen as
specified by Beckman) was viable It soon became clear that these anomalies
observed in clinical practice might be explained by a marked degree of sample
instability seen in the Laboratory which had not previously been reported and
which may or may not have been an issue with previous AMH assays
The data contained in this paper represents the largest retrospective
study on the variability of the DSL assay and the first study on the variability
of the Gen II assay Early studies reported insignificant variation between
repeated AMH measurements suggesting that a single AMH measurement
may be sufficient in assessment of ovarian reserve (La Marca et al 2006
Tsepelidis et al 2007) However these recommendations have been challenged
by a number of groups (Lahlou et al 2006 Wunder et al 2008 Rustamov et al
2011) The current study in a large cohort of patients has demonstrated
substantial sample-to-sample variation in AMH levels using the DSL assay and
an even larger variability using the Gen II assay We suggest that this variability
may be due to sample instability related to specimen processing given that a)
AMH is produced non-cyclically and true biological variation is believed to be
small (Fanchin et al 2005 van Disseldorp et al 2009) and b) the intra-and inter
assay variation in our laboratory for both the DSL and Gen II assays is small
(lt50) suggesting that the observed variation is not due to poor analytical
technique
The population data presented in this paper also suggests that in routine
clinical use the Gen II assay provides AMH results which are 20-40 lower
compared to those measured using the DSL assay This is in contrast to
validation studies for the Gen II assay which showed that this assay gave AMH
values ~40 higher than those found with the DSL assay (Kumar et al 2010
Preissner et al 2010 Wallace et al 2011)
59
All samples in this retrospective study were subject to the same handling
procedures and analyzed by the same laboratory the two populations were
comparable with the same local referral criteria for investigation of infertility
and we are unaware of any other alterations in practice which might produce
such a large effect on AMH we cannot rule out the possibility of other
changes in the population being assayed that were coincident in time with the
assay change However any such change would have to be coincident and
produce a 50 decrease in observed AMH levels to explain our findings We
did note a weak trend towards decreasing AMH over calendar time assuming a
linear trend in the analysis implies that AMH values might be 12 (2-22)
lower when the Gen II assay was being used compared to the Gen I assay
This suggests that the age adjusted analysis of repeat samples on individuals
showing a 21 decrease in AMH with the Gen II assay is currently the best
estimate of the assay difference
This is the first study to compare AMH assays in a routine clinical setting
in a large group of subjects and as such is likely to reflect the true nature of the
relationship between AMH measured by two different ELISA kits and avoids
some of the issues in other published studies Previous laboratory studies have
compared AMH assays in aliquots from the same sample which only provides
data on the within-sample relationship between the two assays (Kumar et al
2010 Preissner et al 2010 Wallace et al 2011) Although it is difficult to give a
definitive explanation for the discrepancy between the previously published
studies (on within-sample relationships) and this study (on between-sample
relationships) we suggest that it may be due to degradation of the specimen in
one (or both) of the assays If AMH in serum is unstable under certain storage
and handling conditions this might result in differing values being generated
because of differential sensitivity of the two assays to degradation products
Unfortunately we cannot suggest which step of sample handling might have
caused this discrepancy since the published studies did not provide detailed
information
The present study used samples which were frozen very soon after
phlebotomy and analysed shortly thereafter hopefully minimising storage
effects The most striking change followed incubation over a period of 7 days
at RT this showed a substantial increase in AMH levels rather than the
expected decline Previously Kumar et al (2010) had shown that the average
variation between fresh serum samples and those stored for seven days to be
60
approximately 4 at 2-8ordmC and lt1 at -20ordmC but presented no data on RT
stability Zhao et al (2007) reported that AMH values were likely to differ by
lt20 in samples incubated at RT for 2 days compared to those frozen
immediately
Several supplementary experiments were performed in order to
investigate this observed increase in AMH when samples were incubated at
RT These included (1) addition of the detergent Tween-20 to assay buffer to
disclose potential antibody-binding sites on the AMH molecule (2) the
removal of heterophilic antibodies from serum using PEG precipitation or
heterophilic blocking tubes None of these approaches affected AMH levels
significantly (results not shown)
Examination of the data presented here shows that in some samples
AMH levels tend towards twice those expected while results greater than that
only occur in two outliers found in Figure 2 The AMH molecule is made up
of two identical 72kDA monomers which are covalently bound (Wilson et al
1993 di Clemente et al 2010) During cytoplasmic transit each monomer is
cleaved to generate 110-kDa N-terminal and 25-kDa C-terminal homodimers
which remain associated in a noncovalent complex The C-terminal
homodimer binds to the receptor but in contrast to other TGF-β superfamily
members AMH is thought to require the N-terminal domain to potentiate this
binding to achieve full bioactivity of the C-terminal domain After activation of
the receptor the N-terminal homodimer is released (Wilson et al 1993) One
possible explanation for our findings is that the N-and C-terminal
homodimers dissociate gradually under certain storage conditions and that
either the two resulting N- and C-terminal components bind to the ELISA
plate or a second binding site on the antigen is exposed by the dissociation
effectively doubling the concentration of AMH It has been shown (di
Clemente et al 2010) that no dissociation occurs once the complex is bound to
immobilised AMH antibodies The observation that in some of our samples
there was no change after one week at RT might be explained by the
supposition that in those samples AMH is already fully dissociated A mixture
of dissociated and complex forms in the same sample would therefore
account for the observed recoveries between 100 and 200 in the
experiments presented in this paper Rapid sample processing and storage of
the resulting serum in a different tube type at -80ordmC might slow down this
breakdown process
61
The change in ionic strength or pH that occurs on dilution also seems to
have the same effect in increasing apparent AMH levels and again may be due
to dissociation or exposure of a second binding site Our results contradict
those reported by Kumar et al (2010) who showed that serum samples in the
range of 36-93pmoll of AMH when diluted in Gen II sample diluent showed
linear results across the dynamic range of the assay with average recoveries on
dilution close to 100 This might be explained if Kumarrsquos samples were
already dissociated before dilution Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample but our results do not seem to support this
These findings have significant clinical relevance given the widespread
use of AMH as the primary tool for assessment of ovarian reserve and as a
marker for tailoring the dose of gonadotrophins in cycles of IVFICSI As no
guideline studies have been published using the new Gen II assay some ART
centres have adopted modified treatment ldquocut off levelsrdquo for ovarian
stimulation programs based on the old DSL assay based ldquocut off levelsrdquo
multiplied by a conversion factor of 14 (Nelson et al 2007 Nelson et al 2009
Wallace et al 2011) The data presented in this paper suggest that this approach
could result in patients being allocated to the wrong ovarian reserve group
Poor performance of the Gen II assay in terms of sample-to-sample variability
(up to 59) could also lead to unreliable allocation to treatment protocols It
is a matter of some urgency therefore that any possible anomalies in the
estimation of AMH using the Gen II assay be thoroughly investigated and that
this work should be repeated in other centres
62
References
Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146
di Clemente N Jamin SP Lugovskoy A Carmillo P Ehrenfels C Picard JY Whitty A Josso N Pepinsky RB Cate RL Processing of anti-mullerian hormone regulates receptor activation by a mechanism distinct from TGF-beta Mol Endocrinol 2010242193-2206
Freour T Mirallie S Bach-Ngohou K Denis M Barriere P and Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
Fanchin R Taieb J Mendez Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Mullerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 2005 20923-927
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
La Marca A Nelson SM Sighinolfi G Manno M Baraldi E Roli L Xella S Marsella T Tagliasacchi D DAmico R Volpe A Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction Reprod Biomed Online 2011 22341-349
Lahlou N Chabbert-Buffet E Gainer E Roger M Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-5
63
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
Lee MM Donahoe PK Hasegawa T Silverman B Crist GB Best S Hasegawa Y Noto RA Schoenfeld D MacLaughlin DT Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 199681571-576
Loh JS Maheshwari A Anti-Mullerian hormone--is it a crystal ball for predicting ovarian ageing Hum Reprod 2011262925-2932
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 201195736-741
Preissner CM MD Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Taieb J Coussieu C Guibourdenche J Picard JY and di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34)18-21
Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormoneconcentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
van Disseldorp J Lambalk CB Kwee J Looman CWN Eijkemans MJC Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2010 25 221-227
64
Vigier B Picard JY Tran D Legeai L Josso N Production of anti-Mullerian hormone another homology between Sertoli and granulosa cells Endocrinology 19841141315-1320
Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-MuSllerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
Wilson CA Di Clemente N Ehrenfels C Pepinsky RB Josso NVigier B Cate RL Muumlllerian inhibiting substance requires its N-terminal domain for maintenance of biological activity a novel finding within the transforming growth-factor-beta superfamily Mol Endocrinol 19937247ndash257
Wunder DM Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrualcycle in reproductive age women Fertil Steril 200889927-933
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 2007 88S17
65
AMH GEN II ASSAY A VALIDATION STUDY OF
OBSERVED VARIABILITY BETWEEN REPEATED
AMH MEASUREMENTS
Oybek Rustamov Richard Russell
Cheryl Fitzgerald Stephen Troup Stephen A Roberts
22
66
Title
AMH Gen II assay A validation study of observed variability between
repeated AMH measurements
Authors
Oybek Rustamov 1 Richard Russell2 Cheryl Fitzgerald1 Stephen Troup2
Stephen A Roberts3
Institutions
1Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospitals NHS Foundation Trust Manchester
M13 9WL UK
2Hewitt Fertility Centre Liverpool Womenrsquos NHS Foundation Trust Hospital
Crown Street Liverpool L8 7SS
3 Centre for Biostatistics Institute of Population Health University of
Manchester Manchester M13 9PL UK
Word count 1782
Conflict of interest Authors have nothing to disclose
Acknowledgment
The authors would like to thank the Biomedical Andrology Laboratory team at
the Hewitt Fertility Centre for their assistance
67
Declaration of authorsrsquo roles
OR coordinated the study conducted the statistical analysis and prepared first
draft of the manuscript RR extracted data prepared the dataset assisted in
preparation of first draft of manuscript CF ST and SR involved in study
design oversaw statistical analysis contributed to the discussion and
preparation of the final version of the manuscript
68
ABSTRACT
Objective
To study the within patient sample-to-sample variability of AMH levels using
the Gen II assay reproduced in an independent population and laboratory
Design Retrospective cohort analysis
SettingTertiary referral IVF Unit in the United Kingdom
Patients Women being investigated for sub-fertility
Interventions
Retrospective measurements were obtained from women who had AMH
measurements using Gen II assay during routine investigation for infertility at a
tertiary referral unit during a 1-year period The patients who had repeated
AMH measurements were identified and within-patient coefficient of variation
(CV) calculated using a mixed effects model with quadratic adjustment for age
Main Outcome Measures
The within-patient coefficient of variation (CV) calculated using a random
effects model with quadratic adjustment for age
Results
There was in total of 76 samples from 38 women with repeated AMH
measurements during the study period The within-patient sample-to-sample
variation (CV) was found to be 62
Conclusions
The study has confirmed that even when samples are processed promptly and
strictly in accordance with the manufacturers instructions substantial
variability exists between repeated samples Thus caution is recommended in
the use of these newer assays to guide treatment decisions Further work is
required to understand the underlying cause of this variability
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA AMH ELISA sample variability
69
INTRODUCTION
Anti-Muumlllerian hormone is a dimeric glycoprotein that is produced by
the granulosa cells of pre-antral and early antral follicles and has been found to
be the primary regulator of oocyte recruitment and folliculogenesis (Durlinger
et al 1999 Durlinger et al 2001) Strong correlation between AMH levels and
primordial follicle count (Hansen et al 2011) and hence a reflection of ovarian
response has promised a valuable tool in the reproductive specialistsrsquo armory
The development of commercially available AMH immunoassay assay kits has
heralded the widespread introduction and routine usage of AMH assessment in
the clinical setting Several studies have demonstrated that AMH serves as a
good predictor of ovarian response to gonadotrophin stimulation during IVF
treatment (van Rooij et al 2002 Nelson et al 2007 Nardo et al 2009) AMH
testing has also been shown to identify patients at risk of excessive ovarian
response and ovarian hyperstimulation syndrome (Yates et al 2011) with
consequent reduction in per cycle treatment costs by adopting an antagonist
approach during controlled ovarian stimulation Sensitivity and specificity of
AMH in detecting extremes of response has been shown to be comparable to
antral follicle count without the apparent technical limitations of the latter
(Broer et al 2009 Broer et al 2011)
It is stated that the sample-to-sample variation of AMH concentration in
individual women is small and therefore a single AMH measurement has been
recommended as standard practice (La Marca et al 2006 Hehenkamp et al
2006) However recent studies based on data from a single centre recently
published in Human Reproduction found that larger variability between
repeated samples exists which is particularly profound when currently
available second generation AMH assay (AMH Gen II ELISA Beckman
Coulter Inc Brea CA USA) is used (Rustamov et al 2012a Rustamov et al
2012b Rustamov et al 2011)
The trial team had 2 objectives firstly to assess whether the controversial
findings from the above study (Rustamov et al 2012a) were reproducible when
performed in the data based on the samples from a different laboratory with
differing populations If our study reached similar conclusions concerns
regarding the AMH Gen II assay and or manufacturers recommendations on
handling and sampling processes would be validated Alternatively if non-
70
similar findings were reported the laboratory performance in the initial study
ought to be questioned Secondly and more importantly if the repeat samples
are found to be within acceptable parameters then the current clinical standard
of a single random AMH measurement in patients is appropriate If the results
of repeated samples are significantly different following adjustment for age it
would suggest that AMH measurement is not a true estimation of the patientrsquos
ovarian reserve
In view of clinical and research implications of these findings we
undertook to replicate the variability study in a second fertility centre The
authors wish to note that Beckman Coulter recently issued a worldwide STOP
SHIP order on all AMH Gen II Elisa assay kits until further notice due to
manufacturing and quality issues
MATERIALS AND METHODS
Population
Women had serum AMH measurements using Gen II AMH assay from
15 April 2011 to 25 May 2012 for investigation of infertility at the Hewitt
Fertility Centre in the Liverpool Womens NHS Foundation Trust Hospital
tertiary referral unit were identified using the Biochemistry Laboratory AMH
samples database and all women within age range of 20-46 years were included
in the study The main reasons for repeating the samples were a) obtaining up-
to-date assessment of ovarian reserve b) patient request and c) for formulation
of a treatment strategy prior to repeat IVF cycles
Institutional Review Board approval was granted by the Audit
Department Liverpool Womenrsquos NHS Foundation Trust Hospital
Assay procedure
Samples were transported immediately to the in-house laboratory of
Liverpool Womenrsquos Hospital for the processing and analysis The serum was
separated within 8 hours from venipuncture and frozen at -50C until analyzed
71
in batches The sample preparation and assay methodology strictly followed
the manufacturers guidelines The AMH analysis of laboratory is regularly
monitored by external quality assessment scheme (UKNEQAS) and
performance has been satisfactory
The samples were analyzed using enzymatically amplified two-site
immunoassay (AMH Gen II ELISA Beckman Coulter Inc Brea CA USA)
The intra-assay CV was 521 and inter-assay CV (n=9) was 276 (low
controls) and 657 (high controls) The working range of the assay was
150pmolL and the minimum detection limit was 057pmolL
The main difference in the assay preparation in this study is that the
samples were processed within 8 hours whilst the samples in the previous
study were processed within 2 hours (Rustamov 2012a) Importantly the kit
insert of Gen II AMH assay does not state any maximum duration of storage
of unprocessed samples or any constraints on the transportation of
unprocessed samples Therefore there appears to be considerable variation in
practice of sample processing between clinics which ranges from processing
samples immediately to shipping unfrozen whole samples to long distances
Statistical analysis
The dataset was obtained from the Biomedical Andrology Laboratory
of the hospital and anonymised by one of the researchers (RR) Data
management and analysis of the anonymised data followed the same
procedures as the previous study (13) and were performed using Stata 12
Statistical Package (StataCorp Texas USA) Approval for data management
analysis and publication was obtained from the Research and Development
Department of Liverpool Womenrsquos Hospital
Between and within-subject sample-to-sample coefficient of variability
(CV) as well as the intra correlation coefficient (ICC) was estimated using a
mixed effects model in log (AMH) with quadratic adjustment for age AMH
levels of the samples that fell below minimum detection limit of the assay
(lt057 pmolL) were arbitrarily assigned a value of 031 pmolL in line with
the previous analysis (Rustamov et al 2012a)
72
RESULTS
During the study period in total of 1719 women had AMH
measurements using Gen II assay Thirty-eight women had repeated AMH
measurements with a total number of 76 repeat samples (Figure 1) The
median age of the women was 318 (IQR 304-364) The median AMH level
was 52pmolL (IQR 15-114) The median interval between samples was 93
days (IQR 49-164) with range of 6-375 days Age-adjusted regression analysis
of samples of these women showed that within-patient sample-to-sample
coefficient of variation (CV) of AMH measurements was 62 while between-
patient CV was 125 An age adjusted intra-correlation coefficient was 079
Figure 1 The repeated AMH measurements by date lines join the
repeats from the same patients (AMH in pmolL)
73
DISCUSSION
A number of studies have recently been published that have expressed
concerns regarding the stability and reproducibility of AMH results Whilst
technical issues regarding reproducibility between assays were known more
recently the reproducibility of results regarding the current Gen II assay has
raised significant concern (Rustamov et al 2012a Rustamov et al 2012b
Rustamov et al 2011) Proponents of the assay have proposed that poor
sample handling and preparation are responsible for these observed concerns
(Nelson et al 2013) Several studies have observed the stability of samples at
room temperature Kumar et al (Kumar et al 2010) observed a 4 variation in
results after 7 days storage compared with those samples analysed immediately
These results were consistent with studies by Fleming and Nelson who also
reported no change in AMH concentration over a period of several days
(Fleming et al 2012) However Rustamov et al reported a measured AMH
increase of 58 in samples stored at room temperature over a seven day
period (Rustamov et al 2012a) Similar concerns were raised regarding the
appropriate freezing process whilst samples frozen at -20C demonstrated
variation in results of between 6 and 22 (Durlinger et al 1999 Rustamov et al
2012a) freezing at -80C obviated a significant variation in assay results (Al-
Qahtani et al 2005 Rustamov et al 2012a) Several studies initially reported
good linearity of dilution (Kumar et al 2012 Preissner et al 2010 Fleming et al
2012) which was contradicted by reports that demonstrated poor linearity in
dilution when fresh samples were utilized (Rustamov et al 2012a) This study
suggested a tendency of AMH results to double with dilution More recently
Beckman Coulter issued a warning on their Gen II AMH ELISA kits that the
dilution of sample may give an erroneous result confirming non linearity of
dilution (King Dave 2012)
A number of studies have looked at the variability of AMH in repeated
samples without account to the menstrual cycle utilizing different assays
Dorgan et al in analyzing DSL samples frozen for prolonged periods
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median-sample interval of one year (Dorgan et al 2012)
Rustamov et al presented a larger series of 186 infertile patients with a median
between-sample interval of 26 months and a CV of 28 in DSL samples
74
(ICC 091 95 CI 090-093(Rustamov et al 2011) In a follow-up study
utilizing the Gen II assay in a group of 84 infertile patients the coefficient
variation of repeated results was 59 (ICC of 084 95 CI 079-090) a
substantial increase in the observed variability of the studies reporting for the
DSL assay (Rustamov et al 2012a) The most recent study to cast doubt on
current practice suggested that repeated measurement of AMH using Gen II
assay resulted in a within-subject variability of 80 (CV) (Hadlow et al 2012)
As a result 7 out of 12 women were subsequently reclassified according to their
originally predicted ovarian response Our study outlined above involving 76
samples from 38 infertile patients demonstrated a within-patient sample-to-
sample coefficient of variation (CV) of AMH measurements was 62
Overall these results suggest that there is significant within patient
variability that may be more pronounced in the Gen II assay Whilst biological
variation has been demonstrated to play a part within this the appreciative
effects of sample handling storage and freezing play a significant part in the
results and it may be that the Gen II assays may be more susceptible to these
changes This study has confirmed that there is significant within-patient
sample-to-sample variability in AMH measurements when the Gen II AMH
assay is used which is not confined to a single population or laboratory It is
important to note that the samples reported by both Rustamov et al 2012
and this study were processed and analyzed strictly according to
manufacturerrsquos recommendations in their respective local laboratories without
external transportation (Rustamov et al 2012a) Therefore it seems reasonable
to suggest that AMH results from other centers and laboratories are likely to
display similar significant sampling variability
Reproducibility of AMH measurements is of paramount importance
given that a single random AMH measurement is used for triaging patients
unsuitable for proceeding with IVFICSI and determining the dose of
gonadotrophins for ovarian stimulation for those patients who proceed with
treatment Similarly other clinical applications of AMH such as an assessment
of the effect of chemotherapy to fertility and follow up of women with history
of granulosa cell tumors also rely on accurate measurement of circulating
hormone levels The present work confirms the high between-sample within-
patient variability The recent warning from Beckman Coulter utilizing their
Gen II ELISA assay kits may give an erroneous result with dilution of samples
further questions the stability of the assay (King David 2012) Subsequently
75
the manufacturer recalled the assay kits due to issues with the instability of
samples and introduced modified protocol for preparation of Gen II assay
samples
Given there can be a substantial difference between two samples from
the same patient the use of such measurements for clinical decision-making
should be questioned and caution is advised
76
References
Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP and Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 2005 63267-273
Broer SL Dolleman M Opmeer BC Fauser BC Mol BW Broekmans FJM AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 20111746-54
Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL and Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
Fleming R and Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
Hadlow Narelle Longhurst Katherine McClements Allison Natalwala Jay Brown Suzanne J and Matson Phillip L Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response (Article in press) Fertil Steril 2012
Hansen KL Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170-5
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 King Dave URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L and Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593 6
77
Nelson S Biomarkers of ovarian response current and future applications Fertil and Steril 201399963-969
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton Reply Reproducibility of AMH Hum Reprod 2012b273641-3642
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Preissner CM Morbeck DE Gada RP and Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011261768-74
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse patient variability Fertil Steril 2011951185-118
78
THE MEASUREMENT OF ANTI-MUumlLLERIAN
HORMONE A CRITICAL APPRAISAL
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan
Luciano G Nardo Philip W Pemberton
The Journal of Clinical Endocrinology amp Metabolism
2014 Mar 99(3) 723-32
3
79
Title
The measurement of Anti-Muumlllerian hormone a critical appraisal
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK d Manchester Royal Infirmary Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Key terms
Anti-Muumlllerian hormone AMH Active MISAMH ELISA Diagnostic
Systems Laboratories AMHMIS ELISA Immunotech AMH Gen II assay
Beckman Coulter
Word Count 3947 (intro ndash general summary text only (no headings)
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
80
Declaration of authorsrsquo roles
The idea was developed during discussion between OR CF and SAR
OR conducted the initial appraisal of the studies prepared and revised the
manuscript SAR and CF contributed to the discussion and interpretation of
the studies and oversaw the revision of the manuscript PWP AY MK
and AS reviewed the data extraction and interpretation contributed to
the discussion of the studies and revision of the manuscript LGN
contributed to the discussion of the studies and revision of the manuscript
81
ABSTRACT
Context
Measurement of AMH is perceived as reliable but the literature reveals
discrepancies in reported within-subject variability and between-assay
conversion factors Recent studies suggest that AMH may be prone to pre-
analytical instability We therefore examined the published evidence on the
performance of current and historic AMH assays in terms of the assessment of
sample stability within-patient variability and comparability of the assay
methods
Evidence Acquisition
Studies (manuscripts or abstracts) measuring AMH published between
01011990 and 01082013 in peer-reviewed journals using appropriate
PubMedMedline searches
Evidence Synthesis
AMH levels in specimens left at room temperature for varying periods
increased by 20 in one study and almost 60 in another depending on
duration and the AMH assay used Even at -20degC increased AMH
concentrations were observed An increase over expected values of 20-30 or
57 respectively was observed following two-fold dilution in two linearity-of-
dilution studies but not in others Several studies investigating within-cycle
variability of AMH reported conflicting results although most studies suggest
variability of AMH within the menstrual cycle appears to be small However
between-sample variability without regard to menstrual cycle as well as within-
sample variation appears to be higher using the Gen II AMH assay than with
previous assays a fact now conceded by the kit manufacturer Studies
comparing first generation AMH assays with each other and with the Gen II
assay reported widely varying differences
Conclusions AMH may exhibit assay-specific pre-analytical instability
Robust protocols for the development and validation of commercial AMH
assays are required
82
INTORDUCTION
In the female AMH produced by granulosa cells of pre-antral and early
antral ovarian follicles regulates oocyte recruitment and folliculogenesis (1 2)
It can assess ovarian reserve (3-5) and guide gonadotrophin stimulation in
assisted reproduction technology (ART) (6) AMH is also used as a granulosa
cell tumour marker a marker of ovarian reserve post-chemotherapy (7 8) and
to predict age at menopause (910)
AMH immunoassays first developed by Hudson et al in 1990 (11) were
introduced commercially by Diagnostic Systems Laboratories (DSL) and
Immunotech (IOT) These assays were integrated into a second-generation
AMH assay GenII (12) by Beckman-Coulter but recent work suggests that this
new assay exhibits clinically important within-patient sample variability (13-
15) Beckman Coulter have recently confirmed this with a field safety notice
(FSN 20434-3) they cite without showing evidence for complement
interference as the problem
ldquoTruerdquo AMH variability comprises both biological and analytical
components (Figure 1) and given the varying antibody specificity and
sensitivity of different AMH assays then logically different kits will respond to
these components to varying degrees This review considers the published
literature on AMH measurement using previous and currently available assays
Potential sources of variation and their contribution to observed AMH
variability were identified
Review structure
This review has been divided into logical subgroups We first address the
stability of AMH at different storage temperatures then the effects of
freezethaw cycles and finally AMH variability in dilution studies Secondly
the within-person variability of AMH measurement is considered
encompassing intra- and inter-menstrual cycle variability and repeat sample
variability in general The final section covers AMH method comparisons
comparing older methods to each other and to the newer now prevalent
GenII method finishing with data on published guidance ranges concerning
the use of AMH in ART A general summary concludes the paper
83
Systematic review
The terms ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting
Substance and MIS were used to search the PubMedMedline MeSH
database between 1st January 1990 and 1st August 2013 for publications in
English commenting on AMH sample stability biological and sample-to-
sample variability or assay method comparison in human clinical or healthy
volunteer samples Titles andor abstracts of 1653 articles were screened to
yield the following eligible publications ten stability studies 17 intrainter-
cycle variability studies and 14 assay method comparability studies
Sample stability
Recent work has established that the GenII-measured AMH is
susceptible to significant preanalytical variability (13 14) not previously
acknowledged which may have influenced results in previous studies with this
assay
Stability of unfrozen samples
Five studies examined AMH stability in samples stored either at room or
fridge temperature (Table 1) (13 16-19) Al-Qahtani et al (16) assessing the
precursor of the DSL ELISA reported that ldquoimmunoreactivity survived the
storage of samples unfrozen for 4 daysrdquo but did not record storage
temperature or sample numbers Evaluating the GenII assay Kumar et al (18)
stored 10 samples at 2-8degC for up to a week and found an average 4
variation compared to samples analysed immediately However their
specimens originally reported as ldquofreshrdquo appear to have been kept cool and
transported overnight Fleming amp Nelson (19) reported no significant change
in the GenII-assayed AMH from 51 samples stored at 4degC Methodological
information was limited but interrogation of their data by Rustamov et al (14)
suggested that AMH levels rose by an average of 27 after 7 days storage
Zhao et al (17) reported a difference of less than 20 between DSL-assayed
AMH in 7 serum samples kept at 22degC for 48 hours when compared to
aliquots from the same samples frozen immediately at -20degC Rustamov et al
(13) measured AMH (GenII) daily in 48 serum samples at room temperature
for 7 days and observed an average 58 increase (from 0 to gt200) whilst
others (20) reported a 31 mean rise in GenII-assayed AMH in whole blood
84
after 90hrs at 20oC whereas serum AMH was virtually unchanged after
prolonged storage at 20oC
Sample stability at -20 o or -80oC and the effects of freezethaw
Rey et al (21) reported a significant increase in AMH (in-house assay)
in samples stored at -20degC for a few weeks attributing this to proteolysis
which could be stabilised with protease inhibitor (see discussion below)
Kumar et al (18) saw 6 variation between GenII-assayed AMH levels from
10 fresh and 10 frozen samples whilst Rustamov et al (13) observed a 22
increase in AMH (GenII) on re-analysis of 8 serum samples after 5 days
storage at -20degC These authors saw no AMH increase in serum stored at -80deg
C for the same period
Linearity of dilution
Six studies examined linearity of dilution on observed AMH
concentrations Long et al (22) recovered between 84 and 105 of the
expected AMH concentration (IOT n=3) AMH dilution curves parallel to
the standard curve were reported by others (16)Kumar et al (18) (n=4) and
Preissner et al (23) ) (n=7) reported GenII-assayed AMH recoveries from 95
to 104 and 96 respectively Sample handling information was limited in
some of these studies (16 23) Fleming amp Nelson (19) (GenII n=10) reported
variances of 8 using assay diluent and 5 using AMH-free serum following
2-fold dilution however interrogation of their data reveals an apparent
dilutional AMH increase of 20-30 in samples stored prior to dilution and
analysis Rustamov et al (13) (GenII n=9) in freshly collected serum observed
an average 57 increase in apparent AMH concentration following two-fold
dilution but with considerable variation
Discussion Sample stability
Sample stability can be a major analytical problem and detailed
examination suggests that previous evidence stating that commercially
measured AMH is stable in storage and exhibits linearity of dilution (12 16 18
19) is weak or conflicting
No study looking at room temperature storage on IOT-assayed AMH
was found and only one using DSL-assayed AMH which showed an increase
85
of less than 20 during storage (17) Studies using the GenII assay to
investigate the effect of storage on AMH variability at room temperature in
the fridge and at -200C reach differing conclusions ranging from stable to an
average 58 increase in measured levels It is important to note here that
sample preparation and storage prior to these experiments was different and
could account for the observed discrepancies The most stable storage
temperature for AMH in serum appears to be -80degC (13 16)
Linearity of dilution studies were also conflicting (13 18 19 23) those
reporting good linearity used samples transported or stored prior to baseline
analysis whereas dilution of fresh samples showed poor linearity In late 2012
Beckman Coulter accepted that the GenII assay did not exhibit linear dilution
and issued a warning on kits that samples should not be diluted They now
suggest that with the newly introduced pre-mixing protocol dilution should
not be a problem
This review highlights the fact that assumptions about AMH stability in
serum were based on a limited number of small studies often providing
limited methodological detail (impairing detailed assessment and comparison
with other studies) using samples stored or transported under unreported
conditions Furthermore conclusions derived using one particular AMH assay
have been applied to other commercial assays without independent validation
The available data suggests that dilution of samples andor storage or
transport in sub-optimal conditions can lead to an increase in apparent AMH
concentration The conditions under which this occurs in each particular AMH
assay are not yet clear and more work is required to understand the underlying
mechanisms Two alternative hypotheses have been proposed firstly that
AMH may undergo proteolytic change as postulated by Rey et al (21) or
conformational change as proposed by Rustamov et al (1314) during storage
resulting in ldquostabilisationrdquo of the molecule in a more immunoreactive form
secondly Beckman have postulated the presence of an interferent
(complement) which degrades on storage (Beckman Coulter field safety notice
FSN 20434-3)
A recent case report found that a falsely high AMH level was corrected
by the use of heterophylic antibody blocking tubes (24) but this does not
explain elevation of AMH on storage (13)
Whatever the mechanism responsible two solutions are available either
inhibit the process completely or force it to completion prior to analysis
86
Rustamov et al (13) and Han et al (15) both suggest pre-dilution of samples to
force the process a protocol now adopted by Beckman Coulter in their revised
GenII assay protocol Any solution must be robustly and independently
validated both experimentally and clinically prior to introduction in clinical
practice Fresh optimal ranges for interpretation of AMH levels in ART will be
needed and the validity of studies carried out using unreported storage
conditions may have to be re-evaluated
Within-person variability
The biological components of AMH variability such as circadian and
interintra-cycle variability have been extensively studied (Table 2 amp
Supplementary table 1)
Circadian variation
Bungum et al (25) evaluated circadian variability measuring AMH
(IOT) two hourly over 24hrs within day 2ndash6 of the menstrual cycle in younger
(20-30 years) and older (35-45 years) women Within-individual CVs of 23
(range 10-230) in the younger group and 68 (range 17-147) in the older
group were observed
Variability within the menstrual cycle
Cook et al (26) observed significant (12) variation in mean AMH (in-
house) levels in 20 healthy women throughout different phases of the
menstrual cycle Intra-cycle variability of IOT-assayed AMH was reported in
three publications (27-29) In two sequential samples were stored at -20degC
until analysis (27 28) Streuli et al (29) did not report on storage La Marca et
al (27) saw no difference in mean follicular phase AMH levels (days 2 4 and 6)
in untreated spontaneous menstrual cycles from 24 women This group went
on to report a small insignificant change (14) in within-group AMH
variability throughout the whole menstrual cycle in 12 healthy women
However this analysis does not appear to allow for correlations within same-
patient samples Streuli et al (29) studied intra-cycle variation of AMH
throughout two menstrual cycles in 10 healthy women and also reported no
significant changes (lt5)
87
The DSL assay was used in eight studies assessing intra-cycle variability
(30-37) Four studied sample storage at -20deg C (30323437) and two studied
samples storage at -80degC (3335) No sample storage data was given in two
publications (31 36) Hehenkamp et al (30) assessed within-subject variation
of AMH in 44 healthy women throughout two consecutive menstrual cycles
and reported an intra-cycle variation of 174 Lahlou et al (31) reported a
ldquodiphasicrdquo pattern of AMH with a significant decrease in levels during the LH
surge from 10 women at various cycle phases Tsepelidis et al (32) reported a
mean intra-cycle coefficient of variation of 14 comparing group mean AMH
levels in 20 women during various stages of the menstrual cycle Wunder et al
(33) reported an intra-cycle variability of around 30 in 36 healthy women
sampling on alternate days They saw a marked fall around ovulation which
might have been missed with less frequent sampling intervals as in other
studies Sowers et al (35) studied within-cycle variability in 20 healthy women
but did not compute an overall estimate instead they selected subgroups of
low and high AMH and reported significant within-cycle variability for women
with high AMH but not those with low AMH - an analysis that has been
questioned (38 39) Robertson et al (36) subgrouped mean AMH levels in 61
women observing that AMH levels were stable in women of reproductive age
and ovulatory women in late reproductive age whilst AMH in other women in
late reproductive age was much more variable Using the data from
Hehenkamp et al (30) van Disseldorp et al (34) calculated intra-class
correlation (ICC) and reported a within-cycle variability of 13 although this
was not clearly defined Using the same data Overbeek et al (37) analyzed the
absolute intra-individual difference in younger (38 years) and older (gt38
years) women This study concluded that the AMH concentration was more
variable in younger women (081059 gL) compared to older women
(031029 gL) during the menstrual cycle (P=0001) thus a single AMH
measurement may be unreliable A recent study using the GenII assay
reported 20 intra-cycle variability in AMH measurements in women (n=12)
with regular ovulatory cycles (40) All the reports considered have findings
consistent with a modest true systematic variability of 10-20 in the level of
AMH in circulation during the menstrual cycle Whilst there have been
suggestions that this variability may differ between subgroups of women these
88
have been based on post-hoc subgroup analyses and there is no convincing
evidence for such subgroups (38)
Variability between menstrual cycles
Three studies (Supplementary table 1) evaluated AMH variability in
samples taken during the early follicular phase of consecutive menstrual cycles
(102941) and three studies have reported on the variability of AMH in repeat
samples from the same patient taken with no regard to the menstrual cycle
(134243) One study employed an in-house assay (41) one study used the
IOT assay (29) three studies used the DSL assay (10 42 43) and one study
(13) used the GenII assay In four infertile women Fanchin et al (41) assessed
the early follicular phase AMH (in-house) variability across three consecutive
menstrual cycles they concluded that inter-sample AMH variability was
characterised by an ICC of 089 (95 CI 083-094) Streuli et al (29)
calculated a between-sample coefficient of variation of 285 in AMH (IOT)
in 10 healthy women In 77 infertile women van Disseldorp et al (10) found
an inter-cycle AMH (DSL) variability of 11 In summary these studies
suggest that the overall inter-cycle variability of AMH ranges from 11 (DSL)
to 28 (IOT) this figure will include both biological and measurement-related
variability
Variability between repeat samples
Variability between repeat samples without regard to menstrual cycle
phase was examined in three studies (Supplementary table 1) In a group of 20
women using samples frozen for prolonged periods Dorgan et al (42)
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median between-sample interval of one year In a larger series
of 186 infertile women Rustamov et al (43) (DSL) found a CV of 28
between repeated samples with a median between-sample interval of 26
months (ICC 091 95 CI 090-093) Rustamov et al (13) found that the
coefficient of variation of repeated GenII-assayed AMH in a group of 84
infertile women was 59 (ICC of 084 95 CI 079-090) substantially higher
than that reported using the DSL assay Similarly a recent study by Hadlow et
al (40) found a within-subject GenII-assayed AMH variability of 80 As a
89
result 5 of the 12 women studied crossed clinical cut-off levels following
repeated measurements
Discussion Within-patient variability
Evidence suggests that repeated measurement of AMH can result in
clinically important variability particularly when using the GenII assay This
questions the assumption that a single AMH measurement is acceptable in
guiding individual treatment strategies in ART
The observed concentration of any analyte measured in a blood
(serum) sample is a function of its ldquotruerdquo concentration and the influence of a
number of other factors (Figure 1) Studies examining the variability of AMH
by repeated measurement of the hormone will therefore reflect both true
biological variation and measurement-related variability introduced by sample
handling andor processing Thus within-sample inter-assay variability used as
an indicator of assay performance may not reflect true measurement-related
variability between samples since it does not take into account the contribution
from pre-analytical variability Measurement-related between-sample variability
can be established in part using blood samples taken simultaneously (to avoid
biological variability) from a group of subjects although even this does not
reflect the full variability in sample processing and storage inherent in real
clinical measurement
Since AMH is only produced by steadily growing ovarian follicles it is
plausible to predict a small true biological variability in serum reflected in the
modest 1-20 variability found within the menstrual cycle In contrast it
appears that the magnitude of measurement-related variability of AMH is more
significant a) within-sample inter-assay variation can be as high as 13 b)
different assays display substantially different variability and c) AMH appears
to be unstable under certain conditions of sample handling and storage (Table
1) Consequently any modest variation in true biological AMH concentration
may be overshadowed by a larger measurement-related variability and careful
experimental designs are required to characterise such differences In general
the reported variability in published studies should be regarded as a measure of
total sample-to-sample variability ie the sum of biological and measurement-
related variability (Figure 1)
90
In repeat samples the available evidence confirms that there is a
significant level of within-patient variability between measurements which is
assay-dependent greater than the estimates of within cycle variability and
therefore likely to be predominantly measurement-related Evidence from
several sources suggests that the effects of sample handling storage and
freezing differ between commercial assays and that the newer GenII assay may
be more susceptible to these changes under clinical conditions When it has
been established that the modified protocol for the GenII assay can produce
reproducible results independent of storage conditions then it will be
necessary to re-examine intra and inter cycle variability of AMH
Assay method comparability
AMH assay comparisons have either used same sample aliquots or
used population-based data with repeat samples Study population
characteristics sample handling inter-method conversion formulae and results
from these comparisons are summarised in Table 3 AMH levels were almost
universally compared using a laboratory based within-sample design The
Rustamov et al study (13) was population-based comparing AMH results in
two different samples from the same patient at different time points using 2
different assays
IOT vs DSL
Table 3 summarises 8 large studies (17 29 30 44-48) that compared the
DSL and IOT AMH assays They demonstrate strikingly different conversion
factors from five-fold higher with the IOT assay to assay equivalence Most
studies carried out both analyses at the same time to avoid analytical variation
(Figure 1) However this does mean that samples were batched and frozen at -
18degC to -80degC prior to analysis which as already outlined may influence pre-
analytical variability and contribute to the observed discrepancies in conversion
factors
IOT vs GenII
Three studies have compared the IOT and Gen II assays (Table 3)
Kumar (18) reported that both assays gave identical AMH concentrations
However Li et al (48) found that the IOT assay produced AMH values 38
91
lower than the Gen II assay whilst Pigny et al (49) found levels that were 2-fold
lower
DSL vs GenII
Four studies analysed same-sample aliquots using the DSL and GenII
assays either simultaneously or sequentially (33 48 50 51) Only Li et al (48)
gave details of sample handling (Table 3) All four studies found that AMH
values that were 35 ndash 50 lower using the DSL compared to the GenII assay
Rustamov et al (13) carried out a between-sample comparison of the assays
measuring AMH in fresh or briefly stored clinical samples from the same
women at different times with values adjusted for patient age (Table 3) In
contrast to within-sample comparisons this study found that the DSL assay gave
results on average 21 higher than with the GenII assay Whilst this
comparison is open to other bias it does reflect the full range of variability
present in clinical samples and avoids issues associated with longer term
sample storage
Discussion Assay method comparability
It is critical for across-method comparison of clinical studies that
reliable conversion factors for AMH are established In-house assays aside
three commercially available AMH ELISAs have been widely available (IOT
DSL and GenII) and the literature demonstrates considerable diversity in
reported conversion factors between first-generation assays (DSL vs IOT)
and between first and second-generation immunoassays (DSLIOT vs GenII)
Although most studies appear to follow manufacturersrsquo protocols
detailed methodological information is sometimes lacking The assessment of
within-sample difference between the two assays involved thawing of a single
sample and simultaneous analysis of two aliquots with each assay Both
aliquots experience the same pre-analytical sample-handling and processing
conditions therefore the results should be reproducible provided the AMH
samples are stable during the post-thaw analytical stage and the study
populations are comparable However this review has identified significant
discrepancies between studies perhaps due to either significant instability of
the sample or significant variation in assay performance Studies comparing
AMH levels measured using different assays in populations during routine
92
clinical use have also come to differing conclusions (13 51) Given the study
designs that workers have used to try to ensure that samples are comparable
the finding of significant discrepancies in the observed conversion factors
between assays is consistent with the proposal that AMH is subject to
instability during the pre-analytical stage of sample handling This coupled
with any differential sensitivity and specificity between these commercial
assays could give rise to the observed results ie some assays are more
sensitive than others to pre analytical effects
AMH guidance in ART
AMH guidance ranges to assess ovarian reserve (52) or subsequent
response to treatment (53 54) have been published The Doctors Laboratory
using the DSL assay advised the following ranges for ovarian reserve (lt
057pmolL-undetectable 057-21 pmolL-very low 22-157 pmolL-low
158-286 pmolL-satisfactory 287-485pmolL-optimal gt485pmolL-very
high) ranges that supposedly increased by 40 on changing to the GenII assay
(51) More recently other authors have attempted to correlate AMH levels with
subsequent birth rates Brodin et al (53) using the DSL assay observed that
higher birth rates were seen in women with an AMH level gt 21 pmolL and
low birth rates were seen in women who had AMH levels lt 143 pmolL In
the UK the National Institute for Health and Care Excellence (NICE) have
recently issued guidance on AMH levels in the assessment of ovarian reserve in
the new clinical guideline on Fertility (54) They advise that an AMH level of le
54 pmolL would indicate a low response to subsequent treatment and an
AMH ge 250 pmolL indicates a possible high response Although not
specifically stated interrogation of the guideline suggests that these levels have
been obtained using the DSL assay which is no longer available in the UK
As discussed above the initial study of comparability between the DSL
and GenII assays reported that GenII generated values 40 higher compared
to the DSL assay clinics were therefore recommended to increase their
treatment guidance ranges accordingly (51) However a more recent study
using fresh samples found that the original GenII assay may actually give
values which are 20-30 lower suggesting that following the above
recommendation may lead to allocation of patients to inappropriate treatment
groups (13) The apparent disparity in assay comparison studies implies that
93
AMH reference ranges and guidance ranges for IVF treatment which have
been established using one assay cannot be reliably used with another assay
method without full independent validation Similarly caution is required
when comparing the outcomes of research studies using different AMH assay
methods
General Summary
Recent publications have suggested that GenII-assayed AMH is
susceptible to pre-analytical change leading to significant variability in
determined AMH concentration an observation now accepted by the kit
manufacturer However this review suggests that all AMH assays may display a
differential response to pre-analytical proteolysis conformational changes of
the AMH dimer or presence of interfering substances The existence of
appreciable sample-to-sample variability and substantial discrepancies in
between-assay conversion factors suggests that sample instability may have
been an issue with previous AMH assays but appears to be more pronounced
with the currently available GenII immunoassay The observed discrepancies
may be explicable in terms of changes in AMH or assay performance that are
dependent on sample handling transport and storage conditions factors
under-reported in the literature We strongly recommend that future studies on
AMH should explicitly report on how samples are collected processed and
stored If it can be clearly demonstrated that the new GenII protocol drives
this process to completion in all samples ensuring stability then a re-
examination of reference and guidance ranges for AMH interpretation will be
necessary There is a clear need for an international reference standard for
AMH and for robust independent evaluation of commercial assays in routine
clinical samples with well-defined sample handling and processing protocols
These issues of sample instability and lack of reliable inter-assay comparability
data should be taken into account in the interpretation of available research
evidence and the application of AMH measurement in clinical practice
94
References
1 Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
2 Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
3 van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
4 Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
5 Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009921586-1593 6 Yates AP Rustamov O Roberts SA Lim HYN Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353ndash2362
7 Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-55
8 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
9 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
10 van Disseldorp J Lambalk CB Kwee J Looman CW Eijkemans MJ Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Muumlllerian hormone and antral follicle counts Hum Reprod 201025221-227
11 Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
95
12 Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642
15 Han X McShane M Sahertian R White C Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Hum Reprod 201328 (suppl 1)i76-i78 (abstract)
16 Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 200563267-273
17 Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 200788S17 (abstract)
18 Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
19 Fleming R Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
20 Fleming R Fairbairn C Blaney C Lucas D Gaudoin M Stability of AMH measurement in blood and avoidance of proteolytic changes Reprod Biomed Online 201326130-132
21 Rey R Lordereau-Richard I Carel JC Barbet P Cate RL Roger M Chaussain JL Josso N Anti-Mullerian hormone and testosterone serum levels are inversely related during normal and precocious pubertal development J Clin Endocrinol Metab 199377 1220ndash1226
22 Long WQ Ranchin V Pautier P Belville C Denizot P Cailla H Lhomme C Picard JY Bidart JM Rey R Detection of minimal levels of serum anti-Mullerian hormone during follow-up of patients with ovarian granulosa cell tumor by means of a highly sensitive enzyme-linked immunosorbent assay J Clin Endocrinol Metab 200085540ndash544
23 Preissner CM Morbeck DE Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54 (abstract)
24 Cappy H Pigny P Leroy-Billiard M Dewailly D Catteau‐Jonard S Falsely elevated serum antimuumlllerian hormone level in a context of heterophilic
96
interference Fertil Steril 2013991729-1732
25 Bungum L Jacobsson AK Roseacuten F Becker C Yding Andersen C Guumlner N Giwercman A Circadian variation in concentration of anti-Mullerian hormone in regularly menstruating females relation to age gonadotrophin and sex steroid levels Hum Reprod 201126678ndash684
26 Cook CL Siow Y Taylor S Fallat ME Serum muumlllerian-inhibiting substance levels during normal menstrual cycles Fertil Steril 200073859-861
27 La Marca A Malmusi S Giulini S Tamaro LF Orvieto R Levratti P Volpe A Anti-Muumlllerian hormone plasma levels in spontaneous menstrual cycle and during treatment with FSH to induce ovulation Hum Reprod 2004192738-2741
28 La Marca A Stabile G Carduccio Artenisio A Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash310729 Streuli I Fraisse T Chapron C Bijaoui G Bischof P de Ziegler D Clinical uses of anti-Mullerian hormone assays pitfalls and promises Fertil Steril 200991226-230
30 Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
31 Lahlou N Chabbert-Buffet N Gainer E Roger M Bouchard P Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11 (abstract)
32 Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
33 Wunder DM Bersinger NA Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrual cycle in reproductive age women Fertil Steril 200889927-933
34 van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormone concentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
35 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 2010 941482-1486
36 Robertson DM Hale GE Fraser IS Hughes CL Burger HG Changes in serum antimuumlllerian hormone levels across the ovulatory menstrual cycle in late reproductive age Menopause 201118521-524
37 Overbeek A Broekmans FJ Hehenkamp WJ Wijdeveld ME van
97
Disseldorp J van Dulmen-den Broeder E Lambalk CB Intra-cycle fluctuations of anti-Mullerian hormone in normal women with a regular cycle a re-analysis Reprod Biomed Online 201224664ndash 669
38 Roberts SA Variability in anti-Mullerian hormone levels a comment on Sowers et al ldquoAnti-Mullerian hormone and inhibin B variability during normal menstrual cyclesrdquo Fertil Steril 201094e59
39 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Reply of the authors Variability in anti-Muumlllerian hormone levels a comment on Sowers et al Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 201094e60
40 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013991791-1797
41 Fanchin R Taieb J Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Muumlllerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 200520923-927
42 Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
43 Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
44 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
45 Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175
46 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
47 Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
48 Li HW Ng EH Wong BP Anderson RA Ho PC Yeung WS Correlation between three assay systems for anti-Mullerian hormone (AMH)
98
determination J Assist Reprod Genet 2012291443-1446
49 Pigny P Dassonneville A Catteau-Jonard S Decanter C Dewailly D Comparative analysis of two-widely used immunoassays for the measurement of serum AMH in women Hum Reprod 2013 28i311-316 (abstract)
50 Gada R Hughes P Amols M Amols M Preissner C Morbeck D Coddington C Validation and comparison of AMH serum levels using the original active MISAMH ELISA to the new active AMH Gen II ELISA Fertil Steril 201195S23 (abstract)
51 Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
52 The Doctors Laboratory Lab Report newsletter ndash Winter 20072008 ndash AMH
53 Brodin T Hadziosmanovic N Berglund L Olovsson M Holte J Antimullerian hormone levels are strongly associated with live birth rates after assisted reproduction J Clin Endocrinol Metab 201398(3)1107-1104
54 National Institute for Care and Health Excellence NICE clinical guideline CG156 Fertility
99
Figure 1 Biological and analytical variability of AMH
100
Table 1 AMH assay validation effect of sample storage conditions freshthaw cycles and linearity of dilution
Study Assay Method Result
Rey et al (21) in-house effect of Long-term storage at -20C (n=4) AMH levels in archival samples were 230 higher than original value
Long et al (22) IOT linearity up to 16-fold dilution (n=3) observed AMH was 84-105 of expected AMH
Al-Qahtani et al (16) in-house a freezethaw stability storage unfrozen for 4 days
b linearity up to 32-fold dilution (n=6)
a immuno-reactivity survived both multiple freeze-thaw cycles and storage unfrozen for 4 days b dilution curves were parallel to the standard curve
Zhao et al (17) DSL
serum frozen immediately at -20C compared to
aliquots stored at 4C or 22C for up to 2 days (n=7) AMH levels increased by 1 at 4C and 9 at 22C after 2 days compared to sample frozen immediately
Kumar et al (18) Gen II
a serum or plasma stored at 2-8C or -20C for up to 7 days (n = 20) b serum or plasma underwent up to three freezethaw cycles (n=20) c linearity of dilution (n=4)
a AMH levels were stable for up to 7 days at 2-8C or -20C
b AMH increased by 15 in serum and by 5 in plasma after 3 cycles c linear results obtained across the dynamic range of the assay
Preissner et al (23) Gen II linearity of dilution (n=7) average agreement with expected result was 97
Rustamov et al (13) Gen II
a stability at RT for up to 7 days (n=48)
b storage for 5 days at -20C or -80C compared to fresh sample (n=8) c linearity on 2-fold dilution (n=9)
a AMH levels increased by an average of 58 over 7 days
b AMH levels increased by 23 at -20C but were unchanged at -80C c AMH levels were on average 157 higher than expected
Fleming amp Nelson (19) Gen II a serum stored at 4C for 7 days (n=48) b linearity of dilution (n=10)
a AMH levels increased by an average of 27 b AMH was 28 amp 33 higher on 2-fold amp 4-fold dilution resp
Fleming et al (20) Gen II
a whole blood stored for up to 90 hours at 4C (n=32) or 20C (n=21)
b serum stored for 5 days at 20C and 2 days at 4C (n=13)
a AMH increased by 11 at 4C and by 31 at 20C b only 1 increase in AMH compared to original value
Han et al (15) Gen II
serum from non-pregnant (n=13) or early pregnant (n=7) women
stored at RT -20C or -80C for up to 7 days
In non-pregnant women AMH increased by 26 after 7 days at RT but was
unchanged at -20C or -80C
In pregnant women AMH increased by 50 at RT and 27 at -80C after 48 hours
101
Table 2 Intra-cycle variability of AMH Study
Subjects
a cycles b day sampled
Assay
a storage b freezethaw c measurement
Result
Authorsrsquo Conclusion
Cook et al (26)
healthy age 22-35 regular cycle (n=20)
a 1 cycle b day 23 LH surge LH surge +7 d
in-house
a -80C b once c inter-assay variation eliminated
day 3 AMH = 14 09ngml
mid cycle AMH = 17 11ngmL
mid luteal AMH = 14 09ngmL
Fluctuations significant (plt0008) AMH may have a regulatory role in folliculogenesis
La Marca et al (27)
healthy age 21-36
regular cycle (n=24)
a follicular phase b alternate days
IOT
a -20C
b once
AMH did not change from days 2 to 6 in spontaneous cycles but decreased progressively in FSH-treated cycles
AMH levels did not change significantly during follicular phase of the menstrual cycle
La Marca et al (28)
healthy age18-24
regular cycle (n=12)
a 1 cycle b alternate days day 0 = day of LH surge
IOT
a -20C
b once
low mean AMH = 3411ngmL (day 14)
high mean AMH =3913ngmL (day 12)
AMH levels did not change significantly throughout menstrual cycle
Lahlou et al (31)
placebo-treated (n=12)
a 1 cycle
b every 3 days
DSL
NR 7 days pre LH surge AMH = 26
32pmolL peak AMH = 191 35pmolL 10 days post LH surge
AMH = 254 43pmolL
AMH levels exhibited a diphasic pattern with levels declining significantly (plt005) during the LH surge
Hehenkamp et al (30)
healthy
fertile regular cycle (n=44)
a 2 cycles
b AMH measured at each of 7 cycle phases
DSL a -20C a sine pattern fitted to AMH data was not significant (p=040) b72 repeat AMH values fell within the same quintile 28 in adjacent quintile
AMH shows no consistent fluctuation through the cycle compared to FSH LH amp E2
van Disseldorp et al (10)
data from Hehenkamp et al (30)
Intra-cycle within-subject variation of AMH was only 13 compared to 31-34 for AFC (dependent on follicle size)
AMH displays less intra-cycle variability than AFC
Overbeek et al (37)
data from Hehenkamp et al (30)
Fluctuations were larger than 05microgL in one cycle in significantly (p = 0001) more women in the younger group than the older one
AMH can fluctuate substantially in younger women during menstrual cycle so a single measurement could be unreliable
102
Tsepelidis
et al (32)
healthy age 18-35 regular cycles (n=20)
a 1 cycle b days 3 7 10-16 18 21 amp 25
DSL
a -20C
b once
Within-cycle differences not significant (p=0408)
AMH levels do not vary during the menstrual cycle
Wunder et al (33)
healthy
age 20-32 regular cycles (n=36)
a 1 cycle
b alternate days
DSL
a -80C
AMH levels were statistically higher in the late follicular phase than at the time of ovulation (p= 0019) or in the early luteal phases (plt00001)
AMH levels vary significantly during the menstrual cycle
Streuli
et al (29)
healthy mean age=241 regular cycles
(n=10)
a 1 cycle b before (LH
-10-5-2-1) and after LH surge (LH +1+2+10)
IOT
a -18C
AMH levels were statistically lower during the early luteal phase compared to early follicular phase (p=0016) and late luteal phase levels (p=002)
In clinical practice AMH can be measured at any time during the menstrual cycle
Sowers et al
(35)
healthy age 30-40 regular cycles
(n=20)
a 1 cycle b daily
DSL
a -80C
b once c simultaneous
Higher AMH levels with significant variation between days 2-7 in the ldquoyounger ovaryrdquo Low AMH levels with little variation in the ldquoaging ovaryrdquo
AMH varies across the menstrual cycle in the ldquoyounger ovaryrdquo
Robertson et al (36)
a age 21-35 regular cycles
(n=43) b age 45-55
variable cycles (n=18)
a 1 cycle + initial stages of succeeding cycle b three times weekly
DSL
NR No intracycle variation in AMH level was found in women in mid reproductive life or in 33 women with regular cycles in late reproductive age In the remaining cycles there was a significant (plt001) two-fold decrease in AMH in 11 cycles and a significant (plt001) 42-fold increase between the follicular amp luteal phases
When AMH levels are substantially reduced they become less reliable markers of ovarian reserve
Hadlow
et al (40)
age 29-43 regular cycles non-PCOS
(n=12)
a 1 cycle b 5-9 samples per subject
Gen II a -20C within 4 hours of sampling b once
c simultaneous
712 women could be reclassified depending on when AMH was measured during the cycle 212 crossed cut-offs predicting hyperstimulation
AMH cycles varied during menstrual cycle and clinical classification of the ovarian response was altered
103
Table 3 Variability in AMH levels between menstrual cycles
Study
Subjects
a cycles b day sampled
Assay
Storage
Result
Authorsrsquo Conclusion
Fanchin et al (41)
infertile
age 25-40 regular cycles
(n=47)
a 3 cycles
b day 3
in-house
(Long et al 2000)
-80C
AMH showed significantly
higher reproducibility than inhibin B (plt003) E2 (plt00001) FSH (plt001) and early AFC (plt00001)
AMH showed improved cycle-to-cycle consistency compared to other markers of ovarian follicular status
Streuli
et al (29)
healthy mean age = 241 regular cycles
(n=10)
a 2 cycles b before (LH -10-5-2-1) and
after LH surge (LH +1+2+10)
IOT
-18C Inter-cycle variability of 285
AMH fluctuations during the cycle were smaller than or equal to the variability between two cycles
van Disseldorp et al (10)
infertile median age =33
PCOS excluded (n=77)
a average 373 cycles b day 3
DSL
-80C
AMH showed a within-subject variability of 11 compared to 27 for AFC
AMH demonstrated less individual inter-cycle variability than AFC
Dorgan
et al (42)
blood donors age 36-44 collected 1977-1981 (n=20)
two samples collected during the same menstrual cycle phase at least 1yr apart
DSL
-70C
between-subject variance in AMH of 219 was large compared to the within-subject variance of 031
AMH was relatively stable over 1 year in pre-menopausal women
Rustamov et al (36)
infertile women age 22-41
(n=186)
random sampling median interval = 26 months
DSL
-70C
within-subject CV for AMH was 28 compared to 27 for FSH
AMH showed significant sample-to-sample variation
Rustamov et al (13)
infertile women age 20-46
(n=87)
random sampling median interval = 51 months
Gen II
-20C
within-subject CV for AMH was 59
AMH demonstrated a large sample-to-sample variation
104
Table 4 Within-subject comparison between AMH methods Study
Assays
Subjects
Simultaneous Analysis
Regression
Summary
Freour et al (44) DSL vs IOT 69 infertile women age 22-40
Yes IOT = 401 x DSL + 098 (microgL) (Deming regression)
DSL = 22 IOT (plt00001)
Hehenkamp et al (30) DSL vs IOT 82 healthy women NR DSL= 0495 x IOT - 003 DSL = 495 IOT
Bersinger et al (45) a DSL vs IOT
b DSL vs IOT
a 11 infertile women
b 55 infertile women
a yes
b no
a DSL= 0180 x IOT
b DSL= 0325 x IOT + 0733
a DSL = 18 IOT
b DSL= 33 IOT
Zhao et al (17) DSL vs IOT 38 donors NR IOT = 15 x DSL + 07 (ngml) DSL = 66 IOT
Taieb et al (46) DSL vs IOT 104 samples NR DSL = 104 x IOT - 149 DSL = 96 IOT
Streuli et al (29) DSL vs IOT 153 normal and infertile No IOT = 107 x DSL - 029 DSL = IOT
Kumar et al (18) IOT vs Gen II 60 female 60 male volunteers NR IOT =10 Gen II IOT=Gen II
Gada et al (50) DSL vs Gen II 42 women NR NR DSL = 63 Gen II
Preissner et al (23) DSL vs Gen II 206 samples NR Gen II = 153 x DSL - 077 DSL = 66 Gen II
Lee et al (47) DSL vs IOT 172 infertile women Yes IOT = 1102 x DSL - 0042 DSL = IOT
Wallace et al (51) DSL vs Gen II 271 women NR Gen II = 140 x DSL - 062 DSL = 71 Gen II
Li et al (48) a DSL vs IOT b DSL vs Gen II c IOT vs Gen II
56 women with PCOS or sub-fertility Yes a IOT = 097 x DSL -296 b Gen II = 133 x DSL - 417 c Gen II = 138 x IOT - 068
a DSL = IOT b DSL = 67 Gen II c IOT = 62 Gen II
Rustamov et al (13) DSL vs Gen II female IVF patients (n=330)
median of 2yr between samples
No NR
DSL = 127 Gen II
(age-adjusted)
Pigny et al (49) IOT vs Gen II 59 women 32 controls 27 with PCOS Yes NR IOT = 200 Gen II
105
Appendix I Flow-chart of the search for publications Database search for sample stability measurement variability and assay-method comparability was conducted simultaneously using the MeSH database of PubMedMedline using the search terms of ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting Substance and MIS which identified n=1653 studies on AMH The initial step of identification involved screening of articles by reading titles andor abstracts Further search involved identification of studies from the reference sections of the initially identified studies
Database Search
n=1653
Sample
Stability
Screening Titles
n=6
Further Search
n=4
Total
n=10
Measurment Variability
Screening Titles
n=14
Further Search
n=3
Total
n=17
Method comparability
Screening Titles
n=10
Further Search
n=4
Total
n=14
106
EXTRACTION PREPARATION AND
COLLATION OF DATASETS FOR THE
ASSESSMENT OF THE ROLE OF THE MARKERS
OF OVARIAN RESERVE IN FEMALE
REPRODUCTION AND IVF TREATMENT
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
Research Database
4
107
Title
Extraction preparation and collation of datasets for the assessment of
the role of the markers of ovarian reserve in female reproduction and
IVF treatment
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
NHS Research Ethics Approval
North West Research Ethics Committee (10H101522)
Word count 5088
Grants or fellowships
No funding was sought for this study
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
108
Declaration of authorsrsquo roles
OR prepared the protocol extracted data from electronic sources and hospital
notes prepared datasets and prepared all versions of the chapter MK assisted
in collection of data from hospital notes SR and CF oversaw and supervised
preparation the protocol extraction of data preparation of datasets and
reviewed the chapter
109
CONTENTS I PROTOCOL Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip110
Methodshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Objectiveshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Inclusion Criteriahelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip114 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 RH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 AFC datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Folliculogram datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Data managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data cleaning and codinghelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118 Merging datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data security and storagehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip119 II RESULTS Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip120 Data extraction and managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 RH AFC and Folliculogram datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 Merging Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip124 Conclusionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip125
110
I PROTOCOL
INTRODUCTION
The aim of the project is to create a series of reliable and validated
datasets which contain all relevant data on the ovarian reserve markers (AMH
AFC FSH) ethnicity BMI reproductive history causes of infertility IVF
treatment parameters for patients that meet inclusion criteria as described
below The datasets will be used for the subsequent research projects of the
MD programme and future research studies on ovarian reserve
Most data can be obtained from following existing clinical electronic
records a) Patient Administration System (PAS) b) Biochemistry Department
data management system c) the hospital database for surgical procedures and
d) AMH dataset and e) ACUBase IVF data management system Following
obtaining original datasets from the administrators of the data management
systems in their original Excel format the datasets will be converted into Stata
format and ldquopreparedrdquo by a) checking and recoding spurious data
transforming the dates from string to numeric format which will be consistent
across all datasets (Day Month Year) and stored in Stata format under
following names ldquoDemographyrdquo ldquoBiochemistryrdquo ldquoAMHrdquo ldquoSurgeryrdquo ldquoIVFrdquo
ldquoFETrdquo ldquoEmbryologyrdquo Copies of original datasets will be kept in the
password-protected and encrypted computer located in the Clinical Records
Room of Reproductive Medicine Department Central Manchester University
Hospitals NHS Foundation Trust which is maintained by IT department of
the Trust (Figure 1)
Data not available in electronic format will be collected from the hospital
records of each patient by researchers Dr Oybek Rustamov and Dr Monica
Krishnan and entered into following datasets Reproductive history (RH)
antral follicle count (AFC) and Folliculogram The hospital notes of all
included patients will be hand-searched The datasets will be transferred to
Stata and each step of data preparation will be recorded using Stata Do files
and the files will be stored under the filenames of ldquoHistoryrdquo ldquoAFCrdquo
Folliculogramrdquo in Stata format In order to ensure the robustness of the data
and for the purpose of validation of the datasets electronic scanned copies of
all available reports of pelvic ultrasound assessments for AFC and
folliculograms will be obtained and stored in the password-protected and
111
encrypted computer located in the Clinical Records Room of Reproductive
Medicine Department Ethics approval for collection of data has already been
obtained (UK-NHS 10H101522)
The datasets will be merged and datasets for each research project with
all available data nested with IVF cycles nested within patients will be created
METHODS
Objectives
The aim of the project is to build a robust database which can reliably
used for the following purposes
1 To estimate the effect of ethnicity BMI endometriosis and the causes
of infertility on ovarian reserve using cross sectional data (Chapter 51)
2 To estimate the effect of salpingectomy ovarian cystectomy and
unilateral salpingo-oopherectomy on ovarian reserve using cross
sectional data (Chapter 52)
3 To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield (Chapter 6)
4 To explore the potential for optimization of AMH-tailored
individualisation of ovarian stimulation using retrospective data
(Chapter 6)
Inclusion criteria
In order to capture the populations for all three studies the database will
have broad inclusion criteria All women from 20 to 50 years of age referred to
Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust will be included if a) they were referred for
management of infertility or fertility preservation and b) had AMH
measurement during the period from 1 September 2008 till 16 November
2011
112
Datasets
PAS dataset
The dataset contains information on the hospital number surname first
name date of birth and the ethnicity of all patients referred to Reproductive
Medicine Department CMFT (Table 1) The data are originally entered during
registration of the patient for clinical care by administrative staff of
Gynaecology and Reproductive Medicine Departments The dataset will be
obtained from the administrators of the Information Unit
The dataset will be obtained in Excel format and transferred into Stata
12 Data Management and Statistical Software The date values (referral date
and date of birth) will be converted into numeric variable using ldquoDate Month
Yearrdquo format (DMY) Ethnicity will be coded using numeric variables in
alphabetical order as pre-specified in the Table 2a
Biochemistry dataset
The dataset contains all blood test results specimen numbers the names
of the tests and the date of sampling of women who had assays for follicle
stimulating hormone (FSH) oestradiol (E2) luteinizing hormone (LH) and
AMH during the study period (Table 1) Data entries were conducted by the
clinical scientists the technicians and the members of administrative team of
the Biochemistry Department The dataset will be obtained from an
administrator of the database
The date of sampling and analyses will be converted to the numeric
ldquoDMYrdquo format The specimen number will be kept unaltered in the string
variable format and used to link the tests that were taken in the same sample
tube The name of the test will be kept as described in the original format
ldquoAMHrdquo ldquoFSHrdquo ldquoLH and ldquoOestrdquo In the original dataset the samples sent
from Reproductive Medicine Department are coded as ldquoIVFrdquo which will be
kept unaltered and the remaining observations will be divided into
ldquoGynaecology Departmentrdquo ldquoNon-IVFGynaecologyrdquo and ldquoUnknownrdquo
categories using the code of referred ward and the names of the consultants
The test results will be converted into numeric format and the results with
minimum detection limit will be coded as 50 of the minimum detection limit
as follows AMH ldquolt061rdquo= 031 pmolL FSH ldquolt05rdquo= 025 mlUml LH
113
ldquolt05rdquo=025 mlUml Oest ldquolt50rdquo=025 pgml The test results that are
higher than the assay ranges will be set to 150 of the maximum range
Interpretation of serum FSH results in conjunction with serum
oestradiol levels is important in establishing true early follicular phase hormone
levels The test results are believed to be inaccurate if serum oestradiol levels
higher than 250pmolL at the time of sampling and therefore a new variable
for FSH results with only serum FSH observations that meet above criteria will
be created and used subsequently All ambiguous data will be checked using
electronic pathology data management system Clinical Work Station (CWS)
Surgery dataset
The electronic dataset will be obtained from Information Department
in Excel format The dataset created using clinical coding software and data
entry conducted during patient treatment episodes by theatre nursing and
medical staff In order to evaluate effect of past reproductive surgery to
ovarian reserve all patients had ovarian cystectomy drainage of ovarian cyst
salpingectomy salpingo-oopherectomy during 1 January 2000-16 November
2011 at Central Manchester University Hospitals NHS Foundation Trust will
be included in the dataset The dataset contains following variables hospital
number surname first name date of birth date of operation name of
operation laterality of operation and name of surgeon
The final dataset will be stored in Stata dta format (Figure 1) The
dataset will be used to validate data on reproductive surgery that was collected
from hospital records in the RH dataset
AMH dataset
The dataset contains the AMH results the dates of sampling the dates
of analyses and the assay generation (DSL or Gen II) for all patients included
in the study (Table 1) The dataset will be obtained from the senior clinical
scientist Dr Philip Pemberton Specialist Assay Laboratory who is responsible
for the data entry and updating of the dataset
There are two separate primary Excel based AMH data files 1) DSL
dataset and 2) Gen II dataset The datasets will be transferred to Stata 12
software separately and following preparation of the datasets which logged
using Stata Do file Stata versions of the data files will be stored under ldquoDSLrdquo
114
and ldquoGen2rdquo names Then the files will be combined by appending ldquoDSLrdquo to
ldquoGen2rdquo in order to create a new combined ldquoAMHrdquo dataset The date variables
the sample date the assay date and the date of birth will be converted into
numeric ldquoDMYrdquo format The samples sent from other NHS trusts and private
clinics will be excluded from the dataset alongside the records from male
patients and the patients outside of the age range of 20-50 years of age The
manufacturers of the assays suggest that haemolysed and partly haemolysed
samples may provide inaccurate test readings Therefore a new variable
without these samples will be created and used in the analyses for all studies
All the ambiguous data will be checked and verified using duplicate datasets
obtained from Biochemistry dataset and the hospital records of the patients
IVF dataset
The IVF dataset will be downloaded from ACUBase Data management
system in original Excel format and contains detailed information on causes of
infertility sperm parameters treatment interventions assessment of oocyte
quantity and quality assessment of embryo quantity and quality and the
outcomes of treatment cycles (Table 1)Data entry to ACUBase was
performed by members of administrative nursing embryology and medical
staff of the Reproductive Medicine Department at the point of care This is
only electronic data management system for ART cycles and used for
monitoring of the clinical performance of the department by internal and
external quality assessment agencies and regulators (eg HFEA CQC)
Therefore the quality of data entry for the main indicators of the performance
of IVFICSI programs (the treatment procedures the outcomes of the cycles
and assessment of embryos) should be fairly accurate
Table 2b describes the coding of the treatment outcomes and the
practitioners of ICSI the ultrasound-guided oocyte retrieval (USOR) and the
embryo transfer (ET) procedures
In addition to the main patient identifier (Hospital Number) this dataset
contains in-built cycle identifier (IVF Reference Number) which will be used
to link the original IVF cycles to corresponding Frozen Embryo Transfer
(FET) cycles and the embryos originating from the index cycle using ldquoFETrdquo
and ldquoEmbryordquo datasets respectively
115
FET dataset
The dataset provides information on the quality and the quantity of
transferred embryos the date of embryo transfer and the outcome of the cycle
in frozen embryo transfer cycles (Table 1) Primary data entry was performed
by the members of the clinical embryology team during the treatment of
patients and will be downloaded from ACUBase by Dr O Rustamov
Together with ldquoIVFrdquo dataset it can be used to study cumulative live birth rate
(LBR) of index cycles The treatment outcomes as well as ICSI USOR and ET
practitioners will be converted to numeric variables using the codes which are
shown in Table 2b The dataset can be linked to the index fresh IVF cycles as
well as to embryos of FET cycles using the IVF Reference number
Embryology dataset
The dataset has comprehensive information on the quality and the
quantity of embryos on each day of their culturing including embryos that
were cryopreserved and those that were discarded (Table 1) The dataset also
includes patient identifiers (name date of birth IVF reference number) and
the dates of embryo transfer The primary data entry into this dataset was
conducted by the members of clinical embryology team during the clinical
episodes and will be downloaded from ACUBase by Dr O Rustamov The
dataset can be linked to index fresh IVF cycle and FET cycles using IVF
Reference numbers of corresponding datasets
RH dataset
This dataset will be created and data entry will be conducted during the
search of the hospital notes Following identification of included patients using
AMH dataset Excel electronic data collection file will be created The hospital
notes of each patient will be searched for by systematically checking all filed
hospital records in Clinical Records Room of Reproductive Medicine
Department by the order of their hospital number Further search for missing
notes will be conducted by checking all hospital notes located in the offices of
nurses doctors and secretaries Electronic hospital notes filed in Medisec
Digital Dictation Database will be used for data extraction for the patients
whose hospital notes were not located
116
All available diagnosis will be recorded under the following columns 1)
female referral diagnosis 2) male referral diagnosis 3) female initial clinic
diagnosis 4) female final clinic diagnosis 5) diagnosis prior 2nd IVF cycle 6)
diagnosis prior 3rd IVF cycle Furthermore other relevant information on
pathology of reproductive system will be documented For instance all possible
iatrogenic causes of poor ovarian reserve (eg oophorectomy ovarian
cystectomy salpingectomy chemotherapy and radiotherapy) will be recorded
In order to establish the existence of polycystic ovary syndrome (PCOS) the
history of oligomenorrhea amenorrhea and diagnosis of polycystic ovaries
(PCO) on pelvic ultrasound scan will be collected and used in conjunction with
serum LH levels of Biochemistry dataset (Table 1)
Male infertility will be defined as ldquosevere male factorrdquo if the sperm
parameters were low enough to meet criteria (lt05 mlnml or retrograde
ejaculation) for Multiple Ejaculation Resuspension and Centrifugation test
(MERC) as part of investigation for infertility A variable for patients
diagnosed with azoospermia will be created and the diagnosis will be recorded
The patients diagnosed with male factor infertility but with the sperm
parameters that did not reach criteria for MERC will be diagnosed with ldquomild
male factorrdquo infertility Patients diagnosed with ldquosevererdquo andor ldquostage IVrdquo
andor ldquostage IIIrdquo endometriosis will be categorized as ldquosevere
endometriosisrdquo while patients diagnosed with mild or moderate endometriosis
will be coded as ldquomild endometriosisrdquo group In diagnosing the tubal factor
infertility only patients with history of bilateral salpingectomy and the patients
with evidence of bilateral tubal blockage on a laparoscopy and dye test will be
diagnosed as ldquosevere tubal factorrdquo The patients with history of unilateral
salpingectomy unilateral tubal block in laparoscopy and dye test or
unilateralbilateral tubal block on hysterosalpingogram will be categorized as
ldquomild tubal factorrdquo infertility Diagnosis of polycystic ovarian syndrome
(PCOS) will be based in Rotterdam criteria existence of two of the following
features 1) oligo- or anovulation 2) clinical andor biochemical signs of
hyperandrgoenism 3) polycystic ovaries Referral for fertility preservation will
be defined as ldquoreferral for consideration of obtaining oocytes orand embryos
andor sperm prior to chemotherapy radiotherapy or surgical management of
a malignant diseaserdquo The length of infertility will be recorded as per proforma
of initial consultation for the patients attended initial clinic appointment
following introduction of serum AMH test 1 September 2008 For patients
117
attended initial consultation prior to introduction of AMH test the length of
infertility will be documented as per the initial clinic proforma plus years till the
patientrsquos first AMH test The patientrsquos body mass index (BMI) documented at
initial assessment will used for patients who had assessment after introduction
of AMH test 1 September 2008 whereas the most up to date BMI result is
collected for the patients seen prior to this date
AFC dataset
Data will be extracted from the hospital notes The data on the
assessment of AFC will be obtained from the pelvic ultrasound scan reports
The date of assessment the AFC in each ovary the name of sonographer will
be recorded (Table 1) Furthermore other relevant ultrasound findings such
as ovarian cyst hydrosalpynx and submucous uterine fibroids will also be
entered in the dataset To permit data validation scanned copies of ultrasound
scan report of each AFC investigation will be stored in PDF format in the
computer that located in the Clinical Notes Room
The department uses a stringent methodology for the assessment of
AFC which consist of counting of all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
The ultrasound assessments are conducted by qualified sonographers who use
the same methodology for the measurement of AFC However it is well
known that the counting of antral follicles may be prone to significant inter-
operator variability Therefore the name of sonographers will be recorded
during primary data collection and coded (Table 2a) so that the estimates of
within- and between-operator variability can be obtained if necessary
Folliculogram dataset
Although most data on IVFICSI cycles are available in ldquoIVFrdquo dataset
certain important data on IVF treatment are recorded only in the hard copy
IVF folliculograms Consequently data on ultrasound follicle tracking the
reasons for changing the doses of stimulation drugs are only available in the
folliculograms Furthermore the length of ldquothe coastingrdquo and the causes for
cycle cancellation are usually recorded in both folliculograms and ldquoIVFrdquo
dataset which can be used to validate accuracy of ldquoIVFrdquo dataset Therefore
118
these data will be collected using the folliculograms that filed in the hospital
notes and the scanned copies of each folliculograms will be stored in the
computer located Clinical Records Room for data validation purposes (Table
1)
The number of follicles on Day 8 and Day 10 ultrasound scans will be
recorded according to the size of the follicles 10-16mm and 17mm
Numeric variables for the follicle numbers will be created and used for
assessment of ovarian response in IVF cycles
Data management
Data cleaning and coding
All datasets will be obtained in Excel format and transferred in the
original unaltered condition into Stata 12 data management and statistical
package (Stata 12 StataCorp Texas USA) and all steps of the data cleaning
and the coding will be recorded using Stata Do files to create audit trails of the
data management process Both original Excel and cleaned Stata versions of
data files will be stored in computer that is located in Clinical Records Room at
Reproductive Medicine Department Uniformity of hospital numbers in all
datasets will be achieved by converting a) leading lower case prefixes ldquosrdquo to
upper case ldquoSrdquo b) dropping suffixes ldquozrdquo and ldquoZrdquo and c) dropping all leading
zeros in the second part of the hospital number (eg ldquos1000235Zrdquo
=rdquoS10235rdquo) The coding of the datasets is shown in the Table 2a and the
Table 2b All ambiguous data will be checked using electronic data
management systems (eg CWS Medisec) and hospital notes
Merging the datasets
The datasets will be structured as such that the data files can be used
independently or merged at a) patient or b) IVF cycle levels using the patient
identifier cycle identifier and date variables (Figure 1) This allows analysis of
outcomes of both ldquoFresh IVF cyclesrdquo and study the cumulative outcomes of
Fresh IVF and Frozen Embryo Transfer cycles originating form index IVF
cycles
Each dataset will contain two main patient identifiers and patient
number (Patient ID) which will be used for linking the datasets in a patient
119
level At the initial stages of the data management the hospital numbers will be
used as the main patient identifier The accuracy of the hospital numbers in
each dataset will be validated using PAS dataset by checking patient surname
first name and date of birth
Following methodology will be used to add study numbers into each
dataset First all dataset will be merged in a wide format using the hospital
numbers which creates Master Datasets for each of the research projects Then
an accuracy of the merger will be checked using DOB surname and first name
Once the dataset is validated several copies of the Patient ID variable will be
created and distributed to each dataset Finally the datasets will be separated
and stored as independent datasets alongside Master Datasets for each research
projects
ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo datasets contain cycle specific IVF
reference numbers which were allocated during the clinical episodes on
ACUBase Using IVF reference number new ID variable (Cycle ID) will be
created and allocated to all datasets using closest observation prior to the IVF
cycle in Master Research Dataset Consequently by using cycle reference
number all patient and cycle related data can be linked in the IVF FET cycle
and embryo level
Data security and storage
The encrypted and password protected hospital computer will be used to
process the data until all the patient identifiers have been removed and the
datasets have been anonymised Once the Master Research Datasets are
validated and research team is satisfied with the quality of the data the dataset
will be anonymised by dropping variables for following patient identifiers
hospital number surname first name date of birth and IVF reference number
The study number and the cycle reference numbers will be used as a patient
and a cycle identifiers and only this anonymised dataset will be used for
statistical analysis A copy of non-anonymised dataset will be stored in the
computer located in Clinical Records Room for data verification and a
reference purposes The datasets will be stored within IVF unit for the
duration of the research projects of the MD programme The necessity of
storage of the datasets and measures of data security will be reviewed every
three years thereafter
120
II RESULTS
INTRODUCTION
According to the protocol all women from 20 to 50 years of age referred
to Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust for management of infertility or fertility
preservation and had AMH measurement during the period from 1 September
2008 till 16 November 2011 have been included in the database In total of
4506 patients met the inclusion criteria with 3381 patients in DSL AMH
assay group and 1125 patients Gen II assay group The following datasets
have been extracted from the clinical electronic data management systems
ldquoPASrdquordquo Biochemistryrdquo ldquoSurgeryrdquo ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo Data
extraction from the paper-based hospital records of 3681 patients (n=3130
DSL and n=551 Gen II) were performed by two researchers Dr ORustamov
(n=2801) and Dr M Krishnan (n=880) In addition data collection using
Medisec Digital Dictation Software for the notes that were not located in DSL
group (n=251 patients) was completed by Dr O Rustamov In view of the
issues with validity of Gen II assay measurements which were observed in the
earlier study of the MD Programme (Chapter 2 AMH variability and assay
method comparison) I decided to base subsequent work for the last three
projects (Chapter 5-7) of the MD programme only on DSL assay
measurements and not to include samples based on Gen II AMH Assay
Therefore I decided not to collect data from the hospital notes for the patients
that had AMH measurements using exclusively Gen II Assay where the notes
were not found during the first round of data collection (n=575)
As a result in DSL group all datasets for 3130 patients were completed
and all but AFC and Folliculogram datasets were completed for 251 (Figure 2)
In Gen II group all datasets were completed for 551 patients and all but RH
AFC and Folliculogram datasets were obtained for 575 patients (Figure 2)
As described above the studies of the last three projects (Chapter 5-7)
are based on DSL assay which is no longer in clinical use The review of
literature presented in Chapter 3 suggests that DSL assay appears to have
provided the most reproducible measurements of AMH compared to that of
other assays Therefore AMH measured using DSL assay is perhaps most
121
reliable in terms addressing the research questions In all three chapters
estimates of the effect sizes are provided in percentage terms and therefore the
results are convertible to any AMH assay
Datasets
Demography dataset
The dataset was obtained from Mr Peter Hoyle Senior Data Analyst of
Information Unit CMFT on 16 October 2012 The dataset includes all patients
referred to Reproductive Medicine Department between 1 January 2006 and 31
August 2012 and contains 5573 patients I created a dataset ldquoDemographyrdquo in
Stata format using the steps of data cleaning coding and management as per
protocol The audit trial of the data management was created using Stata Do
file (Figure 1)
Biochemistry dataset
The biochemistry data file was obtained from Dr Alexander Smith
Senior Clinical Scientist Biochemistry Department on 24 January 2011 The
dataset contains the results of all serum AMH FSH LH and E2 samples
conducted from 01 September 2008 to 31 December 2010 The dataset was in
Excel format that consisted of two datasheets 1) Biochemistry 2008-2009 and
2) Biochemistry 2010 The datasheets transferred to Stata 12 in original
unaltered condition and a single Stata ldquoBiochemistryrdquo dataset was created by
combining datasheets by appending them to each other The dataset contains
in total of 78415 blood results of 11574 patients with 6643 AMH 19175 FSH
28677 LH and 23920 E2 results A wide format of the dataset was prepared by
transferring all blood results of each patient to a single row A variable which
indicates valid FSH results was created by coding FSH results as missing if
corresponding E2 levels were higher than 250 pmolL The audit trial of the
data management was created using a Stata Do file
Surgery dataset
Data management was conducted according to the protocol In total
dataset contained 2096 operations in 1787 patients Data on all operations on
122
Fallopian tubes (eg salpingectomy salpingostomy) and ovaries (eg
cystectomy drainage of cyst) at Central Manchester NHS Foundation Trust
from 1 January 2000 to 16 January 2011 are available in the dataset The
dataset will be used to validate the data on history of reproductive surgery of
Reproductive History dataset
AMH dataset
Both AMH datasets were received from Dr Philip Pemberton Senior
Clinical Scientist of the Specialist Assay Laboratory on 13 January 2012 and
transferred to Stata 12 software in the original format All steps of the data
cleaning and the management were recorded using Stata Do file
There were 3381 patients in DSL dataset and 1125 patients in Gen II
dataset Cleaning and coding of the datasets were achieved using the
methodology described in above protocol and new AMH dataset has been
created
IVF dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov on
08 October 2012 and following importing the dataset into Stata 12 in original
format dataset was prepared according to the protocol The dataset contains all
IVFICSI cycles that took place between 01 January 2004 and 01 October
2012 including the cycles of women who acted as egg donors and egg
recipients There were in total of 4323 patients who had 5737 IVFICSI cycles
with 4123 IVFICSI cycles using own eggs 10 embryo storage 40 oocyte
donation 7 oocyte storage 55 oocyte recipient cycles The dataset has
anonymised unique patient (Patient ID) and cycle identifiers (Cycle ID) and
therefore can be linked to all other datasets including all FET cycles and
embryos originated from the index IVF cycle
FET dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov
in Excel format on 20 October 2012 and transferred to Stata 12 Software in
the original condition The data managed as per above protocol and each step
of the process of preparation of the dataset was recorded in Stata Do file The
dataset comprised of all FET cycles (n= 3709) of all women (n=1991)
123
conducted between 01 January 2004 and 01 October 2010 and the Stata
version of ldquoFETrdquo dataset contains complete data on number of thawed
cleaved discarded and research embryos for all patients The dataset contains
unique patient identifier (Patient N) and unique cycle identifiers (Cycle N) and
therefore can be linked to all datasets in patient and cycle levels including index
IVF cycle and embryos
Embryology dataset
The Excel dataset was downloaded from ACUBase by Dr Oybek
Rustamov on 20 October 2012 and transferred into Stata 12 Software in
unaltered condition The data was managed according to the above protocol
The dataset has details of all 65535 (n=4305 women) embryos that were
created between 01 January 2004 and 01 October 2012 The dataset contains
complete data on quantity and the assessment of embryo quality which
includes grading of number evenness and defragmentation of the cells for
each day of culturing of the embryos Furthermore the destination of each
embryo (eg transferred cryopreserved discarded and donated) and the
outcomes of cycles for transferred embryos are available in the dataset Given
that the Embryology dataset has the unique patient as well as the cycle
identifiers this dataset is nested within patients and IVF cycles Consequently
each embryo can be linked to patient index Fresh IVF cycle and subsequent
FET cycles
Reproductive History AFC and Folliculogram datasets
The hospital notes of all patients (n=4506) were searched during the
period of 1 April 2012 to 15 October 2012 for collection of data for
Reproductive history AFC and Folliculogram datasets as per protocol All case
noted filed in the Clinical Records Room the Nurses Room the Doctors
Room and the Secretaries Room of Reproductive Medicine Department were
searched and relevant notes were pulled and searched for data All ultrasound
scan reports containing data on AFC and all IVFICSI folliculograms of
patients were scanned and electronic copy of scanned documents were stored
in the password protected NHS computer located in the Clinical Records
Room
124
The first round of data gathering achieved following result In DSL
dataset there were in total of 3381 patients with 3130 patients had complete
data extraction from their hospital notes and hospital records of 251 patients
were not found There were in total of 1126 patients in Gen II dataset 551 of
whom had complete data extraction from their hospital records and the case
notes of 575 patients were not located (Figure 2) The main reason for
ldquomissing case notesrdquo was found to be the use of hospital records by clinical
laboratory and administrative members of staff at the time of data collection in
patients undergoing investigation and treatment
In the meantime the results of our previous research study indicated that
Gen II samples provide erroneous results (Chapter II) and therefore we
decided to use only data from the patients in DSL group Data on reproductive
history for the remaining patients in the DSL group (n=251) with missing
hospital records were collected using digital clinic letters stored in Medisec
Digital Dictation Software (Medisec Software UK) The data file that
contained combined datasets of reproductive history and AFC was transferred
to Stata 12 in original condition and data management was conducted
according to the protocol All steps of data management was recorded using
Stata do file for audit trail and to ensure reproducibility of the management of
the data Similarly the management of Folliculogram dataset was achieved
using the procedures described in the protocol and all steps of data
management was logged using Stata Do file As result of above data collection
and management I created three Stata datasets ldquoRHrdquo (reproductive history)
ldquoAFCrdquo and ldquoFolliculogramrdquo
Merging Datasets
First the datasets were merged using a unique patient identifier (hospital
number) as per protocol Validation of the merger using additional patient
identifiers (NHS number name date of birth) revealed existence of duplicate
hospital numbers in patients transferred from secondary care infertility services
to IVF Department of Central Manchester University Hospitals NHS
Foundation Trust I established that in the datasets the combination of the
patientrsquos first name surname and date of birth in a single string variable could
be used as a unique identifier Hence I used this identifier to merge all
datasets achieving a robust merger of all independent datasets into combined
125
final Master Datasets for each of the research projects Following the creation
of an anonymised unique patient identifier (Patient ID) for each patient and
anonymised unique cycle identifier (Cycle ID) for each IVF cycle all patient
identifiers (eg surname forename hospital number IVF ref number) were
dropped (Figure 1) The anonymised independent datasets (eg AMH AFC
IVF etc) and anonymised Master Datasets were stored as per protocol
Subsequently these anonymised datasets were used for the statistical analyses
of the research projects The original unanonymised data files were stored in
two password protected NHS hospital computers in the Clinical Records
Room and Doctors Room of Reproductive Medicine Department and
archived according to the Trust policies thereafter Only members of clinical
staff have access to the computers and only nominated clinical members of the
research group who have specific approval can have access to unanomysed
Fully anonymised datasets have been made available to other members of the
research team with the stipulation that the datasets are stored on secure
password protected servers or fully encrypted computers Fully anonymised
datasets may in the future be shared with other researchers following
consideration of the request by the person responsible for the datasets (Dr
Cheryl Fitzgerald) and appropriate ethical and data protection approval
CONCLUSION
Following extraction and management of the data I have built
comprehensive validated datasets which will enable to study ovarian reserve in
a wide context including a) assessment of ovarian reserve b) evaluation of the
performance of ovarian biomarkers c) study individualization of ovarian
stimulation in IVF d) association of the biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryo live birth) The database will be used
to address the research questions posed in the subsequent chapters of this
thesis and beyond that for future studies on the assessment of ovarian reserve
and IVF treatment
126
Figure 1 Data and program files Datasets and programme files created in preparation of the research datasets File names and types are provided in the brackets
127
Table 1a Available vriables The
available identifiers variables and the source of data for following datasets Ethnicity RH AMH AFC Biochemistry OHSS Folliculogram
Datasets
Clinical ID
Study ID
Variables
Source
Demography Hospital N Surname
First name DOB
Patient ID
Ethnicity Information Department
(PAS)
RH
(Reproductive History)
Hospital N Surname
First name DOB
Patient ID
1 Diagnosis Referral Female Referral Male
Clinic Female Clinic Male
Post Cycle 1 Post cycle 2 Post cycle 3
2 Iatrogenic causes of loss of ovarian reserve Ovarian surgery tubal surgery chemotherapy radiotherapy
3 BMI 4 PCOS (PCO oligomenorrhea amenorrhea hirsutism)
Hospital Records
Surgery Hospital N Surname
First name DOB
Patient ID Date
Procedure Date Operator
Information Department
AMH Hospital N Surname
First name DOB
Patient ID Date
Date of sample Date of assay AMH level Assay generation AMH dataset of Specialist Assay
Lab
AFC Hospital N Surname
First name DOB
Patient ID Date
AFC (up to six AFC scans)
Left ovary Right ovary Date of Scan Sonographer Comments (Ovarian cyst hydrosalpynx fibroid poorly visualized etc)
Hospital Records
Biochemistry Hospital N Surname
First name DOB
Patient ID Date
Oestradiol (Date of sample Date of assay serum level) FSH (Date of sample Date of assay serum level)
LH (Date of sample Date of assay serum level)
Biochemistry Electronic
Database
Folliculogram Hospital N Surname
First name DOB
Patient ID Date
Folliculogram (up to 3 cycles) Date (1st day of ovarian stimulation)
Day 8( 10-16mm) Day 8 (gt17mm) Day 10 (10-16mm) Day 8 (gt17mm)
Comments (Day of HCG OHSS Cancellation Ovarian cyst Hydrosalpynx Coasting etc)
Hospital Records
128
Table 1b Available variables The available identifiers variables and the source of data for IVF dataset
Datasets Clinical ID Study Variables Source
IVF Hospital N Surname First name DOB PCT code
Patient ID Cycle ID Date
GENERAL
Attempt Type Protocol DaysStim InitDose Outcome OutcomeDt Age PartnerAge EggCollect TreatDate ETransfer Add_Drug1 Add_Drug2 Add_Drug3 Add_Drug4 Add_Drug5 Add_Drug6 Add_Drug7 EGG RECOVERY SNumber Follicles TotEgg EggNumber
FERTILISATION IVFEgg IVFCleaved ICSICleaved Cleaved PN2 IVFPN2 ICSI2PN ICSICl ICSIEgg ICSIFPN IVFFPN IVFTransfer ICSITransfer IVFLysed ICSILysed IVFMetII IVFMetI IVFAtretic IVFAbnormal IVFEmptyZona IVFG_Vesicle ICSIMetII ICSIMetI ICSIAtretic ICSIAbnormal ICSIEmptyZona ICSIG_Vesicle
OUTCOME
sacs Hearts Preg ICSIPract STORAGE Frozen IVFFroz ICSIFroz SpermSource SortKeySTAR HISTORY cat_tubal cat_OvFail cat_UtProb cat_unex cat_ MF cat_Meno cat_Genetic cat_endo cat_anov cat_noMale Inf_Since MaleInf
CoupleInf Preg24Wk MiscTOP Ectopic LiveBirth FSH AMH Emb_Recip Surrogate Sperm_Recip StoreEggs EggThaw Treat_Reason IgnoreKPI EMBRYOLOGY
D1LteClCells1 D1LteClCells2 D2Cells2 D2Cells3 D2Cells4 D2Even2 D2Even3 D2Even4 D2Frag2 D2Frag3 D2Frag
SPERM Conc_Init MotA MotB Conc_ Prep MotAP MotBP SemenSource SemenAnalysis STIMULATION BMI TotDose GonadUsed Incubator ICSIRigg AMHBand DHEA EGG
Egg_Recip Own_Eggs Altruistic_D
ACUBASE Electronic Database
129
Table 1c Available variables
The available identifiers variables and the source of the data for FET and Embryo datasets
Datasets Clinical ID Study ID
Variables
Source
FER
Hospital N Surname First name
Patient ID Cycle ID Date
GENERAL treatdate transfer ETDate
OUTCOME preg IUP Outcome OutcomeDt
EMBRYOLOGY
Thawed Survived Cleaved Discarded Research
STORAGE NumStored DtCreated
CLINICIAN ETClinician ETEmbryologist OrigCycle
ACUBASE Electronic Database
Embryo
Hospital N Surname First name DOB
Patient ID Cycle ID Date
GENERAL TreatDate Injected Destination
CELLS CellsD1 CellsD2_AM CellsD2_PM CellsD3_AM CellsD3_PM
EVENNES EvenD2_AM EvenD2_PM EvenD3_AM EvenD3_PM
FRAGMENT FragD1 FragD2_AM FragD2_PM FragD3_AM FragD3_PM
OUTCOMES ICSIPract Maturity PosPreg Hearts SpermSource Age
ACUBASE Electronic Database
130
Table 2a Coding
The codes used to convert ethnicity and diagnosis variables from string to numeric format in PAS and RH datasets
131
Table 2b Coding
The codes used to convert treatment outcomes from string to numeric format in IVF and FET datasets
Datasets Codes for outcomes
IVF
FET
ldquoBiochemical Pregnancyrdquo=1 ldquoCancel (other)rdquo=2
ldquoCancel Hyperstimulationrdquo=3 ldquoCancel Poor responserdquo=4
ldquoCancelled no sperm on day of ECrdquo=5 ldquoCONVERTED IVF TO IUIrdquo=6
ldquoDelayed Miscarriagerdquo=7 ldquoDonatedrdquo=8 ldquoEctopicrdquo=9
ldquoEgg donationrdquo=10 ldquoEmbryos for storagerdquo=11
ldquoEmpty Sacrdquo=12 ldquoFailed Fertilisationrdquo=13
ldquoFor donationrdquo=14 ldquoFreeze Allrdquo=15
ldquoFreeze All (OHSS)rdquo=16 ldquoFreeze All (Other)rdquo=17
ldquoLate Miscarriagerdquo=18 ldquolost to contactrdquo=19
ldquolost to follow uprdquo=19 ldquoNo Eggsrdquo=20
ldquoNo Spermrdquo=21 ldquoNo Normal Embryosrdquo=22
ldquoNot Pregnantrdquo=23 ldquoOngoing Singletonrdquo=24
ldquoOngoing Twinrdquo=25 ldquoPositive hCGrdquo=26
ldquoSingleton Birth=27rdquo ldquoTwin Birthrdquo=28
ldquoTriplet Birthrdquo=29 ldquoStill Birthrdquo=30The
132
Figure 2 Data collection from hospital records
Completeness of data collection from hospital records for RH AFC and Folliculogram datasets
All
patients
DSL
(n=3381)
All Datasets
Complete
n=3130
AFC and Folliculogram
not complete
n=251
Gen II
(n=1126)
All Datasets
Complete
n=551
RH AFC Follicologram
not complete
n=575
133
Table 3 Results Datasets and observation
Summary of the number of patients observations IVFFET cycles and data entry period for all datasets
Datasets Patients Observations Cycles Period
AMH DSL 3381Gen II 1126
DSL-3913 DSL 01 Sep 2008-15 Nov 2010 Gen II 16 Nov 2010-16 Nov 2011
Demography 5573 01 Jan 2006-31 Aug 2012
Biochemistry 11754 Total 78415 6643-AMH 19175-FSH 28677-LH 23920-E2
01 Sep 2008-31 Dec 2010
RH DSL-3381 DSL-3381 01 Sep 2008-01 Oct 2012
Surgery 1787
2096 01 Jan 2000-16 Nov 2011
AFC DSL 2411 DSL Total 4174 Single measurement2411 Repeats 2-1250 3-370 4-105 5-25 6-7 7-1
01 Sep 2008-01 Oct 2012
Folliculogram 1736 2183
01 Sep 2008-01 Oct 2012
IVFICSI 4324 - Total 5737 own eggs-4123 oocyte recipients-55 oocyte donors-40 Embryo storage-10 oocyte storage-7
01 Jan 2004-01 Oct 2012
FET 1991 - 3709
01 Jan 2004-01 Oct 2012
Embryology
4305 65535 embryos - 01 Jan 2004-01 Oct 2012
134
Figure 3 Merging datasets
The process of merging datasets in patient and cycle levels using patient date and cycle IDs
135
ASSESSMENT OF DETERMINANTS OF
ANTI-MUumlLLERIAN HORMONE IN INFERTILE
WOMEN
5
136
THE EFFECT OF ETHNICITY BMI
ENDOMETRIOSIS AND THE CAUSES OF
INFERTILITY ON OVARIAN RESERVE
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
To be submitted to Fertility and Sterility
51
137
Title
The effect of ethnicity BMI endometriosis and the causes of infertility
on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK c Centre for Biostatistics
Institute of Population Health Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Word count 4715
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
138
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all version
of the manuscript MK assisted in data extraction contributed in discussion
and the review of the manuscript SR and CF oversaw and supervised
preparation of dataset statistical analysis contributed in discussion and
reviewed all versions of the manuscript
139
ABSTRACT
Objective
To estimate the effect of ethnicity BMI endometriosis and the causes of
infertility on ovarian reserve
Design Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 2946 patients were included in the study of which 65 did not have
data on ethnicity leaving 2881 women in the sample
Interventions Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH serum basal FSH and basal AFC measurements
Results
Multivariable regression excluding BMI showed that woman of Black ethnicity
and the group defined as ldquoOther ethnicityrdquo had significantly lower AMH
measurements when compared to that of White (-25 p=0013 and -19
p=0047) and overall ethnicity was a significant predictor of AMH (p=0007)
However inclusion of BMI in the model reduced these effects and the overall
effect of ethnicity did not reach statistical significance (p=008) AFC was
significantly reduced in Pakistani and women of ldquoOther ethnicitiesrdquo although
the effect sizes were small (10-14) and the overall effect of ethnicity was
significant in both models (p=004 and p=003) None of the groups showed a
statistically significant difference in FSH although women of ldquoOther Asianrdquo
ethnicity appear to have lower FSH measurements (12) which was close to
statistical significance (-12 p=007)
140
Obese women had higher AMH measurements (16 p=0035) compared to
that with normal BMI and the overall effect of the BMI was significant
(p=003) In the analysis of the effect of BMI to AFC measurements we did
not observe differences that were statistically significant However FSH results
showed that there is a modest association between BMI and FSH with both
overweight and obese women having significantly lower FSH measurements
compared to lean women (-5 p=0003 and -10 p=0003)
In the absence of endometrioma endometriosis was associated with lower
AMH measurements although this did not reach statistical significance
Neither AFC nor FSH was significantly different in the endometriosis group
compared to those without endometriosis In contrast we observed around
31 higher AMH levels in the patients with at least one endometrioma
(p=0034) although this did not reach statistical significance (21 p=01) in
the smaller subset after adjustment for BMI AFC and FSH did not show any
statistically significant association with endometrioma
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to the ones who did not have
unexplained infertility except the analysis that did not include BMI as a
covariate which found a weakly positive correlation (10 p=003) Similarly
the estimation of the effect of the diagnosis of unexplained infertility to AFC
as well as FSH showed that there were weak association between the markers
and diagnosis of unexplained infertility
There was no significant difference in AMH AFC and FSH measurements of
women with mild and severe tubal infertility in the models which included all
covariates except the analysis of FSH and mild tubal factor where we found
weakly negative correlation between these variables
Women diagnosed with male factor infertility had significantly higher AMH
and lower FSH measurements the effect sizes of which were directly
proportional to the severity of the diagnosis In the analysis of AFC we did not
found significant difference in the measurements between patients with male
factor infertility and to that of non-male factor
141
Conclusions
Ethnicity does not appear to play a major role in determination of ovarian
reserve as measured by AMH AFC and FSH whereas there is a significant
positive association with BMI and these markers of ovarian reserve Women
with endometriosis appear to have lower AMH whilst patients with
endometrioma have significantly higher AMH and lower FSH measurements
The study showed that the association between markers of ovarian reserve and
unexplained infertility as well as tubal disease is weak In contrast women
diagnosed with male factor infertility have higher ovarian reserve
Key Words
Ovarian reserve AMH AFC FSH ethnicity BMI infertility endometriosis
endometrioma
142
INTRODUCTION
The ovarian reserve consists of a total number of resting primordial and
growing oocytes which appears to be determined by the initial oocyte pool at
birth and the age-related decline in the oocyte number (Hansen et al 2008
Wallace and Kelsey 2010) Both of these factors appear to be largely
predetermined genetically although certain environmental socioeconomic and
medical factors likely to play a role in the rate of the decline (Schuh-Huerta et
al 2012b Kim et al 2013 Dolleman et al 2013) The understanding of the
formation and the loss of ovarian reserve have been improved greatly due to
recently published data on the histological assessment of ovarian reserve
(Hansen et al 2008) Furthermore the use of the biomarkers has enabled the
evaluation of ovarian reserve in larger population-based samples Biomarkers
such as AMH and AFC can only assess the measurement of growing pre-antral
and early antral follicle activity However some studies suggest that there is a
close correlation between the measurements of these markers and the number
of resting primordial follicles (Hansen et al 2011)
Studies on age related decline of AMH and AFC have played important
roles in understanding the decline of ovarian reserve although most of the
data have been derived from heterogeneous population without full account
for characteristics of individual patients (Nelson et al 2011 Seifer et al 2011
Shebl et al 2011) These studies have demonstrated that there is a significant
between-subject variation in ovarian reserve beyond that due to chronological
age (Kelsey et al 2011) More recent studies reported interesting findings on
the role of demographic anthropometric and clinical factors in the
determination of ovarian reserve Although these studies have employed
better-described samples some have small sample sizes and lack power for the
estimation of the effect of these factors Consequently studies on large and
well-characterised populations are necessary for evaluation of the determinants
of ovarian aging as well as to provide normative data for the individualisation
of the assessment of ovarian reserve
There have been reports of measurable disparities in the reproductive
aging and reproductive endocrinology between various ethnicities For
instance according to a large prospective study White Black and Hispanic
women reported higher rates of premature ovarian failure compared to
143
Chinese and Japanese (Luborsky et al 2002) In contrast the prevalence of
PCOS which is associated with higher ovarian reserve has been reported to be
significantly lower in Chinese (22) compared to British (8) women
(Michelmore et al 1999 Chen et al 2002) Although these disparities may
partially be due to the differences in the local diagnostic criteria it is plausible
to believe that the ethnicity may play a role in the determination of the
reproductive aging With regard to the effect of ethnicity to the markers of
ovarian reserve Seifer et al found that African American and Hispanic women
have lower AMH levels compared to White (Seifer et al 2009) In contrast
Randolph et al reported that African American women had significantly higher
ovarian reserve compared to that of White when determined by FSH
measurements (Randolph et al 2003) These studies indicate that ethnicity may
play a role in the determination of ovarian reserve and therefore warrants
further investigation which should include other major ethnic groups
Body weight appears to be closely associated with human female
reproduction which is evident by its effect on the natural fecundity as well as
the success of the assisted conception treatment cycles (Maheshwari et al
2007) Indeed the relationship of increased body mass index (BMI) and PCOS
is well described although the mechanism of this is not yet fully understood
Consequently a number of recent studies have assessed the effect of BMI to
the various aspects of reproductive endocrinology including ovarian reserve
Studies on the influence of BMI on the markers of ovarian reserve have
provided conflicting results probably due to the limited statistical power in
most of these studies and the difficulties encountered in properly accounting
for confounding factors such as age ethnicity and medical diagnosis (Buyuk et
al 2011 Freeman et al 2007 Su et al 2008 Seifer et al 2008 Sahmay et al 2012
Skalba et al 2011) Therefore there is a need for studies with large datasets and
good adjustment for confounding factors
We therefore designed and undertook a study to estimate the effect of
ethnicity BMI endometriosis and causes of infertility on ovarian reserve as
measured by AMH AFC and FSH using a robust dataset from a large cohort
of patients referred for infertility investigation and treatment in a single centre
144
METHODS
Objectives
The objectives of the study were to assess the role of the ethnicity BMI
and endometriosis and the causes of infertility on ovarian reserve as assessed
by the biomarkers AMH AFC and FSH using a large clinical data obtained
retrospectively
Sample
All women between 20 to 45 years of age referred to the Womenrsquos
Outpatient Department (WOP) and the Reproductive Medicine Department
(RMD) of Central Manchester University Hospitals NHS Foundation Trust for
management of infertility from 1 September 2008 to 16 November 2010 and
who had the measurement of AMH using DSL assay (DSL Active MISAMH
ELISA Diagnostic Systems Laboratories Webster Texas) were included in
this study Patients referred for fertility preservation (eg prior to or after the
treatment of a malignant disorder) and patients with a history of tubal or
ovarian surgery (salpingectomy ovarian cystectomy salpingo-oopherectomy)
and patients diagnosed with polycystic ovaries on ultrasound were excluded
The sample size was determined on pragmatic grounds and represents all
available patients meeting the inclusion criteria
Measurement of AMH
All patients referred to RMD had a measurement of AMH prior to
management of their infertility whereas the patients seen at WOP had AMH
measurements if they had a clinical indication for an assessment of ovarian
reserve
Blood samples for the measurement of AMH were taken at an initial
patient visit without regard to the day of the menstrual cycle and transported
to the in-house Biochemistry Laboratory All samples were processed and
analysed strictly according to the assay kit insert provided by the manufacturer
Serum samples were separated within two hours from venipuncture and frozen
at -20C until analysed in batches using the enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
145
100pmolL with a minimum detection limit of 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first
measurement was selected for this study
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the in-house
Biochemistry Laboratory within two hours of venipuncture for sample
processing and analysis Serum FSH levels were measured using specific
immunoassay kits (Cobas Roche Diagnostics Mannheim Germany) for use
on an autoanalyser platform (Roche Modular Analytics E170 Roche USA)
The intra-assay and inter-assay CVs were 60 and 68 respectively FSH
measurements in samples with high E2 levels (gt250) were defined as non-
representative of early follicular phase and were not included in this study
Where patients had repeated FSH measurements the measurement with the
closest date to that of AMH measurement was used
Measurement of AFC
Measurement of AFC was conducted in all patients undergoing assisted
conception The department uses a stringent protocol for the assessment of
AFC which consists of counting all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
Fully qualified sonographers conducted the ultrasound assessments Where
patients had repeated AFC measurements the AFC closest to the date of the
AMH measurement was used
Data collection
Data was extracted from hospital electronic clinical data management
systems and from written hospital notes of each patient AMH and FSH
measurements were obtained from the Biochemistry Department of the
hospital and validated by checking results of randomly selected 50 patients
146
against the results available in electronic clinical data management system
(Clinical Workstation) Data on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
surgery were gathered from the hospital case notes Data on the ethnicity was
obtained from the hospitalrsquos administrative database (PAS) The datasets were
merged using a unique patient identifier (hospital number) and the validity of
the linkage was validated using other patient identifiers (NHS number
patientrsquos name and date of birth)
Definitions and groups
In our hospital the ethnicity of the patient is established using a patient
questionnaire based on the UK census classification The body mass index
(BMI) of patients was categorised using NHS UK cut-off reference ranges
Underweight (lt185) Normal (185-249) Overweight (25-299) and Obese
(30-40) Causes of infertility were established by searching hospital records
including referral letters clinical entries and the letters generated following
initial and follow up clinic consultations Patients with a history of bilateral
tubal block which was confirmed by laparoscopy and dye test and patients
with a history of bilateral salpingectomy were categorised as having severe
tubal factor infertility Patients with unilateral tubal patency or unilateral
salpingectomy were categorised as having mild tubal factor infertility Patientrsquos
with laparoscopic diagnosis of stage III and Stage IV endometriosis (AFS)
were categorised as diagnosed with severe endometriosis whilst patients with
Stage I and Stage II endometriosis were allocated to group of mild
endometriosis Severe male factor infertility was defined as azoospermia or
severe oligospermia which necessitated Multiple Ejaculation Resuspension and
Centrifugation test (MERC) for assisted conception The criteria for MERC
were a) sperm count of lt05 mlnml or b) retrograde ejaculation Patients with
abnormal sperm count but who did not meet above criteria were classified as
mild male factor infertility
Statistical analysis
Firstly univariate analyses of the effect of age ethnicity BMI
endometriosis with and without endometrioma causes of infertility and
duration of infertility were conducted using two sample t test Then a
147
multivariate linear regression model that included age ethnicity BMI
endometriosis presence of endometrioma and the causes of infertility was
specified for the analyses of the effect of these factors to AMH AFC and
FSH Logarithmically transformed values were used for the statistical analysis
of AMH AFC and FSH The precise age on the day measurement of each of
the marker of ovarian reserve (AMH AFC and FSH) was used and age
adjustment utilised a quadratic function following centring to 30 years of age
Differences between the groups were considered significant at p005
Interactions between all explanatory variables were tested at a significance level
of plt001 In order to estimate the effect of BMI we fitted two different
models with a) BMI not included and b) BMI included in the model
Duration of infertility did not show any clinical or statistically significant
differences for any of the markers and therefore this variable was not included
in the models
RESULTS
In total 2946 patients were included in the study of whom 2880 of these
patient had valid AMH measurements 1810 had measurement of AFC and
2377 had FSH samples The mean and median age of patients were 328 (45)
and 332 (295 365) respectively and the distribution of patients according to
age categories ethnicity BMI endometriosis and the causes of infertility is
shown in the Table 1 The summary statistics for the markers of ovarian
reserve were as follows AMH mean 175 (501) median 142 (76-232) AFC
mean 139 (63) median 13 (10-17) and FSH mean 79 (72) median 7 (58-85)
As expected chronological age was found to be a significant determinant of all
markers of ovarian reserve We observed in average 5 decline in AMH levels
2 decline in AFC and 1 increase in FSH measurements per year (Table 2-
4)
Out of 2946 patients 2021 had data on BMI measurements and in 925
BMI was not available Table 5 describes age AMH AFC and FSH according
to the availability of data on BMI Distribution of patients by their ethnicity
and an availability of data on BMI is provided in Table 6 Similarly patient
distribution by diagnosis and availability of data on BMI can be found in Table
7
148
Ethnicity
The multivariable regression excluding BMI (Table 2) showed that
woman of Black ethnicity and the group defined as ldquoOther ethnicityrdquo had
significantly lower AMH measurements when compared to that of White (-25
p=0013 and -19 p=0047) and the overall ethnicity was a significant
predictor of AMH (p=0007) However inclusion of BMI in the model
reduced these effects and none of the groups had a statistically significant
difference in AMH levels compared to that of White and the overall effect of
ethnicity did not reach statistical significance (p=008)
AFC was significantly reduced in Pakistani and women of ldquoOther
ethnicitiesrdquo (Table 3) although the effect sizes were small (10-14) and the
overall effect of ethnicity was significant in the models with and without BMI
(p=004 and p=003) None of the groups showed statistically significant
differences in FSH (Table 4) although women of ldquoOther Asianrdquo ethnicity
appear to have lower FSH measurements (12) which was close to the level of
statistical significance (-12 p=007)
BMI
Obese women had 16 higher measurements of AMH (p=0035) and
overall effect of the BMI was significant (p=003) No interaction were
detected between BMI and ethnicity causes of infertility or diagnosis of
endometriosis suggesting that effect of BMI was independent of these factors
(Table 2)
In the analysis of the effect of BMI on AFC measurements we did not
observe any differences that were statistically significant (Table 3) However
FSH results showed that there is a modest association between BMI and FSH
with both overweight (Table 4) and obese women having significantly lower
FSH measurements compared to lean women (-5 p=0003 and -10
p=0003)
Endometriosis
In the absence of endometrioma endometriosis was associated with
lower AMH measurements although this did not reach statistical significance
149
(Table 2) Neither AFC nor FSH was significantly different in the
endometriosis group compared to those without endometriosis (Table 3-4)
In contrast we observed around 31 higher AMH levels in the patients
with endometrioma (p=0034) although this reduced to 21 and did not reach
statistical significance (p=010) in the smaller subset after adjustment for BMI
(Table 2) AFC and FSH did not show any statistically significant association
with endometrioma (Table 3-4)
Causes of Infertility
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to those with diagnosis
except the analysis that did not include BMI as a covariate which found a
weakly positive correlation (10 p=003) Similarly the estimation of the
effect of a diagnosis of unexplained infertility on AFC as well as FSH showed
that there were weak association between the markers and a diagnosis of
unexplained infertility (Table 2-4)
There were no significant differences in AMH AFC and FSH in women
with mild and severe tubal infertility in the models which included all
covariates other than weakly negative correlation between FSH and mild tubal
factor (Table 2-4)
Women diagnosed with male factor infertility had significantly higher
AMH and lower FSH measurements the effect sizes of which increased with
the severity of the diagnosis We did not find any significant difference in AFC
between patients with and without male factor infertility (Table 2-4)
DISCUSSION
This is first study investigating the effect of demographic
anthropometric and clinical factors on all three markers of ovarian reserve
using a large cohort of women of reproductive age Furthermore the statistical
analysis adjusted for relevant covariables using multivariable linear regression
models
150
Ethnicity
Our study found that amongst the main British ethnic groups the
effect of ethnicity on ovarian reserve measured using AMH AFC and FSH is
fairly weak and can be accounted for by differences in BMI between the
ethnic groups Recently studies have been published on the relationship of
ethnicity and markers of ovarian reserve all of which compared North
American populations One study which assessed a relatively small number of
women (n=102) at late reproductive age did not find a difference in AMH
levels between White and African American Women OR 123 (056 271
P=070) (Freeman et al 2007) In contrast Seifer et al reported that Black
(n=462) women had around 25 lower AMH measurements (P=0037)
compared to that of White (n=122) (Seifer et al 2009) which is not consistent
with our findings The main differences of this study compared to our study
were a) a majority were HIV infected women b) women were older (median
375 years of age) c) the analysis did not control for possible confounders
related to PCO reproductive pathology and surgery Furthermore unlike our
results the study did not find a correlation between BMI and AMH levels
Similarly Shuh-Huerta and colleagues reported that African American women
(n=200) had significantly lower AMH levels (P=000074) compared to that of
White (n=232) Mean AMH 22817 pmolL and 301+15 pmolL
respectively (Shuh-Huerta et al 2012b) Although the group used very stringent
selection of patients and statistical analysis BMI was not included in the
regression model Indeed our analysis without BMI in the model found similar
results (Table 2) But controlling for BMI has revealed no significant difference
in the AMH levels between White and Black ethnic groups
With regard to AFC measurements Shuh Huerta et al reported no
difference in the follicle counts between White (n=245) and African American
(n=202) women which supports our findings (Shuh-Huerta et al 2012b)
Again similar to our results the authors reported that FSH results of these
ethnic groups provided comparable results (Shuh-Huerta et al 2012a)
Although our results do not support some of previously published data
in view of above arguments we believe the ethnicity does not appear to play a
major role in determination of ovarian reserve However in view of the
discrepant findings of the currently available studies we suggest further studies
151
in large and diverse cohorts should be carried out in order to fully understand
the role of ethnicity
BMI
Our results show that BMI has direct correlation with AMH and AFC
and negative correlation with FSH suggesting women with increased BMI are
likely to have higher ovarian reserve The effect of this association was
significant in the analysis of AMH and FSH obese women appear to have
approximately 16 higher AMH and 10 lower FSH measurements when
compared to women with normal BMI Although the difference in AFC
measurements did not reach statistical significance there was direct correlation
between AFC and BMI
Published data on the effect of BMI to AMH levels provide conflicting
results compared to our study given the studies reported either no association
(Buyuk et al 2011 Freeman et al 2007 Su et al 2008) or a negative correlation
between these factors (Seifer et al 2008 Sahmay et al 2012 Skalba et al 2011)
However most of these studies assessed peremenopausal women or that of
late reproductive age Indeed the studies evaluated the effect of BMI to AMH
measurements in women of reproductive age demonstrated that lower AMH
levels in obese women were due to age rather than increased BMI (La Marca
et al 2012 Streuli et al 2012) Furthermore most of these studies either
employed univariate analysis or multivariate regression models that did not
included all relevant explanatory factors In addition these studies had
significantly smaller numbers of samples ranging from 10 to 809 compared to
our study (n=1953) Indeed other large study (n=3302) with multivariate
analysis supports our findings on the effect of BMI on ovarian reserve
indicating obese women have significantly lower FSH levels (Randolph et al
2004)
Endometriosis
Here we present data on the measurement of all three main markers of
ovarian reserve in women with endometriosis We observed that women with
endometriosis without endometrioma did not have significantly different
AMH AFC or FSH measurements compared to women that do not have this
pathology Intriguingly women who had endometriosis with endometriomata
152
tended to have higher AMH levels Given the data was collected
retrospectively we did not have full information on laparoscopic staging of
endometriosis in all patients and therefore an analysis according to severity or
staging of endometriosis was not feasible However the analysis controlled for
the important variables mentioned above and importantly only included the
patients without previous history of ovarian surgery We therefore we believe
the study provides fairly robust data on relationship of endometriosis and the
markers of ovarian reserve
Although it is generally believed that endometriosis has a damaging
effect on ovarian reserve published literature provides conflicting views
ranging from no correlation between these factors to a significant negative
effect of endometriosis As mentioned above most studies were small and
used matched comparison of patients with endometriosis to control group
using retrospectively collected data Carvalho et al compared women with
endometriosis (n=27) and to that of male factor infertility (n=50) and reported
there was no difference in basal AMH and AFC levels whilst FSH levels of
women with endometriosis was lower Another small study which used similar
methodology where an endometriosis group (n=17) was compared to patients
with tubal factor infertility (n=17) reported opposite results suggesting
endometriosis was associated with lower AMH measurements and there was
no correlation between the pathology and FSH or AFC (Lemos et al 2007)
Shebl et al compared AMH results of women with endometriosis (n=153) to a
matched group that did not have the pathology (n=306) and reported that
women with mild endometriosis had similar AMH levels whereas the group
with severe endometriosis had significantly lower AMH compared to the
control group (Shebl et al 2009) Although using well-matched control groups
is a robust study design direct comparison of the two groups without
controlling for other important covariables may result in inaccurate results
Indeed the study that used multivariate regression analysis was able to
demonstrate that there are number of factors that can affect AMH results and
indeed following controlling for these factors there was no difference between
AMH results of women with endometriosis compared to that of without
disease (Streuli et al 2012) In view of above considerations we believe the
effect of endometriosis to ovarian reserve is poorly understood and warrants
further investigation
153
Regarding the effect of endometrioma on AMH levels current evidence
is conflicting Using univariate analysis without controlling for confounders
Uncu et al reported that women with endometrioma (n=30) had significantly
lower AMH and AFC measurements compared to control (n=30) women
(Uncu et al 2013) Similarly Hwu et al reported that women with
endometrioma (n=141) had significantly lower AMH measurements compared
to that of without pathology (n=1323) pathology (Hwu et al 2013) However
the study population appears to have a disproportionately higher number of
women with history of previous and current history of endometrioma
(3191642) compared to any published studies and therefore the study may
have been subject of selection bias
Kim et al reported lower AMH measurements in women with
endometrioma (n=102) compared to control group (102) meanplusmnSEM
29plusmn03 ngmL_vs 33plusmn03_ngmL although this did not reach statistical
significance (P=028)
In our view the most robust data on measurement of AMH in women
with endometriosis was published by Streuli et al which compared AMH levels
of 313 women with laparoscopically and histologically confirmed
endometriosis to 413 women without pathology (Streuli et al 2009) The group
with endometriosis consisted of women with superficial peritoneal
endometriosis (n=35) deep infiltrating endometriosis (n=183) and ovarian
endometrioma (n=95) and relevant factors such as age parity smoking and
previous ovarian surgery were adjusted for using multivariate regression
analysis In keeping with our findings women with endometriosis did not have
lower AMH levels except for patients with previous history of surgery for
endometrioma Most interestingly the findings of Streuili et al and this study
suggest that women with ovarian endometrioma do not have low AMH levels
In contrast according to our data the presence of endometrioma may be
associated with a significant increase in serum AMH levels Given that an
endometrioma is believed to cause significant damage to ovarian stroma this is
an interesting finding Increased AMH levels in the presence of endometrioma
may be due to acceleration in the rate of recruitment of primordial follicles
andor increased expression of AMH in granulosa cells Furthermore
increased AMH levels in these patients may be due to expressions of AMH in
endometriotic cells A study by Wang et al suggested that AMH is secreted by
human endometrial cells in-vitro (Wang et al 2009) This was the first report of
154
non-ovarian secretion of AMH and suggested that AMH may play important
role in regulation of the function of the human endometrium Subsequently
the findings of Wang et al were independently confirmed by two different
groups Carrarelli et al assessed expression of AMH and AMH type II receptor
(AMHRII) in specimens of endometrium obtained by hysteroscopy from
patients with endometriosis (n=55) and from healthy (n=45) controls
(Carrarelli et al 2014) The study also assessed specimens from patients with
ovarian endometriosis (n=29) and deep peritoneal endometriosis (n=26) The
study found that both AMH and AMHRII were expressed in endometrium
Interestingly ectopic endometrium obtained from patients with endometriosis
had significantly higher AMH and AMHRII levels compared to that of healthy
individuals Furthermore the specimens collected from ovarian and deep
endometriosis had highest AMH and AMHII mRNA expression These
findings confirm that AMH as well as AMHRII are expressed in human
endometrium and AMH may play a role in pathophysiology of endometriosis
A further study conducted by Signorile et al also confirmed expression of
AMH and AMHRII in human endometriosis glands Furthermore the study
found that treatment of endometriosis cells with AMH resulted in a decrease in
cell growth suggesting that AMH may inhibit the growth of endometriotic
cells This suggests that further studies to understand the role of AMH in
pathophysiology of endometriosis are warranted
Causes of infertility
Unlike the above-mentioned factors the association of the various
causes of infertility and the markers of ovarian reserve are poorly studied
Therefore our study appears to provide only available data on AMH AFC and
FSH levels in women with three main causes of infertility unexplained tubal
and male factor
In our study AMH levels of women with unexplained infertility did not
differ from those with a diagnosis Similarly the effect of a diagnosis on AFC
and FSH measurements were weak Women with unexplained infertility do not
have any significant pathology that can account for their infertility However
understanding the role of ovarian reserve in these patients is important Our
study suggests that women with unexplained infertility have comparable AMH
levels to other infertile women
155
We did not find any significant differences in AMH AFC or FSH
measurements of women diagnosed with tubal factor infertility compared to
infertile women without tubal disease Pelvic inflammatory disease and
endometriosis are well known causes of tubal pathology and our regression
model has controlled for the effect of endometriosis in these analyses Our
results suggest that despite having damaging effect to the tubes pelvic
infection does not reduce ovarian reserve
In contrast our analyses showed that women with mild and severe male
factor infertility have significantly increased AMH and lower FSH
measurements which demonstrates that these women have better ovarian
reserve compared to general infertility population
Strengths and Limitations of the study
The study is based on retrospectively collected data and therefore was
subject to the issues related to this methodology However we believe that we
have overcome most problems and improved the validity of our results by
using a robust methodology for data collection large sample size and careful
analysis We included all women who presented during the study period and
met the inclusion criteria of the study Importantly women with previous
history of PCO chemotherapy radiotherapy tubal surgery or ovarian surgery
have been excluded from the study given these factors may have significant
acute impact on ovarian reserve effect of which may be difficult to control for
The analysis showed an interaction between BMI and ethnicity which
could not be explored fully due to missing data on BMI (Tables 6) Therefore
analyses with and without BMI in models have been performed (Tables 2-4)
and the distribution of patients according to availability of data on BMI has
been obtained (Tables 5-7) I suggest further studies with sufficient data should
explore this interaction
I was not able to establish the patients that meet Rotterdam criteria for
diagnosis of PCOS given data on menstrual history and biochemical
assessment of androgenemia were not available Therefore ultrasound
diagnosis of PCO was used to categories patients with polycystic ovaries and
all patients with PCO were excluded from analysis
It is important to note that measurement of AMH using Gen II assay may
provide erroneous results (Rustamov et al 2012a) Therefore only samples
156
obtained using DSL assay have been included in the study The DSL assay
appears to provide more reproducible results than the Gen II assay (Rustamov
et al 2011 and Rustamov et al 2012a) and therefore we believe the estimates
in this study reflect the relationship between circulating AMH and the above
factors
SUMMARY
Our data suggests that there is no strong association between ethnicity
and AMH AFC or FSH whilst women with increased BMI appear to have
higher ovarian reserve There was no evidence of reduced ovarian reserve in
women with endometriosis who do not have a previous history of ovarian
surgery In contrast women with current history of endometrioma may have
higher AMH levels which warrants further investigation Women with a
history of unexplained infertility do not appear to have reduced ovarian
reserve as measured with AMH AFC and FSH compared to general infertile
women Similarly women with tubal factor infertility have comparable ovarian
reserve with women who do not have tubal disease In contrast women with
male factor infertility have significantly higher ovarian reserve compared to
infertile women who do not have male factor infertility
This study has elucidated the effect of demographic anthropometric and
clinical factors on all commonly used markers of ovarian reserve and
demonstrated that some of these factors have significant impact on ovarian
reserve
157
References Buyuk E Seifer DB Illions E Grazi RV and Lieman H Elevated body mass index is associated with lower serum anti-mullerian hormone levels in infertile women with diminished ovarian reserve but not with normal ovarian reserve Fertility and Sterility_ Vol 95 No 7 June 2011 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 2014 1011353ndash8 de Carvalho BR Rosa-e-Silva AC Rosa-e-Silva JC dos Reis RM Ferriani RA de Saacute MFIncreased basal FSH levels as predictors of low-quality follicles in infertile women with endometriosis International Journal of Gynecology and Obstetrics 110 (2010) 208ndash212 Doacutelleman M Verschuren W M M Eijkemans M J C Dolle M E T Jansen E H J M Broekmans F J M and van der Schouw Y T Reproductive and Lifestyle Determinants of Anti-Mullerian Hormone in a Large Population-based Study J Clin Endocrinol Metab May 2013 98(5) 2106ndash2115 Freeman EW Gracia CR Sammel MD Lin H Lim LC Strauss JF 3rd Association of anti-mullerian hormone levels with obesity in late reproductive-age women Fertil Steril 2007 87101-6 Halawaty S ElKattan E Azab H ElGhamry N Al-Inany H Effect of obesity on parameters of ovarian reserve in premenopausal women J Obstet Gynaecol Can 2010 32687ndash690 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699-708 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 2011 95170ndash5 Hwu Y Wu FS Li S Sun F Lin M and Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reproductive Biology and Endocrinology 2011 980 Kelsey TW Wright P Nelson SM Anderson RA Wallace WHB (2011) A Validated Model of Serum Anti-Muumlllerian Hormone from Conception to Menopause PLoS ONE 6(7) e22024 Kim MJ Byung Chul Jee Chang Suk Suh and Kim SH Preoperative Serum Anti-Mullerian Hormone Level in Women with Ovarian Endometrioma and Mature Cystic Teratoma Yonsei Med J Volume 54 Number 4 July 2013 La Marca A Sighinolfi G Papaleo E Cagnacci A Volpe A et al (2013) Prediction of Age at Menopause from Assessment of Ovarian Reserve May Be
158
Improved by Using Body Mass Index and Smoking Status PLoS ONE 8(3) e57005 Lemos NA Arbo E Scalco R Weiler E Rosa V Cunha-Filho JS Decreased anti-Muumlllerian hormone and altered ovarian follicular cohort in infertile patients with mildminimal endometriosis Fertil Steril 2008 May 89(5)1064-8 Luborsky JL Meyer P Sowers MF Gold EB Santoro N Premature menopause in a multi-ethnic population study of the menopause transition Hum Reprod 200218199-206 Maheshwari A Stofberg L Bhattacharya S Effect of overweight and obesity on assisted reproductive technologymdasha systematic review Hum Reprod Update 200713433ndash44 Michelmore K Balen A Dunger D Vessey M Polycystic ovaries and associated clinical and biochemical features in young women Clin Endocrinol (Oxf) 199951779-86 Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 95736-741 e731-7332011 Chen X Yang D Mo Y Li L Chen Y Huang Y Prevalence of polycystic ovary syndrome in unselected women from southern China Eur J Obstet Gynecol Reprod Biol 2008 13959-64 Randolph JF Sowers M Gold EB Mohr BA Luborsky J Santoro M et al Reproductive hormones in early menopausal transition relationship to ethnicity body size and menopausalstatus J Clin Endocrinol Metab Apr2003 88(4)1516ndash1522 [PubMed 12679432] Sahmay S Usta T Erel CT Imamoğlu M Kuuk M Atakul N Seyisoğlu H Is there any correlation between amh and obesity in premenopausal women Arch Gynecol Obstet 2012 Sep 286(3) 661-5 Seifer DB Baker VL and Leader B Age-specific serum anti-Meuroullerian hormone values for 17120 women presenting to fertility centers within the United States Fertility and Sterility_ Vol 95 No 2 February 2011 Seifer DB Golub ET Lambert-Messerlian G Benning L Anastos K Watts H Cohen MH Karim R Young MA Minkoff H and Greenblatt RM Variations in Serum Mullerian Inhibiting Substance Between White Black and Hispanic Women Fertil Steril 2009 November 92(5) 1674ndash1678 Shebl O Ebner T Sir A Schreier-Lechner E Mayer RB Tews GSommergruber M Age-related distribution of basal serum AMH level in women of reproductive age and a presumably healthy cohort Fertil Steril 2011 95 832ndash834
159
Shebl O Ebner T Sommergruber M Sir A Tews G Anti muellerian hormone serum levels in women with endometriosis a case-control study Gynecol Endocrinol 200925713-6 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities Hum Genet (2012b) 1311709ndash1724 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Skałba P Cygal A Madej P Dabkowska-Huc A Sikora J Martirosian G Romanik M Olszanecka-Glinianowicz M Is the plasma anti-Mullerian hormone (AMH) level associated with body weight and metabolic and hormonal disturbances in women with and without polycystic ovary syndrome European Journal of Obstetrics amp Gynecology and Reproductive Biology 158 (2011) 254ndash259 Streuli I de Ziegler D Gayet V Santulli P Bijaoui G de Mouzon J and Chapron C In women with endometriosis anti-Mullerian hormone levels are decreased only in those with previous endometrioma surgery Human Reproduction Vol27 No11 pp 3294ndash3303 2012 Su IH Sammel MD Freeman EW Lin H DeBlasis T Gracia C Body size affects measures of ovarian reserve in late reproductive age women Menopause 2008 15(5) 857ndash861 Uncu G Kasapoglu I Ozerkan K Seyhan A Oral Yilmaztepe A Ata B Prospective assessment of the impact of endometriomas and their removal on ovarian reserve and determinants of the rate of decline in ovarian reserve Hum Reprod 2013 Aug 28(8) 2140-5 Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wallace WHB Kelsey TW (2010) Human Ovarian Reserve from Conception to the Menopause PLoS ONE 5(1) e87
160
Table 1 Distribution of patients
AMH AFC FSH
n Mean (SD) n Mean (SD) n Mean (SD)
All 2880 175150 1810 13972 2377 7972
Ethnicity
White (Reference) 1833 169139 1222 13959 1556 7966
Other White 137 172131 85 14480 107 7637
Black 93 202208 43 16098 73 104135
Indian 108 216169 69 14360 94 7127
Other Asian 46 194157 30 14560 41 6717
Pakistani 276 201164 166 14375 232 81124
Other ethnic 103 158130 63 12448 83 7640
Not disclosed 220 170152 114 13161 157 7937
Data not available 64 183251 18 11952 34 8956
Patients with BMI
Normal (Reference) 1110 172137 917 13861 1011 7844
Underweight 38 179136 30 13947 38 7751
Overweight 679 168134 546 13763 620 7544
Obese 149 220209 90 14167 119 7142
Data not available 904 177163 227 14967 589 88123
Diagnosis
Unexplained 894 156120 667 13354 801 7632
Mild tubal 411 172158 284 13771 370 7530
Severe tubal 40 12685 27 13658 38 7827
Mild male 779 181134 538 14058 668 7342
Severe male 356 198135 197 14661 208 6818
Endometriosis ndash endometrioma 141 137108 91 13658 122 8341
Endometriosis + endometrioma 46 196159 15 14449 42 7123
161
Table 2 Regression models for AMH
AMH (Log)
BMI included
n=1952
BMI excluded
n=2816
Β 95 CI P β 95 CI P
Age -0057 -0069 -0045 00001 -0056 -0067 -0046 00001
age2 -0003 -0005 -0001 00001 -0004 -0006 -0003 00001
Ethnicity 00812 00079
Other White -0046 -0226 0133 0611 0038 -0131 0208 0658
Black 0209 -0038 0457 0097 -0259 -0464 -0054 0013
Indian 0032 -0164 0228 0749 0119 -0071 0310 022
Other Asian 0292 -0014 0598 0061 0250 -0037 0537 0088
Pakistani -0116 -0251 0017 0089 -0100 -0226 0025 0118
Other ethnic -0174 -0390 0041 0113 -0197 -0392 -0002 0047
Not disclosed -0002 -0162 0157 0977 -0104 -0241 0033 0138
BMI 00374
Underweight -0107 -0394 0179 0462
Overweight -0058 -0143 0025 017
Obese 0165 00119 0318 0035
Diagnosis
Unexplained 0039 -0073 0152 0492 0105 0007 0204 0035
Mild tubal 0089 -0033 0212 0153 0113 -000009 0226 005
Severe tubal -0168 -0463 0126 0264 -0133 -0444 0177 0401
Mild male 0118 0009 0227 0033 0180 0084 0275 00001
Severe male 0245 0096 0395 0001 0287 0162 0412 00001
Endometriosis -0136 -0311 0037 0124 -0152 -0324 0018 0081
Endometrioma 0217 -0068 0503 0136 0314 0023 0606 0034
_cons 2731 2616 2847 0 2658 2567 2750 0
162
Table 3 Regression models for AFC
AFC (Log)
BMI Included
n=1589
BMI Excluded
n=1810
Β 95 CI P Β 95 CI P
Age -0028 -0035 -0021 0 -0027 -0033 -0021 0
age2 000009 -00009 0001 086 000007 -00008 0001 0885
Ethnicity 00265 00383
Other White -0024 -0119 0070 0614 0003 -0087 0094 0942
Black 0093 -0037 0224 0162 0049 -0075 0175 0436
Indian -0042 -0148 0064 0438 -0035 -0136 0065 0492
Other Asian 0037 -0125 0200 0651 0037 -0114 0189 0626
Pakistani -0095 -0166 -0024 0008 -0083 -0151 -0015 0016
Other ethnic -0142 -0253 -0031 0012 -0132 -0237 -0027 0013
Not disclosed -0008 -0094 0078 0853 -0067 -0148 0012 0098
BMI 07713
Underweight -0040 -0190 0109 0599
Overweight -0018 -0062 0024 0398
Obese 0012 -0077 0103 0779
Diagnosis
Unexplained -0071 -0131 -0011 0019 -0065 -0121 -0009 0021
Mild tubal -0047 -0112 0017 0151 -0060 -0121 00003 0051
Severe tubal -0110 -0267 0045 0164 -0141 -0294 0010 0069
Mild male -0037 -0095 0020 0201 -0027 -0081 0025 0307
Severe male 0007 -0071 0086 0853 -0021 -0093 0050 0563
Endometriosis -0019 -0114 0076 0691 -0004 -0096 0087 0922
Endometrioma -0079 -0215 0055 0248 -0106 -0231 0019 0097
_cons 2694 2632 2755 0 2691 2636 2745 0
163
Table 4 Regression models for FSH
FSH (Log)
BMI Included
n=1772
BMI Excluded n=2343
Β 95 CI P Β 95 CI P
age 0009 0003 0014 0001 0009 0004 0014 00001
age2 00009 00001 0001 0019 0001 00003 0001 0003
Ethnicity 04415 03329
Other White 0034 -0046 0114 0403 -0017 -0099 0065 0685
Black 0043 -0065 0153 043 0068 -0030 0167 0175
Indian -0010 -0097 0076 0808 -0070 -0157 0017 0116
Other Asian -0119 -0250 0011 0074 -0104 -0234 0026 0117
Pakistani -0031 -0089 0026 029 -0014 -0073 0045 064
Other ethnic 0031 -0062 0125 0508 -0002 -0095 0090 0962
Not disclosed 0022 -0049 0093 0541 0026 -0042 0095 045
BMI 00017
Underweight -0070 -0189 0048 0246
Overweight -0055 -0091 -0018 0003
Obese -0106 -0176 -0036 0003
Diagnosis
Unexplained -0055 -0104 -0006 0028 -0055 -0101 -0009 0018
Mild tubal -0052 -0105 000008 005 -0050 -0103 0001 0056
Severe tubal 0004 -0118 0127 0943 0016 -0120 0154 0809
Mild male -0084 -0132 -0037 00001 -0071 -0116 -0026 0002
Severe male -0127 -0196 -0059 00001 -0102 -0168 -0036 0002
Endometriosis 0035 -0039 0111 0353 0044 -0034 0124 0268
Endometrioma -0074 -0196 0047 0229 -0056 -0186 0074 0402
_cons 1999 1948 2049 0 1958 1915 2002 0
164
Table 5 Distribution of patient characteristics by availability of data on BMI The number of observations and mean (SD) of the markers of ovarian reserve (Age AMH AFC and FSH) described according to an availability of data on BMI
BMI (+)
BMI (-) Total
n Mean (SD) n Mean (SD) n Mean (SD)
Age 1976 32944 904 32750 2880 32946
AMH 1976 175144 904 178164 2880 176150
AFC 1583 13862 227 14968 1810 14063
FSH 1788 7744 589 88123 2377 8073
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
165
Table 6 Distribution of ethnicity by availability of data on BMI Distribution of the number of observations by ethnicity and availability of data on BMI
AMH AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
BMI (+) BMI (-) Total
White 1308 525 1833 1070 152 1222 1201 355 1556
Other White 97 40 137 76 9 85 83 24 107
Black 50 43 93 39 4 43 44 29 73
Indian 81 27 108 60 9 69 70 24 94
Other Asian 32 14 46 25 5 30 30 11 41
Pakistani 193 83 276 148 18 166 177 55 232
Other ethnic 66 37 103 55 8 63 60 23 83
Not disclosed 125 95 220 95 19 114 107 50 157
Data not available 24 40 64 15 3 18 16 18 34
Total 1976 904 2880 1583 227 1810 1788 589 2377
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
166
Table 7 Distribution of diagnosis by availability of data on BMI Distribution of number of observations in each diagnosis group tabulated by availability of data on BMI
AMH
AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
Unexplained 730 164 894 611 56 667 672 129 801
Mild tubal 319 92 411 258 26 284 298 72 370
Severe tubal 36 4 40 26 1 27 36 2 38
Mild male 567 212 779 461 77 538 525 143 668
Severe male 196 160 356 161 36 197 153 55 208
Endometriosis ndash endometrioma 112 29 141 83 8 91 101 21 122
Endometriosis + endometrioma 38 8 46 38 8 46 36 6 42
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
167
THE EFFECT OF SALPINGECTOMY
OVARIAN CYSTECTOMY AND UNILATERAL
SALPINGOOPHERECTOMY ON OVARIAN
RESERVE
Oybek Rustamov Monica Krishnan
Stephen A Roberts Cheryl Fitzgerald
To be submitted to Gynecological Surgery
52
168
Title
Effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Stephen A Robertsc Cheryl Fitzgeralda
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable Word count 2904
Acknowledgement
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
169
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all
versions of the manuscript MK assisted in data extraction contributed in
discussion and the review of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed in discussion
and reviewed all versions of the manuscript
170
ABSTRACT
Objective
To estimate the effect of salpingectomy ovarian cystectomy and unilateral
salpingo-oopherectomy on ovarian reserve
Design
Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 3179 patients were included in the study The AMH measurements
of 66 women were excluded due to haemolysed samples or delay in processing
the samples leaving 3113 women for analysis There were 138 women who
had unilateral or bilateral salpingectomy 36 women with history of unilateral
salpingo-oopherectomy 41 women with history of cystectomy for ovarian
cysts that other than endometrioma and 40 women had cystectomy for
endometrioma
Interventions
Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH basal serum FSH and basal AFC measurements
Results
The analysis did not find any significant differences in AMH (9 p=033)
AFC (-2 p=059) and FSH (-14 p=021) measurements between women
with a history of salpingectomy and those without history of surgery Women
with history of unilateral salpingo-oopherectomy were found to have
significantly lower AMH (-54 p=0001) and AFC (-28 p=034) and
increased FSH (14 p=006) The study did not find any significant
171
association between a previous history of ovarian cystectomy that was for
conditions other than endometrioma and AMH (7 p=062) AFC (13
p=018) or FSH (11 p=016) The analysis of the effect of ovarian
cystectomy for endometrioma showed that women with history of surgery had
around 66 lower AMH (p=0002) Surgery for endometrioma did not
significantly affect AFC (14 p=022) or FSH (10 p=028)
Conclusions
Salpingo-oopherectomy and ovarian cystectomy for endometrioma have a
significant detrimental impact on ovarian reserve Neither salpingectomy nor
ovarian cystectomy for cysts other than endometrioma has an appreciable
effect on ovarian reserve
Key Words
Salpingectomy Ovarian cystectomy Salpingo-oopherectomy ovarian reserve
AMH AFC FSH
172
INTRODUCTION
Human ovarian reserve is determined by the size of oocyte pool at birth
and decline in the oocyte numbers thereafter Both of these processes are
largely under the influence of genetic factors and to date no effective
interventions are available to improve physiological ovarian reserve (Shuh-
Huerta et al 2012) However various other environmental pathological and
iatrogenic factors appear to play a role in the determination of ovarian reserve
and consequently it may be influenced either directly or indirectly Evidently
the use of chemotherapeutic agents certain radio-therapeutic modalities and
surgical interventions that damage ovarian parenchyma can cause substantial
damage to ovarian reserve (Nielsen et al 2013 Somigliana et al 2012)
Estimation of the effect of each of these interventions is of paramount
importance in ascertainment of lesser ootoxic treatment modalities and safer
surgical methods
Age is the main determinant of the number of non-growing follicles
accounting for 84 of its variation and used as marker of ovarian reserve
(Hansen et al 2008) However biomarkers that allow direct assessment of the
dynamics of growing follicles AMH and AFC may provide more accurate
estimation of ovarian reserve Although these markers only reflect
folliculogenesis of already recruited growing follicles there appears to be a
good correlation between their measurements and histologically determined
total ovarian reserve (Hansen et al 2011) Thus the biomarkers can effectively
be utilized for estimation of the effect of above adverse factors on the
primordial oocyte pool
Surgical interventions that lead to disruption of the blood supply to
ovaries or involve direct damage to ovarian tissue may be expected to lead to a
reduction in the primordial follicle pool Indeed a number of studies have
reported an association between surgical interventions to ovaries and reduction
in ovarian reserve (Somigliana et al 2012) However given both underlying
disease and surgery may affect ovarian reserve disentanglement of the
individual effects of these factors may be challenging and requires robust
research methodology Here we present a study that intended to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease
173
METHODS
The effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve were studied using serum AMH AFC and
FSH measurements in a large cross sectional study
Population
All women between the ages of 20 to 45 who were referred to the
Womenrsquos Outpatient Department (WOP) and the Reproductive Medicine
Department (RMD) of Central Manchester University Hospitals NHS
Foundation Trust for management of infertility between 1 September 2008
and 16 November 2010 and had an AMH measurement using the DSL assay
(DSL Active MISAMH ELISA Diagnostic Systems Laboratories Webster
Texas) were included We excluded patients referred for fertility preservation
(eg prior to or after treatment for a malignant disorder) and those with a
diagnosis of polycystic ovaries (PCO) on transvaginal ultrasound scan which
was defined as volume of one or both ovaries more than 10ml Patients with
haemolysed AMH andor FSH samples were not included in the analysis of
these markers Non-smoking is an essential criteria for investigation prior to
assisted conception and therefore to our best knowledge our population
consisted of non-smokers
Measurement of AMH
Blood samples for AMH were taken without regard to the day of
womenrsquos menstrual cycle and serum samples were separated within two hours
of venipuncture in the Biochemistry laboratory of our hospital All samples
were processed strictly according to the manufacturerrsquos recommendations and
frozen at -20C until analysed in batches using the enzymatically amplified two-
site immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
100pmolL and a minimum detection limit was 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first AMH of
the patients were selected
174
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the Biochemistry
Laboratory within two hours of venipuncture for sample processing and
analysis Specific immunoassay kits (Cobas Roche Diagnostics Mannheim
Germany) and an autoanalyser platform was used (Roche Modular Analytics
E170 Roche USA) for analysis of FSH The intra-assay CV was 60 and
inter-assay CV was 68 The FSH measurements in the samples with high E2
levels (gt250pmolL) were excluded from the analysis given these samples are
likely to have been taken outside of early follicular phase of menstrual cycle
In patients with repeated FSH measurements measurements conducted on the
same day as first AMH were selected If the patient did not have FSH
measurement on the day of AMH sampling the measurement with the closest
date to first AMH sample was selected
Measurement of AFC
Measurement of AFC is conducted in patients referred for assisted
conception during their initial work up Our department uses a stringent
protocol for the assessment of AFC and qualified radiographers who have
undergone specific training on measurement of AFC The methodology
consists of counting of all antral follicles measuring 2-6mm in longitudinal and
transverse cross sections of both ovaries using transvaginal ultrasound
scanning at early follicular phase (Day 0-5) of the menstrual cycle The AFC
measurement with the closest date to first AMH sample was selected
Data collection
Data was extracted from electronic clinical data management systems
and from information held in written hospital notes for each patient Data on
AMH and FSH measurements were obtained from the Biochemistry
Department and validated by checking the results documented in the hospital
case notes of randomly selected 50 patients against the results obtained from
electronic clinical data management system (Clinical Workstation) finding
100 concordance Information on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
175
surgery were obtained from the hospital case notes The ethnicity of the
patients was established using a patient questionnaire and data were extracted
from the hospital database for the patient demographics (PAS)
Definitions and groups
First the datasets were merged using a unique patient identifier (hospital
number) Validation of the merger using additional patient identifiers (NHS
number name date of birth) revealed existence of duplicate hospital numbers
in patients transferred from secondary care infertility services of our hospital to
IVF Department We established that in our datasets combination of the
patientrsquos first name surname and date of birth in a continuous string variable
could be used as a unique identifier Hence we used this identifier to merge all
datasets achieving a robust merger of all independent datasets into a combined
final dataset Following creation of an anonymised a unique study number for
each patient all patient identifiers were dropped and the anonymised
combined dataset was used for the analysis
Body mass index (BMI) of patients was categorized using standard NHS
cut-off reference ranges Underweight (lt185) Normal (185-249)
Overweight (25-299) and Obese (30-40) (The Office for National Statistics
2011) Causes of infertility were established by searching the hospital notes
including the referral letters clinical notes and letters generated following clinic
consultations Patients with history of bilateral tubal block which was
confirmed by laparoscopic dye test and patients with history of bilateral
salpingectomy were categorized as having severe tubal factor infertility
Patients with unilateral tubal patency or unilateral salpingectomy were
categorized as having mild tubal factor infertility Severe male factor infertility
was defined as azoospermia or severe oligospermia (lt1mln sperm sample)
Patients with abnormal sperm count but do not meet above criteria were
classified as having mild male factor infertility
Patients with reproductive surgery were categorized as having history of
salpingectomy cystectomy for endometrioma cystectomy for ovarian cysts
other than endometrioma or unilateral salpingo-oopherectomy First
measurement of AMH AFC and FSH following surgery was selected for the
study
176
Statistical analysis
A multivariable regression model that included age ethnicity BMI
endometriosis presence of endometrioma the causes of infertility tubal and
ovarian surgery was fitted for each of the ovarian reserve markers AMH AFC
and FSH Difference between the groups were considered significant at
p005 Preliminary analysis of AMH AFC and FSH indicated that
logarithmically transformed values with a quadratic age term provided adequate
fits The precise age on the day measurement of each of the marker of ovarian
reserve (AMH AFC and FSH) was included in the model as a quadratic
function following centering to 30 years of age
Interactions between all explanatory variables were tested at a
significance level of 001 We observed significant interaction between BMI
and other covariates This may be due to biological complexity in the
relationship of BMI and other factors (eg ethnicity) in determination of
ovarian reserve However given data on BMI was not available in considerable
number of patients the observed interactions may be due to limitation of our
dataset Therefore in order to assist in interpretation of the results analyses
with and without BMI in the models were conducted
RESULTS
In total 3179 patients were included in the study The AMH
measurements of 66 women were excluded due to haemolysed samples or
delay in processing the samples leaving 3113 women for analysis 1934 of
patients had measurement of AFC and 2580 had FSH samples that met
inclusion criteria The mean age AMH AFC and FSH of patients were
328plusmn45 173plusmn148 139plusmn62 80plusmn75 respectively There were 138 women
who had unilateral or bilateral salpingectomy 36 women with history of
unilateral salpingo-oopherectomy 41 women with history of cystectomy for
ovarian cysts that other than endometrioma and 40 women had cystectomy for
endometrioma (Table 1) The results of regression analysis on the effect of
reproductive surgery on AMH AFC and FSH measurements are shown in
Table 2
The analysis did not find any significant differences in AMH (9
p=033) AFC (-2 p=059) and FSH (-14 p=021) measurements in
women with history of salpingectomy compared to women without history of
177
surgery and we did not observe marked change in the estimates in a smaller
subset where BMI was included in the model (Table 2)
Women with history of unilateral salpingo-oopherectomy were found
to have significantly lower AMH (-54 p=0001) and AFC (-28 p=034)
and increased FSH (14 p=006) measurements where effect on AMH
reached the level of statistical significance Similarly the analysis of the model
that included BMI showed significantly lower AMH and AFC and higher FSH
measurements in surgery group where both AMH and FSH analysis were
statistically significant (Table 2)
The study did not find a significant association between previous
history of ovarian cystectomy that was for disease other than endometrioma
and measurement of AMH (7 p=062) AFC (13 p=018) or FSH (11
p=016) which did not change noticeably following adding BMI in the model
(Table 2)
The analysis of the effect of ovarian cystectomy for endometrioma
showed that women with history of surgery had around 66 lower AMH
(p=0002) measurements The effect of surgery for endometrioma was not
significant in assessment of AFC (14 p=022) and FSH (10 p=028)
However in the model with BMI association of the surgery with both AMH (-
64 p=0005) and FSH (24 p=0015) were found to be significant (Table
2)
DISUCUSSION
Salpingectomy
The blood supply to human ovaries is maintained by the direct branches
of aorta ovarian arteries which form anastomoses with ovarian and tubal
branch of uterine arteries in mesovarium and mesosalpynx In salpingectomy
often tubal branches of uterine arteries are excised alongside mesosalpynx and
hence it is believed disruption to blood supply to ovaries may lead to a
reduction of ovarian reserve However in our study we did not observe an
appreciable association between salpingectomy and any of the biomarkers of
ovarian reserve suggesting this surgery does not appreciably affect ovarian
reserve These findings are supported by study that assessed the effect of tubal
178
dissection to AMH AFC FSH levels (n=49) using longitudinal data (Erkan et
al 2012) There were no differences between preoperative and 3 month
postoperative measurements with median AMH (15 vs 14 p=007) AFC
(8437 vs 7941 p=009) FSH (76 21 vs 7721 p=010) da Silva et al
assessed the effect of tubal ligation (n=52) in longer term postoperative period
(1 year) and reported that median AMH (143 IQR 063-262 vs and 130 IQR
053-285 p=023) and mean AFC ( 8 IQR 5-14 vs 11 IQR 7-15 p=012)
measurements did not change significantly Our results and on other published
evidence suggest that salpingectomy or tubal division does not have an
adverse effect to ovarian reserve
Unilateral salpingo-oopherectomy
Although salpingo-oopherectomy is rare in women of reproductive age
significant ovarian pathologies and acute diseases such as ovarian torsion may
necessitate unilateral salpingo-oopherectomy There is a plausible causative
relationship between this surgery and ovarian reserve although to our
knowledge there is no previous published evidence We found that women
with a history of unilateral salpingo-oopherectomy have significantly lower
AMH (-54) and higher FSH (13) measurements suggesting the surgery has
considerable negative impact to ovarian reserve Important clinical question in
this clinical scenario is ldquoDo these patients have comparable reproductive
lifespan or experience accelerated loss of oocytes resulting premature loss of
fertilityrdquo as this would allow appropriate pre-operative counseling of patients
regarding long term effect of the surgery to fertility and age at menopause
Considering our data had relatively small number of patients with a history of
salpingo-oopherectomy we were not able to obtain reliable estimates on age-
related decline of ovarian reserve in this study We suggest that studies with
larger number of patients preferably using longitudinal data should address
this research question
Ovarian cystectomy
In women with a history of ovarian cystectomy for ovarian cysts other
than those due to endometrioma we did not observe any significant
association between the surgery and markers of ovarian reserve However
women that had ovarian cystectomy for endometrioma appear to have
179
significantly lower AMH (-66) measurements compared to those without
history of surgery
During the last few years a number of studies have assessed the effect of
cystectomy on AMH levels in patients with endometrioma (Chang et al 2010
Erkan et al 2010 Lee et al 2011) The studies have been summarised by a
recent systematic review which concluded that cystectomy results in damage
to ovarian reserve (Somigliana et al 2012) Further studies evaluated the
mechanism of damage and these suggest that coagulation for purpose of
hemostasis as well as stripping of the cyst wall may cause direct damage to
ovarian reserve Sonmezer et al compared the effect of diathermy coagulation
(n=15) for hemostasis compared to use of hemostatic matrix (n=13) in a
randomized controlled trial and reported that use of diathermy coagulation is
associated with significantly lower AMH measurements (164 plusmn 093 vs 272 plusmn
149 ngmL) in the first postoperative month
Similarly stripping of the cyst wall also appears to have detrimental
effect of ovarian reserve due to inadvertent removal of ovarian tissue (Donnez
et al 1996) Using histological data Roman et al demonstrated that normal
ovarian tissue was removed in 97 specimens of surgically removed
endometriomata (Roman et al 2010) Furthermore it appears that ovarian
cortex containing endometrioma appears to have significantly reduced density
compared to normal ovarian cortex and therefore loss of oocyte containing
normal ovarian cortex may be unavoidable in cystectomy for endometrioma
(Sanchez et al 2014) Matsuzaki et al conducted histological assessment of
cystectomy specimens and found that normal ovarian tissue adjacent to cyst
wall was found in 58 (71121) of patients with endometrioma whereas
normal ovarian tissue was excised in 54 (356) following cystectomy for
other benign cyst (Matsuzaki et al 2008) Similarly in our study women with a
history of cystectomy for endometrioma had significantly lower AMH
measurements whilst those had cystectomy for other benign cysts do not
appear to have lower AMH measurements In view of our findings and other
published research evidence it seems clear that cystectomy for endometrioma
results in significant reduction in ovarian reserve and women undergoing
surgery should be counseled regarding the adverse effect of surgery
180
Strengths and Limitations
The published studies have used longitudinal data comparing biomarkers
before and after cystectomy and provide reliable estimates on the effect of the
intervention on ovarian reserve However data on the effect of salpingectomy
and unilateral salpingoophorectomy is lacking In addition to reevaluation of
the effect of cystectomy this is study has assessed the impact of salpingectomy
and unilateral salpingoophorectomy on the markers of ovarian reserve In
contrast to published studies this study employed analysis of cross sectional
data Given a robust adjustment for all relevant factors has been conducted
our analysis of the cross sectional data should provide reliable estimates of the
effects of various intervention on the markers of ovarian reserve Furthermore
the effect of surgery on all the main biomarkers of ovarian reserve has been
assessed which improves our understanding of the clinical value of each test in
the assessment of patients with history of tubal or ovarian surgery In addition
the analyses adjusted for other relevant factors that may affect ovarian reserve
In patients with history of cystectomy for endometrioma we estimated
independent effects of pathology and surgery providing important data for
preoperative counseling It is important to note that the study evaluated The
effect of surgery using retrospective data which has limitations due variation in
recording of surgical history and missing data In addition given BMI results
for around one third of patients were not available we were not able to fully
explore the effect of BMI However data on the analyses with and without
BMI in the model have been provided to evaluate the effect of this factor The
study employed the data obtained using first generation DSL AMH assay
which is no longer in use However the paper describes the effects of the
interventions in percentage terms and therefore the results are interpretable in
any AMH assay measurement
Important to note although the effects are significant in population level
there is considerable variation between individuals which is evident from the
fact there is overlap between median and interquartile ranges of the groups
(Figure 1) This indicates that clinicians should exercise caution in predicting
the effect of surgery to ovarian reserve of individual patients Nevertheless
given I used a robust methodology for data extraction and conducted careful
analysis I think that the study provides fairly reliable estimates on the effect of
surgery to ovarian reserve
181
CONCLUSION
This multivariable regression analysis of retrospectively collected cross-
sectional data suggests that neither salpingectomy nor ovarian cystectomy for
cysts other than endometrioma has an appreciable effect on ovarian reserve
determined by AMH AFC and FSH In contrast salpingoophorectomy and
ovarian cystectomy for endometrioma have a significant detrimental impact to
ovarian reserve On the basis of findings of this study and other published
studies women undergoing reproductive should be counseled with regards to
the effect of the surgery on their ovarian reserve
182
References
Biacchiardi CP Piane LD Camanni M Deltetto F Delpiano EM Marchino GL et al Laparoscopic stripping of endometriomas negatively affects ovarian follicular reserve even if performed by experienced surgeons Reprod Biomed Online 201123740ndash6 Chang HJ Han SH Lee JR Jee BC Lee BI Suh CS et al Impact of laparoscopic cystectomy on ovarian reserve serial changes of serum anti-Mullerian hormone levels Fertil Steril 201094343ndash9 Dogan E Ulukus EC Okyay E Ertugrul C Saygili U Koyuncuoglu M Retrospective analysis of follicle loss after laparoscopic excision of endometrioma compared with benign nonendometriotic ovarian cysts Int J Gynaecol Obstet 2011114124ndash7 Ercan CM Sakinci M Duru NK Alanbay I Karasahin KE Baser I (2010) Antimullerian hormone levels after laparoscopic endometrioma stripping surgery Gynecol Endocrinol 201026468ndash72 Ercan CM Duru NK Karasahin KE Coksuer H Dede M Baser I (2011) Ultrasonographic evaluation and anti-mullerian hormone levels after laparoscopic stripping of unilateral endometriomas Eur J Obstet Gynecol Reprod Biol 2011158280ndash4 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hachisuga T Kawarabayashi T Histopathological analysis of laparoscopically treated ovarian endometriotic cysts with special reference to loss of follicles Hum Reprod 200217432ndash5 Hirokawa W Iwase A Goto M Takikawa S Nagatomo Y Nakahara T et al The post-operative decline in serum anti-Mullerian hormone correlates with the bilaterality and severity of endometriosis Hum Reprod 201126904ndash10 Hwu YM Wu FS Li SH Sun FJ Lin MH Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reprod Biol Endocrinol 2011980 Iwase A Hirokawa W Goto M Takikawa S Nagatomo Y Nakahara T et al Serum anti-Mullerian hormone level is a useful marker for evaluating the impact of laparoscopic cystectomy on ovarian reserve Fertil Steril 201094 2846ndash9 Kitajima M Khan KN Hiraki K Inoue T Fujishita A Masuzaki H Changes in serum anti-Mullerian hormone levels may predict damage to residual normal ovarian tissue after laparoscopic surgery for women with ovarian endometrioma Fertil Steril 2011952589ndash91e1 Kitajima M Defr_ere S Dolmans MM Colette S Squifflet J van
183
Langendonckt A et al Endometriomas as a possible cause of reduced ovarian reserve in women with endometriosis Fertil Steril 201196685ndash91 Lee DY Young Kim N Jae Kim M Yoon BK Choi D Effects of laparoscopic surgery on serum anti-Meuroullerian hormone levels in reproductive-aged women with endometrioma Gynecol Endocrinol 201127733ndash6 Matsouzaki S Houlle C Darcha S Pouly JL Mage G Canis M Analysis of risk factors for the removal of normal ovarian tissue during laparoscopic cystectomy for ovarian endometriosis Hum Reprod 2009 241402ndash1406 Muzii L Bianchi A Croc_e C Manci N Panici PB Laparoscopic excision of ovarian cysts is the stripping technique a tissue-sparing procedure Fertil Steril 200277609ndash14 Office for National Statistics (ONS) Social Trends 41 Health 2011 Roman H Tarta O Pura I Opris I Bourdel N Marpeau L et al Direct proportional relationship between endometrioma size and ovarian parenchyma inadvertently removed during cystectomy and its implication on the management of enlarged endometriomas Hum Reprod 201025 1428ndash32 Romualdi D Franco Zannoni G Lanzone A Selvaggi L Tagliaferri V Gaetano Vellone V et al Follicular loss in endoscopic surgery for ovarian endometriosis quantitative and qualitative observations Fertil Steril 201196374ndash8
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642 Sanchez A P Viganograve P Somigliana E Panina-Bordignon P Vercellini and Candiani M The distinguishing cellular and molecular features of the endometriotic ovarian cyst from pathophysiology to the potential endometrioma-mediated damage to the ovary Hum Reprod Update (MarchApril 2014)
Shi J Leng J Cui Q Lang J Follicle loss after laparoscopic treatment of ovarian endometriotic cysts Int J Gynaecol Obstet 2011115277ndash81 Tsolakidis D Pados G Vavilis D Athanatos D Tsalikis T Giannakou A et al The impact on ovarian reserve after laparoscopic ovarian cystectomy versus three-stage management in patients with endometriomas a prospective randomized study Fertil Steril 20109471ndash7 Vicino M Scioscia M Resta L Marzullo A Ceci O Selvaggi LE Fibrotic tissue in the endometrioma capsule surgical and physiopathologic considerations from histologic findings Fertil Steril 200991(4 Suppl)1326ndash8
184
Figure 1 Box plots of AMH by various groups Upper panel shows the raw data and the lower panel the AMH measurement (in pmolL) adjusted for age ethnicity BMI causes of infertility endometriosis endometrioma and surgery Groups (left to right) 1) Endometrioma without history of cystectomy (endoma-no surg) 2) Cystectomy for endometrioma (endoma+surg) 3) Endometriosis without endometrioma (endsisonly) 4) Without endometriosis or any surgery (No end+no surg) 5) Oopherectomy (oe) 6) Cystectomy for cyst other than those for endometrioma (other cyst) 7) Salpingectomy (se)
185
Table1 Distribution of patients
BMI excluded
BMI Included
Age AMH AFC FSH AMH AFC
FSH
Mean (SD) N Mean n Mean (SD) N Mean (SD) n n N
Non-surgery 328plusmn45 2880 175plusmn150 18100 139plusmn63 23770 79plusmn72 1976 15830 17880
Oophorectomy 324plusmn50 36 106plusmn84 2 115plusmn77 34 118plusmn230 25 2 23
Salpingectomy 331plusmn42 138 154plusmn119 91 13plusmn43 122 82plusmn 123 121 84 27
Cystectomy Other 336plusmn42 41 168plusmn132 18 148plusmn50 29 122plusmn249 27 15 20
Cystectomy Endometrioma
327plusmn51 40 119plusmn140 17 137plusmn41 37 89plusmn56 23 10 22
186
Table 2 Multivariable regression analysis Adjusted for age ethnicity causes of infertility endometriosis (without endometrioma) endometrioma and reproductive surgery
BMI(+)
BMI(-)
N
Coeff
95 CI
P
N
Coeff
95 CI
P
Oophorectomy
AMH 2128 -0779 -1135 -0422 00005 3049 -0540 -0868 -0213 0001
AFC 1697 -0278 -0848 0292 0340 1946 -0280 -0857 0298 0342
FSH 1929 0266 0110 0422 0001 2546 0139 -0006 0284 0060
Salpingectomy
AMH 2128 0067 -0118 0252 0476 2128 0094 -0097 0285 0333
AFC 1697 -0027 -0128 0075 0605 1697 -0027 -0126 0072 0595
FSH 1929 -0085 -0167 -0004 0041 1929 -0056 -0143 0032 0210
Cystectomy Other
AMH 2128 0102 -0230 0433 0548 2128 0075 -0226 0376 0626
AFC 1697 0102 -0107 0311 0339 1697 0130 -0064 0323 0189
FSH 1929 0134 -0028 0297 0106 1929 0110 -0044 0265 0161
Cystectomy Endometrioma
AMH 2128 -0647 -1100 -0194 0005 2128 -0667 -1081 -0252 0002
AFC 1697 0115 -0172 0402 0433 1697 0144 -0089 0376 0225
FSH 1929 0243 0047 0439 0015 1929 0103 -0084 0290 0281
187
ASSESSMENT OF DETERMINANTS OF OOCYTE
NUMBER USING RETROSPECTIVE DATA ON
IVF CYCLES AND EXPLORATIVE STUDY OF
THE POTENTIAL FOR OPTIMIZATION OF AMH-
TAILORED STRATIFICATION OF CONTROLLED
OVARIAN HYPERSTIMULATION
Oybek Rustamov
Cheryl Fitzgerald Stephen A Roberts
6
188
Title
Assessment of determinants of oocyte number using large retrospective
data on IVF cycles and explorative study of the potential for
optimization of AMH-tailored stratification of controlled ovarian
stimulation
Authors
Oybek Rustamova Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Centre for Biostatistics Institute of Population Health Manchester
Academic Health Science Centre (MAHSC) University of Manchester
Manchester M13 9PL UK
Word count 7520
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgement
Authors would like to thank Dr Monica Krishnan (Foundation Trainee
Manchester Royal Infirmary) for her assistance in data extraction We would
also like to thank colleagues Dr Greg Horne (Senior Clinical Embryologist)
Ann Hinchliffe (Clinical Biochemistry Department) and Helen Shackleton
(Information Operations Manager) for their help in obtaining datasets for the
study
189
Declaration of authorsrsquo roles
OR prepared the study protocol prepared the dataset conducted statistical
analysis and prepared all versions of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed to the
discussion and reviewed all versions of the manuscript
190
ABSTRACT
Objectives
1) To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield
2) To explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Design
Retrospective cross sectional study using multivariable regression analysis
First the effect of a set of plausible factors that may affect the outcomes have
been established including assessment of the effect of age AMH AFC causes
of infertility attempt of IVFICSI cycle COH protocol changes
gonadotrophin preparations operator for oocyte recovery pituitary
desensitisation regime and initial daily dose of gonadotrophins Then the
regression models that examined the effect of gonadotrophin dose and regime
categories on total and mature oocyte numbers have been developed
Setting
Tertiary referral centre for management of infertility St Maryrsquos Hospital
Central Manchester University Hospitals NHS Foundation Trust
Participants
Women without ultrasound features of polycystic ovaries who underwent
IVFICSI cycle using pituitary desensitisation with GnRH long agonist or
GnRH antagonist regimes and had previous measurement of AMH with the
DSL assay In total of 1847 IVF or ICSI cycles of 1428 patients met the
inclusion criteria for the study AMH measurements of all cycles and AFC
measurements for 1671 cycles (n=1289 patients) were available In the analysis
of total oocytes 1653 cycles were included and the analysis of metaphase II
oocytes comprised of 1101 ICSI cycles
Interventions
None (observational study)
191
Main outcome measures
Total oocyte number Metaphase II oocyte number
Results
After adjustment for all the above factors age remained a negative predictor of
oocyte yield whereas we observed a gradual and significant increase in oocyte
number with increasing AMH and AFC values suggesting all these markers
display an independent association with oocyte yield
Compared to 1st IVF cycles those with 2nd (8 p=001) and particularly 3rd
attempt (24 p=0001) had considerably higher total oocytes The effect of
attempt on mature oocyte yield was not significant (p=045) Similarly there
was significant between-operator variability in total oocyte number when
oocyte recovery practitioners were compared (p=00005) However the effect
of oocyte recovery practitioner on mature oocyte yield did not reach statistical
significance (p=0058) Comparison of the effect of gonadotrophin type
showed that rFSH was associated with higher total oocyte yield compared to
that of HMG (p=0008) although the numbers of mature oocytes were not
significantly different between the groups (p=026)
After adjustment for all above factors compared to a reference group (Agonist
with 75-150 IU hMGrFSH) none of the regime and dose categories provided
higher total oocyte yield and Antagonist with 75-150 IU hMGrFSH (-36
p=00005) provided significantly less total oocyte With regards to the mature
oocyte yield Antagonist with 187-250 IU rFSHhMG (43 p=005) and
Antagonist 375 IU rFSHhMG (47 p=002) were associated with
significantly higher oocyte number compared to that of above reference group
This implies that compared to long Agonist down regulation Antagonist
regime is associated with higher mature oocyte yield
Following adjustment for all above variables we did not observe significant
increase in oocyte number with increasing gonadotrophin dose categories
192
Conclusions
Given there was no expected increase in oocyte number with increasing
gonadotrophin dose categories we believe there may not be significant direct
dose-response effect Consequently strict protocols for tailoring the initial
dose of gonadotrophins may not necessarily improve ovarian performance in
IVF treatment It is important to note our COS protocols instructed the use
of cycle monitoring with ultrasound follicle tracking and oestradiol levels and
corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
Key Words
Ovarian reserve AMH AFC IVF Controlled ovarian stimulation AMH-
tailored ovarian stimulation Individualisation of ovarian stimulation
193
INTRODUCTION
According to the HFEA around 12 of IVF cycles in the UK are
cancelled due to poor or excessive ovarian response in the UK which
highlights the importance of the provision of optimal ovarian stimulation in
improving the outcomes (Kurinczuk et al 2010) Traditionally patientrsquos age and
basal FSH measurements were used for the assessment of ovarian reserve with
subsequent tailoring of the initial dose of gonadotrophins and regime for
pituitary desensitisation for controlled ovarian stimulation in IVF Studies on
the prognostic value of markers of ovarian reserve show that AMH and AFC
are the best predictors of ovarian response in cycles of IVF (Broer et al 2011)
Furthermore unlike most other markers AMH has potential discriminatory
power due to significantly higher between-patient (CV 94) variability
compared to its within-patient (CV 28) variation (Rustamov et al 2011)
which allows stratification of patients into various degrees of (eg low normal
high) ovarian reserve Consequently development of optimal ovarian
stimulation protocol for each band of ovarian reserve using AMH may be
feasible
Controlled ovarian stimulation (COS) based on tailoring the pituitary
desensitisation and initial dose of gonadotrophins to AMH measurements is
known under various names individualisation of ovarian stimulation AMH-
tailored stratification of COS personalization of IVF are the most commonly
used This strategy is believed to be effective and has been widely
recommended (Nelson et al 2013 Dewailly et al 2014 La Marca et al 2014)
Although AMH based assessment of ovarian reserve with pituitary down
regulation in patients with extremes of ovarian reserve may improve the
outcomes of ovarian response compared to conventional ovarian stimulation
protocols (Nelson et al 2009 Yates et al 2011) there is no robust data on
AMH-tailored individualisation of ovarian stimulation To establish
individualisation of ovarian stimulation the studies should ideally assess
various pituitary desensitisation regimes and initial doses of gonadotrophins in
patients across the full range of ovarian reserve For instance in AMH-tailored
individualisation of pituitary desensitisation regime studies should evaluate the
effect of both GnRH Agonist and GnRH Antagonist regimes for the groups
for each band of AMH levels (eg low normal high) necessitating 6
comparison groups (Figure 1) In individualisation of the initial dose of
194
gonadotrophins the groups of each band of AMH should be treated with the
range of doses of gonadotrophins (eg low moderate high dose) which
requires 9 treatment groups (Figure 2) Consequently to evaluate the
individualisation of both the stimulation regime and the initial dose of
gonadotrophin across the full range of AMH measurements in a single study
ideally 18 comparison groups are needed Indeed the study should have a large
enough sample to adjust for the confounders and obtain sufficient power for
the estimates of each treatment group In addition assessment of ovarian
reserve should be based on reliable AMH measurements with minimal sample-
to-sample variation which appears to be an issue at present (Rustamov et al
2013) Finally evidence on AMH-tailored individualisation of ovarian
stimulation should ideally be based on randomized controlled trials given in
this context AMH is being used as a therapeutic intervention At present there
is no single RCT that assessed AMH-tailored individualisation of ovarian
stimulation and most quoted research evidence appear to have been based on
two retrospective studies (Nelson et al 2009 Yates et al 2011) Both studies
display a number of methodological issues including small sample size and
centre-dependent or time-dependent selection of cohorts Therefore the role
of confounding factors on the obtained estimates of these studies is unclear
The first study on AMH-tailored individualisation ovarian stimulation
compared outcomes of the cohorts who had IVF cycles in two different IVF
centers (Nelson et al 2009) In this case control study the patients in the 1st
centre (n=370) had minimal tailoring of dose of gonadotrophins and were
offered mainly GnRH agonist regime for pituitary desensitisation except
patients with very low AMH (lt10pmolL) who had GnRH antagonist regime
In patients undergoing treatment in the 2nd centre (n=168) the daily dose of
the gonadotrophins was tailored on the basis of AMH levels and GnRH
antagonist based protocol employed for women with low (1-5 pmolL) and
high (gt15 pmolL) AMH levels whereas patients with normal (5-15 pmolL)
AMH levels had standard long GnRH agonist regimen In addition the
patients with very low AMH (lt10 pmolL) had modified natural cycle IVF
treatment in 2nd centre The study reported that the group that had significant
tailoring of both mode and degree of stimulation to AMH levels (2nd centre)
had higher pregnancy rate and less cycle cancellation However given the
methodological weaknesses the findings of the study ought to be interpreted
with caution First the study compared the outcomes of small number of
195
patients who had treatment in two different centers suggesting that differences
in the outcomes may be due to variation in the characteristics of patient
populations andor performance of two different centers Moreover both
cohorts had some degree of tailoring of pituitary desensitisation regimens as
well as the daily dose of gonadotrophins to AMH levels suggesting estimation
of the effect of AMH tailoring to the outcome of treatment may not be
reliable
A subsequent study attempted to address the above issues by assessing a
somewhat larger number of IVF cycles from the same fertility centre (Yates et
al 2011) The study compared IVF outcomes of the cohorts that underwent
ovarian stimulation using chronological age and serum FSH (n=346) with
women that had AMH-tailored (n=423) treatment cycles (Yates et al 2011)
The study found that the group that had AMH-tailored ovarian stimulation
had significantly higher pregnancy rate less cycle cancellation due to poor or
excessive ovarian response and had significantly lower treatment costs
However this study also has appreciable weaknesses given that it was based
on retrospective data that compared outcomes of treatment cycles that took
place over two year period During this period apart from introduction of
AMH-tailored stimulation protocols other new interventions were introduced
particularly in the steps involved in embryo culture Although the outcomes of
the ovarian response to stimulation could have mainly been due to
performance of the stimulation protocols downstream outcomes such as
clinical pregnancy rate may be associated with the introduction of new
interventions in embryo culture techniques Nevertheless the study
demonstrated that tailoring of ovarian stimulation protocol to AMH levels
could reduce the incidence of cycle cancellation OHSS and the cost of
treatment supporting the need for more robust studies on the use of AMH in
the individualisation of ovarian stimulation in IVF
It appears despite a lack of good quality evidence that AMH-tailored
individualisation has been widely advocated and has been introduced in clinical
practice in a number of fertility units In the absence of good quality evidence
we decided to obtain more reliable estimates on the feasibility of AMH-tailored
ovarian stimulation using more robust methodology Availability of the data on
a large cohort of patients with AMH measurements who subsequently
underwent IVF treatment cycles in a single centre may allow us to obtain more
reliable estimates on the effectiveness of AMH-tailored COS Furthermore due
196
to changes on COS protocol combination of various regime and initial dose of
gonadotrophin were used for patients in each band of ovarian reserve This
may facilitate development of predictive models for both regime and dose for
the whole range of AMH measurements In addition as a part of the study we
decided to establish the role of patient and treatment related factors in
determination of ovarian response in cycle of IVF I believe that
understanding the effect of various factors on ovarian performance in COS
will improve the methodology of the study and can be used as a guide for
identification of confounders in future studies The first step in such an
analysis is to develop a statistical model to describe the relationship between
ovarian response and patient and treatment factors This can then be utilized
to explore the effects of treatment on outcome and potentially to allow optimal
treatments to be identified for given patient characteristics and ovarian reserve
METHODS
Objective
The objectives of the study were 1) to determine the effect of age AMH
AFC causes of infertility and treatment interventions on oocyte yield and 2) to
explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Population
Women of 21-43 years of age undergoing ovarian stimulation for
IVFICSI treatment using their own eggs at the Reproductive Medicine
Department of St Maryrsquos Hospital Manchester from 1st October 2008 to 8th
August 2012 were included Patients with previous AMH measurements using
DSL assay were included and patients that had AMH measurement with only
Gen II assay were excluded given the observed issues with this assay
(Rustamov et al 2012) The patients with ultrasound features of PCO previous
history of salpingectomy ovarian cystectomy andor unilateral
salpingoophorectomy have been excluded from the analysis Similarly cycles
with ovarian stimulation other than GnRH agonist long down regulation or
Short GnRH antagonist cycles were not included in the study
197
Dataset
The dataset for the study was prepared using a protocol for the data
extraction management linking and validation which is described in Chapter
4 In short first the data contained in clinical data management systems were
obtained on patient demography AMH measurements and IVF treatment
cycles Then data not available in electronic format were collected from the
patient case notes which includes causes of infertility previous history of
reproductive surgery AFC and folliculogram for monitoring of ovarian
stimulation Each dataset was downloaded in original Excel format into Stata
12 Data Management and Statistics Software (StataCorp LP Texas USA) and
analysis datasets were prepared in Stata format All IVF cycles commenced
during the study period were identified and the combined study dataset was
created by linking all datasets in cycle level using the anonymised patient
identifiers and the dates of interventions All steps of data handling have been
recorded using Stata Do files to ensure reproducibility and provide a record of
the data management process
Categorization of diagnosis
Patients with history of unilateral tubal occlusion or unilateral
salpingectomy were categorized as mild tubal factor infertility and patients with
blocked tubes bilaterally or with history of bilateral salpingectomy were
allocated to severe tubal disease Severe male factor infertility was defined if
the partner had azoospermia surgical sperm extraction or severe oligospermia
which necessitated Multiple Ejaculation Resuspension and Centrifugation test
(MERC) for assisted conception Mild male factor was defined as abnormal
sperm count that do not above meet criteria for severe male infertility
Diagnosis of endometriosis was based on a previous history of endometriosis
confirmed using Laparoscopy Diagnosis of endometrioma was established
using transvaginal ultrasound scan prior to IVF treatment In couples without a
definite cause for infertility following investigation the diagnosis was
categorized as unexplained Women with features of polycystic ovaries on
transvaginal ultrasound were categorized as PCO and excluded from analyses
198
Measurement of AMH and AFC
AMH measurements were performed by the in-house laboratory Clinical
Assay Laboratory of Central Manchester NHS Foundation Trust and the
procedure for sample handling and analysis was based on the manufacturerrsquos
recommendations Venous blood samples were taken without regard to the day
of womenrsquos menstrual cycle and serum samples were separated within two
hours of venipuncture Samples were frozen at -20C until analysed in batches
using the enzymatically amplified two-site immunoassay (DSL Active
MISAMH ELISA Diagnostic Systems Laboratories Webster Texas) The
intra-assay coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and
29 (at 56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and
49 (at 56pmoll) Haemolysed samples were not included in the study In
patients with repeated AMH the measurement closest to their IVF treatment
cycle was selected The working range of the assay was up to 100pmolL and a
minimum detection limit was 063pmolLThe results with minimum detection
limit were coded as 50 of the minimum detection limit (031 pmolL) and
the test results that are higher than the assay ranges were coded as 150 of the
maximum range (150 pmolL)
In our department the measurement of AFC is conducted as part of
initial clinical investigation before first consultation with clinicians and prior to
IVF cycle Qualified radiographers performed the assessment of AFC during
early follicular phase (Day 0-5) of menstrual cycle The methodology of
measurement of AFC consisted of the counting of all antral follicles measuring
2-6mm in longitudinal and transverse cross sections of both ovaries using
transvaginal ultrasound scan The AFC closest to the IVF cycle was selected
for the analysis
Description of COS Protocols
On the basis of their AMH measurement patients were stratified into
the treatment bands for ovarian stimulation using COS protocols During the
study two different COS protocols were used in our centre and in addition
three minor modifications were made in the 2nd protocol Time periods AMH
bands down regulation regimes initial dose of gonadotrophins and adjustment
of daily dose of gonadotrophins of the protocols are described in Table 1
Similarly the management of excessive ovarian response was tailored to
199
pretreatment AMH measurements although mainly based on the results of
oestradiol and scan monitoring the cycle stimulation (Table 2) Assessment of
transvaginal ultrasound guided follicle tracking and serum oestradiol levels in
specific days of the stimulation were used for monitoring of COS (Table 2)
The criteria for the cycle cancellation for poor ovarian response were same
across all protocols fewer than 3 follicles gt15mm in size on Day 10 of ovarian
stimulation
In patients undergoing their first IVF cycle AMH measurement
obtained at the initial assessment was used for determination of which band of
COS the patient would be allocated In the patients with repeated IVF cycles
AMH measurements were obtained prior to each IVF cycle unless a last
measurement performed within 12 months of period was available During the
study period two different assay methods for measurement of AMH was used
in our centre DSL Assay (1 October 2008- 16 November 2010) and Gen II
Assay (17 November 2010- 8 August 2012) Correspondingly during the study
period two different COS Protocols were used 1st Protocol (1 October 2008-
31 December 2010) and 2nd Protocol (1 January 2011-8 August 2012)
Consequently allocation into the ovarian reserve bands of the patients of 1st
protocol were based on DSL assay samples whereas the stratification of
patients of 2nd protocol was based either on DSL assay or Gen II assay
samples Specifically the patients with recent DSL measurements (lt12 months
old) who had IVF treatment during the period of 2nd Protocol had
stratification on the basis of their DSL measurements In these patients in
order to obtain equivalent Gen II value the DSL result was multiplied by 14
in accordance with the manufacturerrsquos recommendation at the time In the
patients without previous or recent (lt12 months old) DSL measurements
stratification into ovarian reserve bands was achieved using their most recent
Gen II measurements Therefore DSL measurements presented in this study
may or may not have been used for formulation of the treatment strategies for
individual patients In fact in this study DSL measurements have been
included in order to understand the role of AMH in determination of ovarian
response in IVF cycles rather than an evaluation of AMH-tailored COS
protocols In addition to introduction of 2nd protocol further modifications
were made to the protocol and therefore 2nd protocol comprised of 4 different
versions (Table 1-2) These changes in the protocols allowed us to compare the
effect of the various modifications to COS protocols on oocyte yield
200
Pituitary desensitisation regimes
Selection of pituitary desensitisation regime was based on the patientrsquos
AMH according to the COH protocol at the time of commencement of IVF
cycle (Table 1) Long agonist regime involved daily subcutaneous injection of
250g or 500 g of the GnRH agonist Buseralin acetate (Supercur Sanofi
Aventis Ltd Surrey UK) from the mid-luteal phase (Day 21) of preceding
menstrual cycle which continued throughout ovarian stimulation Women
treated with Antagonist regime had daily subcutaneous administration of
GnRH antagonist Ganirelex (Orgalutran Organon Laboratories Ltd
Cambridge UK) from Day 4 post-stimulation until the day of HCGGnRH
agonist trigger Ovarian stimulation was achieved by injection of daily dose of
hMG Menopuir (Ferring Pharmaceuticals UK) or rFSH Gonal F (Merck
Serono) as per AMH-tailored protocols (Table 1) Oocyte maturation was
triggered using 5000 international units of HCG (Pregnyl Organon
Laboratories Ltd Cambridge UK) and the criteria for timing of HCG
injection was consistent across all protocols one (or more) leading follicle
measuring gt18mm and two (or more) follicle gt17mm
Oocyte collection
Oocyte collection was conducted 34-36 hours following injection of
HCG for follicle maturation An Ultrasound Guided Oocyte Recovery (USOR)
was conducted by experienced clinicians under sedation The names of
practitioners were anonymised and the practitioner with the largest number of
oocyte recovery was categorized as a reference group Practitioners with a
small number (lt10) of oocyte collection were pooled (group J) If the cycle
was cancelled before oocyte recovery it was categorized under the practitioner
who was on-call for oocyte recovery session on the day of cycle cancellation
In cycles with pre-USOR cancellation for excessive ovarian response
total oocyte number was coded as 27 and Metaphase II oocyte number was
coded as 19 This was based on mean oocyte number in the patients who had
post-USOR cancellation for excessive ovarian response or OHSS
Quantitative assessment of total oocytes were conducted immediately
post-USOR by an embryologist In patients undergoing ICSI the assessment
of the quality of oocytes were conducted 4-6 hours post-USOR and the
201
oocytes assessed as in Metaphase II stage (MII) of maturation were categorized
as mature oocytes
Statistical analysis
The total number of collected oocytes in all cycles and the number of
mature oocytes in the subset of ICSI cycles were used as outcome measures
for the study Oocyte was selected as the primary outcome measure for
assessment of ovarian performance as this provides an objective measure
which is largely determined by effectiveness of ovarian stimulation regimens
In contrast downstream measures such as clinical pregnancy and live birth are
influenced by factors related to management gametes and embryos
Statistical analysis was conducted using multivariable regression models
and the process of model building included following steps 1) Analyses of
distribution of the groups and variables 2) Univariate analysis to establish the
factors that likely to affect total oocyte number 3) Evaluation of
representation of continuous variables 4) Analysis of interaction between
explanatory variables 5) Sensitivity analysis
First the distribution of patients the ovarian reserve markers
interventions and the outcomes were explored using cross tabulation
histograms Box Whisker and scatter plots Then in order to establish the
factors that likely to affect the oocyte number univariate analyses of Age
AMH AFC PCO status attempt of IVFICSI ethnicity BMI protocol
regime USOR practitioner and initial dose of gonadotrophins were conducted
Following this all these explanatory variables were run as part of initial
multivariable regression model Adjustment for confounders related to the
modifications of the protocols and unknown time-dependent changes
conducted by inclusion of the COS protocol categories in the regression
model
Evaluation of representation of oocyte number Age AMH AFC initial
dose of gonadotrophins were conducted by establishing best fit on the basis of
Akaike and Bayesian Information Criteria In addition interpretability of the
data and clinical applicability of the results (eg cut off ranges) were used as a
guide for selection of optimal representation Given the oocyte number was
not normally distributed it was represented in logarithmic scale (log(oocyte
number+5) To establish best representation for AMH AFC and initial dose
202
the models in following scales were run for each variable Linear quadratic
cubic 4th order polynomial linear (log) quadratic (log) cubic (log) 4th order
polynomial (log) cut-off ranges according to distribution Age adjustment in
quadratic scale following centering it to 30 years of age was found to provide
the most parsimonious representation AMH was found to be best represented
using following cut-off ranges 0-3 4-5 6-8 9-10 11-12 13-15 16-18 19-22
23-28 and 29-200 The best representation for AFC was found to be cut-off
ranges of 0-7 8-910-1112-14 15-19 20-24 and 25-100 Initial dose of
gonadotrophins were categorized as following 75-150IU 187-250IU 300IU
375IU 450IU
Subsequently interactions between explanatory variables were tested at
significance level of plt001 which revealed there were significant interaction
between PCO status and other covariables Given these interactions were
found to be complex and not easily computable we decided to restrict the
regression analysis to the non-PCO group We observed significant interaction
between regime and initial dose and therefore these variables were fitted with
interaction term in the model Finally sensitivity analyses of final regression
models were conducted Significance of the results was interpreted using p
value (lt005) effect size and clinical significance For assessment of feasibility
of individualization of stimulation regime and initial dose visual representation
of data was achieved using plots for observed and fitted values (Figure 1-4)
RESULTS
Description of data
A total of 1847 IVF or ICSI cycles of 1428 patients met inclusion criteria for
the study AMH measurements of all cycles and AFC measurements for 1671
cycles (n=1289 patients) were available In the analysis of total oocytes 1653
cycles were included and the analysis of MII oocytes comprised of 1101 ICSI
cycles
Mean AMH was found to be 178 (125) mean AFC was 142 56
mean number of total oocytes was 101 64 and mean number of mature
oocytes was 74 53 The distribution of the cycles according to patient
characteristics and interventions is shown in Tables 3
203
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
Table 4a and 4b show that there was a significant negative association of
oocyte yield with age and oocyte number following adjustment for AMH
AFC causes of infertility attempt of IVFICSI cycle USOR practitioner COS
protocol pituitary desensitisation regime type of gonadotrophin preparation
and initial daily dose of gonadotrophins (Table 4a) With each increase of age
by 1 year we observed approximately a 3 reduction in total oocyte
(p=00005) and a 2 decrease in mature oocyte number (p=0006) which was
independent of age and other covariables
In the analysis of AMH there was significant gradual increase in total
oocyte as well as mature oocyte number with increasing AMH following
adjustment for all covariables (Figure 1 and 2) Compared to an AMH range of
0-3 pmolL there was increase of 25 in the range of 4-5 pmolL (p=007)
36 in 6-8 pmolL (p=0008) 60 in 9-10 pmolL (p=00005) 65 in 11-12
pmolL (p=00005) 77 in 13-15 pmolL (p=00005) 83 in 16-18 pmolL
(p=00005) 80 in 19-22 pmolL (p=00005) 95 in 23-28 pmolL
(p=00005) and 112 in the range of 29-150 pmolL (p=00005) in total
oocyte number (Table 4a) Similar but less marked increase in MII oocyte
number was observed with increasing AMH
The data on AFC also showed that there was gradual increase in total
oocyte number with increasing AFC following adjustment of all covariables
(Table 4a) Compared to an AFC of 0-7 there was increase of 14 in the
range of 10-11 (p=003) 22 in AFC of 12-14 (p=0001) 26 in AFC of 15-
19 (p=00005) 34 in AFC of 20-24 (p=00005) and 40 in AFC of gt25
(p=0005) However there was no increase in total oocyte number in AFC
range of 8-9 compared to that of 0-7 AFC-related Increase in MII oocytes was
less marked compared to that of total oocytes (Table 4a)
Causes of infertility
We did not observe any significant associations between the causes of
infertility and number of retrieved oocytes However women diagnosed with
unexplained infertility appear to have marginally higher (10 p=002) total
number of oocytes compared to women whose causes of infertility were
204
known Diagnosis of severe tubal (-37 p=019) and severe male (-37
p=035) factor infertility was found to be associated with lower number of MII
oocytes compared to other causes of infertility However neither of these
parameters reached statistical significance Similarly there was no significant
association between oocyte number and diagnosis of endometriosis with or
without endometriomata compared to women that were not diagnosed with
the disease (Table 4a)
Attempt
Analysis of total number of oocytes showed that women who had their
2nd attempt of IVFICSI cycle had slightly higher (85 p=001) and those
that had their 3rd or 4th attempt of treatment had significantly higher total
oocyte yield (24 p=0001) compared to women undergoing their 1st attempt
of IVFICSI cycle (Table 4a) Similarly overall effect of attempt on total
oocyte yield was significant (p=0001)
However we did not observe any association between the attempt and
MII oocyte number in the analysis of the subset of ICSI cycles (p=045)
USOR practitioner COS protocol and gonadotrophin preparation
There was a significant association (p=00005) between total oocyte yield
with USOR practitioner (Table 4b) However the association of USOR
practitioner with MII oocyte number did not reach statistical significance
(p=0058)
We observed significant association between the COS protocols in the
analysis of total number of oocytes 1st version of 2nd Protocol (-18
p=00005) 2nd amp 3rd versions of 2nd Protocol (-14 p=005) and 4th version of
2nd Protocol (-24 p=0009) provided significantly lower number of total
oocytes compared to 1st Protocol However the effect of the COS Protocol
changes to MII oocyte number was not significant (p=024)
Compared to hMG ovarian stimulation using rFSH provided 13
higher total oocytes (p=0008) In the analysis of Metaphase II oocytes there
was no significant difference in oocyte yield between hMG and rFSH (026)
205
Regime and Initial dose of gonadotrophins
The regression analyses of the regimes for pituitary desensitisation and
initial dose categories were conducted in comparison to the reference group
(Agonist with 75-150IU hMGrFSH) IVFICSI cycles where Antagonist
with 75-100IU of hMGrFSH (-36 p=00005) was used provided
significantly lower total oocyte yield whereas cycles with Agonist and 300IU
hMGrFSH (15 p=005) provided marginally higher total oocyte number
In the analysis of MII oocytes cycles using Antagonist with 187-250IU
of hMGrFSH (43 p=005) Agonist with 300IU of hMGrFSH (25
p=016) and Antagonist with 375IU hMGrFSH (47 p=002) yielded higher
number of oocytes Use of Agonist with 375IU hMGrFSH (-18 p=05) and
Agonist with 450IU of hMGrFSH (-28 p=02) was associated with lower
mature oocyte number although the analysis did not reach statistical
significance
AMH-tailored individualization of COS
The overall effect of initial gonadotrophin dose to total oocyte yield
was found to be significant (plt0001) However other than the lowest dose
category with Antagonist regime the analysis did not show any consistent
dose-response effect on total oocyte number with increasing gonadotrophin
dose (Table 4b Figure 3a Figure 3b Figure 4a and Figure 4b)
In the analysis of MII compared to reference group of 75-150 IU of
initial daily gonadotrophins we observed increased oocyte yield in the
categories of 187-250 IU (43 p=005) and 375 IU (47 p=002) of
gonadotrophins However both of these groups had Antagonist regime for
pituitary desensitisation compared to that of Agonist in the reference group
and therefore the observed effect may be related to the regime of COS rather
than daily dose of gonadotrophins
206
DISCUSSION
In this study we explored the effect of age AMH AFC causes of
infertility attempt of IVF ICSI treatment and interventions of COS on
ovarian performance using a retrospective data on large cohort of IVF ICSI
cycles of non-PCO patients To our knowledge this is largest study to have
conducted a detailed analysis of the effect of AMH and AFC on ovarian
performance in IVFICSI cycles The study utilized a dataset that was
prepared using a robust protocol for data extraction and handling Similarly
the statistical analysis was based on a systematic exploration of the effect of all
relevant factors followed by adjustment for all relevant factors and finally
careful analysis
With regards to the outcome measures the quantitative response of
ovaries were measured using total collected oocytes in IVFICSI cycles and
the MII oocyte number in the subset of ICSI cycles were used as a
measurement of quantitative response of ovaries to COS Arguably oocyte
number is the best outcome measure for determination of ovarian response to
COS given it is mainly determined by patientrsquos true ovarian reserve the quality
of assessment of ovarian reserve and treatment strategies for ovarian
stimulation In contrast downstream outcomes such as clinical pregnancy and
live birth are subject to additional clinical and interventional factors which may
not always be possible to adjust for using retrospective data Indeed large
observational studies suggest that achieving optimal ovarian response is one of
the most important determinants of success of IVFICSI cycles and
recommend to use oocyte number as a surrogate marker for live birth (Sunkara
et al 2011) It appears around 10-15 total oocytes or 3-4 mature oocytes
provide optimal chance for a one live birth in IVFICSI cycles (Sunkara et al
2011 Stoop et al 2012) Therefore oocyte number appears to be most useful
marker for assessment of ovarian response to COS as well as in prediction of
live birth in cycles of IVFICSI
207
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
After adjusting for AMH AFC the patient characteristics and above
mentioned treatment interventions age remained as an independent predictor
of ovarian response to COS Our data showed approximately 3 (p=00005)
decrease in total oocyte and 2 (p=0006) reduction in mature oocyte number
with increase of age by factor of 1 year (Figure 3b and Figure 4b)
Interestingly the effect of AMH was also found to predict oocyte yield
independently of age with an effect actually more pronounced compared to
that of age After adjusting for age and all other factors there was gradual
increase in total oocyte number with increasing AMH which were both
clinically (25-110) and statistically (p=007-p=00005) significant (Table 4a)
We observed a largely similar effect of AMH in the analysis of mature
oocytes It is important to note that due to the issues with Gen II AMH assay
(Rustamov et al 2012) in this study we included only measurements obtained
with the DSL assay Consequently presented cut-off ranges may not be
applicable with current assay methods We suggest that future studies should
revisit the optimality of the cut-off ranges once a reliable assay method has
been established
Similarly after adjusting for all factors the effect of AFC on total
oocytes remained significant (14-40 plt003) However the effect of AFC
appears to be less marked compared to AMH It is important to note that the
AFC assessment in this study is based on the measurement of 2-6mm antral
follicles using two-dimensional transvaginal ultrasound scan The cut-off
ranges may not be applicable in centers where AFC measurement is obtained
using different criteria
Our analysis suggests that age AMH and AFC are independent
determinants of total and MII oocyte number in IVFICSI cycles and can be
used as predictors of ovarian performance irrespective of patient and treatment
characteristics However assessment of oocyte number is the quantitative
response of ovaries to COS and may not necessarily reflect qualitative
outcome
208
Causes Endometriosis Endometrioma
The causes of infertility do not appear to make a significant contribution
in determining total oocyte number after controlling for age AMH AFC the
attempt and treatment interventions Although in the analysis of MII oocytes
we observed reduced oocyte yield in women with severe tubal (-37) and
severe male (-37) infertility this was not statistically significant The analysis
of MII oocytes only included the subset of ICSI cycles consisting of women
with male factor infertility Therefore the effect of severe male factor infertility
may have been more marked in this model
We did not observe a significant difference in total or MII oocyte
number in women with a history of endometriosis with or without
endometriomata Current understanding of the effect of endometriosis in the
outcomes of IVF treatment suggests that the disease has detrimental effect on
IVF outcomes (Barnhart et al 2007 Barnhart et al 2002) However some argue
that no association is observed if the analysis conducted using proper
adjustment for all relevant confounders (Surrey 2013) Our data suggests that
after adjustment for all relevant factors there is no measurable association with
endometriosis (with or without endometriomata) and oocyte number Some
suggest that using ultra-long down regulation using depot GnRH analogue up
tp 3-6 months prior to ovarian stimulation improves ovarian performance in
patients with endometriomata Our dataset did not have information on
pituitary desensitisation prior IVF treatment cycles and we are therefore unable
to assess the effect of this intervention
Attempt
Our study found that 2nd and 3rd cycles were associated with 8
(p=001) and 24 (p=0001) higher total oocytes compared to that of 1st IVF
cycle However the effect of the attempt on MII oocytes was not significant
In our centre only patients with a previously unsuccessful IVF treatment are
offered subsequent cycles and therefore compared to the patients with
repeated attempts the group with first cycle may be expected to have better
oocyte yield However when controlled for all relevant confounders including
adjustment of treatment interventions 1st IVF cycle does not appear to provide
better oocyte yield In keeping with our findings a recent study demonstrated
independence of attempts of IVF cycles in terms of outcomes (Roberts SA and
209
Stylianou C 2012) Increased total oocyte yield with progressed attempts is
likely to be due to the adjustment of COS on the basis of information on the
ovarian response in previous cycles It is important to note that in this study
we assessed oocyte yield as the outcome measure and this may not necessarily
translate into live birth which is desired outcome for the couples Therefore
availability of data on the attempt-dependency of live birth in IVF cycles is
important and we suggest future studies should explore it
USOR practitioner
To our knowledge this is the first study that explored the effect of an
oocyte recovery practitioner on oocyte yield adjusting for all relevant
confounders We observed a considerable operator-dependent effect on total
oocyte yield which may be due to a variation of patients across the days of the
week (p=00005) The practitioners were allocated to the sessions of oocyte
recovery using a specific rota template according to the day of the week Given
in our centre we do not conduct oocyte recovery at weekends there may be
day-dependent variation in selection of patients For instance the patients who
are likely to have maturation of leading follicles during the weekend may have
been scheduled slightly earlier Similarly the patients with confirmed
maturation of leading follicles whose oocyte recovery would have fallen on
weekends may have been scheduled after the weekend allowing maturation of
additional follicles Therefore practitioners conducting the sessions of oocyte
recovery in extremes of weekdays may not necessarily have similar patients
compared to that of other days which may have introduced some bias in
estimating the outcomes of individual practitioners Nevertheless given the
statistical analysis adjusted for age ovarian reserve and treatment interventions
we think there is considerable true between-operator variability on total oocyte
number We suggest that future studies should assess it further by including
adjustment for follicle number and size on the day of HCG
Interestingly overall effect of the operator did not reach statistical
significance in the analysis of MII oocytes in ICSI subset (p=0058) This may
suggest irrespective of total oocyte yield aspiration of only follicles of larger
than a certain size provides oocytes with potential for fertilization
210
COS Protocol
Controlled ovarian hyperstimulation in IVF is conducted using a pre-
defined protocol which contains the policy on selection of regime for pituitary
desensitisation the initial daily dose of gonadotrophins the monitoring of
ovarian response the adjustment of daily dose of gonadotrophins the policy
for cancellation due to poor or excessive ovarian response and criteria for
HCG trigger for final maturation of oocytes Determination of the optimal
treatment regime and the initial dose of gonadotrophins for each patient is
frequently achieved by stratification of patients into various bands of ovarian
reserve on the basis of the assessment of ovarian reserve The assessment of
ovarian reserve prior to IVF cycle is performed using biomarkers which usually
consist of one or combination of following Age AMH AFC and FSH In our
centre stratification of patients into the bands of ovarian reserve was
determined on the basis of the patientrsquos AMH measurements For instance the
patients deemed to have lower ovarian reserve were allocated to the treatment
band with higher daily dose of gonadotrophins and vice versa (Table 1)
The study found that the 2nd protocol was associated with 14-24 lower
total oocyte yield compared to the 1stprotocol The differences in the
interventions between the protocols are described in Table 1 and Table2
Compared to the 1st protocol the 2nd protocol had a) some patients allocated
to COS bands using Gen II assay measurements which later was found to
provide inaccurate measurements b) more AMH cut-off bands for COS
bands c) strict monitoring of ovarian response and corresponding adjustment
of daily dose of gonadotrophins and d) strict criteria for cycle cancellation for
excessive response Therefore our data suggests that the COS protocols with
broader AMH cut-off bands with less strict criteria for adjustment of daily
gonadotrophins may provide higher oocyte yield However given it is
retrospective analysis the limitation of the study should be recognized and we
recommend more robust prospective studies on optimization of AMH tailored
protocols should be conducted
Gonadotrophin type
The study showed that rFSH was associated with higher total oocyte
number (13 p=0008) Interestingly analysis of MII oocyte showed a larger
confidence interval and did not reach statistical significance suggesting the
211
effect of rFSH was not a strong determinant of mature oocytes Perhaps
observation of higher total oocytes in rFSH cycles compared to that of HMG
and yet comparable mature oocyte number in the study suggest that regardless
of total oocyte yield only follicles with a potential for maturation will achieve a
stage of metaphase II
Ovarian stimulation in cycles for IVF is largely achieved by two different
analogues of follicle stimulating hormone human menopausal gonadotrophin
(hMG) and recombinant follicle stimulating hormone r(FSH) Although
purified hMG contains more luteinising hormone compared to rFSH which is
believed to assist endometrial maturation and improve odds of implantation in
cycles of IVF Furthermore the LH component of hMG is believed to assist in
maturation of oocyte with subsequent improvement in live birth On the other
hand historically rFSH was believed to have less batch-to-batch variation
compared to that of HMG which allows administration of more precise daily
dose of gonadotrophins To date a number of studies have been published
comparing these two forms of gonadotrophin preparations which provide
conflicting findings However systematic review that compared of the effect of
these types of gonadotrophins on live birth rate suggests that there is no
significant difference on live birth rate (van Wely et al 2011) This supports our
findings on that irrespective of total oocyte yield clinically useful mature
oocyte number is comparable between the groups
Regime and dose of gonadotrophins
The study found that compared to the reference group (Agonist 75-
150IU) none of the combination of the regime and gonadotrophin dose
provided a higher total oocyte yield Women that were in Antagonist regime
group with an initial daily dose of 75-150 IU gonadotrophins produced
approximately 36 fewer total oocytes (p=00005) However comparison of
MII oocytes of these groups did not reach statistical significance and the effect
size was much smaller (-19 p=023) This and reference groups represent the
patients with high ovarian reserve who had milder ovarian stimulation because
of risk of excessive ovarian response and OHSS Lower total oocyte yield and
comparable mature oocyte number in the Antagonist regime may explain why
this regime is reported to be associated with reduction in the risk of OHSS and
212
yet comparable live birth in patients with high ovarian reserve (Yates et al
2012)
In the analysis of MII oocytes Antagonist with 187-250 IU of
gonadotrophin and Antagonist with 375 IU of gonadotrophin provided around
43 (p=005) and 47 (p=002) more oocytes compared to that of the
reference group (Agonist 75-150 IU) Interestingly total oocytes of these
groups were comparable to that of reference group suggesting that using
Antagonist protocol may be associated with improvement in oocyte
maturation compared to Long Agonist regime Perhaps in addition to the
effect of exogenous HCG endogenous LH may play role in oocyte maturation
in IVFICSI cycles and shorter desensitisation of pituitary using Antagonist
regime may allow secretion of LH during COS in lower quantities
AMH-tailored individualisation of COS
Given that we did not observe a significant dose-dependent effect on
oocyte number we were not able to develop AMH or AFC tailored
individualisation protocols for COS Although the initial dose of
gonadotrophin is believed to be one of the main determinants of oocyte yield
our study suggests that the association between these variables is weak
Consequently strict protocols for tailoring the initial dose of
gonadotrophins may not necessarily improve ovarian performance in IVF
treatment It is important to note that our COS protocols recommended close
monitoring of ovarian response and corresponding dose adjustment starting
from 3rd day of COS which may have masked the effect of initial dose
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
213
Strengths of the study
Here we presented the largest study on assessment of the role of patient
and treatment related factors on oocyte yield and exploration of optimization
of AMH-tailored COS using a validated dataset Statistical analysis included
systematic assessment of the effect possible confounders on measured
outcome including of age AMH AFC causes of infertility attempt of IVF
treatment USOR practitioner type of gonadotrophin pituitary desensitisation
regime and initial dose of gonadotrophins On the basis of above analysis a
robust multivariable regression models for assessment of the effect all above
factors on total and mature oocyte number have been developed
Prior to conducting this study previous projects explored the
performance of AMH assay methods The studies found that Gen II assay may
yield highly non-reproducible measurements compared to that of DSL assay
(Rustamov et al 2012a) Therefore in this study only DSL AMH assay
measurements were included Furthermore previous projects (Chapter 5 and 6)
explored the effect of various patient related factors on AMH AFC and FSH
measurements and found that some of the factors had measurable impact on
ovarian reserve These findings were used in establishing which patient related
factors ought to be explored in the building of regression models for this
study However the DSL assay is no longer available and most clinics are
mainly using Gen II AMH assay in formulation of COS in IVF Given its
observed instability AMH-tailoring based on Gen II samples may lead to
erroneous allocation of patients to the band that is significantly inconsistent
with patientrsquos ovarian reserve Subsequently this may result in the extremes of
ovarian response to COS including severe OHSS and cycle cancellation
Weaknesses of the study
The main weakness of the study is that the analysis is based on
retrospectively collected data The methodology included an extensive
exploration for possible confounders and adjustment for the ones that were
found to be significant However there are may be unmeasured factors that
that might have affected the estimates In addition the study included only
patients that did not have PCO appearance on ultrasound scan The analysis in
all patients showed that interaction of PCO status with other covariables was
complex which could introduce bias in estimation of the effects of other
214
factors Therefore analyses of the groups with and without PCO were run
separately Subsequently results of non-PCO group was presented in the thesis
given it had the largest number of cycles Compared to non-PCO analysis we
did not observe significant difference in the results of PCO model
The study assessed ovarian response using oocyte yield only Other
outcomes of ovarian response such as duration of ovarian stimulation total
dose of gonadotrophins cycle cancellation due to poor or excessive ovarian
response and OHSS have not been analysed Therefore it is important to
interpret the findings of this study in the context of ovarian response
determined by oocyte yield Specifically the study should not be used to
interpret cycle cancellation for excessive ovarian response As described in the
methodology of the study the oocyte number in the cycles with cancellation of
oocyte recovery due to excessive response were recoded with comparable
values with cycles that were cancelled following oocyte recovery for OHSS
Given the main desired outcome of IVF treatment is live birth the
overall success of a treatment cycle should reflect this outcome measure This
study does not assess the effect of above factors to overall success of IVF
treatment However the study provides a robust data on research methodology
in assessment of IVF outcomes which can assist in the assessment of other
outcome measures in future studies
SUMMARY
After adjustment for all the above factors age remained a negative
predictor of oocyte yield whereas we observed a gradual and significant
increase in oocyte number with increasing AMH and AFC values suggesting
all these markers display an independent association with oocyte yield IVF
attempt oocyte recovery practitioner type of gonadotrophin were found to
have significant effect on total oocyte yield However the effect of these
factors on mature oocyte number did not reach statistical significance Whilst
total oocyte number was comparable between pituitary desensitisation regimes
GnRH antagonist cycles were found to provide significantly higher mature
oocytes compared to that of long GnRH agonist regime
In terms of the effect of initial dose on oocyte yield following
adjustment for all above variables we did not observe significant increase in
215
oocyte number with increasing gonadotrophin dose categories Therefore
strict protocols for tailoring the initial dose of gonadotrophins may not
necessarily improve ovarian performance in IVF treatment However further
time series regression analysis with full parameters of cycle monitoring and the
dose adjustments in the model should be conducted in order to ascertain the
role of AMH in tailoring the dose of gonadotrophins in cycles of IVF
This study demonstrates complexity of the factors that determine
ovarian response in IVF cycles Therefore assessment of AMH-tailored
individualisation of ovarian stimulation should be based on a robust
methodology preferably using a large randomized controlled trial
Furthermore measurement of AMH ought to be based on a reliable assay
method which is currently not available In the meantime the limitations of
available evidence on AMH-tailored individualisation of ovarian stimulation
should be taken into account in the management of patients
216
References
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Barnhart K Dunsmoor-Su R Coutifaris C Effect of endometriosis on in vitro fertilization Fertil Steril 2002771148ndash55 Dechaud H Dechanet C Brunet C et al Endometriosis and in vitro fertilization a review Gynecol Endocrinol 200925717ndash21 Dewailly D Andersen CY Balen A Broekmans F Dilaver N Fanchin R Griesinger G Kelsey TW La Marca A Lambalk C Mason H Nelson SM Visser JA Wallace WH Anderson RA The physiology and clinical utility of anti-Mullerian hormone in women Hum Reprod Update 2014 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A and Sunkara S K Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers from theory to practice Human Reproduction Update Vol20 No1 pp 124ndash140 2014
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867-75 Nelson SM Biomarkers of ovarian response current and future applications Fertil Steril 201399963ndash969
Roberts SA Stylianou C The non-independence of treatment outcomes from repeat IVF cycles estimates and consequences Hum Reprod 2012 Feb27(2)436-43
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum
217
Reprod 2012a273085-3091
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071 Stoop D Ermini B Polyzos NP Haentjens P De Vos M Verheyen G and Devroey P Reproductive potential of a metaphase II oocyte retrieved after ovarian stimulation an analysis of 23 354 ICSI cycles Human Reproduction 2012 Vol27 No7 pp 2030ndash2035 2012 Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011 261768ndash1774 Sunkara SK Coomarasamy A Faris R Braude P Khalaf Y Effectiveness of the GnRH agonist long GnRH agonist short and GnRH antagonist regimens in poor responders undergoing IVF treatment a three arm randomised controlled trial (ESHRE) 2013London UK SurreyES Endometriosis and Assisted Reproductive Technologies Maximizing Outcomes Semin Reprod Med 201331154ndash163 van Wely M1 Kwan I Burt AL Thomas J Vail A Van der Veen F Al-Inany HG Recombinant versus urinary gonadotrophin for ovarian stimulation in assisted reproductive technology cycles Cochrane Database Syst Rev 2011 Feb 16(2)CD005354
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
218
Figure 1 Study groups for assessment of Individualisation of pituitary desensitisation regime
Individualisation of pituitary desensitisation regimens can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high ovarian reserve
Individualisation of COS Regime
Low AMH
(eg DSL assay
22-157 pmolL)
GnRH
Antagonist
GnRH
Agonist
Normal AMH
(eg DSL assay
158-288pmolL)
GnRH
Antagonist
GnRH
Agonist
High AMH
(eg DSL assay
gt288 pmolL)
GnRH
Antagonist
GnRH
Agonist
219
Fiure 2 Study groups for assessment of individualisation of initial gonadotrophin dose
Individualisation of daily dose of gonadotrophins can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high
ovarian reserve
Individualisation
Gonadotrophin
Dose
Low AMH
(eg DSL assay 22-157 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
Normal AMH
(eg DSL assay158-288pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
High AMH
(eg DSL assay gt288 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-375 IU)
Low Dose
(eg HMG 75-150 IU)
220
Table 1 AMH-tailored stratification protocols for regime starting dose of hMGrFSH and adjusting daily dose of gonadotrophins (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (V1) (01 Jan 2011-30 Apr 2011)
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Initial dose (Day 1-3) 1) lt22 AMH (DSL) Exclude 2) 22-156 AMH (DSL) Antagonist 300 hMG 3) 157-285 AMH (DSL) Long Agonist 200 rFSH225 hMG 4) gt286 AMH (DSL) Antagonist 150 hMG
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 375 hMG 3) 11-21 AMH (Gen II) Long Agonist 300 hMG 4) 22-30 AMH (Gen II) Long Agonist 225 hMG 5) 31-39 AMH (Gen II) Long Agonist 150 hMG 6) 40-67 AMH (Gen II) without PCO Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCO Long Agonist 125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Long Agonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Long Agonist 1125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 450 hMG 2) 3-10 AMH (Gen II) Long Agonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 rFSH 8) gt67 AMH (Gen II) Antagonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 300 rFSH 2) 3-10 AMH (Gen II) Long Agonist 225 rFSH 3) 11-21 AMH (Gen II) Long Agonist 1875 rFSH 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 hMG 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 hMG 8) gt67 AMH (Gen II) Antagonist 1125 hMG
Dose adjustment No or minimum change on daily dose of gonadotrophin
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
221
Table 2 AMH-tailored stratification protocols for management of suspected excessive response (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (v1) (01 Jan 2011-30 Apr 2011)
amp
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Coasting for excessive response on day 8
Oestradiol gt20000 pgml 30-40 follicles larger than 10mm or Oestradiol gt18000 pgml
30-40 follicles larger than 12mm
No coasting
Coasting for excessive response once follicle maturation meets criteria
Oestradiol gt20000 pgml
30-40 follicles larger than 10mm
25-40 follicles larger than 10mm
25-30 follicles larger than 15mm
Cancellation for excessive response
Day 8 or thereafter Oestradiol lgt20000 pgml and symptoms of OHSS after gt3 days of coasting
Day 8 or thereafter More than 40 follicles larger than 10mm
Day 10 or thereafter More than 40 follicles larger than 15mm
Day 8 or thereafter Cancel only if symptoms of OHSS
222
Table 3 Distribution of patient characteristics and interventions
In total 1847 cycles included in the study
n
Causes
Unexplained 591 32
Mild tubal 325 176
Severe tubal 37 2
Mild male 589 3189
Severe male 18 097
Endometriosis 91 493
Endometrioma 47 28
Attempt
1 1346 7287
2 406 2198
3 91 493
4 4 022
USOR practitioner
A 570 317
B 412 2291
C 147 818
D 15 083
E 153 851
F 86 478
G 118 656
H 136 756
I 141 784
J 20 111
Protocol
1 1265 6849
2 (v1) 399 216
2 (v2ampv3) 79 428
2 (v4) 104 563
FSH preparation
HMG 1594 87
rFSH 237 13
Regime
Long Agonist 820 444
Antagonist 1027 556
Initial dose
75-150IU 298 1617
187-250IU 483 2621
300IU 914 4959
375IU 60 326
450IU 88 477
223
Table 4a Results of multivariable regression analysis for total and MII oocytes
Total oocytes (n=1653) Metaphase II oocytes (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
Age -0031 -004 -002 00005 -0021 -004 -001 0006
age2 -0002 000 000 0047 -0002 -001 000 0206
AMH categories (Ref0-3 pmolL) 00005 00005
4-5 pmolL 0254 -003 054 0078 -0073 -054 040 0761
6-8 pmolL 0368 010 064 0008 0250 -019 069 0267
9-10 pmolL 0605 034 087 00005 0474 004 091 0034
11-12 pmolL 0651 039 091 00005 0305 -016 077 0198
13-15 pmolL 0779 051 104 00005 0372 -008 083 0109
16-18 pmolL 0836 057 111 00005 0655 018 113 0007
19-22 pmolL 0803 051 109 00005 0381 -013 089 0142
23-28 pmolL 0954 067 123 00005 0832 034 132 0001
29-200 pmolL 1126 084 141 00005 0872 035 139 0001
AFC categories (Ref 0-7) 00005 0008
8-9 -0039 -018 010 0589 0001 -024 024 0992
10-11 0145 001 028 0037 0185 -005 042 0119
12-14 0223 009 036 0001 0254 002 049 0031
15-19 0263 013 040 00005 0113 -013 036 0362
20-24 0344 017 052 00005 0456 013 078 0006
25-100 0405 021 060 00005 0455 009 082 0015
Causes of infertility
Unexplained 0103 002 019 0021 0090 -010 028 0354
Mild tubal -0012 -010 008 0797 -0098 -029 009 0307
Severe tubal -0066 -030 017 0579 -0371 -093 019 0194
Mild male 0014 -007 009 0729 0135 -002 029 009
Severe male -0074 -055 040 0758 -0377 -117 042 0351
Endometriosis -0108 -026 005 0169 -0139 -041 013 0314
Endometrioma -0016 -018 015 0843 0043 -035 044 083
Attempt (Ref 1st) 0001 045
2nd 0085 002 015 0016 0080 -006 022 0274
3rd4th attempt 0243 010 039 0001 0116 -014 037 0367
224
Table 4b Results of multivariable regression analysis for total and MII oocytes Continuation of Table 4a)
Total oocyte (n=1653) Metaphase II oocyte (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
USOR Practitioner (Ref A) 00005 0058
B -0009 -009 007 0823 -0129 -031 005 0153
C 0104 -003 024 0129 0111 -012 034 0348
D -0260 -059 007 0125 -0287 -108 051 0478
E -0297 -044 -016 0 -0246 -048 -001 0043
F -0173 -032 -003 0017 -0367 -072 -001 0043
G -0213 -039 -003 002 -0311 -061 -001 0044
H -0007 -012 011 0909 0022 -020 025 0849
I -0149 -025 -004 0005 -0082 -030 014 0462
J -0549 -095 -015 0007 -0408 -095 014 0143
Protocol (Ref 1st) 00003 024
2nd (v1) -0186 -027 -010 0 -0066 -024 010 0449
2nd (v2ampv3) -0140 -028 000 0056 0175 -007 042 0156
2nd (v4) -0244 -043 -006 0009 0002 -031 031 0989
Gonadotrophin (Ref HMG)
rFSH 0137 004 024 0008 0119 -009 033 0262
Dose amp Regime (RefAgonist 75-150IU) 00005 00052
Antagonist 75-150IU -0364 -053 -020 0 -0199 -051 011 0203
Agonist 187-250IU 0104 -003 024 0139 0028 -031 036 0869
Antagonist 187-250IU 0124 -006 030 0176 0436 -002 089 0059
Agonist 300IU 0151 -001 031 0059 0258 -011 062 0165
Antagonist 300IU 0003 -016 017 0968 0143 -022 050 0433
Agonist 375IU 0072 -023 037 0639 -0185 -086 049 0591
Antagonist 375IU 0124 -011 035 0291 0478 005 090 0028
Agonist 450IU -0129 -041 015 037 -0285 -080 023 0278
Antagonist 450IU -0207 -048 006 0134 0046 -041 051 0843
Intercept 1342 102 166 0 0993 043 155 0001
225
Figure 3a Total oocytes
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fit0
Non-PCO
226
Figure 3b Total oocytes
Plots show the raw data as dots Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 stimulation with HMG USOR practitioner-A none of the specific causes of infertility
25 30 35 40
12
510
20
Age
To
tal E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
To
tal E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
To
tal E
gg
s
AFC
fit0
Non-PCO
227
Figure 4a Metaphase II oocytes (ICSI subset)
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fitm0
Non-PCO
228
Figure 4b Metaphase II oocytes (ICSI subset)
Plots show raw data as dot Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 simulation with HMG USOR practitioner-A None of the specific causes of infertility
25 30 35 40
12
510
20
Age
Ma
ture
IC
SI E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
Ma
ture
IC
SI E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
Ma
ture
IC
SI E
gg
s
AFC
fitm0
Non-PCO
229
GENERAL SUMMARY
7
230
GENERAL SUMMARY
Anti-Muumlllerian hormone a dimeric glycoprotein secreted from granulosa cells
of growing ovarian follicles appears to play a central role in the regulation of
oocyte recruitment and folliculogenesis (Durlinger et al 2002)
Serum anti-Muumlllerian hormone concentration has been found to be one of
the best predictors of ovarian performance in IVF treatment (van Rooij et al
2002 Broer et al 2011) Therefore an evaluation of the role of AMH in assisted
conception has been of great interest and consequently a considerable body of
research work has been performed during last two decades Most published
studies with varying methodological quality have suggested that AMH is one
of the most reliable predictors of ovarian performance in IVF treatment cycles
Consequently many fertility centers have introduced measurement of AMH for
the assessment of ovarian reserve and as a tool for formulation of treatment
strategies for controlled ovarian hyperstimulation in assisted conception
However the studies described in this thesis suggest that some assumptions on
the clinical value of AMH particularly reliability of AMH assay methods and
the role of AMH-tailored individualisation of daily dose of gonadotrophins in
IVF were not based on robust data
For the purpose of this thesis I conducted a comprehensive review of the
published literature on the biology of ovarian reserve the role of AMH in
female reproduction the assay methods and clinical application of AMH in
assisted conception (Chapter 1) I established that a) published work on
sampling variability of AMH measurements and comparability of various assay
methods provide conflicting results b) data on the effect of ethnicity BMI
reproductive pathology and surgery is scarce and c) good quality data on
individualisation of AMH-tailored controlled ovarian hyperstimulation in IVF
is lacking Consequently I decided to conduct a series of studies that directed
towards an improvement of the scientific evidence in these areas of research
Our previous work on within-patient variability of the first generation DSL
assay samples showed that AMH measurements may exhibit considerable (CV
28) sample-to-sample variability (Rustamov et al 2011) In view of this it was
decided to evaluate the validity of newly introduced Gen II assay (Chapter
21) In order to achieve adequately powered results all available AMH
samples of women of 20-46 years of age who had investigation for infertility at
231
secondary and tertiary care divisions of St Maryrsquos Hospital during the study
period were selected for the study According to the manufacturerrsquos
recommendation haemolysed AMH samples may provide erroneous results
and therefore women with haemolysed samples were excluded from the
analysis Inclusion of all women during the study period was also important in
reducing the risk of selection bias particularly in this study which compared
historical and current AMH assay Given the referral criteria of patients did not
change throughout the study period I could confidently report that observed
comparison between DSL and Gen II samples were the reflection of true
differences of the assay methods It is important to note that validity and
performance of a new test should ideally be compared to a reliable ldquogold
standardrdquo test However to date there appears to be no gold standard test in
measurement of AMH and hence an evaluation of the performance of assay
methods can be chllanging Given the lack of a gold standard I decided to
assess the quality of the new test in comparison to what was considered the
most reliable test available at that time accepting that such a comparison may
have limitations Previously two AMH assays (DSL and IOT) were in use and
there is no research evidence on the superiority of one assay over other
Therefore in this study the new Gen II assay was compared to the DSL assay
method which was previously available in our clinic
Once I prepared a robust and validated dataset the quality of Gen II assay
was evaluated by taking following steps of investigation First within-patient
between-sample variability of AMH measurements of Gen II assay samples
were obtained and compared to that of DSL assay samples Then the validity
of the manufacturer recommended between-assay conversion factor was
evaluated by comparing the Gen II assay sample measurements to that of DSL
assay method using both cross-sectional and longitudinal datasets The stability
of the Gen II assay samples was assessed by examining a) stability of the
samples in room temperature b) the linearity of dilution of the samples c)
comparing the standard assay preparation method to that of an equivalent
method and d) stability of samples during storage in frozen condition
Worryingly the study found that the Gen II AMH assay which was
reported to be more reliable than previous assays gave significantly higher
sampling variability (CV 59) compared to that of DSL samples (CV 28)
This significant variation in between repeated measurements of Gen II samples
indicated that there might be a profound fault in the assay method The
232
comparison of the assay methods using a large cohort of clinical samples
suggested that Gen II assay provided 40 lower measurements compared to
that of DSL contradicting the manufacturerrsquos reported 40 higher
measurements (Kumar et al 2011) These discrepancies in the sampling
variability and assay-method comparability suggested that Gen II assay samples
may lack stability which had not been observed previously
When different assays are available for a particular analyte it is critical that
the comparability of results is established and reliable conversion factors or
calibration curves are determined The study demonstrated that the difference
between the previously recommended (Kumar et al 2011 Wallace et al 2011)
conversion factor and the conversion formula obtained in this study was as
high as 60-80 All three studies followed the manufacturersrsquo
recommendations as supplied in the kit insert In terms of the study design
and analysis previous studies assessed the within-sample difference between
the two assays considered this involved the thawing of samples splitting into
two different aliquots and analysis of each aliquot with a different assay In
contrast I conducted between-sample comparison of historical DSL
measurements to that of Gen II using cross sectional and longitudinal
population based analyses The laboratory based within-sample conversion
formula should be reproducible in population based between-sample
comparison particularly in longitudinal analysis Observed discrepancies in the
conversion factors again suggested that AMH samples may suffer from pre-
analytical instability
Thus in collaboration with the scientific team of the Clinical Assay
Laboratory of our hospital we investigated the stability of Gen II assay
samples The studies on sample storage and preparation confirmed the Gen II
assay samples exhibited considerable instability under the storage and
processing conditions recommended by the manufacturer It was suggested
that Gen II samples remain stable when stored in unfrozen conditions up to 7
days and many IVF clinics adopted the practice of shipping unfrozen AMH
samples to centralized laboratories for processing and analysis (Kumar et al
2010 Nelson and La Marca 2011) This study demonstrated that storage of
unfrozen samples can affect obtained results considerably Evaluation of the
stability of samples (n=48) at room temperature found that in the majority of
samples AMH levels in serum increased progressively during 7 days of storage
with an overall increase as high as 58 Contrary to the manufacturerrsquos report
233
even storage of samples in frozen condition (-20 ordmC) does not ensure the
stability of the samples Storage at -20ordmC for 5 days increased AMH levels by
23 compared to fresh samples Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample In contrary the study showed that Gen II samples exhibit
considerable increase with the dilution Pre dilution of serum prior to assay
gave AMH levels up to twice that found in the corresponding neat sample
Similarly pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave overall 72 higher readings compared to sequential
addition These experiments confirmed that Gen II assay methodology was
completely flawed and routine clinical samples were likely to provide highly
erroneous results which could lead to adverse clinical consequences in
patients
To evaluate the robustness of our data I validated the study on the
variability of Gen II samples using external data (Chapter 22) Assessment of
samples obtained from different patient population and different assay-
laboratory found that within-patient between-sample variability of Gen II
AMH measurements were similar to that of my study (CV 62) This
confirmed that Gen II assay sampling variability was independent of
population or laboratory and specific to the assay-method
Findings of this series of studies suggested that the use of Gen II
measurements might have considerable clinical implications particularly when
used as a marker for triaging patient to ovarian stimulation regimens in cycles
of IVF In order to obtain equivalent clinical cut-off ranges for Gen II
samples previously used DSL assay based guidance ranges were recommended
to be increased by 40 However my study found that Gen II assay may
actually provide 20-40 lower measurements compared to that of DSL which
might led to allocation of patients to inappropriate treatment regimens Given
that using the above conversion formula may underestimate ovarian reserve by
60-80 the patients may inadvertently be given significantly higher dose of
gonadotrophins than appropriate in the individual IVF treatment cycles This
can increase the patientrsquos risk of excessive ovarian response resulting in
cancellation of IVF cycles andor severe ovarian hyperstimulation syndrome
(OHSS) In addition significant variation of Gen II assay sample
measurements (CV 59) may also lead to inconsistency in allocation of
patients to appropriate cut off ranges Indeed this was demonstrated by a
234
recent study which found that 7 out of 12 patients moved from one cut-off
range to another when Gen II assay was used for AMH measurements
(Hadlow et al 2013) Therefore we suggested that Gen II assay samples should
not be used in allocating patients to ovarian stimulation regimens
Immediate steps were taken to report these findings to the manufacturer
scientists clinicians and the quality assessment agencies The findings of the
study were presented at the annual meetings of European Society of Human
Reproduction and Embryology as well as British Fertility Society The study
was also published in Human Reproduction which generated an important debate
on the validity of Gen II assay measurements Further independent studies by
other research groups and re-evaluation of the assay by the manufacturer have
confirmed our results (Han et al 2013) This led to recognition of the issues of
the Gen II assay by the manufacturer and consequent modification of the assay
method (King 2012) Subsequent evaluation of Gen II assay by the Medicines
and Healthcare Products Regulatory Agency (MHRA) and the National
External Quality Assessment Service (NEQAS) have confirmed the above
findings As a result the Human Fertility and Embryology Authority have
circulated a field safety notice with the regards to the pitfalls of the AMH Gen
II assay We informed National Institute for Health and Care Excellence
(NICE) of the problems of AMH measurements and urged it to review its
current recommendation on the use of AMH in the investigation and
treatment of infertility With regards to the impact of this work it is important
to note that AMH is widely used in fertility clinics around the world and Gen
II assay is the only commercially available kit for the measurement of AMH in
most countries Consequently this study has made a direct significant impact
in the improving safety and effectiveness of fertility investigation and
treatment around the world However further studies are required to
determine the cause of the instability In addition the validity of the modified
protocol for Gen II assay and other new AMH assays need to be evaluated In
the meantime caution should be exercised in the interpretation of Gen II
AMH measurements
Studies above established that invalid commercial AMH assay was
introduced for clinical use without full and independent validation Regretfully
the issues with the assay were not identified early enough to prevent
widespread use of this faulty test in clinical management of patients around the
world In order to avoid above failures and improve reliability of future AMH
235
assays I recommend following steps should be taken 1) International
standards for the evaluation of validity of existing and future AMH assays
should be developed 2) Independent research groups should evaluate validity
of AMH assays before introduction of the test for clinical application 3)
Validity and performance of already introduced AMH assays ought to be
evaluated by independent research groups periodically to ensure timely
detection of the deterioration in the quality of the test
In view of the observed issues with AMH measurements we conducted
a critical appraisal of the published research on the previous and current assay
methods that reported AMH measurement variability assay method
comparison and sample stability (Chapter 3) Following a systematic search
for all published studies on the evaluation of performance of historic and
current AMH assays ten sample stability studies 17 intrainter-cycle variability
studies and 14 assay method comparability studies were identified Previously
most studies reported that variability of AMH in serum was very small and
suggested a random single measurement provides an accurate assessment of
circulating AMH in serum Therefore using a random AMH measurement for
assessment of ovarian reserve has become a routine practice It appears that
both in reporting particularly in its interpretation the term ldquoAMH variabilityrdquo
was used too broadly and had a various meanings Reviewing all published
studies that used term ldquoAMH variabilityrdquo I identified that the term was used in
interpretation of four distinct outcomes for measurement of variability of
AMH in serum 1) circadian 2) within the menstrual cycle 3) between
menstrual cycles and 4) between repeated samples without consideration of the
day of menstrual cycle In order to delineate the reported variability of AMH
for each outcome I divided the variability studies into four separate groups
and reviewed each study within its appropriate group The review found that
most studies were based on small sample sizes and did not report the
methodology for sample processing and analysis fully The studies also appear
to refer to their outcomes as biological variability of AMH without taking into
account the variability arising due to errors in its measurement More
importantly the review demonstrated that there is clinically significant
variability between AMH measurements in repeated samples which was
reported to be markedly higher with currently used Gen II assay compared to
that of historic DSL and IOT assays
236
Appraisal of assay method comparability found that despite using the
standard manufacturer protocols for the sample analysis the studies have
generated strikingly different between-assay conversion factors The studies
comparing first generation AMH assays (DSL vs IOT) reported conversion
factors ranging from five-fold higher with the IOT assay compared to both
assays giving equivalent AMH concentrations Similarly studies comparing first
and second-generation assays (DSL vs Gen II or IOT vs Gen II) derived
conflicting conclusions The apparent disparity in results of the assay
comparison studies implies that AMH reference ranges and guidance ranges
for IVF treatment which have been established using one assay cannot be
reliably used with another assay method without full and independent
validation Similarly caution is required when comparing the outcomes of
research studies using different AMH assay methods Correspondingly the
review of studies on sample stability revealed conflicting reports on the
stability of AMH under normal storage and processing conditions which was
reported to be a more significant issue with the Gen II assay Similarly there
was considerable discrepancy in the reported results on the linearity of dilution
of AMH samples particularly in Gen II studies In view of above findings we
concluded that AMH in serum may exhibit pre-analytical instability which may
vary with assay method Therefore robust international standards for the
development and validation of AMH assays are required
Although AMH assays have been in clinical use for more than a decade
this appears to be first published review that examined the studies on the
performance of AMH assay methods Indeed a number of review articles
comparing clinical performance of AMH test to other markers of ovarian
reserve have been published (Broer et al 2009 Broer et al 2011b La Marca et
al 2009) Reviewing observational studies the articles concluded that AMH
measurement was one of the most robust methods of assessment of ovarian
reserve However there appears to be no review article that specifically
evaluated the validity of the AMH assay methods suggesting AMH assay
methods were assumed to be reliable despite the lack of robust data on the
validity of assay methods
Reassuringly the report of instability of the Gen II assay samples has
generated significant research interest directed towards understanding the
causes of the issue As a result several hypotheses have been proposed and are
undergoing testing by various research groups For instance in the work
237
described here it was proposed that AMH molecule may undergo proteolytic
changes under certain storage and processing conditions exposing additional
antibody binding sites (Rustamov et al 2012a) The manufacturer of the assay
suggested that the sample instability is due to the presence of complement
interference (King 2012) More recent studies have reported the presence of
another form of AMH molecule pro-AMH in the serum may be the source of
erroneous measurements (Pankhurst et al 2014) Furthermore this study
demonstrated that Gen II assay detects both AMH and pro-AMH suggesting
that the mechanism of sample instability may be more complex than previously
thought It is indeed important to continue the quest to determine the cause of
the sample instability in order to develop reliable method for measurement of
AMH in future In the meantime clinicians should exercise caution when using
AMH measurements in the formulation of treatment strategies for individual
patients
Using a robust protocol for extraction of data and preparation of
datasets I have built a large validated research database (Chapter 4) Utilizing
the clinical electronic data management systems and case notes of patients I
have prepared a validated dataset that will enable study of ovarian reserve in a
wide context including a) assessment of ovarian reserve b) evaluation of the
performance of the biomarkers c) study individualization of ovarian
stimulation in IVF d) association of biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryos live birth) The database has been
used to address research questions posed in chapter 5 and chapter 6 of this
thesis In addition it can be utilized for future studies on assessment of ovarian
reserve and IVF treatment interventions
Both formation and decline of ovarian reserve appears to be largely
determined by genetic factors although at present data on genetic markers are
scarce (Shuh-Huerta et al 2012) Therefore availability of data on clinically
measurable determinants of ovarian reserve is important Consequently I
explored the role of ethnicity BMI endometriosis causes of infertility and
reproductive surgery to ovarian reserve using AMH AFC and FSH
measurements of a large cohort of infertile patients (Chapter 51)
Multivariable regression analysis of data on the non-PCO cohort showed the
association between ethnicity and the markers of ovarian reserve is weak In
contrast I observed a clinically significant association between BMI and
ovarian reserve obese women were found to have higher AMH and lower
238
FSH measurements compared to those of non-obese With regard to the role
of the causes of infertility I did not observe a significant association between
the markers of ovarian reserve and subsets diagnosed with unexplained or
tubal factor infertility In contrast those diagnosed with male factor infertility
had significantly higher AMH and lower FSH measurements which increased
with the severity of the disease In conclusion the study demonstrated that
some of the above factors have a significant impact on above biomarkers of
ovarian reserve and therefore I suggest future studies on ovarian reserve
should include adjustment for the effects these factors
The study showed that in the absence of endometrioma endometriosis
was not found to have a strong association with markers of ovarian reserve
compared to those without the disease Interestingly women with an
endometrioma had significantly higher AMH measurements than those
without endometriosis This is the first study that has reported increased
AMH in serum in the presence of endometrioma Interestingly recent studies
have demonstrated that AMH and its receptor are expressed in tissue samples
obtained from ovarian endometriosis (Wang et al 2009 Carelli et al 2014) It
appears that AMH inhibits growth of both epithelial and stromal cells
(Signorille et al 2014) I believe these intriguing findings warrant further
research on the role of AMH in the pathophysiology of endometriosis With
regards to assessment of ovarian reserve AMH may not reflect ovarian reserve
in the presence of endometrioma and therefore caution should be exercised
With respect to reproductive surgery I conducted a study to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease (Chapter 52) Multivariable regression analysis of the
cross-sectional data showed that salpingo-ophorectomy and ovarian
cystectomy for endometrioma have a significant detrimental impact on ovarian
reserve as estimated by AMH AFC and FSH In contrast neither
salpingectomy nor ovarian cystectomy for cysts other than endometrioma was
found to have appreciable effects on the markers of ovarian reserve I suggest
that women undergoing surgery should be counseled regarding the potential
impact of surgical interventions to their fertility However there was
appreciable overlap between the interquartile ranges of the comparison groups
This suggests that although the effects are significant at a population level
there is considerable variation between individuals Therefore clinicians should
239
exercise caution in predicting the effect of surgery on ovarian reserve of
individual patients
Published studies on the prognostic value of AMH in assisted
conception suggested there is a strong correlation between AMH and extremes
of ovarian response in cycles of IVF (Nelson et al 2007 Nardo et al 2007)
Later case control studies showed that tailoring the daily dose of
gonadotrophins to individual patientrsquos AMH levels and pituitary
desensitisation with GnRH antagonist in patients with the extremes of ovarian
reserve improved the outcomes of IVF treatment (Nelson et al 2009 Yates et
al 2012) However these studies displayed a number of methodological issues
largely due to retrospective analysis small sample size and centre-dependent or
time-dependent selection of cohorts Therefore the role of confounding
factors on the obtained estimates of these studies is unclear Ideally clinical
application of these treatment interventions should be based on research
evidence based on large randomized controlled trials In the absence of
controlled trials I decided to obtain best available estimates on the role of
AMH in individualisation of controlled ovarian stimulation using a robust
methodology in my large cohort of treatment cycles (Chapter 6) Oocyte yield
was used as the outcome measure given it is mainly determined by the
effectiveness of treatment strategies for ovarian stimulation which is the
question the study has addressed In contrast downstream outcomes such as
clinical pregnancy and live birth are subject to additional clinical and
interventional factors The study developed multivariable regression models of
total oocyte yield in all included IVF ICSI cycles (n=1653) and Metaphase II
oocytes of the ICSI subset (n=1101) to measure ovarian response to COH In
view of the significant interaction of PCO status with other variables I
restricted the analysis to non-PCO patients First in order to identify the
confounders I established the effect of a set of plausible factors that may affect
the outcomes including assessment of the effect of age AMH AFC causes of
infertility attempt of IVFICSI cycle COH protocol changes gonadotrophin
preparations operator for oocyte recovery pituitary desensitisation regime and
initial daily dose of gonadotrophins Then I developed the regression models
that examined the effect of gonadotrophin dose and regime categories on total
and mature oocyte numbers
240
The study found that after adjustment for all the above factors age
remained a negative predictor of oocyte yield whereas I observed a gradual
and significant increase in oocyte number with increasing AMH and AFC
values suggesting all these markers display an independent association with
oocyte yield Interestingly after adjustment for all above variables in non-PCO
patients I did not observe the expected increase in oocyte number with
increasing gonadotrophin dose categories beyond the very lowest doses This
suggests that there may not be a significant direct dose-response effect and
consequently strict protocols for tailoring the initial dose of gonadotrophins
may not necessarily optimize ovarian performance in IVF treatment It is
important to note our COH protocols utilized extensive cycle monitoring
using ultrasound follicle tracking and measurement of serum oestradiol levels
with corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not demonstrate a significant
impact on oocyte yield Nevertheless further longitudinal regression analysis
including full time course parameters of cycle monitoring and the dose
adjustments in the model should be conducted in order to ascertain the role of
AMH in tailoring the dose of gonadotrophins in cycles of IVF Moreover the
role of AMH on downstream outcomes of IVF cycles particularly on live
birth should be examined in this dataset Now equipped with a better
understanding of the research methodology and a robust database I am
planning to visit these research questions in future work
Although clinical biomarkers have improved the assessment of ovarian
reserve there remains a significant limitation in their performance in terms of
accurate estimation of ovarian reserve Given that ovarian reserve is believed
to be largely determined genetically recent large Genome-Wide Association
Studies (GWASs) have focused on the identification of genetic markers of
ovarian aging A meta-analysis of these 22 studies identified four genes with
nonsynonymous SNPs as being significantly associated with an age at
menopause (Stolk et al 2012 He et al 2012) However these SNPs were found
to account for only 25-41 of association of the age at menopause
Furthermore studies in mice and humans have identified more than 400 genes
that are involved in ovarian development and function (Wood et al 2013)
Given this genetic heterogeneity it is unlikely that a single genetic determinant
241
of ovarian reserve will be identified In addition epigenetic noncoding RNAs
and gene regulatory regions may play an important role in determination of
ovarian reserve which is yet to be fully explored (Bernstein et al 2012) Indeed
further large scale studies for ascertainment of genetic markers of ovarian
reserve are needed However current biomarkers including AMH appear to
remain as the most useful tests for the assessment of ovarian reserve in the
foreseeable future and further efforts to improve the performance of these
tests are therefore important
In summary some of the assumptions on performance of AMH
measurements particularly Gen II assay appear to have been based on weak
research evidence Similarly there are significant methodological limitations in
the published studies on AMH-tailored individualisation of controlled ovarian
hyperstimulation in IVF I believe the studies described in this thesis have
revealed instability of Gen II assay samples and raised awareness of the pitfalls
of AMH measurements These studies have also demonstrated the effect of
clinically measurable factors on ovarian reserve and provided data on the effect
of AMH other patient characteristics and treatment interventions on oocyte
yield in cycles of IVF Furthermore a robust database and statistical models
have been developed which can be used in future studies on ovarian reserve
and IVF treatment interventions I believe the work presented here has
provided a better understanding of the performance of AMH as an
investigative tool and its role in management of infertile women and provided
resource for future work in this area
242
References Bernstein BE Birney E Dunham I Green ED Gunter C Snyder M ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome Nature 2012 489(7414)57ndash74 [PubMed 22955616] Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Broer SL Doacutelleman M Opmeer BC Fauser BC Mol BW Broekmans FJ AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 Jan-Feb 17(1)46-54 Epub 2010 Jul 28 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 20141011353ndash8
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013 May 99(6)1791-7 Han X Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Human ReproductionJun2013 Vol 28 Issue suppl_1 He C Murabito JM Genome-wide association studies of age at menarche and age at natural menopause Mol Cell Endocrinol 2012
King D URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian
243
response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Pankhurst M Chong Y H and McLennan ISEnzyme-linked immunosorbent assay measurements of antimeuroullerian hormone (AMH) in human blood are a composite of the uncleaved and bioactive cleaved forms of AMH Fertility and Sterility2014
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Stolk L Perry JR Chasman DI et al Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways Nat Genet 2012 44(3)260ndash268
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH
244
and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wood M and Rajkovic A Genomic Markers of Ovarian Reserve Semin Reprod Med 2013 31(6) 399ndash415
245
Authors and affiliations
Stephen A Roberts PhD
Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL United Kingdom
Cheryl Fitzgerald MD
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester M13 0JH
United Kingdom
Philip W Pemberton MSc
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Alexander Smith PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Luciano G Nardo MD
Reproductive Medicine and Gynaecology Unit GyneHealth
Manchester M3 4DN United Kingdom
Allen P Yates PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Monica Krishnan MBChB
Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL United Kingdom
246
Acknowledgments
First and foremost I would like to thank my supervisors Dr Stephen A
Roberts and Dr Cheryl Fitzgerald I am indebted to you for introducing me
into the world of science showing its wonders and guiding me through its
terrains Without your 247 advise and support none of these projects would
have been possible Thank you
I would also like to thank other members of our team Dr Philip W
Pemberton Dr Luciano G Nardo Dr Alexander Smith Dr Allen P Yates and
Monica Krishnan It has been exciting and fun to be a part of the Manchester
AMH Group
I am grateful for the support and friendship of all secretaries nurses
embryologists and consultants of IVF Department at St Maryrsquos Hospital I
would like to express my special thanks to Professor Daniel Brison for his
advice on the projects and providing a great opportunity for research I would
like to express my gratitude to Dr Greg Horne Senior Embryologist for his
patience in taking me through tons of IVF data It was a privilege to be part of
this team
Indeed without support of my wife Zilola Navruzova I could not have
completed my MD programme Thank you for being there for me through
thick and thin of life You are love of my life Your optimism can make
anything possible Your sense of humor and kindness brightened my long
research hours after on-call shifts Only because of your enthusiasm we could
juggle work research and family And thanks for pretending that AMH is
interesting
My children Firuza Sitora and Timur You are most great kids Always stay
cool and funny like this Sorry for not taking you to holiday during my never-
ending research during last year Hope I havenrsquot put you off doing research in
future You get lots of conference holidays after research
247
I canrsquot thank enough my mother Karomat Rajobova and father Dr Sohib
Rustamov Your love kindness and wisdom have always been inspiration and a
guide in my life I always strive to follow your example albeit impossible to
achieve
My brother Ulugbek Rustamov thank your selfless support As always you
have been my guide and strength during these three years My friends Odil
Nizomov Dr Rohit Arora Tarek Sharif and Sabiha Sharif I am grateful for
your friendship and support during my MD Programme
248
I would like to dedicate this thesis to my mother father my wife and
children
Shu Doctorlik Dissertaciysini
Onam (Karomat Rajabova)
Dadam (Dr Sohib Rustamov)
Turmush Urtogim (Zilola Navruzova)
Farzandlarim (Firuza Sohibova Sitora Sohibova
Timur Rustamov) ga bagishlayman
Sizlar mani kuzimni nuri sizlar
Yaratgandan sizlarga mustahkam sogliq va quvonch tilayman
_______________________
Oybek
31 March 2014 Manchester United Kingdom
2
TABLE OF CONTENTS Abstracthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip3 Publications arising from the thesishelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip5 Chapter 1 General Introduction amp Literature reviewhelliphelliphelliphelliphelliphelliphelliphellip8 Chapter 2 Evaluation of the Gen II AMH Assay between-sample variability
and assay- method comparabilityhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip43 21 Anti-Muumlllerian hormone serum levels and reproducibility in a large cohort of subjects suggest sample instabilityhelliphelliphelliphellip44 22 AMH Gen II assay A validation study of observed variability between repeated AMH measurementshelliphelliphelliphellip65
Chapter 3 The measurement of anti-Muumlllerian hormone a critical appraisalhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip78
Chapter 4 Extraction preparation and collation of datasets for the
assessment of the role of the markers of ovarian reserve in female reproduction and IVF treatmenthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip106
Chapter 5 Assessment of determinants of anti-Muumlllerian hormone in infertile womenhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip135
51 The effect of ethnicity BMI endometriosis and the causes of infertility on ovarian reservehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip136 52 The effect of salpingectomy ovarian cystectomy and unilateral salpingoopherectomy on ovarian reservehelliphelliphelliphellip167
Chapter 6 Assessment of determinants of oocyte number using large
retrospective data on IVF cycles and explorative study of the potential for optimization of AMH-tailored stratification of controlled ovarian hyperstimulationhellip187
Chapter 7 General Summaryhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip229 Authors and affiliationshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip245 Acknowledgmentshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip246
3
ABSTRACT The University of Manchester Dr Oybek Rustamov Degre MD Title The role of anti-Muumlllerian hormone in assisted reproduction in women Date 30 March 2014
Anti-Muumlllerian hormone appears to play central role in regulation of oocyte recruitment and folliculogenesis Serum AMH concentration was found to be one of the best predictors of ovarian performance in IVF treatment Consequently many fertility centres have introduced AMH for the assessment of ovarian reserve and as a tool for formulation of ovarian stimulation strategies in IVF However published evidence on reliability of AMH assay methods and the role of AMH-tailored individualisation of ovarian stimulation in IVF appear to be weak Consequently I decided to conduct a series of studies that directed towards an improvement of the scientific evidence in these areas of research
The studies on performance of Gen II AMH assay revealed the assay suffers from significant instability and provides erroneous results Consequently the manufacturer introduced a modification on assay method
In view of the observed issues with Gen II assay I conducted a critical appraisal of all published research on the previous and current assay methods that reported AMH variability assay method comparison and sample stability The literature indicated clinically important variability between AMH measurements in repeated samples which was reported to be more significant with Gen II assay The studies on between-assay conversion factors derived conflicting conclusions Correspondingly the review of studies on sample stability revealed conflicting reports on the stability of AMH under normal storage and processing conditions which was reported to be more significant issue in Gen II assay In view of above findings we concluded that AMH in serum may exhibit pre-analytical instability which may vary with assay method Therefore robust international standards for the development and validation of AMH assays are required In the analysis of determinants of ovarian reserve I evaluated the effect of ethnicity BMI endometriosis causes of infertility and reproductive surgery on AMH AFC and FSH measurements using data on a large cohort of infertile patients
Using robust multivariable regression analysis in a large cohort of IVF cycles I established the effect of age AMH AFC diagnosis attempt COS protocol changes gonadotrophin type USOR operator regime and initial dose of gonadotrophins on oocyte yield Then I examined effect of gonadotrophin dose and regime on total and mature oocyte numbers The study found that after adjustment for all above variables there was no increase in oocyte yield with increasing gonadotrophin dose categories beyond the very lowest doses This suggests that there may not be significant direct dose-response effect and consequently strict protocols for tailoring the initial dose of gonadotrophins may not necessarily optimize ovarian performance in IVF treatment
In summary studies described in this thesis have revealed instability of Gen II assay samples and raised awareness of the pitfalls of AMH measurements These studies have also demonstrated the effect of clinically measurable factors on ovarian reserve and provided data on the effect of AMH other patient characteristics and treatment interventions on oocyte yield in cycles of IVF Furthermore a robust database and statistical models have been developed which can be used in future studies on ovarian reserve and IVF treatment interventions
4
DECLARATION
No portion of the work referred to in the thesis has been submitted in support
of an application for another degree or qualification of this or any other
university or other institute of learning
COPYRIGHT STATEMENT
i The author of this thesis (including any appendices andor schedules to this
thesis) owns certain copyright or related rights in it (the ldquoCopyrightrdquo) and she
has given The University of Manchester certain rights to use such Copyright
including for administrative purposes
ii Copies of this thesis either in full or in extracts and whether in hard or
electronic copy may be made only in accordance with the Copyright Designs
and Patents Act 1988 (as amended) and regulations issued under it or where
appropriate in accordance with licensing agreements which the University has
from time to time This page must form part of any such copies made
iii The ownership of certain Copyright patents designs trade marks and
other intellectual property (the ldquoIntellectual Propertyrdquo) and any reproductions
of copyright works in the thesis for example graphs and tables
(ldquoReproductionsrdquo) which may be described in this thesis may not be owned
by the author and may be owned by third parties Such Intellectual Property
and Reproductions cannot and must not be made available for use without the
prior written permission of the owner(s) of the relevant Intellectual Property
andor Reproductions
iv Further information on the conditions under which disclosure publication
and commercialisation of this thesis the Copyright and any Intellectual
Property andor Reproductions described in it may take place is available in
the University IP Policy (see
httpdocumentsmanchesteracukDocuInfoaspxDocID=487) in any
relevant Thesis restriction declarations deposited in the University Library The
University Libraryrsquos regulations (see
httpwwwmanchesteracuklibraryaboutusregulations) and in The
Universityrsquos policy on Presentation of Theses
5
PUBLICATIONS ARISING FROM THE THESIS
Journal Articles
1 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton
The measurement of Anti-Muumlllerian hormone a critical appraisal
The Journal of Clinical Endocrinology amp Metabolism 2014 Mar99(3)723-32
2A Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large
cohort of subjects suggests sample instability Human Reproduction 2012 Oct
27(10) 3085-91
2B Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Human Reproduction Dec2012 Vol 27 Issue 12 p3641
6
Conference presentations
1 O Rustamov S Roberts C Fitzgerald
Ovarian endometrioma is associated with increased AMH levels
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2014 Munich
Poster Presentation
2 O Rustamov M Krishnan R Mathur S Roberts C Fitzgerald
The effect of BMI to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral presentation Dr O Rustamov
3 M Krishnan O Rustamov R Mathur S Roberts C Fitzgerald
The effect of the ethnicity to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral Presentation Dr M Krishnan
4 O Rustamov M Krishnan S Roberts C Fitzgerald
Reproductive surgery and ovarian reserve
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr O Rustamov
5 C Fitzgerald O Rustamov P Pemberton A Smith A Yates M Krishnan
R Russell L Nardo SRoberts
AMH assays A review of the literature on assay method comparability
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr C Fitzgerald
6 M Krishnan O Rustamov R Russell C Fitzgerald S Roberts
The role of the ethnicity and the body weight in determination of AMH levels
in infertile women
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
7
Poster presentation
7 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
AMH Gen II assay - can we believe the measurements
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
8 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Old and new AMH assays Can we rely on current conversion factor
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
9 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Random AMH measurement is not reproducible
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
10 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
The reproducibility of serum Anti-Muumlllerian hormone AMH Gen II assay
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2012 Istanbul
Oral Presentation Dr O Rustamov
8
GENERAL INTRODUCTION
AND LITERATURE REVIEW
1
9
CONTENTS I LITERATURE REVIEWhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10 GENERAL BACKGROUNDhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10
1 OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip12 11 Primordial Follicle Assemblyhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip13 12 Oocyte recruitmenthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip14 13 Theory of neo-oogenesishelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip15 2 MARKERS OF OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 21 Ovarian reserve markers with limited clinical valuehelliphelliphelliphelliphellip16 213 Inhibin Bhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 214 Basal oestradiolhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 215 Dynamic testshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 216 Ovarian volumehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 22 Ovarian reserve markers in routine clinical usehelliphelliphelliphelliphelliphelliphellip18 221 Chronological agehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 222 Basal FSHhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 223 Antral follicle counthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip19 3 ANTI-MUumlLLERIAN HORMONEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 31 Biology of anti-Muumlllerian hormonehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 311 The role of AMH in the ovaryhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip21 312 AMH in women with polycystic ovary syndromehelliphelliphelliphelliphellip22 32 AMH Assayhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip23 33 Variability of AMH measurementshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip24 34 Role of AMH in assessment of ovarian reservehelliphelliphelliphelliphelliphellip25 341 Prediction of poor and excessive ovarian response in IVFhelliphellip25 342 Prediction of live birth in cycles of IVFhelliphelliphelliphelliphelliphelliphelliphelliphellip26
3 5 Role of AMH in ovarian stimulation for cycles of IVFhelliphelliphelliphellip26
4 MULTIVARIATE TESTShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip27
5 SUMMARYhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip28 II GENERAL INTRODUCTIONhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip29 REFERENCEShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip31
10
I LITERATURE REVIEW GENERAL BACKGROUND
Infertility is a disease of the reproductive system defined by the failure to
achieve a pregnancy after 12 months of regular unprotected sexual intercourse
although the criteria for the duration vary between different countries (NICE
2013) Worldwide prevalence of infertility estimated to be around 724 million
couples and around 40 million of those seek medical care (Hull et al 1985) In
the UK 15 couples present with infertility with an annual incidence of 12
couples per 1000 general population (Scott et al 2009) The main causes of
infertility are tubal disease ovulatory disorders male factor and poor ovarian
reserve In a third of couples the cause of failure to achieve pregnancy is not
established which is known as unexplained infertility (NICE 2013) Effective
treatment options include improving lifestyle factors medical andor surgical
treatment of underlying pathology induction of ovulation and Assisted
Reproductive Technology (ART) Assisted Reproduction consist of
intrauterine insemination (IUI) and in vitro fertilisation (IVF) cycles with or
without introcytoplasmic sperm injection (ICSI) as well as treatment involving
donated gametes It is estimated that 75 of infertile couples presenting at
primary care centres in the UK are referred to fertility specialists based at
secondary or tertiary care centres and nearly 50 of those are subsequently
offered IVFICSI treatment (Scott et al 2009) This is supported by figures of
Human Fertility and Embryology Authority (HFEA) which indicates more
than 50000 IVF treatment cycles are performed in the UK annually (HFEA
2008)
An IVF treatment cycle involves a) pituitary down regulation b)
controlled ovarian stimulation c) oocyte recovery c) in vitro fertilisation of eggs
with sperm d) transfer of resulting embryo(s) back to uterus and c) luteal
phase support (NICE 2013) Prevention of premature surge of luteinising
hormone during controlled ovarian stimulation (COS) is achieved by pituitary
down regulation using either preparations of gonadotrophin releasing hormone
agonist which is widely known as ldquoAgonist cyclerdquo or gonadotrophin releasing
hormone antagonist which is known ldquoAntagonist cyclerdquo (Figure 1 and 2)
Controlled ovarian stimulation involves administration of gonadotrophins to
encourage the development of supernumerary preovulatory follicles followed
by administration of exogenous human chorionic gonadotropin (hCG) or
11
recombinant luteinising hormone (rLH) to assist in maturation of oocytes 34-
36 hours prior to egg collection which is usually conducted with guidance of
transvaginal ultrasound scanning Subject to sperm parameters the fertilisation
of oocytes is conducted by in vitro insemination or intracytoplasmic sperm
injection The resulting embryo(s) are cultured under strict laboratory
conditions and undergo regular qualitative and quantitative assessments before
transferring the best quality embryo(s) back into uterus during its cleavage
(Day 2 or Day 3) or blastocyst (Day 5 or Day 6) stage of development In
natural menstrual cycles under the influence of HCG progesterone secreted
by the ovarian corpus luteum ensures proliferative changes in the endometrium
providing the optimal environment for implantation of embryo(s) (van der
Linden et al 2011) However in IVF treatment cycles owing to pituitary down
regulation and lack of HCG progesterone levels are not in sufficiently high
concentration to ensure an adequate endometrial receptivity and therefore
exogenous analogues of this hormone is administered following transfer of
embryo(s) This is called ldquoluteal phase supportrdquo and in patients with viable
pregnancy usually lasts till 12th week of gestation when placenta starts
producing progesterone in sufficient quantities (van der Linden et al 2011)
In IVF programmes the ldquosuccessrdquo of the treatment often defined as
achieving a live birth following IVF cycle and expressed using Live Birth Rate
(LBR) In general success in IVF predominantly determined by womanrsquos age
cause(s) of infertility ovarian reserve previous reproductive history and
lifestyle factors (NICE 2013 Taylor 2003 Lintsen et al 2005) However
effectiveness of medical interventions as well as the quality of care play
important role in determining the outcome of IVF treatment This is evident
from significant variation in live birth rates among fertility clinics given for
instance in the UK LBR for women younger than 35 years of age after IVF
cycles varies from 15 to 61 (HFEA 2008 HFEA 2007) The provision of
effective interventions in both clinical and laboratory aspects of the care
appears to be the key in achieving high success rates Identification of patients
with sufficient ovarian reserve who benefit from IVF cycles followed by
providing optimal ovarian stimulation regimens may be useful in improving the
outcomes of IVF programmes According to HFEA data around 12 of IVF
cycles are cancelled due to poor or excessive ovarian response (Kurinczuk et al
2010) Availability of reliable markers for assessment of ovarian reserve and
tailoring ovarian stimulation regimens to the need of each individual patient
12
may improve selection of patients with sufficient ovarian reserve and reduce
the rate of cycle cancellation consequently improving the success of IVF
cycles (Yates et al 2011)
Assessment of ovarian reserve can be achieved using various biomarkers
and four of those are currently used by most clinics womanrsquos chronological
age (Age) serum follicle stimulating hormone (FSH) antral follicle count
(AFC) and serum anti-Muumlllerian hormone (AMH) More recently AMH has
been a focus of interest given it is the only available endocrine marker that is
suitable for direct assessment of the activity of ovarian follicles in their non-
cyclical stage development providing a window to FSH independent phase of
follicular recruitment Furthermore it appears to be reliable biomarker for a)
both the assessment of ovarian reserve and the optimisation of ovarian
stimulation regimens (Yates et al 2011 La Marca et al 2009) b) screening and
diagnosis of polycystic ovarian syndrome (PCOS) (Cook et al 2002) c)
monitoring of disease activity in women with a history of granulosa cell
tumours (Lane et al 1999) d) prediction of the age of diminished fertility and
the menopause (van Disseldorp et al 2008 Broer et al 2011) and finally (e)
assessment of the long term effect of chemotherapy on ovarian reserve
(Anderson 2011)
In this review I first discuss current knowledge on factors that
determine ovarian reserve including the formation and loss of oocyte pool
Then characteristics of the markers of ovarian reserve are reviewed Finally I
examine current understanding of biology of anti-Muumlllerian hormone and its
role in management of infertility
1 OVARIAN RESERVE
It is important to recognize that there is no universal definition for the
term ldquoovarian reserverdquo and the term can have various meanings depending on
the context in which it is used For instance the scientific literature describing
the biology of ovarian reserve usually refers to ldquothe total number of remaining
oocytes in the ovaries which consists of the number of resting primordial
follicles and growing primary pre-antral and antral folliclesrdquo (Gleicher et al
2011) In contrast the use of the term in the context of clinical studies may
refer to ldquoclinically measurable ovarian reserve established using available
biomarkers of ovarian reserverdquo For the purpose of clarity in this thesis the
13
term ldquoovarian reserverdquo refers to clinically measurable ovarian reserve whilst
true biological ovarian reserve will be termed ldquobiological ovarian reserverdquo
Recent studies have demonstrated that ovarian reserve is highly variable
between women due to the variation in the size of initial ovarian reserve at
birth as well as the rate of loss of ovarian reserve thereafter (Wallace et al
2010) Interestingly the rate of oocyte loss appears to be mainly determined by
the initial ovarian reserve which is believed to be facilitated by most potent
ovarian growth factor anti-Muumlllerian hormone Similarly the size of the initial
ovarian reserve is mainly underpinned by the rate of primordial follicle
assembly in the embryo which is also regulated by AMH Both primordial
follicle assembly and the rate of oocyte loss appear to be primarily under the
influence of genetic factors although developmental and environmental factors
are also believed to play a role (Nilsson et al 2010 Shuh-Huerta et al 2012)
11 Primordial follicle assembly
The process of assembly of primordial follicles in the female embryo
spans from the early embryonic to the early postnatal period and formation of
primordial follicles consists of following stages 1) primordial germ cell (PGC)
2) oogonia 3) primary oocyte and 4) primordial follicle In the human female
fetus around a hundred cells that differentiated from extra-embryonic
ectoderm form early PGCs on the yolk sac and migrate via hindgut to gonadal
ridges during 4th - 6th weeks of gestation (MC et al 1953 Donovan 1998) Once
arrived to the gonadal ridges these cells are called primary oogonia which
consequently undergo several rounds of mitotic division during 6th - 28th weeks
of gestation Interestingly the numbers of oogonia reach as high as six million
during its highest rate of mitotic division at around 20 weeks of gestation
Following the last round of mitotic division oogonia enter meiosis which
marks their new stage of development-primary oocyte Formation of
primordial follicles starts as early as at 8th week of gestation and is characterised
by meiosis of primary oocyte that arrest in diplotyne stage and surrounding of
the oocyte by somatic granulosa cell (Baker et al 1963 Maheshwari and Fowler
2010) Indeed the primordial follicle is the cardinal unit of the biological
ovarian reserve and therefore the rate of formation of primordial follicles is the
main determinant of initial biological ovarian reserve at birth
Interestingly the process of loss of oogonia and oocytes which is also
one of the main determinants of the initial ovarian reserve takes place
14
throughout the period of follicle assembly The formation of the granulosa cell
layer around the oocyte prevents the oocyte from subsequent atresia The
oocyte enveloped in a single layer of granulosa cells which is also known as
primordial follicle remains quiescent until recruitment of the follicle for
growth which may not take place for a number of decades after the formation
of a particular primordial follicle (Skinner 2005 Maheshwari and Fowler 2010)
12 Oocyte recruitment
Follicle growth in women consists of two stages a) the initial non-cyclical
recruitment of primordial follicles and the formation of a primary and a pre-
antral follicles and b) cyclical development of antral follicles with subsequent
selection of usually a single dominant follicle The initial recruitment of
primordial follicles is continuous non-cyclical process that starts as early as
from 18-20 weeks of gestation and lasts till the depletion of follicle pool which
later results in the menopause (McGee and Hsueh 2000) Transformation of
flat granulosa cells into cuboidal cells increases the diameter of the oocyte and
the formation of zona pellicuda completes the stage of formation of a primary
follicle During pre-antral stage oocytes increase in diameter and mitotic
division of granulose cells create a new layer of cells-theca cells The
mechanism of initial recruitment of oocytes is not well understood but it is
clear that the process is independent of influence of pituitary gonadotrophins
and appears to be governed by the genetically pre-programmed interaction of
the oocyte with local growth factors the most important of which appears to
be anti-Muumlllerian hormone and cytokines (McGee and Hsueh 2000)
The cyclical phase of development of oocytes is characterised by the
transformation of secondary follicle into antral follicle and subsequent growth
of antral follicles into pre-ovulatory stages In general the process of cyclic
recruitment starts from puberty under the influence of rising levels of pituitary
follicular stimulating hormone (FSH) During the antral stage oocyte increases
in size even further and the formation of a fluid filled space in follicle is
observed Under the influence of FSH luteinising hormone (LH) and local
growth factorsselection of a single dominant follicle occurs which followsby an
ovulation (McGee and Hsueh 2000)
Oocyte loss is a continuous process and occurs due to atresia of oocytes
during primary secondary and antral stages of development The rate of
oocyte loss appears to increase until the age of around 14 and declines
15
thereafter until the age of the menopause when around 1000 primordial
follicles remain (Hansen et al 2008 Oktem and Oktayl 2008) Furthermore by
the age of 30 years the average age at which women of western societies plan
to start a family around 90 of initial primordial follicles are lost which
illustrates that formation and maintenance of ovarian reserve is wasteful
process in humans (ONS 2012 Wallace and Kelsey 2010) As mentioned
above there is a wide individual variation in both sizes of initial primordial
follicular pool and the rate of oocyte loss which explains variation in the
reproductive lifespan in women Evidently the number of primordial follicles
at birth ranges between around 35000 to 25 million per ovary and similarly
the rate of oocyte loss during its peak at 14 years of age may range between
100 to 7500 primordial follicles per month which is believed to be inversely
proportional to initial size of primordial follicle pool (Wallace and Kelsey
2010)
13 Theory of neo-oogenesis
The traditional view of oogenesis states that the process of the creation
and the mitotic division of oogonia with subsequent formation of primordial
follicles takes place only during embryonic and foetal life (Zuckerman 1951)
According to this central theory of mammalian reproductive biology females
are born with a certain number of germ cells that is gradually lost but not
renewed during postnatal period However Johnson et al have recently
challenged this view and reported that adult mammalian ovary may possesses
mitotically active germ cells that continuously replenish the primordial follicle
pool (Johnson et al 2004) The group reported that ovaries of juvenile and
young adult mice contained large ovoid cells which resemble germ cells of
foetal mouse ovaries Interestingly immunohistochemical staining for a gene
which is expressed exclusively in germ cells have been reported to have
confirmed that these large ovoid cells were of germline lineage Furthermore
application of a mitotic germ cell toxicant busulphan appeared to have
eliminated primordial follicle reserve by early adulthood but did not induce
atresia suggesting the presence of proliferative germ cells in postnatal mouse
ovary (Johnson et al 2004 Bazer 2004) The study has generated enormous
amount of interest as well as debate among reproductive biologists (Notarianni
2011) Some other groups have also reported an evidence of postnatal
oogenesis (Pacchiarott et al 2010 Zou et al 2009 Bukovsky et al 2004)) while
16
others do not support the theory (Bristol-Gould et al 2006 Byskov et al 2005
Begum et al 2008) Furthermore some authors argued that adult mouse
germline stem cells exist and remain quiescent in physiologic conditions and
neo-oogenesis occurs only in response to ovotoxic damage (Tilly et al 2007 De
Felici 2010) Although consensus has yet to emerge to date there is no
conclusive evidence on validity of theory of neo-oogenesis
2 MARKERS FOR ASSESMENT OF OVARIAN RESERVE
Biological ovarian reserve is defined as the number of primordial and
growing follicles left in the ovary at any given time and therefore only
counting the number of primordial follicles by histological assessment can
accurately determine ovarian reserve which is clearly not feasible in clinical
setting However ovarian reserve can be estimated using various biomarkers
dynamic clinical tests and implied from the outcomes of ART cycles
Although a wide range of clinical (age ovarian response in previous IVF
cycles) biochemical (basal FSH Inhibin B basal oestradiol AMH) ultrasound
(ovarian volume antral follicle count (AFC)) and dynamic (clomiphene
challenge test exogenous FSH ovarian reserve test GnRH analogue
stimulating test) tests of ovarian reserve exist only a few of the markers are
reliable and practical enough to be of use in routine clinical practice In this
chapter first I discuss the research evidence on the assessment of the markers
andor tests of ovarian reserve that have limited clinical value Then I
evaluated more reliable markers that are in routine clinical use Age FSH
AFC and combination of these markers in multivariable tests Finally I
conducted detailed review of biology of AMH and the role AMH measurement
in the management of infertility
21 Ovarian reserve markers with limited clinical value
211 Inhibin B
Inhibins are members of TGFβ family and expressed in granulosa cells
of growing follicles Principal role of inhibins is thought to be the negative
feedback regulation of pituitary FSH secretion and therefore the serum level of
circulating hormone is believed to reflect the state of folliculogenesis
17
Consequently several groups have studied the role of serum Inhibin β in the
assessment of ovarian reserve Although initial reports were encouraging
(Seifer et al 1997) more robust studies demonstrated that serum Inhibin β was
less reliable than chronological age or basal FSH (Creus et al 2000 Urbancsek
2005) The systematic review of nine studies demonstrated that accuracy of the
Inhibin β test for predicting poor ovarian response and non-pregnancy in IVF
cycles was modest even at a very low threshold level (Broekmans et al 2006)
Therefore it is recommended that inhibin β at best can be used as only
screening test in the fertility centers where other more reliable markers are not
available (Broekmans et al 2006)
212 Basal oestradiol
Some studies suggested that elevated basal oestradiol levels indicate low
ovarian reserve and are associated with poor fertility prognosis (Johannes et al
1998 Licciardi and Rosenwaks 1995) Johannes et al demonstrated basal
oestradiol in conjunction with serum FSH is more reliable than serum FSH
alone in prediction of cycle cancellation due to the poor response in IVF cycles
(Johannes et al 1998) However there are no published data on the comparison
of basal oestradiol to more reliable markers such as AMH or antral follicle
count (AFC) Moreover a recent systematic review has demonstrated that
basal oestradiol has very low predictive value for poor response and has no
discriminatory power for accuracy of non-pregnancy prediction (Broekmans et
al 2006)
213 Dynamic tests of ovarian reserve
The dynamic tests of ovarian reserve are based on assessment of ovarian
response by measuring serum FSH and oestradiol levels following
administration of exogenous stimulation The following tests are reported in
literature Clomiphene Citrate Challenge Test (CCCT) Exogenous FSH
Ovarian Reserve Test (EFORT) and GnRH agonist stimulation test A recent
systematic review and meta-analysis on the accuracy of these tests showed that
none of them can adequately predict poor response or non-pregnancy in IVF
cycles and therefore are not recommended for use in routine clinical practice
(Maheshwari et al 2009)
18
214 Ovarian volume
There is some evidence that increased age is associated with decreased
ovarian volume and women with smaller ovaries are more likely to have
cancellation of their IVF cycles due to poor ovarian response (Syrop et al 1995
Syrop et al 1999 Templeton 1995) However a meta-analysis of the published
studies on the accuracy of ovarian volume as a predictor of poor response and
non-pregnancy in IVF cycles failed to demonstrate clinical usefulness of the
test and suggested the test is not reliable enough for use in a routine clinical
practice (Broekmans et al 2006)
22 Ovarian reserve markers in routine clinical use
221 Chronological age
Owing to the biological age-related decline of the quantity and arguably
the quality of oocytes the chronological age can be used as a marker of ovarian
reserve Studies have demonstrated that ovarian reserve (Wallace and Kelsey
2010 Kelsey 2011) natural fecundity (Islam et al 1989 and outcomes of ART
(Templeton et al 1996 van Kooij et al 1996) decline significantly from age of
35 when it is believed the ovarian reserve undergoes accelerated decline
Although there is a strong association between chronological age and reduction
in fertility evidently there is a significant variation in age-related ovarian
reserve indicating chronological age alone may not be sufficient to estimate the
individual womanrsquos ovarian reserve reliably (Broekmans et al 2006)
222 Basal FSH
Basal FSH was one of the first endocrine markers introduced in ART
programs and is still utilized in many fertility clinics albeit in conjunction with
other markers which are considered more reliable (Creus et al 2000) Secretion
of FSH is largely governed by the negative feedback effect of steroid
hormones primarily oestradiol and inhibins which are expressed in granulosa
cells of growing ovarian follicles Consequently decreased or diminished
recruitment of ovarian follicles is associated increased serum FSH
measurements and high particularly very high basal FSH reading is considered
as a good marker of very low or diminished ovarian reserve (Abdalla et al
2006) However unlike some other markers FSH measurements do not
appear to have discriminatory power for categorisation of patients to various
19
bands of ovarian reserve Given between-patient variability FSH measurement
(CV 30) is similar to its within-patient variability (27) stratification of
patients to various ranges of ovarian reserve does not appear to be feasible
(Rustamov et al 2011) Indeed a recent systematic review of 37 studies on the
prediction of poor response and non-pregnancy in IVF cycle has concluded
that basal FSH is an adequate test at very high threshold levels and therefore
has limited value in modern ART programs (Broekmans et al 2006)
223 Antral follicle count
Antral follicle count estimation involves ultrasound assessment of
ovaries between 2nd and 4th day of menstrual period and counting ldquofolliclesrdquo
which corresponds to antral stage of folliculogenesis (Broekmans et al 2010)
The test provides direct quantitative assessment of growing follicles and is
known as one of the most reliable markers of ovarian reserve (Broekmans et al
2006) AFC measurement has been reported as having a similar sensitivity and
specificity to AMH in prediction of poor and excessive ovarian response in
IVF cycles (Broekmans et al 2006 Broer et al 2010 Jayaprakasan et al 2010)
Given AFC measurement is available instantly and allows patients to be
counseled immediately the test eliminates the need for an additional patient
visit prior to IVF cycle However AFC is normally performed only in the early
follicular phase of the menstrual cycle given most published data on
measurement of AFC are based on studies that assessed antral follicles during
this stage of the cycle (Broekmans et al 2010a) Interestingly more recent
studies suggest that variability of AFC during menstrual cycle is small
particularly when follicles between 2-6mm are counted and therefore
assessment of AFC without account for the day of menstrual cycle may be
feasible (Deb et al 2013)
One of the main drawbacks of AFC is that the cut off levels for size of
counted follicles remains to be standardised (Broekmans 2010b) Initially
follicles of 2-10mm were introduced as the range for AFC and many studies
were based on this cut off Later counting follicles of 2-6mm was reported to
provide most accurate assessment of ovarian reserve (Jayaprakasan et al 2010b
Haadsma et al 2007) and therefore some newer studies are based on AFC
measurements that used this criterion Consequently direct comparison of the
outcomes of various studies on assessment of AFC requires careful analysis
20
3 ANTI-MUumlLLERIAN HORMONE
31 Biology of Anti-Muumlllerian hormone
AMH is a member of transforming growth factor β superfamily which
was discovered by Jost et al in 1947 and was initially known for its is role in
regression of Muumlllerian ducts in sex differentiation of the male embryo In
women AMH is believed to be solely produced by ovaries and expressed in
granulosa cells of growing follicles of 2-6 mm in size which corresponds to
primary pre-antral and early antral stage of follicular development Although
there has been a report of expression of AMH in endometrial cells to date
there is no other published evidence that supports this finding (Wang et al
2009) Indeed studies that evaluated half-life of AMH in serum have
demonstrated that in women who had bilateral salpingo-oopherectomy AMH
becomes undetectable within 3-5 days of following surgery suggesting ovaries
are the only source of secretion of AMH in appreciable quantity (La Marca et
al 2005b) Anti-Muumlllerian hormone is a dimeric glycoprotein which is
composed of a long N-terminus and short C-terminus and was believed to be
secreted in serum only in this dimeric form (AMH-N C)
Like other members of TGF-β family which includes inhibins activins
bone morphogenic proteins (BMPs) and growth and differentiation factors
(Massague et al 1990) AMH binds to two type of serinethreonine kinase
receptors referred to as type I and type II In order to activate AMH signaling
pathway both receptors have to form a heteromeric complex When AMH
binds to the type II (AMHR-II) receptor (Massague et al 2000) this will
phosphorylate and activate a type I receptor (ALK2 -3 andor -6) which
subsequently activates the SMAD pathway through phosphorylation of
SMAD 1 5 andor 8 These activated SMADs interact with SMAD4 and
translocate to the nucleus regulating the expression of different genes
inhibiting the recruitment of primordial follicles and reducing FSH sensitivity
in growing follicles In addition AMH receptors as well as the other members
of TGF-β family can activate MAPK and PI3KAKT pathways
Studies on AMHR II-deficient male mice demonstrated lack of
regression of Muumlllerian ducts suggesting that type II receptor is essential in
AMH signaling (Mishina et al 1996) Similarly Type I receptors which includes
three members of activin receptor-like kinase (ALK2 ALK3 and ALK6) also
appear to play an important role in the regression of Muumlllerian ducts although
21
the role of ALK 6 in AMH signaling appears not to be crucial (Visser 2003
Clarke et al 2001) The signal transduction pathway of AMH in the ovary is
largely not understood In postnatal mice ovary AMHR-II receptor was
expressed in both granulosa and theca cells of pre-antral and antral follicles
(Visser 2003) AMH type I receptors ALK 2 and ALK 3 is expressed in foetal
as well as adult mouse ovary while ALK 6 is expressed in only adult ovary
(Visser 2003)
311 The role of AMH in the ovary
In the mammalian ovary the role of AMH appears to be one of a
regulation of size of the primordial follicle pool by its inhibitory effect on the
formation as well as the growth of primordial follicles (Nilsson et al 2011) In
the embryonic mouse ovary AMH inhibits the initiation of the assembly of
follicles when the process of apoptosis of the majority of oocytes is observed
(Nilsson et al 2011) Consequently AMH reduces the rate of oocyte loss
which plays an important role in the determination of the size of initial follicle
pool Similarly in the adult mouse ovary AMH plays a central role in
maintaining the follicle pool AMH inhibits both the processes of the initial
(non-cyclical) recruitment of primordial follicles and subsequent FSH-
dependent cyclical growth of antral follicles (Figure 3) Inhibition of the initial
recruitment of a new cohort of follicles is believed to be achieved by a
paracrine negative feedback effect of the rising levels of AMH secreted from
already recruited growing follicles (Durlinger et al 1999) Durlinger et al
compared the complete follicle population of AMHnull mice and wild type
mice of different ages of 25 days 4 months old and 13 months old and found
that the ovaries of 25 day and 4 months old AMHnull females contained
significantly higher number of growing pre-antral and antral follicles but
significantly fewer primordial follicles compared to wild-type females
(Durlinger et al 1999) Interestingly almost no primordial follicles were
detected in 13 months old AMHnull mice ovaries suggesting AMH is a potent
inhibitor of the recruitment of primordial follicles and in the absence of AMH
ovaries undergo premature depletion of primordial follicles due to an
accelerated recruitment Subsequent study conducted by the group
demonstrated that in addition to its inhibitory effect to the resting follicles
AMH also suppresses the development of the growing follicles (Durlinger et al
2001 Durlinger et al 2002 Themmen 2005) It appears that AMH inhibits
22
FSH-induced follicle growth by reducing the sensitivity of growing follicles to
FSH which has been confirmed by in vivo as well as in vitro studies (Durlinger
et al 1999 Durlinger et al 2001) In the initial study the group observed that
despite lower levels of serum FSH concentration ovaries of AMHnull mice
contained more growing follicles than that of their wild-type littermates which
has been supported by the findings of subsequent in vitro study (Durlinger et al
1999) Addition of AMH to the culture inhibited FSH-induced follicle growth
of pre-antral mouse follicles due to reduction in granulosa cell proliferation
(Durlinger et al 2001)
In the human embryo the expression of AMH commences in the late
foetal life and can be detected only from 36 weeks of gestation (Rajpert-De et
al 1999 Lee et al 1996) Following a small decline in first two years of life
AMH levels gradually increase to peak at (mean 5 ngml) around age of 24
years In line with the pattern of oocyte loss serum hormone levels gradually
decline with increasing age and become undetectable around 5 years prior to
menopause (Kelsey et al 2011 Nelson et al 2011)
It has been suggested that anti-Muumlllerian hormone plays a central role in
determining the pace of recruitment of primordial follicles hence maintaining
the primordial follicle pool of postnatal mammalian ovary Consequently a
reduction in the concentration of circulating AMH signals the exhaustion of
the primordial follicle pool and the decline of ovarian function
312 AMH in women with polycystic ovary syndrome
Polycystic ovary syndrome (PCOS) endocrine abnormality characterised
by increased ovarian androgen secretion infrequent ovulation and the
appearance of ldquopolycysticrdquo ovaries on ultrasound scan (Dunaif 1997 Homburg
et al 1993) It is the commonest endocrine abnormality in women of
reproductive age and affects around 15-20 of women PCOS is also one of
the main causes of anovulation and subsequent sub-fertility (Webber et al
2003) Although the role of anti-Muumlllerian hormone in the development of
PCOS is not fully understood it is becoming increasingly evident that the
hormone plays an important role in its pathogenesis (Pehlivanov et al 2011)
There is a strong association between serum AMH levels and PCOS and it
appears that women diagnosed with PCOS have two to three fold higher
serum AMH concentration compared to normo-ovulatory women (Cook et al
2002 Pigny et al 2003) Similarly women with PCOS are found to have
23
significantly higher number antral follicles Interestingly the expression of
AMH in granulosa cells of follicles were found to be 75 times higher in women
with PCOS compared to those without a the disease suggesting increased
serum AMH in PCOS may be due to increased secretion of hormone per
follicle rather than due to an increased number of antral follicles (Pellat et al
2007) High AMH concentrations may act as the main facilitator of abnormal
folliculogenesis in PCOS given the follicles appear to arrest when they reach
an antral stage (2-6mm) of development (Rajpert-De et al 1999) Indeed the
studies of Durlinger et al have demonstrated that AMH inhibits selection of
dominant follicle when follicles reach antral stage of development (Durlinger et
al 2001) Serum AMH levels appear to decrease with treatment of PCOS
which may play important role in restoration of ovulatory cycles Studies have
reported a significant reduction in serum concentration of AMH following
treatment of PCOS with metformin and laparoscopic ovarian diathermy (Falbo
et al 2010 Amer et al 2009 Elmashad 2011) Similarly reduction of BMI
following intensified endurance exercise training for treatment of PCOS may
also lead to a significant reduction in serum AMH levels (Moran et al 2011)
This suggests that there is strong association between serum concentration of
AMH and abnormal folliculogenesis in PCOS and therefore understanding the
molecular mechanisms of this interaction should be one of the priorities of
future research
32 AMH Assays
Enzyme-linked immunosorbent assay specific for measurement of anti-
Muumlllerian hormone was first developed in 1990 and was recognised as a
significant step in the assessment of ovarian reserve (Hudson et al 1990)
Subsequently a number of non-commercial immunoassays were developed
which were mainly used in research settings (Lee et al 1996) Later Diagnostic
Systems Ltd (DSL) and Immunotech Beckman Coulter Ltd (IOT) introduced
two commercial immunoassays for the routine clinical assessment of ovarian
reserve which are known as ldquofirst generation AMH assaysrdquo (Nelson and La
Marca 2011) These assays employed two different antibodies against AMH
and used different standards for calibration providing non-comparable
measurements (Nelson and La Marca 2011) Consequently several studies
attempted to develop a reliable between-assay conversion factor which
interestingly revealed from five-fold higher with the IOT assay to assay
24
equivalence causing significant impact to reliability of AMH measurements and
interpretation of research findings (Hehenkamp et al 2006 Freour et al 2007
Bersinger et al 2007 Taieb et al 2008 Lee et al 2011)
Later the manufacturer of IOT assay (Beckmann Coulter Ltd)
consolidated the manufacturer of the DSL assay (Diagnostic Systems
Laboratories Inc) and introduced a new assay ldquoGen II AMH assayrdquo which is
only available commercial immunoassay in most countries including the UK
AMH Gen II assay was developed using the antibodies derived from first
generation DSL assay and calibrated using the standards used for IOT assay
and was believed to be considerably more stable compared to the first
generation immunoassays providing more reliable measurements (Kumar et al
2010 Nelson and La Marca 2011) The manufacturer as well as initial external
validation study recommended when compared to old DSL assay AMH Gen
II assay provides around 40 higher measurements and therefore previously
reported DSL-based clinical cut-off levels for estimation of ovarian reserve
should be increased by 40 in order to use Gen II-based AMH results (Kumar
et al 2010 Wallace et al 2011 Nelson and La Marca 2011)
33 Variability of AMH measurements
It is generally believed that AMH values do not change throughout the
menstrual cycle and early studies reported that variation in AMH
measurements between repeated measurements of same patient was negligible
(van Disseldorp et al 2010 La Marca 2010) On the basis of these studies
sampling at a random time in the menstrual cycle was introduced as a method
for measurement of AMH in routine clinical practice However the
methodologies of some of these studies do not appear to be robust enough to
reliably estimate sample-to-sample variability of AMH which is mainly due to
small sample sizes (Rustamov et al 2011) Consequently in a recent study we
assessed sample-to-sample variability of AMH using DSL assay and found that
within-subject coefficient of variation (CV) of AMH between samples were as
high as 28 which cannot be attributed to any patient or cycle characteristics
(Rustamov et al 2011) Although there is no consensus in the causes of this
observed variability in AMH measurements we believe it is largely attributable
to instability of AMH samples given initial recruitment of primordial follicles
and growth of AMH producing pre-antral and antral follicles are continuous
process and therefore the true biological variation between samples is unlikely
25
to be high However given the importance of establishing true variability of
AMH in both understanding of the biology of hormone and clinical
application of the test future studies should be conducted to establish the
source of variability in the clinical samples
3 4 The role of AMH in the assessment of ovarian reserve
341 Prediction of poor and excessive ovarian response in cycles of
IVF
A number of studies have assessed the role of AMH in the prediction of
poor ovarian response in IVF cycles using first generation AMH assays and
found that AMH and AFC were the best predictors of poor ovarian response
compared to other markers of ovarian reserve Nardo et al showed that the
predictive value of AMH in receiver operating characteristic curve (ROC)
analysis was similar to (AUC 088) that of AFC (AUC 081) and found that
AMH cut offs of gt375 ngmL and lt10 ngmL would have modest
sensitivity and specificity in predicting the extremes of response (Nardo et al
2009) These findings were largely supported by subsequent prospective studies
and a systematic review (Nelson et al 2007 Jayaprakasan et al 2010 Broer et al
2011) Similarly comparison of chronological age basal FSH ovarian volume
AFC and AMH found that only AMH (AUC 090) and AFC (AUC 093) were
reliable predictors of poor ovarian response in cycles of IVF Subsequent
combination of the effect of AMH and AFC using multivariable regression
analysis did not improve the level of prediction of poor ovarian response
significantly (AUC 094) suggesting both AMH and AFC can be used as
independent markers (Jayaprakasan et al 2010)
Similarly most studies agree that AMH and AFC are the best predictors
of excessive ovarian response and ovarian hyperstimulation syndrome (OHSS)
compared to other clinical endocrine and ultrasound markers (Nardo et al
2009 Nelson et al 2007) Broer et al compared these two tests in systematic
review of 14 studies and reported that the summary estimates of the sensitivity
and the specificity for AMH were 82 and 76 respectively and for AFC 82
and 80 respectively (Broer et al 2011) Consequently the study concluded
that AMH and AFC were equally predictive and the difference in the predictive
value between the tests was not statistically significant
26
342 Prediction of live birth rate (LBR) in cycles of IVF
Lee at al reported that AMH and chronological age were more accurate
than basal FSH AFC BMI and causes of infertility in the prediction of live
birth rate (Lee et al 2009) Similarly La Marca et al suggested that odds of live
birth could be reliably predicted using AMH (La Marca et al 2010b) although
subsequent review of the study questioned strength of the evidence (Loh and
Maheshwari 2011)
A study conducted by Nelson et al found that higher AMH levels had
stronger association with increased live birth rate compared to age and FSH
(Nelson et al 2007) However the study also suggested that this association
was mainly confined in the women with low AMH levels and there was no
additional increase in live birth in women with AMH levels of higher than 710
pmolL This may suggest that achieving a live birth may be under the
influence of number of other factors and that markers of ovarian reserve alone
may not be able predict this outcome reliably
35 The role of AMH in individualisation of ovarian stimulation in
IVF cycles
Prediction of ovarian response to the stimulation of ovaries in cycles of
IVF plays an important role in the counseling of couples undergoing treatment
programmes and hence many clinical studies on AMH have focused on the
prognostic value of AMH measurements However data on using AMH as a
tool for improving the clinical outcomes in IVF cycles appear to be lacking
considering AMH may be useful tool in tailoring treatment strategies to an
individual patientrsquos ovarian reserve Unlike most other markers AMH has
discriminatory power in determining various degrees of ovarian reserve due to
significantly higher between patient (CV 94) variability compared to its
within-patient (CV 28) variation (Rustamov et al 2011) which allows
stratification of patients into various degrees of (eg low normal high) ovarian
reserve Subsequently most optimal ovarian stimulation protocol may be
established for each band of ovarian reserve Consequently reference ranges
on the basis of distribution of AMH in infertile women were developed which
were subsequently adopted by fertility clinics for a tailoring the mode of
27
ovarian stimulation and daily dose of gonadotrophins in IVF (The Doctors
Laboratory 2008 However currently available clinical reference ranges are
based on the first generation DSL assay and may not be reliably convertible to
currently available Gen II assay measurements (Wallace et al 2011) Indeed the
findings of the studies on comparability of the first generation AMH assays
suggest that establishing a reliable between assay conversion factor between
AMH assays may not be straightforward Furthermore the reference ranges
appear to reflect the distribution of AMH measurements within a specific
population and may therefore not be directly applicable for the prediction of
response to ovarian stimulation in IVF patients (The Doctors Laboratory
2008)
More importantly despite lack of good quality evidence on the
effectiveness of AMH-tailored ovarian stimulation protocols a number of
fertility clinics appear to have introduced various AMH-based COH protocols
in their IVF programs At present research evidence on AMH-tailored
ovarian stimulation in IVF is largely based on two retrospective studies
(Nelson et al 2009 Yates et al 2012) Both of these studies display considerable
methodological limitations including small sample size and centre-related or
period-related selection of their cohorts In this context AMH is used as a tool
for therapeutic intervention and therefore the research evidence should ideally
be derived from randomised controlled trials However recruitment of large
enough patients in IVF setting may take considerable time and resources In
the meantime given AMH-tailored ovarian stimulation has already been
introduced in clinical practice and there is urgent need for more reliable data
the studies with a larger cohorts and robust methodology should assess the role
of AMH in individualisation of ovarian stimulation in IVF treatment cycles
4 Multivariate models of assessment of ovarian reserve
In view of the fact there is not a single marker of ovarian reserve that
can accurately predict ovarian response various models for combination of
multiple ovarian markers have been developed (Verhagen et al 2008) A
number of studies reported that multivariate models are better predictors of
poor ovarian response in IVF compared to a single marker (Bancsi et al 2002
Balasch et al 1996 Creus et al 2000 Durmusoglu et al 2004) However a meta-
analysis showed that when compared to a single marker (AFC) multivariate
28
model has a similar accuracy in terms of prediction of poor ovarian response
(Verhagen et al 2008) In contrast a more recent study demonstrated that
multivariate score was superior to chronological age basal FSH or AFC alone
in predicting likelihood of poor ovarian response and clinical pregnancy
(Younis et al 2010) However the study did not include one of the most
reliable markers AMH in either arm necessitating further assessment of the
role of combined tests which include all reliable biomarkers
4 SUMMARY
During the last two decades a significant leap has been taken towards
understanding the biology of anti-Muumlllerian hormone and its role in female
reproduction (Durlinger et al 2002 Themmen et al 2005) Availability of
commercial AMH assays has resulted in significant increase in interest in the
role of the measurement of serum AMH in the assessment of ovarian reserve
which has been followed by the introduction of the test into routine clinical
practice (Nelson et al 2011) However more recent studies suggest that current
methodologies for the measurement of AMH may provide significant sampling
variability (Rustamov et al 2011) Furthermore the studies that compared first
generation commercial assay methods appear to provide non-reproducible
results suggesting there may be underlying issues with assay methodologies
(Lee et al 2011) Similarly despite lack of sufficient evidence in the role of
AMH in individualisation of ovarian stimulation protocols in IVF AMH-
tailored IVF protocols have been introduced in routine clinical practice of
many fertility clinics around the world
Consequently it appears that clinical application of AMH test has
surpassed the research evidence in some aspects of fertility treatment and
therefore future projects should be directed toward areas where gaps in
research evidence exist On the basis of the review of literature we believe that
evaluation of the performance of assay methods understanding the role of
AMH in assessment ovarian reserve and establishing its role in
individualisation of ovarian stimulation protocols should be research priority
29
II GENERAL INTRODUCTION
On the basis of the review of published literature I have identified that
the following areas of research on the clinical application of AMH in the
management of infertility requires further investigation 1) Within-patient
variability of measurement of AMH using Gen II assay method 2)
Establishment of clinically measurable determinants of AMH levels and 3) The
role of AMH in individualisation of ovarian stimulation in IVF treatment
cycles
In our previous study we estimated that there was significant sample-to-
sample variation (CV 28) in AMH measurements when the first generation
DSL assay was used (Rustamov et al 2011) The source of variability is likely to
be related to the assay method given that biological within-cycle variation of
AMH is believed to be small (La Marca et al 2006) Therefore assessment of
sample-to-sample variability of AMH using the newly introduced Gen II assay
which is believed to be significantly more stable and sensitive compared to that
of DSL assay should enable us to establish the measurement related variability
of AMH Furthermore given I am planning to use data from both DSL and
Gen II assays I need to establish between-assay conversion factor for these
assays using data on clinical samples
There appears to be a lack of good quality data on the effect of
ethnicity BMI causes of infertility reproductive history and reproductive
surgery on ovarian reserve Therefore I am planning to ascertain the role of
above factors on determination of ovarian reserve by analysing AMH
measurements of a large cohort of patients
There is a strong correlation between AMH and ovarian performance
in IVF treatment when conventional ovarian stimulation using GnRH agonist
regimens with a standard daily dose of gonadotrophins are used (Nelson et al
2007 Nardo et al 2007) Furthermore studies suggest tailoring the ovarian
stimulation protocols to AMH measurement may improve ovarian
performance and subsequently the success of IVF treatment (Nelson et al
2011 Yates et al 2012) However given methodologies of the published
studies the effectiveness of currently proposed AMH-tailored ovarian
stimulation protocols remains unknown Therefore I am planning to develop
individualised ovarian stimulation protocols by establishing the most optimal
mode of pituitary down regulation and starting dose of gonadotrophins for
30
each AMH cut-off bands using a robust research methodology However
development of individualised ovarian stimulation protocols on the basis of
retrospective data requires a reliable and validated database containing a large
number of observations In the IVF Department of St Maryrsquos Hospital we
have data on a large number of patients who underwent ovarian stimulation
following the introduction of AMH However the data on various aspects of
investigation and treatment of patients is stored in different clinical data
management systems and may not be easily linkable In addition it appears that
data on certain important variables (eg causes of infertility AFC) are available
only in the hospital records necessitating searching for data from the hospital
records of each patient Consequently I designed a project for building a
research database which will have comprehensive and validated datasets that
are necessary for investigation of the research questions of the MD
programme
In conclusion I am planning to conduct a series of studies to improve
the understanding of the role of AMH in the management of women with
infertility Specifically I am intending to evaluate 1) sample-to-sample variability
of Gen II AMH measurements 2) conversion factor between DSL and Gen II
assays in clinical samples 3) the effect of ethnicity BMI causes of infertility
endometriosis reproductive history and reproductive surgery to ovarian
reserve and explore AMH-tailored individualisation of ovarian stimulation in
IVF cycles
31
References
Abbeel E The Istanbul consensus workshop on embryo assessment proceedings of an expert meeting Human reproduction 2011 26 p 1270-83 Abdalla HT M Y Repeated testing of basal FSH levels has no predictive value for IVF outcome in women with elevated basal FSH Human reproduction 2006 21(1) p 171-4 Amer SA LT Ledger WL The value of measuring anti-Mullerian hormone in women with polycystic ovary syndrome undergoing laparoscopic ovarian diathermy Human reproduction 2009 24 p 2760-6 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343 Balaban B BD Calderoacuten G Catt J Conaghan J Cowan L Ebner T Gardner D Hardarson T Lundin K Cristina Magli M Mortimer D Mortimer S Munneacute S Royere D Scott L Smitz J Thornhill A van Blerkom J Van den Baker A quantitative and cytological study of germ cells in human ovaries Proc R Soc Lond B Biol Sci 1963 158 p 417-433 Balasch J CM Fabregues F Carmona F Casamitjana R Ascaso and VJ C Inhibin follicle-stimulating hormone and age as predictors of ovarian response in in vitro fertilization cycles stimulated with gonadotropin-releasing hormone agonist-gonadotropin treatment Am J Obstet Gynecol 1996 175 p 1226-1230 Bancsi LF BF Eiijekemans MJ at al Predictors of poor ovarian response in in vitro fertilisation a prospective study comparing basal markers of ovarian reserve Fertility and Sterility 2002 77 p 328-336 Bazer FW Strong science challenges conventional wisdom new perspectives on ovarian biology Reprod Biol Endocrinol 2004 2 p 28 Begum S VE Papaioannou and RG Gosden The oocyte population is not renewed in transplanted or irradiated adult ovaries Hum Reprod 2008 23(10) p 2326-30
Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175 Bristol-Gould SK et al Fate of the initial follicle pool empirical and mathematical evidence supporting its sufficiency for adult fertility Dev Biol 2006 298(1) p 149-54 Broekmans FJ et al A systematic review of tests predicting ovarian reserve and IVF outcome Hum Reprod Update 2006 12(6) p 685-718
32
Broekmans Frank J M de Ziegler Dominique Howles Colin M Gougeon Alain Trew Geoffrey and Olivennes Francois The antral follicle count practical recommendations for better standardization Fertility and Sterility 2010 94 p 1044-51 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011 Aug96(8)2532-9
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Bukovsky A et al Origin of germ cells and formation of new primary follicles in adult human ovaries Reprod Biol Endocrinol 2004 2 p 20 Byskov AG et al Eggs forever Differentiation 2005 73(9-10) p 438-46 Clarke TR et al Mullerian inhibiting substance signaling uses a bone morphogenetic protein (BMP)-like pathway mediated by ALK2 and induces SMAD6 expression Mol Endocrinol 2001 15(6) p 946-59
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146 Creus M PJ Faacutebregues F Vidal E Carmona F Casamitjana R and BJ Vanrell JA Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-2346 Creus M PaJ Fabregues F Vidal E Carmona F Casamitjana R et al Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-6 Cook CL SY Brenner AG et al Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertility and Sterility 2002 77 p 141-6 Deb S Campbell B K Clewis JS Pincott-Allen C and Raine-Fenning NJ Intracycle variation in number of antral follicles stratified by size and in endocrine markers of ovarian reserve in women with normal ovulatory menstrual cycles Ultrasound Obstet Gynecol 2013 41 216ndash222 De Felici M Germ stem cells in the mammalian adult ovary considerations by a fan of the primordial germ cells 2010 Mol Hum Reprod 16(9) p 632-6 Donovan PJ (1998) The germ cell ndash the mother of all stem cells Int J Dev Biol 42 1043ndash50 Dunaif A Insulin resistance and the polycystic ovary syndrome mechanism adn implications for pathogenesis Endocr Rev 1997 18 p 774-800
33
Durlinger AL et al Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 1999 140(12) p 5789-96 Durlinger AL et al Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 2001 142(11) p 4891-9 Durlinger AL JA Visser and AP Themmen Regulation of ovarian function the role of anti-Mullerian hormone Reproduction 2002 124(5) p 601-9 Durmusoglu F EK Yoruk P Erenus M Combining day 7 follicle count with the basal antral follicle count improves the prediction of ovarian response Fertility and Sterility 2004 81 p 1073-78 Ebner T et al Basal level of anti-Mullerian hormone is associated with oocyte quality in stimulated cycles Hum Reprod 2006 21(8) p 2022-6 Elmashad AI Impact of laparoscopic ovarian drilling on anti-Muumlllerian hormone levels and ovarian stromal blood flow using three-dimensional power Doppler in women with anovulatory polycystic ovary syndrome Fertility and Sterility 2011 95 p 2342-6 Falbo A RM Russo T DEttore A Tolino A Zullo F Orio F Palomba S Serum and follicular anti-Mullerian hormone levels in women with polycystic ovary syndrome (PCOS) under metformin J Ovarian Resere 2010 Jul p 16 Fanchin R et al Anti-Mullerian hormone concentrations in the follicular fluid of the preovulatory follicle are predictive of the implantation potential of the ensuing embryo obtained by in vitro fertilization J Clin Endocrinol Metab 2007 92(5) p 1796-802 Fasouliotis SJ A Simon and N Laufer Evaluation and treatment of low responders in assisted reproductive technology a challenge to meet J Assist Reprod Genet 2000 17(7) p 357-73 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164 Gleicher N A Weghofer and DH Barad Defining ovarian reserve to better understand ovarian aging Reprod Biol Endocrinol 9 p 23 Haadsma ML BA Groen H Roeloffzen EM Groenewoud ER Heineman MJ et al The number of small antral follicles (2ndash6 mm) determines the outcome of endocrine ovarian reserve tests in a subfertile population Human reproduction 2007 22 p 1925-31 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699ndash708
34
Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hazout A et al Serum antimullerian hormonemullerian-inhibiting substance appears to be a more discriminatory marker of assisted reproductive technology outcome than follicle-stimulating hormone inhibin B or estradiol Fertil Steril 2004 82(5) p 1323-9
Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 HFEA Fertility Figures 2005 2007 HFEA HFEA Fertility Facts and Figures 2008 HFEA 2010 Homburg R BD Levy T Feldberg D Ashkenazi J Ben-Rafael Z In vitro fertilisation and embryo transfer for the treatment of infertility associated with polycystic ovary syndrome Fertility and Sterility 1993 60 p 858-863 Hudson PL et al An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 1990 70(1) p 16-22 Hull MG GC Kelly NJ et al Population study of causes treatment and outcome of infertility Br Med J Clin Res Ed 1985 291 p 1693-1697 Islam MN and MM Islam Biological and behavioural determinants of fertility in Bangladesh 1975-1989 Asia Pac Popul J 1993 8(1) p 3-18 Jayaprakasan K et al A prospective comparative analysis of anti-Mullerian hormone inhibin-B and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation(2010a) Fertil Steril 2010 93(3) p 855-64 Jayaprakasan et al (2010b) The cohort of antral follicles measuring 2ndash6 mmreflects the quantitative status of ovarian reserve as assessed by serum levels of anti-Mullerian hormone and response to controlled ovarian stimulation Fertil Steril_ 2010941775ndash81 Johannes L H Evers MD Peronneke Slaats MS Jolande A Land MD John C M Dumoulin PhD and Gerard A J Dunselman MD Elevated Levels of Basal Estradiol-17β Predict Poor Response in Patients with Normal Basal Levels of Follicle-Stimulating Hormone Undergoing In Vitro Fertilization Fertility and Sterility 1998(69) p 1010-4 Johnson J et al Germline stem cells and follicular renewal in the postnatal mammalian ovary Nature 2004 428(6979) p 145-50 Kelsey TW et al A validated model of serum anti-mullerian hormone from conception to menopause PLoS One 2011 6(7) p e22024
35
Kumar A et al Development of a second generation anti-Mullerian hormone (AMH) ELISA J Immunol Methods 362(1-2) p 51-9 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A De Leo V Giulini S Orvieto R Malmusi S Giannella L Volpe A Anti-Mullerian hormone in premenopausal women and after spontaneous or surgically induced menopause J Soc Gynecol Investig 2005b12545-548 La Marca A et al Normal serum concentrations of anti-Mullerian hormone in women with regular menstrual cycles (2010a) Reprod Biomed Online 2010 21(4) p 463-9 La Marca A et al Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction (2010b) Reprod Biomed Online 2010 22(4) p 341-9 La Marca A et al Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART) Hum Reprod Update 16(2) p 113-30 La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351 Lee MM et al Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 1996 81(2) p 571-6 Lee TH et al Impact of female age and male infertility on ovarian reserve markers to predict outcome of assisted reproduction technology cycles Reprod Biol Endocrinol 2009 7 p 100
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604 Licciardi FL LH Rosenwaks Z Day 3 estradiol serum concentrations as prognosticators of ovarian stimulation response and pregnancy outcome in patients undergoing in vitro fertilization Fertility and Sterility 1995 64 p 991-4 Lie Fong S et al Anti-Mullerian hormone a marker for oocyte quantity oocyte quality and embryo quality Reprod Biomed Online 2008 16(5) p 664-70 Lintsen AM et al Effects of subfertility cause smoking and body weight on the success rate of IVF Hum Reprod 2005 20(7) p 1867-75 Maheshwari A and PA Fowler Primordial follicular assembly in humans--
36
revisited Zygote 2008 16(4) p 285-96 Maheshwari A et al Dynamic tests of ovarian reserve a systematic review of diagnostic accuracy Reprod Biomed Online 2009 18(5) p 717-34 Massague J et al TGF-beta receptors and TGF-beta binding proteoglycans recent progress in identifying their functional properties Ann N Y Acad Sci 1990 593 p 59-72 Massague J and YG Chen Controlling TGF-beta signaling Genes Dev 2000 14(6) p 627-44 Mc KD HA Adams EC Danziger S Histochemical observations on the germ cells of human embryos Anat Rec 1953 2 p 201-219 McGee EA and AJ Hsueh Initial and cyclic recruitment of ovarian follicles Endocr Rev 2000 21(2) p 200-14 Mishina Y et al Genetic analysis of the Mullerian-inhibiting substance signal transduction pathway in mammalian sexual differentiation Genes Dev 1996 10(20) p 2577-87 Moran LJ HC Hutchinson SK Stepto NK Strauss BJ Teede HJ Exercise decreases anti-Mullerian horomone in anovulatory overweight women with polycystic ovary syndrome-A pilot study Horm Metab Res 2011 October Nardo LG et al Circulating basal anti-Mullerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 92(5) p 1586-93 Nelson SM RW Yates and R Fleming Serum anti-Mullerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007 22(9) p 2414-21 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867 Nelson SM and A La Marca The journey from the old to the new AMH assay how to avoid getting lost in the values 2011 Reprod Biomed Online Nelson SM et al External validation of nomogram for the decline in serum anti-Mullerian hormone in women a population study of 15834 infertility patients Reprod Biomed Online 2011 23(2) p 204-6 NICE Assessment and treatment for people with fertility problems NICE Guidelines 2013 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS ONE 5(7) e11637 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS
37
ONE 2010 5(7) 11637 Nilsson EE et al Inhibitory actions of Anti-Mullerian Hormone (AMH) on ovarian primordial follicle assembly PLoS One 2011 6(5) p e20087 Notarianni E Reinterpretation of evidence advanced for neo-oogenesis in mammals in terms of a finite oocyte reserve 2011 J Ovarian Res 4(1) p 1 Office of National Statistics 2012 1 2011 Live Births in England and Wales by Characteristics of Mother Oktem O and B Urman Understanding follicle growth in vivo Hum Reprod 25(12) p 2944-54 Oktem O and K Oktay The ovary anatomy and function throughout human life Ann N Y Acad Sci 2008 1127 p 1-9 Ottosen LD et al Pregnancy prediction models and eSET criteria for IVF patients--do we need more information J Assist Reprod Genet 2007 24(1) p 29-36 Pacchiarotti J et al Differentiation potential of germ line stem cells derived from the postnatal mouse ovary Differentiation 2010 79(3) p 159-70 Paternot G WA Thonon F Vansteenbrugge A Willemen D Devroe J Debrock S DHooghe TM Spiessens C Intra- and interobserver analysis in the morphological assessment of early stage embryos during an IVF procedure a multicentre study Reprod Biol Endocrinol 2011 9 p 127 Pehlivanov B OM Anti-Muumlllerian hormone in women with polycystic ovary syndrome Folia Medica 2011 53 p 5-10 Pellat L HL Brincat M et al Granulosa cell production of anti-Muumlllerian hormone is increased in polycystic ovaries J Clin Endocrinol Metab 2007 92 p 240-5 Pigny P ME Robert Y et al Elevated serum level of anti-Mullerian hormone in patients with polycystic ovary syndrome relationship to the ovarian follicle excess and the follicular arrest J Clin Endocrinol Metab 2003 88 p 5957-62 Porter RN et al Induction of ovulation for in-vitro fertilisation using buserelin and gonadotropins Lancet 1984 2(8414) p 1284-5 Rajpert-De Meyts E et al Expression of anti-Mullerian hormone during normal and pathological gonadal development association with differentiation of Sertoli and granulosa cells J Clin Endocrinol Metab 1999 84(10) p 3836-44
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-
38
Scott Wilkes Murdoch Alison DC and Greg Rubin Epidimiology and management of infertility a poppulation-based study in UK primary care Family Practice 2009 26 p 269-274 Seifer DB L-MG Hogan JW Gardiner AC Blaza AS Berk CA Day 3 serum inhibin-B is predictive of assisted reproductive technologies outcome Fertility and Sterility 1997 67 p 110-4 Skinner MK (2005) Regulation of primordial follicle assembly and development Hum Reprod Update 11 461ndash71 Syrop CH et al Ovarian volume may predict assisted reproductive outcomes better than follicle stimulating hormone concentration on day 3 Hum Reprod 1999 14(7) p 1752-6 Syrop CH A Willhoite and BJ Van Voorhis Ovarian volume a novel outcome predictor for assisted reproduction Fertil Steril 1995 64(6) p 1167-71 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Taylor A ABC of subfertility Making a diagnosis Br Med J Clin Res Ed 2003 327 p 799-801 Templeton A JK Morris and W Parslow Factors that affect outcome of in-vitro fertilisation treatment Lancet 1996 348(9039) p 1402-6 Templeton A Infertility-epidemiology aetiology and effective management Health Bull (Edinb) 1995 53(5) p 294-8 TDL test update AMH Stability Hormones and OCPs The Doctors Laboratory Guide 2008 page 29 Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34) p 18-21 Tilly JL and J Johnson Recent arguments against germ cell renewal in the adult human ovary is an absence of marker gene expression really acceptable evidence of an absence of oogenesis Cell Cycle 2007 6(8) p 879-83 Urbancsek J Use of serum inhibin B levels at the start of ovarian stimulation and at oocyte pickup in the prediction of assisted reproduction treatment outcome Fertility and Sterility 2005 83(2) p 341-348 van der Linden M BK Farquhar C Kremer JAM Metwally M Luteal phase support for assisted reproduction cycles (Review) Cochrane Library 2011 October
39
van Disseldorp J et al Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2011 25(1) p 221-7 van Kooij RJ et al Age-dependent decrease in embryo implantation rate after in vitro fertilization Fertil Steril 1996 66(5) p 769-75 van Rooij IA et al Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002 17(12) p 3065-71 Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539 van Disseldorp J Kwee CBL J Looman CWN Eijkemans MJC and FJ Broekmans Comparison of inter- and intra-cycle variability of anti-Muuml llerian hormone and antral follicle counts Human reproduction 2010 25 p 221-227 Verberg MF et al Predictors of low response to mild ovarian stimulation initiated on cycle day 5 for IVF Hum Reprod 2007 22(7) p 1919-24 Verhagen TE et al The accuracy of multivariate models predicting ovarian reserve and pregnancy after in vitro fertilization a meta-analysis Hum Reprod Update 2008 14(2) p 95-100 Visser JA AMH signaling from receptor to target gene Mol Cell Endocrinol 2003 211(1-2) p 65-73 Wallace WH and TW Kelsey Human ovarian reserve from conception to the menopause PLoS One 5(1) p e8772 Wallace AM et al A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 2011 48(Pt 4) p 370-3 Webber L J SS Stark J Trew G H Margara R Hardy K Franks S Formation and early development of follicles in the polycystic ovary Lancet 2003 362(September) p 1017-1021
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362 Younis JS et al A simple multivariate score could predict ovarian reserve as well as pregnancy rate in infertile women Fertil Steril 2010 94(2) p 655-61 Zou K et al Production of offspring from a germline stem cell line derived from neonatal ovaries Nat Cell Biol 2009 11(5) p 631-6 Zuckerman The number of oocytes in the mature ovary Recent Prog Horm Res 1951 6(63-108)
Figure 1 Schematic representation of a long GnRH agonist cycle
In a long agonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH agonist preparations starting from mid-luteal phase of the preceding menstrual cycle till the day of administration of HCG
Cycle Started
Menstrual Period
Daily GnRH agonist
From mid-luteal phase
Daily GnRH agonist
Menstrual
Period
Daily GnRH agonist
amp
Daily hMG
Day 2-10
HCG
USOR
amp
ET
41
Figure 2 Schematic representation of GnRH antagonist cycle
In an antagonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH antagonist preparations starting from the 5th day of IVF cycle till the day of administration of HCG Therefore an ldquoAntagonistrdquo cycle is significantly shorter than an ldquoAgonistrdquo cycle
Cycle Started
Menstrual Period
Daily GnRH antagonist
(Day 5-10)
amp
Daily hMG
(Day 2-10)
HCG
USOR
amp
ET
42
Figure 3 The role of AMH in regulation of oocyte recruitment and folliculogenesis
It appears that AMH plays an important role in a) the recruitment of primordial follicles and b) the selection of a dominant follicle from a cohort of antral follicles AMH is believed to be the main regulator of ovarian reserve which is achieved by its paracrine negative feedback effect to resting primordial follicles (Durlinger et al 1999) AMH was found to play an important role
in the regulation of the selection of a dominant follicle by inhibition of the FSH-induced follicle growth (Durlinger et al 2001)
EVALUATION OF THE GEN II AMH ASSAY BETWEEN-SAMPLE VARIABILITY AND
ASSAY-METHOD COMPARABILITY
2
44
ANTI-MUumlLLERIAN HORMONE SERUM LEVELS AND REPRODUCIBILITY
IN A LARGE COHORT OF SUBJECTS SUGGEST
SAMPLE INSTABILITY
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G
Nardo Philip W Pemberton
Human Reproduction 2012a 273085-3091
21
45
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large
cohort of subjects suggest sample instability
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester Foundation Trust
Manchester M13 9WL UK
c Health Sciences - Methodology Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
d School of Medicine University of Manchester Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Corresponding author
Oybek Rustamov MRCOG
Research Fellow in Reproductive Medicine
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
E-mail oybekrustamovcmftnhsuk oybek_rustamovyahoocouk
Word count 3909
Conflicts of Interest There are no potential conflicts of interest
Acknowledgement of financial support
Dr Steve Roberts is supported by the NIHR Manchester Biomedical Research Centre
46
Declaration of authorsrsquo roles
OR led on clinical aspects of this study with responsibility for collation of the
clinical database and the analysis of the clinical data OR prepared the first
draft of the clinical work and was involved in preparation of the whole paper
and submission of the final manuscript CF and LGN contributed to clinical
data analysis draft preparation and approval of the final manuscript MK was
involved in clinical data collation and approval of the final draft PWP was the
laboratory lead responsible for all of the laboratory based experiments and for
the routine analysis of clinical samples PWP prepared the first draft of the
laboratory work and was involved in the preparation of the whole paper and
submission of the final manuscript AS suggested the sample stability studies
and was involved in discussion draft preparation and approval of the final
manuscript APY was involved in some of the routine clinical analyses and
progression of drafts to approval of the final manuscript SAR was involved in
clinical study design oversaw the statistical analysis and progression of drafts
through to approval of the final manuscript OR and PWP should be
considered as joint first authors
47
ABSTRACT
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large cohort of
subjects suggest sample instability
Study question
What is the variability of anti-muumlllerian hormone (AMH) concentration in
repeat samples from the same individual when using the Gen II assay and how
do values compare to Gen I (DSL) assay results
Summary answer
Both AMH assays displayed appreciable variability which can be explained by
sample instability
What is known already
AMH is the primary predictor of ovarian performance and is used to tailor
gonadatrophin dosage in cycles of IVFICSI and in other routine clinical
settings A robust reproducible and sensitive method for AMH analysis is of
paramount importance The Beckman Coulter Gen II ELISA for AMH was
introduced to replace earlier DSL and Immunotech assays The performance
of the Gen II assay has not previously been studied in a clinical setting
Study design size and duration
For AMH concentration study we studied an unselected group of 5007
women referred for fertility problems between 1st September 2008 to 25th
October 2011 AMH was measured initially using the DSL AMH ELISA and
subsequently using the Gen II assay AMH values in the two populations were
compared using a regression model in log(AMH) with a quadratic adjustment
for age Additionally women (n=330) in whom AMH had been determined in
different samples using both the DSL and Gen II assays (paired samples)
identified and the difference in AMH levels between the DSL and Gen II
assays was estimated using the age adjusted regression analysis
In AMH variability study 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay and 87 women had repeated AMH
determinations using the Gen II assay (n=177 samples) were identified A
mixed effects model in log (AMH) was utilised to estimate the sample-to-
48
sample (within-subject) coefficients of variation of AMH adjusting for age
Laboratory experiments including sample stability at room temperature
linearity of dilution and storage conditions used anonymised samples
Main results and the role of chance
In clinical practice Gen II AMH values were ~20 lower than those
generated using the DSL assay instead of the 40 increase predicted by the kit
manufacturer Both assays displayed high within-subject variability (Gen II
assay CV=59 DSL assay CV=32) In the laboratory AMH levels in serum
from 48 subjects incubated at RT for up to 7 days increased progressively in
the majority of samples (58 increase overall) Pre dilution of serum prior to
assay gave AMH levels up to twice that found in the corresponding neat
sample Pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave higher readings (72 overall) compared to sequential
addition Storage at -20ordmC for 5 days increased AMH levels by 23 compared
to fresh samples The statistical significance of results was assessed where
appropriate
Limitations reasons for caution
The analysis of AMH levels is a retrospective study and therefore we cannot
entirely rule out the existence of differences in referral practices or changes in
the two populations
Wider implications of the findings
Our data suggests that AMH may not be stable under some storage or assay
conditions and that this may be more pronounced with the Gen II assay The
published conversion factors between the Gen II and DSL assays appear to be
inappropriate for routine clinical practice Further studies are urgently required
to confirm our observations and to determine the cause of the apparent
instability In the meantime caution should be exercised in the interpretation
of AMH levels in the clinical setting
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA DSL Active MIS AMH ELISA sample stability
49
INTRODUCTION
AMH in women is secreted by the granulosa cells of pre-antral and small
antral follicles (Vigier et al 1984 Themmen 2005) and circulating levels reflect
the ovarian pool from which follicles can be recruited (Loh amp Maheshwari
2011) Measurement of AMH has become of paramount significance in clinical
practice in IVF units to assign candidates to the most suitable controlled
ovarian hyperstimulation protocol and its level is used to predict poor or
excessive ovarian response (Nelson et al 2007 Nardo et al 2009 Yates et al
2011) It is also of increasing importance in (a) prediction of live birth rate in
IVF cycles (La Marca et al 2011) (b) screeningdiagnosis of polycystic ovarian
syndrome (Cook et al 2002) (c) follow up of women with a history of
granulosa cell tumours (Lane et al 1999) (d) prediction of the age of onset of
infertility due to the menopause (van Disseldorp et al 2008 Broer et al 2011)
and finally (e) assessment of the long term effect of chemotherapy on fertility
(Anderson 2011)
Following development of the first laboratory AMH assay in 1990
(Hudson et al 1990 Lee et al 1996) first generation commercially available
immunoassays were introduced by Diagnostic Systems Ltd (DSL) and
Immunotech Ltd (IOT) These assays used different antibodies and standards
(Nelson amp La Marca 2011) and the resulting AMH concentrations obtained
using the IOT assay were found to be higher than those produced using the
DSL assay by most but not all authors (Freour et al 2007Taieb et al 2008 Lee
et al 2011) The AMH Gen II Assay (Beckman-Coulter Ltd) replaced both of
these assays using the DSL Gen I antibody with the IOT standards AMH
values obtained using this kit were predicted to correlate with but be higher
than those using the old DSL kit (Kumar et al 2010 Nelson amp La Marca
2011) This was confirmed (Wallace et al 2011) with the AMH Gen II assay
giving values approximately 40 higher than the DSL assay The
recommended conversion factor of 14 (AMH Gen II = DSL x 14) was also
applied to the DSL reference ranges but this recommendation does not appear
to have been independently validated
It is generally accepted that serum AMH concentrations are highly
reproducible within and across several menstrual cycles and therefore a single
blood sampling for AMH measurement has been accepted as routine practice
50
(Hehenkamp et al 2006 La Marca et al 2006 Tsepelidis et al 2007) However
we recently challenged this view and reported significant sample-to-sample
variation in AMH levels using the DSL assay in women who had repeated
measurements 28 difference between samples taken from the same patient
with a median time between sampling of 26 months and taking no account of
menstrual cycle (Rustamov et al 2011) Although we could not explain the
cause of this variability we speculated that it might be due to true biological
variation in secretion of AMH or due to post-sampling pre-analytical
instability of the specimen
Given the widespread adoption of AMH in Clinical Units it is critical
that the sources of variability in any AMH assay are understood and quantified
This paper presents the results of clinical and laboratory studies on routine
clinical samples using the new AMH Gen II assay specifically comparing assay
values with the older DSL assay assessing between sample variability and
investigating analytical and pre-analytical factors affecting AMH measurement
METHODS
Study population
Samples were obtained from women of 20-46 years of age attending for
investigation of infertility requiring AMH assessment at the secondary
(Gynecology Department) and tertiary (Reproductive Medicine Department)
care divisions of St Maryrsquos Hospital Manchester from 1st September 2008 to
25th October 2011 Samples which were lipaemic or haemolysed and samples
not frozen within 2 hours of venepuncture were excluded from the study
Anonymised samples from this pool of patients were used for stability studies
after routine AMH measurements had been completed The full dataset
comprised AMH results on 5868 samples from 5007 women meeting the
inclusion criteria Additionally we identified women in whom AMH had been
determined in different samples using both the DSL and Gen II assays (paired
samples from 330 women)
51
Sample processing
Collection and handling of all AMH samples was conducted according
to the standards set out by the manufacturers and did not vary between the
different assays Serum samples were transported immediately to the
Department of Clinical Biochemistry based in the same hospital and
separated within 2 hours of venepuncture using the Modular Pre-Analytics
Evo (Roche Diagnostics Burgess Hill West Sussex UK) Samples were frozen
in aliquots at -20C until analysis normally within one week of receipt The
laboratory participates in the pilot National external quality assessment scheme
(UKNEQAS) for AMH in Edinburgh and performance has been satisfactory
AMH analysis
All AMH assays were carried out strictly according to the protocols
provided by the manufacturer and sample collection and storage also
conformed to these recommendations All AMH samples were analysed in
duplicate and the mean of the two replicates was reported as the final result
1) The DSL AMH assay The enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) was used for measurement of AMH prior to 17th
November 2010 The working range of the assay was up to 100pmolL with a
minimum detection limit of 063pmolL The intra-assay coefficient of
variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at 56pmoll) The
inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at 56pmoll)
2) The Beckman Coulter Gen II assay After 17th November 2010
AMH was measured using the enzymatically amplified two-site immunoassay
(AMH Gen II ELISA Beckman Coulter Inc Brea CA USA) The working
range of the assay is up to 150pmolL with a minimum detection limit of
057pmolL The intra-assay CV (n=16) is 292 (at 18pmoll) and 203 (at
60pmoll) The inter-assay coefficient of variation (n=28) is 357 (at
18pmoll) and 364 (at 60pmoll)
Sample Stability Studies
(1) Stability of AMH in serum at room temperature (RT) serum samples
(n = 48) were allowed to thaw and then left at RT for one week At 0 1 2 4
and 7 days 100microl aliquots were removed and immediately stored at -80 ordmC in
52
2ml screw-capped polypropylene tubes (Alpha Laboratories Eastleigh UK)
Two freezethaw cycles had no effect on AMH concentration (results not
shown) Samples from individual subjects were analysed for AMH on the same
GenII microtitre plate to eliminate inter-assay variability Results were
expressed as a percentage of the day 0 value
(2) Linearity of Dilution 100microl fresh serum (n = 9) was added to 100microl
AMH Gen II sample diluent incubated for 30min at RT and the mixture
analysed using the standard GenII assay procedure
(3) Comparison between the Standard Assay method and an equivalent
procedure in the standard GenII ELISA assay method the first steps involve
the addition of calibrators controls or serum samples to microtitration wells
coated with anti-AMH antibody Assay buffer is then added to each well As a
comparison serum and assay buffer were mixed in a separate tube incubated
for 10min at RT and then added in exactly the same volume and proportions
to the microtitre plate Thereafter the assay was performed using the standard
protocol
(4) Stability of AMH during storage fresh serum samples (n = 8)
analysed on the day of reception were compared with aliquots from the same
samples that had been frozen for 5 days either in polystyrene tubes at -20degC or
polypropylene tubes at -80degC
Statistical Analysis
Data analysis was performed using the Stata 12 analytical package
(StataCorp Texas USA) Data management and analysis of clinical data was
conducted by one of the researchers (OR) and verified independently by
another member of the research team (SR) using different statistical software
(R statistical environment) Approval for the use of the data was obtained from
the Local Research Ethics Committee (UK-NHS 10H101522) The age-
related relationship of the DSL and Gen II assays to AMH was visualised using
scatter plots and quadratic fit on a logarithmic scale (Nelson et al 2011) The
age adjusted regression analysis of paired samples was used to estimate the
difference in AMH levels between the DSL and Gen II assays A mixed effects
model in log (AMH) was utilised to estimate the sample-to-sample (within-
subject) coefficients of variation of AMH levels in women who had repeated
53
measurements within a 1 year period from the patientrsquos first AMH sample
adjusting for age as above In the sample stability studies percentage changes
are expressed as mean plusmn SEM In the stability of AMH in serum at RT study a
paired t-test determined the level of significance between baseline and
subsequent days
RESULTS
Population studies and variability
AMH concentration
Table 1 summarizes the results of AMH determinations in our
population of women attending the IVF Clinic prior to the 17th November
2010 (using the DSL assay) and after that date (using the Gen II assay) A
second analysis compares AMH levels in women who had AMH measured
using both assays at different times Results were consistent with lower serum
levels of AMH observed when samples were analysed using the Gen II assay
compared to the DSL assay Figure 1 shows the correlation of AMH with age
for the unselected groups After adjustment for age the total cohorts showed
Gen II giving AMH values 34 lower than those for DSL Analysis restricted
to patients with AMH determinations using both assays gave an age-adjusted
difference of 21
AMH variability
During the study period 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay with 295 patients having two samples 17
three samples and one five samples The median time between samples was 51
months Eighty seven women had repeated AMH determinations using the
Gen II assay (n=177 samples) with 84 women having two samples and 3
having three samples The median interval between repeat samples was 32
months Both assays exhibit high sample-to-sample variability (CV) this was
32 in the DSL assay group (our previous finding (Rustamov et al 2011) in a
smaller group was 28) variability in the Gen II assay group was much higher
(59)
54
Table 1 Median and inter-quartile range for the two assays in the
different datasets along with the mean difference from an age-
adjusted regression model expressed as a percentage
DSL Gen II
difference ()
n age AMH (pmoll
)
n Age
AMH (pmoll
)
all data
3934
33 (29 36)
147 (78250
)
1934 33 (29 36)
112 (45 216)
-335 (-395 to -
275)
paired sample
s
330 32 (29 36)
149 (74 247)
330 34 (30 37)
110 (56 209)
-214 (-362 to -64)
Figure 1 Unselected AMH values from DSL (circles) and Gen II
(triangles) assays as a function of age Lines show the regression
fits of log(AMH) against a quadratic function of age solid lines
Gen II broken lined DSL
20 25 30 35 40 45
Age
AM
H [p
mo
lL
]
DSLGen II
11
01
00
55
Sample stability studies
(1) Stability of AMH in serum at room temperature
AMH levels in 11 of the 48 individuals remained relatively unchanged
giving values within plusmn10 of the original activity over the period of a week
and one patient had an undetectable AMH at all time points The remaining 36
serum samples had AMH values that increased progressively with time In the
47 samples with detectable AMH levels increased significantly (plt0001) for
each time interval compared to baseline the increase at day 7 being 1584 plusmn 76
(Figure 2)
Figure 2 Stability of AMH in serum at RT
Results at each time interval are expressed as a percentage of the patientrsquos AMH concentration at day 0 Means plusmn SEM are indicated
56
(2) Linearity of Dilution
In a group of nine anonymised samples proportionality with two-fold
sample dilution does not hold and on average there is a 574 plusmn 123 increase
in the apparent AMH concentration on dilution compared to neat sample (see
table 2a) Two samples which gave the highest increases were diluted further It
was apparent that after the anomalous doubling of AMH concentration on
initial two-fold dilution subsequent dilutions gave a much more proportional
result (see Table 2b) Linearity of dilution was maintained only in samples that
showed no initial increase on two-fold dilution
Table 2a Proportionality with two-fold dilution of serum
AMH (pmoll)
sample no neat serum x2 dilution recovery
1 1105 2294 2076 2 4941 9900 2004 3 415 483 1164 4 923 1122 1216 5 2801 3066 1091 6 362 628 1734 7 2739 3962 1447 8 553 1034 1870 9 1849 2892 1564
Table 2b Linearity with multiple dilution of serum
AMH (pmoll)
sample no dilution Measured expected recovery ()
1 x1 1105 1105 100 x2 1147 5525 2076 x4 5532 2763 2002 x7 3072 1579 1946 x10 2145 1105 1941
2 x1 4941 4941 100
x2 4950 2471 2003 x4 2286 1235 1851 x7 1228 706 1739 x10 857 494 1735
57
(3) Comparison between the Standard Assay method and an equivalent
procedure Serum samples that had been pre-mixed with buffer prior to
addition gave on average 718 plusmn 48 higher readings than those added
sequentially using the standard procedure (see table 3)
Table 3 Comparison between equivalent ELISA procedures
AMH (pmoll)
sample no A B BA ()
1 1466 2284 1558 2 839 1642 1957 3 3151 6446 2046 4 1244 2014 1619 5 1393 2276 1634 6 701 1246 1777 7 778 1358 1746 8 1693 3298 1948 9 955 1793 1877 10 2849 5437 1908
11 1365 2062 1511 12 1773 2868 1617 13 1468 2429 1655 14 1499 2115 1411 15 249 357 1434 16 1284 2289 1783
A = 20microl serum added directly to the plate followed by 100microl assay buffer
B = 60microl serum + 300microl assay buffer mixed amp incubated at RT for 10min 120microl mixture added to the plate
(4) Stability of AMH during storage AMH levels in samples stored at -20degC
showed an average increase of 225 plusmn 111 over 5 days compared with fresh
values while those samples stored at -80degC showed no change (18 plusmn 31)
(see Table 4)
Table 4 Stability of AMH in serum on storage
AMH (pmoll)
sample no
fresh -20ordmC PS -80ordmC PP
1 1241 1551 1312 2 4217 7542 4508 3 1193 1712 1239 4 1042 1282 1228 5 956 905 879 6 1902 2601 1884 7 2402 2016 2362 8 145 137 132
PS = polystyrene LP4 tube PP = polypropylene 2ml tube
58
DISCUSSION
This publication arose from two initially separate pieces of work in the
Clinical IVF Unit at St Maryrsquos Hospital and in the Specialist Assay Laboratory
at Central Manchester Foundation Trust The IVF Unit had become
concerned with their observed increase in variation in AMH values and
consequently with the reliability of their AMH-tailored treatment guidance
The Laboratory wished to establish whether the practice of sending samples in
the post (which has been adopted by many laboratories rather than frozen as
specified by Beckman) was viable It soon became clear that these anomalies
observed in clinical practice might be explained by a marked degree of sample
instability seen in the Laboratory which had not previously been reported and
which may or may not have been an issue with previous AMH assays
The data contained in this paper represents the largest retrospective
study on the variability of the DSL assay and the first study on the variability
of the Gen II assay Early studies reported insignificant variation between
repeated AMH measurements suggesting that a single AMH measurement
may be sufficient in assessment of ovarian reserve (La Marca et al 2006
Tsepelidis et al 2007) However these recommendations have been challenged
by a number of groups (Lahlou et al 2006 Wunder et al 2008 Rustamov et al
2011) The current study in a large cohort of patients has demonstrated
substantial sample-to-sample variation in AMH levels using the DSL assay and
an even larger variability using the Gen II assay We suggest that this variability
may be due to sample instability related to specimen processing given that a)
AMH is produced non-cyclically and true biological variation is believed to be
small (Fanchin et al 2005 van Disseldorp et al 2009) and b) the intra-and inter
assay variation in our laboratory for both the DSL and Gen II assays is small
(lt50) suggesting that the observed variation is not due to poor analytical
technique
The population data presented in this paper also suggests that in routine
clinical use the Gen II assay provides AMH results which are 20-40 lower
compared to those measured using the DSL assay This is in contrast to
validation studies for the Gen II assay which showed that this assay gave AMH
values ~40 higher than those found with the DSL assay (Kumar et al 2010
Preissner et al 2010 Wallace et al 2011)
59
All samples in this retrospective study were subject to the same handling
procedures and analyzed by the same laboratory the two populations were
comparable with the same local referral criteria for investigation of infertility
and we are unaware of any other alterations in practice which might produce
such a large effect on AMH we cannot rule out the possibility of other
changes in the population being assayed that were coincident in time with the
assay change However any such change would have to be coincident and
produce a 50 decrease in observed AMH levels to explain our findings We
did note a weak trend towards decreasing AMH over calendar time assuming a
linear trend in the analysis implies that AMH values might be 12 (2-22)
lower when the Gen II assay was being used compared to the Gen I assay
This suggests that the age adjusted analysis of repeat samples on individuals
showing a 21 decrease in AMH with the Gen II assay is currently the best
estimate of the assay difference
This is the first study to compare AMH assays in a routine clinical setting
in a large group of subjects and as such is likely to reflect the true nature of the
relationship between AMH measured by two different ELISA kits and avoids
some of the issues in other published studies Previous laboratory studies have
compared AMH assays in aliquots from the same sample which only provides
data on the within-sample relationship between the two assays (Kumar et al
2010 Preissner et al 2010 Wallace et al 2011) Although it is difficult to give a
definitive explanation for the discrepancy between the previously published
studies (on within-sample relationships) and this study (on between-sample
relationships) we suggest that it may be due to degradation of the specimen in
one (or both) of the assays If AMH in serum is unstable under certain storage
and handling conditions this might result in differing values being generated
because of differential sensitivity of the two assays to degradation products
Unfortunately we cannot suggest which step of sample handling might have
caused this discrepancy since the published studies did not provide detailed
information
The present study used samples which were frozen very soon after
phlebotomy and analysed shortly thereafter hopefully minimising storage
effects The most striking change followed incubation over a period of 7 days
at RT this showed a substantial increase in AMH levels rather than the
expected decline Previously Kumar et al (2010) had shown that the average
variation between fresh serum samples and those stored for seven days to be
60
approximately 4 at 2-8ordmC and lt1 at -20ordmC but presented no data on RT
stability Zhao et al (2007) reported that AMH values were likely to differ by
lt20 in samples incubated at RT for 2 days compared to those frozen
immediately
Several supplementary experiments were performed in order to
investigate this observed increase in AMH when samples were incubated at
RT These included (1) addition of the detergent Tween-20 to assay buffer to
disclose potential antibody-binding sites on the AMH molecule (2) the
removal of heterophilic antibodies from serum using PEG precipitation or
heterophilic blocking tubes None of these approaches affected AMH levels
significantly (results not shown)
Examination of the data presented here shows that in some samples
AMH levels tend towards twice those expected while results greater than that
only occur in two outliers found in Figure 2 The AMH molecule is made up
of two identical 72kDA monomers which are covalently bound (Wilson et al
1993 di Clemente et al 2010) During cytoplasmic transit each monomer is
cleaved to generate 110-kDa N-terminal and 25-kDa C-terminal homodimers
which remain associated in a noncovalent complex The C-terminal
homodimer binds to the receptor but in contrast to other TGF-β superfamily
members AMH is thought to require the N-terminal domain to potentiate this
binding to achieve full bioactivity of the C-terminal domain After activation of
the receptor the N-terminal homodimer is released (Wilson et al 1993) One
possible explanation for our findings is that the N-and C-terminal
homodimers dissociate gradually under certain storage conditions and that
either the two resulting N- and C-terminal components bind to the ELISA
plate or a second binding site on the antigen is exposed by the dissociation
effectively doubling the concentration of AMH It has been shown (di
Clemente et al 2010) that no dissociation occurs once the complex is bound to
immobilised AMH antibodies The observation that in some of our samples
there was no change after one week at RT might be explained by the
supposition that in those samples AMH is already fully dissociated A mixture
of dissociated and complex forms in the same sample would therefore
account for the observed recoveries between 100 and 200 in the
experiments presented in this paper Rapid sample processing and storage of
the resulting serum in a different tube type at -80ordmC might slow down this
breakdown process
61
The change in ionic strength or pH that occurs on dilution also seems to
have the same effect in increasing apparent AMH levels and again may be due
to dissociation or exposure of a second binding site Our results contradict
those reported by Kumar et al (2010) who showed that serum samples in the
range of 36-93pmoll of AMH when diluted in Gen II sample diluent showed
linear results across the dynamic range of the assay with average recoveries on
dilution close to 100 This might be explained if Kumarrsquos samples were
already dissociated before dilution Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample but our results do not seem to support this
These findings have significant clinical relevance given the widespread
use of AMH as the primary tool for assessment of ovarian reserve and as a
marker for tailoring the dose of gonadotrophins in cycles of IVFICSI As no
guideline studies have been published using the new Gen II assay some ART
centres have adopted modified treatment ldquocut off levelsrdquo for ovarian
stimulation programs based on the old DSL assay based ldquocut off levelsrdquo
multiplied by a conversion factor of 14 (Nelson et al 2007 Nelson et al 2009
Wallace et al 2011) The data presented in this paper suggest that this approach
could result in patients being allocated to the wrong ovarian reserve group
Poor performance of the Gen II assay in terms of sample-to-sample variability
(up to 59) could also lead to unreliable allocation to treatment protocols It
is a matter of some urgency therefore that any possible anomalies in the
estimation of AMH using the Gen II assay be thoroughly investigated and that
this work should be repeated in other centres
62
References
Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146
di Clemente N Jamin SP Lugovskoy A Carmillo P Ehrenfels C Picard JY Whitty A Josso N Pepinsky RB Cate RL Processing of anti-mullerian hormone regulates receptor activation by a mechanism distinct from TGF-beta Mol Endocrinol 2010242193-2206
Freour T Mirallie S Bach-Ngohou K Denis M Barriere P and Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
Fanchin R Taieb J Mendez Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Mullerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 2005 20923-927
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
La Marca A Nelson SM Sighinolfi G Manno M Baraldi E Roli L Xella S Marsella T Tagliasacchi D DAmico R Volpe A Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction Reprod Biomed Online 2011 22341-349
Lahlou N Chabbert-Buffet E Gainer E Roger M Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-5
63
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
Lee MM Donahoe PK Hasegawa T Silverman B Crist GB Best S Hasegawa Y Noto RA Schoenfeld D MacLaughlin DT Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 199681571-576
Loh JS Maheshwari A Anti-Mullerian hormone--is it a crystal ball for predicting ovarian ageing Hum Reprod 2011262925-2932
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 201195736-741
Preissner CM MD Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Taieb J Coussieu C Guibourdenche J Picard JY and di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34)18-21
Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormoneconcentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
van Disseldorp J Lambalk CB Kwee J Looman CWN Eijkemans MJC Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2010 25 221-227
64
Vigier B Picard JY Tran D Legeai L Josso N Production of anti-Mullerian hormone another homology between Sertoli and granulosa cells Endocrinology 19841141315-1320
Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-MuSllerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
Wilson CA Di Clemente N Ehrenfels C Pepinsky RB Josso NVigier B Cate RL Muumlllerian inhibiting substance requires its N-terminal domain for maintenance of biological activity a novel finding within the transforming growth-factor-beta superfamily Mol Endocrinol 19937247ndash257
Wunder DM Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrualcycle in reproductive age women Fertil Steril 200889927-933
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 2007 88S17
65
AMH GEN II ASSAY A VALIDATION STUDY OF
OBSERVED VARIABILITY BETWEEN REPEATED
AMH MEASUREMENTS
Oybek Rustamov Richard Russell
Cheryl Fitzgerald Stephen Troup Stephen A Roberts
22
66
Title
AMH Gen II assay A validation study of observed variability between
repeated AMH measurements
Authors
Oybek Rustamov 1 Richard Russell2 Cheryl Fitzgerald1 Stephen Troup2
Stephen A Roberts3
Institutions
1Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospitals NHS Foundation Trust Manchester
M13 9WL UK
2Hewitt Fertility Centre Liverpool Womenrsquos NHS Foundation Trust Hospital
Crown Street Liverpool L8 7SS
3 Centre for Biostatistics Institute of Population Health University of
Manchester Manchester M13 9PL UK
Word count 1782
Conflict of interest Authors have nothing to disclose
Acknowledgment
The authors would like to thank the Biomedical Andrology Laboratory team at
the Hewitt Fertility Centre for their assistance
67
Declaration of authorsrsquo roles
OR coordinated the study conducted the statistical analysis and prepared first
draft of the manuscript RR extracted data prepared the dataset assisted in
preparation of first draft of manuscript CF ST and SR involved in study
design oversaw statistical analysis contributed to the discussion and
preparation of the final version of the manuscript
68
ABSTRACT
Objective
To study the within patient sample-to-sample variability of AMH levels using
the Gen II assay reproduced in an independent population and laboratory
Design Retrospective cohort analysis
SettingTertiary referral IVF Unit in the United Kingdom
Patients Women being investigated for sub-fertility
Interventions
Retrospective measurements were obtained from women who had AMH
measurements using Gen II assay during routine investigation for infertility at a
tertiary referral unit during a 1-year period The patients who had repeated
AMH measurements were identified and within-patient coefficient of variation
(CV) calculated using a mixed effects model with quadratic adjustment for age
Main Outcome Measures
The within-patient coefficient of variation (CV) calculated using a random
effects model with quadratic adjustment for age
Results
There was in total of 76 samples from 38 women with repeated AMH
measurements during the study period The within-patient sample-to-sample
variation (CV) was found to be 62
Conclusions
The study has confirmed that even when samples are processed promptly and
strictly in accordance with the manufacturers instructions substantial
variability exists between repeated samples Thus caution is recommended in
the use of these newer assays to guide treatment decisions Further work is
required to understand the underlying cause of this variability
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA AMH ELISA sample variability
69
INTRODUCTION
Anti-Muumlllerian hormone is a dimeric glycoprotein that is produced by
the granulosa cells of pre-antral and early antral follicles and has been found to
be the primary regulator of oocyte recruitment and folliculogenesis (Durlinger
et al 1999 Durlinger et al 2001) Strong correlation between AMH levels and
primordial follicle count (Hansen et al 2011) and hence a reflection of ovarian
response has promised a valuable tool in the reproductive specialistsrsquo armory
The development of commercially available AMH immunoassay assay kits has
heralded the widespread introduction and routine usage of AMH assessment in
the clinical setting Several studies have demonstrated that AMH serves as a
good predictor of ovarian response to gonadotrophin stimulation during IVF
treatment (van Rooij et al 2002 Nelson et al 2007 Nardo et al 2009) AMH
testing has also been shown to identify patients at risk of excessive ovarian
response and ovarian hyperstimulation syndrome (Yates et al 2011) with
consequent reduction in per cycle treatment costs by adopting an antagonist
approach during controlled ovarian stimulation Sensitivity and specificity of
AMH in detecting extremes of response has been shown to be comparable to
antral follicle count without the apparent technical limitations of the latter
(Broer et al 2009 Broer et al 2011)
It is stated that the sample-to-sample variation of AMH concentration in
individual women is small and therefore a single AMH measurement has been
recommended as standard practice (La Marca et al 2006 Hehenkamp et al
2006) However recent studies based on data from a single centre recently
published in Human Reproduction found that larger variability between
repeated samples exists which is particularly profound when currently
available second generation AMH assay (AMH Gen II ELISA Beckman
Coulter Inc Brea CA USA) is used (Rustamov et al 2012a Rustamov et al
2012b Rustamov et al 2011)
The trial team had 2 objectives firstly to assess whether the controversial
findings from the above study (Rustamov et al 2012a) were reproducible when
performed in the data based on the samples from a different laboratory with
differing populations If our study reached similar conclusions concerns
regarding the AMH Gen II assay and or manufacturers recommendations on
handling and sampling processes would be validated Alternatively if non-
70
similar findings were reported the laboratory performance in the initial study
ought to be questioned Secondly and more importantly if the repeat samples
are found to be within acceptable parameters then the current clinical standard
of a single random AMH measurement in patients is appropriate If the results
of repeated samples are significantly different following adjustment for age it
would suggest that AMH measurement is not a true estimation of the patientrsquos
ovarian reserve
In view of clinical and research implications of these findings we
undertook to replicate the variability study in a second fertility centre The
authors wish to note that Beckman Coulter recently issued a worldwide STOP
SHIP order on all AMH Gen II Elisa assay kits until further notice due to
manufacturing and quality issues
MATERIALS AND METHODS
Population
Women had serum AMH measurements using Gen II AMH assay from
15 April 2011 to 25 May 2012 for investigation of infertility at the Hewitt
Fertility Centre in the Liverpool Womens NHS Foundation Trust Hospital
tertiary referral unit were identified using the Biochemistry Laboratory AMH
samples database and all women within age range of 20-46 years were included
in the study The main reasons for repeating the samples were a) obtaining up-
to-date assessment of ovarian reserve b) patient request and c) for formulation
of a treatment strategy prior to repeat IVF cycles
Institutional Review Board approval was granted by the Audit
Department Liverpool Womenrsquos NHS Foundation Trust Hospital
Assay procedure
Samples were transported immediately to the in-house laboratory of
Liverpool Womenrsquos Hospital for the processing and analysis The serum was
separated within 8 hours from venipuncture and frozen at -50C until analyzed
71
in batches The sample preparation and assay methodology strictly followed
the manufacturers guidelines The AMH analysis of laboratory is regularly
monitored by external quality assessment scheme (UKNEQAS) and
performance has been satisfactory
The samples were analyzed using enzymatically amplified two-site
immunoassay (AMH Gen II ELISA Beckman Coulter Inc Brea CA USA)
The intra-assay CV was 521 and inter-assay CV (n=9) was 276 (low
controls) and 657 (high controls) The working range of the assay was
150pmolL and the minimum detection limit was 057pmolL
The main difference in the assay preparation in this study is that the
samples were processed within 8 hours whilst the samples in the previous
study were processed within 2 hours (Rustamov 2012a) Importantly the kit
insert of Gen II AMH assay does not state any maximum duration of storage
of unprocessed samples or any constraints on the transportation of
unprocessed samples Therefore there appears to be considerable variation in
practice of sample processing between clinics which ranges from processing
samples immediately to shipping unfrozen whole samples to long distances
Statistical analysis
The dataset was obtained from the Biomedical Andrology Laboratory
of the hospital and anonymised by one of the researchers (RR) Data
management and analysis of the anonymised data followed the same
procedures as the previous study (13) and were performed using Stata 12
Statistical Package (StataCorp Texas USA) Approval for data management
analysis and publication was obtained from the Research and Development
Department of Liverpool Womenrsquos Hospital
Between and within-subject sample-to-sample coefficient of variability
(CV) as well as the intra correlation coefficient (ICC) was estimated using a
mixed effects model in log (AMH) with quadratic adjustment for age AMH
levels of the samples that fell below minimum detection limit of the assay
(lt057 pmolL) were arbitrarily assigned a value of 031 pmolL in line with
the previous analysis (Rustamov et al 2012a)
72
RESULTS
During the study period in total of 1719 women had AMH
measurements using Gen II assay Thirty-eight women had repeated AMH
measurements with a total number of 76 repeat samples (Figure 1) The
median age of the women was 318 (IQR 304-364) The median AMH level
was 52pmolL (IQR 15-114) The median interval between samples was 93
days (IQR 49-164) with range of 6-375 days Age-adjusted regression analysis
of samples of these women showed that within-patient sample-to-sample
coefficient of variation (CV) of AMH measurements was 62 while between-
patient CV was 125 An age adjusted intra-correlation coefficient was 079
Figure 1 The repeated AMH measurements by date lines join the
repeats from the same patients (AMH in pmolL)
73
DISCUSSION
A number of studies have recently been published that have expressed
concerns regarding the stability and reproducibility of AMH results Whilst
technical issues regarding reproducibility between assays were known more
recently the reproducibility of results regarding the current Gen II assay has
raised significant concern (Rustamov et al 2012a Rustamov et al 2012b
Rustamov et al 2011) Proponents of the assay have proposed that poor
sample handling and preparation are responsible for these observed concerns
(Nelson et al 2013) Several studies have observed the stability of samples at
room temperature Kumar et al (Kumar et al 2010) observed a 4 variation in
results after 7 days storage compared with those samples analysed immediately
These results were consistent with studies by Fleming and Nelson who also
reported no change in AMH concentration over a period of several days
(Fleming et al 2012) However Rustamov et al reported a measured AMH
increase of 58 in samples stored at room temperature over a seven day
period (Rustamov et al 2012a) Similar concerns were raised regarding the
appropriate freezing process whilst samples frozen at -20C demonstrated
variation in results of between 6 and 22 (Durlinger et al 1999 Rustamov et al
2012a) freezing at -80C obviated a significant variation in assay results (Al-
Qahtani et al 2005 Rustamov et al 2012a) Several studies initially reported
good linearity of dilution (Kumar et al 2012 Preissner et al 2010 Fleming et al
2012) which was contradicted by reports that demonstrated poor linearity in
dilution when fresh samples were utilized (Rustamov et al 2012a) This study
suggested a tendency of AMH results to double with dilution More recently
Beckman Coulter issued a warning on their Gen II AMH ELISA kits that the
dilution of sample may give an erroneous result confirming non linearity of
dilution (King Dave 2012)
A number of studies have looked at the variability of AMH in repeated
samples without account to the menstrual cycle utilizing different assays
Dorgan et al in analyzing DSL samples frozen for prolonged periods
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median-sample interval of one year (Dorgan et al 2012)
Rustamov et al presented a larger series of 186 infertile patients with a median
between-sample interval of 26 months and a CV of 28 in DSL samples
74
(ICC 091 95 CI 090-093(Rustamov et al 2011) In a follow-up study
utilizing the Gen II assay in a group of 84 infertile patients the coefficient
variation of repeated results was 59 (ICC of 084 95 CI 079-090) a
substantial increase in the observed variability of the studies reporting for the
DSL assay (Rustamov et al 2012a) The most recent study to cast doubt on
current practice suggested that repeated measurement of AMH using Gen II
assay resulted in a within-subject variability of 80 (CV) (Hadlow et al 2012)
As a result 7 out of 12 women were subsequently reclassified according to their
originally predicted ovarian response Our study outlined above involving 76
samples from 38 infertile patients demonstrated a within-patient sample-to-
sample coefficient of variation (CV) of AMH measurements was 62
Overall these results suggest that there is significant within patient
variability that may be more pronounced in the Gen II assay Whilst biological
variation has been demonstrated to play a part within this the appreciative
effects of sample handling storage and freezing play a significant part in the
results and it may be that the Gen II assays may be more susceptible to these
changes This study has confirmed that there is significant within-patient
sample-to-sample variability in AMH measurements when the Gen II AMH
assay is used which is not confined to a single population or laboratory It is
important to note that the samples reported by both Rustamov et al 2012
and this study were processed and analyzed strictly according to
manufacturerrsquos recommendations in their respective local laboratories without
external transportation (Rustamov et al 2012a) Therefore it seems reasonable
to suggest that AMH results from other centers and laboratories are likely to
display similar significant sampling variability
Reproducibility of AMH measurements is of paramount importance
given that a single random AMH measurement is used for triaging patients
unsuitable for proceeding with IVFICSI and determining the dose of
gonadotrophins for ovarian stimulation for those patients who proceed with
treatment Similarly other clinical applications of AMH such as an assessment
of the effect of chemotherapy to fertility and follow up of women with history
of granulosa cell tumors also rely on accurate measurement of circulating
hormone levels The present work confirms the high between-sample within-
patient variability The recent warning from Beckman Coulter utilizing their
Gen II ELISA assay kits may give an erroneous result with dilution of samples
further questions the stability of the assay (King David 2012) Subsequently
75
the manufacturer recalled the assay kits due to issues with the instability of
samples and introduced modified protocol for preparation of Gen II assay
samples
Given there can be a substantial difference between two samples from
the same patient the use of such measurements for clinical decision-making
should be questioned and caution is advised
76
References
Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP and Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 2005 63267-273
Broer SL Dolleman M Opmeer BC Fauser BC Mol BW Broekmans FJM AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 20111746-54
Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL and Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
Fleming R and Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
Hadlow Narelle Longhurst Katherine McClements Allison Natalwala Jay Brown Suzanne J and Matson Phillip L Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response (Article in press) Fertil Steril 2012
Hansen KL Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170-5
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 King Dave URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L and Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593 6
77
Nelson S Biomarkers of ovarian response current and future applications Fertil and Steril 201399963-969
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton Reply Reproducibility of AMH Hum Reprod 2012b273641-3642
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Preissner CM Morbeck DE Gada RP and Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011261768-74
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse patient variability Fertil Steril 2011951185-118
78
THE MEASUREMENT OF ANTI-MUumlLLERIAN
HORMONE A CRITICAL APPRAISAL
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan
Luciano G Nardo Philip W Pemberton
The Journal of Clinical Endocrinology amp Metabolism
2014 Mar 99(3) 723-32
3
79
Title
The measurement of Anti-Muumlllerian hormone a critical appraisal
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK d Manchester Royal Infirmary Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Key terms
Anti-Muumlllerian hormone AMH Active MISAMH ELISA Diagnostic
Systems Laboratories AMHMIS ELISA Immunotech AMH Gen II assay
Beckman Coulter
Word Count 3947 (intro ndash general summary text only (no headings)
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
80
Declaration of authorsrsquo roles
The idea was developed during discussion between OR CF and SAR
OR conducted the initial appraisal of the studies prepared and revised the
manuscript SAR and CF contributed to the discussion and interpretation of
the studies and oversaw the revision of the manuscript PWP AY MK
and AS reviewed the data extraction and interpretation contributed to
the discussion of the studies and revision of the manuscript LGN
contributed to the discussion of the studies and revision of the manuscript
81
ABSTRACT
Context
Measurement of AMH is perceived as reliable but the literature reveals
discrepancies in reported within-subject variability and between-assay
conversion factors Recent studies suggest that AMH may be prone to pre-
analytical instability We therefore examined the published evidence on the
performance of current and historic AMH assays in terms of the assessment of
sample stability within-patient variability and comparability of the assay
methods
Evidence Acquisition
Studies (manuscripts or abstracts) measuring AMH published between
01011990 and 01082013 in peer-reviewed journals using appropriate
PubMedMedline searches
Evidence Synthesis
AMH levels in specimens left at room temperature for varying periods
increased by 20 in one study and almost 60 in another depending on
duration and the AMH assay used Even at -20degC increased AMH
concentrations were observed An increase over expected values of 20-30 or
57 respectively was observed following two-fold dilution in two linearity-of-
dilution studies but not in others Several studies investigating within-cycle
variability of AMH reported conflicting results although most studies suggest
variability of AMH within the menstrual cycle appears to be small However
between-sample variability without regard to menstrual cycle as well as within-
sample variation appears to be higher using the Gen II AMH assay than with
previous assays a fact now conceded by the kit manufacturer Studies
comparing first generation AMH assays with each other and with the Gen II
assay reported widely varying differences
Conclusions AMH may exhibit assay-specific pre-analytical instability
Robust protocols for the development and validation of commercial AMH
assays are required
82
INTORDUCTION
In the female AMH produced by granulosa cells of pre-antral and early
antral ovarian follicles regulates oocyte recruitment and folliculogenesis (1 2)
It can assess ovarian reserve (3-5) and guide gonadotrophin stimulation in
assisted reproduction technology (ART) (6) AMH is also used as a granulosa
cell tumour marker a marker of ovarian reserve post-chemotherapy (7 8) and
to predict age at menopause (910)
AMH immunoassays first developed by Hudson et al in 1990 (11) were
introduced commercially by Diagnostic Systems Laboratories (DSL) and
Immunotech (IOT) These assays were integrated into a second-generation
AMH assay GenII (12) by Beckman-Coulter but recent work suggests that this
new assay exhibits clinically important within-patient sample variability (13-
15) Beckman Coulter have recently confirmed this with a field safety notice
(FSN 20434-3) they cite without showing evidence for complement
interference as the problem
ldquoTruerdquo AMH variability comprises both biological and analytical
components (Figure 1) and given the varying antibody specificity and
sensitivity of different AMH assays then logically different kits will respond to
these components to varying degrees This review considers the published
literature on AMH measurement using previous and currently available assays
Potential sources of variation and their contribution to observed AMH
variability were identified
Review structure
This review has been divided into logical subgroups We first address the
stability of AMH at different storage temperatures then the effects of
freezethaw cycles and finally AMH variability in dilution studies Secondly
the within-person variability of AMH measurement is considered
encompassing intra- and inter-menstrual cycle variability and repeat sample
variability in general The final section covers AMH method comparisons
comparing older methods to each other and to the newer now prevalent
GenII method finishing with data on published guidance ranges concerning
the use of AMH in ART A general summary concludes the paper
83
Systematic review
The terms ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting
Substance and MIS were used to search the PubMedMedline MeSH
database between 1st January 1990 and 1st August 2013 for publications in
English commenting on AMH sample stability biological and sample-to-
sample variability or assay method comparison in human clinical or healthy
volunteer samples Titles andor abstracts of 1653 articles were screened to
yield the following eligible publications ten stability studies 17 intrainter-
cycle variability studies and 14 assay method comparability studies
Sample stability
Recent work has established that the GenII-measured AMH is
susceptible to significant preanalytical variability (13 14) not previously
acknowledged which may have influenced results in previous studies with this
assay
Stability of unfrozen samples
Five studies examined AMH stability in samples stored either at room or
fridge temperature (Table 1) (13 16-19) Al-Qahtani et al (16) assessing the
precursor of the DSL ELISA reported that ldquoimmunoreactivity survived the
storage of samples unfrozen for 4 daysrdquo but did not record storage
temperature or sample numbers Evaluating the GenII assay Kumar et al (18)
stored 10 samples at 2-8degC for up to a week and found an average 4
variation compared to samples analysed immediately However their
specimens originally reported as ldquofreshrdquo appear to have been kept cool and
transported overnight Fleming amp Nelson (19) reported no significant change
in the GenII-assayed AMH from 51 samples stored at 4degC Methodological
information was limited but interrogation of their data by Rustamov et al (14)
suggested that AMH levels rose by an average of 27 after 7 days storage
Zhao et al (17) reported a difference of less than 20 between DSL-assayed
AMH in 7 serum samples kept at 22degC for 48 hours when compared to
aliquots from the same samples frozen immediately at -20degC Rustamov et al
(13) measured AMH (GenII) daily in 48 serum samples at room temperature
for 7 days and observed an average 58 increase (from 0 to gt200) whilst
others (20) reported a 31 mean rise in GenII-assayed AMH in whole blood
84
after 90hrs at 20oC whereas serum AMH was virtually unchanged after
prolonged storage at 20oC
Sample stability at -20 o or -80oC and the effects of freezethaw
Rey et al (21) reported a significant increase in AMH (in-house assay)
in samples stored at -20degC for a few weeks attributing this to proteolysis
which could be stabilised with protease inhibitor (see discussion below)
Kumar et al (18) saw 6 variation between GenII-assayed AMH levels from
10 fresh and 10 frozen samples whilst Rustamov et al (13) observed a 22
increase in AMH (GenII) on re-analysis of 8 serum samples after 5 days
storage at -20degC These authors saw no AMH increase in serum stored at -80deg
C for the same period
Linearity of dilution
Six studies examined linearity of dilution on observed AMH
concentrations Long et al (22) recovered between 84 and 105 of the
expected AMH concentration (IOT n=3) AMH dilution curves parallel to
the standard curve were reported by others (16)Kumar et al (18) (n=4) and
Preissner et al (23) ) (n=7) reported GenII-assayed AMH recoveries from 95
to 104 and 96 respectively Sample handling information was limited in
some of these studies (16 23) Fleming amp Nelson (19) (GenII n=10) reported
variances of 8 using assay diluent and 5 using AMH-free serum following
2-fold dilution however interrogation of their data reveals an apparent
dilutional AMH increase of 20-30 in samples stored prior to dilution and
analysis Rustamov et al (13) (GenII n=9) in freshly collected serum observed
an average 57 increase in apparent AMH concentration following two-fold
dilution but with considerable variation
Discussion Sample stability
Sample stability can be a major analytical problem and detailed
examination suggests that previous evidence stating that commercially
measured AMH is stable in storage and exhibits linearity of dilution (12 16 18
19) is weak or conflicting
No study looking at room temperature storage on IOT-assayed AMH
was found and only one using DSL-assayed AMH which showed an increase
85
of less than 20 during storage (17) Studies using the GenII assay to
investigate the effect of storage on AMH variability at room temperature in
the fridge and at -200C reach differing conclusions ranging from stable to an
average 58 increase in measured levels It is important to note here that
sample preparation and storage prior to these experiments was different and
could account for the observed discrepancies The most stable storage
temperature for AMH in serum appears to be -80degC (13 16)
Linearity of dilution studies were also conflicting (13 18 19 23) those
reporting good linearity used samples transported or stored prior to baseline
analysis whereas dilution of fresh samples showed poor linearity In late 2012
Beckman Coulter accepted that the GenII assay did not exhibit linear dilution
and issued a warning on kits that samples should not be diluted They now
suggest that with the newly introduced pre-mixing protocol dilution should
not be a problem
This review highlights the fact that assumptions about AMH stability in
serum were based on a limited number of small studies often providing
limited methodological detail (impairing detailed assessment and comparison
with other studies) using samples stored or transported under unreported
conditions Furthermore conclusions derived using one particular AMH assay
have been applied to other commercial assays without independent validation
The available data suggests that dilution of samples andor storage or
transport in sub-optimal conditions can lead to an increase in apparent AMH
concentration The conditions under which this occurs in each particular AMH
assay are not yet clear and more work is required to understand the underlying
mechanisms Two alternative hypotheses have been proposed firstly that
AMH may undergo proteolytic change as postulated by Rey et al (21) or
conformational change as proposed by Rustamov et al (1314) during storage
resulting in ldquostabilisationrdquo of the molecule in a more immunoreactive form
secondly Beckman have postulated the presence of an interferent
(complement) which degrades on storage (Beckman Coulter field safety notice
FSN 20434-3)
A recent case report found that a falsely high AMH level was corrected
by the use of heterophylic antibody blocking tubes (24) but this does not
explain elevation of AMH on storage (13)
Whatever the mechanism responsible two solutions are available either
inhibit the process completely or force it to completion prior to analysis
86
Rustamov et al (13) and Han et al (15) both suggest pre-dilution of samples to
force the process a protocol now adopted by Beckman Coulter in their revised
GenII assay protocol Any solution must be robustly and independently
validated both experimentally and clinically prior to introduction in clinical
practice Fresh optimal ranges for interpretation of AMH levels in ART will be
needed and the validity of studies carried out using unreported storage
conditions may have to be re-evaluated
Within-person variability
The biological components of AMH variability such as circadian and
interintra-cycle variability have been extensively studied (Table 2 amp
Supplementary table 1)
Circadian variation
Bungum et al (25) evaluated circadian variability measuring AMH
(IOT) two hourly over 24hrs within day 2ndash6 of the menstrual cycle in younger
(20-30 years) and older (35-45 years) women Within-individual CVs of 23
(range 10-230) in the younger group and 68 (range 17-147) in the older
group were observed
Variability within the menstrual cycle
Cook et al (26) observed significant (12) variation in mean AMH (in-
house) levels in 20 healthy women throughout different phases of the
menstrual cycle Intra-cycle variability of IOT-assayed AMH was reported in
three publications (27-29) In two sequential samples were stored at -20degC
until analysis (27 28) Streuli et al (29) did not report on storage La Marca et
al (27) saw no difference in mean follicular phase AMH levels (days 2 4 and 6)
in untreated spontaneous menstrual cycles from 24 women This group went
on to report a small insignificant change (14) in within-group AMH
variability throughout the whole menstrual cycle in 12 healthy women
However this analysis does not appear to allow for correlations within same-
patient samples Streuli et al (29) studied intra-cycle variation of AMH
throughout two menstrual cycles in 10 healthy women and also reported no
significant changes (lt5)
87
The DSL assay was used in eight studies assessing intra-cycle variability
(30-37) Four studied sample storage at -20deg C (30323437) and two studied
samples storage at -80degC (3335) No sample storage data was given in two
publications (31 36) Hehenkamp et al (30) assessed within-subject variation
of AMH in 44 healthy women throughout two consecutive menstrual cycles
and reported an intra-cycle variation of 174 Lahlou et al (31) reported a
ldquodiphasicrdquo pattern of AMH with a significant decrease in levels during the LH
surge from 10 women at various cycle phases Tsepelidis et al (32) reported a
mean intra-cycle coefficient of variation of 14 comparing group mean AMH
levels in 20 women during various stages of the menstrual cycle Wunder et al
(33) reported an intra-cycle variability of around 30 in 36 healthy women
sampling on alternate days They saw a marked fall around ovulation which
might have been missed with less frequent sampling intervals as in other
studies Sowers et al (35) studied within-cycle variability in 20 healthy women
but did not compute an overall estimate instead they selected subgroups of
low and high AMH and reported significant within-cycle variability for women
with high AMH but not those with low AMH - an analysis that has been
questioned (38 39) Robertson et al (36) subgrouped mean AMH levels in 61
women observing that AMH levels were stable in women of reproductive age
and ovulatory women in late reproductive age whilst AMH in other women in
late reproductive age was much more variable Using the data from
Hehenkamp et al (30) van Disseldorp et al (34) calculated intra-class
correlation (ICC) and reported a within-cycle variability of 13 although this
was not clearly defined Using the same data Overbeek et al (37) analyzed the
absolute intra-individual difference in younger (38 years) and older (gt38
years) women This study concluded that the AMH concentration was more
variable in younger women (081059 gL) compared to older women
(031029 gL) during the menstrual cycle (P=0001) thus a single AMH
measurement may be unreliable A recent study using the GenII assay
reported 20 intra-cycle variability in AMH measurements in women (n=12)
with regular ovulatory cycles (40) All the reports considered have findings
consistent with a modest true systematic variability of 10-20 in the level of
AMH in circulation during the menstrual cycle Whilst there have been
suggestions that this variability may differ between subgroups of women these
88
have been based on post-hoc subgroup analyses and there is no convincing
evidence for such subgroups (38)
Variability between menstrual cycles
Three studies (Supplementary table 1) evaluated AMH variability in
samples taken during the early follicular phase of consecutive menstrual cycles
(102941) and three studies have reported on the variability of AMH in repeat
samples from the same patient taken with no regard to the menstrual cycle
(134243) One study employed an in-house assay (41) one study used the
IOT assay (29) three studies used the DSL assay (10 42 43) and one study
(13) used the GenII assay In four infertile women Fanchin et al (41) assessed
the early follicular phase AMH (in-house) variability across three consecutive
menstrual cycles they concluded that inter-sample AMH variability was
characterised by an ICC of 089 (95 CI 083-094) Streuli et al (29)
calculated a between-sample coefficient of variation of 285 in AMH (IOT)
in 10 healthy women In 77 infertile women van Disseldorp et al (10) found
an inter-cycle AMH (DSL) variability of 11 In summary these studies
suggest that the overall inter-cycle variability of AMH ranges from 11 (DSL)
to 28 (IOT) this figure will include both biological and measurement-related
variability
Variability between repeat samples
Variability between repeat samples without regard to menstrual cycle
phase was examined in three studies (Supplementary table 1) In a group of 20
women using samples frozen for prolonged periods Dorgan et al (42)
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median between-sample interval of one year In a larger series
of 186 infertile women Rustamov et al (43) (DSL) found a CV of 28
between repeated samples with a median between-sample interval of 26
months (ICC 091 95 CI 090-093) Rustamov et al (13) found that the
coefficient of variation of repeated GenII-assayed AMH in a group of 84
infertile women was 59 (ICC of 084 95 CI 079-090) substantially higher
than that reported using the DSL assay Similarly a recent study by Hadlow et
al (40) found a within-subject GenII-assayed AMH variability of 80 As a
89
result 5 of the 12 women studied crossed clinical cut-off levels following
repeated measurements
Discussion Within-patient variability
Evidence suggests that repeated measurement of AMH can result in
clinically important variability particularly when using the GenII assay This
questions the assumption that a single AMH measurement is acceptable in
guiding individual treatment strategies in ART
The observed concentration of any analyte measured in a blood
(serum) sample is a function of its ldquotruerdquo concentration and the influence of a
number of other factors (Figure 1) Studies examining the variability of AMH
by repeated measurement of the hormone will therefore reflect both true
biological variation and measurement-related variability introduced by sample
handling andor processing Thus within-sample inter-assay variability used as
an indicator of assay performance may not reflect true measurement-related
variability between samples since it does not take into account the contribution
from pre-analytical variability Measurement-related between-sample variability
can be established in part using blood samples taken simultaneously (to avoid
biological variability) from a group of subjects although even this does not
reflect the full variability in sample processing and storage inherent in real
clinical measurement
Since AMH is only produced by steadily growing ovarian follicles it is
plausible to predict a small true biological variability in serum reflected in the
modest 1-20 variability found within the menstrual cycle In contrast it
appears that the magnitude of measurement-related variability of AMH is more
significant a) within-sample inter-assay variation can be as high as 13 b)
different assays display substantially different variability and c) AMH appears
to be unstable under certain conditions of sample handling and storage (Table
1) Consequently any modest variation in true biological AMH concentration
may be overshadowed by a larger measurement-related variability and careful
experimental designs are required to characterise such differences In general
the reported variability in published studies should be regarded as a measure of
total sample-to-sample variability ie the sum of biological and measurement-
related variability (Figure 1)
90
In repeat samples the available evidence confirms that there is a
significant level of within-patient variability between measurements which is
assay-dependent greater than the estimates of within cycle variability and
therefore likely to be predominantly measurement-related Evidence from
several sources suggests that the effects of sample handling storage and
freezing differ between commercial assays and that the newer GenII assay may
be more susceptible to these changes under clinical conditions When it has
been established that the modified protocol for the GenII assay can produce
reproducible results independent of storage conditions then it will be
necessary to re-examine intra and inter cycle variability of AMH
Assay method comparability
AMH assay comparisons have either used same sample aliquots or
used population-based data with repeat samples Study population
characteristics sample handling inter-method conversion formulae and results
from these comparisons are summarised in Table 3 AMH levels were almost
universally compared using a laboratory based within-sample design The
Rustamov et al study (13) was population-based comparing AMH results in
two different samples from the same patient at different time points using 2
different assays
IOT vs DSL
Table 3 summarises 8 large studies (17 29 30 44-48) that compared the
DSL and IOT AMH assays They demonstrate strikingly different conversion
factors from five-fold higher with the IOT assay to assay equivalence Most
studies carried out both analyses at the same time to avoid analytical variation
(Figure 1) However this does mean that samples were batched and frozen at -
18degC to -80degC prior to analysis which as already outlined may influence pre-
analytical variability and contribute to the observed discrepancies in conversion
factors
IOT vs GenII
Three studies have compared the IOT and Gen II assays (Table 3)
Kumar (18) reported that both assays gave identical AMH concentrations
However Li et al (48) found that the IOT assay produced AMH values 38
91
lower than the Gen II assay whilst Pigny et al (49) found levels that were 2-fold
lower
DSL vs GenII
Four studies analysed same-sample aliquots using the DSL and GenII
assays either simultaneously or sequentially (33 48 50 51) Only Li et al (48)
gave details of sample handling (Table 3) All four studies found that AMH
values that were 35 ndash 50 lower using the DSL compared to the GenII assay
Rustamov et al (13) carried out a between-sample comparison of the assays
measuring AMH in fresh or briefly stored clinical samples from the same
women at different times with values adjusted for patient age (Table 3) In
contrast to within-sample comparisons this study found that the DSL assay gave
results on average 21 higher than with the GenII assay Whilst this
comparison is open to other bias it does reflect the full range of variability
present in clinical samples and avoids issues associated with longer term
sample storage
Discussion Assay method comparability
It is critical for across-method comparison of clinical studies that
reliable conversion factors for AMH are established In-house assays aside
three commercially available AMH ELISAs have been widely available (IOT
DSL and GenII) and the literature demonstrates considerable diversity in
reported conversion factors between first-generation assays (DSL vs IOT)
and between first and second-generation immunoassays (DSLIOT vs GenII)
Although most studies appear to follow manufacturersrsquo protocols
detailed methodological information is sometimes lacking The assessment of
within-sample difference between the two assays involved thawing of a single
sample and simultaneous analysis of two aliquots with each assay Both
aliquots experience the same pre-analytical sample-handling and processing
conditions therefore the results should be reproducible provided the AMH
samples are stable during the post-thaw analytical stage and the study
populations are comparable However this review has identified significant
discrepancies between studies perhaps due to either significant instability of
the sample or significant variation in assay performance Studies comparing
AMH levels measured using different assays in populations during routine
92
clinical use have also come to differing conclusions (13 51) Given the study
designs that workers have used to try to ensure that samples are comparable
the finding of significant discrepancies in the observed conversion factors
between assays is consistent with the proposal that AMH is subject to
instability during the pre-analytical stage of sample handling This coupled
with any differential sensitivity and specificity between these commercial
assays could give rise to the observed results ie some assays are more
sensitive than others to pre analytical effects
AMH guidance in ART
AMH guidance ranges to assess ovarian reserve (52) or subsequent
response to treatment (53 54) have been published The Doctors Laboratory
using the DSL assay advised the following ranges for ovarian reserve (lt
057pmolL-undetectable 057-21 pmolL-very low 22-157 pmolL-low
158-286 pmolL-satisfactory 287-485pmolL-optimal gt485pmolL-very
high) ranges that supposedly increased by 40 on changing to the GenII assay
(51) More recently other authors have attempted to correlate AMH levels with
subsequent birth rates Brodin et al (53) using the DSL assay observed that
higher birth rates were seen in women with an AMH level gt 21 pmolL and
low birth rates were seen in women who had AMH levels lt 143 pmolL In
the UK the National Institute for Health and Care Excellence (NICE) have
recently issued guidance on AMH levels in the assessment of ovarian reserve in
the new clinical guideline on Fertility (54) They advise that an AMH level of le
54 pmolL would indicate a low response to subsequent treatment and an
AMH ge 250 pmolL indicates a possible high response Although not
specifically stated interrogation of the guideline suggests that these levels have
been obtained using the DSL assay which is no longer available in the UK
As discussed above the initial study of comparability between the DSL
and GenII assays reported that GenII generated values 40 higher compared
to the DSL assay clinics were therefore recommended to increase their
treatment guidance ranges accordingly (51) However a more recent study
using fresh samples found that the original GenII assay may actually give
values which are 20-30 lower suggesting that following the above
recommendation may lead to allocation of patients to inappropriate treatment
groups (13) The apparent disparity in assay comparison studies implies that
93
AMH reference ranges and guidance ranges for IVF treatment which have
been established using one assay cannot be reliably used with another assay
method without full independent validation Similarly caution is required
when comparing the outcomes of research studies using different AMH assay
methods
General Summary
Recent publications have suggested that GenII-assayed AMH is
susceptible to pre-analytical change leading to significant variability in
determined AMH concentration an observation now accepted by the kit
manufacturer However this review suggests that all AMH assays may display a
differential response to pre-analytical proteolysis conformational changes of
the AMH dimer or presence of interfering substances The existence of
appreciable sample-to-sample variability and substantial discrepancies in
between-assay conversion factors suggests that sample instability may have
been an issue with previous AMH assays but appears to be more pronounced
with the currently available GenII immunoassay The observed discrepancies
may be explicable in terms of changes in AMH or assay performance that are
dependent on sample handling transport and storage conditions factors
under-reported in the literature We strongly recommend that future studies on
AMH should explicitly report on how samples are collected processed and
stored If it can be clearly demonstrated that the new GenII protocol drives
this process to completion in all samples ensuring stability then a re-
examination of reference and guidance ranges for AMH interpretation will be
necessary There is a clear need for an international reference standard for
AMH and for robust independent evaluation of commercial assays in routine
clinical samples with well-defined sample handling and processing protocols
These issues of sample instability and lack of reliable inter-assay comparability
data should be taken into account in the interpretation of available research
evidence and the application of AMH measurement in clinical practice
94
References
1 Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
2 Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
3 van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
4 Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
5 Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009921586-1593 6 Yates AP Rustamov O Roberts SA Lim HYN Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353ndash2362
7 Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-55
8 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
9 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
10 van Disseldorp J Lambalk CB Kwee J Looman CW Eijkemans MJ Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Muumlllerian hormone and antral follicle counts Hum Reprod 201025221-227
11 Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
95
12 Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642
15 Han X McShane M Sahertian R White C Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Hum Reprod 201328 (suppl 1)i76-i78 (abstract)
16 Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 200563267-273
17 Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 200788S17 (abstract)
18 Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
19 Fleming R Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
20 Fleming R Fairbairn C Blaney C Lucas D Gaudoin M Stability of AMH measurement in blood and avoidance of proteolytic changes Reprod Biomed Online 201326130-132
21 Rey R Lordereau-Richard I Carel JC Barbet P Cate RL Roger M Chaussain JL Josso N Anti-Mullerian hormone and testosterone serum levels are inversely related during normal and precocious pubertal development J Clin Endocrinol Metab 199377 1220ndash1226
22 Long WQ Ranchin V Pautier P Belville C Denizot P Cailla H Lhomme C Picard JY Bidart JM Rey R Detection of minimal levels of serum anti-Mullerian hormone during follow-up of patients with ovarian granulosa cell tumor by means of a highly sensitive enzyme-linked immunosorbent assay J Clin Endocrinol Metab 200085540ndash544
23 Preissner CM Morbeck DE Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54 (abstract)
24 Cappy H Pigny P Leroy-Billiard M Dewailly D Catteau‐Jonard S Falsely elevated serum antimuumlllerian hormone level in a context of heterophilic
96
interference Fertil Steril 2013991729-1732
25 Bungum L Jacobsson AK Roseacuten F Becker C Yding Andersen C Guumlner N Giwercman A Circadian variation in concentration of anti-Mullerian hormone in regularly menstruating females relation to age gonadotrophin and sex steroid levels Hum Reprod 201126678ndash684
26 Cook CL Siow Y Taylor S Fallat ME Serum muumlllerian-inhibiting substance levels during normal menstrual cycles Fertil Steril 200073859-861
27 La Marca A Malmusi S Giulini S Tamaro LF Orvieto R Levratti P Volpe A Anti-Muumlllerian hormone plasma levels in spontaneous menstrual cycle and during treatment with FSH to induce ovulation Hum Reprod 2004192738-2741
28 La Marca A Stabile G Carduccio Artenisio A Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash310729 Streuli I Fraisse T Chapron C Bijaoui G Bischof P de Ziegler D Clinical uses of anti-Mullerian hormone assays pitfalls and promises Fertil Steril 200991226-230
30 Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
31 Lahlou N Chabbert-Buffet N Gainer E Roger M Bouchard P Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11 (abstract)
32 Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
33 Wunder DM Bersinger NA Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrual cycle in reproductive age women Fertil Steril 200889927-933
34 van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormone concentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
35 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 2010 941482-1486
36 Robertson DM Hale GE Fraser IS Hughes CL Burger HG Changes in serum antimuumlllerian hormone levels across the ovulatory menstrual cycle in late reproductive age Menopause 201118521-524
37 Overbeek A Broekmans FJ Hehenkamp WJ Wijdeveld ME van
97
Disseldorp J van Dulmen-den Broeder E Lambalk CB Intra-cycle fluctuations of anti-Mullerian hormone in normal women with a regular cycle a re-analysis Reprod Biomed Online 201224664ndash 669
38 Roberts SA Variability in anti-Mullerian hormone levels a comment on Sowers et al ldquoAnti-Mullerian hormone and inhibin B variability during normal menstrual cyclesrdquo Fertil Steril 201094e59
39 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Reply of the authors Variability in anti-Muumlllerian hormone levels a comment on Sowers et al Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 201094e60
40 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013991791-1797
41 Fanchin R Taieb J Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Muumlllerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 200520923-927
42 Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
43 Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
44 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
45 Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175
46 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
47 Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
48 Li HW Ng EH Wong BP Anderson RA Ho PC Yeung WS Correlation between three assay systems for anti-Mullerian hormone (AMH)
98
determination J Assist Reprod Genet 2012291443-1446
49 Pigny P Dassonneville A Catteau-Jonard S Decanter C Dewailly D Comparative analysis of two-widely used immunoassays for the measurement of serum AMH in women Hum Reprod 2013 28i311-316 (abstract)
50 Gada R Hughes P Amols M Amols M Preissner C Morbeck D Coddington C Validation and comparison of AMH serum levels using the original active MISAMH ELISA to the new active AMH Gen II ELISA Fertil Steril 201195S23 (abstract)
51 Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
52 The Doctors Laboratory Lab Report newsletter ndash Winter 20072008 ndash AMH
53 Brodin T Hadziosmanovic N Berglund L Olovsson M Holte J Antimullerian hormone levels are strongly associated with live birth rates after assisted reproduction J Clin Endocrinol Metab 201398(3)1107-1104
54 National Institute for Care and Health Excellence NICE clinical guideline CG156 Fertility
99
Figure 1 Biological and analytical variability of AMH
100
Table 1 AMH assay validation effect of sample storage conditions freshthaw cycles and linearity of dilution
Study Assay Method Result
Rey et al (21) in-house effect of Long-term storage at -20C (n=4) AMH levels in archival samples were 230 higher than original value
Long et al (22) IOT linearity up to 16-fold dilution (n=3) observed AMH was 84-105 of expected AMH
Al-Qahtani et al (16) in-house a freezethaw stability storage unfrozen for 4 days
b linearity up to 32-fold dilution (n=6)
a immuno-reactivity survived both multiple freeze-thaw cycles and storage unfrozen for 4 days b dilution curves were parallel to the standard curve
Zhao et al (17) DSL
serum frozen immediately at -20C compared to
aliquots stored at 4C or 22C for up to 2 days (n=7) AMH levels increased by 1 at 4C and 9 at 22C after 2 days compared to sample frozen immediately
Kumar et al (18) Gen II
a serum or plasma stored at 2-8C or -20C for up to 7 days (n = 20) b serum or plasma underwent up to three freezethaw cycles (n=20) c linearity of dilution (n=4)
a AMH levels were stable for up to 7 days at 2-8C or -20C
b AMH increased by 15 in serum and by 5 in plasma after 3 cycles c linear results obtained across the dynamic range of the assay
Preissner et al (23) Gen II linearity of dilution (n=7) average agreement with expected result was 97
Rustamov et al (13) Gen II
a stability at RT for up to 7 days (n=48)
b storage for 5 days at -20C or -80C compared to fresh sample (n=8) c linearity on 2-fold dilution (n=9)
a AMH levels increased by an average of 58 over 7 days
b AMH levels increased by 23 at -20C but were unchanged at -80C c AMH levels were on average 157 higher than expected
Fleming amp Nelson (19) Gen II a serum stored at 4C for 7 days (n=48) b linearity of dilution (n=10)
a AMH levels increased by an average of 27 b AMH was 28 amp 33 higher on 2-fold amp 4-fold dilution resp
Fleming et al (20) Gen II
a whole blood stored for up to 90 hours at 4C (n=32) or 20C (n=21)
b serum stored for 5 days at 20C and 2 days at 4C (n=13)
a AMH increased by 11 at 4C and by 31 at 20C b only 1 increase in AMH compared to original value
Han et al (15) Gen II
serum from non-pregnant (n=13) or early pregnant (n=7) women
stored at RT -20C or -80C for up to 7 days
In non-pregnant women AMH increased by 26 after 7 days at RT but was
unchanged at -20C or -80C
In pregnant women AMH increased by 50 at RT and 27 at -80C after 48 hours
101
Table 2 Intra-cycle variability of AMH Study
Subjects
a cycles b day sampled
Assay
a storage b freezethaw c measurement
Result
Authorsrsquo Conclusion
Cook et al (26)
healthy age 22-35 regular cycle (n=20)
a 1 cycle b day 23 LH surge LH surge +7 d
in-house
a -80C b once c inter-assay variation eliminated
day 3 AMH = 14 09ngml
mid cycle AMH = 17 11ngmL
mid luteal AMH = 14 09ngmL
Fluctuations significant (plt0008) AMH may have a regulatory role in folliculogenesis
La Marca et al (27)
healthy age 21-36
regular cycle (n=24)
a follicular phase b alternate days
IOT
a -20C
b once
AMH did not change from days 2 to 6 in spontaneous cycles but decreased progressively in FSH-treated cycles
AMH levels did not change significantly during follicular phase of the menstrual cycle
La Marca et al (28)
healthy age18-24
regular cycle (n=12)
a 1 cycle b alternate days day 0 = day of LH surge
IOT
a -20C
b once
low mean AMH = 3411ngmL (day 14)
high mean AMH =3913ngmL (day 12)
AMH levels did not change significantly throughout menstrual cycle
Lahlou et al (31)
placebo-treated (n=12)
a 1 cycle
b every 3 days
DSL
NR 7 days pre LH surge AMH = 26
32pmolL peak AMH = 191 35pmolL 10 days post LH surge
AMH = 254 43pmolL
AMH levels exhibited a diphasic pattern with levels declining significantly (plt005) during the LH surge
Hehenkamp et al (30)
healthy
fertile regular cycle (n=44)
a 2 cycles
b AMH measured at each of 7 cycle phases
DSL a -20C a sine pattern fitted to AMH data was not significant (p=040) b72 repeat AMH values fell within the same quintile 28 in adjacent quintile
AMH shows no consistent fluctuation through the cycle compared to FSH LH amp E2
van Disseldorp et al (10)
data from Hehenkamp et al (30)
Intra-cycle within-subject variation of AMH was only 13 compared to 31-34 for AFC (dependent on follicle size)
AMH displays less intra-cycle variability than AFC
Overbeek et al (37)
data from Hehenkamp et al (30)
Fluctuations were larger than 05microgL in one cycle in significantly (p = 0001) more women in the younger group than the older one
AMH can fluctuate substantially in younger women during menstrual cycle so a single measurement could be unreliable
102
Tsepelidis
et al (32)
healthy age 18-35 regular cycles (n=20)
a 1 cycle b days 3 7 10-16 18 21 amp 25
DSL
a -20C
b once
Within-cycle differences not significant (p=0408)
AMH levels do not vary during the menstrual cycle
Wunder et al (33)
healthy
age 20-32 regular cycles (n=36)
a 1 cycle
b alternate days
DSL
a -80C
AMH levels were statistically higher in the late follicular phase than at the time of ovulation (p= 0019) or in the early luteal phases (plt00001)
AMH levels vary significantly during the menstrual cycle
Streuli
et al (29)
healthy mean age=241 regular cycles
(n=10)
a 1 cycle b before (LH
-10-5-2-1) and after LH surge (LH +1+2+10)
IOT
a -18C
AMH levels were statistically lower during the early luteal phase compared to early follicular phase (p=0016) and late luteal phase levels (p=002)
In clinical practice AMH can be measured at any time during the menstrual cycle
Sowers et al
(35)
healthy age 30-40 regular cycles
(n=20)
a 1 cycle b daily
DSL
a -80C
b once c simultaneous
Higher AMH levels with significant variation between days 2-7 in the ldquoyounger ovaryrdquo Low AMH levels with little variation in the ldquoaging ovaryrdquo
AMH varies across the menstrual cycle in the ldquoyounger ovaryrdquo
Robertson et al (36)
a age 21-35 regular cycles
(n=43) b age 45-55
variable cycles (n=18)
a 1 cycle + initial stages of succeeding cycle b three times weekly
DSL
NR No intracycle variation in AMH level was found in women in mid reproductive life or in 33 women with regular cycles in late reproductive age In the remaining cycles there was a significant (plt001) two-fold decrease in AMH in 11 cycles and a significant (plt001) 42-fold increase between the follicular amp luteal phases
When AMH levels are substantially reduced they become less reliable markers of ovarian reserve
Hadlow
et al (40)
age 29-43 regular cycles non-PCOS
(n=12)
a 1 cycle b 5-9 samples per subject
Gen II a -20C within 4 hours of sampling b once
c simultaneous
712 women could be reclassified depending on when AMH was measured during the cycle 212 crossed cut-offs predicting hyperstimulation
AMH cycles varied during menstrual cycle and clinical classification of the ovarian response was altered
103
Table 3 Variability in AMH levels between menstrual cycles
Study
Subjects
a cycles b day sampled
Assay
Storage
Result
Authorsrsquo Conclusion
Fanchin et al (41)
infertile
age 25-40 regular cycles
(n=47)
a 3 cycles
b day 3
in-house
(Long et al 2000)
-80C
AMH showed significantly
higher reproducibility than inhibin B (plt003) E2 (plt00001) FSH (plt001) and early AFC (plt00001)
AMH showed improved cycle-to-cycle consistency compared to other markers of ovarian follicular status
Streuli
et al (29)
healthy mean age = 241 regular cycles
(n=10)
a 2 cycles b before (LH -10-5-2-1) and
after LH surge (LH +1+2+10)
IOT
-18C Inter-cycle variability of 285
AMH fluctuations during the cycle were smaller than or equal to the variability between two cycles
van Disseldorp et al (10)
infertile median age =33
PCOS excluded (n=77)
a average 373 cycles b day 3
DSL
-80C
AMH showed a within-subject variability of 11 compared to 27 for AFC
AMH demonstrated less individual inter-cycle variability than AFC
Dorgan
et al (42)
blood donors age 36-44 collected 1977-1981 (n=20)
two samples collected during the same menstrual cycle phase at least 1yr apart
DSL
-70C
between-subject variance in AMH of 219 was large compared to the within-subject variance of 031
AMH was relatively stable over 1 year in pre-menopausal women
Rustamov et al (36)
infertile women age 22-41
(n=186)
random sampling median interval = 26 months
DSL
-70C
within-subject CV for AMH was 28 compared to 27 for FSH
AMH showed significant sample-to-sample variation
Rustamov et al (13)
infertile women age 20-46
(n=87)
random sampling median interval = 51 months
Gen II
-20C
within-subject CV for AMH was 59
AMH demonstrated a large sample-to-sample variation
104
Table 4 Within-subject comparison between AMH methods Study
Assays
Subjects
Simultaneous Analysis
Regression
Summary
Freour et al (44) DSL vs IOT 69 infertile women age 22-40
Yes IOT = 401 x DSL + 098 (microgL) (Deming regression)
DSL = 22 IOT (plt00001)
Hehenkamp et al (30) DSL vs IOT 82 healthy women NR DSL= 0495 x IOT - 003 DSL = 495 IOT
Bersinger et al (45) a DSL vs IOT
b DSL vs IOT
a 11 infertile women
b 55 infertile women
a yes
b no
a DSL= 0180 x IOT
b DSL= 0325 x IOT + 0733
a DSL = 18 IOT
b DSL= 33 IOT
Zhao et al (17) DSL vs IOT 38 donors NR IOT = 15 x DSL + 07 (ngml) DSL = 66 IOT
Taieb et al (46) DSL vs IOT 104 samples NR DSL = 104 x IOT - 149 DSL = 96 IOT
Streuli et al (29) DSL vs IOT 153 normal and infertile No IOT = 107 x DSL - 029 DSL = IOT
Kumar et al (18) IOT vs Gen II 60 female 60 male volunteers NR IOT =10 Gen II IOT=Gen II
Gada et al (50) DSL vs Gen II 42 women NR NR DSL = 63 Gen II
Preissner et al (23) DSL vs Gen II 206 samples NR Gen II = 153 x DSL - 077 DSL = 66 Gen II
Lee et al (47) DSL vs IOT 172 infertile women Yes IOT = 1102 x DSL - 0042 DSL = IOT
Wallace et al (51) DSL vs Gen II 271 women NR Gen II = 140 x DSL - 062 DSL = 71 Gen II
Li et al (48) a DSL vs IOT b DSL vs Gen II c IOT vs Gen II
56 women with PCOS or sub-fertility Yes a IOT = 097 x DSL -296 b Gen II = 133 x DSL - 417 c Gen II = 138 x IOT - 068
a DSL = IOT b DSL = 67 Gen II c IOT = 62 Gen II
Rustamov et al (13) DSL vs Gen II female IVF patients (n=330)
median of 2yr between samples
No NR
DSL = 127 Gen II
(age-adjusted)
Pigny et al (49) IOT vs Gen II 59 women 32 controls 27 with PCOS Yes NR IOT = 200 Gen II
105
Appendix I Flow-chart of the search for publications Database search for sample stability measurement variability and assay-method comparability was conducted simultaneously using the MeSH database of PubMedMedline using the search terms of ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting Substance and MIS which identified n=1653 studies on AMH The initial step of identification involved screening of articles by reading titles andor abstracts Further search involved identification of studies from the reference sections of the initially identified studies
Database Search
n=1653
Sample
Stability
Screening Titles
n=6
Further Search
n=4
Total
n=10
Measurment Variability
Screening Titles
n=14
Further Search
n=3
Total
n=17
Method comparability
Screening Titles
n=10
Further Search
n=4
Total
n=14
106
EXTRACTION PREPARATION AND
COLLATION OF DATASETS FOR THE
ASSESSMENT OF THE ROLE OF THE MARKERS
OF OVARIAN RESERVE IN FEMALE
REPRODUCTION AND IVF TREATMENT
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
Research Database
4
107
Title
Extraction preparation and collation of datasets for the assessment of
the role of the markers of ovarian reserve in female reproduction and
IVF treatment
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
NHS Research Ethics Approval
North West Research Ethics Committee (10H101522)
Word count 5088
Grants or fellowships
No funding was sought for this study
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
108
Declaration of authorsrsquo roles
OR prepared the protocol extracted data from electronic sources and hospital
notes prepared datasets and prepared all versions of the chapter MK assisted
in collection of data from hospital notes SR and CF oversaw and supervised
preparation the protocol extraction of data preparation of datasets and
reviewed the chapter
109
CONTENTS I PROTOCOL Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip110
Methodshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Objectiveshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Inclusion Criteriahelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip114 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 RH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 AFC datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Folliculogram datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Data managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data cleaning and codinghelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118 Merging datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data security and storagehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip119 II RESULTS Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip120 Data extraction and managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 RH AFC and Folliculogram datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 Merging Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip124 Conclusionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip125
110
I PROTOCOL
INTRODUCTION
The aim of the project is to create a series of reliable and validated
datasets which contain all relevant data on the ovarian reserve markers (AMH
AFC FSH) ethnicity BMI reproductive history causes of infertility IVF
treatment parameters for patients that meet inclusion criteria as described
below The datasets will be used for the subsequent research projects of the
MD programme and future research studies on ovarian reserve
Most data can be obtained from following existing clinical electronic
records a) Patient Administration System (PAS) b) Biochemistry Department
data management system c) the hospital database for surgical procedures and
d) AMH dataset and e) ACUBase IVF data management system Following
obtaining original datasets from the administrators of the data management
systems in their original Excel format the datasets will be converted into Stata
format and ldquopreparedrdquo by a) checking and recoding spurious data
transforming the dates from string to numeric format which will be consistent
across all datasets (Day Month Year) and stored in Stata format under
following names ldquoDemographyrdquo ldquoBiochemistryrdquo ldquoAMHrdquo ldquoSurgeryrdquo ldquoIVFrdquo
ldquoFETrdquo ldquoEmbryologyrdquo Copies of original datasets will be kept in the
password-protected and encrypted computer located in the Clinical Records
Room of Reproductive Medicine Department Central Manchester University
Hospitals NHS Foundation Trust which is maintained by IT department of
the Trust (Figure 1)
Data not available in electronic format will be collected from the hospital
records of each patient by researchers Dr Oybek Rustamov and Dr Monica
Krishnan and entered into following datasets Reproductive history (RH)
antral follicle count (AFC) and Folliculogram The hospital notes of all
included patients will be hand-searched The datasets will be transferred to
Stata and each step of data preparation will be recorded using Stata Do files
and the files will be stored under the filenames of ldquoHistoryrdquo ldquoAFCrdquo
Folliculogramrdquo in Stata format In order to ensure the robustness of the data
and for the purpose of validation of the datasets electronic scanned copies of
all available reports of pelvic ultrasound assessments for AFC and
folliculograms will be obtained and stored in the password-protected and
111
encrypted computer located in the Clinical Records Room of Reproductive
Medicine Department Ethics approval for collection of data has already been
obtained (UK-NHS 10H101522)
The datasets will be merged and datasets for each research project with
all available data nested with IVF cycles nested within patients will be created
METHODS
Objectives
The aim of the project is to build a robust database which can reliably
used for the following purposes
1 To estimate the effect of ethnicity BMI endometriosis and the causes
of infertility on ovarian reserve using cross sectional data (Chapter 51)
2 To estimate the effect of salpingectomy ovarian cystectomy and
unilateral salpingo-oopherectomy on ovarian reserve using cross
sectional data (Chapter 52)
3 To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield (Chapter 6)
4 To explore the potential for optimization of AMH-tailored
individualisation of ovarian stimulation using retrospective data
(Chapter 6)
Inclusion criteria
In order to capture the populations for all three studies the database will
have broad inclusion criteria All women from 20 to 50 years of age referred to
Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust will be included if a) they were referred for
management of infertility or fertility preservation and b) had AMH
measurement during the period from 1 September 2008 till 16 November
2011
112
Datasets
PAS dataset
The dataset contains information on the hospital number surname first
name date of birth and the ethnicity of all patients referred to Reproductive
Medicine Department CMFT (Table 1) The data are originally entered during
registration of the patient for clinical care by administrative staff of
Gynaecology and Reproductive Medicine Departments The dataset will be
obtained from the administrators of the Information Unit
The dataset will be obtained in Excel format and transferred into Stata
12 Data Management and Statistical Software The date values (referral date
and date of birth) will be converted into numeric variable using ldquoDate Month
Yearrdquo format (DMY) Ethnicity will be coded using numeric variables in
alphabetical order as pre-specified in the Table 2a
Biochemistry dataset
The dataset contains all blood test results specimen numbers the names
of the tests and the date of sampling of women who had assays for follicle
stimulating hormone (FSH) oestradiol (E2) luteinizing hormone (LH) and
AMH during the study period (Table 1) Data entries were conducted by the
clinical scientists the technicians and the members of administrative team of
the Biochemistry Department The dataset will be obtained from an
administrator of the database
The date of sampling and analyses will be converted to the numeric
ldquoDMYrdquo format The specimen number will be kept unaltered in the string
variable format and used to link the tests that were taken in the same sample
tube The name of the test will be kept as described in the original format
ldquoAMHrdquo ldquoFSHrdquo ldquoLH and ldquoOestrdquo In the original dataset the samples sent
from Reproductive Medicine Department are coded as ldquoIVFrdquo which will be
kept unaltered and the remaining observations will be divided into
ldquoGynaecology Departmentrdquo ldquoNon-IVFGynaecologyrdquo and ldquoUnknownrdquo
categories using the code of referred ward and the names of the consultants
The test results will be converted into numeric format and the results with
minimum detection limit will be coded as 50 of the minimum detection limit
as follows AMH ldquolt061rdquo= 031 pmolL FSH ldquolt05rdquo= 025 mlUml LH
113
ldquolt05rdquo=025 mlUml Oest ldquolt50rdquo=025 pgml The test results that are
higher than the assay ranges will be set to 150 of the maximum range
Interpretation of serum FSH results in conjunction with serum
oestradiol levels is important in establishing true early follicular phase hormone
levels The test results are believed to be inaccurate if serum oestradiol levels
higher than 250pmolL at the time of sampling and therefore a new variable
for FSH results with only serum FSH observations that meet above criteria will
be created and used subsequently All ambiguous data will be checked using
electronic pathology data management system Clinical Work Station (CWS)
Surgery dataset
The electronic dataset will be obtained from Information Department
in Excel format The dataset created using clinical coding software and data
entry conducted during patient treatment episodes by theatre nursing and
medical staff In order to evaluate effect of past reproductive surgery to
ovarian reserve all patients had ovarian cystectomy drainage of ovarian cyst
salpingectomy salpingo-oopherectomy during 1 January 2000-16 November
2011 at Central Manchester University Hospitals NHS Foundation Trust will
be included in the dataset The dataset contains following variables hospital
number surname first name date of birth date of operation name of
operation laterality of operation and name of surgeon
The final dataset will be stored in Stata dta format (Figure 1) The
dataset will be used to validate data on reproductive surgery that was collected
from hospital records in the RH dataset
AMH dataset
The dataset contains the AMH results the dates of sampling the dates
of analyses and the assay generation (DSL or Gen II) for all patients included
in the study (Table 1) The dataset will be obtained from the senior clinical
scientist Dr Philip Pemberton Specialist Assay Laboratory who is responsible
for the data entry and updating of the dataset
There are two separate primary Excel based AMH data files 1) DSL
dataset and 2) Gen II dataset The datasets will be transferred to Stata 12
software separately and following preparation of the datasets which logged
using Stata Do file Stata versions of the data files will be stored under ldquoDSLrdquo
114
and ldquoGen2rdquo names Then the files will be combined by appending ldquoDSLrdquo to
ldquoGen2rdquo in order to create a new combined ldquoAMHrdquo dataset The date variables
the sample date the assay date and the date of birth will be converted into
numeric ldquoDMYrdquo format The samples sent from other NHS trusts and private
clinics will be excluded from the dataset alongside the records from male
patients and the patients outside of the age range of 20-50 years of age The
manufacturers of the assays suggest that haemolysed and partly haemolysed
samples may provide inaccurate test readings Therefore a new variable
without these samples will be created and used in the analyses for all studies
All the ambiguous data will be checked and verified using duplicate datasets
obtained from Biochemistry dataset and the hospital records of the patients
IVF dataset
The IVF dataset will be downloaded from ACUBase Data management
system in original Excel format and contains detailed information on causes of
infertility sperm parameters treatment interventions assessment of oocyte
quantity and quality assessment of embryo quantity and quality and the
outcomes of treatment cycles (Table 1)Data entry to ACUBase was
performed by members of administrative nursing embryology and medical
staff of the Reproductive Medicine Department at the point of care This is
only electronic data management system for ART cycles and used for
monitoring of the clinical performance of the department by internal and
external quality assessment agencies and regulators (eg HFEA CQC)
Therefore the quality of data entry for the main indicators of the performance
of IVFICSI programs (the treatment procedures the outcomes of the cycles
and assessment of embryos) should be fairly accurate
Table 2b describes the coding of the treatment outcomes and the
practitioners of ICSI the ultrasound-guided oocyte retrieval (USOR) and the
embryo transfer (ET) procedures
In addition to the main patient identifier (Hospital Number) this dataset
contains in-built cycle identifier (IVF Reference Number) which will be used
to link the original IVF cycles to corresponding Frozen Embryo Transfer
(FET) cycles and the embryos originating from the index cycle using ldquoFETrdquo
and ldquoEmbryordquo datasets respectively
115
FET dataset
The dataset provides information on the quality and the quantity of
transferred embryos the date of embryo transfer and the outcome of the cycle
in frozen embryo transfer cycles (Table 1) Primary data entry was performed
by the members of the clinical embryology team during the treatment of
patients and will be downloaded from ACUBase by Dr O Rustamov
Together with ldquoIVFrdquo dataset it can be used to study cumulative live birth rate
(LBR) of index cycles The treatment outcomes as well as ICSI USOR and ET
practitioners will be converted to numeric variables using the codes which are
shown in Table 2b The dataset can be linked to the index fresh IVF cycles as
well as to embryos of FET cycles using the IVF Reference number
Embryology dataset
The dataset has comprehensive information on the quality and the
quantity of embryos on each day of their culturing including embryos that
were cryopreserved and those that were discarded (Table 1) The dataset also
includes patient identifiers (name date of birth IVF reference number) and
the dates of embryo transfer The primary data entry into this dataset was
conducted by the members of clinical embryology team during the clinical
episodes and will be downloaded from ACUBase by Dr O Rustamov The
dataset can be linked to index fresh IVF cycle and FET cycles using IVF
Reference numbers of corresponding datasets
RH dataset
This dataset will be created and data entry will be conducted during the
search of the hospital notes Following identification of included patients using
AMH dataset Excel electronic data collection file will be created The hospital
notes of each patient will be searched for by systematically checking all filed
hospital records in Clinical Records Room of Reproductive Medicine
Department by the order of their hospital number Further search for missing
notes will be conducted by checking all hospital notes located in the offices of
nurses doctors and secretaries Electronic hospital notes filed in Medisec
Digital Dictation Database will be used for data extraction for the patients
whose hospital notes were not located
116
All available diagnosis will be recorded under the following columns 1)
female referral diagnosis 2) male referral diagnosis 3) female initial clinic
diagnosis 4) female final clinic diagnosis 5) diagnosis prior 2nd IVF cycle 6)
diagnosis prior 3rd IVF cycle Furthermore other relevant information on
pathology of reproductive system will be documented For instance all possible
iatrogenic causes of poor ovarian reserve (eg oophorectomy ovarian
cystectomy salpingectomy chemotherapy and radiotherapy) will be recorded
In order to establish the existence of polycystic ovary syndrome (PCOS) the
history of oligomenorrhea amenorrhea and diagnosis of polycystic ovaries
(PCO) on pelvic ultrasound scan will be collected and used in conjunction with
serum LH levels of Biochemistry dataset (Table 1)
Male infertility will be defined as ldquosevere male factorrdquo if the sperm
parameters were low enough to meet criteria (lt05 mlnml or retrograde
ejaculation) for Multiple Ejaculation Resuspension and Centrifugation test
(MERC) as part of investigation for infertility A variable for patients
diagnosed with azoospermia will be created and the diagnosis will be recorded
The patients diagnosed with male factor infertility but with the sperm
parameters that did not reach criteria for MERC will be diagnosed with ldquomild
male factorrdquo infertility Patients diagnosed with ldquosevererdquo andor ldquostage IVrdquo
andor ldquostage IIIrdquo endometriosis will be categorized as ldquosevere
endometriosisrdquo while patients diagnosed with mild or moderate endometriosis
will be coded as ldquomild endometriosisrdquo group In diagnosing the tubal factor
infertility only patients with history of bilateral salpingectomy and the patients
with evidence of bilateral tubal blockage on a laparoscopy and dye test will be
diagnosed as ldquosevere tubal factorrdquo The patients with history of unilateral
salpingectomy unilateral tubal block in laparoscopy and dye test or
unilateralbilateral tubal block on hysterosalpingogram will be categorized as
ldquomild tubal factorrdquo infertility Diagnosis of polycystic ovarian syndrome
(PCOS) will be based in Rotterdam criteria existence of two of the following
features 1) oligo- or anovulation 2) clinical andor biochemical signs of
hyperandrgoenism 3) polycystic ovaries Referral for fertility preservation will
be defined as ldquoreferral for consideration of obtaining oocytes orand embryos
andor sperm prior to chemotherapy radiotherapy or surgical management of
a malignant diseaserdquo The length of infertility will be recorded as per proforma
of initial consultation for the patients attended initial clinic appointment
following introduction of serum AMH test 1 September 2008 For patients
117
attended initial consultation prior to introduction of AMH test the length of
infertility will be documented as per the initial clinic proforma plus years till the
patientrsquos first AMH test The patientrsquos body mass index (BMI) documented at
initial assessment will used for patients who had assessment after introduction
of AMH test 1 September 2008 whereas the most up to date BMI result is
collected for the patients seen prior to this date
AFC dataset
Data will be extracted from the hospital notes The data on the
assessment of AFC will be obtained from the pelvic ultrasound scan reports
The date of assessment the AFC in each ovary the name of sonographer will
be recorded (Table 1) Furthermore other relevant ultrasound findings such
as ovarian cyst hydrosalpynx and submucous uterine fibroids will also be
entered in the dataset To permit data validation scanned copies of ultrasound
scan report of each AFC investigation will be stored in PDF format in the
computer that located in the Clinical Notes Room
The department uses a stringent methodology for the assessment of
AFC which consist of counting of all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
The ultrasound assessments are conducted by qualified sonographers who use
the same methodology for the measurement of AFC However it is well
known that the counting of antral follicles may be prone to significant inter-
operator variability Therefore the name of sonographers will be recorded
during primary data collection and coded (Table 2a) so that the estimates of
within- and between-operator variability can be obtained if necessary
Folliculogram dataset
Although most data on IVFICSI cycles are available in ldquoIVFrdquo dataset
certain important data on IVF treatment are recorded only in the hard copy
IVF folliculograms Consequently data on ultrasound follicle tracking the
reasons for changing the doses of stimulation drugs are only available in the
folliculograms Furthermore the length of ldquothe coastingrdquo and the causes for
cycle cancellation are usually recorded in both folliculograms and ldquoIVFrdquo
dataset which can be used to validate accuracy of ldquoIVFrdquo dataset Therefore
118
these data will be collected using the folliculograms that filed in the hospital
notes and the scanned copies of each folliculograms will be stored in the
computer located Clinical Records Room for data validation purposes (Table
1)
The number of follicles on Day 8 and Day 10 ultrasound scans will be
recorded according to the size of the follicles 10-16mm and 17mm
Numeric variables for the follicle numbers will be created and used for
assessment of ovarian response in IVF cycles
Data management
Data cleaning and coding
All datasets will be obtained in Excel format and transferred in the
original unaltered condition into Stata 12 data management and statistical
package (Stata 12 StataCorp Texas USA) and all steps of the data cleaning
and the coding will be recorded using Stata Do files to create audit trails of the
data management process Both original Excel and cleaned Stata versions of
data files will be stored in computer that is located in Clinical Records Room at
Reproductive Medicine Department Uniformity of hospital numbers in all
datasets will be achieved by converting a) leading lower case prefixes ldquosrdquo to
upper case ldquoSrdquo b) dropping suffixes ldquozrdquo and ldquoZrdquo and c) dropping all leading
zeros in the second part of the hospital number (eg ldquos1000235Zrdquo
=rdquoS10235rdquo) The coding of the datasets is shown in the Table 2a and the
Table 2b All ambiguous data will be checked using electronic data
management systems (eg CWS Medisec) and hospital notes
Merging the datasets
The datasets will be structured as such that the data files can be used
independently or merged at a) patient or b) IVF cycle levels using the patient
identifier cycle identifier and date variables (Figure 1) This allows analysis of
outcomes of both ldquoFresh IVF cyclesrdquo and study the cumulative outcomes of
Fresh IVF and Frozen Embryo Transfer cycles originating form index IVF
cycles
Each dataset will contain two main patient identifiers and patient
number (Patient ID) which will be used for linking the datasets in a patient
119
level At the initial stages of the data management the hospital numbers will be
used as the main patient identifier The accuracy of the hospital numbers in
each dataset will be validated using PAS dataset by checking patient surname
first name and date of birth
Following methodology will be used to add study numbers into each
dataset First all dataset will be merged in a wide format using the hospital
numbers which creates Master Datasets for each of the research projects Then
an accuracy of the merger will be checked using DOB surname and first name
Once the dataset is validated several copies of the Patient ID variable will be
created and distributed to each dataset Finally the datasets will be separated
and stored as independent datasets alongside Master Datasets for each research
projects
ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo datasets contain cycle specific IVF
reference numbers which were allocated during the clinical episodes on
ACUBase Using IVF reference number new ID variable (Cycle ID) will be
created and allocated to all datasets using closest observation prior to the IVF
cycle in Master Research Dataset Consequently by using cycle reference
number all patient and cycle related data can be linked in the IVF FET cycle
and embryo level
Data security and storage
The encrypted and password protected hospital computer will be used to
process the data until all the patient identifiers have been removed and the
datasets have been anonymised Once the Master Research Datasets are
validated and research team is satisfied with the quality of the data the dataset
will be anonymised by dropping variables for following patient identifiers
hospital number surname first name date of birth and IVF reference number
The study number and the cycle reference numbers will be used as a patient
and a cycle identifiers and only this anonymised dataset will be used for
statistical analysis A copy of non-anonymised dataset will be stored in the
computer located in Clinical Records Room for data verification and a
reference purposes The datasets will be stored within IVF unit for the
duration of the research projects of the MD programme The necessity of
storage of the datasets and measures of data security will be reviewed every
three years thereafter
120
II RESULTS
INTRODUCTION
According to the protocol all women from 20 to 50 years of age referred
to Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust for management of infertility or fertility
preservation and had AMH measurement during the period from 1 September
2008 till 16 November 2011 have been included in the database In total of
4506 patients met the inclusion criteria with 3381 patients in DSL AMH
assay group and 1125 patients Gen II assay group The following datasets
have been extracted from the clinical electronic data management systems
ldquoPASrdquordquo Biochemistryrdquo ldquoSurgeryrdquo ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo Data
extraction from the paper-based hospital records of 3681 patients (n=3130
DSL and n=551 Gen II) were performed by two researchers Dr ORustamov
(n=2801) and Dr M Krishnan (n=880) In addition data collection using
Medisec Digital Dictation Software for the notes that were not located in DSL
group (n=251 patients) was completed by Dr O Rustamov In view of the
issues with validity of Gen II assay measurements which were observed in the
earlier study of the MD Programme (Chapter 2 AMH variability and assay
method comparison) I decided to base subsequent work for the last three
projects (Chapter 5-7) of the MD programme only on DSL assay
measurements and not to include samples based on Gen II AMH Assay
Therefore I decided not to collect data from the hospital notes for the patients
that had AMH measurements using exclusively Gen II Assay where the notes
were not found during the first round of data collection (n=575)
As a result in DSL group all datasets for 3130 patients were completed
and all but AFC and Folliculogram datasets were completed for 251 (Figure 2)
In Gen II group all datasets were completed for 551 patients and all but RH
AFC and Folliculogram datasets were obtained for 575 patients (Figure 2)
As described above the studies of the last three projects (Chapter 5-7)
are based on DSL assay which is no longer in clinical use The review of
literature presented in Chapter 3 suggests that DSL assay appears to have
provided the most reproducible measurements of AMH compared to that of
other assays Therefore AMH measured using DSL assay is perhaps most
121
reliable in terms addressing the research questions In all three chapters
estimates of the effect sizes are provided in percentage terms and therefore the
results are convertible to any AMH assay
Datasets
Demography dataset
The dataset was obtained from Mr Peter Hoyle Senior Data Analyst of
Information Unit CMFT on 16 October 2012 The dataset includes all patients
referred to Reproductive Medicine Department between 1 January 2006 and 31
August 2012 and contains 5573 patients I created a dataset ldquoDemographyrdquo in
Stata format using the steps of data cleaning coding and management as per
protocol The audit trial of the data management was created using Stata Do
file (Figure 1)
Biochemistry dataset
The biochemistry data file was obtained from Dr Alexander Smith
Senior Clinical Scientist Biochemistry Department on 24 January 2011 The
dataset contains the results of all serum AMH FSH LH and E2 samples
conducted from 01 September 2008 to 31 December 2010 The dataset was in
Excel format that consisted of two datasheets 1) Biochemistry 2008-2009 and
2) Biochemistry 2010 The datasheets transferred to Stata 12 in original
unaltered condition and a single Stata ldquoBiochemistryrdquo dataset was created by
combining datasheets by appending them to each other The dataset contains
in total of 78415 blood results of 11574 patients with 6643 AMH 19175 FSH
28677 LH and 23920 E2 results A wide format of the dataset was prepared by
transferring all blood results of each patient to a single row A variable which
indicates valid FSH results was created by coding FSH results as missing if
corresponding E2 levels were higher than 250 pmolL The audit trial of the
data management was created using a Stata Do file
Surgery dataset
Data management was conducted according to the protocol In total
dataset contained 2096 operations in 1787 patients Data on all operations on
122
Fallopian tubes (eg salpingectomy salpingostomy) and ovaries (eg
cystectomy drainage of cyst) at Central Manchester NHS Foundation Trust
from 1 January 2000 to 16 January 2011 are available in the dataset The
dataset will be used to validate the data on history of reproductive surgery of
Reproductive History dataset
AMH dataset
Both AMH datasets were received from Dr Philip Pemberton Senior
Clinical Scientist of the Specialist Assay Laboratory on 13 January 2012 and
transferred to Stata 12 software in the original format All steps of the data
cleaning and the management were recorded using Stata Do file
There were 3381 patients in DSL dataset and 1125 patients in Gen II
dataset Cleaning and coding of the datasets were achieved using the
methodology described in above protocol and new AMH dataset has been
created
IVF dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov on
08 October 2012 and following importing the dataset into Stata 12 in original
format dataset was prepared according to the protocol The dataset contains all
IVFICSI cycles that took place between 01 January 2004 and 01 October
2012 including the cycles of women who acted as egg donors and egg
recipients There were in total of 4323 patients who had 5737 IVFICSI cycles
with 4123 IVFICSI cycles using own eggs 10 embryo storage 40 oocyte
donation 7 oocyte storage 55 oocyte recipient cycles The dataset has
anonymised unique patient (Patient ID) and cycle identifiers (Cycle ID) and
therefore can be linked to all other datasets including all FET cycles and
embryos originated from the index IVF cycle
FET dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov
in Excel format on 20 October 2012 and transferred to Stata 12 Software in
the original condition The data managed as per above protocol and each step
of the process of preparation of the dataset was recorded in Stata Do file The
dataset comprised of all FET cycles (n= 3709) of all women (n=1991)
123
conducted between 01 January 2004 and 01 October 2010 and the Stata
version of ldquoFETrdquo dataset contains complete data on number of thawed
cleaved discarded and research embryos for all patients The dataset contains
unique patient identifier (Patient N) and unique cycle identifiers (Cycle N) and
therefore can be linked to all datasets in patient and cycle levels including index
IVF cycle and embryos
Embryology dataset
The Excel dataset was downloaded from ACUBase by Dr Oybek
Rustamov on 20 October 2012 and transferred into Stata 12 Software in
unaltered condition The data was managed according to the above protocol
The dataset has details of all 65535 (n=4305 women) embryos that were
created between 01 January 2004 and 01 October 2012 The dataset contains
complete data on quantity and the assessment of embryo quality which
includes grading of number evenness and defragmentation of the cells for
each day of culturing of the embryos Furthermore the destination of each
embryo (eg transferred cryopreserved discarded and donated) and the
outcomes of cycles for transferred embryos are available in the dataset Given
that the Embryology dataset has the unique patient as well as the cycle
identifiers this dataset is nested within patients and IVF cycles Consequently
each embryo can be linked to patient index Fresh IVF cycle and subsequent
FET cycles
Reproductive History AFC and Folliculogram datasets
The hospital notes of all patients (n=4506) were searched during the
period of 1 April 2012 to 15 October 2012 for collection of data for
Reproductive history AFC and Folliculogram datasets as per protocol All case
noted filed in the Clinical Records Room the Nurses Room the Doctors
Room and the Secretaries Room of Reproductive Medicine Department were
searched and relevant notes were pulled and searched for data All ultrasound
scan reports containing data on AFC and all IVFICSI folliculograms of
patients were scanned and electronic copy of scanned documents were stored
in the password protected NHS computer located in the Clinical Records
Room
124
The first round of data gathering achieved following result In DSL
dataset there were in total of 3381 patients with 3130 patients had complete
data extraction from their hospital notes and hospital records of 251 patients
were not found There were in total of 1126 patients in Gen II dataset 551 of
whom had complete data extraction from their hospital records and the case
notes of 575 patients were not located (Figure 2) The main reason for
ldquomissing case notesrdquo was found to be the use of hospital records by clinical
laboratory and administrative members of staff at the time of data collection in
patients undergoing investigation and treatment
In the meantime the results of our previous research study indicated that
Gen II samples provide erroneous results (Chapter II) and therefore we
decided to use only data from the patients in DSL group Data on reproductive
history for the remaining patients in the DSL group (n=251) with missing
hospital records were collected using digital clinic letters stored in Medisec
Digital Dictation Software (Medisec Software UK) The data file that
contained combined datasets of reproductive history and AFC was transferred
to Stata 12 in original condition and data management was conducted
according to the protocol All steps of data management was recorded using
Stata do file for audit trail and to ensure reproducibility of the management of
the data Similarly the management of Folliculogram dataset was achieved
using the procedures described in the protocol and all steps of data
management was logged using Stata Do file As result of above data collection
and management I created three Stata datasets ldquoRHrdquo (reproductive history)
ldquoAFCrdquo and ldquoFolliculogramrdquo
Merging Datasets
First the datasets were merged using a unique patient identifier (hospital
number) as per protocol Validation of the merger using additional patient
identifiers (NHS number name date of birth) revealed existence of duplicate
hospital numbers in patients transferred from secondary care infertility services
to IVF Department of Central Manchester University Hospitals NHS
Foundation Trust I established that in the datasets the combination of the
patientrsquos first name surname and date of birth in a single string variable could
be used as a unique identifier Hence I used this identifier to merge all
datasets achieving a robust merger of all independent datasets into combined
125
final Master Datasets for each of the research projects Following the creation
of an anonymised unique patient identifier (Patient ID) for each patient and
anonymised unique cycle identifier (Cycle ID) for each IVF cycle all patient
identifiers (eg surname forename hospital number IVF ref number) were
dropped (Figure 1) The anonymised independent datasets (eg AMH AFC
IVF etc) and anonymised Master Datasets were stored as per protocol
Subsequently these anonymised datasets were used for the statistical analyses
of the research projects The original unanonymised data files were stored in
two password protected NHS hospital computers in the Clinical Records
Room and Doctors Room of Reproductive Medicine Department and
archived according to the Trust policies thereafter Only members of clinical
staff have access to the computers and only nominated clinical members of the
research group who have specific approval can have access to unanomysed
Fully anonymised datasets have been made available to other members of the
research team with the stipulation that the datasets are stored on secure
password protected servers or fully encrypted computers Fully anonymised
datasets may in the future be shared with other researchers following
consideration of the request by the person responsible for the datasets (Dr
Cheryl Fitzgerald) and appropriate ethical and data protection approval
CONCLUSION
Following extraction and management of the data I have built
comprehensive validated datasets which will enable to study ovarian reserve in
a wide context including a) assessment of ovarian reserve b) evaluation of the
performance of ovarian biomarkers c) study individualization of ovarian
stimulation in IVF d) association of the biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryo live birth) The database will be used
to address the research questions posed in the subsequent chapters of this
thesis and beyond that for future studies on the assessment of ovarian reserve
and IVF treatment
126
Figure 1 Data and program files Datasets and programme files created in preparation of the research datasets File names and types are provided in the brackets
127
Table 1a Available vriables The
available identifiers variables and the source of data for following datasets Ethnicity RH AMH AFC Biochemistry OHSS Folliculogram
Datasets
Clinical ID
Study ID
Variables
Source
Demography Hospital N Surname
First name DOB
Patient ID
Ethnicity Information Department
(PAS)
RH
(Reproductive History)
Hospital N Surname
First name DOB
Patient ID
1 Diagnosis Referral Female Referral Male
Clinic Female Clinic Male
Post Cycle 1 Post cycle 2 Post cycle 3
2 Iatrogenic causes of loss of ovarian reserve Ovarian surgery tubal surgery chemotherapy radiotherapy
3 BMI 4 PCOS (PCO oligomenorrhea amenorrhea hirsutism)
Hospital Records
Surgery Hospital N Surname
First name DOB
Patient ID Date
Procedure Date Operator
Information Department
AMH Hospital N Surname
First name DOB
Patient ID Date
Date of sample Date of assay AMH level Assay generation AMH dataset of Specialist Assay
Lab
AFC Hospital N Surname
First name DOB
Patient ID Date
AFC (up to six AFC scans)
Left ovary Right ovary Date of Scan Sonographer Comments (Ovarian cyst hydrosalpynx fibroid poorly visualized etc)
Hospital Records
Biochemistry Hospital N Surname
First name DOB
Patient ID Date
Oestradiol (Date of sample Date of assay serum level) FSH (Date of sample Date of assay serum level)
LH (Date of sample Date of assay serum level)
Biochemistry Electronic
Database
Folliculogram Hospital N Surname
First name DOB
Patient ID Date
Folliculogram (up to 3 cycles) Date (1st day of ovarian stimulation)
Day 8( 10-16mm) Day 8 (gt17mm) Day 10 (10-16mm) Day 8 (gt17mm)
Comments (Day of HCG OHSS Cancellation Ovarian cyst Hydrosalpynx Coasting etc)
Hospital Records
128
Table 1b Available variables The available identifiers variables and the source of data for IVF dataset
Datasets Clinical ID Study Variables Source
IVF Hospital N Surname First name DOB PCT code
Patient ID Cycle ID Date
GENERAL
Attempt Type Protocol DaysStim InitDose Outcome OutcomeDt Age PartnerAge EggCollect TreatDate ETransfer Add_Drug1 Add_Drug2 Add_Drug3 Add_Drug4 Add_Drug5 Add_Drug6 Add_Drug7 EGG RECOVERY SNumber Follicles TotEgg EggNumber
FERTILISATION IVFEgg IVFCleaved ICSICleaved Cleaved PN2 IVFPN2 ICSI2PN ICSICl ICSIEgg ICSIFPN IVFFPN IVFTransfer ICSITransfer IVFLysed ICSILysed IVFMetII IVFMetI IVFAtretic IVFAbnormal IVFEmptyZona IVFG_Vesicle ICSIMetII ICSIMetI ICSIAtretic ICSIAbnormal ICSIEmptyZona ICSIG_Vesicle
OUTCOME
sacs Hearts Preg ICSIPract STORAGE Frozen IVFFroz ICSIFroz SpermSource SortKeySTAR HISTORY cat_tubal cat_OvFail cat_UtProb cat_unex cat_ MF cat_Meno cat_Genetic cat_endo cat_anov cat_noMale Inf_Since MaleInf
CoupleInf Preg24Wk MiscTOP Ectopic LiveBirth FSH AMH Emb_Recip Surrogate Sperm_Recip StoreEggs EggThaw Treat_Reason IgnoreKPI EMBRYOLOGY
D1LteClCells1 D1LteClCells2 D2Cells2 D2Cells3 D2Cells4 D2Even2 D2Even3 D2Even4 D2Frag2 D2Frag3 D2Frag
SPERM Conc_Init MotA MotB Conc_ Prep MotAP MotBP SemenSource SemenAnalysis STIMULATION BMI TotDose GonadUsed Incubator ICSIRigg AMHBand DHEA EGG
Egg_Recip Own_Eggs Altruistic_D
ACUBASE Electronic Database
129
Table 1c Available variables
The available identifiers variables and the source of the data for FET and Embryo datasets
Datasets Clinical ID Study ID
Variables
Source
FER
Hospital N Surname First name
Patient ID Cycle ID Date
GENERAL treatdate transfer ETDate
OUTCOME preg IUP Outcome OutcomeDt
EMBRYOLOGY
Thawed Survived Cleaved Discarded Research
STORAGE NumStored DtCreated
CLINICIAN ETClinician ETEmbryologist OrigCycle
ACUBASE Electronic Database
Embryo
Hospital N Surname First name DOB
Patient ID Cycle ID Date
GENERAL TreatDate Injected Destination
CELLS CellsD1 CellsD2_AM CellsD2_PM CellsD3_AM CellsD3_PM
EVENNES EvenD2_AM EvenD2_PM EvenD3_AM EvenD3_PM
FRAGMENT FragD1 FragD2_AM FragD2_PM FragD3_AM FragD3_PM
OUTCOMES ICSIPract Maturity PosPreg Hearts SpermSource Age
ACUBASE Electronic Database
130
Table 2a Coding
The codes used to convert ethnicity and diagnosis variables from string to numeric format in PAS and RH datasets
131
Table 2b Coding
The codes used to convert treatment outcomes from string to numeric format in IVF and FET datasets
Datasets Codes for outcomes
IVF
FET
ldquoBiochemical Pregnancyrdquo=1 ldquoCancel (other)rdquo=2
ldquoCancel Hyperstimulationrdquo=3 ldquoCancel Poor responserdquo=4
ldquoCancelled no sperm on day of ECrdquo=5 ldquoCONVERTED IVF TO IUIrdquo=6
ldquoDelayed Miscarriagerdquo=7 ldquoDonatedrdquo=8 ldquoEctopicrdquo=9
ldquoEgg donationrdquo=10 ldquoEmbryos for storagerdquo=11
ldquoEmpty Sacrdquo=12 ldquoFailed Fertilisationrdquo=13
ldquoFor donationrdquo=14 ldquoFreeze Allrdquo=15
ldquoFreeze All (OHSS)rdquo=16 ldquoFreeze All (Other)rdquo=17
ldquoLate Miscarriagerdquo=18 ldquolost to contactrdquo=19
ldquolost to follow uprdquo=19 ldquoNo Eggsrdquo=20
ldquoNo Spermrdquo=21 ldquoNo Normal Embryosrdquo=22
ldquoNot Pregnantrdquo=23 ldquoOngoing Singletonrdquo=24
ldquoOngoing Twinrdquo=25 ldquoPositive hCGrdquo=26
ldquoSingleton Birth=27rdquo ldquoTwin Birthrdquo=28
ldquoTriplet Birthrdquo=29 ldquoStill Birthrdquo=30The
132
Figure 2 Data collection from hospital records
Completeness of data collection from hospital records for RH AFC and Folliculogram datasets
All
patients
DSL
(n=3381)
All Datasets
Complete
n=3130
AFC and Folliculogram
not complete
n=251
Gen II
(n=1126)
All Datasets
Complete
n=551
RH AFC Follicologram
not complete
n=575
133
Table 3 Results Datasets and observation
Summary of the number of patients observations IVFFET cycles and data entry period for all datasets
Datasets Patients Observations Cycles Period
AMH DSL 3381Gen II 1126
DSL-3913 DSL 01 Sep 2008-15 Nov 2010 Gen II 16 Nov 2010-16 Nov 2011
Demography 5573 01 Jan 2006-31 Aug 2012
Biochemistry 11754 Total 78415 6643-AMH 19175-FSH 28677-LH 23920-E2
01 Sep 2008-31 Dec 2010
RH DSL-3381 DSL-3381 01 Sep 2008-01 Oct 2012
Surgery 1787
2096 01 Jan 2000-16 Nov 2011
AFC DSL 2411 DSL Total 4174 Single measurement2411 Repeats 2-1250 3-370 4-105 5-25 6-7 7-1
01 Sep 2008-01 Oct 2012
Folliculogram 1736 2183
01 Sep 2008-01 Oct 2012
IVFICSI 4324 - Total 5737 own eggs-4123 oocyte recipients-55 oocyte donors-40 Embryo storage-10 oocyte storage-7
01 Jan 2004-01 Oct 2012
FET 1991 - 3709
01 Jan 2004-01 Oct 2012
Embryology
4305 65535 embryos - 01 Jan 2004-01 Oct 2012
134
Figure 3 Merging datasets
The process of merging datasets in patient and cycle levels using patient date and cycle IDs
135
ASSESSMENT OF DETERMINANTS OF
ANTI-MUumlLLERIAN HORMONE IN INFERTILE
WOMEN
5
136
THE EFFECT OF ETHNICITY BMI
ENDOMETRIOSIS AND THE CAUSES OF
INFERTILITY ON OVARIAN RESERVE
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
To be submitted to Fertility and Sterility
51
137
Title
The effect of ethnicity BMI endometriosis and the causes of infertility
on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK c Centre for Biostatistics
Institute of Population Health Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Word count 4715
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
138
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all version
of the manuscript MK assisted in data extraction contributed in discussion
and the review of the manuscript SR and CF oversaw and supervised
preparation of dataset statistical analysis contributed in discussion and
reviewed all versions of the manuscript
139
ABSTRACT
Objective
To estimate the effect of ethnicity BMI endometriosis and the causes of
infertility on ovarian reserve
Design Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 2946 patients were included in the study of which 65 did not have
data on ethnicity leaving 2881 women in the sample
Interventions Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH serum basal FSH and basal AFC measurements
Results
Multivariable regression excluding BMI showed that woman of Black ethnicity
and the group defined as ldquoOther ethnicityrdquo had significantly lower AMH
measurements when compared to that of White (-25 p=0013 and -19
p=0047) and overall ethnicity was a significant predictor of AMH (p=0007)
However inclusion of BMI in the model reduced these effects and the overall
effect of ethnicity did not reach statistical significance (p=008) AFC was
significantly reduced in Pakistani and women of ldquoOther ethnicitiesrdquo although
the effect sizes were small (10-14) and the overall effect of ethnicity was
significant in both models (p=004 and p=003) None of the groups showed a
statistically significant difference in FSH although women of ldquoOther Asianrdquo
ethnicity appear to have lower FSH measurements (12) which was close to
statistical significance (-12 p=007)
140
Obese women had higher AMH measurements (16 p=0035) compared to
that with normal BMI and the overall effect of the BMI was significant
(p=003) In the analysis of the effect of BMI to AFC measurements we did
not observe differences that were statistically significant However FSH results
showed that there is a modest association between BMI and FSH with both
overweight and obese women having significantly lower FSH measurements
compared to lean women (-5 p=0003 and -10 p=0003)
In the absence of endometrioma endometriosis was associated with lower
AMH measurements although this did not reach statistical significance
Neither AFC nor FSH was significantly different in the endometriosis group
compared to those without endometriosis In contrast we observed around
31 higher AMH levels in the patients with at least one endometrioma
(p=0034) although this did not reach statistical significance (21 p=01) in
the smaller subset after adjustment for BMI AFC and FSH did not show any
statistically significant association with endometrioma
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to the ones who did not have
unexplained infertility except the analysis that did not include BMI as a
covariate which found a weakly positive correlation (10 p=003) Similarly
the estimation of the effect of the diagnosis of unexplained infertility to AFC
as well as FSH showed that there were weak association between the markers
and diagnosis of unexplained infertility
There was no significant difference in AMH AFC and FSH measurements of
women with mild and severe tubal infertility in the models which included all
covariates except the analysis of FSH and mild tubal factor where we found
weakly negative correlation between these variables
Women diagnosed with male factor infertility had significantly higher AMH
and lower FSH measurements the effect sizes of which were directly
proportional to the severity of the diagnosis In the analysis of AFC we did not
found significant difference in the measurements between patients with male
factor infertility and to that of non-male factor
141
Conclusions
Ethnicity does not appear to play a major role in determination of ovarian
reserve as measured by AMH AFC and FSH whereas there is a significant
positive association with BMI and these markers of ovarian reserve Women
with endometriosis appear to have lower AMH whilst patients with
endometrioma have significantly higher AMH and lower FSH measurements
The study showed that the association between markers of ovarian reserve and
unexplained infertility as well as tubal disease is weak In contrast women
diagnosed with male factor infertility have higher ovarian reserve
Key Words
Ovarian reserve AMH AFC FSH ethnicity BMI infertility endometriosis
endometrioma
142
INTRODUCTION
The ovarian reserve consists of a total number of resting primordial and
growing oocytes which appears to be determined by the initial oocyte pool at
birth and the age-related decline in the oocyte number (Hansen et al 2008
Wallace and Kelsey 2010) Both of these factors appear to be largely
predetermined genetically although certain environmental socioeconomic and
medical factors likely to play a role in the rate of the decline (Schuh-Huerta et
al 2012b Kim et al 2013 Dolleman et al 2013) The understanding of the
formation and the loss of ovarian reserve have been improved greatly due to
recently published data on the histological assessment of ovarian reserve
(Hansen et al 2008) Furthermore the use of the biomarkers has enabled the
evaluation of ovarian reserve in larger population-based samples Biomarkers
such as AMH and AFC can only assess the measurement of growing pre-antral
and early antral follicle activity However some studies suggest that there is a
close correlation between the measurements of these markers and the number
of resting primordial follicles (Hansen et al 2011)
Studies on age related decline of AMH and AFC have played important
roles in understanding the decline of ovarian reserve although most of the
data have been derived from heterogeneous population without full account
for characteristics of individual patients (Nelson et al 2011 Seifer et al 2011
Shebl et al 2011) These studies have demonstrated that there is a significant
between-subject variation in ovarian reserve beyond that due to chronological
age (Kelsey et al 2011) More recent studies reported interesting findings on
the role of demographic anthropometric and clinical factors in the
determination of ovarian reserve Although these studies have employed
better-described samples some have small sample sizes and lack power for the
estimation of the effect of these factors Consequently studies on large and
well-characterised populations are necessary for evaluation of the determinants
of ovarian aging as well as to provide normative data for the individualisation
of the assessment of ovarian reserve
There have been reports of measurable disparities in the reproductive
aging and reproductive endocrinology between various ethnicities For
instance according to a large prospective study White Black and Hispanic
women reported higher rates of premature ovarian failure compared to
143
Chinese and Japanese (Luborsky et al 2002) In contrast the prevalence of
PCOS which is associated with higher ovarian reserve has been reported to be
significantly lower in Chinese (22) compared to British (8) women
(Michelmore et al 1999 Chen et al 2002) Although these disparities may
partially be due to the differences in the local diagnostic criteria it is plausible
to believe that the ethnicity may play a role in the determination of the
reproductive aging With regard to the effect of ethnicity to the markers of
ovarian reserve Seifer et al found that African American and Hispanic women
have lower AMH levels compared to White (Seifer et al 2009) In contrast
Randolph et al reported that African American women had significantly higher
ovarian reserve compared to that of White when determined by FSH
measurements (Randolph et al 2003) These studies indicate that ethnicity may
play a role in the determination of ovarian reserve and therefore warrants
further investigation which should include other major ethnic groups
Body weight appears to be closely associated with human female
reproduction which is evident by its effect on the natural fecundity as well as
the success of the assisted conception treatment cycles (Maheshwari et al
2007) Indeed the relationship of increased body mass index (BMI) and PCOS
is well described although the mechanism of this is not yet fully understood
Consequently a number of recent studies have assessed the effect of BMI to
the various aspects of reproductive endocrinology including ovarian reserve
Studies on the influence of BMI on the markers of ovarian reserve have
provided conflicting results probably due to the limited statistical power in
most of these studies and the difficulties encountered in properly accounting
for confounding factors such as age ethnicity and medical diagnosis (Buyuk et
al 2011 Freeman et al 2007 Su et al 2008 Seifer et al 2008 Sahmay et al 2012
Skalba et al 2011) Therefore there is a need for studies with large datasets and
good adjustment for confounding factors
We therefore designed and undertook a study to estimate the effect of
ethnicity BMI endometriosis and causes of infertility on ovarian reserve as
measured by AMH AFC and FSH using a robust dataset from a large cohort
of patients referred for infertility investigation and treatment in a single centre
144
METHODS
Objectives
The objectives of the study were to assess the role of the ethnicity BMI
and endometriosis and the causes of infertility on ovarian reserve as assessed
by the biomarkers AMH AFC and FSH using a large clinical data obtained
retrospectively
Sample
All women between 20 to 45 years of age referred to the Womenrsquos
Outpatient Department (WOP) and the Reproductive Medicine Department
(RMD) of Central Manchester University Hospitals NHS Foundation Trust for
management of infertility from 1 September 2008 to 16 November 2010 and
who had the measurement of AMH using DSL assay (DSL Active MISAMH
ELISA Diagnostic Systems Laboratories Webster Texas) were included in
this study Patients referred for fertility preservation (eg prior to or after the
treatment of a malignant disorder) and patients with a history of tubal or
ovarian surgery (salpingectomy ovarian cystectomy salpingo-oopherectomy)
and patients diagnosed with polycystic ovaries on ultrasound were excluded
The sample size was determined on pragmatic grounds and represents all
available patients meeting the inclusion criteria
Measurement of AMH
All patients referred to RMD had a measurement of AMH prior to
management of their infertility whereas the patients seen at WOP had AMH
measurements if they had a clinical indication for an assessment of ovarian
reserve
Blood samples for the measurement of AMH were taken at an initial
patient visit without regard to the day of the menstrual cycle and transported
to the in-house Biochemistry Laboratory All samples were processed and
analysed strictly according to the assay kit insert provided by the manufacturer
Serum samples were separated within two hours from venipuncture and frozen
at -20C until analysed in batches using the enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
145
100pmolL with a minimum detection limit of 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first
measurement was selected for this study
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the in-house
Biochemistry Laboratory within two hours of venipuncture for sample
processing and analysis Serum FSH levels were measured using specific
immunoassay kits (Cobas Roche Diagnostics Mannheim Germany) for use
on an autoanalyser platform (Roche Modular Analytics E170 Roche USA)
The intra-assay and inter-assay CVs were 60 and 68 respectively FSH
measurements in samples with high E2 levels (gt250) were defined as non-
representative of early follicular phase and were not included in this study
Where patients had repeated FSH measurements the measurement with the
closest date to that of AMH measurement was used
Measurement of AFC
Measurement of AFC was conducted in all patients undergoing assisted
conception The department uses a stringent protocol for the assessment of
AFC which consists of counting all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
Fully qualified sonographers conducted the ultrasound assessments Where
patients had repeated AFC measurements the AFC closest to the date of the
AMH measurement was used
Data collection
Data was extracted from hospital electronic clinical data management
systems and from written hospital notes of each patient AMH and FSH
measurements were obtained from the Biochemistry Department of the
hospital and validated by checking results of randomly selected 50 patients
146
against the results available in electronic clinical data management system
(Clinical Workstation) Data on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
surgery were gathered from the hospital case notes Data on the ethnicity was
obtained from the hospitalrsquos administrative database (PAS) The datasets were
merged using a unique patient identifier (hospital number) and the validity of
the linkage was validated using other patient identifiers (NHS number
patientrsquos name and date of birth)
Definitions and groups
In our hospital the ethnicity of the patient is established using a patient
questionnaire based on the UK census classification The body mass index
(BMI) of patients was categorised using NHS UK cut-off reference ranges
Underweight (lt185) Normal (185-249) Overweight (25-299) and Obese
(30-40) Causes of infertility were established by searching hospital records
including referral letters clinical entries and the letters generated following
initial and follow up clinic consultations Patients with a history of bilateral
tubal block which was confirmed by laparoscopy and dye test and patients
with a history of bilateral salpingectomy were categorised as having severe
tubal factor infertility Patients with unilateral tubal patency or unilateral
salpingectomy were categorised as having mild tubal factor infertility Patientrsquos
with laparoscopic diagnosis of stage III and Stage IV endometriosis (AFS)
were categorised as diagnosed with severe endometriosis whilst patients with
Stage I and Stage II endometriosis were allocated to group of mild
endometriosis Severe male factor infertility was defined as azoospermia or
severe oligospermia which necessitated Multiple Ejaculation Resuspension and
Centrifugation test (MERC) for assisted conception The criteria for MERC
were a) sperm count of lt05 mlnml or b) retrograde ejaculation Patients with
abnormal sperm count but who did not meet above criteria were classified as
mild male factor infertility
Statistical analysis
Firstly univariate analyses of the effect of age ethnicity BMI
endometriosis with and without endometrioma causes of infertility and
duration of infertility were conducted using two sample t test Then a
147
multivariate linear regression model that included age ethnicity BMI
endometriosis presence of endometrioma and the causes of infertility was
specified for the analyses of the effect of these factors to AMH AFC and
FSH Logarithmically transformed values were used for the statistical analysis
of AMH AFC and FSH The precise age on the day measurement of each of
the marker of ovarian reserve (AMH AFC and FSH) was used and age
adjustment utilised a quadratic function following centring to 30 years of age
Differences between the groups were considered significant at p005
Interactions between all explanatory variables were tested at a significance level
of plt001 In order to estimate the effect of BMI we fitted two different
models with a) BMI not included and b) BMI included in the model
Duration of infertility did not show any clinical or statistically significant
differences for any of the markers and therefore this variable was not included
in the models
RESULTS
In total 2946 patients were included in the study of whom 2880 of these
patient had valid AMH measurements 1810 had measurement of AFC and
2377 had FSH samples The mean and median age of patients were 328 (45)
and 332 (295 365) respectively and the distribution of patients according to
age categories ethnicity BMI endometriosis and the causes of infertility is
shown in the Table 1 The summary statistics for the markers of ovarian
reserve were as follows AMH mean 175 (501) median 142 (76-232) AFC
mean 139 (63) median 13 (10-17) and FSH mean 79 (72) median 7 (58-85)
As expected chronological age was found to be a significant determinant of all
markers of ovarian reserve We observed in average 5 decline in AMH levels
2 decline in AFC and 1 increase in FSH measurements per year (Table 2-
4)
Out of 2946 patients 2021 had data on BMI measurements and in 925
BMI was not available Table 5 describes age AMH AFC and FSH according
to the availability of data on BMI Distribution of patients by their ethnicity
and an availability of data on BMI is provided in Table 6 Similarly patient
distribution by diagnosis and availability of data on BMI can be found in Table
7
148
Ethnicity
The multivariable regression excluding BMI (Table 2) showed that
woman of Black ethnicity and the group defined as ldquoOther ethnicityrdquo had
significantly lower AMH measurements when compared to that of White (-25
p=0013 and -19 p=0047) and the overall ethnicity was a significant
predictor of AMH (p=0007) However inclusion of BMI in the model
reduced these effects and none of the groups had a statistically significant
difference in AMH levels compared to that of White and the overall effect of
ethnicity did not reach statistical significance (p=008)
AFC was significantly reduced in Pakistani and women of ldquoOther
ethnicitiesrdquo (Table 3) although the effect sizes were small (10-14) and the
overall effect of ethnicity was significant in the models with and without BMI
(p=004 and p=003) None of the groups showed statistically significant
differences in FSH (Table 4) although women of ldquoOther Asianrdquo ethnicity
appear to have lower FSH measurements (12) which was close to the level of
statistical significance (-12 p=007)
BMI
Obese women had 16 higher measurements of AMH (p=0035) and
overall effect of the BMI was significant (p=003) No interaction were
detected between BMI and ethnicity causes of infertility or diagnosis of
endometriosis suggesting that effect of BMI was independent of these factors
(Table 2)
In the analysis of the effect of BMI on AFC measurements we did not
observe any differences that were statistically significant (Table 3) However
FSH results showed that there is a modest association between BMI and FSH
with both overweight (Table 4) and obese women having significantly lower
FSH measurements compared to lean women (-5 p=0003 and -10
p=0003)
Endometriosis
In the absence of endometrioma endometriosis was associated with
lower AMH measurements although this did not reach statistical significance
149
(Table 2) Neither AFC nor FSH was significantly different in the
endometriosis group compared to those without endometriosis (Table 3-4)
In contrast we observed around 31 higher AMH levels in the patients
with endometrioma (p=0034) although this reduced to 21 and did not reach
statistical significance (p=010) in the smaller subset after adjustment for BMI
(Table 2) AFC and FSH did not show any statistically significant association
with endometrioma (Table 3-4)
Causes of Infertility
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to those with diagnosis
except the analysis that did not include BMI as a covariate which found a
weakly positive correlation (10 p=003) Similarly the estimation of the
effect of a diagnosis of unexplained infertility on AFC as well as FSH showed
that there were weak association between the markers and a diagnosis of
unexplained infertility (Table 2-4)
There were no significant differences in AMH AFC and FSH in women
with mild and severe tubal infertility in the models which included all
covariates other than weakly negative correlation between FSH and mild tubal
factor (Table 2-4)
Women diagnosed with male factor infertility had significantly higher
AMH and lower FSH measurements the effect sizes of which increased with
the severity of the diagnosis We did not find any significant difference in AFC
between patients with and without male factor infertility (Table 2-4)
DISCUSSION
This is first study investigating the effect of demographic
anthropometric and clinical factors on all three markers of ovarian reserve
using a large cohort of women of reproductive age Furthermore the statistical
analysis adjusted for relevant covariables using multivariable linear regression
models
150
Ethnicity
Our study found that amongst the main British ethnic groups the
effect of ethnicity on ovarian reserve measured using AMH AFC and FSH is
fairly weak and can be accounted for by differences in BMI between the
ethnic groups Recently studies have been published on the relationship of
ethnicity and markers of ovarian reserve all of which compared North
American populations One study which assessed a relatively small number of
women (n=102) at late reproductive age did not find a difference in AMH
levels between White and African American Women OR 123 (056 271
P=070) (Freeman et al 2007) In contrast Seifer et al reported that Black
(n=462) women had around 25 lower AMH measurements (P=0037)
compared to that of White (n=122) (Seifer et al 2009) which is not consistent
with our findings The main differences of this study compared to our study
were a) a majority were HIV infected women b) women were older (median
375 years of age) c) the analysis did not control for possible confounders
related to PCO reproductive pathology and surgery Furthermore unlike our
results the study did not find a correlation between BMI and AMH levels
Similarly Shuh-Huerta and colleagues reported that African American women
(n=200) had significantly lower AMH levels (P=000074) compared to that of
White (n=232) Mean AMH 22817 pmolL and 301+15 pmolL
respectively (Shuh-Huerta et al 2012b) Although the group used very stringent
selection of patients and statistical analysis BMI was not included in the
regression model Indeed our analysis without BMI in the model found similar
results (Table 2) But controlling for BMI has revealed no significant difference
in the AMH levels between White and Black ethnic groups
With regard to AFC measurements Shuh Huerta et al reported no
difference in the follicle counts between White (n=245) and African American
(n=202) women which supports our findings (Shuh-Huerta et al 2012b)
Again similar to our results the authors reported that FSH results of these
ethnic groups provided comparable results (Shuh-Huerta et al 2012a)
Although our results do not support some of previously published data
in view of above arguments we believe the ethnicity does not appear to play a
major role in determination of ovarian reserve However in view of the
discrepant findings of the currently available studies we suggest further studies
151
in large and diverse cohorts should be carried out in order to fully understand
the role of ethnicity
BMI
Our results show that BMI has direct correlation with AMH and AFC
and negative correlation with FSH suggesting women with increased BMI are
likely to have higher ovarian reserve The effect of this association was
significant in the analysis of AMH and FSH obese women appear to have
approximately 16 higher AMH and 10 lower FSH measurements when
compared to women with normal BMI Although the difference in AFC
measurements did not reach statistical significance there was direct correlation
between AFC and BMI
Published data on the effect of BMI to AMH levels provide conflicting
results compared to our study given the studies reported either no association
(Buyuk et al 2011 Freeman et al 2007 Su et al 2008) or a negative correlation
between these factors (Seifer et al 2008 Sahmay et al 2012 Skalba et al 2011)
However most of these studies assessed peremenopausal women or that of
late reproductive age Indeed the studies evaluated the effect of BMI to AMH
measurements in women of reproductive age demonstrated that lower AMH
levels in obese women were due to age rather than increased BMI (La Marca
et al 2012 Streuli et al 2012) Furthermore most of these studies either
employed univariate analysis or multivariate regression models that did not
included all relevant explanatory factors In addition these studies had
significantly smaller numbers of samples ranging from 10 to 809 compared to
our study (n=1953) Indeed other large study (n=3302) with multivariate
analysis supports our findings on the effect of BMI on ovarian reserve
indicating obese women have significantly lower FSH levels (Randolph et al
2004)
Endometriosis
Here we present data on the measurement of all three main markers of
ovarian reserve in women with endometriosis We observed that women with
endometriosis without endometrioma did not have significantly different
AMH AFC or FSH measurements compared to women that do not have this
pathology Intriguingly women who had endometriosis with endometriomata
152
tended to have higher AMH levels Given the data was collected
retrospectively we did not have full information on laparoscopic staging of
endometriosis in all patients and therefore an analysis according to severity or
staging of endometriosis was not feasible However the analysis controlled for
the important variables mentioned above and importantly only included the
patients without previous history of ovarian surgery We therefore we believe
the study provides fairly robust data on relationship of endometriosis and the
markers of ovarian reserve
Although it is generally believed that endometriosis has a damaging
effect on ovarian reserve published literature provides conflicting views
ranging from no correlation between these factors to a significant negative
effect of endometriosis As mentioned above most studies were small and
used matched comparison of patients with endometriosis to control group
using retrospectively collected data Carvalho et al compared women with
endometriosis (n=27) and to that of male factor infertility (n=50) and reported
there was no difference in basal AMH and AFC levels whilst FSH levels of
women with endometriosis was lower Another small study which used similar
methodology where an endometriosis group (n=17) was compared to patients
with tubal factor infertility (n=17) reported opposite results suggesting
endometriosis was associated with lower AMH measurements and there was
no correlation between the pathology and FSH or AFC (Lemos et al 2007)
Shebl et al compared AMH results of women with endometriosis (n=153) to a
matched group that did not have the pathology (n=306) and reported that
women with mild endometriosis had similar AMH levels whereas the group
with severe endometriosis had significantly lower AMH compared to the
control group (Shebl et al 2009) Although using well-matched control groups
is a robust study design direct comparison of the two groups without
controlling for other important covariables may result in inaccurate results
Indeed the study that used multivariate regression analysis was able to
demonstrate that there are number of factors that can affect AMH results and
indeed following controlling for these factors there was no difference between
AMH results of women with endometriosis compared to that of without
disease (Streuli et al 2012) In view of above considerations we believe the
effect of endometriosis to ovarian reserve is poorly understood and warrants
further investigation
153
Regarding the effect of endometrioma on AMH levels current evidence
is conflicting Using univariate analysis without controlling for confounders
Uncu et al reported that women with endometrioma (n=30) had significantly
lower AMH and AFC measurements compared to control (n=30) women
(Uncu et al 2013) Similarly Hwu et al reported that women with
endometrioma (n=141) had significantly lower AMH measurements compared
to that of without pathology (n=1323) pathology (Hwu et al 2013) However
the study population appears to have a disproportionately higher number of
women with history of previous and current history of endometrioma
(3191642) compared to any published studies and therefore the study may
have been subject of selection bias
Kim et al reported lower AMH measurements in women with
endometrioma (n=102) compared to control group (102) meanplusmnSEM
29plusmn03 ngmL_vs 33plusmn03_ngmL although this did not reach statistical
significance (P=028)
In our view the most robust data on measurement of AMH in women
with endometriosis was published by Streuli et al which compared AMH levels
of 313 women with laparoscopically and histologically confirmed
endometriosis to 413 women without pathology (Streuli et al 2009) The group
with endometriosis consisted of women with superficial peritoneal
endometriosis (n=35) deep infiltrating endometriosis (n=183) and ovarian
endometrioma (n=95) and relevant factors such as age parity smoking and
previous ovarian surgery were adjusted for using multivariate regression
analysis In keeping with our findings women with endometriosis did not have
lower AMH levels except for patients with previous history of surgery for
endometrioma Most interestingly the findings of Streuili et al and this study
suggest that women with ovarian endometrioma do not have low AMH levels
In contrast according to our data the presence of endometrioma may be
associated with a significant increase in serum AMH levels Given that an
endometrioma is believed to cause significant damage to ovarian stroma this is
an interesting finding Increased AMH levels in the presence of endometrioma
may be due to acceleration in the rate of recruitment of primordial follicles
andor increased expression of AMH in granulosa cells Furthermore
increased AMH levels in these patients may be due to expressions of AMH in
endometriotic cells A study by Wang et al suggested that AMH is secreted by
human endometrial cells in-vitro (Wang et al 2009) This was the first report of
154
non-ovarian secretion of AMH and suggested that AMH may play important
role in regulation of the function of the human endometrium Subsequently
the findings of Wang et al were independently confirmed by two different
groups Carrarelli et al assessed expression of AMH and AMH type II receptor
(AMHRII) in specimens of endometrium obtained by hysteroscopy from
patients with endometriosis (n=55) and from healthy (n=45) controls
(Carrarelli et al 2014) The study also assessed specimens from patients with
ovarian endometriosis (n=29) and deep peritoneal endometriosis (n=26) The
study found that both AMH and AMHRII were expressed in endometrium
Interestingly ectopic endometrium obtained from patients with endometriosis
had significantly higher AMH and AMHRII levels compared to that of healthy
individuals Furthermore the specimens collected from ovarian and deep
endometriosis had highest AMH and AMHII mRNA expression These
findings confirm that AMH as well as AMHRII are expressed in human
endometrium and AMH may play a role in pathophysiology of endometriosis
A further study conducted by Signorile et al also confirmed expression of
AMH and AMHRII in human endometriosis glands Furthermore the study
found that treatment of endometriosis cells with AMH resulted in a decrease in
cell growth suggesting that AMH may inhibit the growth of endometriotic
cells This suggests that further studies to understand the role of AMH in
pathophysiology of endometriosis are warranted
Causes of infertility
Unlike the above-mentioned factors the association of the various
causes of infertility and the markers of ovarian reserve are poorly studied
Therefore our study appears to provide only available data on AMH AFC and
FSH levels in women with three main causes of infertility unexplained tubal
and male factor
In our study AMH levels of women with unexplained infertility did not
differ from those with a diagnosis Similarly the effect of a diagnosis on AFC
and FSH measurements were weak Women with unexplained infertility do not
have any significant pathology that can account for their infertility However
understanding the role of ovarian reserve in these patients is important Our
study suggests that women with unexplained infertility have comparable AMH
levels to other infertile women
155
We did not find any significant differences in AMH AFC or FSH
measurements of women diagnosed with tubal factor infertility compared to
infertile women without tubal disease Pelvic inflammatory disease and
endometriosis are well known causes of tubal pathology and our regression
model has controlled for the effect of endometriosis in these analyses Our
results suggest that despite having damaging effect to the tubes pelvic
infection does not reduce ovarian reserve
In contrast our analyses showed that women with mild and severe male
factor infertility have significantly increased AMH and lower FSH
measurements which demonstrates that these women have better ovarian
reserve compared to general infertility population
Strengths and Limitations of the study
The study is based on retrospectively collected data and therefore was
subject to the issues related to this methodology However we believe that we
have overcome most problems and improved the validity of our results by
using a robust methodology for data collection large sample size and careful
analysis We included all women who presented during the study period and
met the inclusion criteria of the study Importantly women with previous
history of PCO chemotherapy radiotherapy tubal surgery or ovarian surgery
have been excluded from the study given these factors may have significant
acute impact on ovarian reserve effect of which may be difficult to control for
The analysis showed an interaction between BMI and ethnicity which
could not be explored fully due to missing data on BMI (Tables 6) Therefore
analyses with and without BMI in models have been performed (Tables 2-4)
and the distribution of patients according to availability of data on BMI has
been obtained (Tables 5-7) I suggest further studies with sufficient data should
explore this interaction
I was not able to establish the patients that meet Rotterdam criteria for
diagnosis of PCOS given data on menstrual history and biochemical
assessment of androgenemia were not available Therefore ultrasound
diagnosis of PCO was used to categories patients with polycystic ovaries and
all patients with PCO were excluded from analysis
It is important to note that measurement of AMH using Gen II assay may
provide erroneous results (Rustamov et al 2012a) Therefore only samples
156
obtained using DSL assay have been included in the study The DSL assay
appears to provide more reproducible results than the Gen II assay (Rustamov
et al 2011 and Rustamov et al 2012a) and therefore we believe the estimates
in this study reflect the relationship between circulating AMH and the above
factors
SUMMARY
Our data suggests that there is no strong association between ethnicity
and AMH AFC or FSH whilst women with increased BMI appear to have
higher ovarian reserve There was no evidence of reduced ovarian reserve in
women with endometriosis who do not have a previous history of ovarian
surgery In contrast women with current history of endometrioma may have
higher AMH levels which warrants further investigation Women with a
history of unexplained infertility do not appear to have reduced ovarian
reserve as measured with AMH AFC and FSH compared to general infertile
women Similarly women with tubal factor infertility have comparable ovarian
reserve with women who do not have tubal disease In contrast women with
male factor infertility have significantly higher ovarian reserve compared to
infertile women who do not have male factor infertility
This study has elucidated the effect of demographic anthropometric and
clinical factors on all commonly used markers of ovarian reserve and
demonstrated that some of these factors have significant impact on ovarian
reserve
157
References Buyuk E Seifer DB Illions E Grazi RV and Lieman H Elevated body mass index is associated with lower serum anti-mullerian hormone levels in infertile women with diminished ovarian reserve but not with normal ovarian reserve Fertility and Sterility_ Vol 95 No 7 June 2011 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 2014 1011353ndash8 de Carvalho BR Rosa-e-Silva AC Rosa-e-Silva JC dos Reis RM Ferriani RA de Saacute MFIncreased basal FSH levels as predictors of low-quality follicles in infertile women with endometriosis International Journal of Gynecology and Obstetrics 110 (2010) 208ndash212 Doacutelleman M Verschuren W M M Eijkemans M J C Dolle M E T Jansen E H J M Broekmans F J M and van der Schouw Y T Reproductive and Lifestyle Determinants of Anti-Mullerian Hormone in a Large Population-based Study J Clin Endocrinol Metab May 2013 98(5) 2106ndash2115 Freeman EW Gracia CR Sammel MD Lin H Lim LC Strauss JF 3rd Association of anti-mullerian hormone levels with obesity in late reproductive-age women Fertil Steril 2007 87101-6 Halawaty S ElKattan E Azab H ElGhamry N Al-Inany H Effect of obesity on parameters of ovarian reserve in premenopausal women J Obstet Gynaecol Can 2010 32687ndash690 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699-708 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 2011 95170ndash5 Hwu Y Wu FS Li S Sun F Lin M and Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reproductive Biology and Endocrinology 2011 980 Kelsey TW Wright P Nelson SM Anderson RA Wallace WHB (2011) A Validated Model of Serum Anti-Muumlllerian Hormone from Conception to Menopause PLoS ONE 6(7) e22024 Kim MJ Byung Chul Jee Chang Suk Suh and Kim SH Preoperative Serum Anti-Mullerian Hormone Level in Women with Ovarian Endometrioma and Mature Cystic Teratoma Yonsei Med J Volume 54 Number 4 July 2013 La Marca A Sighinolfi G Papaleo E Cagnacci A Volpe A et al (2013) Prediction of Age at Menopause from Assessment of Ovarian Reserve May Be
158
Improved by Using Body Mass Index and Smoking Status PLoS ONE 8(3) e57005 Lemos NA Arbo E Scalco R Weiler E Rosa V Cunha-Filho JS Decreased anti-Muumlllerian hormone and altered ovarian follicular cohort in infertile patients with mildminimal endometriosis Fertil Steril 2008 May 89(5)1064-8 Luborsky JL Meyer P Sowers MF Gold EB Santoro N Premature menopause in a multi-ethnic population study of the menopause transition Hum Reprod 200218199-206 Maheshwari A Stofberg L Bhattacharya S Effect of overweight and obesity on assisted reproductive technologymdasha systematic review Hum Reprod Update 200713433ndash44 Michelmore K Balen A Dunger D Vessey M Polycystic ovaries and associated clinical and biochemical features in young women Clin Endocrinol (Oxf) 199951779-86 Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 95736-741 e731-7332011 Chen X Yang D Mo Y Li L Chen Y Huang Y Prevalence of polycystic ovary syndrome in unselected women from southern China Eur J Obstet Gynecol Reprod Biol 2008 13959-64 Randolph JF Sowers M Gold EB Mohr BA Luborsky J Santoro M et al Reproductive hormones in early menopausal transition relationship to ethnicity body size and menopausalstatus J Clin Endocrinol Metab Apr2003 88(4)1516ndash1522 [PubMed 12679432] Sahmay S Usta T Erel CT Imamoğlu M Kuuk M Atakul N Seyisoğlu H Is there any correlation between amh and obesity in premenopausal women Arch Gynecol Obstet 2012 Sep 286(3) 661-5 Seifer DB Baker VL and Leader B Age-specific serum anti-Meuroullerian hormone values for 17120 women presenting to fertility centers within the United States Fertility and Sterility_ Vol 95 No 2 February 2011 Seifer DB Golub ET Lambert-Messerlian G Benning L Anastos K Watts H Cohen MH Karim R Young MA Minkoff H and Greenblatt RM Variations in Serum Mullerian Inhibiting Substance Between White Black and Hispanic Women Fertil Steril 2009 November 92(5) 1674ndash1678 Shebl O Ebner T Sir A Schreier-Lechner E Mayer RB Tews GSommergruber M Age-related distribution of basal serum AMH level in women of reproductive age and a presumably healthy cohort Fertil Steril 2011 95 832ndash834
159
Shebl O Ebner T Sommergruber M Sir A Tews G Anti muellerian hormone serum levels in women with endometriosis a case-control study Gynecol Endocrinol 200925713-6 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities Hum Genet (2012b) 1311709ndash1724 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Skałba P Cygal A Madej P Dabkowska-Huc A Sikora J Martirosian G Romanik M Olszanecka-Glinianowicz M Is the plasma anti-Mullerian hormone (AMH) level associated with body weight and metabolic and hormonal disturbances in women with and without polycystic ovary syndrome European Journal of Obstetrics amp Gynecology and Reproductive Biology 158 (2011) 254ndash259 Streuli I de Ziegler D Gayet V Santulli P Bijaoui G de Mouzon J and Chapron C In women with endometriosis anti-Mullerian hormone levels are decreased only in those with previous endometrioma surgery Human Reproduction Vol27 No11 pp 3294ndash3303 2012 Su IH Sammel MD Freeman EW Lin H DeBlasis T Gracia C Body size affects measures of ovarian reserve in late reproductive age women Menopause 2008 15(5) 857ndash861 Uncu G Kasapoglu I Ozerkan K Seyhan A Oral Yilmaztepe A Ata B Prospective assessment of the impact of endometriomas and their removal on ovarian reserve and determinants of the rate of decline in ovarian reserve Hum Reprod 2013 Aug 28(8) 2140-5 Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wallace WHB Kelsey TW (2010) Human Ovarian Reserve from Conception to the Menopause PLoS ONE 5(1) e87
160
Table 1 Distribution of patients
AMH AFC FSH
n Mean (SD) n Mean (SD) n Mean (SD)
All 2880 175150 1810 13972 2377 7972
Ethnicity
White (Reference) 1833 169139 1222 13959 1556 7966
Other White 137 172131 85 14480 107 7637
Black 93 202208 43 16098 73 104135
Indian 108 216169 69 14360 94 7127
Other Asian 46 194157 30 14560 41 6717
Pakistani 276 201164 166 14375 232 81124
Other ethnic 103 158130 63 12448 83 7640
Not disclosed 220 170152 114 13161 157 7937
Data not available 64 183251 18 11952 34 8956
Patients with BMI
Normal (Reference) 1110 172137 917 13861 1011 7844
Underweight 38 179136 30 13947 38 7751
Overweight 679 168134 546 13763 620 7544
Obese 149 220209 90 14167 119 7142
Data not available 904 177163 227 14967 589 88123
Diagnosis
Unexplained 894 156120 667 13354 801 7632
Mild tubal 411 172158 284 13771 370 7530
Severe tubal 40 12685 27 13658 38 7827
Mild male 779 181134 538 14058 668 7342
Severe male 356 198135 197 14661 208 6818
Endometriosis ndash endometrioma 141 137108 91 13658 122 8341
Endometriosis + endometrioma 46 196159 15 14449 42 7123
161
Table 2 Regression models for AMH
AMH (Log)
BMI included
n=1952
BMI excluded
n=2816
Β 95 CI P β 95 CI P
Age -0057 -0069 -0045 00001 -0056 -0067 -0046 00001
age2 -0003 -0005 -0001 00001 -0004 -0006 -0003 00001
Ethnicity 00812 00079
Other White -0046 -0226 0133 0611 0038 -0131 0208 0658
Black 0209 -0038 0457 0097 -0259 -0464 -0054 0013
Indian 0032 -0164 0228 0749 0119 -0071 0310 022
Other Asian 0292 -0014 0598 0061 0250 -0037 0537 0088
Pakistani -0116 -0251 0017 0089 -0100 -0226 0025 0118
Other ethnic -0174 -0390 0041 0113 -0197 -0392 -0002 0047
Not disclosed -0002 -0162 0157 0977 -0104 -0241 0033 0138
BMI 00374
Underweight -0107 -0394 0179 0462
Overweight -0058 -0143 0025 017
Obese 0165 00119 0318 0035
Diagnosis
Unexplained 0039 -0073 0152 0492 0105 0007 0204 0035
Mild tubal 0089 -0033 0212 0153 0113 -000009 0226 005
Severe tubal -0168 -0463 0126 0264 -0133 -0444 0177 0401
Mild male 0118 0009 0227 0033 0180 0084 0275 00001
Severe male 0245 0096 0395 0001 0287 0162 0412 00001
Endometriosis -0136 -0311 0037 0124 -0152 -0324 0018 0081
Endometrioma 0217 -0068 0503 0136 0314 0023 0606 0034
_cons 2731 2616 2847 0 2658 2567 2750 0
162
Table 3 Regression models for AFC
AFC (Log)
BMI Included
n=1589
BMI Excluded
n=1810
Β 95 CI P Β 95 CI P
Age -0028 -0035 -0021 0 -0027 -0033 -0021 0
age2 000009 -00009 0001 086 000007 -00008 0001 0885
Ethnicity 00265 00383
Other White -0024 -0119 0070 0614 0003 -0087 0094 0942
Black 0093 -0037 0224 0162 0049 -0075 0175 0436
Indian -0042 -0148 0064 0438 -0035 -0136 0065 0492
Other Asian 0037 -0125 0200 0651 0037 -0114 0189 0626
Pakistani -0095 -0166 -0024 0008 -0083 -0151 -0015 0016
Other ethnic -0142 -0253 -0031 0012 -0132 -0237 -0027 0013
Not disclosed -0008 -0094 0078 0853 -0067 -0148 0012 0098
BMI 07713
Underweight -0040 -0190 0109 0599
Overweight -0018 -0062 0024 0398
Obese 0012 -0077 0103 0779
Diagnosis
Unexplained -0071 -0131 -0011 0019 -0065 -0121 -0009 0021
Mild tubal -0047 -0112 0017 0151 -0060 -0121 00003 0051
Severe tubal -0110 -0267 0045 0164 -0141 -0294 0010 0069
Mild male -0037 -0095 0020 0201 -0027 -0081 0025 0307
Severe male 0007 -0071 0086 0853 -0021 -0093 0050 0563
Endometriosis -0019 -0114 0076 0691 -0004 -0096 0087 0922
Endometrioma -0079 -0215 0055 0248 -0106 -0231 0019 0097
_cons 2694 2632 2755 0 2691 2636 2745 0
163
Table 4 Regression models for FSH
FSH (Log)
BMI Included
n=1772
BMI Excluded n=2343
Β 95 CI P Β 95 CI P
age 0009 0003 0014 0001 0009 0004 0014 00001
age2 00009 00001 0001 0019 0001 00003 0001 0003
Ethnicity 04415 03329
Other White 0034 -0046 0114 0403 -0017 -0099 0065 0685
Black 0043 -0065 0153 043 0068 -0030 0167 0175
Indian -0010 -0097 0076 0808 -0070 -0157 0017 0116
Other Asian -0119 -0250 0011 0074 -0104 -0234 0026 0117
Pakistani -0031 -0089 0026 029 -0014 -0073 0045 064
Other ethnic 0031 -0062 0125 0508 -0002 -0095 0090 0962
Not disclosed 0022 -0049 0093 0541 0026 -0042 0095 045
BMI 00017
Underweight -0070 -0189 0048 0246
Overweight -0055 -0091 -0018 0003
Obese -0106 -0176 -0036 0003
Diagnosis
Unexplained -0055 -0104 -0006 0028 -0055 -0101 -0009 0018
Mild tubal -0052 -0105 000008 005 -0050 -0103 0001 0056
Severe tubal 0004 -0118 0127 0943 0016 -0120 0154 0809
Mild male -0084 -0132 -0037 00001 -0071 -0116 -0026 0002
Severe male -0127 -0196 -0059 00001 -0102 -0168 -0036 0002
Endometriosis 0035 -0039 0111 0353 0044 -0034 0124 0268
Endometrioma -0074 -0196 0047 0229 -0056 -0186 0074 0402
_cons 1999 1948 2049 0 1958 1915 2002 0
164
Table 5 Distribution of patient characteristics by availability of data on BMI The number of observations and mean (SD) of the markers of ovarian reserve (Age AMH AFC and FSH) described according to an availability of data on BMI
BMI (+)
BMI (-) Total
n Mean (SD) n Mean (SD) n Mean (SD)
Age 1976 32944 904 32750 2880 32946
AMH 1976 175144 904 178164 2880 176150
AFC 1583 13862 227 14968 1810 14063
FSH 1788 7744 589 88123 2377 8073
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
165
Table 6 Distribution of ethnicity by availability of data on BMI Distribution of the number of observations by ethnicity and availability of data on BMI
AMH AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
BMI (+) BMI (-) Total
White 1308 525 1833 1070 152 1222 1201 355 1556
Other White 97 40 137 76 9 85 83 24 107
Black 50 43 93 39 4 43 44 29 73
Indian 81 27 108 60 9 69 70 24 94
Other Asian 32 14 46 25 5 30 30 11 41
Pakistani 193 83 276 148 18 166 177 55 232
Other ethnic 66 37 103 55 8 63 60 23 83
Not disclosed 125 95 220 95 19 114 107 50 157
Data not available 24 40 64 15 3 18 16 18 34
Total 1976 904 2880 1583 227 1810 1788 589 2377
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
166
Table 7 Distribution of diagnosis by availability of data on BMI Distribution of number of observations in each diagnosis group tabulated by availability of data on BMI
AMH
AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
Unexplained 730 164 894 611 56 667 672 129 801
Mild tubal 319 92 411 258 26 284 298 72 370
Severe tubal 36 4 40 26 1 27 36 2 38
Mild male 567 212 779 461 77 538 525 143 668
Severe male 196 160 356 161 36 197 153 55 208
Endometriosis ndash endometrioma 112 29 141 83 8 91 101 21 122
Endometriosis + endometrioma 38 8 46 38 8 46 36 6 42
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
167
THE EFFECT OF SALPINGECTOMY
OVARIAN CYSTECTOMY AND UNILATERAL
SALPINGOOPHERECTOMY ON OVARIAN
RESERVE
Oybek Rustamov Monica Krishnan
Stephen A Roberts Cheryl Fitzgerald
To be submitted to Gynecological Surgery
52
168
Title
Effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Stephen A Robertsc Cheryl Fitzgeralda
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable Word count 2904
Acknowledgement
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
169
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all
versions of the manuscript MK assisted in data extraction contributed in
discussion and the review of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed in discussion
and reviewed all versions of the manuscript
170
ABSTRACT
Objective
To estimate the effect of salpingectomy ovarian cystectomy and unilateral
salpingo-oopherectomy on ovarian reserve
Design
Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 3179 patients were included in the study The AMH measurements
of 66 women were excluded due to haemolysed samples or delay in processing
the samples leaving 3113 women for analysis There were 138 women who
had unilateral or bilateral salpingectomy 36 women with history of unilateral
salpingo-oopherectomy 41 women with history of cystectomy for ovarian
cysts that other than endometrioma and 40 women had cystectomy for
endometrioma
Interventions
Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH basal serum FSH and basal AFC measurements
Results
The analysis did not find any significant differences in AMH (9 p=033)
AFC (-2 p=059) and FSH (-14 p=021) measurements between women
with a history of salpingectomy and those without history of surgery Women
with history of unilateral salpingo-oopherectomy were found to have
significantly lower AMH (-54 p=0001) and AFC (-28 p=034) and
increased FSH (14 p=006) The study did not find any significant
171
association between a previous history of ovarian cystectomy that was for
conditions other than endometrioma and AMH (7 p=062) AFC (13
p=018) or FSH (11 p=016) The analysis of the effect of ovarian
cystectomy for endometrioma showed that women with history of surgery had
around 66 lower AMH (p=0002) Surgery for endometrioma did not
significantly affect AFC (14 p=022) or FSH (10 p=028)
Conclusions
Salpingo-oopherectomy and ovarian cystectomy for endometrioma have a
significant detrimental impact on ovarian reserve Neither salpingectomy nor
ovarian cystectomy for cysts other than endometrioma has an appreciable
effect on ovarian reserve
Key Words
Salpingectomy Ovarian cystectomy Salpingo-oopherectomy ovarian reserve
AMH AFC FSH
172
INTRODUCTION
Human ovarian reserve is determined by the size of oocyte pool at birth
and decline in the oocyte numbers thereafter Both of these processes are
largely under the influence of genetic factors and to date no effective
interventions are available to improve physiological ovarian reserve (Shuh-
Huerta et al 2012) However various other environmental pathological and
iatrogenic factors appear to play a role in the determination of ovarian reserve
and consequently it may be influenced either directly or indirectly Evidently
the use of chemotherapeutic agents certain radio-therapeutic modalities and
surgical interventions that damage ovarian parenchyma can cause substantial
damage to ovarian reserve (Nielsen et al 2013 Somigliana et al 2012)
Estimation of the effect of each of these interventions is of paramount
importance in ascertainment of lesser ootoxic treatment modalities and safer
surgical methods
Age is the main determinant of the number of non-growing follicles
accounting for 84 of its variation and used as marker of ovarian reserve
(Hansen et al 2008) However biomarkers that allow direct assessment of the
dynamics of growing follicles AMH and AFC may provide more accurate
estimation of ovarian reserve Although these markers only reflect
folliculogenesis of already recruited growing follicles there appears to be a
good correlation between their measurements and histologically determined
total ovarian reserve (Hansen et al 2011) Thus the biomarkers can effectively
be utilized for estimation of the effect of above adverse factors on the
primordial oocyte pool
Surgical interventions that lead to disruption of the blood supply to
ovaries or involve direct damage to ovarian tissue may be expected to lead to a
reduction in the primordial follicle pool Indeed a number of studies have
reported an association between surgical interventions to ovaries and reduction
in ovarian reserve (Somigliana et al 2012) However given both underlying
disease and surgery may affect ovarian reserve disentanglement of the
individual effects of these factors may be challenging and requires robust
research methodology Here we present a study that intended to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease
173
METHODS
The effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve were studied using serum AMH AFC and
FSH measurements in a large cross sectional study
Population
All women between the ages of 20 to 45 who were referred to the
Womenrsquos Outpatient Department (WOP) and the Reproductive Medicine
Department (RMD) of Central Manchester University Hospitals NHS
Foundation Trust for management of infertility between 1 September 2008
and 16 November 2010 and had an AMH measurement using the DSL assay
(DSL Active MISAMH ELISA Diagnostic Systems Laboratories Webster
Texas) were included We excluded patients referred for fertility preservation
(eg prior to or after treatment for a malignant disorder) and those with a
diagnosis of polycystic ovaries (PCO) on transvaginal ultrasound scan which
was defined as volume of one or both ovaries more than 10ml Patients with
haemolysed AMH andor FSH samples were not included in the analysis of
these markers Non-smoking is an essential criteria for investigation prior to
assisted conception and therefore to our best knowledge our population
consisted of non-smokers
Measurement of AMH
Blood samples for AMH were taken without regard to the day of
womenrsquos menstrual cycle and serum samples were separated within two hours
of venipuncture in the Biochemistry laboratory of our hospital All samples
were processed strictly according to the manufacturerrsquos recommendations and
frozen at -20C until analysed in batches using the enzymatically amplified two-
site immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
100pmolL and a minimum detection limit was 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first AMH of
the patients were selected
174
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the Biochemistry
Laboratory within two hours of venipuncture for sample processing and
analysis Specific immunoassay kits (Cobas Roche Diagnostics Mannheim
Germany) and an autoanalyser platform was used (Roche Modular Analytics
E170 Roche USA) for analysis of FSH The intra-assay CV was 60 and
inter-assay CV was 68 The FSH measurements in the samples with high E2
levels (gt250pmolL) were excluded from the analysis given these samples are
likely to have been taken outside of early follicular phase of menstrual cycle
In patients with repeated FSH measurements measurements conducted on the
same day as first AMH were selected If the patient did not have FSH
measurement on the day of AMH sampling the measurement with the closest
date to first AMH sample was selected
Measurement of AFC
Measurement of AFC is conducted in patients referred for assisted
conception during their initial work up Our department uses a stringent
protocol for the assessment of AFC and qualified radiographers who have
undergone specific training on measurement of AFC The methodology
consists of counting of all antral follicles measuring 2-6mm in longitudinal and
transverse cross sections of both ovaries using transvaginal ultrasound
scanning at early follicular phase (Day 0-5) of the menstrual cycle The AFC
measurement with the closest date to first AMH sample was selected
Data collection
Data was extracted from electronic clinical data management systems
and from information held in written hospital notes for each patient Data on
AMH and FSH measurements were obtained from the Biochemistry
Department and validated by checking the results documented in the hospital
case notes of randomly selected 50 patients against the results obtained from
electronic clinical data management system (Clinical Workstation) finding
100 concordance Information on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
175
surgery were obtained from the hospital case notes The ethnicity of the
patients was established using a patient questionnaire and data were extracted
from the hospital database for the patient demographics (PAS)
Definitions and groups
First the datasets were merged using a unique patient identifier (hospital
number) Validation of the merger using additional patient identifiers (NHS
number name date of birth) revealed existence of duplicate hospital numbers
in patients transferred from secondary care infertility services of our hospital to
IVF Department We established that in our datasets combination of the
patientrsquos first name surname and date of birth in a continuous string variable
could be used as a unique identifier Hence we used this identifier to merge all
datasets achieving a robust merger of all independent datasets into a combined
final dataset Following creation of an anonymised a unique study number for
each patient all patient identifiers were dropped and the anonymised
combined dataset was used for the analysis
Body mass index (BMI) of patients was categorized using standard NHS
cut-off reference ranges Underweight (lt185) Normal (185-249)
Overweight (25-299) and Obese (30-40) (The Office for National Statistics
2011) Causes of infertility were established by searching the hospital notes
including the referral letters clinical notes and letters generated following clinic
consultations Patients with history of bilateral tubal block which was
confirmed by laparoscopic dye test and patients with history of bilateral
salpingectomy were categorized as having severe tubal factor infertility
Patients with unilateral tubal patency or unilateral salpingectomy were
categorized as having mild tubal factor infertility Severe male factor infertility
was defined as azoospermia or severe oligospermia (lt1mln sperm sample)
Patients with abnormal sperm count but do not meet above criteria were
classified as having mild male factor infertility
Patients with reproductive surgery were categorized as having history of
salpingectomy cystectomy for endometrioma cystectomy for ovarian cysts
other than endometrioma or unilateral salpingo-oopherectomy First
measurement of AMH AFC and FSH following surgery was selected for the
study
176
Statistical analysis
A multivariable regression model that included age ethnicity BMI
endometriosis presence of endometrioma the causes of infertility tubal and
ovarian surgery was fitted for each of the ovarian reserve markers AMH AFC
and FSH Difference between the groups were considered significant at
p005 Preliminary analysis of AMH AFC and FSH indicated that
logarithmically transformed values with a quadratic age term provided adequate
fits The precise age on the day measurement of each of the marker of ovarian
reserve (AMH AFC and FSH) was included in the model as a quadratic
function following centering to 30 years of age
Interactions between all explanatory variables were tested at a
significance level of 001 We observed significant interaction between BMI
and other covariates This may be due to biological complexity in the
relationship of BMI and other factors (eg ethnicity) in determination of
ovarian reserve However given data on BMI was not available in considerable
number of patients the observed interactions may be due to limitation of our
dataset Therefore in order to assist in interpretation of the results analyses
with and without BMI in the models were conducted
RESULTS
In total 3179 patients were included in the study The AMH
measurements of 66 women were excluded due to haemolysed samples or
delay in processing the samples leaving 3113 women for analysis 1934 of
patients had measurement of AFC and 2580 had FSH samples that met
inclusion criteria The mean age AMH AFC and FSH of patients were
328plusmn45 173plusmn148 139plusmn62 80plusmn75 respectively There were 138 women
who had unilateral or bilateral salpingectomy 36 women with history of
unilateral salpingo-oopherectomy 41 women with history of cystectomy for
ovarian cysts that other than endometrioma and 40 women had cystectomy for
endometrioma (Table 1) The results of regression analysis on the effect of
reproductive surgery on AMH AFC and FSH measurements are shown in
Table 2
The analysis did not find any significant differences in AMH (9
p=033) AFC (-2 p=059) and FSH (-14 p=021) measurements in
women with history of salpingectomy compared to women without history of
177
surgery and we did not observe marked change in the estimates in a smaller
subset where BMI was included in the model (Table 2)
Women with history of unilateral salpingo-oopherectomy were found
to have significantly lower AMH (-54 p=0001) and AFC (-28 p=034)
and increased FSH (14 p=006) measurements where effect on AMH
reached the level of statistical significance Similarly the analysis of the model
that included BMI showed significantly lower AMH and AFC and higher FSH
measurements in surgery group where both AMH and FSH analysis were
statistically significant (Table 2)
The study did not find a significant association between previous
history of ovarian cystectomy that was for disease other than endometrioma
and measurement of AMH (7 p=062) AFC (13 p=018) or FSH (11
p=016) which did not change noticeably following adding BMI in the model
(Table 2)
The analysis of the effect of ovarian cystectomy for endometrioma
showed that women with history of surgery had around 66 lower AMH
(p=0002) measurements The effect of surgery for endometrioma was not
significant in assessment of AFC (14 p=022) and FSH (10 p=028)
However in the model with BMI association of the surgery with both AMH (-
64 p=0005) and FSH (24 p=0015) were found to be significant (Table
2)
DISUCUSSION
Salpingectomy
The blood supply to human ovaries is maintained by the direct branches
of aorta ovarian arteries which form anastomoses with ovarian and tubal
branch of uterine arteries in mesovarium and mesosalpynx In salpingectomy
often tubal branches of uterine arteries are excised alongside mesosalpynx and
hence it is believed disruption to blood supply to ovaries may lead to a
reduction of ovarian reserve However in our study we did not observe an
appreciable association between salpingectomy and any of the biomarkers of
ovarian reserve suggesting this surgery does not appreciably affect ovarian
reserve These findings are supported by study that assessed the effect of tubal
178
dissection to AMH AFC FSH levels (n=49) using longitudinal data (Erkan et
al 2012) There were no differences between preoperative and 3 month
postoperative measurements with median AMH (15 vs 14 p=007) AFC
(8437 vs 7941 p=009) FSH (76 21 vs 7721 p=010) da Silva et al
assessed the effect of tubal ligation (n=52) in longer term postoperative period
(1 year) and reported that median AMH (143 IQR 063-262 vs and 130 IQR
053-285 p=023) and mean AFC ( 8 IQR 5-14 vs 11 IQR 7-15 p=012)
measurements did not change significantly Our results and on other published
evidence suggest that salpingectomy or tubal division does not have an
adverse effect to ovarian reserve
Unilateral salpingo-oopherectomy
Although salpingo-oopherectomy is rare in women of reproductive age
significant ovarian pathologies and acute diseases such as ovarian torsion may
necessitate unilateral salpingo-oopherectomy There is a plausible causative
relationship between this surgery and ovarian reserve although to our
knowledge there is no previous published evidence We found that women
with a history of unilateral salpingo-oopherectomy have significantly lower
AMH (-54) and higher FSH (13) measurements suggesting the surgery has
considerable negative impact to ovarian reserve Important clinical question in
this clinical scenario is ldquoDo these patients have comparable reproductive
lifespan or experience accelerated loss of oocytes resulting premature loss of
fertilityrdquo as this would allow appropriate pre-operative counseling of patients
regarding long term effect of the surgery to fertility and age at menopause
Considering our data had relatively small number of patients with a history of
salpingo-oopherectomy we were not able to obtain reliable estimates on age-
related decline of ovarian reserve in this study We suggest that studies with
larger number of patients preferably using longitudinal data should address
this research question
Ovarian cystectomy
In women with a history of ovarian cystectomy for ovarian cysts other
than those due to endometrioma we did not observe any significant
association between the surgery and markers of ovarian reserve However
women that had ovarian cystectomy for endometrioma appear to have
179
significantly lower AMH (-66) measurements compared to those without
history of surgery
During the last few years a number of studies have assessed the effect of
cystectomy on AMH levels in patients with endometrioma (Chang et al 2010
Erkan et al 2010 Lee et al 2011) The studies have been summarised by a
recent systematic review which concluded that cystectomy results in damage
to ovarian reserve (Somigliana et al 2012) Further studies evaluated the
mechanism of damage and these suggest that coagulation for purpose of
hemostasis as well as stripping of the cyst wall may cause direct damage to
ovarian reserve Sonmezer et al compared the effect of diathermy coagulation
(n=15) for hemostasis compared to use of hemostatic matrix (n=13) in a
randomized controlled trial and reported that use of diathermy coagulation is
associated with significantly lower AMH measurements (164 plusmn 093 vs 272 plusmn
149 ngmL) in the first postoperative month
Similarly stripping of the cyst wall also appears to have detrimental
effect of ovarian reserve due to inadvertent removal of ovarian tissue (Donnez
et al 1996) Using histological data Roman et al demonstrated that normal
ovarian tissue was removed in 97 specimens of surgically removed
endometriomata (Roman et al 2010) Furthermore it appears that ovarian
cortex containing endometrioma appears to have significantly reduced density
compared to normal ovarian cortex and therefore loss of oocyte containing
normal ovarian cortex may be unavoidable in cystectomy for endometrioma
(Sanchez et al 2014) Matsuzaki et al conducted histological assessment of
cystectomy specimens and found that normal ovarian tissue adjacent to cyst
wall was found in 58 (71121) of patients with endometrioma whereas
normal ovarian tissue was excised in 54 (356) following cystectomy for
other benign cyst (Matsuzaki et al 2008) Similarly in our study women with a
history of cystectomy for endometrioma had significantly lower AMH
measurements whilst those had cystectomy for other benign cysts do not
appear to have lower AMH measurements In view of our findings and other
published research evidence it seems clear that cystectomy for endometrioma
results in significant reduction in ovarian reserve and women undergoing
surgery should be counseled regarding the adverse effect of surgery
180
Strengths and Limitations
The published studies have used longitudinal data comparing biomarkers
before and after cystectomy and provide reliable estimates on the effect of the
intervention on ovarian reserve However data on the effect of salpingectomy
and unilateral salpingoophorectomy is lacking In addition to reevaluation of
the effect of cystectomy this is study has assessed the impact of salpingectomy
and unilateral salpingoophorectomy on the markers of ovarian reserve In
contrast to published studies this study employed analysis of cross sectional
data Given a robust adjustment for all relevant factors has been conducted
our analysis of the cross sectional data should provide reliable estimates of the
effects of various intervention on the markers of ovarian reserve Furthermore
the effect of surgery on all the main biomarkers of ovarian reserve has been
assessed which improves our understanding of the clinical value of each test in
the assessment of patients with history of tubal or ovarian surgery In addition
the analyses adjusted for other relevant factors that may affect ovarian reserve
In patients with history of cystectomy for endometrioma we estimated
independent effects of pathology and surgery providing important data for
preoperative counseling It is important to note that the study evaluated The
effect of surgery using retrospective data which has limitations due variation in
recording of surgical history and missing data In addition given BMI results
for around one third of patients were not available we were not able to fully
explore the effect of BMI However data on the analyses with and without
BMI in the model have been provided to evaluate the effect of this factor The
study employed the data obtained using first generation DSL AMH assay
which is no longer in use However the paper describes the effects of the
interventions in percentage terms and therefore the results are interpretable in
any AMH assay measurement
Important to note although the effects are significant in population level
there is considerable variation between individuals which is evident from the
fact there is overlap between median and interquartile ranges of the groups
(Figure 1) This indicates that clinicians should exercise caution in predicting
the effect of surgery to ovarian reserve of individual patients Nevertheless
given I used a robust methodology for data extraction and conducted careful
analysis I think that the study provides fairly reliable estimates on the effect of
surgery to ovarian reserve
181
CONCLUSION
This multivariable regression analysis of retrospectively collected cross-
sectional data suggests that neither salpingectomy nor ovarian cystectomy for
cysts other than endometrioma has an appreciable effect on ovarian reserve
determined by AMH AFC and FSH In contrast salpingoophorectomy and
ovarian cystectomy for endometrioma have a significant detrimental impact to
ovarian reserve On the basis of findings of this study and other published
studies women undergoing reproductive should be counseled with regards to
the effect of the surgery on their ovarian reserve
182
References
Biacchiardi CP Piane LD Camanni M Deltetto F Delpiano EM Marchino GL et al Laparoscopic stripping of endometriomas negatively affects ovarian follicular reserve even if performed by experienced surgeons Reprod Biomed Online 201123740ndash6 Chang HJ Han SH Lee JR Jee BC Lee BI Suh CS et al Impact of laparoscopic cystectomy on ovarian reserve serial changes of serum anti-Mullerian hormone levels Fertil Steril 201094343ndash9 Dogan E Ulukus EC Okyay E Ertugrul C Saygili U Koyuncuoglu M Retrospective analysis of follicle loss after laparoscopic excision of endometrioma compared with benign nonendometriotic ovarian cysts Int J Gynaecol Obstet 2011114124ndash7 Ercan CM Sakinci M Duru NK Alanbay I Karasahin KE Baser I (2010) Antimullerian hormone levels after laparoscopic endometrioma stripping surgery Gynecol Endocrinol 201026468ndash72 Ercan CM Duru NK Karasahin KE Coksuer H Dede M Baser I (2011) Ultrasonographic evaluation and anti-mullerian hormone levels after laparoscopic stripping of unilateral endometriomas Eur J Obstet Gynecol Reprod Biol 2011158280ndash4 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hachisuga T Kawarabayashi T Histopathological analysis of laparoscopically treated ovarian endometriotic cysts with special reference to loss of follicles Hum Reprod 200217432ndash5 Hirokawa W Iwase A Goto M Takikawa S Nagatomo Y Nakahara T et al The post-operative decline in serum anti-Mullerian hormone correlates with the bilaterality and severity of endometriosis Hum Reprod 201126904ndash10 Hwu YM Wu FS Li SH Sun FJ Lin MH Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reprod Biol Endocrinol 2011980 Iwase A Hirokawa W Goto M Takikawa S Nagatomo Y Nakahara T et al Serum anti-Mullerian hormone level is a useful marker for evaluating the impact of laparoscopic cystectomy on ovarian reserve Fertil Steril 201094 2846ndash9 Kitajima M Khan KN Hiraki K Inoue T Fujishita A Masuzaki H Changes in serum anti-Mullerian hormone levels may predict damage to residual normal ovarian tissue after laparoscopic surgery for women with ovarian endometrioma Fertil Steril 2011952589ndash91e1 Kitajima M Defr_ere S Dolmans MM Colette S Squifflet J van
183
Langendonckt A et al Endometriomas as a possible cause of reduced ovarian reserve in women with endometriosis Fertil Steril 201196685ndash91 Lee DY Young Kim N Jae Kim M Yoon BK Choi D Effects of laparoscopic surgery on serum anti-Meuroullerian hormone levels in reproductive-aged women with endometrioma Gynecol Endocrinol 201127733ndash6 Matsouzaki S Houlle C Darcha S Pouly JL Mage G Canis M Analysis of risk factors for the removal of normal ovarian tissue during laparoscopic cystectomy for ovarian endometriosis Hum Reprod 2009 241402ndash1406 Muzii L Bianchi A Croc_e C Manci N Panici PB Laparoscopic excision of ovarian cysts is the stripping technique a tissue-sparing procedure Fertil Steril 200277609ndash14 Office for National Statistics (ONS) Social Trends 41 Health 2011 Roman H Tarta O Pura I Opris I Bourdel N Marpeau L et al Direct proportional relationship between endometrioma size and ovarian parenchyma inadvertently removed during cystectomy and its implication on the management of enlarged endometriomas Hum Reprod 201025 1428ndash32 Romualdi D Franco Zannoni G Lanzone A Selvaggi L Tagliaferri V Gaetano Vellone V et al Follicular loss in endoscopic surgery for ovarian endometriosis quantitative and qualitative observations Fertil Steril 201196374ndash8
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642 Sanchez A P Viganograve P Somigliana E Panina-Bordignon P Vercellini and Candiani M The distinguishing cellular and molecular features of the endometriotic ovarian cyst from pathophysiology to the potential endometrioma-mediated damage to the ovary Hum Reprod Update (MarchApril 2014)
Shi J Leng J Cui Q Lang J Follicle loss after laparoscopic treatment of ovarian endometriotic cysts Int J Gynaecol Obstet 2011115277ndash81 Tsolakidis D Pados G Vavilis D Athanatos D Tsalikis T Giannakou A et al The impact on ovarian reserve after laparoscopic ovarian cystectomy versus three-stage management in patients with endometriomas a prospective randomized study Fertil Steril 20109471ndash7 Vicino M Scioscia M Resta L Marzullo A Ceci O Selvaggi LE Fibrotic tissue in the endometrioma capsule surgical and physiopathologic considerations from histologic findings Fertil Steril 200991(4 Suppl)1326ndash8
184
Figure 1 Box plots of AMH by various groups Upper panel shows the raw data and the lower panel the AMH measurement (in pmolL) adjusted for age ethnicity BMI causes of infertility endometriosis endometrioma and surgery Groups (left to right) 1) Endometrioma without history of cystectomy (endoma-no surg) 2) Cystectomy for endometrioma (endoma+surg) 3) Endometriosis without endometrioma (endsisonly) 4) Without endometriosis or any surgery (No end+no surg) 5) Oopherectomy (oe) 6) Cystectomy for cyst other than those for endometrioma (other cyst) 7) Salpingectomy (se)
185
Table1 Distribution of patients
BMI excluded
BMI Included
Age AMH AFC FSH AMH AFC
FSH
Mean (SD) N Mean n Mean (SD) N Mean (SD) n n N
Non-surgery 328plusmn45 2880 175plusmn150 18100 139plusmn63 23770 79plusmn72 1976 15830 17880
Oophorectomy 324plusmn50 36 106plusmn84 2 115plusmn77 34 118plusmn230 25 2 23
Salpingectomy 331plusmn42 138 154plusmn119 91 13plusmn43 122 82plusmn 123 121 84 27
Cystectomy Other 336plusmn42 41 168plusmn132 18 148plusmn50 29 122plusmn249 27 15 20
Cystectomy Endometrioma
327plusmn51 40 119plusmn140 17 137plusmn41 37 89plusmn56 23 10 22
186
Table 2 Multivariable regression analysis Adjusted for age ethnicity causes of infertility endometriosis (without endometrioma) endometrioma and reproductive surgery
BMI(+)
BMI(-)
N
Coeff
95 CI
P
N
Coeff
95 CI
P
Oophorectomy
AMH 2128 -0779 -1135 -0422 00005 3049 -0540 -0868 -0213 0001
AFC 1697 -0278 -0848 0292 0340 1946 -0280 -0857 0298 0342
FSH 1929 0266 0110 0422 0001 2546 0139 -0006 0284 0060
Salpingectomy
AMH 2128 0067 -0118 0252 0476 2128 0094 -0097 0285 0333
AFC 1697 -0027 -0128 0075 0605 1697 -0027 -0126 0072 0595
FSH 1929 -0085 -0167 -0004 0041 1929 -0056 -0143 0032 0210
Cystectomy Other
AMH 2128 0102 -0230 0433 0548 2128 0075 -0226 0376 0626
AFC 1697 0102 -0107 0311 0339 1697 0130 -0064 0323 0189
FSH 1929 0134 -0028 0297 0106 1929 0110 -0044 0265 0161
Cystectomy Endometrioma
AMH 2128 -0647 -1100 -0194 0005 2128 -0667 -1081 -0252 0002
AFC 1697 0115 -0172 0402 0433 1697 0144 -0089 0376 0225
FSH 1929 0243 0047 0439 0015 1929 0103 -0084 0290 0281
187
ASSESSMENT OF DETERMINANTS OF OOCYTE
NUMBER USING RETROSPECTIVE DATA ON
IVF CYCLES AND EXPLORATIVE STUDY OF
THE POTENTIAL FOR OPTIMIZATION OF AMH-
TAILORED STRATIFICATION OF CONTROLLED
OVARIAN HYPERSTIMULATION
Oybek Rustamov
Cheryl Fitzgerald Stephen A Roberts
6
188
Title
Assessment of determinants of oocyte number using large retrospective
data on IVF cycles and explorative study of the potential for
optimization of AMH-tailored stratification of controlled ovarian
stimulation
Authors
Oybek Rustamova Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Centre for Biostatistics Institute of Population Health Manchester
Academic Health Science Centre (MAHSC) University of Manchester
Manchester M13 9PL UK
Word count 7520
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgement
Authors would like to thank Dr Monica Krishnan (Foundation Trainee
Manchester Royal Infirmary) for her assistance in data extraction We would
also like to thank colleagues Dr Greg Horne (Senior Clinical Embryologist)
Ann Hinchliffe (Clinical Biochemistry Department) and Helen Shackleton
(Information Operations Manager) for their help in obtaining datasets for the
study
189
Declaration of authorsrsquo roles
OR prepared the study protocol prepared the dataset conducted statistical
analysis and prepared all versions of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed to the
discussion and reviewed all versions of the manuscript
190
ABSTRACT
Objectives
1) To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield
2) To explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Design
Retrospective cross sectional study using multivariable regression analysis
First the effect of a set of plausible factors that may affect the outcomes have
been established including assessment of the effect of age AMH AFC causes
of infertility attempt of IVFICSI cycle COH protocol changes
gonadotrophin preparations operator for oocyte recovery pituitary
desensitisation regime and initial daily dose of gonadotrophins Then the
regression models that examined the effect of gonadotrophin dose and regime
categories on total and mature oocyte numbers have been developed
Setting
Tertiary referral centre for management of infertility St Maryrsquos Hospital
Central Manchester University Hospitals NHS Foundation Trust
Participants
Women without ultrasound features of polycystic ovaries who underwent
IVFICSI cycle using pituitary desensitisation with GnRH long agonist or
GnRH antagonist regimes and had previous measurement of AMH with the
DSL assay In total of 1847 IVF or ICSI cycles of 1428 patients met the
inclusion criteria for the study AMH measurements of all cycles and AFC
measurements for 1671 cycles (n=1289 patients) were available In the analysis
of total oocytes 1653 cycles were included and the analysis of metaphase II
oocytes comprised of 1101 ICSI cycles
Interventions
None (observational study)
191
Main outcome measures
Total oocyte number Metaphase II oocyte number
Results
After adjustment for all the above factors age remained a negative predictor of
oocyte yield whereas we observed a gradual and significant increase in oocyte
number with increasing AMH and AFC values suggesting all these markers
display an independent association with oocyte yield
Compared to 1st IVF cycles those with 2nd (8 p=001) and particularly 3rd
attempt (24 p=0001) had considerably higher total oocytes The effect of
attempt on mature oocyte yield was not significant (p=045) Similarly there
was significant between-operator variability in total oocyte number when
oocyte recovery practitioners were compared (p=00005) However the effect
of oocyte recovery practitioner on mature oocyte yield did not reach statistical
significance (p=0058) Comparison of the effect of gonadotrophin type
showed that rFSH was associated with higher total oocyte yield compared to
that of HMG (p=0008) although the numbers of mature oocytes were not
significantly different between the groups (p=026)
After adjustment for all above factors compared to a reference group (Agonist
with 75-150 IU hMGrFSH) none of the regime and dose categories provided
higher total oocyte yield and Antagonist with 75-150 IU hMGrFSH (-36
p=00005) provided significantly less total oocyte With regards to the mature
oocyte yield Antagonist with 187-250 IU rFSHhMG (43 p=005) and
Antagonist 375 IU rFSHhMG (47 p=002) were associated with
significantly higher oocyte number compared to that of above reference group
This implies that compared to long Agonist down regulation Antagonist
regime is associated with higher mature oocyte yield
Following adjustment for all above variables we did not observe significant
increase in oocyte number with increasing gonadotrophin dose categories
192
Conclusions
Given there was no expected increase in oocyte number with increasing
gonadotrophin dose categories we believe there may not be significant direct
dose-response effect Consequently strict protocols for tailoring the initial
dose of gonadotrophins may not necessarily improve ovarian performance in
IVF treatment It is important to note our COS protocols instructed the use
of cycle monitoring with ultrasound follicle tracking and oestradiol levels and
corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
Key Words
Ovarian reserve AMH AFC IVF Controlled ovarian stimulation AMH-
tailored ovarian stimulation Individualisation of ovarian stimulation
193
INTRODUCTION
According to the HFEA around 12 of IVF cycles in the UK are
cancelled due to poor or excessive ovarian response in the UK which
highlights the importance of the provision of optimal ovarian stimulation in
improving the outcomes (Kurinczuk et al 2010) Traditionally patientrsquos age and
basal FSH measurements were used for the assessment of ovarian reserve with
subsequent tailoring of the initial dose of gonadotrophins and regime for
pituitary desensitisation for controlled ovarian stimulation in IVF Studies on
the prognostic value of markers of ovarian reserve show that AMH and AFC
are the best predictors of ovarian response in cycles of IVF (Broer et al 2011)
Furthermore unlike most other markers AMH has potential discriminatory
power due to significantly higher between-patient (CV 94) variability
compared to its within-patient (CV 28) variation (Rustamov et al 2011)
which allows stratification of patients into various degrees of (eg low normal
high) ovarian reserve Consequently development of optimal ovarian
stimulation protocol for each band of ovarian reserve using AMH may be
feasible
Controlled ovarian stimulation (COS) based on tailoring the pituitary
desensitisation and initial dose of gonadotrophins to AMH measurements is
known under various names individualisation of ovarian stimulation AMH-
tailored stratification of COS personalization of IVF are the most commonly
used This strategy is believed to be effective and has been widely
recommended (Nelson et al 2013 Dewailly et al 2014 La Marca et al 2014)
Although AMH based assessment of ovarian reserve with pituitary down
regulation in patients with extremes of ovarian reserve may improve the
outcomes of ovarian response compared to conventional ovarian stimulation
protocols (Nelson et al 2009 Yates et al 2011) there is no robust data on
AMH-tailored individualisation of ovarian stimulation To establish
individualisation of ovarian stimulation the studies should ideally assess
various pituitary desensitisation regimes and initial doses of gonadotrophins in
patients across the full range of ovarian reserve For instance in AMH-tailored
individualisation of pituitary desensitisation regime studies should evaluate the
effect of both GnRH Agonist and GnRH Antagonist regimes for the groups
for each band of AMH levels (eg low normal high) necessitating 6
comparison groups (Figure 1) In individualisation of the initial dose of
194
gonadotrophins the groups of each band of AMH should be treated with the
range of doses of gonadotrophins (eg low moderate high dose) which
requires 9 treatment groups (Figure 2) Consequently to evaluate the
individualisation of both the stimulation regime and the initial dose of
gonadotrophin across the full range of AMH measurements in a single study
ideally 18 comparison groups are needed Indeed the study should have a large
enough sample to adjust for the confounders and obtain sufficient power for
the estimates of each treatment group In addition assessment of ovarian
reserve should be based on reliable AMH measurements with minimal sample-
to-sample variation which appears to be an issue at present (Rustamov et al
2013) Finally evidence on AMH-tailored individualisation of ovarian
stimulation should ideally be based on randomized controlled trials given in
this context AMH is being used as a therapeutic intervention At present there
is no single RCT that assessed AMH-tailored individualisation of ovarian
stimulation and most quoted research evidence appear to have been based on
two retrospective studies (Nelson et al 2009 Yates et al 2011) Both studies
display a number of methodological issues including small sample size and
centre-dependent or time-dependent selection of cohorts Therefore the role
of confounding factors on the obtained estimates of these studies is unclear
The first study on AMH-tailored individualisation ovarian stimulation
compared outcomes of the cohorts who had IVF cycles in two different IVF
centers (Nelson et al 2009) In this case control study the patients in the 1st
centre (n=370) had minimal tailoring of dose of gonadotrophins and were
offered mainly GnRH agonist regime for pituitary desensitisation except
patients with very low AMH (lt10pmolL) who had GnRH antagonist regime
In patients undergoing treatment in the 2nd centre (n=168) the daily dose of
the gonadotrophins was tailored on the basis of AMH levels and GnRH
antagonist based protocol employed for women with low (1-5 pmolL) and
high (gt15 pmolL) AMH levels whereas patients with normal (5-15 pmolL)
AMH levels had standard long GnRH agonist regimen In addition the
patients with very low AMH (lt10 pmolL) had modified natural cycle IVF
treatment in 2nd centre The study reported that the group that had significant
tailoring of both mode and degree of stimulation to AMH levels (2nd centre)
had higher pregnancy rate and less cycle cancellation However given the
methodological weaknesses the findings of the study ought to be interpreted
with caution First the study compared the outcomes of small number of
195
patients who had treatment in two different centers suggesting that differences
in the outcomes may be due to variation in the characteristics of patient
populations andor performance of two different centers Moreover both
cohorts had some degree of tailoring of pituitary desensitisation regimens as
well as the daily dose of gonadotrophins to AMH levels suggesting estimation
of the effect of AMH tailoring to the outcome of treatment may not be
reliable
A subsequent study attempted to address the above issues by assessing a
somewhat larger number of IVF cycles from the same fertility centre (Yates et
al 2011) The study compared IVF outcomes of the cohorts that underwent
ovarian stimulation using chronological age and serum FSH (n=346) with
women that had AMH-tailored (n=423) treatment cycles (Yates et al 2011)
The study found that the group that had AMH-tailored ovarian stimulation
had significantly higher pregnancy rate less cycle cancellation due to poor or
excessive ovarian response and had significantly lower treatment costs
However this study also has appreciable weaknesses given that it was based
on retrospective data that compared outcomes of treatment cycles that took
place over two year period During this period apart from introduction of
AMH-tailored stimulation protocols other new interventions were introduced
particularly in the steps involved in embryo culture Although the outcomes of
the ovarian response to stimulation could have mainly been due to
performance of the stimulation protocols downstream outcomes such as
clinical pregnancy rate may be associated with the introduction of new
interventions in embryo culture techniques Nevertheless the study
demonstrated that tailoring of ovarian stimulation protocol to AMH levels
could reduce the incidence of cycle cancellation OHSS and the cost of
treatment supporting the need for more robust studies on the use of AMH in
the individualisation of ovarian stimulation in IVF
It appears despite a lack of good quality evidence that AMH-tailored
individualisation has been widely advocated and has been introduced in clinical
practice in a number of fertility units In the absence of good quality evidence
we decided to obtain more reliable estimates on the feasibility of AMH-tailored
ovarian stimulation using more robust methodology Availability of the data on
a large cohort of patients with AMH measurements who subsequently
underwent IVF treatment cycles in a single centre may allow us to obtain more
reliable estimates on the effectiveness of AMH-tailored COS Furthermore due
196
to changes on COS protocol combination of various regime and initial dose of
gonadotrophin were used for patients in each band of ovarian reserve This
may facilitate development of predictive models for both regime and dose for
the whole range of AMH measurements In addition as a part of the study we
decided to establish the role of patient and treatment related factors in
determination of ovarian response in cycle of IVF I believe that
understanding the effect of various factors on ovarian performance in COS
will improve the methodology of the study and can be used as a guide for
identification of confounders in future studies The first step in such an
analysis is to develop a statistical model to describe the relationship between
ovarian response and patient and treatment factors This can then be utilized
to explore the effects of treatment on outcome and potentially to allow optimal
treatments to be identified for given patient characteristics and ovarian reserve
METHODS
Objective
The objectives of the study were 1) to determine the effect of age AMH
AFC causes of infertility and treatment interventions on oocyte yield and 2) to
explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Population
Women of 21-43 years of age undergoing ovarian stimulation for
IVFICSI treatment using their own eggs at the Reproductive Medicine
Department of St Maryrsquos Hospital Manchester from 1st October 2008 to 8th
August 2012 were included Patients with previous AMH measurements using
DSL assay were included and patients that had AMH measurement with only
Gen II assay were excluded given the observed issues with this assay
(Rustamov et al 2012) The patients with ultrasound features of PCO previous
history of salpingectomy ovarian cystectomy andor unilateral
salpingoophorectomy have been excluded from the analysis Similarly cycles
with ovarian stimulation other than GnRH agonist long down regulation or
Short GnRH antagonist cycles were not included in the study
197
Dataset
The dataset for the study was prepared using a protocol for the data
extraction management linking and validation which is described in Chapter
4 In short first the data contained in clinical data management systems were
obtained on patient demography AMH measurements and IVF treatment
cycles Then data not available in electronic format were collected from the
patient case notes which includes causes of infertility previous history of
reproductive surgery AFC and folliculogram for monitoring of ovarian
stimulation Each dataset was downloaded in original Excel format into Stata
12 Data Management and Statistics Software (StataCorp LP Texas USA) and
analysis datasets were prepared in Stata format All IVF cycles commenced
during the study period were identified and the combined study dataset was
created by linking all datasets in cycle level using the anonymised patient
identifiers and the dates of interventions All steps of data handling have been
recorded using Stata Do files to ensure reproducibility and provide a record of
the data management process
Categorization of diagnosis
Patients with history of unilateral tubal occlusion or unilateral
salpingectomy were categorized as mild tubal factor infertility and patients with
blocked tubes bilaterally or with history of bilateral salpingectomy were
allocated to severe tubal disease Severe male factor infertility was defined if
the partner had azoospermia surgical sperm extraction or severe oligospermia
which necessitated Multiple Ejaculation Resuspension and Centrifugation test
(MERC) for assisted conception Mild male factor was defined as abnormal
sperm count that do not above meet criteria for severe male infertility
Diagnosis of endometriosis was based on a previous history of endometriosis
confirmed using Laparoscopy Diagnosis of endometrioma was established
using transvaginal ultrasound scan prior to IVF treatment In couples without a
definite cause for infertility following investigation the diagnosis was
categorized as unexplained Women with features of polycystic ovaries on
transvaginal ultrasound were categorized as PCO and excluded from analyses
198
Measurement of AMH and AFC
AMH measurements were performed by the in-house laboratory Clinical
Assay Laboratory of Central Manchester NHS Foundation Trust and the
procedure for sample handling and analysis was based on the manufacturerrsquos
recommendations Venous blood samples were taken without regard to the day
of womenrsquos menstrual cycle and serum samples were separated within two
hours of venipuncture Samples were frozen at -20C until analysed in batches
using the enzymatically amplified two-site immunoassay (DSL Active
MISAMH ELISA Diagnostic Systems Laboratories Webster Texas) The
intra-assay coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and
29 (at 56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and
49 (at 56pmoll) Haemolysed samples were not included in the study In
patients with repeated AMH the measurement closest to their IVF treatment
cycle was selected The working range of the assay was up to 100pmolL and a
minimum detection limit was 063pmolLThe results with minimum detection
limit were coded as 50 of the minimum detection limit (031 pmolL) and
the test results that are higher than the assay ranges were coded as 150 of the
maximum range (150 pmolL)
In our department the measurement of AFC is conducted as part of
initial clinical investigation before first consultation with clinicians and prior to
IVF cycle Qualified radiographers performed the assessment of AFC during
early follicular phase (Day 0-5) of menstrual cycle The methodology of
measurement of AFC consisted of the counting of all antral follicles measuring
2-6mm in longitudinal and transverse cross sections of both ovaries using
transvaginal ultrasound scan The AFC closest to the IVF cycle was selected
for the analysis
Description of COS Protocols
On the basis of their AMH measurement patients were stratified into
the treatment bands for ovarian stimulation using COS protocols During the
study two different COS protocols were used in our centre and in addition
three minor modifications were made in the 2nd protocol Time periods AMH
bands down regulation regimes initial dose of gonadotrophins and adjustment
of daily dose of gonadotrophins of the protocols are described in Table 1
Similarly the management of excessive ovarian response was tailored to
199
pretreatment AMH measurements although mainly based on the results of
oestradiol and scan monitoring the cycle stimulation (Table 2) Assessment of
transvaginal ultrasound guided follicle tracking and serum oestradiol levels in
specific days of the stimulation were used for monitoring of COS (Table 2)
The criteria for the cycle cancellation for poor ovarian response were same
across all protocols fewer than 3 follicles gt15mm in size on Day 10 of ovarian
stimulation
In patients undergoing their first IVF cycle AMH measurement
obtained at the initial assessment was used for determination of which band of
COS the patient would be allocated In the patients with repeated IVF cycles
AMH measurements were obtained prior to each IVF cycle unless a last
measurement performed within 12 months of period was available During the
study period two different assay methods for measurement of AMH was used
in our centre DSL Assay (1 October 2008- 16 November 2010) and Gen II
Assay (17 November 2010- 8 August 2012) Correspondingly during the study
period two different COS Protocols were used 1st Protocol (1 October 2008-
31 December 2010) and 2nd Protocol (1 January 2011-8 August 2012)
Consequently allocation into the ovarian reserve bands of the patients of 1st
protocol were based on DSL assay samples whereas the stratification of
patients of 2nd protocol was based either on DSL assay or Gen II assay
samples Specifically the patients with recent DSL measurements (lt12 months
old) who had IVF treatment during the period of 2nd Protocol had
stratification on the basis of their DSL measurements In these patients in
order to obtain equivalent Gen II value the DSL result was multiplied by 14
in accordance with the manufacturerrsquos recommendation at the time In the
patients without previous or recent (lt12 months old) DSL measurements
stratification into ovarian reserve bands was achieved using their most recent
Gen II measurements Therefore DSL measurements presented in this study
may or may not have been used for formulation of the treatment strategies for
individual patients In fact in this study DSL measurements have been
included in order to understand the role of AMH in determination of ovarian
response in IVF cycles rather than an evaluation of AMH-tailored COS
protocols In addition to introduction of 2nd protocol further modifications
were made to the protocol and therefore 2nd protocol comprised of 4 different
versions (Table 1-2) These changes in the protocols allowed us to compare the
effect of the various modifications to COS protocols on oocyte yield
200
Pituitary desensitisation regimes
Selection of pituitary desensitisation regime was based on the patientrsquos
AMH according to the COH protocol at the time of commencement of IVF
cycle (Table 1) Long agonist regime involved daily subcutaneous injection of
250g or 500 g of the GnRH agonist Buseralin acetate (Supercur Sanofi
Aventis Ltd Surrey UK) from the mid-luteal phase (Day 21) of preceding
menstrual cycle which continued throughout ovarian stimulation Women
treated with Antagonist regime had daily subcutaneous administration of
GnRH antagonist Ganirelex (Orgalutran Organon Laboratories Ltd
Cambridge UK) from Day 4 post-stimulation until the day of HCGGnRH
agonist trigger Ovarian stimulation was achieved by injection of daily dose of
hMG Menopuir (Ferring Pharmaceuticals UK) or rFSH Gonal F (Merck
Serono) as per AMH-tailored protocols (Table 1) Oocyte maturation was
triggered using 5000 international units of HCG (Pregnyl Organon
Laboratories Ltd Cambridge UK) and the criteria for timing of HCG
injection was consistent across all protocols one (or more) leading follicle
measuring gt18mm and two (or more) follicle gt17mm
Oocyte collection
Oocyte collection was conducted 34-36 hours following injection of
HCG for follicle maturation An Ultrasound Guided Oocyte Recovery (USOR)
was conducted by experienced clinicians under sedation The names of
practitioners were anonymised and the practitioner with the largest number of
oocyte recovery was categorized as a reference group Practitioners with a
small number (lt10) of oocyte collection were pooled (group J) If the cycle
was cancelled before oocyte recovery it was categorized under the practitioner
who was on-call for oocyte recovery session on the day of cycle cancellation
In cycles with pre-USOR cancellation for excessive ovarian response
total oocyte number was coded as 27 and Metaphase II oocyte number was
coded as 19 This was based on mean oocyte number in the patients who had
post-USOR cancellation for excessive ovarian response or OHSS
Quantitative assessment of total oocytes were conducted immediately
post-USOR by an embryologist In patients undergoing ICSI the assessment
of the quality of oocytes were conducted 4-6 hours post-USOR and the
201
oocytes assessed as in Metaphase II stage (MII) of maturation were categorized
as mature oocytes
Statistical analysis
The total number of collected oocytes in all cycles and the number of
mature oocytes in the subset of ICSI cycles were used as outcome measures
for the study Oocyte was selected as the primary outcome measure for
assessment of ovarian performance as this provides an objective measure
which is largely determined by effectiveness of ovarian stimulation regimens
In contrast downstream measures such as clinical pregnancy and live birth are
influenced by factors related to management gametes and embryos
Statistical analysis was conducted using multivariable regression models
and the process of model building included following steps 1) Analyses of
distribution of the groups and variables 2) Univariate analysis to establish the
factors that likely to affect total oocyte number 3) Evaluation of
representation of continuous variables 4) Analysis of interaction between
explanatory variables 5) Sensitivity analysis
First the distribution of patients the ovarian reserve markers
interventions and the outcomes were explored using cross tabulation
histograms Box Whisker and scatter plots Then in order to establish the
factors that likely to affect the oocyte number univariate analyses of Age
AMH AFC PCO status attempt of IVFICSI ethnicity BMI protocol
regime USOR practitioner and initial dose of gonadotrophins were conducted
Following this all these explanatory variables were run as part of initial
multivariable regression model Adjustment for confounders related to the
modifications of the protocols and unknown time-dependent changes
conducted by inclusion of the COS protocol categories in the regression
model
Evaluation of representation of oocyte number Age AMH AFC initial
dose of gonadotrophins were conducted by establishing best fit on the basis of
Akaike and Bayesian Information Criteria In addition interpretability of the
data and clinical applicability of the results (eg cut off ranges) were used as a
guide for selection of optimal representation Given the oocyte number was
not normally distributed it was represented in logarithmic scale (log(oocyte
number+5) To establish best representation for AMH AFC and initial dose
202
the models in following scales were run for each variable Linear quadratic
cubic 4th order polynomial linear (log) quadratic (log) cubic (log) 4th order
polynomial (log) cut-off ranges according to distribution Age adjustment in
quadratic scale following centering it to 30 years of age was found to provide
the most parsimonious representation AMH was found to be best represented
using following cut-off ranges 0-3 4-5 6-8 9-10 11-12 13-15 16-18 19-22
23-28 and 29-200 The best representation for AFC was found to be cut-off
ranges of 0-7 8-910-1112-14 15-19 20-24 and 25-100 Initial dose of
gonadotrophins were categorized as following 75-150IU 187-250IU 300IU
375IU 450IU
Subsequently interactions between explanatory variables were tested at
significance level of plt001 which revealed there were significant interaction
between PCO status and other covariables Given these interactions were
found to be complex and not easily computable we decided to restrict the
regression analysis to the non-PCO group We observed significant interaction
between regime and initial dose and therefore these variables were fitted with
interaction term in the model Finally sensitivity analyses of final regression
models were conducted Significance of the results was interpreted using p
value (lt005) effect size and clinical significance For assessment of feasibility
of individualization of stimulation regime and initial dose visual representation
of data was achieved using plots for observed and fitted values (Figure 1-4)
RESULTS
Description of data
A total of 1847 IVF or ICSI cycles of 1428 patients met inclusion criteria for
the study AMH measurements of all cycles and AFC measurements for 1671
cycles (n=1289 patients) were available In the analysis of total oocytes 1653
cycles were included and the analysis of MII oocytes comprised of 1101 ICSI
cycles
Mean AMH was found to be 178 (125) mean AFC was 142 56
mean number of total oocytes was 101 64 and mean number of mature
oocytes was 74 53 The distribution of the cycles according to patient
characteristics and interventions is shown in Tables 3
203
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
Table 4a and 4b show that there was a significant negative association of
oocyte yield with age and oocyte number following adjustment for AMH
AFC causes of infertility attempt of IVFICSI cycle USOR practitioner COS
protocol pituitary desensitisation regime type of gonadotrophin preparation
and initial daily dose of gonadotrophins (Table 4a) With each increase of age
by 1 year we observed approximately a 3 reduction in total oocyte
(p=00005) and a 2 decrease in mature oocyte number (p=0006) which was
independent of age and other covariables
In the analysis of AMH there was significant gradual increase in total
oocyte as well as mature oocyte number with increasing AMH following
adjustment for all covariables (Figure 1 and 2) Compared to an AMH range of
0-3 pmolL there was increase of 25 in the range of 4-5 pmolL (p=007)
36 in 6-8 pmolL (p=0008) 60 in 9-10 pmolL (p=00005) 65 in 11-12
pmolL (p=00005) 77 in 13-15 pmolL (p=00005) 83 in 16-18 pmolL
(p=00005) 80 in 19-22 pmolL (p=00005) 95 in 23-28 pmolL
(p=00005) and 112 in the range of 29-150 pmolL (p=00005) in total
oocyte number (Table 4a) Similar but less marked increase in MII oocyte
number was observed with increasing AMH
The data on AFC also showed that there was gradual increase in total
oocyte number with increasing AFC following adjustment of all covariables
(Table 4a) Compared to an AFC of 0-7 there was increase of 14 in the
range of 10-11 (p=003) 22 in AFC of 12-14 (p=0001) 26 in AFC of 15-
19 (p=00005) 34 in AFC of 20-24 (p=00005) and 40 in AFC of gt25
(p=0005) However there was no increase in total oocyte number in AFC
range of 8-9 compared to that of 0-7 AFC-related Increase in MII oocytes was
less marked compared to that of total oocytes (Table 4a)
Causes of infertility
We did not observe any significant associations between the causes of
infertility and number of retrieved oocytes However women diagnosed with
unexplained infertility appear to have marginally higher (10 p=002) total
number of oocytes compared to women whose causes of infertility were
204
known Diagnosis of severe tubal (-37 p=019) and severe male (-37
p=035) factor infertility was found to be associated with lower number of MII
oocytes compared to other causes of infertility However neither of these
parameters reached statistical significance Similarly there was no significant
association between oocyte number and diagnosis of endometriosis with or
without endometriomata compared to women that were not diagnosed with
the disease (Table 4a)
Attempt
Analysis of total number of oocytes showed that women who had their
2nd attempt of IVFICSI cycle had slightly higher (85 p=001) and those
that had their 3rd or 4th attempt of treatment had significantly higher total
oocyte yield (24 p=0001) compared to women undergoing their 1st attempt
of IVFICSI cycle (Table 4a) Similarly overall effect of attempt on total
oocyte yield was significant (p=0001)
However we did not observe any association between the attempt and
MII oocyte number in the analysis of the subset of ICSI cycles (p=045)
USOR practitioner COS protocol and gonadotrophin preparation
There was a significant association (p=00005) between total oocyte yield
with USOR practitioner (Table 4b) However the association of USOR
practitioner with MII oocyte number did not reach statistical significance
(p=0058)
We observed significant association between the COS protocols in the
analysis of total number of oocytes 1st version of 2nd Protocol (-18
p=00005) 2nd amp 3rd versions of 2nd Protocol (-14 p=005) and 4th version of
2nd Protocol (-24 p=0009) provided significantly lower number of total
oocytes compared to 1st Protocol However the effect of the COS Protocol
changes to MII oocyte number was not significant (p=024)
Compared to hMG ovarian stimulation using rFSH provided 13
higher total oocytes (p=0008) In the analysis of Metaphase II oocytes there
was no significant difference in oocyte yield between hMG and rFSH (026)
205
Regime and Initial dose of gonadotrophins
The regression analyses of the regimes for pituitary desensitisation and
initial dose categories were conducted in comparison to the reference group
(Agonist with 75-150IU hMGrFSH) IVFICSI cycles where Antagonist
with 75-100IU of hMGrFSH (-36 p=00005) was used provided
significantly lower total oocyte yield whereas cycles with Agonist and 300IU
hMGrFSH (15 p=005) provided marginally higher total oocyte number
In the analysis of MII oocytes cycles using Antagonist with 187-250IU
of hMGrFSH (43 p=005) Agonist with 300IU of hMGrFSH (25
p=016) and Antagonist with 375IU hMGrFSH (47 p=002) yielded higher
number of oocytes Use of Agonist with 375IU hMGrFSH (-18 p=05) and
Agonist with 450IU of hMGrFSH (-28 p=02) was associated with lower
mature oocyte number although the analysis did not reach statistical
significance
AMH-tailored individualization of COS
The overall effect of initial gonadotrophin dose to total oocyte yield
was found to be significant (plt0001) However other than the lowest dose
category with Antagonist regime the analysis did not show any consistent
dose-response effect on total oocyte number with increasing gonadotrophin
dose (Table 4b Figure 3a Figure 3b Figure 4a and Figure 4b)
In the analysis of MII compared to reference group of 75-150 IU of
initial daily gonadotrophins we observed increased oocyte yield in the
categories of 187-250 IU (43 p=005) and 375 IU (47 p=002) of
gonadotrophins However both of these groups had Antagonist regime for
pituitary desensitisation compared to that of Agonist in the reference group
and therefore the observed effect may be related to the regime of COS rather
than daily dose of gonadotrophins
206
DISCUSSION
In this study we explored the effect of age AMH AFC causes of
infertility attempt of IVF ICSI treatment and interventions of COS on
ovarian performance using a retrospective data on large cohort of IVF ICSI
cycles of non-PCO patients To our knowledge this is largest study to have
conducted a detailed analysis of the effect of AMH and AFC on ovarian
performance in IVFICSI cycles The study utilized a dataset that was
prepared using a robust protocol for data extraction and handling Similarly
the statistical analysis was based on a systematic exploration of the effect of all
relevant factors followed by adjustment for all relevant factors and finally
careful analysis
With regards to the outcome measures the quantitative response of
ovaries were measured using total collected oocytes in IVFICSI cycles and
the MII oocyte number in the subset of ICSI cycles were used as a
measurement of quantitative response of ovaries to COS Arguably oocyte
number is the best outcome measure for determination of ovarian response to
COS given it is mainly determined by patientrsquos true ovarian reserve the quality
of assessment of ovarian reserve and treatment strategies for ovarian
stimulation In contrast downstream outcomes such as clinical pregnancy and
live birth are subject to additional clinical and interventional factors which may
not always be possible to adjust for using retrospective data Indeed large
observational studies suggest that achieving optimal ovarian response is one of
the most important determinants of success of IVFICSI cycles and
recommend to use oocyte number as a surrogate marker for live birth (Sunkara
et al 2011) It appears around 10-15 total oocytes or 3-4 mature oocytes
provide optimal chance for a one live birth in IVFICSI cycles (Sunkara et al
2011 Stoop et al 2012) Therefore oocyte number appears to be most useful
marker for assessment of ovarian response to COS as well as in prediction of
live birth in cycles of IVFICSI
207
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
After adjusting for AMH AFC the patient characteristics and above
mentioned treatment interventions age remained as an independent predictor
of ovarian response to COS Our data showed approximately 3 (p=00005)
decrease in total oocyte and 2 (p=0006) reduction in mature oocyte number
with increase of age by factor of 1 year (Figure 3b and Figure 4b)
Interestingly the effect of AMH was also found to predict oocyte yield
independently of age with an effect actually more pronounced compared to
that of age After adjusting for age and all other factors there was gradual
increase in total oocyte number with increasing AMH which were both
clinically (25-110) and statistically (p=007-p=00005) significant (Table 4a)
We observed a largely similar effect of AMH in the analysis of mature
oocytes It is important to note that due to the issues with Gen II AMH assay
(Rustamov et al 2012) in this study we included only measurements obtained
with the DSL assay Consequently presented cut-off ranges may not be
applicable with current assay methods We suggest that future studies should
revisit the optimality of the cut-off ranges once a reliable assay method has
been established
Similarly after adjusting for all factors the effect of AFC on total
oocytes remained significant (14-40 plt003) However the effect of AFC
appears to be less marked compared to AMH It is important to note that the
AFC assessment in this study is based on the measurement of 2-6mm antral
follicles using two-dimensional transvaginal ultrasound scan The cut-off
ranges may not be applicable in centers where AFC measurement is obtained
using different criteria
Our analysis suggests that age AMH and AFC are independent
determinants of total and MII oocyte number in IVFICSI cycles and can be
used as predictors of ovarian performance irrespective of patient and treatment
characteristics However assessment of oocyte number is the quantitative
response of ovaries to COS and may not necessarily reflect qualitative
outcome
208
Causes Endometriosis Endometrioma
The causes of infertility do not appear to make a significant contribution
in determining total oocyte number after controlling for age AMH AFC the
attempt and treatment interventions Although in the analysis of MII oocytes
we observed reduced oocyte yield in women with severe tubal (-37) and
severe male (-37) infertility this was not statistically significant The analysis
of MII oocytes only included the subset of ICSI cycles consisting of women
with male factor infertility Therefore the effect of severe male factor infertility
may have been more marked in this model
We did not observe a significant difference in total or MII oocyte
number in women with a history of endometriosis with or without
endometriomata Current understanding of the effect of endometriosis in the
outcomes of IVF treatment suggests that the disease has detrimental effect on
IVF outcomes (Barnhart et al 2007 Barnhart et al 2002) However some argue
that no association is observed if the analysis conducted using proper
adjustment for all relevant confounders (Surrey 2013) Our data suggests that
after adjustment for all relevant factors there is no measurable association with
endometriosis (with or without endometriomata) and oocyte number Some
suggest that using ultra-long down regulation using depot GnRH analogue up
tp 3-6 months prior to ovarian stimulation improves ovarian performance in
patients with endometriomata Our dataset did not have information on
pituitary desensitisation prior IVF treatment cycles and we are therefore unable
to assess the effect of this intervention
Attempt
Our study found that 2nd and 3rd cycles were associated with 8
(p=001) and 24 (p=0001) higher total oocytes compared to that of 1st IVF
cycle However the effect of the attempt on MII oocytes was not significant
In our centre only patients with a previously unsuccessful IVF treatment are
offered subsequent cycles and therefore compared to the patients with
repeated attempts the group with first cycle may be expected to have better
oocyte yield However when controlled for all relevant confounders including
adjustment of treatment interventions 1st IVF cycle does not appear to provide
better oocyte yield In keeping with our findings a recent study demonstrated
independence of attempts of IVF cycles in terms of outcomes (Roberts SA and
209
Stylianou C 2012) Increased total oocyte yield with progressed attempts is
likely to be due to the adjustment of COS on the basis of information on the
ovarian response in previous cycles It is important to note that in this study
we assessed oocyte yield as the outcome measure and this may not necessarily
translate into live birth which is desired outcome for the couples Therefore
availability of data on the attempt-dependency of live birth in IVF cycles is
important and we suggest future studies should explore it
USOR practitioner
To our knowledge this is the first study that explored the effect of an
oocyte recovery practitioner on oocyte yield adjusting for all relevant
confounders We observed a considerable operator-dependent effect on total
oocyte yield which may be due to a variation of patients across the days of the
week (p=00005) The practitioners were allocated to the sessions of oocyte
recovery using a specific rota template according to the day of the week Given
in our centre we do not conduct oocyte recovery at weekends there may be
day-dependent variation in selection of patients For instance the patients who
are likely to have maturation of leading follicles during the weekend may have
been scheduled slightly earlier Similarly the patients with confirmed
maturation of leading follicles whose oocyte recovery would have fallen on
weekends may have been scheduled after the weekend allowing maturation of
additional follicles Therefore practitioners conducting the sessions of oocyte
recovery in extremes of weekdays may not necessarily have similar patients
compared to that of other days which may have introduced some bias in
estimating the outcomes of individual practitioners Nevertheless given the
statistical analysis adjusted for age ovarian reserve and treatment interventions
we think there is considerable true between-operator variability on total oocyte
number We suggest that future studies should assess it further by including
adjustment for follicle number and size on the day of HCG
Interestingly overall effect of the operator did not reach statistical
significance in the analysis of MII oocytes in ICSI subset (p=0058) This may
suggest irrespective of total oocyte yield aspiration of only follicles of larger
than a certain size provides oocytes with potential for fertilization
210
COS Protocol
Controlled ovarian hyperstimulation in IVF is conducted using a pre-
defined protocol which contains the policy on selection of regime for pituitary
desensitisation the initial daily dose of gonadotrophins the monitoring of
ovarian response the adjustment of daily dose of gonadotrophins the policy
for cancellation due to poor or excessive ovarian response and criteria for
HCG trigger for final maturation of oocytes Determination of the optimal
treatment regime and the initial dose of gonadotrophins for each patient is
frequently achieved by stratification of patients into various bands of ovarian
reserve on the basis of the assessment of ovarian reserve The assessment of
ovarian reserve prior to IVF cycle is performed using biomarkers which usually
consist of one or combination of following Age AMH AFC and FSH In our
centre stratification of patients into the bands of ovarian reserve was
determined on the basis of the patientrsquos AMH measurements For instance the
patients deemed to have lower ovarian reserve were allocated to the treatment
band with higher daily dose of gonadotrophins and vice versa (Table 1)
The study found that the 2nd protocol was associated with 14-24 lower
total oocyte yield compared to the 1stprotocol The differences in the
interventions between the protocols are described in Table 1 and Table2
Compared to the 1st protocol the 2nd protocol had a) some patients allocated
to COS bands using Gen II assay measurements which later was found to
provide inaccurate measurements b) more AMH cut-off bands for COS
bands c) strict monitoring of ovarian response and corresponding adjustment
of daily dose of gonadotrophins and d) strict criteria for cycle cancellation for
excessive response Therefore our data suggests that the COS protocols with
broader AMH cut-off bands with less strict criteria for adjustment of daily
gonadotrophins may provide higher oocyte yield However given it is
retrospective analysis the limitation of the study should be recognized and we
recommend more robust prospective studies on optimization of AMH tailored
protocols should be conducted
Gonadotrophin type
The study showed that rFSH was associated with higher total oocyte
number (13 p=0008) Interestingly analysis of MII oocyte showed a larger
confidence interval and did not reach statistical significance suggesting the
211
effect of rFSH was not a strong determinant of mature oocytes Perhaps
observation of higher total oocytes in rFSH cycles compared to that of HMG
and yet comparable mature oocyte number in the study suggest that regardless
of total oocyte yield only follicles with a potential for maturation will achieve a
stage of metaphase II
Ovarian stimulation in cycles for IVF is largely achieved by two different
analogues of follicle stimulating hormone human menopausal gonadotrophin
(hMG) and recombinant follicle stimulating hormone r(FSH) Although
purified hMG contains more luteinising hormone compared to rFSH which is
believed to assist endometrial maturation and improve odds of implantation in
cycles of IVF Furthermore the LH component of hMG is believed to assist in
maturation of oocyte with subsequent improvement in live birth On the other
hand historically rFSH was believed to have less batch-to-batch variation
compared to that of HMG which allows administration of more precise daily
dose of gonadotrophins To date a number of studies have been published
comparing these two forms of gonadotrophin preparations which provide
conflicting findings However systematic review that compared of the effect of
these types of gonadotrophins on live birth rate suggests that there is no
significant difference on live birth rate (van Wely et al 2011) This supports our
findings on that irrespective of total oocyte yield clinically useful mature
oocyte number is comparable between the groups
Regime and dose of gonadotrophins
The study found that compared to the reference group (Agonist 75-
150IU) none of the combination of the regime and gonadotrophin dose
provided a higher total oocyte yield Women that were in Antagonist regime
group with an initial daily dose of 75-150 IU gonadotrophins produced
approximately 36 fewer total oocytes (p=00005) However comparison of
MII oocytes of these groups did not reach statistical significance and the effect
size was much smaller (-19 p=023) This and reference groups represent the
patients with high ovarian reserve who had milder ovarian stimulation because
of risk of excessive ovarian response and OHSS Lower total oocyte yield and
comparable mature oocyte number in the Antagonist regime may explain why
this regime is reported to be associated with reduction in the risk of OHSS and
212
yet comparable live birth in patients with high ovarian reserve (Yates et al
2012)
In the analysis of MII oocytes Antagonist with 187-250 IU of
gonadotrophin and Antagonist with 375 IU of gonadotrophin provided around
43 (p=005) and 47 (p=002) more oocytes compared to that of the
reference group (Agonist 75-150 IU) Interestingly total oocytes of these
groups were comparable to that of reference group suggesting that using
Antagonist protocol may be associated with improvement in oocyte
maturation compared to Long Agonist regime Perhaps in addition to the
effect of exogenous HCG endogenous LH may play role in oocyte maturation
in IVFICSI cycles and shorter desensitisation of pituitary using Antagonist
regime may allow secretion of LH during COS in lower quantities
AMH-tailored individualisation of COS
Given that we did not observe a significant dose-dependent effect on
oocyte number we were not able to develop AMH or AFC tailored
individualisation protocols for COS Although the initial dose of
gonadotrophin is believed to be one of the main determinants of oocyte yield
our study suggests that the association between these variables is weak
Consequently strict protocols for tailoring the initial dose of
gonadotrophins may not necessarily improve ovarian performance in IVF
treatment It is important to note that our COS protocols recommended close
monitoring of ovarian response and corresponding dose adjustment starting
from 3rd day of COS which may have masked the effect of initial dose
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
213
Strengths of the study
Here we presented the largest study on assessment of the role of patient
and treatment related factors on oocyte yield and exploration of optimization
of AMH-tailored COS using a validated dataset Statistical analysis included
systematic assessment of the effect possible confounders on measured
outcome including of age AMH AFC causes of infertility attempt of IVF
treatment USOR practitioner type of gonadotrophin pituitary desensitisation
regime and initial dose of gonadotrophins On the basis of above analysis a
robust multivariable regression models for assessment of the effect all above
factors on total and mature oocyte number have been developed
Prior to conducting this study previous projects explored the
performance of AMH assay methods The studies found that Gen II assay may
yield highly non-reproducible measurements compared to that of DSL assay
(Rustamov et al 2012a) Therefore in this study only DSL AMH assay
measurements were included Furthermore previous projects (Chapter 5 and 6)
explored the effect of various patient related factors on AMH AFC and FSH
measurements and found that some of the factors had measurable impact on
ovarian reserve These findings were used in establishing which patient related
factors ought to be explored in the building of regression models for this
study However the DSL assay is no longer available and most clinics are
mainly using Gen II AMH assay in formulation of COS in IVF Given its
observed instability AMH-tailoring based on Gen II samples may lead to
erroneous allocation of patients to the band that is significantly inconsistent
with patientrsquos ovarian reserve Subsequently this may result in the extremes of
ovarian response to COS including severe OHSS and cycle cancellation
Weaknesses of the study
The main weakness of the study is that the analysis is based on
retrospectively collected data The methodology included an extensive
exploration for possible confounders and adjustment for the ones that were
found to be significant However there are may be unmeasured factors that
that might have affected the estimates In addition the study included only
patients that did not have PCO appearance on ultrasound scan The analysis in
all patients showed that interaction of PCO status with other covariables was
complex which could introduce bias in estimation of the effects of other
214
factors Therefore analyses of the groups with and without PCO were run
separately Subsequently results of non-PCO group was presented in the thesis
given it had the largest number of cycles Compared to non-PCO analysis we
did not observe significant difference in the results of PCO model
The study assessed ovarian response using oocyte yield only Other
outcomes of ovarian response such as duration of ovarian stimulation total
dose of gonadotrophins cycle cancellation due to poor or excessive ovarian
response and OHSS have not been analysed Therefore it is important to
interpret the findings of this study in the context of ovarian response
determined by oocyte yield Specifically the study should not be used to
interpret cycle cancellation for excessive ovarian response As described in the
methodology of the study the oocyte number in the cycles with cancellation of
oocyte recovery due to excessive response were recoded with comparable
values with cycles that were cancelled following oocyte recovery for OHSS
Given the main desired outcome of IVF treatment is live birth the
overall success of a treatment cycle should reflect this outcome measure This
study does not assess the effect of above factors to overall success of IVF
treatment However the study provides a robust data on research methodology
in assessment of IVF outcomes which can assist in the assessment of other
outcome measures in future studies
SUMMARY
After adjustment for all the above factors age remained a negative
predictor of oocyte yield whereas we observed a gradual and significant
increase in oocyte number with increasing AMH and AFC values suggesting
all these markers display an independent association with oocyte yield IVF
attempt oocyte recovery practitioner type of gonadotrophin were found to
have significant effect on total oocyte yield However the effect of these
factors on mature oocyte number did not reach statistical significance Whilst
total oocyte number was comparable between pituitary desensitisation regimes
GnRH antagonist cycles were found to provide significantly higher mature
oocytes compared to that of long GnRH agonist regime
In terms of the effect of initial dose on oocyte yield following
adjustment for all above variables we did not observe significant increase in
215
oocyte number with increasing gonadotrophin dose categories Therefore
strict protocols for tailoring the initial dose of gonadotrophins may not
necessarily improve ovarian performance in IVF treatment However further
time series regression analysis with full parameters of cycle monitoring and the
dose adjustments in the model should be conducted in order to ascertain the
role of AMH in tailoring the dose of gonadotrophins in cycles of IVF
This study demonstrates complexity of the factors that determine
ovarian response in IVF cycles Therefore assessment of AMH-tailored
individualisation of ovarian stimulation should be based on a robust
methodology preferably using a large randomized controlled trial
Furthermore measurement of AMH ought to be based on a reliable assay
method which is currently not available In the meantime the limitations of
available evidence on AMH-tailored individualisation of ovarian stimulation
should be taken into account in the management of patients
216
References
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Barnhart K Dunsmoor-Su R Coutifaris C Effect of endometriosis on in vitro fertilization Fertil Steril 2002771148ndash55 Dechaud H Dechanet C Brunet C et al Endometriosis and in vitro fertilization a review Gynecol Endocrinol 200925717ndash21 Dewailly D Andersen CY Balen A Broekmans F Dilaver N Fanchin R Griesinger G Kelsey TW La Marca A Lambalk C Mason H Nelson SM Visser JA Wallace WH Anderson RA The physiology and clinical utility of anti-Mullerian hormone in women Hum Reprod Update 2014 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A and Sunkara S K Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers from theory to practice Human Reproduction Update Vol20 No1 pp 124ndash140 2014
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867-75 Nelson SM Biomarkers of ovarian response current and future applications Fertil Steril 201399963ndash969
Roberts SA Stylianou C The non-independence of treatment outcomes from repeat IVF cycles estimates and consequences Hum Reprod 2012 Feb27(2)436-43
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum
217
Reprod 2012a273085-3091
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071 Stoop D Ermini B Polyzos NP Haentjens P De Vos M Verheyen G and Devroey P Reproductive potential of a metaphase II oocyte retrieved after ovarian stimulation an analysis of 23 354 ICSI cycles Human Reproduction 2012 Vol27 No7 pp 2030ndash2035 2012 Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011 261768ndash1774 Sunkara SK Coomarasamy A Faris R Braude P Khalaf Y Effectiveness of the GnRH agonist long GnRH agonist short and GnRH antagonist regimens in poor responders undergoing IVF treatment a three arm randomised controlled trial (ESHRE) 2013London UK SurreyES Endometriosis and Assisted Reproductive Technologies Maximizing Outcomes Semin Reprod Med 201331154ndash163 van Wely M1 Kwan I Burt AL Thomas J Vail A Van der Veen F Al-Inany HG Recombinant versus urinary gonadotrophin for ovarian stimulation in assisted reproductive technology cycles Cochrane Database Syst Rev 2011 Feb 16(2)CD005354
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
218
Figure 1 Study groups for assessment of Individualisation of pituitary desensitisation regime
Individualisation of pituitary desensitisation regimens can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high ovarian reserve
Individualisation of COS Regime
Low AMH
(eg DSL assay
22-157 pmolL)
GnRH
Antagonist
GnRH
Agonist
Normal AMH
(eg DSL assay
158-288pmolL)
GnRH
Antagonist
GnRH
Agonist
High AMH
(eg DSL assay
gt288 pmolL)
GnRH
Antagonist
GnRH
Agonist
219
Fiure 2 Study groups for assessment of individualisation of initial gonadotrophin dose
Individualisation of daily dose of gonadotrophins can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high
ovarian reserve
Individualisation
Gonadotrophin
Dose
Low AMH
(eg DSL assay 22-157 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
Normal AMH
(eg DSL assay158-288pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
High AMH
(eg DSL assay gt288 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-375 IU)
Low Dose
(eg HMG 75-150 IU)
220
Table 1 AMH-tailored stratification protocols for regime starting dose of hMGrFSH and adjusting daily dose of gonadotrophins (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (V1) (01 Jan 2011-30 Apr 2011)
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Initial dose (Day 1-3) 1) lt22 AMH (DSL) Exclude 2) 22-156 AMH (DSL) Antagonist 300 hMG 3) 157-285 AMH (DSL) Long Agonist 200 rFSH225 hMG 4) gt286 AMH (DSL) Antagonist 150 hMG
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 375 hMG 3) 11-21 AMH (Gen II) Long Agonist 300 hMG 4) 22-30 AMH (Gen II) Long Agonist 225 hMG 5) 31-39 AMH (Gen II) Long Agonist 150 hMG 6) 40-67 AMH (Gen II) without PCO Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCO Long Agonist 125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Long Agonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Long Agonist 1125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 450 hMG 2) 3-10 AMH (Gen II) Long Agonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 rFSH 8) gt67 AMH (Gen II) Antagonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 300 rFSH 2) 3-10 AMH (Gen II) Long Agonist 225 rFSH 3) 11-21 AMH (Gen II) Long Agonist 1875 rFSH 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 hMG 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 hMG 8) gt67 AMH (Gen II) Antagonist 1125 hMG
Dose adjustment No or minimum change on daily dose of gonadotrophin
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
221
Table 2 AMH-tailored stratification protocols for management of suspected excessive response (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (v1) (01 Jan 2011-30 Apr 2011)
amp
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Coasting for excessive response on day 8
Oestradiol gt20000 pgml 30-40 follicles larger than 10mm or Oestradiol gt18000 pgml
30-40 follicles larger than 12mm
No coasting
Coasting for excessive response once follicle maturation meets criteria
Oestradiol gt20000 pgml
30-40 follicles larger than 10mm
25-40 follicles larger than 10mm
25-30 follicles larger than 15mm
Cancellation for excessive response
Day 8 or thereafter Oestradiol lgt20000 pgml and symptoms of OHSS after gt3 days of coasting
Day 8 or thereafter More than 40 follicles larger than 10mm
Day 10 or thereafter More than 40 follicles larger than 15mm
Day 8 or thereafter Cancel only if symptoms of OHSS
222
Table 3 Distribution of patient characteristics and interventions
In total 1847 cycles included in the study
n
Causes
Unexplained 591 32
Mild tubal 325 176
Severe tubal 37 2
Mild male 589 3189
Severe male 18 097
Endometriosis 91 493
Endometrioma 47 28
Attempt
1 1346 7287
2 406 2198
3 91 493
4 4 022
USOR practitioner
A 570 317
B 412 2291
C 147 818
D 15 083
E 153 851
F 86 478
G 118 656
H 136 756
I 141 784
J 20 111
Protocol
1 1265 6849
2 (v1) 399 216
2 (v2ampv3) 79 428
2 (v4) 104 563
FSH preparation
HMG 1594 87
rFSH 237 13
Regime
Long Agonist 820 444
Antagonist 1027 556
Initial dose
75-150IU 298 1617
187-250IU 483 2621
300IU 914 4959
375IU 60 326
450IU 88 477
223
Table 4a Results of multivariable regression analysis for total and MII oocytes
Total oocytes (n=1653) Metaphase II oocytes (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
Age -0031 -004 -002 00005 -0021 -004 -001 0006
age2 -0002 000 000 0047 -0002 -001 000 0206
AMH categories (Ref0-3 pmolL) 00005 00005
4-5 pmolL 0254 -003 054 0078 -0073 -054 040 0761
6-8 pmolL 0368 010 064 0008 0250 -019 069 0267
9-10 pmolL 0605 034 087 00005 0474 004 091 0034
11-12 pmolL 0651 039 091 00005 0305 -016 077 0198
13-15 pmolL 0779 051 104 00005 0372 -008 083 0109
16-18 pmolL 0836 057 111 00005 0655 018 113 0007
19-22 pmolL 0803 051 109 00005 0381 -013 089 0142
23-28 pmolL 0954 067 123 00005 0832 034 132 0001
29-200 pmolL 1126 084 141 00005 0872 035 139 0001
AFC categories (Ref 0-7) 00005 0008
8-9 -0039 -018 010 0589 0001 -024 024 0992
10-11 0145 001 028 0037 0185 -005 042 0119
12-14 0223 009 036 0001 0254 002 049 0031
15-19 0263 013 040 00005 0113 -013 036 0362
20-24 0344 017 052 00005 0456 013 078 0006
25-100 0405 021 060 00005 0455 009 082 0015
Causes of infertility
Unexplained 0103 002 019 0021 0090 -010 028 0354
Mild tubal -0012 -010 008 0797 -0098 -029 009 0307
Severe tubal -0066 -030 017 0579 -0371 -093 019 0194
Mild male 0014 -007 009 0729 0135 -002 029 009
Severe male -0074 -055 040 0758 -0377 -117 042 0351
Endometriosis -0108 -026 005 0169 -0139 -041 013 0314
Endometrioma -0016 -018 015 0843 0043 -035 044 083
Attempt (Ref 1st) 0001 045
2nd 0085 002 015 0016 0080 -006 022 0274
3rd4th attempt 0243 010 039 0001 0116 -014 037 0367
224
Table 4b Results of multivariable regression analysis for total and MII oocytes Continuation of Table 4a)
Total oocyte (n=1653) Metaphase II oocyte (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
USOR Practitioner (Ref A) 00005 0058
B -0009 -009 007 0823 -0129 -031 005 0153
C 0104 -003 024 0129 0111 -012 034 0348
D -0260 -059 007 0125 -0287 -108 051 0478
E -0297 -044 -016 0 -0246 -048 -001 0043
F -0173 -032 -003 0017 -0367 -072 -001 0043
G -0213 -039 -003 002 -0311 -061 -001 0044
H -0007 -012 011 0909 0022 -020 025 0849
I -0149 -025 -004 0005 -0082 -030 014 0462
J -0549 -095 -015 0007 -0408 -095 014 0143
Protocol (Ref 1st) 00003 024
2nd (v1) -0186 -027 -010 0 -0066 -024 010 0449
2nd (v2ampv3) -0140 -028 000 0056 0175 -007 042 0156
2nd (v4) -0244 -043 -006 0009 0002 -031 031 0989
Gonadotrophin (Ref HMG)
rFSH 0137 004 024 0008 0119 -009 033 0262
Dose amp Regime (RefAgonist 75-150IU) 00005 00052
Antagonist 75-150IU -0364 -053 -020 0 -0199 -051 011 0203
Agonist 187-250IU 0104 -003 024 0139 0028 -031 036 0869
Antagonist 187-250IU 0124 -006 030 0176 0436 -002 089 0059
Agonist 300IU 0151 -001 031 0059 0258 -011 062 0165
Antagonist 300IU 0003 -016 017 0968 0143 -022 050 0433
Agonist 375IU 0072 -023 037 0639 -0185 -086 049 0591
Antagonist 375IU 0124 -011 035 0291 0478 005 090 0028
Agonist 450IU -0129 -041 015 037 -0285 -080 023 0278
Antagonist 450IU -0207 -048 006 0134 0046 -041 051 0843
Intercept 1342 102 166 0 0993 043 155 0001
225
Figure 3a Total oocytes
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fit0
Non-PCO
226
Figure 3b Total oocytes
Plots show the raw data as dots Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 stimulation with HMG USOR practitioner-A none of the specific causes of infertility
25 30 35 40
12
510
20
Age
To
tal E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
To
tal E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
To
tal E
gg
s
AFC
fit0
Non-PCO
227
Figure 4a Metaphase II oocytes (ICSI subset)
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fitm0
Non-PCO
228
Figure 4b Metaphase II oocytes (ICSI subset)
Plots show raw data as dot Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 simulation with HMG USOR practitioner-A None of the specific causes of infertility
25 30 35 40
12
510
20
Age
Ma
ture
IC
SI E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
Ma
ture
IC
SI E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
Ma
ture
IC
SI E
gg
s
AFC
fitm0
Non-PCO
229
GENERAL SUMMARY
7
230
GENERAL SUMMARY
Anti-Muumlllerian hormone a dimeric glycoprotein secreted from granulosa cells
of growing ovarian follicles appears to play a central role in the regulation of
oocyte recruitment and folliculogenesis (Durlinger et al 2002)
Serum anti-Muumlllerian hormone concentration has been found to be one of
the best predictors of ovarian performance in IVF treatment (van Rooij et al
2002 Broer et al 2011) Therefore an evaluation of the role of AMH in assisted
conception has been of great interest and consequently a considerable body of
research work has been performed during last two decades Most published
studies with varying methodological quality have suggested that AMH is one
of the most reliable predictors of ovarian performance in IVF treatment cycles
Consequently many fertility centers have introduced measurement of AMH for
the assessment of ovarian reserve and as a tool for formulation of treatment
strategies for controlled ovarian hyperstimulation in assisted conception
However the studies described in this thesis suggest that some assumptions on
the clinical value of AMH particularly reliability of AMH assay methods and
the role of AMH-tailored individualisation of daily dose of gonadotrophins in
IVF were not based on robust data
For the purpose of this thesis I conducted a comprehensive review of the
published literature on the biology of ovarian reserve the role of AMH in
female reproduction the assay methods and clinical application of AMH in
assisted conception (Chapter 1) I established that a) published work on
sampling variability of AMH measurements and comparability of various assay
methods provide conflicting results b) data on the effect of ethnicity BMI
reproductive pathology and surgery is scarce and c) good quality data on
individualisation of AMH-tailored controlled ovarian hyperstimulation in IVF
is lacking Consequently I decided to conduct a series of studies that directed
towards an improvement of the scientific evidence in these areas of research
Our previous work on within-patient variability of the first generation DSL
assay samples showed that AMH measurements may exhibit considerable (CV
28) sample-to-sample variability (Rustamov et al 2011) In view of this it was
decided to evaluate the validity of newly introduced Gen II assay (Chapter
21) In order to achieve adequately powered results all available AMH
samples of women of 20-46 years of age who had investigation for infertility at
231
secondary and tertiary care divisions of St Maryrsquos Hospital during the study
period were selected for the study According to the manufacturerrsquos
recommendation haemolysed AMH samples may provide erroneous results
and therefore women with haemolysed samples were excluded from the
analysis Inclusion of all women during the study period was also important in
reducing the risk of selection bias particularly in this study which compared
historical and current AMH assay Given the referral criteria of patients did not
change throughout the study period I could confidently report that observed
comparison between DSL and Gen II samples were the reflection of true
differences of the assay methods It is important to note that validity and
performance of a new test should ideally be compared to a reliable ldquogold
standardrdquo test However to date there appears to be no gold standard test in
measurement of AMH and hence an evaluation of the performance of assay
methods can be chllanging Given the lack of a gold standard I decided to
assess the quality of the new test in comparison to what was considered the
most reliable test available at that time accepting that such a comparison may
have limitations Previously two AMH assays (DSL and IOT) were in use and
there is no research evidence on the superiority of one assay over other
Therefore in this study the new Gen II assay was compared to the DSL assay
method which was previously available in our clinic
Once I prepared a robust and validated dataset the quality of Gen II assay
was evaluated by taking following steps of investigation First within-patient
between-sample variability of AMH measurements of Gen II assay samples
were obtained and compared to that of DSL assay samples Then the validity
of the manufacturer recommended between-assay conversion factor was
evaluated by comparing the Gen II assay sample measurements to that of DSL
assay method using both cross-sectional and longitudinal datasets The stability
of the Gen II assay samples was assessed by examining a) stability of the
samples in room temperature b) the linearity of dilution of the samples c)
comparing the standard assay preparation method to that of an equivalent
method and d) stability of samples during storage in frozen condition
Worryingly the study found that the Gen II AMH assay which was
reported to be more reliable than previous assays gave significantly higher
sampling variability (CV 59) compared to that of DSL samples (CV 28)
This significant variation in between repeated measurements of Gen II samples
indicated that there might be a profound fault in the assay method The
232
comparison of the assay methods using a large cohort of clinical samples
suggested that Gen II assay provided 40 lower measurements compared to
that of DSL contradicting the manufacturerrsquos reported 40 higher
measurements (Kumar et al 2011) These discrepancies in the sampling
variability and assay-method comparability suggested that Gen II assay samples
may lack stability which had not been observed previously
When different assays are available for a particular analyte it is critical that
the comparability of results is established and reliable conversion factors or
calibration curves are determined The study demonstrated that the difference
between the previously recommended (Kumar et al 2011 Wallace et al 2011)
conversion factor and the conversion formula obtained in this study was as
high as 60-80 All three studies followed the manufacturersrsquo
recommendations as supplied in the kit insert In terms of the study design
and analysis previous studies assessed the within-sample difference between
the two assays considered this involved the thawing of samples splitting into
two different aliquots and analysis of each aliquot with a different assay In
contrast I conducted between-sample comparison of historical DSL
measurements to that of Gen II using cross sectional and longitudinal
population based analyses The laboratory based within-sample conversion
formula should be reproducible in population based between-sample
comparison particularly in longitudinal analysis Observed discrepancies in the
conversion factors again suggested that AMH samples may suffer from pre-
analytical instability
Thus in collaboration with the scientific team of the Clinical Assay
Laboratory of our hospital we investigated the stability of Gen II assay
samples The studies on sample storage and preparation confirmed the Gen II
assay samples exhibited considerable instability under the storage and
processing conditions recommended by the manufacturer It was suggested
that Gen II samples remain stable when stored in unfrozen conditions up to 7
days and many IVF clinics adopted the practice of shipping unfrozen AMH
samples to centralized laboratories for processing and analysis (Kumar et al
2010 Nelson and La Marca 2011) This study demonstrated that storage of
unfrozen samples can affect obtained results considerably Evaluation of the
stability of samples (n=48) at room temperature found that in the majority of
samples AMH levels in serum increased progressively during 7 days of storage
with an overall increase as high as 58 Contrary to the manufacturerrsquos report
233
even storage of samples in frozen condition (-20 ordmC) does not ensure the
stability of the samples Storage at -20ordmC for 5 days increased AMH levels by
23 compared to fresh samples Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample In contrary the study showed that Gen II samples exhibit
considerable increase with the dilution Pre dilution of serum prior to assay
gave AMH levels up to twice that found in the corresponding neat sample
Similarly pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave overall 72 higher readings compared to sequential
addition These experiments confirmed that Gen II assay methodology was
completely flawed and routine clinical samples were likely to provide highly
erroneous results which could lead to adverse clinical consequences in
patients
To evaluate the robustness of our data I validated the study on the
variability of Gen II samples using external data (Chapter 22) Assessment of
samples obtained from different patient population and different assay-
laboratory found that within-patient between-sample variability of Gen II
AMH measurements were similar to that of my study (CV 62) This
confirmed that Gen II assay sampling variability was independent of
population or laboratory and specific to the assay-method
Findings of this series of studies suggested that the use of Gen II
measurements might have considerable clinical implications particularly when
used as a marker for triaging patient to ovarian stimulation regimens in cycles
of IVF In order to obtain equivalent clinical cut-off ranges for Gen II
samples previously used DSL assay based guidance ranges were recommended
to be increased by 40 However my study found that Gen II assay may
actually provide 20-40 lower measurements compared to that of DSL which
might led to allocation of patients to inappropriate treatment regimens Given
that using the above conversion formula may underestimate ovarian reserve by
60-80 the patients may inadvertently be given significantly higher dose of
gonadotrophins than appropriate in the individual IVF treatment cycles This
can increase the patientrsquos risk of excessive ovarian response resulting in
cancellation of IVF cycles andor severe ovarian hyperstimulation syndrome
(OHSS) In addition significant variation of Gen II assay sample
measurements (CV 59) may also lead to inconsistency in allocation of
patients to appropriate cut off ranges Indeed this was demonstrated by a
234
recent study which found that 7 out of 12 patients moved from one cut-off
range to another when Gen II assay was used for AMH measurements
(Hadlow et al 2013) Therefore we suggested that Gen II assay samples should
not be used in allocating patients to ovarian stimulation regimens
Immediate steps were taken to report these findings to the manufacturer
scientists clinicians and the quality assessment agencies The findings of the
study were presented at the annual meetings of European Society of Human
Reproduction and Embryology as well as British Fertility Society The study
was also published in Human Reproduction which generated an important debate
on the validity of Gen II assay measurements Further independent studies by
other research groups and re-evaluation of the assay by the manufacturer have
confirmed our results (Han et al 2013) This led to recognition of the issues of
the Gen II assay by the manufacturer and consequent modification of the assay
method (King 2012) Subsequent evaluation of Gen II assay by the Medicines
and Healthcare Products Regulatory Agency (MHRA) and the National
External Quality Assessment Service (NEQAS) have confirmed the above
findings As a result the Human Fertility and Embryology Authority have
circulated a field safety notice with the regards to the pitfalls of the AMH Gen
II assay We informed National Institute for Health and Care Excellence
(NICE) of the problems of AMH measurements and urged it to review its
current recommendation on the use of AMH in the investigation and
treatment of infertility With regards to the impact of this work it is important
to note that AMH is widely used in fertility clinics around the world and Gen
II assay is the only commercially available kit for the measurement of AMH in
most countries Consequently this study has made a direct significant impact
in the improving safety and effectiveness of fertility investigation and
treatment around the world However further studies are required to
determine the cause of the instability In addition the validity of the modified
protocol for Gen II assay and other new AMH assays need to be evaluated In
the meantime caution should be exercised in the interpretation of Gen II
AMH measurements
Studies above established that invalid commercial AMH assay was
introduced for clinical use without full and independent validation Regretfully
the issues with the assay were not identified early enough to prevent
widespread use of this faulty test in clinical management of patients around the
world In order to avoid above failures and improve reliability of future AMH
235
assays I recommend following steps should be taken 1) International
standards for the evaluation of validity of existing and future AMH assays
should be developed 2) Independent research groups should evaluate validity
of AMH assays before introduction of the test for clinical application 3)
Validity and performance of already introduced AMH assays ought to be
evaluated by independent research groups periodically to ensure timely
detection of the deterioration in the quality of the test
In view of the observed issues with AMH measurements we conducted
a critical appraisal of the published research on the previous and current assay
methods that reported AMH measurement variability assay method
comparison and sample stability (Chapter 3) Following a systematic search
for all published studies on the evaluation of performance of historic and
current AMH assays ten sample stability studies 17 intrainter-cycle variability
studies and 14 assay method comparability studies were identified Previously
most studies reported that variability of AMH in serum was very small and
suggested a random single measurement provides an accurate assessment of
circulating AMH in serum Therefore using a random AMH measurement for
assessment of ovarian reserve has become a routine practice It appears that
both in reporting particularly in its interpretation the term ldquoAMH variabilityrdquo
was used too broadly and had a various meanings Reviewing all published
studies that used term ldquoAMH variabilityrdquo I identified that the term was used in
interpretation of four distinct outcomes for measurement of variability of
AMH in serum 1) circadian 2) within the menstrual cycle 3) between
menstrual cycles and 4) between repeated samples without consideration of the
day of menstrual cycle In order to delineate the reported variability of AMH
for each outcome I divided the variability studies into four separate groups
and reviewed each study within its appropriate group The review found that
most studies were based on small sample sizes and did not report the
methodology for sample processing and analysis fully The studies also appear
to refer to their outcomes as biological variability of AMH without taking into
account the variability arising due to errors in its measurement More
importantly the review demonstrated that there is clinically significant
variability between AMH measurements in repeated samples which was
reported to be markedly higher with currently used Gen II assay compared to
that of historic DSL and IOT assays
236
Appraisal of assay method comparability found that despite using the
standard manufacturer protocols for the sample analysis the studies have
generated strikingly different between-assay conversion factors The studies
comparing first generation AMH assays (DSL vs IOT) reported conversion
factors ranging from five-fold higher with the IOT assay compared to both
assays giving equivalent AMH concentrations Similarly studies comparing first
and second-generation assays (DSL vs Gen II or IOT vs Gen II) derived
conflicting conclusions The apparent disparity in results of the assay
comparison studies implies that AMH reference ranges and guidance ranges
for IVF treatment which have been established using one assay cannot be
reliably used with another assay method without full and independent
validation Similarly caution is required when comparing the outcomes of
research studies using different AMH assay methods Correspondingly the
review of studies on sample stability revealed conflicting reports on the
stability of AMH under normal storage and processing conditions which was
reported to be a more significant issue with the Gen II assay Similarly there
was considerable discrepancy in the reported results on the linearity of dilution
of AMH samples particularly in Gen II studies In view of above findings we
concluded that AMH in serum may exhibit pre-analytical instability which may
vary with assay method Therefore robust international standards for the
development and validation of AMH assays are required
Although AMH assays have been in clinical use for more than a decade
this appears to be first published review that examined the studies on the
performance of AMH assay methods Indeed a number of review articles
comparing clinical performance of AMH test to other markers of ovarian
reserve have been published (Broer et al 2009 Broer et al 2011b La Marca et
al 2009) Reviewing observational studies the articles concluded that AMH
measurement was one of the most robust methods of assessment of ovarian
reserve However there appears to be no review article that specifically
evaluated the validity of the AMH assay methods suggesting AMH assay
methods were assumed to be reliable despite the lack of robust data on the
validity of assay methods
Reassuringly the report of instability of the Gen II assay samples has
generated significant research interest directed towards understanding the
causes of the issue As a result several hypotheses have been proposed and are
undergoing testing by various research groups For instance in the work
237
described here it was proposed that AMH molecule may undergo proteolytic
changes under certain storage and processing conditions exposing additional
antibody binding sites (Rustamov et al 2012a) The manufacturer of the assay
suggested that the sample instability is due to the presence of complement
interference (King 2012) More recent studies have reported the presence of
another form of AMH molecule pro-AMH in the serum may be the source of
erroneous measurements (Pankhurst et al 2014) Furthermore this study
demonstrated that Gen II assay detects both AMH and pro-AMH suggesting
that the mechanism of sample instability may be more complex than previously
thought It is indeed important to continue the quest to determine the cause of
the sample instability in order to develop reliable method for measurement of
AMH in future In the meantime clinicians should exercise caution when using
AMH measurements in the formulation of treatment strategies for individual
patients
Using a robust protocol for extraction of data and preparation of
datasets I have built a large validated research database (Chapter 4) Utilizing
the clinical electronic data management systems and case notes of patients I
have prepared a validated dataset that will enable study of ovarian reserve in a
wide context including a) assessment of ovarian reserve b) evaluation of the
performance of the biomarkers c) study individualization of ovarian
stimulation in IVF d) association of biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryos live birth) The database has been
used to address research questions posed in chapter 5 and chapter 6 of this
thesis In addition it can be utilized for future studies on assessment of ovarian
reserve and IVF treatment interventions
Both formation and decline of ovarian reserve appears to be largely
determined by genetic factors although at present data on genetic markers are
scarce (Shuh-Huerta et al 2012) Therefore availability of data on clinically
measurable determinants of ovarian reserve is important Consequently I
explored the role of ethnicity BMI endometriosis causes of infertility and
reproductive surgery to ovarian reserve using AMH AFC and FSH
measurements of a large cohort of infertile patients (Chapter 51)
Multivariable regression analysis of data on the non-PCO cohort showed the
association between ethnicity and the markers of ovarian reserve is weak In
contrast I observed a clinically significant association between BMI and
ovarian reserve obese women were found to have higher AMH and lower
238
FSH measurements compared to those of non-obese With regard to the role
of the causes of infertility I did not observe a significant association between
the markers of ovarian reserve and subsets diagnosed with unexplained or
tubal factor infertility In contrast those diagnosed with male factor infertility
had significantly higher AMH and lower FSH measurements which increased
with the severity of the disease In conclusion the study demonstrated that
some of the above factors have a significant impact on above biomarkers of
ovarian reserve and therefore I suggest future studies on ovarian reserve
should include adjustment for the effects these factors
The study showed that in the absence of endometrioma endometriosis
was not found to have a strong association with markers of ovarian reserve
compared to those without the disease Interestingly women with an
endometrioma had significantly higher AMH measurements than those
without endometriosis This is the first study that has reported increased
AMH in serum in the presence of endometrioma Interestingly recent studies
have demonstrated that AMH and its receptor are expressed in tissue samples
obtained from ovarian endometriosis (Wang et al 2009 Carelli et al 2014) It
appears that AMH inhibits growth of both epithelial and stromal cells
(Signorille et al 2014) I believe these intriguing findings warrant further
research on the role of AMH in the pathophysiology of endometriosis With
regards to assessment of ovarian reserve AMH may not reflect ovarian reserve
in the presence of endometrioma and therefore caution should be exercised
With respect to reproductive surgery I conducted a study to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease (Chapter 52) Multivariable regression analysis of the
cross-sectional data showed that salpingo-ophorectomy and ovarian
cystectomy for endometrioma have a significant detrimental impact on ovarian
reserve as estimated by AMH AFC and FSH In contrast neither
salpingectomy nor ovarian cystectomy for cysts other than endometrioma was
found to have appreciable effects on the markers of ovarian reserve I suggest
that women undergoing surgery should be counseled regarding the potential
impact of surgical interventions to their fertility However there was
appreciable overlap between the interquartile ranges of the comparison groups
This suggests that although the effects are significant at a population level
there is considerable variation between individuals Therefore clinicians should
239
exercise caution in predicting the effect of surgery on ovarian reserve of
individual patients
Published studies on the prognostic value of AMH in assisted
conception suggested there is a strong correlation between AMH and extremes
of ovarian response in cycles of IVF (Nelson et al 2007 Nardo et al 2007)
Later case control studies showed that tailoring the daily dose of
gonadotrophins to individual patientrsquos AMH levels and pituitary
desensitisation with GnRH antagonist in patients with the extremes of ovarian
reserve improved the outcomes of IVF treatment (Nelson et al 2009 Yates et
al 2012) However these studies displayed a number of methodological issues
largely due to retrospective analysis small sample size and centre-dependent or
time-dependent selection of cohorts Therefore the role of confounding
factors on the obtained estimates of these studies is unclear Ideally clinical
application of these treatment interventions should be based on research
evidence based on large randomized controlled trials In the absence of
controlled trials I decided to obtain best available estimates on the role of
AMH in individualisation of controlled ovarian stimulation using a robust
methodology in my large cohort of treatment cycles (Chapter 6) Oocyte yield
was used as the outcome measure given it is mainly determined by the
effectiveness of treatment strategies for ovarian stimulation which is the
question the study has addressed In contrast downstream outcomes such as
clinical pregnancy and live birth are subject to additional clinical and
interventional factors The study developed multivariable regression models of
total oocyte yield in all included IVF ICSI cycles (n=1653) and Metaphase II
oocytes of the ICSI subset (n=1101) to measure ovarian response to COH In
view of the significant interaction of PCO status with other variables I
restricted the analysis to non-PCO patients First in order to identify the
confounders I established the effect of a set of plausible factors that may affect
the outcomes including assessment of the effect of age AMH AFC causes of
infertility attempt of IVFICSI cycle COH protocol changes gonadotrophin
preparations operator for oocyte recovery pituitary desensitisation regime and
initial daily dose of gonadotrophins Then I developed the regression models
that examined the effect of gonadotrophin dose and regime categories on total
and mature oocyte numbers
240
The study found that after adjustment for all the above factors age
remained a negative predictor of oocyte yield whereas I observed a gradual
and significant increase in oocyte number with increasing AMH and AFC
values suggesting all these markers display an independent association with
oocyte yield Interestingly after adjustment for all above variables in non-PCO
patients I did not observe the expected increase in oocyte number with
increasing gonadotrophin dose categories beyond the very lowest doses This
suggests that there may not be a significant direct dose-response effect and
consequently strict protocols for tailoring the initial dose of gonadotrophins
may not necessarily optimize ovarian performance in IVF treatment It is
important to note our COH protocols utilized extensive cycle monitoring
using ultrasound follicle tracking and measurement of serum oestradiol levels
with corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not demonstrate a significant
impact on oocyte yield Nevertheless further longitudinal regression analysis
including full time course parameters of cycle monitoring and the dose
adjustments in the model should be conducted in order to ascertain the role of
AMH in tailoring the dose of gonadotrophins in cycles of IVF Moreover the
role of AMH on downstream outcomes of IVF cycles particularly on live
birth should be examined in this dataset Now equipped with a better
understanding of the research methodology and a robust database I am
planning to visit these research questions in future work
Although clinical biomarkers have improved the assessment of ovarian
reserve there remains a significant limitation in their performance in terms of
accurate estimation of ovarian reserve Given that ovarian reserve is believed
to be largely determined genetically recent large Genome-Wide Association
Studies (GWASs) have focused on the identification of genetic markers of
ovarian aging A meta-analysis of these 22 studies identified four genes with
nonsynonymous SNPs as being significantly associated with an age at
menopause (Stolk et al 2012 He et al 2012) However these SNPs were found
to account for only 25-41 of association of the age at menopause
Furthermore studies in mice and humans have identified more than 400 genes
that are involved in ovarian development and function (Wood et al 2013)
Given this genetic heterogeneity it is unlikely that a single genetic determinant
241
of ovarian reserve will be identified In addition epigenetic noncoding RNAs
and gene regulatory regions may play an important role in determination of
ovarian reserve which is yet to be fully explored (Bernstein et al 2012) Indeed
further large scale studies for ascertainment of genetic markers of ovarian
reserve are needed However current biomarkers including AMH appear to
remain as the most useful tests for the assessment of ovarian reserve in the
foreseeable future and further efforts to improve the performance of these
tests are therefore important
In summary some of the assumptions on performance of AMH
measurements particularly Gen II assay appear to have been based on weak
research evidence Similarly there are significant methodological limitations in
the published studies on AMH-tailored individualisation of controlled ovarian
hyperstimulation in IVF I believe the studies described in this thesis have
revealed instability of Gen II assay samples and raised awareness of the pitfalls
of AMH measurements These studies have also demonstrated the effect of
clinically measurable factors on ovarian reserve and provided data on the effect
of AMH other patient characteristics and treatment interventions on oocyte
yield in cycles of IVF Furthermore a robust database and statistical models
have been developed which can be used in future studies on ovarian reserve
and IVF treatment interventions I believe the work presented here has
provided a better understanding of the performance of AMH as an
investigative tool and its role in management of infertile women and provided
resource for future work in this area
242
References Bernstein BE Birney E Dunham I Green ED Gunter C Snyder M ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome Nature 2012 489(7414)57ndash74 [PubMed 22955616] Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Broer SL Doacutelleman M Opmeer BC Fauser BC Mol BW Broekmans FJ AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 Jan-Feb 17(1)46-54 Epub 2010 Jul 28 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 20141011353ndash8
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013 May 99(6)1791-7 Han X Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Human ReproductionJun2013 Vol 28 Issue suppl_1 He C Murabito JM Genome-wide association studies of age at menarche and age at natural menopause Mol Cell Endocrinol 2012
King D URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian
243
response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Pankhurst M Chong Y H and McLennan ISEnzyme-linked immunosorbent assay measurements of antimeuroullerian hormone (AMH) in human blood are a composite of the uncleaved and bioactive cleaved forms of AMH Fertility and Sterility2014
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Stolk L Perry JR Chasman DI et al Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways Nat Genet 2012 44(3)260ndash268
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH
244
and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wood M and Rajkovic A Genomic Markers of Ovarian Reserve Semin Reprod Med 2013 31(6) 399ndash415
245
Authors and affiliations
Stephen A Roberts PhD
Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL United Kingdom
Cheryl Fitzgerald MD
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester M13 0JH
United Kingdom
Philip W Pemberton MSc
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Alexander Smith PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Luciano G Nardo MD
Reproductive Medicine and Gynaecology Unit GyneHealth
Manchester M3 4DN United Kingdom
Allen P Yates PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Monica Krishnan MBChB
Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL United Kingdom
246
Acknowledgments
First and foremost I would like to thank my supervisors Dr Stephen A
Roberts and Dr Cheryl Fitzgerald I am indebted to you for introducing me
into the world of science showing its wonders and guiding me through its
terrains Without your 247 advise and support none of these projects would
have been possible Thank you
I would also like to thank other members of our team Dr Philip W
Pemberton Dr Luciano G Nardo Dr Alexander Smith Dr Allen P Yates and
Monica Krishnan It has been exciting and fun to be a part of the Manchester
AMH Group
I am grateful for the support and friendship of all secretaries nurses
embryologists and consultants of IVF Department at St Maryrsquos Hospital I
would like to express my special thanks to Professor Daniel Brison for his
advice on the projects and providing a great opportunity for research I would
like to express my gratitude to Dr Greg Horne Senior Embryologist for his
patience in taking me through tons of IVF data It was a privilege to be part of
this team
Indeed without support of my wife Zilola Navruzova I could not have
completed my MD programme Thank you for being there for me through
thick and thin of life You are love of my life Your optimism can make
anything possible Your sense of humor and kindness brightened my long
research hours after on-call shifts Only because of your enthusiasm we could
juggle work research and family And thanks for pretending that AMH is
interesting
My children Firuza Sitora and Timur You are most great kids Always stay
cool and funny like this Sorry for not taking you to holiday during my never-
ending research during last year Hope I havenrsquot put you off doing research in
future You get lots of conference holidays after research
247
I canrsquot thank enough my mother Karomat Rajobova and father Dr Sohib
Rustamov Your love kindness and wisdom have always been inspiration and a
guide in my life I always strive to follow your example albeit impossible to
achieve
My brother Ulugbek Rustamov thank your selfless support As always you
have been my guide and strength during these three years My friends Odil
Nizomov Dr Rohit Arora Tarek Sharif and Sabiha Sharif I am grateful for
your friendship and support during my MD Programme
248
I would like to dedicate this thesis to my mother father my wife and
children
Shu Doctorlik Dissertaciysini
Onam (Karomat Rajabova)
Dadam (Dr Sohib Rustamov)
Turmush Urtogim (Zilola Navruzova)
Farzandlarim (Firuza Sohibova Sitora Sohibova
Timur Rustamov) ga bagishlayman
Sizlar mani kuzimni nuri sizlar
Yaratgandan sizlarga mustahkam sogliq va quvonch tilayman
_______________________
Oybek
31 March 2014 Manchester United Kingdom
3
ABSTRACT The University of Manchester Dr Oybek Rustamov Degre MD Title The role of anti-Muumlllerian hormone in assisted reproduction in women Date 30 March 2014
Anti-Muumlllerian hormone appears to play central role in regulation of oocyte recruitment and folliculogenesis Serum AMH concentration was found to be one of the best predictors of ovarian performance in IVF treatment Consequently many fertility centres have introduced AMH for the assessment of ovarian reserve and as a tool for formulation of ovarian stimulation strategies in IVF However published evidence on reliability of AMH assay methods and the role of AMH-tailored individualisation of ovarian stimulation in IVF appear to be weak Consequently I decided to conduct a series of studies that directed towards an improvement of the scientific evidence in these areas of research
The studies on performance of Gen II AMH assay revealed the assay suffers from significant instability and provides erroneous results Consequently the manufacturer introduced a modification on assay method
In view of the observed issues with Gen II assay I conducted a critical appraisal of all published research on the previous and current assay methods that reported AMH variability assay method comparison and sample stability The literature indicated clinically important variability between AMH measurements in repeated samples which was reported to be more significant with Gen II assay The studies on between-assay conversion factors derived conflicting conclusions Correspondingly the review of studies on sample stability revealed conflicting reports on the stability of AMH under normal storage and processing conditions which was reported to be more significant issue in Gen II assay In view of above findings we concluded that AMH in serum may exhibit pre-analytical instability which may vary with assay method Therefore robust international standards for the development and validation of AMH assays are required In the analysis of determinants of ovarian reserve I evaluated the effect of ethnicity BMI endometriosis causes of infertility and reproductive surgery on AMH AFC and FSH measurements using data on a large cohort of infertile patients
Using robust multivariable regression analysis in a large cohort of IVF cycles I established the effect of age AMH AFC diagnosis attempt COS protocol changes gonadotrophin type USOR operator regime and initial dose of gonadotrophins on oocyte yield Then I examined effect of gonadotrophin dose and regime on total and mature oocyte numbers The study found that after adjustment for all above variables there was no increase in oocyte yield with increasing gonadotrophin dose categories beyond the very lowest doses This suggests that there may not be significant direct dose-response effect and consequently strict protocols for tailoring the initial dose of gonadotrophins may not necessarily optimize ovarian performance in IVF treatment
In summary studies described in this thesis have revealed instability of Gen II assay samples and raised awareness of the pitfalls of AMH measurements These studies have also demonstrated the effect of clinically measurable factors on ovarian reserve and provided data on the effect of AMH other patient characteristics and treatment interventions on oocyte yield in cycles of IVF Furthermore a robust database and statistical models have been developed which can be used in future studies on ovarian reserve and IVF treatment interventions
4
DECLARATION
No portion of the work referred to in the thesis has been submitted in support
of an application for another degree or qualification of this or any other
university or other institute of learning
COPYRIGHT STATEMENT
i The author of this thesis (including any appendices andor schedules to this
thesis) owns certain copyright or related rights in it (the ldquoCopyrightrdquo) and she
has given The University of Manchester certain rights to use such Copyright
including for administrative purposes
ii Copies of this thesis either in full or in extracts and whether in hard or
electronic copy may be made only in accordance with the Copyright Designs
and Patents Act 1988 (as amended) and regulations issued under it or where
appropriate in accordance with licensing agreements which the University has
from time to time This page must form part of any such copies made
iii The ownership of certain Copyright patents designs trade marks and
other intellectual property (the ldquoIntellectual Propertyrdquo) and any reproductions
of copyright works in the thesis for example graphs and tables
(ldquoReproductionsrdquo) which may be described in this thesis may not be owned
by the author and may be owned by third parties Such Intellectual Property
and Reproductions cannot and must not be made available for use without the
prior written permission of the owner(s) of the relevant Intellectual Property
andor Reproductions
iv Further information on the conditions under which disclosure publication
and commercialisation of this thesis the Copyright and any Intellectual
Property andor Reproductions described in it may take place is available in
the University IP Policy (see
httpdocumentsmanchesteracukDocuInfoaspxDocID=487) in any
relevant Thesis restriction declarations deposited in the University Library The
University Libraryrsquos regulations (see
httpwwwmanchesteracuklibraryaboutusregulations) and in The
Universityrsquos policy on Presentation of Theses
5
PUBLICATIONS ARISING FROM THE THESIS
Journal Articles
1 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton
The measurement of Anti-Muumlllerian hormone a critical appraisal
The Journal of Clinical Endocrinology amp Metabolism 2014 Mar99(3)723-32
2A Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large
cohort of subjects suggests sample instability Human Reproduction 2012 Oct
27(10) 3085-91
2B Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Human Reproduction Dec2012 Vol 27 Issue 12 p3641
6
Conference presentations
1 O Rustamov S Roberts C Fitzgerald
Ovarian endometrioma is associated with increased AMH levels
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2014 Munich
Poster Presentation
2 O Rustamov M Krishnan R Mathur S Roberts C Fitzgerald
The effect of BMI to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral presentation Dr O Rustamov
3 M Krishnan O Rustamov R Mathur S Roberts C Fitzgerald
The effect of the ethnicity to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral Presentation Dr M Krishnan
4 O Rustamov M Krishnan S Roberts C Fitzgerald
Reproductive surgery and ovarian reserve
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr O Rustamov
5 C Fitzgerald O Rustamov P Pemberton A Smith A Yates M Krishnan
R Russell L Nardo SRoberts
AMH assays A review of the literature on assay method comparability
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr C Fitzgerald
6 M Krishnan O Rustamov R Russell C Fitzgerald S Roberts
The role of the ethnicity and the body weight in determination of AMH levels
in infertile women
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
7
Poster presentation
7 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
AMH Gen II assay - can we believe the measurements
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
8 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Old and new AMH assays Can we rely on current conversion factor
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
9 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Random AMH measurement is not reproducible
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
10 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
The reproducibility of serum Anti-Muumlllerian hormone AMH Gen II assay
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2012 Istanbul
Oral Presentation Dr O Rustamov
8
GENERAL INTRODUCTION
AND LITERATURE REVIEW
1
9
CONTENTS I LITERATURE REVIEWhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10 GENERAL BACKGROUNDhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10
1 OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip12 11 Primordial Follicle Assemblyhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip13 12 Oocyte recruitmenthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip14 13 Theory of neo-oogenesishelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip15 2 MARKERS OF OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 21 Ovarian reserve markers with limited clinical valuehelliphelliphelliphelliphellip16 213 Inhibin Bhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 214 Basal oestradiolhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 215 Dynamic testshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 216 Ovarian volumehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 22 Ovarian reserve markers in routine clinical usehelliphelliphelliphelliphelliphelliphellip18 221 Chronological agehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 222 Basal FSHhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 223 Antral follicle counthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip19 3 ANTI-MUumlLLERIAN HORMONEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 31 Biology of anti-Muumlllerian hormonehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 311 The role of AMH in the ovaryhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip21 312 AMH in women with polycystic ovary syndromehelliphelliphelliphelliphellip22 32 AMH Assayhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip23 33 Variability of AMH measurementshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip24 34 Role of AMH in assessment of ovarian reservehelliphelliphelliphelliphelliphellip25 341 Prediction of poor and excessive ovarian response in IVFhelliphellip25 342 Prediction of live birth in cycles of IVFhelliphelliphelliphelliphelliphelliphelliphelliphellip26
3 5 Role of AMH in ovarian stimulation for cycles of IVFhelliphelliphelliphellip26
4 MULTIVARIATE TESTShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip27
5 SUMMARYhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip28 II GENERAL INTRODUCTIONhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip29 REFERENCEShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip31
10
I LITERATURE REVIEW GENERAL BACKGROUND
Infertility is a disease of the reproductive system defined by the failure to
achieve a pregnancy after 12 months of regular unprotected sexual intercourse
although the criteria for the duration vary between different countries (NICE
2013) Worldwide prevalence of infertility estimated to be around 724 million
couples and around 40 million of those seek medical care (Hull et al 1985) In
the UK 15 couples present with infertility with an annual incidence of 12
couples per 1000 general population (Scott et al 2009) The main causes of
infertility are tubal disease ovulatory disorders male factor and poor ovarian
reserve In a third of couples the cause of failure to achieve pregnancy is not
established which is known as unexplained infertility (NICE 2013) Effective
treatment options include improving lifestyle factors medical andor surgical
treatment of underlying pathology induction of ovulation and Assisted
Reproductive Technology (ART) Assisted Reproduction consist of
intrauterine insemination (IUI) and in vitro fertilisation (IVF) cycles with or
without introcytoplasmic sperm injection (ICSI) as well as treatment involving
donated gametes It is estimated that 75 of infertile couples presenting at
primary care centres in the UK are referred to fertility specialists based at
secondary or tertiary care centres and nearly 50 of those are subsequently
offered IVFICSI treatment (Scott et al 2009) This is supported by figures of
Human Fertility and Embryology Authority (HFEA) which indicates more
than 50000 IVF treatment cycles are performed in the UK annually (HFEA
2008)
An IVF treatment cycle involves a) pituitary down regulation b)
controlled ovarian stimulation c) oocyte recovery c) in vitro fertilisation of eggs
with sperm d) transfer of resulting embryo(s) back to uterus and c) luteal
phase support (NICE 2013) Prevention of premature surge of luteinising
hormone during controlled ovarian stimulation (COS) is achieved by pituitary
down regulation using either preparations of gonadotrophin releasing hormone
agonist which is widely known as ldquoAgonist cyclerdquo or gonadotrophin releasing
hormone antagonist which is known ldquoAntagonist cyclerdquo (Figure 1 and 2)
Controlled ovarian stimulation involves administration of gonadotrophins to
encourage the development of supernumerary preovulatory follicles followed
by administration of exogenous human chorionic gonadotropin (hCG) or
11
recombinant luteinising hormone (rLH) to assist in maturation of oocytes 34-
36 hours prior to egg collection which is usually conducted with guidance of
transvaginal ultrasound scanning Subject to sperm parameters the fertilisation
of oocytes is conducted by in vitro insemination or intracytoplasmic sperm
injection The resulting embryo(s) are cultured under strict laboratory
conditions and undergo regular qualitative and quantitative assessments before
transferring the best quality embryo(s) back into uterus during its cleavage
(Day 2 or Day 3) or blastocyst (Day 5 or Day 6) stage of development In
natural menstrual cycles under the influence of HCG progesterone secreted
by the ovarian corpus luteum ensures proliferative changes in the endometrium
providing the optimal environment for implantation of embryo(s) (van der
Linden et al 2011) However in IVF treatment cycles owing to pituitary down
regulation and lack of HCG progesterone levels are not in sufficiently high
concentration to ensure an adequate endometrial receptivity and therefore
exogenous analogues of this hormone is administered following transfer of
embryo(s) This is called ldquoluteal phase supportrdquo and in patients with viable
pregnancy usually lasts till 12th week of gestation when placenta starts
producing progesterone in sufficient quantities (van der Linden et al 2011)
In IVF programmes the ldquosuccessrdquo of the treatment often defined as
achieving a live birth following IVF cycle and expressed using Live Birth Rate
(LBR) In general success in IVF predominantly determined by womanrsquos age
cause(s) of infertility ovarian reserve previous reproductive history and
lifestyle factors (NICE 2013 Taylor 2003 Lintsen et al 2005) However
effectiveness of medical interventions as well as the quality of care play
important role in determining the outcome of IVF treatment This is evident
from significant variation in live birth rates among fertility clinics given for
instance in the UK LBR for women younger than 35 years of age after IVF
cycles varies from 15 to 61 (HFEA 2008 HFEA 2007) The provision of
effective interventions in both clinical and laboratory aspects of the care
appears to be the key in achieving high success rates Identification of patients
with sufficient ovarian reserve who benefit from IVF cycles followed by
providing optimal ovarian stimulation regimens may be useful in improving the
outcomes of IVF programmes According to HFEA data around 12 of IVF
cycles are cancelled due to poor or excessive ovarian response (Kurinczuk et al
2010) Availability of reliable markers for assessment of ovarian reserve and
tailoring ovarian stimulation regimens to the need of each individual patient
12
may improve selection of patients with sufficient ovarian reserve and reduce
the rate of cycle cancellation consequently improving the success of IVF
cycles (Yates et al 2011)
Assessment of ovarian reserve can be achieved using various biomarkers
and four of those are currently used by most clinics womanrsquos chronological
age (Age) serum follicle stimulating hormone (FSH) antral follicle count
(AFC) and serum anti-Muumlllerian hormone (AMH) More recently AMH has
been a focus of interest given it is the only available endocrine marker that is
suitable for direct assessment of the activity of ovarian follicles in their non-
cyclical stage development providing a window to FSH independent phase of
follicular recruitment Furthermore it appears to be reliable biomarker for a)
both the assessment of ovarian reserve and the optimisation of ovarian
stimulation regimens (Yates et al 2011 La Marca et al 2009) b) screening and
diagnosis of polycystic ovarian syndrome (PCOS) (Cook et al 2002) c)
monitoring of disease activity in women with a history of granulosa cell
tumours (Lane et al 1999) d) prediction of the age of diminished fertility and
the menopause (van Disseldorp et al 2008 Broer et al 2011) and finally (e)
assessment of the long term effect of chemotherapy on ovarian reserve
(Anderson 2011)
In this review I first discuss current knowledge on factors that
determine ovarian reserve including the formation and loss of oocyte pool
Then characteristics of the markers of ovarian reserve are reviewed Finally I
examine current understanding of biology of anti-Muumlllerian hormone and its
role in management of infertility
1 OVARIAN RESERVE
It is important to recognize that there is no universal definition for the
term ldquoovarian reserverdquo and the term can have various meanings depending on
the context in which it is used For instance the scientific literature describing
the biology of ovarian reserve usually refers to ldquothe total number of remaining
oocytes in the ovaries which consists of the number of resting primordial
follicles and growing primary pre-antral and antral folliclesrdquo (Gleicher et al
2011) In contrast the use of the term in the context of clinical studies may
refer to ldquoclinically measurable ovarian reserve established using available
biomarkers of ovarian reserverdquo For the purpose of clarity in this thesis the
13
term ldquoovarian reserverdquo refers to clinically measurable ovarian reserve whilst
true biological ovarian reserve will be termed ldquobiological ovarian reserverdquo
Recent studies have demonstrated that ovarian reserve is highly variable
between women due to the variation in the size of initial ovarian reserve at
birth as well as the rate of loss of ovarian reserve thereafter (Wallace et al
2010) Interestingly the rate of oocyte loss appears to be mainly determined by
the initial ovarian reserve which is believed to be facilitated by most potent
ovarian growth factor anti-Muumlllerian hormone Similarly the size of the initial
ovarian reserve is mainly underpinned by the rate of primordial follicle
assembly in the embryo which is also regulated by AMH Both primordial
follicle assembly and the rate of oocyte loss appear to be primarily under the
influence of genetic factors although developmental and environmental factors
are also believed to play a role (Nilsson et al 2010 Shuh-Huerta et al 2012)
11 Primordial follicle assembly
The process of assembly of primordial follicles in the female embryo
spans from the early embryonic to the early postnatal period and formation of
primordial follicles consists of following stages 1) primordial germ cell (PGC)
2) oogonia 3) primary oocyte and 4) primordial follicle In the human female
fetus around a hundred cells that differentiated from extra-embryonic
ectoderm form early PGCs on the yolk sac and migrate via hindgut to gonadal
ridges during 4th - 6th weeks of gestation (MC et al 1953 Donovan 1998) Once
arrived to the gonadal ridges these cells are called primary oogonia which
consequently undergo several rounds of mitotic division during 6th - 28th weeks
of gestation Interestingly the numbers of oogonia reach as high as six million
during its highest rate of mitotic division at around 20 weeks of gestation
Following the last round of mitotic division oogonia enter meiosis which
marks their new stage of development-primary oocyte Formation of
primordial follicles starts as early as at 8th week of gestation and is characterised
by meiosis of primary oocyte that arrest in diplotyne stage and surrounding of
the oocyte by somatic granulosa cell (Baker et al 1963 Maheshwari and Fowler
2010) Indeed the primordial follicle is the cardinal unit of the biological
ovarian reserve and therefore the rate of formation of primordial follicles is the
main determinant of initial biological ovarian reserve at birth
Interestingly the process of loss of oogonia and oocytes which is also
one of the main determinants of the initial ovarian reserve takes place
14
throughout the period of follicle assembly The formation of the granulosa cell
layer around the oocyte prevents the oocyte from subsequent atresia The
oocyte enveloped in a single layer of granulosa cells which is also known as
primordial follicle remains quiescent until recruitment of the follicle for
growth which may not take place for a number of decades after the formation
of a particular primordial follicle (Skinner 2005 Maheshwari and Fowler 2010)
12 Oocyte recruitment
Follicle growth in women consists of two stages a) the initial non-cyclical
recruitment of primordial follicles and the formation of a primary and a pre-
antral follicles and b) cyclical development of antral follicles with subsequent
selection of usually a single dominant follicle The initial recruitment of
primordial follicles is continuous non-cyclical process that starts as early as
from 18-20 weeks of gestation and lasts till the depletion of follicle pool which
later results in the menopause (McGee and Hsueh 2000) Transformation of
flat granulosa cells into cuboidal cells increases the diameter of the oocyte and
the formation of zona pellicuda completes the stage of formation of a primary
follicle During pre-antral stage oocytes increase in diameter and mitotic
division of granulose cells create a new layer of cells-theca cells The
mechanism of initial recruitment of oocytes is not well understood but it is
clear that the process is independent of influence of pituitary gonadotrophins
and appears to be governed by the genetically pre-programmed interaction of
the oocyte with local growth factors the most important of which appears to
be anti-Muumlllerian hormone and cytokines (McGee and Hsueh 2000)
The cyclical phase of development of oocytes is characterised by the
transformation of secondary follicle into antral follicle and subsequent growth
of antral follicles into pre-ovulatory stages In general the process of cyclic
recruitment starts from puberty under the influence of rising levels of pituitary
follicular stimulating hormone (FSH) During the antral stage oocyte increases
in size even further and the formation of a fluid filled space in follicle is
observed Under the influence of FSH luteinising hormone (LH) and local
growth factorsselection of a single dominant follicle occurs which followsby an
ovulation (McGee and Hsueh 2000)
Oocyte loss is a continuous process and occurs due to atresia of oocytes
during primary secondary and antral stages of development The rate of
oocyte loss appears to increase until the age of around 14 and declines
15
thereafter until the age of the menopause when around 1000 primordial
follicles remain (Hansen et al 2008 Oktem and Oktayl 2008) Furthermore by
the age of 30 years the average age at which women of western societies plan
to start a family around 90 of initial primordial follicles are lost which
illustrates that formation and maintenance of ovarian reserve is wasteful
process in humans (ONS 2012 Wallace and Kelsey 2010) As mentioned
above there is a wide individual variation in both sizes of initial primordial
follicular pool and the rate of oocyte loss which explains variation in the
reproductive lifespan in women Evidently the number of primordial follicles
at birth ranges between around 35000 to 25 million per ovary and similarly
the rate of oocyte loss during its peak at 14 years of age may range between
100 to 7500 primordial follicles per month which is believed to be inversely
proportional to initial size of primordial follicle pool (Wallace and Kelsey
2010)
13 Theory of neo-oogenesis
The traditional view of oogenesis states that the process of the creation
and the mitotic division of oogonia with subsequent formation of primordial
follicles takes place only during embryonic and foetal life (Zuckerman 1951)
According to this central theory of mammalian reproductive biology females
are born with a certain number of germ cells that is gradually lost but not
renewed during postnatal period However Johnson et al have recently
challenged this view and reported that adult mammalian ovary may possesses
mitotically active germ cells that continuously replenish the primordial follicle
pool (Johnson et al 2004) The group reported that ovaries of juvenile and
young adult mice contained large ovoid cells which resemble germ cells of
foetal mouse ovaries Interestingly immunohistochemical staining for a gene
which is expressed exclusively in germ cells have been reported to have
confirmed that these large ovoid cells were of germline lineage Furthermore
application of a mitotic germ cell toxicant busulphan appeared to have
eliminated primordial follicle reserve by early adulthood but did not induce
atresia suggesting the presence of proliferative germ cells in postnatal mouse
ovary (Johnson et al 2004 Bazer 2004) The study has generated enormous
amount of interest as well as debate among reproductive biologists (Notarianni
2011) Some other groups have also reported an evidence of postnatal
oogenesis (Pacchiarott et al 2010 Zou et al 2009 Bukovsky et al 2004)) while
16
others do not support the theory (Bristol-Gould et al 2006 Byskov et al 2005
Begum et al 2008) Furthermore some authors argued that adult mouse
germline stem cells exist and remain quiescent in physiologic conditions and
neo-oogenesis occurs only in response to ovotoxic damage (Tilly et al 2007 De
Felici 2010) Although consensus has yet to emerge to date there is no
conclusive evidence on validity of theory of neo-oogenesis
2 MARKERS FOR ASSESMENT OF OVARIAN RESERVE
Biological ovarian reserve is defined as the number of primordial and
growing follicles left in the ovary at any given time and therefore only
counting the number of primordial follicles by histological assessment can
accurately determine ovarian reserve which is clearly not feasible in clinical
setting However ovarian reserve can be estimated using various biomarkers
dynamic clinical tests and implied from the outcomes of ART cycles
Although a wide range of clinical (age ovarian response in previous IVF
cycles) biochemical (basal FSH Inhibin B basal oestradiol AMH) ultrasound
(ovarian volume antral follicle count (AFC)) and dynamic (clomiphene
challenge test exogenous FSH ovarian reserve test GnRH analogue
stimulating test) tests of ovarian reserve exist only a few of the markers are
reliable and practical enough to be of use in routine clinical practice In this
chapter first I discuss the research evidence on the assessment of the markers
andor tests of ovarian reserve that have limited clinical value Then I
evaluated more reliable markers that are in routine clinical use Age FSH
AFC and combination of these markers in multivariable tests Finally I
conducted detailed review of biology of AMH and the role AMH measurement
in the management of infertility
21 Ovarian reserve markers with limited clinical value
211 Inhibin B
Inhibins are members of TGFβ family and expressed in granulosa cells
of growing follicles Principal role of inhibins is thought to be the negative
feedback regulation of pituitary FSH secretion and therefore the serum level of
circulating hormone is believed to reflect the state of folliculogenesis
17
Consequently several groups have studied the role of serum Inhibin β in the
assessment of ovarian reserve Although initial reports were encouraging
(Seifer et al 1997) more robust studies demonstrated that serum Inhibin β was
less reliable than chronological age or basal FSH (Creus et al 2000 Urbancsek
2005) The systematic review of nine studies demonstrated that accuracy of the
Inhibin β test for predicting poor ovarian response and non-pregnancy in IVF
cycles was modest even at a very low threshold level (Broekmans et al 2006)
Therefore it is recommended that inhibin β at best can be used as only
screening test in the fertility centers where other more reliable markers are not
available (Broekmans et al 2006)
212 Basal oestradiol
Some studies suggested that elevated basal oestradiol levels indicate low
ovarian reserve and are associated with poor fertility prognosis (Johannes et al
1998 Licciardi and Rosenwaks 1995) Johannes et al demonstrated basal
oestradiol in conjunction with serum FSH is more reliable than serum FSH
alone in prediction of cycle cancellation due to the poor response in IVF cycles
(Johannes et al 1998) However there are no published data on the comparison
of basal oestradiol to more reliable markers such as AMH or antral follicle
count (AFC) Moreover a recent systematic review has demonstrated that
basal oestradiol has very low predictive value for poor response and has no
discriminatory power for accuracy of non-pregnancy prediction (Broekmans et
al 2006)
213 Dynamic tests of ovarian reserve
The dynamic tests of ovarian reserve are based on assessment of ovarian
response by measuring serum FSH and oestradiol levels following
administration of exogenous stimulation The following tests are reported in
literature Clomiphene Citrate Challenge Test (CCCT) Exogenous FSH
Ovarian Reserve Test (EFORT) and GnRH agonist stimulation test A recent
systematic review and meta-analysis on the accuracy of these tests showed that
none of them can adequately predict poor response or non-pregnancy in IVF
cycles and therefore are not recommended for use in routine clinical practice
(Maheshwari et al 2009)
18
214 Ovarian volume
There is some evidence that increased age is associated with decreased
ovarian volume and women with smaller ovaries are more likely to have
cancellation of their IVF cycles due to poor ovarian response (Syrop et al 1995
Syrop et al 1999 Templeton 1995) However a meta-analysis of the published
studies on the accuracy of ovarian volume as a predictor of poor response and
non-pregnancy in IVF cycles failed to demonstrate clinical usefulness of the
test and suggested the test is not reliable enough for use in a routine clinical
practice (Broekmans et al 2006)
22 Ovarian reserve markers in routine clinical use
221 Chronological age
Owing to the biological age-related decline of the quantity and arguably
the quality of oocytes the chronological age can be used as a marker of ovarian
reserve Studies have demonstrated that ovarian reserve (Wallace and Kelsey
2010 Kelsey 2011) natural fecundity (Islam et al 1989 and outcomes of ART
(Templeton et al 1996 van Kooij et al 1996) decline significantly from age of
35 when it is believed the ovarian reserve undergoes accelerated decline
Although there is a strong association between chronological age and reduction
in fertility evidently there is a significant variation in age-related ovarian
reserve indicating chronological age alone may not be sufficient to estimate the
individual womanrsquos ovarian reserve reliably (Broekmans et al 2006)
222 Basal FSH
Basal FSH was one of the first endocrine markers introduced in ART
programs and is still utilized in many fertility clinics albeit in conjunction with
other markers which are considered more reliable (Creus et al 2000) Secretion
of FSH is largely governed by the negative feedback effect of steroid
hormones primarily oestradiol and inhibins which are expressed in granulosa
cells of growing ovarian follicles Consequently decreased or diminished
recruitment of ovarian follicles is associated increased serum FSH
measurements and high particularly very high basal FSH reading is considered
as a good marker of very low or diminished ovarian reserve (Abdalla et al
2006) However unlike some other markers FSH measurements do not
appear to have discriminatory power for categorisation of patients to various
19
bands of ovarian reserve Given between-patient variability FSH measurement
(CV 30) is similar to its within-patient variability (27) stratification of
patients to various ranges of ovarian reserve does not appear to be feasible
(Rustamov et al 2011) Indeed a recent systematic review of 37 studies on the
prediction of poor response and non-pregnancy in IVF cycle has concluded
that basal FSH is an adequate test at very high threshold levels and therefore
has limited value in modern ART programs (Broekmans et al 2006)
223 Antral follicle count
Antral follicle count estimation involves ultrasound assessment of
ovaries between 2nd and 4th day of menstrual period and counting ldquofolliclesrdquo
which corresponds to antral stage of folliculogenesis (Broekmans et al 2010)
The test provides direct quantitative assessment of growing follicles and is
known as one of the most reliable markers of ovarian reserve (Broekmans et al
2006) AFC measurement has been reported as having a similar sensitivity and
specificity to AMH in prediction of poor and excessive ovarian response in
IVF cycles (Broekmans et al 2006 Broer et al 2010 Jayaprakasan et al 2010)
Given AFC measurement is available instantly and allows patients to be
counseled immediately the test eliminates the need for an additional patient
visit prior to IVF cycle However AFC is normally performed only in the early
follicular phase of the menstrual cycle given most published data on
measurement of AFC are based on studies that assessed antral follicles during
this stage of the cycle (Broekmans et al 2010a) Interestingly more recent
studies suggest that variability of AFC during menstrual cycle is small
particularly when follicles between 2-6mm are counted and therefore
assessment of AFC without account for the day of menstrual cycle may be
feasible (Deb et al 2013)
One of the main drawbacks of AFC is that the cut off levels for size of
counted follicles remains to be standardised (Broekmans 2010b) Initially
follicles of 2-10mm were introduced as the range for AFC and many studies
were based on this cut off Later counting follicles of 2-6mm was reported to
provide most accurate assessment of ovarian reserve (Jayaprakasan et al 2010b
Haadsma et al 2007) and therefore some newer studies are based on AFC
measurements that used this criterion Consequently direct comparison of the
outcomes of various studies on assessment of AFC requires careful analysis
20
3 ANTI-MUumlLLERIAN HORMONE
31 Biology of Anti-Muumlllerian hormone
AMH is a member of transforming growth factor β superfamily which
was discovered by Jost et al in 1947 and was initially known for its is role in
regression of Muumlllerian ducts in sex differentiation of the male embryo In
women AMH is believed to be solely produced by ovaries and expressed in
granulosa cells of growing follicles of 2-6 mm in size which corresponds to
primary pre-antral and early antral stage of follicular development Although
there has been a report of expression of AMH in endometrial cells to date
there is no other published evidence that supports this finding (Wang et al
2009) Indeed studies that evaluated half-life of AMH in serum have
demonstrated that in women who had bilateral salpingo-oopherectomy AMH
becomes undetectable within 3-5 days of following surgery suggesting ovaries
are the only source of secretion of AMH in appreciable quantity (La Marca et
al 2005b) Anti-Muumlllerian hormone is a dimeric glycoprotein which is
composed of a long N-terminus and short C-terminus and was believed to be
secreted in serum only in this dimeric form (AMH-N C)
Like other members of TGF-β family which includes inhibins activins
bone morphogenic proteins (BMPs) and growth and differentiation factors
(Massague et al 1990) AMH binds to two type of serinethreonine kinase
receptors referred to as type I and type II In order to activate AMH signaling
pathway both receptors have to form a heteromeric complex When AMH
binds to the type II (AMHR-II) receptor (Massague et al 2000) this will
phosphorylate and activate a type I receptor (ALK2 -3 andor -6) which
subsequently activates the SMAD pathway through phosphorylation of
SMAD 1 5 andor 8 These activated SMADs interact with SMAD4 and
translocate to the nucleus regulating the expression of different genes
inhibiting the recruitment of primordial follicles and reducing FSH sensitivity
in growing follicles In addition AMH receptors as well as the other members
of TGF-β family can activate MAPK and PI3KAKT pathways
Studies on AMHR II-deficient male mice demonstrated lack of
regression of Muumlllerian ducts suggesting that type II receptor is essential in
AMH signaling (Mishina et al 1996) Similarly Type I receptors which includes
three members of activin receptor-like kinase (ALK2 ALK3 and ALK6) also
appear to play an important role in the regression of Muumlllerian ducts although
21
the role of ALK 6 in AMH signaling appears not to be crucial (Visser 2003
Clarke et al 2001) The signal transduction pathway of AMH in the ovary is
largely not understood In postnatal mice ovary AMHR-II receptor was
expressed in both granulosa and theca cells of pre-antral and antral follicles
(Visser 2003) AMH type I receptors ALK 2 and ALK 3 is expressed in foetal
as well as adult mouse ovary while ALK 6 is expressed in only adult ovary
(Visser 2003)
311 The role of AMH in the ovary
In the mammalian ovary the role of AMH appears to be one of a
regulation of size of the primordial follicle pool by its inhibitory effect on the
formation as well as the growth of primordial follicles (Nilsson et al 2011) In
the embryonic mouse ovary AMH inhibits the initiation of the assembly of
follicles when the process of apoptosis of the majority of oocytes is observed
(Nilsson et al 2011) Consequently AMH reduces the rate of oocyte loss
which plays an important role in the determination of the size of initial follicle
pool Similarly in the adult mouse ovary AMH plays a central role in
maintaining the follicle pool AMH inhibits both the processes of the initial
(non-cyclical) recruitment of primordial follicles and subsequent FSH-
dependent cyclical growth of antral follicles (Figure 3) Inhibition of the initial
recruitment of a new cohort of follicles is believed to be achieved by a
paracrine negative feedback effect of the rising levels of AMH secreted from
already recruited growing follicles (Durlinger et al 1999) Durlinger et al
compared the complete follicle population of AMHnull mice and wild type
mice of different ages of 25 days 4 months old and 13 months old and found
that the ovaries of 25 day and 4 months old AMHnull females contained
significantly higher number of growing pre-antral and antral follicles but
significantly fewer primordial follicles compared to wild-type females
(Durlinger et al 1999) Interestingly almost no primordial follicles were
detected in 13 months old AMHnull mice ovaries suggesting AMH is a potent
inhibitor of the recruitment of primordial follicles and in the absence of AMH
ovaries undergo premature depletion of primordial follicles due to an
accelerated recruitment Subsequent study conducted by the group
demonstrated that in addition to its inhibitory effect to the resting follicles
AMH also suppresses the development of the growing follicles (Durlinger et al
2001 Durlinger et al 2002 Themmen 2005) It appears that AMH inhibits
22
FSH-induced follicle growth by reducing the sensitivity of growing follicles to
FSH which has been confirmed by in vivo as well as in vitro studies (Durlinger
et al 1999 Durlinger et al 2001) In the initial study the group observed that
despite lower levels of serum FSH concentration ovaries of AMHnull mice
contained more growing follicles than that of their wild-type littermates which
has been supported by the findings of subsequent in vitro study (Durlinger et al
1999) Addition of AMH to the culture inhibited FSH-induced follicle growth
of pre-antral mouse follicles due to reduction in granulosa cell proliferation
(Durlinger et al 2001)
In the human embryo the expression of AMH commences in the late
foetal life and can be detected only from 36 weeks of gestation (Rajpert-De et
al 1999 Lee et al 1996) Following a small decline in first two years of life
AMH levels gradually increase to peak at (mean 5 ngml) around age of 24
years In line with the pattern of oocyte loss serum hormone levels gradually
decline with increasing age and become undetectable around 5 years prior to
menopause (Kelsey et al 2011 Nelson et al 2011)
It has been suggested that anti-Muumlllerian hormone plays a central role in
determining the pace of recruitment of primordial follicles hence maintaining
the primordial follicle pool of postnatal mammalian ovary Consequently a
reduction in the concentration of circulating AMH signals the exhaustion of
the primordial follicle pool and the decline of ovarian function
312 AMH in women with polycystic ovary syndrome
Polycystic ovary syndrome (PCOS) endocrine abnormality characterised
by increased ovarian androgen secretion infrequent ovulation and the
appearance of ldquopolycysticrdquo ovaries on ultrasound scan (Dunaif 1997 Homburg
et al 1993) It is the commonest endocrine abnormality in women of
reproductive age and affects around 15-20 of women PCOS is also one of
the main causes of anovulation and subsequent sub-fertility (Webber et al
2003) Although the role of anti-Muumlllerian hormone in the development of
PCOS is not fully understood it is becoming increasingly evident that the
hormone plays an important role in its pathogenesis (Pehlivanov et al 2011)
There is a strong association between serum AMH levels and PCOS and it
appears that women diagnosed with PCOS have two to three fold higher
serum AMH concentration compared to normo-ovulatory women (Cook et al
2002 Pigny et al 2003) Similarly women with PCOS are found to have
23
significantly higher number antral follicles Interestingly the expression of
AMH in granulosa cells of follicles were found to be 75 times higher in women
with PCOS compared to those without a the disease suggesting increased
serum AMH in PCOS may be due to increased secretion of hormone per
follicle rather than due to an increased number of antral follicles (Pellat et al
2007) High AMH concentrations may act as the main facilitator of abnormal
folliculogenesis in PCOS given the follicles appear to arrest when they reach
an antral stage (2-6mm) of development (Rajpert-De et al 1999) Indeed the
studies of Durlinger et al have demonstrated that AMH inhibits selection of
dominant follicle when follicles reach antral stage of development (Durlinger et
al 2001) Serum AMH levels appear to decrease with treatment of PCOS
which may play important role in restoration of ovulatory cycles Studies have
reported a significant reduction in serum concentration of AMH following
treatment of PCOS with metformin and laparoscopic ovarian diathermy (Falbo
et al 2010 Amer et al 2009 Elmashad 2011) Similarly reduction of BMI
following intensified endurance exercise training for treatment of PCOS may
also lead to a significant reduction in serum AMH levels (Moran et al 2011)
This suggests that there is strong association between serum concentration of
AMH and abnormal folliculogenesis in PCOS and therefore understanding the
molecular mechanisms of this interaction should be one of the priorities of
future research
32 AMH Assays
Enzyme-linked immunosorbent assay specific for measurement of anti-
Muumlllerian hormone was first developed in 1990 and was recognised as a
significant step in the assessment of ovarian reserve (Hudson et al 1990)
Subsequently a number of non-commercial immunoassays were developed
which were mainly used in research settings (Lee et al 1996) Later Diagnostic
Systems Ltd (DSL) and Immunotech Beckman Coulter Ltd (IOT) introduced
two commercial immunoassays for the routine clinical assessment of ovarian
reserve which are known as ldquofirst generation AMH assaysrdquo (Nelson and La
Marca 2011) These assays employed two different antibodies against AMH
and used different standards for calibration providing non-comparable
measurements (Nelson and La Marca 2011) Consequently several studies
attempted to develop a reliable between-assay conversion factor which
interestingly revealed from five-fold higher with the IOT assay to assay
24
equivalence causing significant impact to reliability of AMH measurements and
interpretation of research findings (Hehenkamp et al 2006 Freour et al 2007
Bersinger et al 2007 Taieb et al 2008 Lee et al 2011)
Later the manufacturer of IOT assay (Beckmann Coulter Ltd)
consolidated the manufacturer of the DSL assay (Diagnostic Systems
Laboratories Inc) and introduced a new assay ldquoGen II AMH assayrdquo which is
only available commercial immunoassay in most countries including the UK
AMH Gen II assay was developed using the antibodies derived from first
generation DSL assay and calibrated using the standards used for IOT assay
and was believed to be considerably more stable compared to the first
generation immunoassays providing more reliable measurements (Kumar et al
2010 Nelson and La Marca 2011) The manufacturer as well as initial external
validation study recommended when compared to old DSL assay AMH Gen
II assay provides around 40 higher measurements and therefore previously
reported DSL-based clinical cut-off levels for estimation of ovarian reserve
should be increased by 40 in order to use Gen II-based AMH results (Kumar
et al 2010 Wallace et al 2011 Nelson and La Marca 2011)
33 Variability of AMH measurements
It is generally believed that AMH values do not change throughout the
menstrual cycle and early studies reported that variation in AMH
measurements between repeated measurements of same patient was negligible
(van Disseldorp et al 2010 La Marca 2010) On the basis of these studies
sampling at a random time in the menstrual cycle was introduced as a method
for measurement of AMH in routine clinical practice However the
methodologies of some of these studies do not appear to be robust enough to
reliably estimate sample-to-sample variability of AMH which is mainly due to
small sample sizes (Rustamov et al 2011) Consequently in a recent study we
assessed sample-to-sample variability of AMH using DSL assay and found that
within-subject coefficient of variation (CV) of AMH between samples were as
high as 28 which cannot be attributed to any patient or cycle characteristics
(Rustamov et al 2011) Although there is no consensus in the causes of this
observed variability in AMH measurements we believe it is largely attributable
to instability of AMH samples given initial recruitment of primordial follicles
and growth of AMH producing pre-antral and antral follicles are continuous
process and therefore the true biological variation between samples is unlikely
25
to be high However given the importance of establishing true variability of
AMH in both understanding of the biology of hormone and clinical
application of the test future studies should be conducted to establish the
source of variability in the clinical samples
3 4 The role of AMH in the assessment of ovarian reserve
341 Prediction of poor and excessive ovarian response in cycles of
IVF
A number of studies have assessed the role of AMH in the prediction of
poor ovarian response in IVF cycles using first generation AMH assays and
found that AMH and AFC were the best predictors of poor ovarian response
compared to other markers of ovarian reserve Nardo et al showed that the
predictive value of AMH in receiver operating characteristic curve (ROC)
analysis was similar to (AUC 088) that of AFC (AUC 081) and found that
AMH cut offs of gt375 ngmL and lt10 ngmL would have modest
sensitivity and specificity in predicting the extremes of response (Nardo et al
2009) These findings were largely supported by subsequent prospective studies
and a systematic review (Nelson et al 2007 Jayaprakasan et al 2010 Broer et al
2011) Similarly comparison of chronological age basal FSH ovarian volume
AFC and AMH found that only AMH (AUC 090) and AFC (AUC 093) were
reliable predictors of poor ovarian response in cycles of IVF Subsequent
combination of the effect of AMH and AFC using multivariable regression
analysis did not improve the level of prediction of poor ovarian response
significantly (AUC 094) suggesting both AMH and AFC can be used as
independent markers (Jayaprakasan et al 2010)
Similarly most studies agree that AMH and AFC are the best predictors
of excessive ovarian response and ovarian hyperstimulation syndrome (OHSS)
compared to other clinical endocrine and ultrasound markers (Nardo et al
2009 Nelson et al 2007) Broer et al compared these two tests in systematic
review of 14 studies and reported that the summary estimates of the sensitivity
and the specificity for AMH were 82 and 76 respectively and for AFC 82
and 80 respectively (Broer et al 2011) Consequently the study concluded
that AMH and AFC were equally predictive and the difference in the predictive
value between the tests was not statistically significant
26
342 Prediction of live birth rate (LBR) in cycles of IVF
Lee at al reported that AMH and chronological age were more accurate
than basal FSH AFC BMI and causes of infertility in the prediction of live
birth rate (Lee et al 2009) Similarly La Marca et al suggested that odds of live
birth could be reliably predicted using AMH (La Marca et al 2010b) although
subsequent review of the study questioned strength of the evidence (Loh and
Maheshwari 2011)
A study conducted by Nelson et al found that higher AMH levels had
stronger association with increased live birth rate compared to age and FSH
(Nelson et al 2007) However the study also suggested that this association
was mainly confined in the women with low AMH levels and there was no
additional increase in live birth in women with AMH levels of higher than 710
pmolL This may suggest that achieving a live birth may be under the
influence of number of other factors and that markers of ovarian reserve alone
may not be able predict this outcome reliably
35 The role of AMH in individualisation of ovarian stimulation in
IVF cycles
Prediction of ovarian response to the stimulation of ovaries in cycles of
IVF plays an important role in the counseling of couples undergoing treatment
programmes and hence many clinical studies on AMH have focused on the
prognostic value of AMH measurements However data on using AMH as a
tool for improving the clinical outcomes in IVF cycles appear to be lacking
considering AMH may be useful tool in tailoring treatment strategies to an
individual patientrsquos ovarian reserve Unlike most other markers AMH has
discriminatory power in determining various degrees of ovarian reserve due to
significantly higher between patient (CV 94) variability compared to its
within-patient (CV 28) variation (Rustamov et al 2011) which allows
stratification of patients into various degrees of (eg low normal high) ovarian
reserve Subsequently most optimal ovarian stimulation protocol may be
established for each band of ovarian reserve Consequently reference ranges
on the basis of distribution of AMH in infertile women were developed which
were subsequently adopted by fertility clinics for a tailoring the mode of
27
ovarian stimulation and daily dose of gonadotrophins in IVF (The Doctors
Laboratory 2008 However currently available clinical reference ranges are
based on the first generation DSL assay and may not be reliably convertible to
currently available Gen II assay measurements (Wallace et al 2011) Indeed the
findings of the studies on comparability of the first generation AMH assays
suggest that establishing a reliable between assay conversion factor between
AMH assays may not be straightforward Furthermore the reference ranges
appear to reflect the distribution of AMH measurements within a specific
population and may therefore not be directly applicable for the prediction of
response to ovarian stimulation in IVF patients (The Doctors Laboratory
2008)
More importantly despite lack of good quality evidence on the
effectiveness of AMH-tailored ovarian stimulation protocols a number of
fertility clinics appear to have introduced various AMH-based COH protocols
in their IVF programs At present research evidence on AMH-tailored
ovarian stimulation in IVF is largely based on two retrospective studies
(Nelson et al 2009 Yates et al 2012) Both of these studies display considerable
methodological limitations including small sample size and centre-related or
period-related selection of their cohorts In this context AMH is used as a tool
for therapeutic intervention and therefore the research evidence should ideally
be derived from randomised controlled trials However recruitment of large
enough patients in IVF setting may take considerable time and resources In
the meantime given AMH-tailored ovarian stimulation has already been
introduced in clinical practice and there is urgent need for more reliable data
the studies with a larger cohorts and robust methodology should assess the role
of AMH in individualisation of ovarian stimulation in IVF treatment cycles
4 Multivariate models of assessment of ovarian reserve
In view of the fact there is not a single marker of ovarian reserve that
can accurately predict ovarian response various models for combination of
multiple ovarian markers have been developed (Verhagen et al 2008) A
number of studies reported that multivariate models are better predictors of
poor ovarian response in IVF compared to a single marker (Bancsi et al 2002
Balasch et al 1996 Creus et al 2000 Durmusoglu et al 2004) However a meta-
analysis showed that when compared to a single marker (AFC) multivariate
28
model has a similar accuracy in terms of prediction of poor ovarian response
(Verhagen et al 2008) In contrast a more recent study demonstrated that
multivariate score was superior to chronological age basal FSH or AFC alone
in predicting likelihood of poor ovarian response and clinical pregnancy
(Younis et al 2010) However the study did not include one of the most
reliable markers AMH in either arm necessitating further assessment of the
role of combined tests which include all reliable biomarkers
4 SUMMARY
During the last two decades a significant leap has been taken towards
understanding the biology of anti-Muumlllerian hormone and its role in female
reproduction (Durlinger et al 2002 Themmen et al 2005) Availability of
commercial AMH assays has resulted in significant increase in interest in the
role of the measurement of serum AMH in the assessment of ovarian reserve
which has been followed by the introduction of the test into routine clinical
practice (Nelson et al 2011) However more recent studies suggest that current
methodologies for the measurement of AMH may provide significant sampling
variability (Rustamov et al 2011) Furthermore the studies that compared first
generation commercial assay methods appear to provide non-reproducible
results suggesting there may be underlying issues with assay methodologies
(Lee et al 2011) Similarly despite lack of sufficient evidence in the role of
AMH in individualisation of ovarian stimulation protocols in IVF AMH-
tailored IVF protocols have been introduced in routine clinical practice of
many fertility clinics around the world
Consequently it appears that clinical application of AMH test has
surpassed the research evidence in some aspects of fertility treatment and
therefore future projects should be directed toward areas where gaps in
research evidence exist On the basis of the review of literature we believe that
evaluation of the performance of assay methods understanding the role of
AMH in assessment ovarian reserve and establishing its role in
individualisation of ovarian stimulation protocols should be research priority
29
II GENERAL INTRODUCTION
On the basis of the review of published literature I have identified that
the following areas of research on the clinical application of AMH in the
management of infertility requires further investigation 1) Within-patient
variability of measurement of AMH using Gen II assay method 2)
Establishment of clinically measurable determinants of AMH levels and 3) The
role of AMH in individualisation of ovarian stimulation in IVF treatment
cycles
In our previous study we estimated that there was significant sample-to-
sample variation (CV 28) in AMH measurements when the first generation
DSL assay was used (Rustamov et al 2011) The source of variability is likely to
be related to the assay method given that biological within-cycle variation of
AMH is believed to be small (La Marca et al 2006) Therefore assessment of
sample-to-sample variability of AMH using the newly introduced Gen II assay
which is believed to be significantly more stable and sensitive compared to that
of DSL assay should enable us to establish the measurement related variability
of AMH Furthermore given I am planning to use data from both DSL and
Gen II assays I need to establish between-assay conversion factor for these
assays using data on clinical samples
There appears to be a lack of good quality data on the effect of
ethnicity BMI causes of infertility reproductive history and reproductive
surgery on ovarian reserve Therefore I am planning to ascertain the role of
above factors on determination of ovarian reserve by analysing AMH
measurements of a large cohort of patients
There is a strong correlation between AMH and ovarian performance
in IVF treatment when conventional ovarian stimulation using GnRH agonist
regimens with a standard daily dose of gonadotrophins are used (Nelson et al
2007 Nardo et al 2007) Furthermore studies suggest tailoring the ovarian
stimulation protocols to AMH measurement may improve ovarian
performance and subsequently the success of IVF treatment (Nelson et al
2011 Yates et al 2012) However given methodologies of the published
studies the effectiveness of currently proposed AMH-tailored ovarian
stimulation protocols remains unknown Therefore I am planning to develop
individualised ovarian stimulation protocols by establishing the most optimal
mode of pituitary down regulation and starting dose of gonadotrophins for
30
each AMH cut-off bands using a robust research methodology However
development of individualised ovarian stimulation protocols on the basis of
retrospective data requires a reliable and validated database containing a large
number of observations In the IVF Department of St Maryrsquos Hospital we
have data on a large number of patients who underwent ovarian stimulation
following the introduction of AMH However the data on various aspects of
investigation and treatment of patients is stored in different clinical data
management systems and may not be easily linkable In addition it appears that
data on certain important variables (eg causes of infertility AFC) are available
only in the hospital records necessitating searching for data from the hospital
records of each patient Consequently I designed a project for building a
research database which will have comprehensive and validated datasets that
are necessary for investigation of the research questions of the MD
programme
In conclusion I am planning to conduct a series of studies to improve
the understanding of the role of AMH in the management of women with
infertility Specifically I am intending to evaluate 1) sample-to-sample variability
of Gen II AMH measurements 2) conversion factor between DSL and Gen II
assays in clinical samples 3) the effect of ethnicity BMI causes of infertility
endometriosis reproductive history and reproductive surgery to ovarian
reserve and explore AMH-tailored individualisation of ovarian stimulation in
IVF cycles
31
References
Abbeel E The Istanbul consensus workshop on embryo assessment proceedings of an expert meeting Human reproduction 2011 26 p 1270-83 Abdalla HT M Y Repeated testing of basal FSH levels has no predictive value for IVF outcome in women with elevated basal FSH Human reproduction 2006 21(1) p 171-4 Amer SA LT Ledger WL The value of measuring anti-Mullerian hormone in women with polycystic ovary syndrome undergoing laparoscopic ovarian diathermy Human reproduction 2009 24 p 2760-6 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343 Balaban B BD Calderoacuten G Catt J Conaghan J Cowan L Ebner T Gardner D Hardarson T Lundin K Cristina Magli M Mortimer D Mortimer S Munneacute S Royere D Scott L Smitz J Thornhill A van Blerkom J Van den Baker A quantitative and cytological study of germ cells in human ovaries Proc R Soc Lond B Biol Sci 1963 158 p 417-433 Balasch J CM Fabregues F Carmona F Casamitjana R Ascaso and VJ C Inhibin follicle-stimulating hormone and age as predictors of ovarian response in in vitro fertilization cycles stimulated with gonadotropin-releasing hormone agonist-gonadotropin treatment Am J Obstet Gynecol 1996 175 p 1226-1230 Bancsi LF BF Eiijekemans MJ at al Predictors of poor ovarian response in in vitro fertilisation a prospective study comparing basal markers of ovarian reserve Fertility and Sterility 2002 77 p 328-336 Bazer FW Strong science challenges conventional wisdom new perspectives on ovarian biology Reprod Biol Endocrinol 2004 2 p 28 Begum S VE Papaioannou and RG Gosden The oocyte population is not renewed in transplanted or irradiated adult ovaries Hum Reprod 2008 23(10) p 2326-30
Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175 Bristol-Gould SK et al Fate of the initial follicle pool empirical and mathematical evidence supporting its sufficiency for adult fertility Dev Biol 2006 298(1) p 149-54 Broekmans FJ et al A systematic review of tests predicting ovarian reserve and IVF outcome Hum Reprod Update 2006 12(6) p 685-718
32
Broekmans Frank J M de Ziegler Dominique Howles Colin M Gougeon Alain Trew Geoffrey and Olivennes Francois The antral follicle count practical recommendations for better standardization Fertility and Sterility 2010 94 p 1044-51 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011 Aug96(8)2532-9
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Bukovsky A et al Origin of germ cells and formation of new primary follicles in adult human ovaries Reprod Biol Endocrinol 2004 2 p 20 Byskov AG et al Eggs forever Differentiation 2005 73(9-10) p 438-46 Clarke TR et al Mullerian inhibiting substance signaling uses a bone morphogenetic protein (BMP)-like pathway mediated by ALK2 and induces SMAD6 expression Mol Endocrinol 2001 15(6) p 946-59
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146 Creus M PJ Faacutebregues F Vidal E Carmona F Casamitjana R and BJ Vanrell JA Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-2346 Creus M PaJ Fabregues F Vidal E Carmona F Casamitjana R et al Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-6 Cook CL SY Brenner AG et al Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertility and Sterility 2002 77 p 141-6 Deb S Campbell B K Clewis JS Pincott-Allen C and Raine-Fenning NJ Intracycle variation in number of antral follicles stratified by size and in endocrine markers of ovarian reserve in women with normal ovulatory menstrual cycles Ultrasound Obstet Gynecol 2013 41 216ndash222 De Felici M Germ stem cells in the mammalian adult ovary considerations by a fan of the primordial germ cells 2010 Mol Hum Reprod 16(9) p 632-6 Donovan PJ (1998) The germ cell ndash the mother of all stem cells Int J Dev Biol 42 1043ndash50 Dunaif A Insulin resistance and the polycystic ovary syndrome mechanism adn implications for pathogenesis Endocr Rev 1997 18 p 774-800
33
Durlinger AL et al Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 1999 140(12) p 5789-96 Durlinger AL et al Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 2001 142(11) p 4891-9 Durlinger AL JA Visser and AP Themmen Regulation of ovarian function the role of anti-Mullerian hormone Reproduction 2002 124(5) p 601-9 Durmusoglu F EK Yoruk P Erenus M Combining day 7 follicle count with the basal antral follicle count improves the prediction of ovarian response Fertility and Sterility 2004 81 p 1073-78 Ebner T et al Basal level of anti-Mullerian hormone is associated with oocyte quality in stimulated cycles Hum Reprod 2006 21(8) p 2022-6 Elmashad AI Impact of laparoscopic ovarian drilling on anti-Muumlllerian hormone levels and ovarian stromal blood flow using three-dimensional power Doppler in women with anovulatory polycystic ovary syndrome Fertility and Sterility 2011 95 p 2342-6 Falbo A RM Russo T DEttore A Tolino A Zullo F Orio F Palomba S Serum and follicular anti-Mullerian hormone levels in women with polycystic ovary syndrome (PCOS) under metformin J Ovarian Resere 2010 Jul p 16 Fanchin R et al Anti-Mullerian hormone concentrations in the follicular fluid of the preovulatory follicle are predictive of the implantation potential of the ensuing embryo obtained by in vitro fertilization J Clin Endocrinol Metab 2007 92(5) p 1796-802 Fasouliotis SJ A Simon and N Laufer Evaluation and treatment of low responders in assisted reproductive technology a challenge to meet J Assist Reprod Genet 2000 17(7) p 357-73 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164 Gleicher N A Weghofer and DH Barad Defining ovarian reserve to better understand ovarian aging Reprod Biol Endocrinol 9 p 23 Haadsma ML BA Groen H Roeloffzen EM Groenewoud ER Heineman MJ et al The number of small antral follicles (2ndash6 mm) determines the outcome of endocrine ovarian reserve tests in a subfertile population Human reproduction 2007 22 p 1925-31 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699ndash708
34
Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hazout A et al Serum antimullerian hormonemullerian-inhibiting substance appears to be a more discriminatory marker of assisted reproductive technology outcome than follicle-stimulating hormone inhibin B or estradiol Fertil Steril 2004 82(5) p 1323-9
Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 HFEA Fertility Figures 2005 2007 HFEA HFEA Fertility Facts and Figures 2008 HFEA 2010 Homburg R BD Levy T Feldberg D Ashkenazi J Ben-Rafael Z In vitro fertilisation and embryo transfer for the treatment of infertility associated with polycystic ovary syndrome Fertility and Sterility 1993 60 p 858-863 Hudson PL et al An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 1990 70(1) p 16-22 Hull MG GC Kelly NJ et al Population study of causes treatment and outcome of infertility Br Med J Clin Res Ed 1985 291 p 1693-1697 Islam MN and MM Islam Biological and behavioural determinants of fertility in Bangladesh 1975-1989 Asia Pac Popul J 1993 8(1) p 3-18 Jayaprakasan K et al A prospective comparative analysis of anti-Mullerian hormone inhibin-B and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation(2010a) Fertil Steril 2010 93(3) p 855-64 Jayaprakasan et al (2010b) The cohort of antral follicles measuring 2ndash6 mmreflects the quantitative status of ovarian reserve as assessed by serum levels of anti-Mullerian hormone and response to controlled ovarian stimulation Fertil Steril_ 2010941775ndash81 Johannes L H Evers MD Peronneke Slaats MS Jolande A Land MD John C M Dumoulin PhD and Gerard A J Dunselman MD Elevated Levels of Basal Estradiol-17β Predict Poor Response in Patients with Normal Basal Levels of Follicle-Stimulating Hormone Undergoing In Vitro Fertilization Fertility and Sterility 1998(69) p 1010-4 Johnson J et al Germline stem cells and follicular renewal in the postnatal mammalian ovary Nature 2004 428(6979) p 145-50 Kelsey TW et al A validated model of serum anti-mullerian hormone from conception to menopause PLoS One 2011 6(7) p e22024
35
Kumar A et al Development of a second generation anti-Mullerian hormone (AMH) ELISA J Immunol Methods 362(1-2) p 51-9 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A De Leo V Giulini S Orvieto R Malmusi S Giannella L Volpe A Anti-Mullerian hormone in premenopausal women and after spontaneous or surgically induced menopause J Soc Gynecol Investig 2005b12545-548 La Marca A et al Normal serum concentrations of anti-Mullerian hormone in women with regular menstrual cycles (2010a) Reprod Biomed Online 2010 21(4) p 463-9 La Marca A et al Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction (2010b) Reprod Biomed Online 2010 22(4) p 341-9 La Marca A et al Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART) Hum Reprod Update 16(2) p 113-30 La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351 Lee MM et al Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 1996 81(2) p 571-6 Lee TH et al Impact of female age and male infertility on ovarian reserve markers to predict outcome of assisted reproduction technology cycles Reprod Biol Endocrinol 2009 7 p 100
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604 Licciardi FL LH Rosenwaks Z Day 3 estradiol serum concentrations as prognosticators of ovarian stimulation response and pregnancy outcome in patients undergoing in vitro fertilization Fertility and Sterility 1995 64 p 991-4 Lie Fong S et al Anti-Mullerian hormone a marker for oocyte quantity oocyte quality and embryo quality Reprod Biomed Online 2008 16(5) p 664-70 Lintsen AM et al Effects of subfertility cause smoking and body weight on the success rate of IVF Hum Reprod 2005 20(7) p 1867-75 Maheshwari A and PA Fowler Primordial follicular assembly in humans--
36
revisited Zygote 2008 16(4) p 285-96 Maheshwari A et al Dynamic tests of ovarian reserve a systematic review of diagnostic accuracy Reprod Biomed Online 2009 18(5) p 717-34 Massague J et al TGF-beta receptors and TGF-beta binding proteoglycans recent progress in identifying their functional properties Ann N Y Acad Sci 1990 593 p 59-72 Massague J and YG Chen Controlling TGF-beta signaling Genes Dev 2000 14(6) p 627-44 Mc KD HA Adams EC Danziger S Histochemical observations on the germ cells of human embryos Anat Rec 1953 2 p 201-219 McGee EA and AJ Hsueh Initial and cyclic recruitment of ovarian follicles Endocr Rev 2000 21(2) p 200-14 Mishina Y et al Genetic analysis of the Mullerian-inhibiting substance signal transduction pathway in mammalian sexual differentiation Genes Dev 1996 10(20) p 2577-87 Moran LJ HC Hutchinson SK Stepto NK Strauss BJ Teede HJ Exercise decreases anti-Mullerian horomone in anovulatory overweight women with polycystic ovary syndrome-A pilot study Horm Metab Res 2011 October Nardo LG et al Circulating basal anti-Mullerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 92(5) p 1586-93 Nelson SM RW Yates and R Fleming Serum anti-Mullerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007 22(9) p 2414-21 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867 Nelson SM and A La Marca The journey from the old to the new AMH assay how to avoid getting lost in the values 2011 Reprod Biomed Online Nelson SM et al External validation of nomogram for the decline in serum anti-Mullerian hormone in women a population study of 15834 infertility patients Reprod Biomed Online 2011 23(2) p 204-6 NICE Assessment and treatment for people with fertility problems NICE Guidelines 2013 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS ONE 5(7) e11637 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS
37
ONE 2010 5(7) 11637 Nilsson EE et al Inhibitory actions of Anti-Mullerian Hormone (AMH) on ovarian primordial follicle assembly PLoS One 2011 6(5) p e20087 Notarianni E Reinterpretation of evidence advanced for neo-oogenesis in mammals in terms of a finite oocyte reserve 2011 J Ovarian Res 4(1) p 1 Office of National Statistics 2012 1 2011 Live Births in England and Wales by Characteristics of Mother Oktem O and B Urman Understanding follicle growth in vivo Hum Reprod 25(12) p 2944-54 Oktem O and K Oktay The ovary anatomy and function throughout human life Ann N Y Acad Sci 2008 1127 p 1-9 Ottosen LD et al Pregnancy prediction models and eSET criteria for IVF patients--do we need more information J Assist Reprod Genet 2007 24(1) p 29-36 Pacchiarotti J et al Differentiation potential of germ line stem cells derived from the postnatal mouse ovary Differentiation 2010 79(3) p 159-70 Paternot G WA Thonon F Vansteenbrugge A Willemen D Devroe J Debrock S DHooghe TM Spiessens C Intra- and interobserver analysis in the morphological assessment of early stage embryos during an IVF procedure a multicentre study Reprod Biol Endocrinol 2011 9 p 127 Pehlivanov B OM Anti-Muumlllerian hormone in women with polycystic ovary syndrome Folia Medica 2011 53 p 5-10 Pellat L HL Brincat M et al Granulosa cell production of anti-Muumlllerian hormone is increased in polycystic ovaries J Clin Endocrinol Metab 2007 92 p 240-5 Pigny P ME Robert Y et al Elevated serum level of anti-Mullerian hormone in patients with polycystic ovary syndrome relationship to the ovarian follicle excess and the follicular arrest J Clin Endocrinol Metab 2003 88 p 5957-62 Porter RN et al Induction of ovulation for in-vitro fertilisation using buserelin and gonadotropins Lancet 1984 2(8414) p 1284-5 Rajpert-De Meyts E et al Expression of anti-Mullerian hormone during normal and pathological gonadal development association with differentiation of Sertoli and granulosa cells J Clin Endocrinol Metab 1999 84(10) p 3836-44
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-
38
Scott Wilkes Murdoch Alison DC and Greg Rubin Epidimiology and management of infertility a poppulation-based study in UK primary care Family Practice 2009 26 p 269-274 Seifer DB L-MG Hogan JW Gardiner AC Blaza AS Berk CA Day 3 serum inhibin-B is predictive of assisted reproductive technologies outcome Fertility and Sterility 1997 67 p 110-4 Skinner MK (2005) Regulation of primordial follicle assembly and development Hum Reprod Update 11 461ndash71 Syrop CH et al Ovarian volume may predict assisted reproductive outcomes better than follicle stimulating hormone concentration on day 3 Hum Reprod 1999 14(7) p 1752-6 Syrop CH A Willhoite and BJ Van Voorhis Ovarian volume a novel outcome predictor for assisted reproduction Fertil Steril 1995 64(6) p 1167-71 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Taylor A ABC of subfertility Making a diagnosis Br Med J Clin Res Ed 2003 327 p 799-801 Templeton A JK Morris and W Parslow Factors that affect outcome of in-vitro fertilisation treatment Lancet 1996 348(9039) p 1402-6 Templeton A Infertility-epidemiology aetiology and effective management Health Bull (Edinb) 1995 53(5) p 294-8 TDL test update AMH Stability Hormones and OCPs The Doctors Laboratory Guide 2008 page 29 Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34) p 18-21 Tilly JL and J Johnson Recent arguments against germ cell renewal in the adult human ovary is an absence of marker gene expression really acceptable evidence of an absence of oogenesis Cell Cycle 2007 6(8) p 879-83 Urbancsek J Use of serum inhibin B levels at the start of ovarian stimulation and at oocyte pickup in the prediction of assisted reproduction treatment outcome Fertility and Sterility 2005 83(2) p 341-348 van der Linden M BK Farquhar C Kremer JAM Metwally M Luteal phase support for assisted reproduction cycles (Review) Cochrane Library 2011 October
39
van Disseldorp J et al Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2011 25(1) p 221-7 van Kooij RJ et al Age-dependent decrease in embryo implantation rate after in vitro fertilization Fertil Steril 1996 66(5) p 769-75 van Rooij IA et al Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002 17(12) p 3065-71 Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539 van Disseldorp J Kwee CBL J Looman CWN Eijkemans MJC and FJ Broekmans Comparison of inter- and intra-cycle variability of anti-Muuml llerian hormone and antral follicle counts Human reproduction 2010 25 p 221-227 Verberg MF et al Predictors of low response to mild ovarian stimulation initiated on cycle day 5 for IVF Hum Reprod 2007 22(7) p 1919-24 Verhagen TE et al The accuracy of multivariate models predicting ovarian reserve and pregnancy after in vitro fertilization a meta-analysis Hum Reprod Update 2008 14(2) p 95-100 Visser JA AMH signaling from receptor to target gene Mol Cell Endocrinol 2003 211(1-2) p 65-73 Wallace WH and TW Kelsey Human ovarian reserve from conception to the menopause PLoS One 5(1) p e8772 Wallace AM et al A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 2011 48(Pt 4) p 370-3 Webber L J SS Stark J Trew G H Margara R Hardy K Franks S Formation and early development of follicles in the polycystic ovary Lancet 2003 362(September) p 1017-1021
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362 Younis JS et al A simple multivariate score could predict ovarian reserve as well as pregnancy rate in infertile women Fertil Steril 2010 94(2) p 655-61 Zou K et al Production of offspring from a germline stem cell line derived from neonatal ovaries Nat Cell Biol 2009 11(5) p 631-6 Zuckerman The number of oocytes in the mature ovary Recent Prog Horm Res 1951 6(63-108)
Figure 1 Schematic representation of a long GnRH agonist cycle
In a long agonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH agonist preparations starting from mid-luteal phase of the preceding menstrual cycle till the day of administration of HCG
Cycle Started
Menstrual Period
Daily GnRH agonist
From mid-luteal phase
Daily GnRH agonist
Menstrual
Period
Daily GnRH agonist
amp
Daily hMG
Day 2-10
HCG
USOR
amp
ET
41
Figure 2 Schematic representation of GnRH antagonist cycle
In an antagonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH antagonist preparations starting from the 5th day of IVF cycle till the day of administration of HCG Therefore an ldquoAntagonistrdquo cycle is significantly shorter than an ldquoAgonistrdquo cycle
Cycle Started
Menstrual Period
Daily GnRH antagonist
(Day 5-10)
amp
Daily hMG
(Day 2-10)
HCG
USOR
amp
ET
42
Figure 3 The role of AMH in regulation of oocyte recruitment and folliculogenesis
It appears that AMH plays an important role in a) the recruitment of primordial follicles and b) the selection of a dominant follicle from a cohort of antral follicles AMH is believed to be the main regulator of ovarian reserve which is achieved by its paracrine negative feedback effect to resting primordial follicles (Durlinger et al 1999) AMH was found to play an important role
in the regulation of the selection of a dominant follicle by inhibition of the FSH-induced follicle growth (Durlinger et al 2001)
EVALUATION OF THE GEN II AMH ASSAY BETWEEN-SAMPLE VARIABILITY AND
ASSAY-METHOD COMPARABILITY
2
44
ANTI-MUumlLLERIAN HORMONE SERUM LEVELS AND REPRODUCIBILITY
IN A LARGE COHORT OF SUBJECTS SUGGEST
SAMPLE INSTABILITY
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G
Nardo Philip W Pemberton
Human Reproduction 2012a 273085-3091
21
45
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large
cohort of subjects suggest sample instability
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester Foundation Trust
Manchester M13 9WL UK
c Health Sciences - Methodology Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
d School of Medicine University of Manchester Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Corresponding author
Oybek Rustamov MRCOG
Research Fellow in Reproductive Medicine
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
E-mail oybekrustamovcmftnhsuk oybek_rustamovyahoocouk
Word count 3909
Conflicts of Interest There are no potential conflicts of interest
Acknowledgement of financial support
Dr Steve Roberts is supported by the NIHR Manchester Biomedical Research Centre
46
Declaration of authorsrsquo roles
OR led on clinical aspects of this study with responsibility for collation of the
clinical database and the analysis of the clinical data OR prepared the first
draft of the clinical work and was involved in preparation of the whole paper
and submission of the final manuscript CF and LGN contributed to clinical
data analysis draft preparation and approval of the final manuscript MK was
involved in clinical data collation and approval of the final draft PWP was the
laboratory lead responsible for all of the laboratory based experiments and for
the routine analysis of clinical samples PWP prepared the first draft of the
laboratory work and was involved in the preparation of the whole paper and
submission of the final manuscript AS suggested the sample stability studies
and was involved in discussion draft preparation and approval of the final
manuscript APY was involved in some of the routine clinical analyses and
progression of drafts to approval of the final manuscript SAR was involved in
clinical study design oversaw the statistical analysis and progression of drafts
through to approval of the final manuscript OR and PWP should be
considered as joint first authors
47
ABSTRACT
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large cohort of
subjects suggest sample instability
Study question
What is the variability of anti-muumlllerian hormone (AMH) concentration in
repeat samples from the same individual when using the Gen II assay and how
do values compare to Gen I (DSL) assay results
Summary answer
Both AMH assays displayed appreciable variability which can be explained by
sample instability
What is known already
AMH is the primary predictor of ovarian performance and is used to tailor
gonadatrophin dosage in cycles of IVFICSI and in other routine clinical
settings A robust reproducible and sensitive method for AMH analysis is of
paramount importance The Beckman Coulter Gen II ELISA for AMH was
introduced to replace earlier DSL and Immunotech assays The performance
of the Gen II assay has not previously been studied in a clinical setting
Study design size and duration
For AMH concentration study we studied an unselected group of 5007
women referred for fertility problems between 1st September 2008 to 25th
October 2011 AMH was measured initially using the DSL AMH ELISA and
subsequently using the Gen II assay AMH values in the two populations were
compared using a regression model in log(AMH) with a quadratic adjustment
for age Additionally women (n=330) in whom AMH had been determined in
different samples using both the DSL and Gen II assays (paired samples)
identified and the difference in AMH levels between the DSL and Gen II
assays was estimated using the age adjusted regression analysis
In AMH variability study 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay and 87 women had repeated AMH
determinations using the Gen II assay (n=177 samples) were identified A
mixed effects model in log (AMH) was utilised to estimate the sample-to-
48
sample (within-subject) coefficients of variation of AMH adjusting for age
Laboratory experiments including sample stability at room temperature
linearity of dilution and storage conditions used anonymised samples
Main results and the role of chance
In clinical practice Gen II AMH values were ~20 lower than those
generated using the DSL assay instead of the 40 increase predicted by the kit
manufacturer Both assays displayed high within-subject variability (Gen II
assay CV=59 DSL assay CV=32) In the laboratory AMH levels in serum
from 48 subjects incubated at RT for up to 7 days increased progressively in
the majority of samples (58 increase overall) Pre dilution of serum prior to
assay gave AMH levels up to twice that found in the corresponding neat
sample Pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave higher readings (72 overall) compared to sequential
addition Storage at -20ordmC for 5 days increased AMH levels by 23 compared
to fresh samples The statistical significance of results was assessed where
appropriate
Limitations reasons for caution
The analysis of AMH levels is a retrospective study and therefore we cannot
entirely rule out the existence of differences in referral practices or changes in
the two populations
Wider implications of the findings
Our data suggests that AMH may not be stable under some storage or assay
conditions and that this may be more pronounced with the Gen II assay The
published conversion factors between the Gen II and DSL assays appear to be
inappropriate for routine clinical practice Further studies are urgently required
to confirm our observations and to determine the cause of the apparent
instability In the meantime caution should be exercised in the interpretation
of AMH levels in the clinical setting
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA DSL Active MIS AMH ELISA sample stability
49
INTRODUCTION
AMH in women is secreted by the granulosa cells of pre-antral and small
antral follicles (Vigier et al 1984 Themmen 2005) and circulating levels reflect
the ovarian pool from which follicles can be recruited (Loh amp Maheshwari
2011) Measurement of AMH has become of paramount significance in clinical
practice in IVF units to assign candidates to the most suitable controlled
ovarian hyperstimulation protocol and its level is used to predict poor or
excessive ovarian response (Nelson et al 2007 Nardo et al 2009 Yates et al
2011) It is also of increasing importance in (a) prediction of live birth rate in
IVF cycles (La Marca et al 2011) (b) screeningdiagnosis of polycystic ovarian
syndrome (Cook et al 2002) (c) follow up of women with a history of
granulosa cell tumours (Lane et al 1999) (d) prediction of the age of onset of
infertility due to the menopause (van Disseldorp et al 2008 Broer et al 2011)
and finally (e) assessment of the long term effect of chemotherapy on fertility
(Anderson 2011)
Following development of the first laboratory AMH assay in 1990
(Hudson et al 1990 Lee et al 1996) first generation commercially available
immunoassays were introduced by Diagnostic Systems Ltd (DSL) and
Immunotech Ltd (IOT) These assays used different antibodies and standards
(Nelson amp La Marca 2011) and the resulting AMH concentrations obtained
using the IOT assay were found to be higher than those produced using the
DSL assay by most but not all authors (Freour et al 2007Taieb et al 2008 Lee
et al 2011) The AMH Gen II Assay (Beckman-Coulter Ltd) replaced both of
these assays using the DSL Gen I antibody with the IOT standards AMH
values obtained using this kit were predicted to correlate with but be higher
than those using the old DSL kit (Kumar et al 2010 Nelson amp La Marca
2011) This was confirmed (Wallace et al 2011) with the AMH Gen II assay
giving values approximately 40 higher than the DSL assay The
recommended conversion factor of 14 (AMH Gen II = DSL x 14) was also
applied to the DSL reference ranges but this recommendation does not appear
to have been independently validated
It is generally accepted that serum AMH concentrations are highly
reproducible within and across several menstrual cycles and therefore a single
blood sampling for AMH measurement has been accepted as routine practice
50
(Hehenkamp et al 2006 La Marca et al 2006 Tsepelidis et al 2007) However
we recently challenged this view and reported significant sample-to-sample
variation in AMH levels using the DSL assay in women who had repeated
measurements 28 difference between samples taken from the same patient
with a median time between sampling of 26 months and taking no account of
menstrual cycle (Rustamov et al 2011) Although we could not explain the
cause of this variability we speculated that it might be due to true biological
variation in secretion of AMH or due to post-sampling pre-analytical
instability of the specimen
Given the widespread adoption of AMH in Clinical Units it is critical
that the sources of variability in any AMH assay are understood and quantified
This paper presents the results of clinical and laboratory studies on routine
clinical samples using the new AMH Gen II assay specifically comparing assay
values with the older DSL assay assessing between sample variability and
investigating analytical and pre-analytical factors affecting AMH measurement
METHODS
Study population
Samples were obtained from women of 20-46 years of age attending for
investigation of infertility requiring AMH assessment at the secondary
(Gynecology Department) and tertiary (Reproductive Medicine Department)
care divisions of St Maryrsquos Hospital Manchester from 1st September 2008 to
25th October 2011 Samples which were lipaemic or haemolysed and samples
not frozen within 2 hours of venepuncture were excluded from the study
Anonymised samples from this pool of patients were used for stability studies
after routine AMH measurements had been completed The full dataset
comprised AMH results on 5868 samples from 5007 women meeting the
inclusion criteria Additionally we identified women in whom AMH had been
determined in different samples using both the DSL and Gen II assays (paired
samples from 330 women)
51
Sample processing
Collection and handling of all AMH samples was conducted according
to the standards set out by the manufacturers and did not vary between the
different assays Serum samples were transported immediately to the
Department of Clinical Biochemistry based in the same hospital and
separated within 2 hours of venepuncture using the Modular Pre-Analytics
Evo (Roche Diagnostics Burgess Hill West Sussex UK) Samples were frozen
in aliquots at -20C until analysis normally within one week of receipt The
laboratory participates in the pilot National external quality assessment scheme
(UKNEQAS) for AMH in Edinburgh and performance has been satisfactory
AMH analysis
All AMH assays were carried out strictly according to the protocols
provided by the manufacturer and sample collection and storage also
conformed to these recommendations All AMH samples were analysed in
duplicate and the mean of the two replicates was reported as the final result
1) The DSL AMH assay The enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) was used for measurement of AMH prior to 17th
November 2010 The working range of the assay was up to 100pmolL with a
minimum detection limit of 063pmolL The intra-assay coefficient of
variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at 56pmoll) The
inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at 56pmoll)
2) The Beckman Coulter Gen II assay After 17th November 2010
AMH was measured using the enzymatically amplified two-site immunoassay
(AMH Gen II ELISA Beckman Coulter Inc Brea CA USA) The working
range of the assay is up to 150pmolL with a minimum detection limit of
057pmolL The intra-assay CV (n=16) is 292 (at 18pmoll) and 203 (at
60pmoll) The inter-assay coefficient of variation (n=28) is 357 (at
18pmoll) and 364 (at 60pmoll)
Sample Stability Studies
(1) Stability of AMH in serum at room temperature (RT) serum samples
(n = 48) were allowed to thaw and then left at RT for one week At 0 1 2 4
and 7 days 100microl aliquots were removed and immediately stored at -80 ordmC in
52
2ml screw-capped polypropylene tubes (Alpha Laboratories Eastleigh UK)
Two freezethaw cycles had no effect on AMH concentration (results not
shown) Samples from individual subjects were analysed for AMH on the same
GenII microtitre plate to eliminate inter-assay variability Results were
expressed as a percentage of the day 0 value
(2) Linearity of Dilution 100microl fresh serum (n = 9) was added to 100microl
AMH Gen II sample diluent incubated for 30min at RT and the mixture
analysed using the standard GenII assay procedure
(3) Comparison between the Standard Assay method and an equivalent
procedure in the standard GenII ELISA assay method the first steps involve
the addition of calibrators controls or serum samples to microtitration wells
coated with anti-AMH antibody Assay buffer is then added to each well As a
comparison serum and assay buffer were mixed in a separate tube incubated
for 10min at RT and then added in exactly the same volume and proportions
to the microtitre plate Thereafter the assay was performed using the standard
protocol
(4) Stability of AMH during storage fresh serum samples (n = 8)
analysed on the day of reception were compared with aliquots from the same
samples that had been frozen for 5 days either in polystyrene tubes at -20degC or
polypropylene tubes at -80degC
Statistical Analysis
Data analysis was performed using the Stata 12 analytical package
(StataCorp Texas USA) Data management and analysis of clinical data was
conducted by one of the researchers (OR) and verified independently by
another member of the research team (SR) using different statistical software
(R statistical environment) Approval for the use of the data was obtained from
the Local Research Ethics Committee (UK-NHS 10H101522) The age-
related relationship of the DSL and Gen II assays to AMH was visualised using
scatter plots and quadratic fit on a logarithmic scale (Nelson et al 2011) The
age adjusted regression analysis of paired samples was used to estimate the
difference in AMH levels between the DSL and Gen II assays A mixed effects
model in log (AMH) was utilised to estimate the sample-to-sample (within-
subject) coefficients of variation of AMH levels in women who had repeated
53
measurements within a 1 year period from the patientrsquos first AMH sample
adjusting for age as above In the sample stability studies percentage changes
are expressed as mean plusmn SEM In the stability of AMH in serum at RT study a
paired t-test determined the level of significance between baseline and
subsequent days
RESULTS
Population studies and variability
AMH concentration
Table 1 summarizes the results of AMH determinations in our
population of women attending the IVF Clinic prior to the 17th November
2010 (using the DSL assay) and after that date (using the Gen II assay) A
second analysis compares AMH levels in women who had AMH measured
using both assays at different times Results were consistent with lower serum
levels of AMH observed when samples were analysed using the Gen II assay
compared to the DSL assay Figure 1 shows the correlation of AMH with age
for the unselected groups After adjustment for age the total cohorts showed
Gen II giving AMH values 34 lower than those for DSL Analysis restricted
to patients with AMH determinations using both assays gave an age-adjusted
difference of 21
AMH variability
During the study period 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay with 295 patients having two samples 17
three samples and one five samples The median time between samples was 51
months Eighty seven women had repeated AMH determinations using the
Gen II assay (n=177 samples) with 84 women having two samples and 3
having three samples The median interval between repeat samples was 32
months Both assays exhibit high sample-to-sample variability (CV) this was
32 in the DSL assay group (our previous finding (Rustamov et al 2011) in a
smaller group was 28) variability in the Gen II assay group was much higher
(59)
54
Table 1 Median and inter-quartile range for the two assays in the
different datasets along with the mean difference from an age-
adjusted regression model expressed as a percentage
DSL Gen II
difference ()
n age AMH (pmoll
)
n Age
AMH (pmoll
)
all data
3934
33 (29 36)
147 (78250
)
1934 33 (29 36)
112 (45 216)
-335 (-395 to -
275)
paired sample
s
330 32 (29 36)
149 (74 247)
330 34 (30 37)
110 (56 209)
-214 (-362 to -64)
Figure 1 Unselected AMH values from DSL (circles) and Gen II
(triangles) assays as a function of age Lines show the regression
fits of log(AMH) against a quadratic function of age solid lines
Gen II broken lined DSL
20 25 30 35 40 45
Age
AM
H [p
mo
lL
]
DSLGen II
11
01
00
55
Sample stability studies
(1) Stability of AMH in serum at room temperature
AMH levels in 11 of the 48 individuals remained relatively unchanged
giving values within plusmn10 of the original activity over the period of a week
and one patient had an undetectable AMH at all time points The remaining 36
serum samples had AMH values that increased progressively with time In the
47 samples with detectable AMH levels increased significantly (plt0001) for
each time interval compared to baseline the increase at day 7 being 1584 plusmn 76
(Figure 2)
Figure 2 Stability of AMH in serum at RT
Results at each time interval are expressed as a percentage of the patientrsquos AMH concentration at day 0 Means plusmn SEM are indicated
56
(2) Linearity of Dilution
In a group of nine anonymised samples proportionality with two-fold
sample dilution does not hold and on average there is a 574 plusmn 123 increase
in the apparent AMH concentration on dilution compared to neat sample (see
table 2a) Two samples which gave the highest increases were diluted further It
was apparent that after the anomalous doubling of AMH concentration on
initial two-fold dilution subsequent dilutions gave a much more proportional
result (see Table 2b) Linearity of dilution was maintained only in samples that
showed no initial increase on two-fold dilution
Table 2a Proportionality with two-fold dilution of serum
AMH (pmoll)
sample no neat serum x2 dilution recovery
1 1105 2294 2076 2 4941 9900 2004 3 415 483 1164 4 923 1122 1216 5 2801 3066 1091 6 362 628 1734 7 2739 3962 1447 8 553 1034 1870 9 1849 2892 1564
Table 2b Linearity with multiple dilution of serum
AMH (pmoll)
sample no dilution Measured expected recovery ()
1 x1 1105 1105 100 x2 1147 5525 2076 x4 5532 2763 2002 x7 3072 1579 1946 x10 2145 1105 1941
2 x1 4941 4941 100
x2 4950 2471 2003 x4 2286 1235 1851 x7 1228 706 1739 x10 857 494 1735
57
(3) Comparison between the Standard Assay method and an equivalent
procedure Serum samples that had been pre-mixed with buffer prior to
addition gave on average 718 plusmn 48 higher readings than those added
sequentially using the standard procedure (see table 3)
Table 3 Comparison between equivalent ELISA procedures
AMH (pmoll)
sample no A B BA ()
1 1466 2284 1558 2 839 1642 1957 3 3151 6446 2046 4 1244 2014 1619 5 1393 2276 1634 6 701 1246 1777 7 778 1358 1746 8 1693 3298 1948 9 955 1793 1877 10 2849 5437 1908
11 1365 2062 1511 12 1773 2868 1617 13 1468 2429 1655 14 1499 2115 1411 15 249 357 1434 16 1284 2289 1783
A = 20microl serum added directly to the plate followed by 100microl assay buffer
B = 60microl serum + 300microl assay buffer mixed amp incubated at RT for 10min 120microl mixture added to the plate
(4) Stability of AMH during storage AMH levels in samples stored at -20degC
showed an average increase of 225 plusmn 111 over 5 days compared with fresh
values while those samples stored at -80degC showed no change (18 plusmn 31)
(see Table 4)
Table 4 Stability of AMH in serum on storage
AMH (pmoll)
sample no
fresh -20ordmC PS -80ordmC PP
1 1241 1551 1312 2 4217 7542 4508 3 1193 1712 1239 4 1042 1282 1228 5 956 905 879 6 1902 2601 1884 7 2402 2016 2362 8 145 137 132
PS = polystyrene LP4 tube PP = polypropylene 2ml tube
58
DISCUSSION
This publication arose from two initially separate pieces of work in the
Clinical IVF Unit at St Maryrsquos Hospital and in the Specialist Assay Laboratory
at Central Manchester Foundation Trust The IVF Unit had become
concerned with their observed increase in variation in AMH values and
consequently with the reliability of their AMH-tailored treatment guidance
The Laboratory wished to establish whether the practice of sending samples in
the post (which has been adopted by many laboratories rather than frozen as
specified by Beckman) was viable It soon became clear that these anomalies
observed in clinical practice might be explained by a marked degree of sample
instability seen in the Laboratory which had not previously been reported and
which may or may not have been an issue with previous AMH assays
The data contained in this paper represents the largest retrospective
study on the variability of the DSL assay and the first study on the variability
of the Gen II assay Early studies reported insignificant variation between
repeated AMH measurements suggesting that a single AMH measurement
may be sufficient in assessment of ovarian reserve (La Marca et al 2006
Tsepelidis et al 2007) However these recommendations have been challenged
by a number of groups (Lahlou et al 2006 Wunder et al 2008 Rustamov et al
2011) The current study in a large cohort of patients has demonstrated
substantial sample-to-sample variation in AMH levels using the DSL assay and
an even larger variability using the Gen II assay We suggest that this variability
may be due to sample instability related to specimen processing given that a)
AMH is produced non-cyclically and true biological variation is believed to be
small (Fanchin et al 2005 van Disseldorp et al 2009) and b) the intra-and inter
assay variation in our laboratory for both the DSL and Gen II assays is small
(lt50) suggesting that the observed variation is not due to poor analytical
technique
The population data presented in this paper also suggests that in routine
clinical use the Gen II assay provides AMH results which are 20-40 lower
compared to those measured using the DSL assay This is in contrast to
validation studies for the Gen II assay which showed that this assay gave AMH
values ~40 higher than those found with the DSL assay (Kumar et al 2010
Preissner et al 2010 Wallace et al 2011)
59
All samples in this retrospective study were subject to the same handling
procedures and analyzed by the same laboratory the two populations were
comparable with the same local referral criteria for investigation of infertility
and we are unaware of any other alterations in practice which might produce
such a large effect on AMH we cannot rule out the possibility of other
changes in the population being assayed that were coincident in time with the
assay change However any such change would have to be coincident and
produce a 50 decrease in observed AMH levels to explain our findings We
did note a weak trend towards decreasing AMH over calendar time assuming a
linear trend in the analysis implies that AMH values might be 12 (2-22)
lower when the Gen II assay was being used compared to the Gen I assay
This suggests that the age adjusted analysis of repeat samples on individuals
showing a 21 decrease in AMH with the Gen II assay is currently the best
estimate of the assay difference
This is the first study to compare AMH assays in a routine clinical setting
in a large group of subjects and as such is likely to reflect the true nature of the
relationship between AMH measured by two different ELISA kits and avoids
some of the issues in other published studies Previous laboratory studies have
compared AMH assays in aliquots from the same sample which only provides
data on the within-sample relationship between the two assays (Kumar et al
2010 Preissner et al 2010 Wallace et al 2011) Although it is difficult to give a
definitive explanation for the discrepancy between the previously published
studies (on within-sample relationships) and this study (on between-sample
relationships) we suggest that it may be due to degradation of the specimen in
one (or both) of the assays If AMH in serum is unstable under certain storage
and handling conditions this might result in differing values being generated
because of differential sensitivity of the two assays to degradation products
Unfortunately we cannot suggest which step of sample handling might have
caused this discrepancy since the published studies did not provide detailed
information
The present study used samples which were frozen very soon after
phlebotomy and analysed shortly thereafter hopefully minimising storage
effects The most striking change followed incubation over a period of 7 days
at RT this showed a substantial increase in AMH levels rather than the
expected decline Previously Kumar et al (2010) had shown that the average
variation between fresh serum samples and those stored for seven days to be
60
approximately 4 at 2-8ordmC and lt1 at -20ordmC but presented no data on RT
stability Zhao et al (2007) reported that AMH values were likely to differ by
lt20 in samples incubated at RT for 2 days compared to those frozen
immediately
Several supplementary experiments were performed in order to
investigate this observed increase in AMH when samples were incubated at
RT These included (1) addition of the detergent Tween-20 to assay buffer to
disclose potential antibody-binding sites on the AMH molecule (2) the
removal of heterophilic antibodies from serum using PEG precipitation or
heterophilic blocking tubes None of these approaches affected AMH levels
significantly (results not shown)
Examination of the data presented here shows that in some samples
AMH levels tend towards twice those expected while results greater than that
only occur in two outliers found in Figure 2 The AMH molecule is made up
of two identical 72kDA monomers which are covalently bound (Wilson et al
1993 di Clemente et al 2010) During cytoplasmic transit each monomer is
cleaved to generate 110-kDa N-terminal and 25-kDa C-terminal homodimers
which remain associated in a noncovalent complex The C-terminal
homodimer binds to the receptor but in contrast to other TGF-β superfamily
members AMH is thought to require the N-terminal domain to potentiate this
binding to achieve full bioactivity of the C-terminal domain After activation of
the receptor the N-terminal homodimer is released (Wilson et al 1993) One
possible explanation for our findings is that the N-and C-terminal
homodimers dissociate gradually under certain storage conditions and that
either the two resulting N- and C-terminal components bind to the ELISA
plate or a second binding site on the antigen is exposed by the dissociation
effectively doubling the concentration of AMH It has been shown (di
Clemente et al 2010) that no dissociation occurs once the complex is bound to
immobilised AMH antibodies The observation that in some of our samples
there was no change after one week at RT might be explained by the
supposition that in those samples AMH is already fully dissociated A mixture
of dissociated and complex forms in the same sample would therefore
account for the observed recoveries between 100 and 200 in the
experiments presented in this paper Rapid sample processing and storage of
the resulting serum in a different tube type at -80ordmC might slow down this
breakdown process
61
The change in ionic strength or pH that occurs on dilution also seems to
have the same effect in increasing apparent AMH levels and again may be due
to dissociation or exposure of a second binding site Our results contradict
those reported by Kumar et al (2010) who showed that serum samples in the
range of 36-93pmoll of AMH when diluted in Gen II sample diluent showed
linear results across the dynamic range of the assay with average recoveries on
dilution close to 100 This might be explained if Kumarrsquos samples were
already dissociated before dilution Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample but our results do not seem to support this
These findings have significant clinical relevance given the widespread
use of AMH as the primary tool for assessment of ovarian reserve and as a
marker for tailoring the dose of gonadotrophins in cycles of IVFICSI As no
guideline studies have been published using the new Gen II assay some ART
centres have adopted modified treatment ldquocut off levelsrdquo for ovarian
stimulation programs based on the old DSL assay based ldquocut off levelsrdquo
multiplied by a conversion factor of 14 (Nelson et al 2007 Nelson et al 2009
Wallace et al 2011) The data presented in this paper suggest that this approach
could result in patients being allocated to the wrong ovarian reserve group
Poor performance of the Gen II assay in terms of sample-to-sample variability
(up to 59) could also lead to unreliable allocation to treatment protocols It
is a matter of some urgency therefore that any possible anomalies in the
estimation of AMH using the Gen II assay be thoroughly investigated and that
this work should be repeated in other centres
62
References
Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146
di Clemente N Jamin SP Lugovskoy A Carmillo P Ehrenfels C Picard JY Whitty A Josso N Pepinsky RB Cate RL Processing of anti-mullerian hormone regulates receptor activation by a mechanism distinct from TGF-beta Mol Endocrinol 2010242193-2206
Freour T Mirallie S Bach-Ngohou K Denis M Barriere P and Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
Fanchin R Taieb J Mendez Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Mullerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 2005 20923-927
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
La Marca A Nelson SM Sighinolfi G Manno M Baraldi E Roli L Xella S Marsella T Tagliasacchi D DAmico R Volpe A Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction Reprod Biomed Online 2011 22341-349
Lahlou N Chabbert-Buffet E Gainer E Roger M Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-5
63
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
Lee MM Donahoe PK Hasegawa T Silverman B Crist GB Best S Hasegawa Y Noto RA Schoenfeld D MacLaughlin DT Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 199681571-576
Loh JS Maheshwari A Anti-Mullerian hormone--is it a crystal ball for predicting ovarian ageing Hum Reprod 2011262925-2932
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 201195736-741
Preissner CM MD Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Taieb J Coussieu C Guibourdenche J Picard JY and di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34)18-21
Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormoneconcentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
van Disseldorp J Lambalk CB Kwee J Looman CWN Eijkemans MJC Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2010 25 221-227
64
Vigier B Picard JY Tran D Legeai L Josso N Production of anti-Mullerian hormone another homology between Sertoli and granulosa cells Endocrinology 19841141315-1320
Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-MuSllerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
Wilson CA Di Clemente N Ehrenfels C Pepinsky RB Josso NVigier B Cate RL Muumlllerian inhibiting substance requires its N-terminal domain for maintenance of biological activity a novel finding within the transforming growth-factor-beta superfamily Mol Endocrinol 19937247ndash257
Wunder DM Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrualcycle in reproductive age women Fertil Steril 200889927-933
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 2007 88S17
65
AMH GEN II ASSAY A VALIDATION STUDY OF
OBSERVED VARIABILITY BETWEEN REPEATED
AMH MEASUREMENTS
Oybek Rustamov Richard Russell
Cheryl Fitzgerald Stephen Troup Stephen A Roberts
22
66
Title
AMH Gen II assay A validation study of observed variability between
repeated AMH measurements
Authors
Oybek Rustamov 1 Richard Russell2 Cheryl Fitzgerald1 Stephen Troup2
Stephen A Roberts3
Institutions
1Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospitals NHS Foundation Trust Manchester
M13 9WL UK
2Hewitt Fertility Centre Liverpool Womenrsquos NHS Foundation Trust Hospital
Crown Street Liverpool L8 7SS
3 Centre for Biostatistics Institute of Population Health University of
Manchester Manchester M13 9PL UK
Word count 1782
Conflict of interest Authors have nothing to disclose
Acknowledgment
The authors would like to thank the Biomedical Andrology Laboratory team at
the Hewitt Fertility Centre for their assistance
67
Declaration of authorsrsquo roles
OR coordinated the study conducted the statistical analysis and prepared first
draft of the manuscript RR extracted data prepared the dataset assisted in
preparation of first draft of manuscript CF ST and SR involved in study
design oversaw statistical analysis contributed to the discussion and
preparation of the final version of the manuscript
68
ABSTRACT
Objective
To study the within patient sample-to-sample variability of AMH levels using
the Gen II assay reproduced in an independent population and laboratory
Design Retrospective cohort analysis
SettingTertiary referral IVF Unit in the United Kingdom
Patients Women being investigated for sub-fertility
Interventions
Retrospective measurements were obtained from women who had AMH
measurements using Gen II assay during routine investigation for infertility at a
tertiary referral unit during a 1-year period The patients who had repeated
AMH measurements were identified and within-patient coefficient of variation
(CV) calculated using a mixed effects model with quadratic adjustment for age
Main Outcome Measures
The within-patient coefficient of variation (CV) calculated using a random
effects model with quadratic adjustment for age
Results
There was in total of 76 samples from 38 women with repeated AMH
measurements during the study period The within-patient sample-to-sample
variation (CV) was found to be 62
Conclusions
The study has confirmed that even when samples are processed promptly and
strictly in accordance with the manufacturers instructions substantial
variability exists between repeated samples Thus caution is recommended in
the use of these newer assays to guide treatment decisions Further work is
required to understand the underlying cause of this variability
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA AMH ELISA sample variability
69
INTRODUCTION
Anti-Muumlllerian hormone is a dimeric glycoprotein that is produced by
the granulosa cells of pre-antral and early antral follicles and has been found to
be the primary regulator of oocyte recruitment and folliculogenesis (Durlinger
et al 1999 Durlinger et al 2001) Strong correlation between AMH levels and
primordial follicle count (Hansen et al 2011) and hence a reflection of ovarian
response has promised a valuable tool in the reproductive specialistsrsquo armory
The development of commercially available AMH immunoassay assay kits has
heralded the widespread introduction and routine usage of AMH assessment in
the clinical setting Several studies have demonstrated that AMH serves as a
good predictor of ovarian response to gonadotrophin stimulation during IVF
treatment (van Rooij et al 2002 Nelson et al 2007 Nardo et al 2009) AMH
testing has also been shown to identify patients at risk of excessive ovarian
response and ovarian hyperstimulation syndrome (Yates et al 2011) with
consequent reduction in per cycle treatment costs by adopting an antagonist
approach during controlled ovarian stimulation Sensitivity and specificity of
AMH in detecting extremes of response has been shown to be comparable to
antral follicle count without the apparent technical limitations of the latter
(Broer et al 2009 Broer et al 2011)
It is stated that the sample-to-sample variation of AMH concentration in
individual women is small and therefore a single AMH measurement has been
recommended as standard practice (La Marca et al 2006 Hehenkamp et al
2006) However recent studies based on data from a single centre recently
published in Human Reproduction found that larger variability between
repeated samples exists which is particularly profound when currently
available second generation AMH assay (AMH Gen II ELISA Beckman
Coulter Inc Brea CA USA) is used (Rustamov et al 2012a Rustamov et al
2012b Rustamov et al 2011)
The trial team had 2 objectives firstly to assess whether the controversial
findings from the above study (Rustamov et al 2012a) were reproducible when
performed in the data based on the samples from a different laboratory with
differing populations If our study reached similar conclusions concerns
regarding the AMH Gen II assay and or manufacturers recommendations on
handling and sampling processes would be validated Alternatively if non-
70
similar findings were reported the laboratory performance in the initial study
ought to be questioned Secondly and more importantly if the repeat samples
are found to be within acceptable parameters then the current clinical standard
of a single random AMH measurement in patients is appropriate If the results
of repeated samples are significantly different following adjustment for age it
would suggest that AMH measurement is not a true estimation of the patientrsquos
ovarian reserve
In view of clinical and research implications of these findings we
undertook to replicate the variability study in a second fertility centre The
authors wish to note that Beckman Coulter recently issued a worldwide STOP
SHIP order on all AMH Gen II Elisa assay kits until further notice due to
manufacturing and quality issues
MATERIALS AND METHODS
Population
Women had serum AMH measurements using Gen II AMH assay from
15 April 2011 to 25 May 2012 for investigation of infertility at the Hewitt
Fertility Centre in the Liverpool Womens NHS Foundation Trust Hospital
tertiary referral unit were identified using the Biochemistry Laboratory AMH
samples database and all women within age range of 20-46 years were included
in the study The main reasons for repeating the samples were a) obtaining up-
to-date assessment of ovarian reserve b) patient request and c) for formulation
of a treatment strategy prior to repeat IVF cycles
Institutional Review Board approval was granted by the Audit
Department Liverpool Womenrsquos NHS Foundation Trust Hospital
Assay procedure
Samples were transported immediately to the in-house laboratory of
Liverpool Womenrsquos Hospital for the processing and analysis The serum was
separated within 8 hours from venipuncture and frozen at -50C until analyzed
71
in batches The sample preparation and assay methodology strictly followed
the manufacturers guidelines The AMH analysis of laboratory is regularly
monitored by external quality assessment scheme (UKNEQAS) and
performance has been satisfactory
The samples were analyzed using enzymatically amplified two-site
immunoassay (AMH Gen II ELISA Beckman Coulter Inc Brea CA USA)
The intra-assay CV was 521 and inter-assay CV (n=9) was 276 (low
controls) and 657 (high controls) The working range of the assay was
150pmolL and the minimum detection limit was 057pmolL
The main difference in the assay preparation in this study is that the
samples were processed within 8 hours whilst the samples in the previous
study were processed within 2 hours (Rustamov 2012a) Importantly the kit
insert of Gen II AMH assay does not state any maximum duration of storage
of unprocessed samples or any constraints on the transportation of
unprocessed samples Therefore there appears to be considerable variation in
practice of sample processing between clinics which ranges from processing
samples immediately to shipping unfrozen whole samples to long distances
Statistical analysis
The dataset was obtained from the Biomedical Andrology Laboratory
of the hospital and anonymised by one of the researchers (RR) Data
management and analysis of the anonymised data followed the same
procedures as the previous study (13) and were performed using Stata 12
Statistical Package (StataCorp Texas USA) Approval for data management
analysis and publication was obtained from the Research and Development
Department of Liverpool Womenrsquos Hospital
Between and within-subject sample-to-sample coefficient of variability
(CV) as well as the intra correlation coefficient (ICC) was estimated using a
mixed effects model in log (AMH) with quadratic adjustment for age AMH
levels of the samples that fell below minimum detection limit of the assay
(lt057 pmolL) were arbitrarily assigned a value of 031 pmolL in line with
the previous analysis (Rustamov et al 2012a)
72
RESULTS
During the study period in total of 1719 women had AMH
measurements using Gen II assay Thirty-eight women had repeated AMH
measurements with a total number of 76 repeat samples (Figure 1) The
median age of the women was 318 (IQR 304-364) The median AMH level
was 52pmolL (IQR 15-114) The median interval between samples was 93
days (IQR 49-164) with range of 6-375 days Age-adjusted regression analysis
of samples of these women showed that within-patient sample-to-sample
coefficient of variation (CV) of AMH measurements was 62 while between-
patient CV was 125 An age adjusted intra-correlation coefficient was 079
Figure 1 The repeated AMH measurements by date lines join the
repeats from the same patients (AMH in pmolL)
73
DISCUSSION
A number of studies have recently been published that have expressed
concerns regarding the stability and reproducibility of AMH results Whilst
technical issues regarding reproducibility between assays were known more
recently the reproducibility of results regarding the current Gen II assay has
raised significant concern (Rustamov et al 2012a Rustamov et al 2012b
Rustamov et al 2011) Proponents of the assay have proposed that poor
sample handling and preparation are responsible for these observed concerns
(Nelson et al 2013) Several studies have observed the stability of samples at
room temperature Kumar et al (Kumar et al 2010) observed a 4 variation in
results after 7 days storage compared with those samples analysed immediately
These results were consistent with studies by Fleming and Nelson who also
reported no change in AMH concentration over a period of several days
(Fleming et al 2012) However Rustamov et al reported a measured AMH
increase of 58 in samples stored at room temperature over a seven day
period (Rustamov et al 2012a) Similar concerns were raised regarding the
appropriate freezing process whilst samples frozen at -20C demonstrated
variation in results of between 6 and 22 (Durlinger et al 1999 Rustamov et al
2012a) freezing at -80C obviated a significant variation in assay results (Al-
Qahtani et al 2005 Rustamov et al 2012a) Several studies initially reported
good linearity of dilution (Kumar et al 2012 Preissner et al 2010 Fleming et al
2012) which was contradicted by reports that demonstrated poor linearity in
dilution when fresh samples were utilized (Rustamov et al 2012a) This study
suggested a tendency of AMH results to double with dilution More recently
Beckman Coulter issued a warning on their Gen II AMH ELISA kits that the
dilution of sample may give an erroneous result confirming non linearity of
dilution (King Dave 2012)
A number of studies have looked at the variability of AMH in repeated
samples without account to the menstrual cycle utilizing different assays
Dorgan et al in analyzing DSL samples frozen for prolonged periods
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median-sample interval of one year (Dorgan et al 2012)
Rustamov et al presented a larger series of 186 infertile patients with a median
between-sample interval of 26 months and a CV of 28 in DSL samples
74
(ICC 091 95 CI 090-093(Rustamov et al 2011) In a follow-up study
utilizing the Gen II assay in a group of 84 infertile patients the coefficient
variation of repeated results was 59 (ICC of 084 95 CI 079-090) a
substantial increase in the observed variability of the studies reporting for the
DSL assay (Rustamov et al 2012a) The most recent study to cast doubt on
current practice suggested that repeated measurement of AMH using Gen II
assay resulted in a within-subject variability of 80 (CV) (Hadlow et al 2012)
As a result 7 out of 12 women were subsequently reclassified according to their
originally predicted ovarian response Our study outlined above involving 76
samples from 38 infertile patients demonstrated a within-patient sample-to-
sample coefficient of variation (CV) of AMH measurements was 62
Overall these results suggest that there is significant within patient
variability that may be more pronounced in the Gen II assay Whilst biological
variation has been demonstrated to play a part within this the appreciative
effects of sample handling storage and freezing play a significant part in the
results and it may be that the Gen II assays may be more susceptible to these
changes This study has confirmed that there is significant within-patient
sample-to-sample variability in AMH measurements when the Gen II AMH
assay is used which is not confined to a single population or laboratory It is
important to note that the samples reported by both Rustamov et al 2012
and this study were processed and analyzed strictly according to
manufacturerrsquos recommendations in their respective local laboratories without
external transportation (Rustamov et al 2012a) Therefore it seems reasonable
to suggest that AMH results from other centers and laboratories are likely to
display similar significant sampling variability
Reproducibility of AMH measurements is of paramount importance
given that a single random AMH measurement is used for triaging patients
unsuitable for proceeding with IVFICSI and determining the dose of
gonadotrophins for ovarian stimulation for those patients who proceed with
treatment Similarly other clinical applications of AMH such as an assessment
of the effect of chemotherapy to fertility and follow up of women with history
of granulosa cell tumors also rely on accurate measurement of circulating
hormone levels The present work confirms the high between-sample within-
patient variability The recent warning from Beckman Coulter utilizing their
Gen II ELISA assay kits may give an erroneous result with dilution of samples
further questions the stability of the assay (King David 2012) Subsequently
75
the manufacturer recalled the assay kits due to issues with the instability of
samples and introduced modified protocol for preparation of Gen II assay
samples
Given there can be a substantial difference between two samples from
the same patient the use of such measurements for clinical decision-making
should be questioned and caution is advised
76
References
Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP and Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 2005 63267-273
Broer SL Dolleman M Opmeer BC Fauser BC Mol BW Broekmans FJM AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 20111746-54
Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL and Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
Fleming R and Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
Hadlow Narelle Longhurst Katherine McClements Allison Natalwala Jay Brown Suzanne J and Matson Phillip L Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response (Article in press) Fertil Steril 2012
Hansen KL Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170-5
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 King Dave URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L and Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593 6
77
Nelson S Biomarkers of ovarian response current and future applications Fertil and Steril 201399963-969
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton Reply Reproducibility of AMH Hum Reprod 2012b273641-3642
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Preissner CM Morbeck DE Gada RP and Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011261768-74
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse patient variability Fertil Steril 2011951185-118
78
THE MEASUREMENT OF ANTI-MUumlLLERIAN
HORMONE A CRITICAL APPRAISAL
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan
Luciano G Nardo Philip W Pemberton
The Journal of Clinical Endocrinology amp Metabolism
2014 Mar 99(3) 723-32
3
79
Title
The measurement of Anti-Muumlllerian hormone a critical appraisal
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK d Manchester Royal Infirmary Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Key terms
Anti-Muumlllerian hormone AMH Active MISAMH ELISA Diagnostic
Systems Laboratories AMHMIS ELISA Immunotech AMH Gen II assay
Beckman Coulter
Word Count 3947 (intro ndash general summary text only (no headings)
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
80
Declaration of authorsrsquo roles
The idea was developed during discussion between OR CF and SAR
OR conducted the initial appraisal of the studies prepared and revised the
manuscript SAR and CF contributed to the discussion and interpretation of
the studies and oversaw the revision of the manuscript PWP AY MK
and AS reviewed the data extraction and interpretation contributed to
the discussion of the studies and revision of the manuscript LGN
contributed to the discussion of the studies and revision of the manuscript
81
ABSTRACT
Context
Measurement of AMH is perceived as reliable but the literature reveals
discrepancies in reported within-subject variability and between-assay
conversion factors Recent studies suggest that AMH may be prone to pre-
analytical instability We therefore examined the published evidence on the
performance of current and historic AMH assays in terms of the assessment of
sample stability within-patient variability and comparability of the assay
methods
Evidence Acquisition
Studies (manuscripts or abstracts) measuring AMH published between
01011990 and 01082013 in peer-reviewed journals using appropriate
PubMedMedline searches
Evidence Synthesis
AMH levels in specimens left at room temperature for varying periods
increased by 20 in one study and almost 60 in another depending on
duration and the AMH assay used Even at -20degC increased AMH
concentrations were observed An increase over expected values of 20-30 or
57 respectively was observed following two-fold dilution in two linearity-of-
dilution studies but not in others Several studies investigating within-cycle
variability of AMH reported conflicting results although most studies suggest
variability of AMH within the menstrual cycle appears to be small However
between-sample variability without regard to menstrual cycle as well as within-
sample variation appears to be higher using the Gen II AMH assay than with
previous assays a fact now conceded by the kit manufacturer Studies
comparing first generation AMH assays with each other and with the Gen II
assay reported widely varying differences
Conclusions AMH may exhibit assay-specific pre-analytical instability
Robust protocols for the development and validation of commercial AMH
assays are required
82
INTORDUCTION
In the female AMH produced by granulosa cells of pre-antral and early
antral ovarian follicles regulates oocyte recruitment and folliculogenesis (1 2)
It can assess ovarian reserve (3-5) and guide gonadotrophin stimulation in
assisted reproduction technology (ART) (6) AMH is also used as a granulosa
cell tumour marker a marker of ovarian reserve post-chemotherapy (7 8) and
to predict age at menopause (910)
AMH immunoassays first developed by Hudson et al in 1990 (11) were
introduced commercially by Diagnostic Systems Laboratories (DSL) and
Immunotech (IOT) These assays were integrated into a second-generation
AMH assay GenII (12) by Beckman-Coulter but recent work suggests that this
new assay exhibits clinically important within-patient sample variability (13-
15) Beckman Coulter have recently confirmed this with a field safety notice
(FSN 20434-3) they cite without showing evidence for complement
interference as the problem
ldquoTruerdquo AMH variability comprises both biological and analytical
components (Figure 1) and given the varying antibody specificity and
sensitivity of different AMH assays then logically different kits will respond to
these components to varying degrees This review considers the published
literature on AMH measurement using previous and currently available assays
Potential sources of variation and their contribution to observed AMH
variability were identified
Review structure
This review has been divided into logical subgroups We first address the
stability of AMH at different storage temperatures then the effects of
freezethaw cycles and finally AMH variability in dilution studies Secondly
the within-person variability of AMH measurement is considered
encompassing intra- and inter-menstrual cycle variability and repeat sample
variability in general The final section covers AMH method comparisons
comparing older methods to each other and to the newer now prevalent
GenII method finishing with data on published guidance ranges concerning
the use of AMH in ART A general summary concludes the paper
83
Systematic review
The terms ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting
Substance and MIS were used to search the PubMedMedline MeSH
database between 1st January 1990 and 1st August 2013 for publications in
English commenting on AMH sample stability biological and sample-to-
sample variability or assay method comparison in human clinical or healthy
volunteer samples Titles andor abstracts of 1653 articles were screened to
yield the following eligible publications ten stability studies 17 intrainter-
cycle variability studies and 14 assay method comparability studies
Sample stability
Recent work has established that the GenII-measured AMH is
susceptible to significant preanalytical variability (13 14) not previously
acknowledged which may have influenced results in previous studies with this
assay
Stability of unfrozen samples
Five studies examined AMH stability in samples stored either at room or
fridge temperature (Table 1) (13 16-19) Al-Qahtani et al (16) assessing the
precursor of the DSL ELISA reported that ldquoimmunoreactivity survived the
storage of samples unfrozen for 4 daysrdquo but did not record storage
temperature or sample numbers Evaluating the GenII assay Kumar et al (18)
stored 10 samples at 2-8degC for up to a week and found an average 4
variation compared to samples analysed immediately However their
specimens originally reported as ldquofreshrdquo appear to have been kept cool and
transported overnight Fleming amp Nelson (19) reported no significant change
in the GenII-assayed AMH from 51 samples stored at 4degC Methodological
information was limited but interrogation of their data by Rustamov et al (14)
suggested that AMH levels rose by an average of 27 after 7 days storage
Zhao et al (17) reported a difference of less than 20 between DSL-assayed
AMH in 7 serum samples kept at 22degC for 48 hours when compared to
aliquots from the same samples frozen immediately at -20degC Rustamov et al
(13) measured AMH (GenII) daily in 48 serum samples at room temperature
for 7 days and observed an average 58 increase (from 0 to gt200) whilst
others (20) reported a 31 mean rise in GenII-assayed AMH in whole blood
84
after 90hrs at 20oC whereas serum AMH was virtually unchanged after
prolonged storage at 20oC
Sample stability at -20 o or -80oC and the effects of freezethaw
Rey et al (21) reported a significant increase in AMH (in-house assay)
in samples stored at -20degC for a few weeks attributing this to proteolysis
which could be stabilised with protease inhibitor (see discussion below)
Kumar et al (18) saw 6 variation between GenII-assayed AMH levels from
10 fresh and 10 frozen samples whilst Rustamov et al (13) observed a 22
increase in AMH (GenII) on re-analysis of 8 serum samples after 5 days
storage at -20degC These authors saw no AMH increase in serum stored at -80deg
C for the same period
Linearity of dilution
Six studies examined linearity of dilution on observed AMH
concentrations Long et al (22) recovered between 84 and 105 of the
expected AMH concentration (IOT n=3) AMH dilution curves parallel to
the standard curve were reported by others (16)Kumar et al (18) (n=4) and
Preissner et al (23) ) (n=7) reported GenII-assayed AMH recoveries from 95
to 104 and 96 respectively Sample handling information was limited in
some of these studies (16 23) Fleming amp Nelson (19) (GenII n=10) reported
variances of 8 using assay diluent and 5 using AMH-free serum following
2-fold dilution however interrogation of their data reveals an apparent
dilutional AMH increase of 20-30 in samples stored prior to dilution and
analysis Rustamov et al (13) (GenII n=9) in freshly collected serum observed
an average 57 increase in apparent AMH concentration following two-fold
dilution but with considerable variation
Discussion Sample stability
Sample stability can be a major analytical problem and detailed
examination suggests that previous evidence stating that commercially
measured AMH is stable in storage and exhibits linearity of dilution (12 16 18
19) is weak or conflicting
No study looking at room temperature storage on IOT-assayed AMH
was found and only one using DSL-assayed AMH which showed an increase
85
of less than 20 during storage (17) Studies using the GenII assay to
investigate the effect of storage on AMH variability at room temperature in
the fridge and at -200C reach differing conclusions ranging from stable to an
average 58 increase in measured levels It is important to note here that
sample preparation and storage prior to these experiments was different and
could account for the observed discrepancies The most stable storage
temperature for AMH in serum appears to be -80degC (13 16)
Linearity of dilution studies were also conflicting (13 18 19 23) those
reporting good linearity used samples transported or stored prior to baseline
analysis whereas dilution of fresh samples showed poor linearity In late 2012
Beckman Coulter accepted that the GenII assay did not exhibit linear dilution
and issued a warning on kits that samples should not be diluted They now
suggest that with the newly introduced pre-mixing protocol dilution should
not be a problem
This review highlights the fact that assumptions about AMH stability in
serum were based on a limited number of small studies often providing
limited methodological detail (impairing detailed assessment and comparison
with other studies) using samples stored or transported under unreported
conditions Furthermore conclusions derived using one particular AMH assay
have been applied to other commercial assays without independent validation
The available data suggests that dilution of samples andor storage or
transport in sub-optimal conditions can lead to an increase in apparent AMH
concentration The conditions under which this occurs in each particular AMH
assay are not yet clear and more work is required to understand the underlying
mechanisms Two alternative hypotheses have been proposed firstly that
AMH may undergo proteolytic change as postulated by Rey et al (21) or
conformational change as proposed by Rustamov et al (1314) during storage
resulting in ldquostabilisationrdquo of the molecule in a more immunoreactive form
secondly Beckman have postulated the presence of an interferent
(complement) which degrades on storage (Beckman Coulter field safety notice
FSN 20434-3)
A recent case report found that a falsely high AMH level was corrected
by the use of heterophylic antibody blocking tubes (24) but this does not
explain elevation of AMH on storage (13)
Whatever the mechanism responsible two solutions are available either
inhibit the process completely or force it to completion prior to analysis
86
Rustamov et al (13) and Han et al (15) both suggest pre-dilution of samples to
force the process a protocol now adopted by Beckman Coulter in their revised
GenII assay protocol Any solution must be robustly and independently
validated both experimentally and clinically prior to introduction in clinical
practice Fresh optimal ranges for interpretation of AMH levels in ART will be
needed and the validity of studies carried out using unreported storage
conditions may have to be re-evaluated
Within-person variability
The biological components of AMH variability such as circadian and
interintra-cycle variability have been extensively studied (Table 2 amp
Supplementary table 1)
Circadian variation
Bungum et al (25) evaluated circadian variability measuring AMH
(IOT) two hourly over 24hrs within day 2ndash6 of the menstrual cycle in younger
(20-30 years) and older (35-45 years) women Within-individual CVs of 23
(range 10-230) in the younger group and 68 (range 17-147) in the older
group were observed
Variability within the menstrual cycle
Cook et al (26) observed significant (12) variation in mean AMH (in-
house) levels in 20 healthy women throughout different phases of the
menstrual cycle Intra-cycle variability of IOT-assayed AMH was reported in
three publications (27-29) In two sequential samples were stored at -20degC
until analysis (27 28) Streuli et al (29) did not report on storage La Marca et
al (27) saw no difference in mean follicular phase AMH levels (days 2 4 and 6)
in untreated spontaneous menstrual cycles from 24 women This group went
on to report a small insignificant change (14) in within-group AMH
variability throughout the whole menstrual cycle in 12 healthy women
However this analysis does not appear to allow for correlations within same-
patient samples Streuli et al (29) studied intra-cycle variation of AMH
throughout two menstrual cycles in 10 healthy women and also reported no
significant changes (lt5)
87
The DSL assay was used in eight studies assessing intra-cycle variability
(30-37) Four studied sample storage at -20deg C (30323437) and two studied
samples storage at -80degC (3335) No sample storage data was given in two
publications (31 36) Hehenkamp et al (30) assessed within-subject variation
of AMH in 44 healthy women throughout two consecutive menstrual cycles
and reported an intra-cycle variation of 174 Lahlou et al (31) reported a
ldquodiphasicrdquo pattern of AMH with a significant decrease in levels during the LH
surge from 10 women at various cycle phases Tsepelidis et al (32) reported a
mean intra-cycle coefficient of variation of 14 comparing group mean AMH
levels in 20 women during various stages of the menstrual cycle Wunder et al
(33) reported an intra-cycle variability of around 30 in 36 healthy women
sampling on alternate days They saw a marked fall around ovulation which
might have been missed with less frequent sampling intervals as in other
studies Sowers et al (35) studied within-cycle variability in 20 healthy women
but did not compute an overall estimate instead they selected subgroups of
low and high AMH and reported significant within-cycle variability for women
with high AMH but not those with low AMH - an analysis that has been
questioned (38 39) Robertson et al (36) subgrouped mean AMH levels in 61
women observing that AMH levels were stable in women of reproductive age
and ovulatory women in late reproductive age whilst AMH in other women in
late reproductive age was much more variable Using the data from
Hehenkamp et al (30) van Disseldorp et al (34) calculated intra-class
correlation (ICC) and reported a within-cycle variability of 13 although this
was not clearly defined Using the same data Overbeek et al (37) analyzed the
absolute intra-individual difference in younger (38 years) and older (gt38
years) women This study concluded that the AMH concentration was more
variable in younger women (081059 gL) compared to older women
(031029 gL) during the menstrual cycle (P=0001) thus a single AMH
measurement may be unreliable A recent study using the GenII assay
reported 20 intra-cycle variability in AMH measurements in women (n=12)
with regular ovulatory cycles (40) All the reports considered have findings
consistent with a modest true systematic variability of 10-20 in the level of
AMH in circulation during the menstrual cycle Whilst there have been
suggestions that this variability may differ between subgroups of women these
88
have been based on post-hoc subgroup analyses and there is no convincing
evidence for such subgroups (38)
Variability between menstrual cycles
Three studies (Supplementary table 1) evaluated AMH variability in
samples taken during the early follicular phase of consecutive menstrual cycles
(102941) and three studies have reported on the variability of AMH in repeat
samples from the same patient taken with no regard to the menstrual cycle
(134243) One study employed an in-house assay (41) one study used the
IOT assay (29) three studies used the DSL assay (10 42 43) and one study
(13) used the GenII assay In four infertile women Fanchin et al (41) assessed
the early follicular phase AMH (in-house) variability across three consecutive
menstrual cycles they concluded that inter-sample AMH variability was
characterised by an ICC of 089 (95 CI 083-094) Streuli et al (29)
calculated a between-sample coefficient of variation of 285 in AMH (IOT)
in 10 healthy women In 77 infertile women van Disseldorp et al (10) found
an inter-cycle AMH (DSL) variability of 11 In summary these studies
suggest that the overall inter-cycle variability of AMH ranges from 11 (DSL)
to 28 (IOT) this figure will include both biological and measurement-related
variability
Variability between repeat samples
Variability between repeat samples without regard to menstrual cycle
phase was examined in three studies (Supplementary table 1) In a group of 20
women using samples frozen for prolonged periods Dorgan et al (42)
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median between-sample interval of one year In a larger series
of 186 infertile women Rustamov et al (43) (DSL) found a CV of 28
between repeated samples with a median between-sample interval of 26
months (ICC 091 95 CI 090-093) Rustamov et al (13) found that the
coefficient of variation of repeated GenII-assayed AMH in a group of 84
infertile women was 59 (ICC of 084 95 CI 079-090) substantially higher
than that reported using the DSL assay Similarly a recent study by Hadlow et
al (40) found a within-subject GenII-assayed AMH variability of 80 As a
89
result 5 of the 12 women studied crossed clinical cut-off levels following
repeated measurements
Discussion Within-patient variability
Evidence suggests that repeated measurement of AMH can result in
clinically important variability particularly when using the GenII assay This
questions the assumption that a single AMH measurement is acceptable in
guiding individual treatment strategies in ART
The observed concentration of any analyte measured in a blood
(serum) sample is a function of its ldquotruerdquo concentration and the influence of a
number of other factors (Figure 1) Studies examining the variability of AMH
by repeated measurement of the hormone will therefore reflect both true
biological variation and measurement-related variability introduced by sample
handling andor processing Thus within-sample inter-assay variability used as
an indicator of assay performance may not reflect true measurement-related
variability between samples since it does not take into account the contribution
from pre-analytical variability Measurement-related between-sample variability
can be established in part using blood samples taken simultaneously (to avoid
biological variability) from a group of subjects although even this does not
reflect the full variability in sample processing and storage inherent in real
clinical measurement
Since AMH is only produced by steadily growing ovarian follicles it is
plausible to predict a small true biological variability in serum reflected in the
modest 1-20 variability found within the menstrual cycle In contrast it
appears that the magnitude of measurement-related variability of AMH is more
significant a) within-sample inter-assay variation can be as high as 13 b)
different assays display substantially different variability and c) AMH appears
to be unstable under certain conditions of sample handling and storage (Table
1) Consequently any modest variation in true biological AMH concentration
may be overshadowed by a larger measurement-related variability and careful
experimental designs are required to characterise such differences In general
the reported variability in published studies should be regarded as a measure of
total sample-to-sample variability ie the sum of biological and measurement-
related variability (Figure 1)
90
In repeat samples the available evidence confirms that there is a
significant level of within-patient variability between measurements which is
assay-dependent greater than the estimates of within cycle variability and
therefore likely to be predominantly measurement-related Evidence from
several sources suggests that the effects of sample handling storage and
freezing differ between commercial assays and that the newer GenII assay may
be more susceptible to these changes under clinical conditions When it has
been established that the modified protocol for the GenII assay can produce
reproducible results independent of storage conditions then it will be
necessary to re-examine intra and inter cycle variability of AMH
Assay method comparability
AMH assay comparisons have either used same sample aliquots or
used population-based data with repeat samples Study population
characteristics sample handling inter-method conversion formulae and results
from these comparisons are summarised in Table 3 AMH levels were almost
universally compared using a laboratory based within-sample design The
Rustamov et al study (13) was population-based comparing AMH results in
two different samples from the same patient at different time points using 2
different assays
IOT vs DSL
Table 3 summarises 8 large studies (17 29 30 44-48) that compared the
DSL and IOT AMH assays They demonstrate strikingly different conversion
factors from five-fold higher with the IOT assay to assay equivalence Most
studies carried out both analyses at the same time to avoid analytical variation
(Figure 1) However this does mean that samples were batched and frozen at -
18degC to -80degC prior to analysis which as already outlined may influence pre-
analytical variability and contribute to the observed discrepancies in conversion
factors
IOT vs GenII
Three studies have compared the IOT and Gen II assays (Table 3)
Kumar (18) reported that both assays gave identical AMH concentrations
However Li et al (48) found that the IOT assay produced AMH values 38
91
lower than the Gen II assay whilst Pigny et al (49) found levels that were 2-fold
lower
DSL vs GenII
Four studies analysed same-sample aliquots using the DSL and GenII
assays either simultaneously or sequentially (33 48 50 51) Only Li et al (48)
gave details of sample handling (Table 3) All four studies found that AMH
values that were 35 ndash 50 lower using the DSL compared to the GenII assay
Rustamov et al (13) carried out a between-sample comparison of the assays
measuring AMH in fresh or briefly stored clinical samples from the same
women at different times with values adjusted for patient age (Table 3) In
contrast to within-sample comparisons this study found that the DSL assay gave
results on average 21 higher than with the GenII assay Whilst this
comparison is open to other bias it does reflect the full range of variability
present in clinical samples and avoids issues associated with longer term
sample storage
Discussion Assay method comparability
It is critical for across-method comparison of clinical studies that
reliable conversion factors for AMH are established In-house assays aside
three commercially available AMH ELISAs have been widely available (IOT
DSL and GenII) and the literature demonstrates considerable diversity in
reported conversion factors between first-generation assays (DSL vs IOT)
and between first and second-generation immunoassays (DSLIOT vs GenII)
Although most studies appear to follow manufacturersrsquo protocols
detailed methodological information is sometimes lacking The assessment of
within-sample difference between the two assays involved thawing of a single
sample and simultaneous analysis of two aliquots with each assay Both
aliquots experience the same pre-analytical sample-handling and processing
conditions therefore the results should be reproducible provided the AMH
samples are stable during the post-thaw analytical stage and the study
populations are comparable However this review has identified significant
discrepancies between studies perhaps due to either significant instability of
the sample or significant variation in assay performance Studies comparing
AMH levels measured using different assays in populations during routine
92
clinical use have also come to differing conclusions (13 51) Given the study
designs that workers have used to try to ensure that samples are comparable
the finding of significant discrepancies in the observed conversion factors
between assays is consistent with the proposal that AMH is subject to
instability during the pre-analytical stage of sample handling This coupled
with any differential sensitivity and specificity between these commercial
assays could give rise to the observed results ie some assays are more
sensitive than others to pre analytical effects
AMH guidance in ART
AMH guidance ranges to assess ovarian reserve (52) or subsequent
response to treatment (53 54) have been published The Doctors Laboratory
using the DSL assay advised the following ranges for ovarian reserve (lt
057pmolL-undetectable 057-21 pmolL-very low 22-157 pmolL-low
158-286 pmolL-satisfactory 287-485pmolL-optimal gt485pmolL-very
high) ranges that supposedly increased by 40 on changing to the GenII assay
(51) More recently other authors have attempted to correlate AMH levels with
subsequent birth rates Brodin et al (53) using the DSL assay observed that
higher birth rates were seen in women with an AMH level gt 21 pmolL and
low birth rates were seen in women who had AMH levels lt 143 pmolL In
the UK the National Institute for Health and Care Excellence (NICE) have
recently issued guidance on AMH levels in the assessment of ovarian reserve in
the new clinical guideline on Fertility (54) They advise that an AMH level of le
54 pmolL would indicate a low response to subsequent treatment and an
AMH ge 250 pmolL indicates a possible high response Although not
specifically stated interrogation of the guideline suggests that these levels have
been obtained using the DSL assay which is no longer available in the UK
As discussed above the initial study of comparability between the DSL
and GenII assays reported that GenII generated values 40 higher compared
to the DSL assay clinics were therefore recommended to increase their
treatment guidance ranges accordingly (51) However a more recent study
using fresh samples found that the original GenII assay may actually give
values which are 20-30 lower suggesting that following the above
recommendation may lead to allocation of patients to inappropriate treatment
groups (13) The apparent disparity in assay comparison studies implies that
93
AMH reference ranges and guidance ranges for IVF treatment which have
been established using one assay cannot be reliably used with another assay
method without full independent validation Similarly caution is required
when comparing the outcomes of research studies using different AMH assay
methods
General Summary
Recent publications have suggested that GenII-assayed AMH is
susceptible to pre-analytical change leading to significant variability in
determined AMH concentration an observation now accepted by the kit
manufacturer However this review suggests that all AMH assays may display a
differential response to pre-analytical proteolysis conformational changes of
the AMH dimer or presence of interfering substances The existence of
appreciable sample-to-sample variability and substantial discrepancies in
between-assay conversion factors suggests that sample instability may have
been an issue with previous AMH assays but appears to be more pronounced
with the currently available GenII immunoassay The observed discrepancies
may be explicable in terms of changes in AMH or assay performance that are
dependent on sample handling transport and storage conditions factors
under-reported in the literature We strongly recommend that future studies on
AMH should explicitly report on how samples are collected processed and
stored If it can be clearly demonstrated that the new GenII protocol drives
this process to completion in all samples ensuring stability then a re-
examination of reference and guidance ranges for AMH interpretation will be
necessary There is a clear need for an international reference standard for
AMH and for robust independent evaluation of commercial assays in routine
clinical samples with well-defined sample handling and processing protocols
These issues of sample instability and lack of reliable inter-assay comparability
data should be taken into account in the interpretation of available research
evidence and the application of AMH measurement in clinical practice
94
References
1 Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
2 Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
3 van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
4 Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
5 Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009921586-1593 6 Yates AP Rustamov O Roberts SA Lim HYN Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353ndash2362
7 Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-55
8 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
9 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
10 van Disseldorp J Lambalk CB Kwee J Looman CW Eijkemans MJ Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Muumlllerian hormone and antral follicle counts Hum Reprod 201025221-227
11 Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
95
12 Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642
15 Han X McShane M Sahertian R White C Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Hum Reprod 201328 (suppl 1)i76-i78 (abstract)
16 Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 200563267-273
17 Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 200788S17 (abstract)
18 Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
19 Fleming R Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
20 Fleming R Fairbairn C Blaney C Lucas D Gaudoin M Stability of AMH measurement in blood and avoidance of proteolytic changes Reprod Biomed Online 201326130-132
21 Rey R Lordereau-Richard I Carel JC Barbet P Cate RL Roger M Chaussain JL Josso N Anti-Mullerian hormone and testosterone serum levels are inversely related during normal and precocious pubertal development J Clin Endocrinol Metab 199377 1220ndash1226
22 Long WQ Ranchin V Pautier P Belville C Denizot P Cailla H Lhomme C Picard JY Bidart JM Rey R Detection of minimal levels of serum anti-Mullerian hormone during follow-up of patients with ovarian granulosa cell tumor by means of a highly sensitive enzyme-linked immunosorbent assay J Clin Endocrinol Metab 200085540ndash544
23 Preissner CM Morbeck DE Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54 (abstract)
24 Cappy H Pigny P Leroy-Billiard M Dewailly D Catteau‐Jonard S Falsely elevated serum antimuumlllerian hormone level in a context of heterophilic
96
interference Fertil Steril 2013991729-1732
25 Bungum L Jacobsson AK Roseacuten F Becker C Yding Andersen C Guumlner N Giwercman A Circadian variation in concentration of anti-Mullerian hormone in regularly menstruating females relation to age gonadotrophin and sex steroid levels Hum Reprod 201126678ndash684
26 Cook CL Siow Y Taylor S Fallat ME Serum muumlllerian-inhibiting substance levels during normal menstrual cycles Fertil Steril 200073859-861
27 La Marca A Malmusi S Giulini S Tamaro LF Orvieto R Levratti P Volpe A Anti-Muumlllerian hormone plasma levels in spontaneous menstrual cycle and during treatment with FSH to induce ovulation Hum Reprod 2004192738-2741
28 La Marca A Stabile G Carduccio Artenisio A Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash310729 Streuli I Fraisse T Chapron C Bijaoui G Bischof P de Ziegler D Clinical uses of anti-Mullerian hormone assays pitfalls and promises Fertil Steril 200991226-230
30 Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
31 Lahlou N Chabbert-Buffet N Gainer E Roger M Bouchard P Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11 (abstract)
32 Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
33 Wunder DM Bersinger NA Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrual cycle in reproductive age women Fertil Steril 200889927-933
34 van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormone concentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
35 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 2010 941482-1486
36 Robertson DM Hale GE Fraser IS Hughes CL Burger HG Changes in serum antimuumlllerian hormone levels across the ovulatory menstrual cycle in late reproductive age Menopause 201118521-524
37 Overbeek A Broekmans FJ Hehenkamp WJ Wijdeveld ME van
97
Disseldorp J van Dulmen-den Broeder E Lambalk CB Intra-cycle fluctuations of anti-Mullerian hormone in normal women with a regular cycle a re-analysis Reprod Biomed Online 201224664ndash 669
38 Roberts SA Variability in anti-Mullerian hormone levels a comment on Sowers et al ldquoAnti-Mullerian hormone and inhibin B variability during normal menstrual cyclesrdquo Fertil Steril 201094e59
39 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Reply of the authors Variability in anti-Muumlllerian hormone levels a comment on Sowers et al Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 201094e60
40 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013991791-1797
41 Fanchin R Taieb J Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Muumlllerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 200520923-927
42 Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
43 Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
44 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
45 Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175
46 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
47 Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
48 Li HW Ng EH Wong BP Anderson RA Ho PC Yeung WS Correlation between three assay systems for anti-Mullerian hormone (AMH)
98
determination J Assist Reprod Genet 2012291443-1446
49 Pigny P Dassonneville A Catteau-Jonard S Decanter C Dewailly D Comparative analysis of two-widely used immunoassays for the measurement of serum AMH in women Hum Reprod 2013 28i311-316 (abstract)
50 Gada R Hughes P Amols M Amols M Preissner C Morbeck D Coddington C Validation and comparison of AMH serum levels using the original active MISAMH ELISA to the new active AMH Gen II ELISA Fertil Steril 201195S23 (abstract)
51 Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
52 The Doctors Laboratory Lab Report newsletter ndash Winter 20072008 ndash AMH
53 Brodin T Hadziosmanovic N Berglund L Olovsson M Holte J Antimullerian hormone levels are strongly associated with live birth rates after assisted reproduction J Clin Endocrinol Metab 201398(3)1107-1104
54 National Institute for Care and Health Excellence NICE clinical guideline CG156 Fertility
99
Figure 1 Biological and analytical variability of AMH
100
Table 1 AMH assay validation effect of sample storage conditions freshthaw cycles and linearity of dilution
Study Assay Method Result
Rey et al (21) in-house effect of Long-term storage at -20C (n=4) AMH levels in archival samples were 230 higher than original value
Long et al (22) IOT linearity up to 16-fold dilution (n=3) observed AMH was 84-105 of expected AMH
Al-Qahtani et al (16) in-house a freezethaw stability storage unfrozen for 4 days
b linearity up to 32-fold dilution (n=6)
a immuno-reactivity survived both multiple freeze-thaw cycles and storage unfrozen for 4 days b dilution curves were parallel to the standard curve
Zhao et al (17) DSL
serum frozen immediately at -20C compared to
aliquots stored at 4C or 22C for up to 2 days (n=7) AMH levels increased by 1 at 4C and 9 at 22C after 2 days compared to sample frozen immediately
Kumar et al (18) Gen II
a serum or plasma stored at 2-8C or -20C for up to 7 days (n = 20) b serum or plasma underwent up to three freezethaw cycles (n=20) c linearity of dilution (n=4)
a AMH levels were stable for up to 7 days at 2-8C or -20C
b AMH increased by 15 in serum and by 5 in plasma after 3 cycles c linear results obtained across the dynamic range of the assay
Preissner et al (23) Gen II linearity of dilution (n=7) average agreement with expected result was 97
Rustamov et al (13) Gen II
a stability at RT for up to 7 days (n=48)
b storage for 5 days at -20C or -80C compared to fresh sample (n=8) c linearity on 2-fold dilution (n=9)
a AMH levels increased by an average of 58 over 7 days
b AMH levels increased by 23 at -20C but were unchanged at -80C c AMH levels were on average 157 higher than expected
Fleming amp Nelson (19) Gen II a serum stored at 4C for 7 days (n=48) b linearity of dilution (n=10)
a AMH levels increased by an average of 27 b AMH was 28 amp 33 higher on 2-fold amp 4-fold dilution resp
Fleming et al (20) Gen II
a whole blood stored for up to 90 hours at 4C (n=32) or 20C (n=21)
b serum stored for 5 days at 20C and 2 days at 4C (n=13)
a AMH increased by 11 at 4C and by 31 at 20C b only 1 increase in AMH compared to original value
Han et al (15) Gen II
serum from non-pregnant (n=13) or early pregnant (n=7) women
stored at RT -20C or -80C for up to 7 days
In non-pregnant women AMH increased by 26 after 7 days at RT but was
unchanged at -20C or -80C
In pregnant women AMH increased by 50 at RT and 27 at -80C after 48 hours
101
Table 2 Intra-cycle variability of AMH Study
Subjects
a cycles b day sampled
Assay
a storage b freezethaw c measurement
Result
Authorsrsquo Conclusion
Cook et al (26)
healthy age 22-35 regular cycle (n=20)
a 1 cycle b day 23 LH surge LH surge +7 d
in-house
a -80C b once c inter-assay variation eliminated
day 3 AMH = 14 09ngml
mid cycle AMH = 17 11ngmL
mid luteal AMH = 14 09ngmL
Fluctuations significant (plt0008) AMH may have a regulatory role in folliculogenesis
La Marca et al (27)
healthy age 21-36
regular cycle (n=24)
a follicular phase b alternate days
IOT
a -20C
b once
AMH did not change from days 2 to 6 in spontaneous cycles but decreased progressively in FSH-treated cycles
AMH levels did not change significantly during follicular phase of the menstrual cycle
La Marca et al (28)
healthy age18-24
regular cycle (n=12)
a 1 cycle b alternate days day 0 = day of LH surge
IOT
a -20C
b once
low mean AMH = 3411ngmL (day 14)
high mean AMH =3913ngmL (day 12)
AMH levels did not change significantly throughout menstrual cycle
Lahlou et al (31)
placebo-treated (n=12)
a 1 cycle
b every 3 days
DSL
NR 7 days pre LH surge AMH = 26
32pmolL peak AMH = 191 35pmolL 10 days post LH surge
AMH = 254 43pmolL
AMH levels exhibited a diphasic pattern with levels declining significantly (plt005) during the LH surge
Hehenkamp et al (30)
healthy
fertile regular cycle (n=44)
a 2 cycles
b AMH measured at each of 7 cycle phases
DSL a -20C a sine pattern fitted to AMH data was not significant (p=040) b72 repeat AMH values fell within the same quintile 28 in adjacent quintile
AMH shows no consistent fluctuation through the cycle compared to FSH LH amp E2
van Disseldorp et al (10)
data from Hehenkamp et al (30)
Intra-cycle within-subject variation of AMH was only 13 compared to 31-34 for AFC (dependent on follicle size)
AMH displays less intra-cycle variability than AFC
Overbeek et al (37)
data from Hehenkamp et al (30)
Fluctuations were larger than 05microgL in one cycle in significantly (p = 0001) more women in the younger group than the older one
AMH can fluctuate substantially in younger women during menstrual cycle so a single measurement could be unreliable
102
Tsepelidis
et al (32)
healthy age 18-35 regular cycles (n=20)
a 1 cycle b days 3 7 10-16 18 21 amp 25
DSL
a -20C
b once
Within-cycle differences not significant (p=0408)
AMH levels do not vary during the menstrual cycle
Wunder et al (33)
healthy
age 20-32 regular cycles (n=36)
a 1 cycle
b alternate days
DSL
a -80C
AMH levels were statistically higher in the late follicular phase than at the time of ovulation (p= 0019) or in the early luteal phases (plt00001)
AMH levels vary significantly during the menstrual cycle
Streuli
et al (29)
healthy mean age=241 regular cycles
(n=10)
a 1 cycle b before (LH
-10-5-2-1) and after LH surge (LH +1+2+10)
IOT
a -18C
AMH levels were statistically lower during the early luteal phase compared to early follicular phase (p=0016) and late luteal phase levels (p=002)
In clinical practice AMH can be measured at any time during the menstrual cycle
Sowers et al
(35)
healthy age 30-40 regular cycles
(n=20)
a 1 cycle b daily
DSL
a -80C
b once c simultaneous
Higher AMH levels with significant variation between days 2-7 in the ldquoyounger ovaryrdquo Low AMH levels with little variation in the ldquoaging ovaryrdquo
AMH varies across the menstrual cycle in the ldquoyounger ovaryrdquo
Robertson et al (36)
a age 21-35 regular cycles
(n=43) b age 45-55
variable cycles (n=18)
a 1 cycle + initial stages of succeeding cycle b three times weekly
DSL
NR No intracycle variation in AMH level was found in women in mid reproductive life or in 33 women with regular cycles in late reproductive age In the remaining cycles there was a significant (plt001) two-fold decrease in AMH in 11 cycles and a significant (plt001) 42-fold increase between the follicular amp luteal phases
When AMH levels are substantially reduced they become less reliable markers of ovarian reserve
Hadlow
et al (40)
age 29-43 regular cycles non-PCOS
(n=12)
a 1 cycle b 5-9 samples per subject
Gen II a -20C within 4 hours of sampling b once
c simultaneous
712 women could be reclassified depending on when AMH was measured during the cycle 212 crossed cut-offs predicting hyperstimulation
AMH cycles varied during menstrual cycle and clinical classification of the ovarian response was altered
103
Table 3 Variability in AMH levels between menstrual cycles
Study
Subjects
a cycles b day sampled
Assay
Storage
Result
Authorsrsquo Conclusion
Fanchin et al (41)
infertile
age 25-40 regular cycles
(n=47)
a 3 cycles
b day 3
in-house
(Long et al 2000)
-80C
AMH showed significantly
higher reproducibility than inhibin B (plt003) E2 (plt00001) FSH (plt001) and early AFC (plt00001)
AMH showed improved cycle-to-cycle consistency compared to other markers of ovarian follicular status
Streuli
et al (29)
healthy mean age = 241 regular cycles
(n=10)
a 2 cycles b before (LH -10-5-2-1) and
after LH surge (LH +1+2+10)
IOT
-18C Inter-cycle variability of 285
AMH fluctuations during the cycle were smaller than or equal to the variability between two cycles
van Disseldorp et al (10)
infertile median age =33
PCOS excluded (n=77)
a average 373 cycles b day 3
DSL
-80C
AMH showed a within-subject variability of 11 compared to 27 for AFC
AMH demonstrated less individual inter-cycle variability than AFC
Dorgan
et al (42)
blood donors age 36-44 collected 1977-1981 (n=20)
two samples collected during the same menstrual cycle phase at least 1yr apart
DSL
-70C
between-subject variance in AMH of 219 was large compared to the within-subject variance of 031
AMH was relatively stable over 1 year in pre-menopausal women
Rustamov et al (36)
infertile women age 22-41
(n=186)
random sampling median interval = 26 months
DSL
-70C
within-subject CV for AMH was 28 compared to 27 for FSH
AMH showed significant sample-to-sample variation
Rustamov et al (13)
infertile women age 20-46
(n=87)
random sampling median interval = 51 months
Gen II
-20C
within-subject CV for AMH was 59
AMH demonstrated a large sample-to-sample variation
104
Table 4 Within-subject comparison between AMH methods Study
Assays
Subjects
Simultaneous Analysis
Regression
Summary
Freour et al (44) DSL vs IOT 69 infertile women age 22-40
Yes IOT = 401 x DSL + 098 (microgL) (Deming regression)
DSL = 22 IOT (plt00001)
Hehenkamp et al (30) DSL vs IOT 82 healthy women NR DSL= 0495 x IOT - 003 DSL = 495 IOT
Bersinger et al (45) a DSL vs IOT
b DSL vs IOT
a 11 infertile women
b 55 infertile women
a yes
b no
a DSL= 0180 x IOT
b DSL= 0325 x IOT + 0733
a DSL = 18 IOT
b DSL= 33 IOT
Zhao et al (17) DSL vs IOT 38 donors NR IOT = 15 x DSL + 07 (ngml) DSL = 66 IOT
Taieb et al (46) DSL vs IOT 104 samples NR DSL = 104 x IOT - 149 DSL = 96 IOT
Streuli et al (29) DSL vs IOT 153 normal and infertile No IOT = 107 x DSL - 029 DSL = IOT
Kumar et al (18) IOT vs Gen II 60 female 60 male volunteers NR IOT =10 Gen II IOT=Gen II
Gada et al (50) DSL vs Gen II 42 women NR NR DSL = 63 Gen II
Preissner et al (23) DSL vs Gen II 206 samples NR Gen II = 153 x DSL - 077 DSL = 66 Gen II
Lee et al (47) DSL vs IOT 172 infertile women Yes IOT = 1102 x DSL - 0042 DSL = IOT
Wallace et al (51) DSL vs Gen II 271 women NR Gen II = 140 x DSL - 062 DSL = 71 Gen II
Li et al (48) a DSL vs IOT b DSL vs Gen II c IOT vs Gen II
56 women with PCOS or sub-fertility Yes a IOT = 097 x DSL -296 b Gen II = 133 x DSL - 417 c Gen II = 138 x IOT - 068
a DSL = IOT b DSL = 67 Gen II c IOT = 62 Gen II
Rustamov et al (13) DSL vs Gen II female IVF patients (n=330)
median of 2yr between samples
No NR
DSL = 127 Gen II
(age-adjusted)
Pigny et al (49) IOT vs Gen II 59 women 32 controls 27 with PCOS Yes NR IOT = 200 Gen II
105
Appendix I Flow-chart of the search for publications Database search for sample stability measurement variability and assay-method comparability was conducted simultaneously using the MeSH database of PubMedMedline using the search terms of ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting Substance and MIS which identified n=1653 studies on AMH The initial step of identification involved screening of articles by reading titles andor abstracts Further search involved identification of studies from the reference sections of the initially identified studies
Database Search
n=1653
Sample
Stability
Screening Titles
n=6
Further Search
n=4
Total
n=10
Measurment Variability
Screening Titles
n=14
Further Search
n=3
Total
n=17
Method comparability
Screening Titles
n=10
Further Search
n=4
Total
n=14
106
EXTRACTION PREPARATION AND
COLLATION OF DATASETS FOR THE
ASSESSMENT OF THE ROLE OF THE MARKERS
OF OVARIAN RESERVE IN FEMALE
REPRODUCTION AND IVF TREATMENT
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
Research Database
4
107
Title
Extraction preparation and collation of datasets for the assessment of
the role of the markers of ovarian reserve in female reproduction and
IVF treatment
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
NHS Research Ethics Approval
North West Research Ethics Committee (10H101522)
Word count 5088
Grants or fellowships
No funding was sought for this study
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
108
Declaration of authorsrsquo roles
OR prepared the protocol extracted data from electronic sources and hospital
notes prepared datasets and prepared all versions of the chapter MK assisted
in collection of data from hospital notes SR and CF oversaw and supervised
preparation the protocol extraction of data preparation of datasets and
reviewed the chapter
109
CONTENTS I PROTOCOL Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip110
Methodshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Objectiveshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Inclusion Criteriahelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip114 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 RH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 AFC datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Folliculogram datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Data managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data cleaning and codinghelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118 Merging datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data security and storagehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip119 II RESULTS Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip120 Data extraction and managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 RH AFC and Folliculogram datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 Merging Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip124 Conclusionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip125
110
I PROTOCOL
INTRODUCTION
The aim of the project is to create a series of reliable and validated
datasets which contain all relevant data on the ovarian reserve markers (AMH
AFC FSH) ethnicity BMI reproductive history causes of infertility IVF
treatment parameters for patients that meet inclusion criteria as described
below The datasets will be used for the subsequent research projects of the
MD programme and future research studies on ovarian reserve
Most data can be obtained from following existing clinical electronic
records a) Patient Administration System (PAS) b) Biochemistry Department
data management system c) the hospital database for surgical procedures and
d) AMH dataset and e) ACUBase IVF data management system Following
obtaining original datasets from the administrators of the data management
systems in their original Excel format the datasets will be converted into Stata
format and ldquopreparedrdquo by a) checking and recoding spurious data
transforming the dates from string to numeric format which will be consistent
across all datasets (Day Month Year) and stored in Stata format under
following names ldquoDemographyrdquo ldquoBiochemistryrdquo ldquoAMHrdquo ldquoSurgeryrdquo ldquoIVFrdquo
ldquoFETrdquo ldquoEmbryologyrdquo Copies of original datasets will be kept in the
password-protected and encrypted computer located in the Clinical Records
Room of Reproductive Medicine Department Central Manchester University
Hospitals NHS Foundation Trust which is maintained by IT department of
the Trust (Figure 1)
Data not available in electronic format will be collected from the hospital
records of each patient by researchers Dr Oybek Rustamov and Dr Monica
Krishnan and entered into following datasets Reproductive history (RH)
antral follicle count (AFC) and Folliculogram The hospital notes of all
included patients will be hand-searched The datasets will be transferred to
Stata and each step of data preparation will be recorded using Stata Do files
and the files will be stored under the filenames of ldquoHistoryrdquo ldquoAFCrdquo
Folliculogramrdquo in Stata format In order to ensure the robustness of the data
and for the purpose of validation of the datasets electronic scanned copies of
all available reports of pelvic ultrasound assessments for AFC and
folliculograms will be obtained and stored in the password-protected and
111
encrypted computer located in the Clinical Records Room of Reproductive
Medicine Department Ethics approval for collection of data has already been
obtained (UK-NHS 10H101522)
The datasets will be merged and datasets for each research project with
all available data nested with IVF cycles nested within patients will be created
METHODS
Objectives
The aim of the project is to build a robust database which can reliably
used for the following purposes
1 To estimate the effect of ethnicity BMI endometriosis and the causes
of infertility on ovarian reserve using cross sectional data (Chapter 51)
2 To estimate the effect of salpingectomy ovarian cystectomy and
unilateral salpingo-oopherectomy on ovarian reserve using cross
sectional data (Chapter 52)
3 To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield (Chapter 6)
4 To explore the potential for optimization of AMH-tailored
individualisation of ovarian stimulation using retrospective data
(Chapter 6)
Inclusion criteria
In order to capture the populations for all three studies the database will
have broad inclusion criteria All women from 20 to 50 years of age referred to
Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust will be included if a) they were referred for
management of infertility or fertility preservation and b) had AMH
measurement during the period from 1 September 2008 till 16 November
2011
112
Datasets
PAS dataset
The dataset contains information on the hospital number surname first
name date of birth and the ethnicity of all patients referred to Reproductive
Medicine Department CMFT (Table 1) The data are originally entered during
registration of the patient for clinical care by administrative staff of
Gynaecology and Reproductive Medicine Departments The dataset will be
obtained from the administrators of the Information Unit
The dataset will be obtained in Excel format and transferred into Stata
12 Data Management and Statistical Software The date values (referral date
and date of birth) will be converted into numeric variable using ldquoDate Month
Yearrdquo format (DMY) Ethnicity will be coded using numeric variables in
alphabetical order as pre-specified in the Table 2a
Biochemistry dataset
The dataset contains all blood test results specimen numbers the names
of the tests and the date of sampling of women who had assays for follicle
stimulating hormone (FSH) oestradiol (E2) luteinizing hormone (LH) and
AMH during the study period (Table 1) Data entries were conducted by the
clinical scientists the technicians and the members of administrative team of
the Biochemistry Department The dataset will be obtained from an
administrator of the database
The date of sampling and analyses will be converted to the numeric
ldquoDMYrdquo format The specimen number will be kept unaltered in the string
variable format and used to link the tests that were taken in the same sample
tube The name of the test will be kept as described in the original format
ldquoAMHrdquo ldquoFSHrdquo ldquoLH and ldquoOestrdquo In the original dataset the samples sent
from Reproductive Medicine Department are coded as ldquoIVFrdquo which will be
kept unaltered and the remaining observations will be divided into
ldquoGynaecology Departmentrdquo ldquoNon-IVFGynaecologyrdquo and ldquoUnknownrdquo
categories using the code of referred ward and the names of the consultants
The test results will be converted into numeric format and the results with
minimum detection limit will be coded as 50 of the minimum detection limit
as follows AMH ldquolt061rdquo= 031 pmolL FSH ldquolt05rdquo= 025 mlUml LH
113
ldquolt05rdquo=025 mlUml Oest ldquolt50rdquo=025 pgml The test results that are
higher than the assay ranges will be set to 150 of the maximum range
Interpretation of serum FSH results in conjunction with serum
oestradiol levels is important in establishing true early follicular phase hormone
levels The test results are believed to be inaccurate if serum oestradiol levels
higher than 250pmolL at the time of sampling and therefore a new variable
for FSH results with only serum FSH observations that meet above criteria will
be created and used subsequently All ambiguous data will be checked using
electronic pathology data management system Clinical Work Station (CWS)
Surgery dataset
The electronic dataset will be obtained from Information Department
in Excel format The dataset created using clinical coding software and data
entry conducted during patient treatment episodes by theatre nursing and
medical staff In order to evaluate effect of past reproductive surgery to
ovarian reserve all patients had ovarian cystectomy drainage of ovarian cyst
salpingectomy salpingo-oopherectomy during 1 January 2000-16 November
2011 at Central Manchester University Hospitals NHS Foundation Trust will
be included in the dataset The dataset contains following variables hospital
number surname first name date of birth date of operation name of
operation laterality of operation and name of surgeon
The final dataset will be stored in Stata dta format (Figure 1) The
dataset will be used to validate data on reproductive surgery that was collected
from hospital records in the RH dataset
AMH dataset
The dataset contains the AMH results the dates of sampling the dates
of analyses and the assay generation (DSL or Gen II) for all patients included
in the study (Table 1) The dataset will be obtained from the senior clinical
scientist Dr Philip Pemberton Specialist Assay Laboratory who is responsible
for the data entry and updating of the dataset
There are two separate primary Excel based AMH data files 1) DSL
dataset and 2) Gen II dataset The datasets will be transferred to Stata 12
software separately and following preparation of the datasets which logged
using Stata Do file Stata versions of the data files will be stored under ldquoDSLrdquo
114
and ldquoGen2rdquo names Then the files will be combined by appending ldquoDSLrdquo to
ldquoGen2rdquo in order to create a new combined ldquoAMHrdquo dataset The date variables
the sample date the assay date and the date of birth will be converted into
numeric ldquoDMYrdquo format The samples sent from other NHS trusts and private
clinics will be excluded from the dataset alongside the records from male
patients and the patients outside of the age range of 20-50 years of age The
manufacturers of the assays suggest that haemolysed and partly haemolysed
samples may provide inaccurate test readings Therefore a new variable
without these samples will be created and used in the analyses for all studies
All the ambiguous data will be checked and verified using duplicate datasets
obtained from Biochemistry dataset and the hospital records of the patients
IVF dataset
The IVF dataset will be downloaded from ACUBase Data management
system in original Excel format and contains detailed information on causes of
infertility sperm parameters treatment interventions assessment of oocyte
quantity and quality assessment of embryo quantity and quality and the
outcomes of treatment cycles (Table 1)Data entry to ACUBase was
performed by members of administrative nursing embryology and medical
staff of the Reproductive Medicine Department at the point of care This is
only electronic data management system for ART cycles and used for
monitoring of the clinical performance of the department by internal and
external quality assessment agencies and regulators (eg HFEA CQC)
Therefore the quality of data entry for the main indicators of the performance
of IVFICSI programs (the treatment procedures the outcomes of the cycles
and assessment of embryos) should be fairly accurate
Table 2b describes the coding of the treatment outcomes and the
practitioners of ICSI the ultrasound-guided oocyte retrieval (USOR) and the
embryo transfer (ET) procedures
In addition to the main patient identifier (Hospital Number) this dataset
contains in-built cycle identifier (IVF Reference Number) which will be used
to link the original IVF cycles to corresponding Frozen Embryo Transfer
(FET) cycles and the embryos originating from the index cycle using ldquoFETrdquo
and ldquoEmbryordquo datasets respectively
115
FET dataset
The dataset provides information on the quality and the quantity of
transferred embryos the date of embryo transfer and the outcome of the cycle
in frozen embryo transfer cycles (Table 1) Primary data entry was performed
by the members of the clinical embryology team during the treatment of
patients and will be downloaded from ACUBase by Dr O Rustamov
Together with ldquoIVFrdquo dataset it can be used to study cumulative live birth rate
(LBR) of index cycles The treatment outcomes as well as ICSI USOR and ET
practitioners will be converted to numeric variables using the codes which are
shown in Table 2b The dataset can be linked to the index fresh IVF cycles as
well as to embryos of FET cycles using the IVF Reference number
Embryology dataset
The dataset has comprehensive information on the quality and the
quantity of embryos on each day of their culturing including embryos that
were cryopreserved and those that were discarded (Table 1) The dataset also
includes patient identifiers (name date of birth IVF reference number) and
the dates of embryo transfer The primary data entry into this dataset was
conducted by the members of clinical embryology team during the clinical
episodes and will be downloaded from ACUBase by Dr O Rustamov The
dataset can be linked to index fresh IVF cycle and FET cycles using IVF
Reference numbers of corresponding datasets
RH dataset
This dataset will be created and data entry will be conducted during the
search of the hospital notes Following identification of included patients using
AMH dataset Excel electronic data collection file will be created The hospital
notes of each patient will be searched for by systematically checking all filed
hospital records in Clinical Records Room of Reproductive Medicine
Department by the order of their hospital number Further search for missing
notes will be conducted by checking all hospital notes located in the offices of
nurses doctors and secretaries Electronic hospital notes filed in Medisec
Digital Dictation Database will be used for data extraction for the patients
whose hospital notes were not located
116
All available diagnosis will be recorded under the following columns 1)
female referral diagnosis 2) male referral diagnosis 3) female initial clinic
diagnosis 4) female final clinic diagnosis 5) diagnosis prior 2nd IVF cycle 6)
diagnosis prior 3rd IVF cycle Furthermore other relevant information on
pathology of reproductive system will be documented For instance all possible
iatrogenic causes of poor ovarian reserve (eg oophorectomy ovarian
cystectomy salpingectomy chemotherapy and radiotherapy) will be recorded
In order to establish the existence of polycystic ovary syndrome (PCOS) the
history of oligomenorrhea amenorrhea and diagnosis of polycystic ovaries
(PCO) on pelvic ultrasound scan will be collected and used in conjunction with
serum LH levels of Biochemistry dataset (Table 1)
Male infertility will be defined as ldquosevere male factorrdquo if the sperm
parameters were low enough to meet criteria (lt05 mlnml or retrograde
ejaculation) for Multiple Ejaculation Resuspension and Centrifugation test
(MERC) as part of investigation for infertility A variable for patients
diagnosed with azoospermia will be created and the diagnosis will be recorded
The patients diagnosed with male factor infertility but with the sperm
parameters that did not reach criteria for MERC will be diagnosed with ldquomild
male factorrdquo infertility Patients diagnosed with ldquosevererdquo andor ldquostage IVrdquo
andor ldquostage IIIrdquo endometriosis will be categorized as ldquosevere
endometriosisrdquo while patients diagnosed with mild or moderate endometriosis
will be coded as ldquomild endometriosisrdquo group In diagnosing the tubal factor
infertility only patients with history of bilateral salpingectomy and the patients
with evidence of bilateral tubal blockage on a laparoscopy and dye test will be
diagnosed as ldquosevere tubal factorrdquo The patients with history of unilateral
salpingectomy unilateral tubal block in laparoscopy and dye test or
unilateralbilateral tubal block on hysterosalpingogram will be categorized as
ldquomild tubal factorrdquo infertility Diagnosis of polycystic ovarian syndrome
(PCOS) will be based in Rotterdam criteria existence of two of the following
features 1) oligo- or anovulation 2) clinical andor biochemical signs of
hyperandrgoenism 3) polycystic ovaries Referral for fertility preservation will
be defined as ldquoreferral for consideration of obtaining oocytes orand embryos
andor sperm prior to chemotherapy radiotherapy or surgical management of
a malignant diseaserdquo The length of infertility will be recorded as per proforma
of initial consultation for the patients attended initial clinic appointment
following introduction of serum AMH test 1 September 2008 For patients
117
attended initial consultation prior to introduction of AMH test the length of
infertility will be documented as per the initial clinic proforma plus years till the
patientrsquos first AMH test The patientrsquos body mass index (BMI) documented at
initial assessment will used for patients who had assessment after introduction
of AMH test 1 September 2008 whereas the most up to date BMI result is
collected for the patients seen prior to this date
AFC dataset
Data will be extracted from the hospital notes The data on the
assessment of AFC will be obtained from the pelvic ultrasound scan reports
The date of assessment the AFC in each ovary the name of sonographer will
be recorded (Table 1) Furthermore other relevant ultrasound findings such
as ovarian cyst hydrosalpynx and submucous uterine fibroids will also be
entered in the dataset To permit data validation scanned copies of ultrasound
scan report of each AFC investigation will be stored in PDF format in the
computer that located in the Clinical Notes Room
The department uses a stringent methodology for the assessment of
AFC which consist of counting of all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
The ultrasound assessments are conducted by qualified sonographers who use
the same methodology for the measurement of AFC However it is well
known that the counting of antral follicles may be prone to significant inter-
operator variability Therefore the name of sonographers will be recorded
during primary data collection and coded (Table 2a) so that the estimates of
within- and between-operator variability can be obtained if necessary
Folliculogram dataset
Although most data on IVFICSI cycles are available in ldquoIVFrdquo dataset
certain important data on IVF treatment are recorded only in the hard copy
IVF folliculograms Consequently data on ultrasound follicle tracking the
reasons for changing the doses of stimulation drugs are only available in the
folliculograms Furthermore the length of ldquothe coastingrdquo and the causes for
cycle cancellation are usually recorded in both folliculograms and ldquoIVFrdquo
dataset which can be used to validate accuracy of ldquoIVFrdquo dataset Therefore
118
these data will be collected using the folliculograms that filed in the hospital
notes and the scanned copies of each folliculograms will be stored in the
computer located Clinical Records Room for data validation purposes (Table
1)
The number of follicles on Day 8 and Day 10 ultrasound scans will be
recorded according to the size of the follicles 10-16mm and 17mm
Numeric variables for the follicle numbers will be created and used for
assessment of ovarian response in IVF cycles
Data management
Data cleaning and coding
All datasets will be obtained in Excel format and transferred in the
original unaltered condition into Stata 12 data management and statistical
package (Stata 12 StataCorp Texas USA) and all steps of the data cleaning
and the coding will be recorded using Stata Do files to create audit trails of the
data management process Both original Excel and cleaned Stata versions of
data files will be stored in computer that is located in Clinical Records Room at
Reproductive Medicine Department Uniformity of hospital numbers in all
datasets will be achieved by converting a) leading lower case prefixes ldquosrdquo to
upper case ldquoSrdquo b) dropping suffixes ldquozrdquo and ldquoZrdquo and c) dropping all leading
zeros in the second part of the hospital number (eg ldquos1000235Zrdquo
=rdquoS10235rdquo) The coding of the datasets is shown in the Table 2a and the
Table 2b All ambiguous data will be checked using electronic data
management systems (eg CWS Medisec) and hospital notes
Merging the datasets
The datasets will be structured as such that the data files can be used
independently or merged at a) patient or b) IVF cycle levels using the patient
identifier cycle identifier and date variables (Figure 1) This allows analysis of
outcomes of both ldquoFresh IVF cyclesrdquo and study the cumulative outcomes of
Fresh IVF and Frozen Embryo Transfer cycles originating form index IVF
cycles
Each dataset will contain two main patient identifiers and patient
number (Patient ID) which will be used for linking the datasets in a patient
119
level At the initial stages of the data management the hospital numbers will be
used as the main patient identifier The accuracy of the hospital numbers in
each dataset will be validated using PAS dataset by checking patient surname
first name and date of birth
Following methodology will be used to add study numbers into each
dataset First all dataset will be merged in a wide format using the hospital
numbers which creates Master Datasets for each of the research projects Then
an accuracy of the merger will be checked using DOB surname and first name
Once the dataset is validated several copies of the Patient ID variable will be
created and distributed to each dataset Finally the datasets will be separated
and stored as independent datasets alongside Master Datasets for each research
projects
ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo datasets contain cycle specific IVF
reference numbers which were allocated during the clinical episodes on
ACUBase Using IVF reference number new ID variable (Cycle ID) will be
created and allocated to all datasets using closest observation prior to the IVF
cycle in Master Research Dataset Consequently by using cycle reference
number all patient and cycle related data can be linked in the IVF FET cycle
and embryo level
Data security and storage
The encrypted and password protected hospital computer will be used to
process the data until all the patient identifiers have been removed and the
datasets have been anonymised Once the Master Research Datasets are
validated and research team is satisfied with the quality of the data the dataset
will be anonymised by dropping variables for following patient identifiers
hospital number surname first name date of birth and IVF reference number
The study number and the cycle reference numbers will be used as a patient
and a cycle identifiers and only this anonymised dataset will be used for
statistical analysis A copy of non-anonymised dataset will be stored in the
computer located in Clinical Records Room for data verification and a
reference purposes The datasets will be stored within IVF unit for the
duration of the research projects of the MD programme The necessity of
storage of the datasets and measures of data security will be reviewed every
three years thereafter
120
II RESULTS
INTRODUCTION
According to the protocol all women from 20 to 50 years of age referred
to Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust for management of infertility or fertility
preservation and had AMH measurement during the period from 1 September
2008 till 16 November 2011 have been included in the database In total of
4506 patients met the inclusion criteria with 3381 patients in DSL AMH
assay group and 1125 patients Gen II assay group The following datasets
have been extracted from the clinical electronic data management systems
ldquoPASrdquordquo Biochemistryrdquo ldquoSurgeryrdquo ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo Data
extraction from the paper-based hospital records of 3681 patients (n=3130
DSL and n=551 Gen II) were performed by two researchers Dr ORustamov
(n=2801) and Dr M Krishnan (n=880) In addition data collection using
Medisec Digital Dictation Software for the notes that were not located in DSL
group (n=251 patients) was completed by Dr O Rustamov In view of the
issues with validity of Gen II assay measurements which were observed in the
earlier study of the MD Programme (Chapter 2 AMH variability and assay
method comparison) I decided to base subsequent work for the last three
projects (Chapter 5-7) of the MD programme only on DSL assay
measurements and not to include samples based on Gen II AMH Assay
Therefore I decided not to collect data from the hospital notes for the patients
that had AMH measurements using exclusively Gen II Assay where the notes
were not found during the first round of data collection (n=575)
As a result in DSL group all datasets for 3130 patients were completed
and all but AFC and Folliculogram datasets were completed for 251 (Figure 2)
In Gen II group all datasets were completed for 551 patients and all but RH
AFC and Folliculogram datasets were obtained for 575 patients (Figure 2)
As described above the studies of the last three projects (Chapter 5-7)
are based on DSL assay which is no longer in clinical use The review of
literature presented in Chapter 3 suggests that DSL assay appears to have
provided the most reproducible measurements of AMH compared to that of
other assays Therefore AMH measured using DSL assay is perhaps most
121
reliable in terms addressing the research questions In all three chapters
estimates of the effect sizes are provided in percentage terms and therefore the
results are convertible to any AMH assay
Datasets
Demography dataset
The dataset was obtained from Mr Peter Hoyle Senior Data Analyst of
Information Unit CMFT on 16 October 2012 The dataset includes all patients
referred to Reproductive Medicine Department between 1 January 2006 and 31
August 2012 and contains 5573 patients I created a dataset ldquoDemographyrdquo in
Stata format using the steps of data cleaning coding and management as per
protocol The audit trial of the data management was created using Stata Do
file (Figure 1)
Biochemistry dataset
The biochemistry data file was obtained from Dr Alexander Smith
Senior Clinical Scientist Biochemistry Department on 24 January 2011 The
dataset contains the results of all serum AMH FSH LH and E2 samples
conducted from 01 September 2008 to 31 December 2010 The dataset was in
Excel format that consisted of two datasheets 1) Biochemistry 2008-2009 and
2) Biochemistry 2010 The datasheets transferred to Stata 12 in original
unaltered condition and a single Stata ldquoBiochemistryrdquo dataset was created by
combining datasheets by appending them to each other The dataset contains
in total of 78415 blood results of 11574 patients with 6643 AMH 19175 FSH
28677 LH and 23920 E2 results A wide format of the dataset was prepared by
transferring all blood results of each patient to a single row A variable which
indicates valid FSH results was created by coding FSH results as missing if
corresponding E2 levels were higher than 250 pmolL The audit trial of the
data management was created using a Stata Do file
Surgery dataset
Data management was conducted according to the protocol In total
dataset contained 2096 operations in 1787 patients Data on all operations on
122
Fallopian tubes (eg salpingectomy salpingostomy) and ovaries (eg
cystectomy drainage of cyst) at Central Manchester NHS Foundation Trust
from 1 January 2000 to 16 January 2011 are available in the dataset The
dataset will be used to validate the data on history of reproductive surgery of
Reproductive History dataset
AMH dataset
Both AMH datasets were received from Dr Philip Pemberton Senior
Clinical Scientist of the Specialist Assay Laboratory on 13 January 2012 and
transferred to Stata 12 software in the original format All steps of the data
cleaning and the management were recorded using Stata Do file
There were 3381 patients in DSL dataset and 1125 patients in Gen II
dataset Cleaning and coding of the datasets were achieved using the
methodology described in above protocol and new AMH dataset has been
created
IVF dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov on
08 October 2012 and following importing the dataset into Stata 12 in original
format dataset was prepared according to the protocol The dataset contains all
IVFICSI cycles that took place between 01 January 2004 and 01 October
2012 including the cycles of women who acted as egg donors and egg
recipients There were in total of 4323 patients who had 5737 IVFICSI cycles
with 4123 IVFICSI cycles using own eggs 10 embryo storage 40 oocyte
donation 7 oocyte storage 55 oocyte recipient cycles The dataset has
anonymised unique patient (Patient ID) and cycle identifiers (Cycle ID) and
therefore can be linked to all other datasets including all FET cycles and
embryos originated from the index IVF cycle
FET dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov
in Excel format on 20 October 2012 and transferred to Stata 12 Software in
the original condition The data managed as per above protocol and each step
of the process of preparation of the dataset was recorded in Stata Do file The
dataset comprised of all FET cycles (n= 3709) of all women (n=1991)
123
conducted between 01 January 2004 and 01 October 2010 and the Stata
version of ldquoFETrdquo dataset contains complete data on number of thawed
cleaved discarded and research embryos for all patients The dataset contains
unique patient identifier (Patient N) and unique cycle identifiers (Cycle N) and
therefore can be linked to all datasets in patient and cycle levels including index
IVF cycle and embryos
Embryology dataset
The Excel dataset was downloaded from ACUBase by Dr Oybek
Rustamov on 20 October 2012 and transferred into Stata 12 Software in
unaltered condition The data was managed according to the above protocol
The dataset has details of all 65535 (n=4305 women) embryos that were
created between 01 January 2004 and 01 October 2012 The dataset contains
complete data on quantity and the assessment of embryo quality which
includes grading of number evenness and defragmentation of the cells for
each day of culturing of the embryos Furthermore the destination of each
embryo (eg transferred cryopreserved discarded and donated) and the
outcomes of cycles for transferred embryos are available in the dataset Given
that the Embryology dataset has the unique patient as well as the cycle
identifiers this dataset is nested within patients and IVF cycles Consequently
each embryo can be linked to patient index Fresh IVF cycle and subsequent
FET cycles
Reproductive History AFC and Folliculogram datasets
The hospital notes of all patients (n=4506) were searched during the
period of 1 April 2012 to 15 October 2012 for collection of data for
Reproductive history AFC and Folliculogram datasets as per protocol All case
noted filed in the Clinical Records Room the Nurses Room the Doctors
Room and the Secretaries Room of Reproductive Medicine Department were
searched and relevant notes were pulled and searched for data All ultrasound
scan reports containing data on AFC and all IVFICSI folliculograms of
patients were scanned and electronic copy of scanned documents were stored
in the password protected NHS computer located in the Clinical Records
Room
124
The first round of data gathering achieved following result In DSL
dataset there were in total of 3381 patients with 3130 patients had complete
data extraction from their hospital notes and hospital records of 251 patients
were not found There were in total of 1126 patients in Gen II dataset 551 of
whom had complete data extraction from their hospital records and the case
notes of 575 patients were not located (Figure 2) The main reason for
ldquomissing case notesrdquo was found to be the use of hospital records by clinical
laboratory and administrative members of staff at the time of data collection in
patients undergoing investigation and treatment
In the meantime the results of our previous research study indicated that
Gen II samples provide erroneous results (Chapter II) and therefore we
decided to use only data from the patients in DSL group Data on reproductive
history for the remaining patients in the DSL group (n=251) with missing
hospital records were collected using digital clinic letters stored in Medisec
Digital Dictation Software (Medisec Software UK) The data file that
contained combined datasets of reproductive history and AFC was transferred
to Stata 12 in original condition and data management was conducted
according to the protocol All steps of data management was recorded using
Stata do file for audit trail and to ensure reproducibility of the management of
the data Similarly the management of Folliculogram dataset was achieved
using the procedures described in the protocol and all steps of data
management was logged using Stata Do file As result of above data collection
and management I created three Stata datasets ldquoRHrdquo (reproductive history)
ldquoAFCrdquo and ldquoFolliculogramrdquo
Merging Datasets
First the datasets were merged using a unique patient identifier (hospital
number) as per protocol Validation of the merger using additional patient
identifiers (NHS number name date of birth) revealed existence of duplicate
hospital numbers in patients transferred from secondary care infertility services
to IVF Department of Central Manchester University Hospitals NHS
Foundation Trust I established that in the datasets the combination of the
patientrsquos first name surname and date of birth in a single string variable could
be used as a unique identifier Hence I used this identifier to merge all
datasets achieving a robust merger of all independent datasets into combined
125
final Master Datasets for each of the research projects Following the creation
of an anonymised unique patient identifier (Patient ID) for each patient and
anonymised unique cycle identifier (Cycle ID) for each IVF cycle all patient
identifiers (eg surname forename hospital number IVF ref number) were
dropped (Figure 1) The anonymised independent datasets (eg AMH AFC
IVF etc) and anonymised Master Datasets were stored as per protocol
Subsequently these anonymised datasets were used for the statistical analyses
of the research projects The original unanonymised data files were stored in
two password protected NHS hospital computers in the Clinical Records
Room and Doctors Room of Reproductive Medicine Department and
archived according to the Trust policies thereafter Only members of clinical
staff have access to the computers and only nominated clinical members of the
research group who have specific approval can have access to unanomysed
Fully anonymised datasets have been made available to other members of the
research team with the stipulation that the datasets are stored on secure
password protected servers or fully encrypted computers Fully anonymised
datasets may in the future be shared with other researchers following
consideration of the request by the person responsible for the datasets (Dr
Cheryl Fitzgerald) and appropriate ethical and data protection approval
CONCLUSION
Following extraction and management of the data I have built
comprehensive validated datasets which will enable to study ovarian reserve in
a wide context including a) assessment of ovarian reserve b) evaluation of the
performance of ovarian biomarkers c) study individualization of ovarian
stimulation in IVF d) association of the biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryo live birth) The database will be used
to address the research questions posed in the subsequent chapters of this
thesis and beyond that for future studies on the assessment of ovarian reserve
and IVF treatment
126
Figure 1 Data and program files Datasets and programme files created in preparation of the research datasets File names and types are provided in the brackets
127
Table 1a Available vriables The
available identifiers variables and the source of data for following datasets Ethnicity RH AMH AFC Biochemistry OHSS Folliculogram
Datasets
Clinical ID
Study ID
Variables
Source
Demography Hospital N Surname
First name DOB
Patient ID
Ethnicity Information Department
(PAS)
RH
(Reproductive History)
Hospital N Surname
First name DOB
Patient ID
1 Diagnosis Referral Female Referral Male
Clinic Female Clinic Male
Post Cycle 1 Post cycle 2 Post cycle 3
2 Iatrogenic causes of loss of ovarian reserve Ovarian surgery tubal surgery chemotherapy radiotherapy
3 BMI 4 PCOS (PCO oligomenorrhea amenorrhea hirsutism)
Hospital Records
Surgery Hospital N Surname
First name DOB
Patient ID Date
Procedure Date Operator
Information Department
AMH Hospital N Surname
First name DOB
Patient ID Date
Date of sample Date of assay AMH level Assay generation AMH dataset of Specialist Assay
Lab
AFC Hospital N Surname
First name DOB
Patient ID Date
AFC (up to six AFC scans)
Left ovary Right ovary Date of Scan Sonographer Comments (Ovarian cyst hydrosalpynx fibroid poorly visualized etc)
Hospital Records
Biochemistry Hospital N Surname
First name DOB
Patient ID Date
Oestradiol (Date of sample Date of assay serum level) FSH (Date of sample Date of assay serum level)
LH (Date of sample Date of assay serum level)
Biochemistry Electronic
Database
Folliculogram Hospital N Surname
First name DOB
Patient ID Date
Folliculogram (up to 3 cycles) Date (1st day of ovarian stimulation)
Day 8( 10-16mm) Day 8 (gt17mm) Day 10 (10-16mm) Day 8 (gt17mm)
Comments (Day of HCG OHSS Cancellation Ovarian cyst Hydrosalpynx Coasting etc)
Hospital Records
128
Table 1b Available variables The available identifiers variables and the source of data for IVF dataset
Datasets Clinical ID Study Variables Source
IVF Hospital N Surname First name DOB PCT code
Patient ID Cycle ID Date
GENERAL
Attempt Type Protocol DaysStim InitDose Outcome OutcomeDt Age PartnerAge EggCollect TreatDate ETransfer Add_Drug1 Add_Drug2 Add_Drug3 Add_Drug4 Add_Drug5 Add_Drug6 Add_Drug7 EGG RECOVERY SNumber Follicles TotEgg EggNumber
FERTILISATION IVFEgg IVFCleaved ICSICleaved Cleaved PN2 IVFPN2 ICSI2PN ICSICl ICSIEgg ICSIFPN IVFFPN IVFTransfer ICSITransfer IVFLysed ICSILysed IVFMetII IVFMetI IVFAtretic IVFAbnormal IVFEmptyZona IVFG_Vesicle ICSIMetII ICSIMetI ICSIAtretic ICSIAbnormal ICSIEmptyZona ICSIG_Vesicle
OUTCOME
sacs Hearts Preg ICSIPract STORAGE Frozen IVFFroz ICSIFroz SpermSource SortKeySTAR HISTORY cat_tubal cat_OvFail cat_UtProb cat_unex cat_ MF cat_Meno cat_Genetic cat_endo cat_anov cat_noMale Inf_Since MaleInf
CoupleInf Preg24Wk MiscTOP Ectopic LiveBirth FSH AMH Emb_Recip Surrogate Sperm_Recip StoreEggs EggThaw Treat_Reason IgnoreKPI EMBRYOLOGY
D1LteClCells1 D1LteClCells2 D2Cells2 D2Cells3 D2Cells4 D2Even2 D2Even3 D2Even4 D2Frag2 D2Frag3 D2Frag
SPERM Conc_Init MotA MotB Conc_ Prep MotAP MotBP SemenSource SemenAnalysis STIMULATION BMI TotDose GonadUsed Incubator ICSIRigg AMHBand DHEA EGG
Egg_Recip Own_Eggs Altruistic_D
ACUBASE Electronic Database
129
Table 1c Available variables
The available identifiers variables and the source of the data for FET and Embryo datasets
Datasets Clinical ID Study ID
Variables
Source
FER
Hospital N Surname First name
Patient ID Cycle ID Date
GENERAL treatdate transfer ETDate
OUTCOME preg IUP Outcome OutcomeDt
EMBRYOLOGY
Thawed Survived Cleaved Discarded Research
STORAGE NumStored DtCreated
CLINICIAN ETClinician ETEmbryologist OrigCycle
ACUBASE Electronic Database
Embryo
Hospital N Surname First name DOB
Patient ID Cycle ID Date
GENERAL TreatDate Injected Destination
CELLS CellsD1 CellsD2_AM CellsD2_PM CellsD3_AM CellsD3_PM
EVENNES EvenD2_AM EvenD2_PM EvenD3_AM EvenD3_PM
FRAGMENT FragD1 FragD2_AM FragD2_PM FragD3_AM FragD3_PM
OUTCOMES ICSIPract Maturity PosPreg Hearts SpermSource Age
ACUBASE Electronic Database
130
Table 2a Coding
The codes used to convert ethnicity and diagnosis variables from string to numeric format in PAS and RH datasets
131
Table 2b Coding
The codes used to convert treatment outcomes from string to numeric format in IVF and FET datasets
Datasets Codes for outcomes
IVF
FET
ldquoBiochemical Pregnancyrdquo=1 ldquoCancel (other)rdquo=2
ldquoCancel Hyperstimulationrdquo=3 ldquoCancel Poor responserdquo=4
ldquoCancelled no sperm on day of ECrdquo=5 ldquoCONVERTED IVF TO IUIrdquo=6
ldquoDelayed Miscarriagerdquo=7 ldquoDonatedrdquo=8 ldquoEctopicrdquo=9
ldquoEgg donationrdquo=10 ldquoEmbryos for storagerdquo=11
ldquoEmpty Sacrdquo=12 ldquoFailed Fertilisationrdquo=13
ldquoFor donationrdquo=14 ldquoFreeze Allrdquo=15
ldquoFreeze All (OHSS)rdquo=16 ldquoFreeze All (Other)rdquo=17
ldquoLate Miscarriagerdquo=18 ldquolost to contactrdquo=19
ldquolost to follow uprdquo=19 ldquoNo Eggsrdquo=20
ldquoNo Spermrdquo=21 ldquoNo Normal Embryosrdquo=22
ldquoNot Pregnantrdquo=23 ldquoOngoing Singletonrdquo=24
ldquoOngoing Twinrdquo=25 ldquoPositive hCGrdquo=26
ldquoSingleton Birth=27rdquo ldquoTwin Birthrdquo=28
ldquoTriplet Birthrdquo=29 ldquoStill Birthrdquo=30The
132
Figure 2 Data collection from hospital records
Completeness of data collection from hospital records for RH AFC and Folliculogram datasets
All
patients
DSL
(n=3381)
All Datasets
Complete
n=3130
AFC and Folliculogram
not complete
n=251
Gen II
(n=1126)
All Datasets
Complete
n=551
RH AFC Follicologram
not complete
n=575
133
Table 3 Results Datasets and observation
Summary of the number of patients observations IVFFET cycles and data entry period for all datasets
Datasets Patients Observations Cycles Period
AMH DSL 3381Gen II 1126
DSL-3913 DSL 01 Sep 2008-15 Nov 2010 Gen II 16 Nov 2010-16 Nov 2011
Demography 5573 01 Jan 2006-31 Aug 2012
Biochemistry 11754 Total 78415 6643-AMH 19175-FSH 28677-LH 23920-E2
01 Sep 2008-31 Dec 2010
RH DSL-3381 DSL-3381 01 Sep 2008-01 Oct 2012
Surgery 1787
2096 01 Jan 2000-16 Nov 2011
AFC DSL 2411 DSL Total 4174 Single measurement2411 Repeats 2-1250 3-370 4-105 5-25 6-7 7-1
01 Sep 2008-01 Oct 2012
Folliculogram 1736 2183
01 Sep 2008-01 Oct 2012
IVFICSI 4324 - Total 5737 own eggs-4123 oocyte recipients-55 oocyte donors-40 Embryo storage-10 oocyte storage-7
01 Jan 2004-01 Oct 2012
FET 1991 - 3709
01 Jan 2004-01 Oct 2012
Embryology
4305 65535 embryos - 01 Jan 2004-01 Oct 2012
134
Figure 3 Merging datasets
The process of merging datasets in patient and cycle levels using patient date and cycle IDs
135
ASSESSMENT OF DETERMINANTS OF
ANTI-MUumlLLERIAN HORMONE IN INFERTILE
WOMEN
5
136
THE EFFECT OF ETHNICITY BMI
ENDOMETRIOSIS AND THE CAUSES OF
INFERTILITY ON OVARIAN RESERVE
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
To be submitted to Fertility and Sterility
51
137
Title
The effect of ethnicity BMI endometriosis and the causes of infertility
on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK c Centre for Biostatistics
Institute of Population Health Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Word count 4715
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
138
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all version
of the manuscript MK assisted in data extraction contributed in discussion
and the review of the manuscript SR and CF oversaw and supervised
preparation of dataset statistical analysis contributed in discussion and
reviewed all versions of the manuscript
139
ABSTRACT
Objective
To estimate the effect of ethnicity BMI endometriosis and the causes of
infertility on ovarian reserve
Design Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 2946 patients were included in the study of which 65 did not have
data on ethnicity leaving 2881 women in the sample
Interventions Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH serum basal FSH and basal AFC measurements
Results
Multivariable regression excluding BMI showed that woman of Black ethnicity
and the group defined as ldquoOther ethnicityrdquo had significantly lower AMH
measurements when compared to that of White (-25 p=0013 and -19
p=0047) and overall ethnicity was a significant predictor of AMH (p=0007)
However inclusion of BMI in the model reduced these effects and the overall
effect of ethnicity did not reach statistical significance (p=008) AFC was
significantly reduced in Pakistani and women of ldquoOther ethnicitiesrdquo although
the effect sizes were small (10-14) and the overall effect of ethnicity was
significant in both models (p=004 and p=003) None of the groups showed a
statistically significant difference in FSH although women of ldquoOther Asianrdquo
ethnicity appear to have lower FSH measurements (12) which was close to
statistical significance (-12 p=007)
140
Obese women had higher AMH measurements (16 p=0035) compared to
that with normal BMI and the overall effect of the BMI was significant
(p=003) In the analysis of the effect of BMI to AFC measurements we did
not observe differences that were statistically significant However FSH results
showed that there is a modest association between BMI and FSH with both
overweight and obese women having significantly lower FSH measurements
compared to lean women (-5 p=0003 and -10 p=0003)
In the absence of endometrioma endometriosis was associated with lower
AMH measurements although this did not reach statistical significance
Neither AFC nor FSH was significantly different in the endometriosis group
compared to those without endometriosis In contrast we observed around
31 higher AMH levels in the patients with at least one endometrioma
(p=0034) although this did not reach statistical significance (21 p=01) in
the smaller subset after adjustment for BMI AFC and FSH did not show any
statistically significant association with endometrioma
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to the ones who did not have
unexplained infertility except the analysis that did not include BMI as a
covariate which found a weakly positive correlation (10 p=003) Similarly
the estimation of the effect of the diagnosis of unexplained infertility to AFC
as well as FSH showed that there were weak association between the markers
and diagnosis of unexplained infertility
There was no significant difference in AMH AFC and FSH measurements of
women with mild and severe tubal infertility in the models which included all
covariates except the analysis of FSH and mild tubal factor where we found
weakly negative correlation between these variables
Women diagnosed with male factor infertility had significantly higher AMH
and lower FSH measurements the effect sizes of which were directly
proportional to the severity of the diagnosis In the analysis of AFC we did not
found significant difference in the measurements between patients with male
factor infertility and to that of non-male factor
141
Conclusions
Ethnicity does not appear to play a major role in determination of ovarian
reserve as measured by AMH AFC and FSH whereas there is a significant
positive association with BMI and these markers of ovarian reserve Women
with endometriosis appear to have lower AMH whilst patients with
endometrioma have significantly higher AMH and lower FSH measurements
The study showed that the association between markers of ovarian reserve and
unexplained infertility as well as tubal disease is weak In contrast women
diagnosed with male factor infertility have higher ovarian reserve
Key Words
Ovarian reserve AMH AFC FSH ethnicity BMI infertility endometriosis
endometrioma
142
INTRODUCTION
The ovarian reserve consists of a total number of resting primordial and
growing oocytes which appears to be determined by the initial oocyte pool at
birth and the age-related decline in the oocyte number (Hansen et al 2008
Wallace and Kelsey 2010) Both of these factors appear to be largely
predetermined genetically although certain environmental socioeconomic and
medical factors likely to play a role in the rate of the decline (Schuh-Huerta et
al 2012b Kim et al 2013 Dolleman et al 2013) The understanding of the
formation and the loss of ovarian reserve have been improved greatly due to
recently published data on the histological assessment of ovarian reserve
(Hansen et al 2008) Furthermore the use of the biomarkers has enabled the
evaluation of ovarian reserve in larger population-based samples Biomarkers
such as AMH and AFC can only assess the measurement of growing pre-antral
and early antral follicle activity However some studies suggest that there is a
close correlation between the measurements of these markers and the number
of resting primordial follicles (Hansen et al 2011)
Studies on age related decline of AMH and AFC have played important
roles in understanding the decline of ovarian reserve although most of the
data have been derived from heterogeneous population without full account
for characteristics of individual patients (Nelson et al 2011 Seifer et al 2011
Shebl et al 2011) These studies have demonstrated that there is a significant
between-subject variation in ovarian reserve beyond that due to chronological
age (Kelsey et al 2011) More recent studies reported interesting findings on
the role of demographic anthropometric and clinical factors in the
determination of ovarian reserve Although these studies have employed
better-described samples some have small sample sizes and lack power for the
estimation of the effect of these factors Consequently studies on large and
well-characterised populations are necessary for evaluation of the determinants
of ovarian aging as well as to provide normative data for the individualisation
of the assessment of ovarian reserve
There have been reports of measurable disparities in the reproductive
aging and reproductive endocrinology between various ethnicities For
instance according to a large prospective study White Black and Hispanic
women reported higher rates of premature ovarian failure compared to
143
Chinese and Japanese (Luborsky et al 2002) In contrast the prevalence of
PCOS which is associated with higher ovarian reserve has been reported to be
significantly lower in Chinese (22) compared to British (8) women
(Michelmore et al 1999 Chen et al 2002) Although these disparities may
partially be due to the differences in the local diagnostic criteria it is plausible
to believe that the ethnicity may play a role in the determination of the
reproductive aging With regard to the effect of ethnicity to the markers of
ovarian reserve Seifer et al found that African American and Hispanic women
have lower AMH levels compared to White (Seifer et al 2009) In contrast
Randolph et al reported that African American women had significantly higher
ovarian reserve compared to that of White when determined by FSH
measurements (Randolph et al 2003) These studies indicate that ethnicity may
play a role in the determination of ovarian reserve and therefore warrants
further investigation which should include other major ethnic groups
Body weight appears to be closely associated with human female
reproduction which is evident by its effect on the natural fecundity as well as
the success of the assisted conception treatment cycles (Maheshwari et al
2007) Indeed the relationship of increased body mass index (BMI) and PCOS
is well described although the mechanism of this is not yet fully understood
Consequently a number of recent studies have assessed the effect of BMI to
the various aspects of reproductive endocrinology including ovarian reserve
Studies on the influence of BMI on the markers of ovarian reserve have
provided conflicting results probably due to the limited statistical power in
most of these studies and the difficulties encountered in properly accounting
for confounding factors such as age ethnicity and medical diagnosis (Buyuk et
al 2011 Freeman et al 2007 Su et al 2008 Seifer et al 2008 Sahmay et al 2012
Skalba et al 2011) Therefore there is a need for studies with large datasets and
good adjustment for confounding factors
We therefore designed and undertook a study to estimate the effect of
ethnicity BMI endometriosis and causes of infertility on ovarian reserve as
measured by AMH AFC and FSH using a robust dataset from a large cohort
of patients referred for infertility investigation and treatment in a single centre
144
METHODS
Objectives
The objectives of the study were to assess the role of the ethnicity BMI
and endometriosis and the causes of infertility on ovarian reserve as assessed
by the biomarkers AMH AFC and FSH using a large clinical data obtained
retrospectively
Sample
All women between 20 to 45 years of age referred to the Womenrsquos
Outpatient Department (WOP) and the Reproductive Medicine Department
(RMD) of Central Manchester University Hospitals NHS Foundation Trust for
management of infertility from 1 September 2008 to 16 November 2010 and
who had the measurement of AMH using DSL assay (DSL Active MISAMH
ELISA Diagnostic Systems Laboratories Webster Texas) were included in
this study Patients referred for fertility preservation (eg prior to or after the
treatment of a malignant disorder) and patients with a history of tubal or
ovarian surgery (salpingectomy ovarian cystectomy salpingo-oopherectomy)
and patients diagnosed with polycystic ovaries on ultrasound were excluded
The sample size was determined on pragmatic grounds and represents all
available patients meeting the inclusion criteria
Measurement of AMH
All patients referred to RMD had a measurement of AMH prior to
management of their infertility whereas the patients seen at WOP had AMH
measurements if they had a clinical indication for an assessment of ovarian
reserve
Blood samples for the measurement of AMH were taken at an initial
patient visit without regard to the day of the menstrual cycle and transported
to the in-house Biochemistry Laboratory All samples were processed and
analysed strictly according to the assay kit insert provided by the manufacturer
Serum samples were separated within two hours from venipuncture and frozen
at -20C until analysed in batches using the enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
145
100pmolL with a minimum detection limit of 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first
measurement was selected for this study
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the in-house
Biochemistry Laboratory within two hours of venipuncture for sample
processing and analysis Serum FSH levels were measured using specific
immunoassay kits (Cobas Roche Diagnostics Mannheim Germany) for use
on an autoanalyser platform (Roche Modular Analytics E170 Roche USA)
The intra-assay and inter-assay CVs were 60 and 68 respectively FSH
measurements in samples with high E2 levels (gt250) were defined as non-
representative of early follicular phase and were not included in this study
Where patients had repeated FSH measurements the measurement with the
closest date to that of AMH measurement was used
Measurement of AFC
Measurement of AFC was conducted in all patients undergoing assisted
conception The department uses a stringent protocol for the assessment of
AFC which consists of counting all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
Fully qualified sonographers conducted the ultrasound assessments Where
patients had repeated AFC measurements the AFC closest to the date of the
AMH measurement was used
Data collection
Data was extracted from hospital electronic clinical data management
systems and from written hospital notes of each patient AMH and FSH
measurements were obtained from the Biochemistry Department of the
hospital and validated by checking results of randomly selected 50 patients
146
against the results available in electronic clinical data management system
(Clinical Workstation) Data on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
surgery were gathered from the hospital case notes Data on the ethnicity was
obtained from the hospitalrsquos administrative database (PAS) The datasets were
merged using a unique patient identifier (hospital number) and the validity of
the linkage was validated using other patient identifiers (NHS number
patientrsquos name and date of birth)
Definitions and groups
In our hospital the ethnicity of the patient is established using a patient
questionnaire based on the UK census classification The body mass index
(BMI) of patients was categorised using NHS UK cut-off reference ranges
Underweight (lt185) Normal (185-249) Overweight (25-299) and Obese
(30-40) Causes of infertility were established by searching hospital records
including referral letters clinical entries and the letters generated following
initial and follow up clinic consultations Patients with a history of bilateral
tubal block which was confirmed by laparoscopy and dye test and patients
with a history of bilateral salpingectomy were categorised as having severe
tubal factor infertility Patients with unilateral tubal patency or unilateral
salpingectomy were categorised as having mild tubal factor infertility Patientrsquos
with laparoscopic diagnosis of stage III and Stage IV endometriosis (AFS)
were categorised as diagnosed with severe endometriosis whilst patients with
Stage I and Stage II endometriosis were allocated to group of mild
endometriosis Severe male factor infertility was defined as azoospermia or
severe oligospermia which necessitated Multiple Ejaculation Resuspension and
Centrifugation test (MERC) for assisted conception The criteria for MERC
were a) sperm count of lt05 mlnml or b) retrograde ejaculation Patients with
abnormal sperm count but who did not meet above criteria were classified as
mild male factor infertility
Statistical analysis
Firstly univariate analyses of the effect of age ethnicity BMI
endometriosis with and without endometrioma causes of infertility and
duration of infertility were conducted using two sample t test Then a
147
multivariate linear regression model that included age ethnicity BMI
endometriosis presence of endometrioma and the causes of infertility was
specified for the analyses of the effect of these factors to AMH AFC and
FSH Logarithmically transformed values were used for the statistical analysis
of AMH AFC and FSH The precise age on the day measurement of each of
the marker of ovarian reserve (AMH AFC and FSH) was used and age
adjustment utilised a quadratic function following centring to 30 years of age
Differences between the groups were considered significant at p005
Interactions between all explanatory variables were tested at a significance level
of plt001 In order to estimate the effect of BMI we fitted two different
models with a) BMI not included and b) BMI included in the model
Duration of infertility did not show any clinical or statistically significant
differences for any of the markers and therefore this variable was not included
in the models
RESULTS
In total 2946 patients were included in the study of whom 2880 of these
patient had valid AMH measurements 1810 had measurement of AFC and
2377 had FSH samples The mean and median age of patients were 328 (45)
and 332 (295 365) respectively and the distribution of patients according to
age categories ethnicity BMI endometriosis and the causes of infertility is
shown in the Table 1 The summary statistics for the markers of ovarian
reserve were as follows AMH mean 175 (501) median 142 (76-232) AFC
mean 139 (63) median 13 (10-17) and FSH mean 79 (72) median 7 (58-85)
As expected chronological age was found to be a significant determinant of all
markers of ovarian reserve We observed in average 5 decline in AMH levels
2 decline in AFC and 1 increase in FSH measurements per year (Table 2-
4)
Out of 2946 patients 2021 had data on BMI measurements and in 925
BMI was not available Table 5 describes age AMH AFC and FSH according
to the availability of data on BMI Distribution of patients by their ethnicity
and an availability of data on BMI is provided in Table 6 Similarly patient
distribution by diagnosis and availability of data on BMI can be found in Table
7
148
Ethnicity
The multivariable regression excluding BMI (Table 2) showed that
woman of Black ethnicity and the group defined as ldquoOther ethnicityrdquo had
significantly lower AMH measurements when compared to that of White (-25
p=0013 and -19 p=0047) and the overall ethnicity was a significant
predictor of AMH (p=0007) However inclusion of BMI in the model
reduced these effects and none of the groups had a statistically significant
difference in AMH levels compared to that of White and the overall effect of
ethnicity did not reach statistical significance (p=008)
AFC was significantly reduced in Pakistani and women of ldquoOther
ethnicitiesrdquo (Table 3) although the effect sizes were small (10-14) and the
overall effect of ethnicity was significant in the models with and without BMI
(p=004 and p=003) None of the groups showed statistically significant
differences in FSH (Table 4) although women of ldquoOther Asianrdquo ethnicity
appear to have lower FSH measurements (12) which was close to the level of
statistical significance (-12 p=007)
BMI
Obese women had 16 higher measurements of AMH (p=0035) and
overall effect of the BMI was significant (p=003) No interaction were
detected between BMI and ethnicity causes of infertility or diagnosis of
endometriosis suggesting that effect of BMI was independent of these factors
(Table 2)
In the analysis of the effect of BMI on AFC measurements we did not
observe any differences that were statistically significant (Table 3) However
FSH results showed that there is a modest association between BMI and FSH
with both overweight (Table 4) and obese women having significantly lower
FSH measurements compared to lean women (-5 p=0003 and -10
p=0003)
Endometriosis
In the absence of endometrioma endometriosis was associated with
lower AMH measurements although this did not reach statistical significance
149
(Table 2) Neither AFC nor FSH was significantly different in the
endometriosis group compared to those without endometriosis (Table 3-4)
In contrast we observed around 31 higher AMH levels in the patients
with endometrioma (p=0034) although this reduced to 21 and did not reach
statistical significance (p=010) in the smaller subset after adjustment for BMI
(Table 2) AFC and FSH did not show any statistically significant association
with endometrioma (Table 3-4)
Causes of Infertility
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to those with diagnosis
except the analysis that did not include BMI as a covariate which found a
weakly positive correlation (10 p=003) Similarly the estimation of the
effect of a diagnosis of unexplained infertility on AFC as well as FSH showed
that there were weak association between the markers and a diagnosis of
unexplained infertility (Table 2-4)
There were no significant differences in AMH AFC and FSH in women
with mild and severe tubal infertility in the models which included all
covariates other than weakly negative correlation between FSH and mild tubal
factor (Table 2-4)
Women diagnosed with male factor infertility had significantly higher
AMH and lower FSH measurements the effect sizes of which increased with
the severity of the diagnosis We did not find any significant difference in AFC
between patients with and without male factor infertility (Table 2-4)
DISCUSSION
This is first study investigating the effect of demographic
anthropometric and clinical factors on all three markers of ovarian reserve
using a large cohort of women of reproductive age Furthermore the statistical
analysis adjusted for relevant covariables using multivariable linear regression
models
150
Ethnicity
Our study found that amongst the main British ethnic groups the
effect of ethnicity on ovarian reserve measured using AMH AFC and FSH is
fairly weak and can be accounted for by differences in BMI between the
ethnic groups Recently studies have been published on the relationship of
ethnicity and markers of ovarian reserve all of which compared North
American populations One study which assessed a relatively small number of
women (n=102) at late reproductive age did not find a difference in AMH
levels between White and African American Women OR 123 (056 271
P=070) (Freeman et al 2007) In contrast Seifer et al reported that Black
(n=462) women had around 25 lower AMH measurements (P=0037)
compared to that of White (n=122) (Seifer et al 2009) which is not consistent
with our findings The main differences of this study compared to our study
were a) a majority were HIV infected women b) women were older (median
375 years of age) c) the analysis did not control for possible confounders
related to PCO reproductive pathology and surgery Furthermore unlike our
results the study did not find a correlation between BMI and AMH levels
Similarly Shuh-Huerta and colleagues reported that African American women
(n=200) had significantly lower AMH levels (P=000074) compared to that of
White (n=232) Mean AMH 22817 pmolL and 301+15 pmolL
respectively (Shuh-Huerta et al 2012b) Although the group used very stringent
selection of patients and statistical analysis BMI was not included in the
regression model Indeed our analysis without BMI in the model found similar
results (Table 2) But controlling for BMI has revealed no significant difference
in the AMH levels between White and Black ethnic groups
With regard to AFC measurements Shuh Huerta et al reported no
difference in the follicle counts between White (n=245) and African American
(n=202) women which supports our findings (Shuh-Huerta et al 2012b)
Again similar to our results the authors reported that FSH results of these
ethnic groups provided comparable results (Shuh-Huerta et al 2012a)
Although our results do not support some of previously published data
in view of above arguments we believe the ethnicity does not appear to play a
major role in determination of ovarian reserve However in view of the
discrepant findings of the currently available studies we suggest further studies
151
in large and diverse cohorts should be carried out in order to fully understand
the role of ethnicity
BMI
Our results show that BMI has direct correlation with AMH and AFC
and negative correlation with FSH suggesting women with increased BMI are
likely to have higher ovarian reserve The effect of this association was
significant in the analysis of AMH and FSH obese women appear to have
approximately 16 higher AMH and 10 lower FSH measurements when
compared to women with normal BMI Although the difference in AFC
measurements did not reach statistical significance there was direct correlation
between AFC and BMI
Published data on the effect of BMI to AMH levels provide conflicting
results compared to our study given the studies reported either no association
(Buyuk et al 2011 Freeman et al 2007 Su et al 2008) or a negative correlation
between these factors (Seifer et al 2008 Sahmay et al 2012 Skalba et al 2011)
However most of these studies assessed peremenopausal women or that of
late reproductive age Indeed the studies evaluated the effect of BMI to AMH
measurements in women of reproductive age demonstrated that lower AMH
levels in obese women were due to age rather than increased BMI (La Marca
et al 2012 Streuli et al 2012) Furthermore most of these studies either
employed univariate analysis or multivariate regression models that did not
included all relevant explanatory factors In addition these studies had
significantly smaller numbers of samples ranging from 10 to 809 compared to
our study (n=1953) Indeed other large study (n=3302) with multivariate
analysis supports our findings on the effect of BMI on ovarian reserve
indicating obese women have significantly lower FSH levels (Randolph et al
2004)
Endometriosis
Here we present data on the measurement of all three main markers of
ovarian reserve in women with endometriosis We observed that women with
endometriosis without endometrioma did not have significantly different
AMH AFC or FSH measurements compared to women that do not have this
pathology Intriguingly women who had endometriosis with endometriomata
152
tended to have higher AMH levels Given the data was collected
retrospectively we did not have full information on laparoscopic staging of
endometriosis in all patients and therefore an analysis according to severity or
staging of endometriosis was not feasible However the analysis controlled for
the important variables mentioned above and importantly only included the
patients without previous history of ovarian surgery We therefore we believe
the study provides fairly robust data on relationship of endometriosis and the
markers of ovarian reserve
Although it is generally believed that endometriosis has a damaging
effect on ovarian reserve published literature provides conflicting views
ranging from no correlation between these factors to a significant negative
effect of endometriosis As mentioned above most studies were small and
used matched comparison of patients with endometriosis to control group
using retrospectively collected data Carvalho et al compared women with
endometriosis (n=27) and to that of male factor infertility (n=50) and reported
there was no difference in basal AMH and AFC levels whilst FSH levels of
women with endometriosis was lower Another small study which used similar
methodology where an endometriosis group (n=17) was compared to patients
with tubal factor infertility (n=17) reported opposite results suggesting
endometriosis was associated with lower AMH measurements and there was
no correlation between the pathology and FSH or AFC (Lemos et al 2007)
Shebl et al compared AMH results of women with endometriosis (n=153) to a
matched group that did not have the pathology (n=306) and reported that
women with mild endometriosis had similar AMH levels whereas the group
with severe endometriosis had significantly lower AMH compared to the
control group (Shebl et al 2009) Although using well-matched control groups
is a robust study design direct comparison of the two groups without
controlling for other important covariables may result in inaccurate results
Indeed the study that used multivariate regression analysis was able to
demonstrate that there are number of factors that can affect AMH results and
indeed following controlling for these factors there was no difference between
AMH results of women with endometriosis compared to that of without
disease (Streuli et al 2012) In view of above considerations we believe the
effect of endometriosis to ovarian reserve is poorly understood and warrants
further investigation
153
Regarding the effect of endometrioma on AMH levels current evidence
is conflicting Using univariate analysis without controlling for confounders
Uncu et al reported that women with endometrioma (n=30) had significantly
lower AMH and AFC measurements compared to control (n=30) women
(Uncu et al 2013) Similarly Hwu et al reported that women with
endometrioma (n=141) had significantly lower AMH measurements compared
to that of without pathology (n=1323) pathology (Hwu et al 2013) However
the study population appears to have a disproportionately higher number of
women with history of previous and current history of endometrioma
(3191642) compared to any published studies and therefore the study may
have been subject of selection bias
Kim et al reported lower AMH measurements in women with
endometrioma (n=102) compared to control group (102) meanplusmnSEM
29plusmn03 ngmL_vs 33plusmn03_ngmL although this did not reach statistical
significance (P=028)
In our view the most robust data on measurement of AMH in women
with endometriosis was published by Streuli et al which compared AMH levels
of 313 women with laparoscopically and histologically confirmed
endometriosis to 413 women without pathology (Streuli et al 2009) The group
with endometriosis consisted of women with superficial peritoneal
endometriosis (n=35) deep infiltrating endometriosis (n=183) and ovarian
endometrioma (n=95) and relevant factors such as age parity smoking and
previous ovarian surgery were adjusted for using multivariate regression
analysis In keeping with our findings women with endometriosis did not have
lower AMH levels except for patients with previous history of surgery for
endometrioma Most interestingly the findings of Streuili et al and this study
suggest that women with ovarian endometrioma do not have low AMH levels
In contrast according to our data the presence of endometrioma may be
associated with a significant increase in serum AMH levels Given that an
endometrioma is believed to cause significant damage to ovarian stroma this is
an interesting finding Increased AMH levels in the presence of endometrioma
may be due to acceleration in the rate of recruitment of primordial follicles
andor increased expression of AMH in granulosa cells Furthermore
increased AMH levels in these patients may be due to expressions of AMH in
endometriotic cells A study by Wang et al suggested that AMH is secreted by
human endometrial cells in-vitro (Wang et al 2009) This was the first report of
154
non-ovarian secretion of AMH and suggested that AMH may play important
role in regulation of the function of the human endometrium Subsequently
the findings of Wang et al were independently confirmed by two different
groups Carrarelli et al assessed expression of AMH and AMH type II receptor
(AMHRII) in specimens of endometrium obtained by hysteroscopy from
patients with endometriosis (n=55) and from healthy (n=45) controls
(Carrarelli et al 2014) The study also assessed specimens from patients with
ovarian endometriosis (n=29) and deep peritoneal endometriosis (n=26) The
study found that both AMH and AMHRII were expressed in endometrium
Interestingly ectopic endometrium obtained from patients with endometriosis
had significantly higher AMH and AMHRII levels compared to that of healthy
individuals Furthermore the specimens collected from ovarian and deep
endometriosis had highest AMH and AMHII mRNA expression These
findings confirm that AMH as well as AMHRII are expressed in human
endometrium and AMH may play a role in pathophysiology of endometriosis
A further study conducted by Signorile et al also confirmed expression of
AMH and AMHRII in human endometriosis glands Furthermore the study
found that treatment of endometriosis cells with AMH resulted in a decrease in
cell growth suggesting that AMH may inhibit the growth of endometriotic
cells This suggests that further studies to understand the role of AMH in
pathophysiology of endometriosis are warranted
Causes of infertility
Unlike the above-mentioned factors the association of the various
causes of infertility and the markers of ovarian reserve are poorly studied
Therefore our study appears to provide only available data on AMH AFC and
FSH levels in women with three main causes of infertility unexplained tubal
and male factor
In our study AMH levels of women with unexplained infertility did not
differ from those with a diagnosis Similarly the effect of a diagnosis on AFC
and FSH measurements were weak Women with unexplained infertility do not
have any significant pathology that can account for their infertility However
understanding the role of ovarian reserve in these patients is important Our
study suggests that women with unexplained infertility have comparable AMH
levels to other infertile women
155
We did not find any significant differences in AMH AFC or FSH
measurements of women diagnosed with tubal factor infertility compared to
infertile women without tubal disease Pelvic inflammatory disease and
endometriosis are well known causes of tubal pathology and our regression
model has controlled for the effect of endometriosis in these analyses Our
results suggest that despite having damaging effect to the tubes pelvic
infection does not reduce ovarian reserve
In contrast our analyses showed that women with mild and severe male
factor infertility have significantly increased AMH and lower FSH
measurements which demonstrates that these women have better ovarian
reserve compared to general infertility population
Strengths and Limitations of the study
The study is based on retrospectively collected data and therefore was
subject to the issues related to this methodology However we believe that we
have overcome most problems and improved the validity of our results by
using a robust methodology for data collection large sample size and careful
analysis We included all women who presented during the study period and
met the inclusion criteria of the study Importantly women with previous
history of PCO chemotherapy radiotherapy tubal surgery or ovarian surgery
have been excluded from the study given these factors may have significant
acute impact on ovarian reserve effect of which may be difficult to control for
The analysis showed an interaction between BMI and ethnicity which
could not be explored fully due to missing data on BMI (Tables 6) Therefore
analyses with and without BMI in models have been performed (Tables 2-4)
and the distribution of patients according to availability of data on BMI has
been obtained (Tables 5-7) I suggest further studies with sufficient data should
explore this interaction
I was not able to establish the patients that meet Rotterdam criteria for
diagnosis of PCOS given data on menstrual history and biochemical
assessment of androgenemia were not available Therefore ultrasound
diagnosis of PCO was used to categories patients with polycystic ovaries and
all patients with PCO were excluded from analysis
It is important to note that measurement of AMH using Gen II assay may
provide erroneous results (Rustamov et al 2012a) Therefore only samples
156
obtained using DSL assay have been included in the study The DSL assay
appears to provide more reproducible results than the Gen II assay (Rustamov
et al 2011 and Rustamov et al 2012a) and therefore we believe the estimates
in this study reflect the relationship between circulating AMH and the above
factors
SUMMARY
Our data suggests that there is no strong association between ethnicity
and AMH AFC or FSH whilst women with increased BMI appear to have
higher ovarian reserve There was no evidence of reduced ovarian reserve in
women with endometriosis who do not have a previous history of ovarian
surgery In contrast women with current history of endometrioma may have
higher AMH levels which warrants further investigation Women with a
history of unexplained infertility do not appear to have reduced ovarian
reserve as measured with AMH AFC and FSH compared to general infertile
women Similarly women with tubal factor infertility have comparable ovarian
reserve with women who do not have tubal disease In contrast women with
male factor infertility have significantly higher ovarian reserve compared to
infertile women who do not have male factor infertility
This study has elucidated the effect of demographic anthropometric and
clinical factors on all commonly used markers of ovarian reserve and
demonstrated that some of these factors have significant impact on ovarian
reserve
157
References Buyuk E Seifer DB Illions E Grazi RV and Lieman H Elevated body mass index is associated with lower serum anti-mullerian hormone levels in infertile women with diminished ovarian reserve but not with normal ovarian reserve Fertility and Sterility_ Vol 95 No 7 June 2011 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 2014 1011353ndash8 de Carvalho BR Rosa-e-Silva AC Rosa-e-Silva JC dos Reis RM Ferriani RA de Saacute MFIncreased basal FSH levels as predictors of low-quality follicles in infertile women with endometriosis International Journal of Gynecology and Obstetrics 110 (2010) 208ndash212 Doacutelleman M Verschuren W M M Eijkemans M J C Dolle M E T Jansen E H J M Broekmans F J M and van der Schouw Y T Reproductive and Lifestyle Determinants of Anti-Mullerian Hormone in a Large Population-based Study J Clin Endocrinol Metab May 2013 98(5) 2106ndash2115 Freeman EW Gracia CR Sammel MD Lin H Lim LC Strauss JF 3rd Association of anti-mullerian hormone levels with obesity in late reproductive-age women Fertil Steril 2007 87101-6 Halawaty S ElKattan E Azab H ElGhamry N Al-Inany H Effect of obesity on parameters of ovarian reserve in premenopausal women J Obstet Gynaecol Can 2010 32687ndash690 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699-708 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 2011 95170ndash5 Hwu Y Wu FS Li S Sun F Lin M and Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reproductive Biology and Endocrinology 2011 980 Kelsey TW Wright P Nelson SM Anderson RA Wallace WHB (2011) A Validated Model of Serum Anti-Muumlllerian Hormone from Conception to Menopause PLoS ONE 6(7) e22024 Kim MJ Byung Chul Jee Chang Suk Suh and Kim SH Preoperative Serum Anti-Mullerian Hormone Level in Women with Ovarian Endometrioma and Mature Cystic Teratoma Yonsei Med J Volume 54 Number 4 July 2013 La Marca A Sighinolfi G Papaleo E Cagnacci A Volpe A et al (2013) Prediction of Age at Menopause from Assessment of Ovarian Reserve May Be
158
Improved by Using Body Mass Index and Smoking Status PLoS ONE 8(3) e57005 Lemos NA Arbo E Scalco R Weiler E Rosa V Cunha-Filho JS Decreased anti-Muumlllerian hormone and altered ovarian follicular cohort in infertile patients with mildminimal endometriosis Fertil Steril 2008 May 89(5)1064-8 Luborsky JL Meyer P Sowers MF Gold EB Santoro N Premature menopause in a multi-ethnic population study of the menopause transition Hum Reprod 200218199-206 Maheshwari A Stofberg L Bhattacharya S Effect of overweight and obesity on assisted reproductive technologymdasha systematic review Hum Reprod Update 200713433ndash44 Michelmore K Balen A Dunger D Vessey M Polycystic ovaries and associated clinical and biochemical features in young women Clin Endocrinol (Oxf) 199951779-86 Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 95736-741 e731-7332011 Chen X Yang D Mo Y Li L Chen Y Huang Y Prevalence of polycystic ovary syndrome in unselected women from southern China Eur J Obstet Gynecol Reprod Biol 2008 13959-64 Randolph JF Sowers M Gold EB Mohr BA Luborsky J Santoro M et al Reproductive hormones in early menopausal transition relationship to ethnicity body size and menopausalstatus J Clin Endocrinol Metab Apr2003 88(4)1516ndash1522 [PubMed 12679432] Sahmay S Usta T Erel CT Imamoğlu M Kuuk M Atakul N Seyisoğlu H Is there any correlation between amh and obesity in premenopausal women Arch Gynecol Obstet 2012 Sep 286(3) 661-5 Seifer DB Baker VL and Leader B Age-specific serum anti-Meuroullerian hormone values for 17120 women presenting to fertility centers within the United States Fertility and Sterility_ Vol 95 No 2 February 2011 Seifer DB Golub ET Lambert-Messerlian G Benning L Anastos K Watts H Cohen MH Karim R Young MA Minkoff H and Greenblatt RM Variations in Serum Mullerian Inhibiting Substance Between White Black and Hispanic Women Fertil Steril 2009 November 92(5) 1674ndash1678 Shebl O Ebner T Sir A Schreier-Lechner E Mayer RB Tews GSommergruber M Age-related distribution of basal serum AMH level in women of reproductive age and a presumably healthy cohort Fertil Steril 2011 95 832ndash834
159
Shebl O Ebner T Sommergruber M Sir A Tews G Anti muellerian hormone serum levels in women with endometriosis a case-control study Gynecol Endocrinol 200925713-6 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities Hum Genet (2012b) 1311709ndash1724 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Skałba P Cygal A Madej P Dabkowska-Huc A Sikora J Martirosian G Romanik M Olszanecka-Glinianowicz M Is the plasma anti-Mullerian hormone (AMH) level associated with body weight and metabolic and hormonal disturbances in women with and without polycystic ovary syndrome European Journal of Obstetrics amp Gynecology and Reproductive Biology 158 (2011) 254ndash259 Streuli I de Ziegler D Gayet V Santulli P Bijaoui G de Mouzon J and Chapron C In women with endometriosis anti-Mullerian hormone levels are decreased only in those with previous endometrioma surgery Human Reproduction Vol27 No11 pp 3294ndash3303 2012 Su IH Sammel MD Freeman EW Lin H DeBlasis T Gracia C Body size affects measures of ovarian reserve in late reproductive age women Menopause 2008 15(5) 857ndash861 Uncu G Kasapoglu I Ozerkan K Seyhan A Oral Yilmaztepe A Ata B Prospective assessment of the impact of endometriomas and their removal on ovarian reserve and determinants of the rate of decline in ovarian reserve Hum Reprod 2013 Aug 28(8) 2140-5 Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wallace WHB Kelsey TW (2010) Human Ovarian Reserve from Conception to the Menopause PLoS ONE 5(1) e87
160
Table 1 Distribution of patients
AMH AFC FSH
n Mean (SD) n Mean (SD) n Mean (SD)
All 2880 175150 1810 13972 2377 7972
Ethnicity
White (Reference) 1833 169139 1222 13959 1556 7966
Other White 137 172131 85 14480 107 7637
Black 93 202208 43 16098 73 104135
Indian 108 216169 69 14360 94 7127
Other Asian 46 194157 30 14560 41 6717
Pakistani 276 201164 166 14375 232 81124
Other ethnic 103 158130 63 12448 83 7640
Not disclosed 220 170152 114 13161 157 7937
Data not available 64 183251 18 11952 34 8956
Patients with BMI
Normal (Reference) 1110 172137 917 13861 1011 7844
Underweight 38 179136 30 13947 38 7751
Overweight 679 168134 546 13763 620 7544
Obese 149 220209 90 14167 119 7142
Data not available 904 177163 227 14967 589 88123
Diagnosis
Unexplained 894 156120 667 13354 801 7632
Mild tubal 411 172158 284 13771 370 7530
Severe tubal 40 12685 27 13658 38 7827
Mild male 779 181134 538 14058 668 7342
Severe male 356 198135 197 14661 208 6818
Endometriosis ndash endometrioma 141 137108 91 13658 122 8341
Endometriosis + endometrioma 46 196159 15 14449 42 7123
161
Table 2 Regression models for AMH
AMH (Log)
BMI included
n=1952
BMI excluded
n=2816
Β 95 CI P β 95 CI P
Age -0057 -0069 -0045 00001 -0056 -0067 -0046 00001
age2 -0003 -0005 -0001 00001 -0004 -0006 -0003 00001
Ethnicity 00812 00079
Other White -0046 -0226 0133 0611 0038 -0131 0208 0658
Black 0209 -0038 0457 0097 -0259 -0464 -0054 0013
Indian 0032 -0164 0228 0749 0119 -0071 0310 022
Other Asian 0292 -0014 0598 0061 0250 -0037 0537 0088
Pakistani -0116 -0251 0017 0089 -0100 -0226 0025 0118
Other ethnic -0174 -0390 0041 0113 -0197 -0392 -0002 0047
Not disclosed -0002 -0162 0157 0977 -0104 -0241 0033 0138
BMI 00374
Underweight -0107 -0394 0179 0462
Overweight -0058 -0143 0025 017
Obese 0165 00119 0318 0035
Diagnosis
Unexplained 0039 -0073 0152 0492 0105 0007 0204 0035
Mild tubal 0089 -0033 0212 0153 0113 -000009 0226 005
Severe tubal -0168 -0463 0126 0264 -0133 -0444 0177 0401
Mild male 0118 0009 0227 0033 0180 0084 0275 00001
Severe male 0245 0096 0395 0001 0287 0162 0412 00001
Endometriosis -0136 -0311 0037 0124 -0152 -0324 0018 0081
Endometrioma 0217 -0068 0503 0136 0314 0023 0606 0034
_cons 2731 2616 2847 0 2658 2567 2750 0
162
Table 3 Regression models for AFC
AFC (Log)
BMI Included
n=1589
BMI Excluded
n=1810
Β 95 CI P Β 95 CI P
Age -0028 -0035 -0021 0 -0027 -0033 -0021 0
age2 000009 -00009 0001 086 000007 -00008 0001 0885
Ethnicity 00265 00383
Other White -0024 -0119 0070 0614 0003 -0087 0094 0942
Black 0093 -0037 0224 0162 0049 -0075 0175 0436
Indian -0042 -0148 0064 0438 -0035 -0136 0065 0492
Other Asian 0037 -0125 0200 0651 0037 -0114 0189 0626
Pakistani -0095 -0166 -0024 0008 -0083 -0151 -0015 0016
Other ethnic -0142 -0253 -0031 0012 -0132 -0237 -0027 0013
Not disclosed -0008 -0094 0078 0853 -0067 -0148 0012 0098
BMI 07713
Underweight -0040 -0190 0109 0599
Overweight -0018 -0062 0024 0398
Obese 0012 -0077 0103 0779
Diagnosis
Unexplained -0071 -0131 -0011 0019 -0065 -0121 -0009 0021
Mild tubal -0047 -0112 0017 0151 -0060 -0121 00003 0051
Severe tubal -0110 -0267 0045 0164 -0141 -0294 0010 0069
Mild male -0037 -0095 0020 0201 -0027 -0081 0025 0307
Severe male 0007 -0071 0086 0853 -0021 -0093 0050 0563
Endometriosis -0019 -0114 0076 0691 -0004 -0096 0087 0922
Endometrioma -0079 -0215 0055 0248 -0106 -0231 0019 0097
_cons 2694 2632 2755 0 2691 2636 2745 0
163
Table 4 Regression models for FSH
FSH (Log)
BMI Included
n=1772
BMI Excluded n=2343
Β 95 CI P Β 95 CI P
age 0009 0003 0014 0001 0009 0004 0014 00001
age2 00009 00001 0001 0019 0001 00003 0001 0003
Ethnicity 04415 03329
Other White 0034 -0046 0114 0403 -0017 -0099 0065 0685
Black 0043 -0065 0153 043 0068 -0030 0167 0175
Indian -0010 -0097 0076 0808 -0070 -0157 0017 0116
Other Asian -0119 -0250 0011 0074 -0104 -0234 0026 0117
Pakistani -0031 -0089 0026 029 -0014 -0073 0045 064
Other ethnic 0031 -0062 0125 0508 -0002 -0095 0090 0962
Not disclosed 0022 -0049 0093 0541 0026 -0042 0095 045
BMI 00017
Underweight -0070 -0189 0048 0246
Overweight -0055 -0091 -0018 0003
Obese -0106 -0176 -0036 0003
Diagnosis
Unexplained -0055 -0104 -0006 0028 -0055 -0101 -0009 0018
Mild tubal -0052 -0105 000008 005 -0050 -0103 0001 0056
Severe tubal 0004 -0118 0127 0943 0016 -0120 0154 0809
Mild male -0084 -0132 -0037 00001 -0071 -0116 -0026 0002
Severe male -0127 -0196 -0059 00001 -0102 -0168 -0036 0002
Endometriosis 0035 -0039 0111 0353 0044 -0034 0124 0268
Endometrioma -0074 -0196 0047 0229 -0056 -0186 0074 0402
_cons 1999 1948 2049 0 1958 1915 2002 0
164
Table 5 Distribution of patient characteristics by availability of data on BMI The number of observations and mean (SD) of the markers of ovarian reserve (Age AMH AFC and FSH) described according to an availability of data on BMI
BMI (+)
BMI (-) Total
n Mean (SD) n Mean (SD) n Mean (SD)
Age 1976 32944 904 32750 2880 32946
AMH 1976 175144 904 178164 2880 176150
AFC 1583 13862 227 14968 1810 14063
FSH 1788 7744 589 88123 2377 8073
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
165
Table 6 Distribution of ethnicity by availability of data on BMI Distribution of the number of observations by ethnicity and availability of data on BMI
AMH AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
BMI (+) BMI (-) Total
White 1308 525 1833 1070 152 1222 1201 355 1556
Other White 97 40 137 76 9 85 83 24 107
Black 50 43 93 39 4 43 44 29 73
Indian 81 27 108 60 9 69 70 24 94
Other Asian 32 14 46 25 5 30 30 11 41
Pakistani 193 83 276 148 18 166 177 55 232
Other ethnic 66 37 103 55 8 63 60 23 83
Not disclosed 125 95 220 95 19 114 107 50 157
Data not available 24 40 64 15 3 18 16 18 34
Total 1976 904 2880 1583 227 1810 1788 589 2377
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
166
Table 7 Distribution of diagnosis by availability of data on BMI Distribution of number of observations in each diagnosis group tabulated by availability of data on BMI
AMH
AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
Unexplained 730 164 894 611 56 667 672 129 801
Mild tubal 319 92 411 258 26 284 298 72 370
Severe tubal 36 4 40 26 1 27 36 2 38
Mild male 567 212 779 461 77 538 525 143 668
Severe male 196 160 356 161 36 197 153 55 208
Endometriosis ndash endometrioma 112 29 141 83 8 91 101 21 122
Endometriosis + endometrioma 38 8 46 38 8 46 36 6 42
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
167
THE EFFECT OF SALPINGECTOMY
OVARIAN CYSTECTOMY AND UNILATERAL
SALPINGOOPHERECTOMY ON OVARIAN
RESERVE
Oybek Rustamov Monica Krishnan
Stephen A Roberts Cheryl Fitzgerald
To be submitted to Gynecological Surgery
52
168
Title
Effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Stephen A Robertsc Cheryl Fitzgeralda
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable Word count 2904
Acknowledgement
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
169
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all
versions of the manuscript MK assisted in data extraction contributed in
discussion and the review of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed in discussion
and reviewed all versions of the manuscript
170
ABSTRACT
Objective
To estimate the effect of salpingectomy ovarian cystectomy and unilateral
salpingo-oopherectomy on ovarian reserve
Design
Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 3179 patients were included in the study The AMH measurements
of 66 women were excluded due to haemolysed samples or delay in processing
the samples leaving 3113 women for analysis There were 138 women who
had unilateral or bilateral salpingectomy 36 women with history of unilateral
salpingo-oopherectomy 41 women with history of cystectomy for ovarian
cysts that other than endometrioma and 40 women had cystectomy for
endometrioma
Interventions
Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH basal serum FSH and basal AFC measurements
Results
The analysis did not find any significant differences in AMH (9 p=033)
AFC (-2 p=059) and FSH (-14 p=021) measurements between women
with a history of salpingectomy and those without history of surgery Women
with history of unilateral salpingo-oopherectomy were found to have
significantly lower AMH (-54 p=0001) and AFC (-28 p=034) and
increased FSH (14 p=006) The study did not find any significant
171
association between a previous history of ovarian cystectomy that was for
conditions other than endometrioma and AMH (7 p=062) AFC (13
p=018) or FSH (11 p=016) The analysis of the effect of ovarian
cystectomy for endometrioma showed that women with history of surgery had
around 66 lower AMH (p=0002) Surgery for endometrioma did not
significantly affect AFC (14 p=022) or FSH (10 p=028)
Conclusions
Salpingo-oopherectomy and ovarian cystectomy for endometrioma have a
significant detrimental impact on ovarian reserve Neither salpingectomy nor
ovarian cystectomy for cysts other than endometrioma has an appreciable
effect on ovarian reserve
Key Words
Salpingectomy Ovarian cystectomy Salpingo-oopherectomy ovarian reserve
AMH AFC FSH
172
INTRODUCTION
Human ovarian reserve is determined by the size of oocyte pool at birth
and decline in the oocyte numbers thereafter Both of these processes are
largely under the influence of genetic factors and to date no effective
interventions are available to improve physiological ovarian reserve (Shuh-
Huerta et al 2012) However various other environmental pathological and
iatrogenic factors appear to play a role in the determination of ovarian reserve
and consequently it may be influenced either directly or indirectly Evidently
the use of chemotherapeutic agents certain radio-therapeutic modalities and
surgical interventions that damage ovarian parenchyma can cause substantial
damage to ovarian reserve (Nielsen et al 2013 Somigliana et al 2012)
Estimation of the effect of each of these interventions is of paramount
importance in ascertainment of lesser ootoxic treatment modalities and safer
surgical methods
Age is the main determinant of the number of non-growing follicles
accounting for 84 of its variation and used as marker of ovarian reserve
(Hansen et al 2008) However biomarkers that allow direct assessment of the
dynamics of growing follicles AMH and AFC may provide more accurate
estimation of ovarian reserve Although these markers only reflect
folliculogenesis of already recruited growing follicles there appears to be a
good correlation between their measurements and histologically determined
total ovarian reserve (Hansen et al 2011) Thus the biomarkers can effectively
be utilized for estimation of the effect of above adverse factors on the
primordial oocyte pool
Surgical interventions that lead to disruption of the blood supply to
ovaries or involve direct damage to ovarian tissue may be expected to lead to a
reduction in the primordial follicle pool Indeed a number of studies have
reported an association between surgical interventions to ovaries and reduction
in ovarian reserve (Somigliana et al 2012) However given both underlying
disease and surgery may affect ovarian reserve disentanglement of the
individual effects of these factors may be challenging and requires robust
research methodology Here we present a study that intended to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease
173
METHODS
The effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve were studied using serum AMH AFC and
FSH measurements in a large cross sectional study
Population
All women between the ages of 20 to 45 who were referred to the
Womenrsquos Outpatient Department (WOP) and the Reproductive Medicine
Department (RMD) of Central Manchester University Hospitals NHS
Foundation Trust for management of infertility between 1 September 2008
and 16 November 2010 and had an AMH measurement using the DSL assay
(DSL Active MISAMH ELISA Diagnostic Systems Laboratories Webster
Texas) were included We excluded patients referred for fertility preservation
(eg prior to or after treatment for a malignant disorder) and those with a
diagnosis of polycystic ovaries (PCO) on transvaginal ultrasound scan which
was defined as volume of one or both ovaries more than 10ml Patients with
haemolysed AMH andor FSH samples were not included in the analysis of
these markers Non-smoking is an essential criteria for investigation prior to
assisted conception and therefore to our best knowledge our population
consisted of non-smokers
Measurement of AMH
Blood samples for AMH were taken without regard to the day of
womenrsquos menstrual cycle and serum samples were separated within two hours
of venipuncture in the Biochemistry laboratory of our hospital All samples
were processed strictly according to the manufacturerrsquos recommendations and
frozen at -20C until analysed in batches using the enzymatically amplified two-
site immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
100pmolL and a minimum detection limit was 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first AMH of
the patients were selected
174
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the Biochemistry
Laboratory within two hours of venipuncture for sample processing and
analysis Specific immunoassay kits (Cobas Roche Diagnostics Mannheim
Germany) and an autoanalyser platform was used (Roche Modular Analytics
E170 Roche USA) for analysis of FSH The intra-assay CV was 60 and
inter-assay CV was 68 The FSH measurements in the samples with high E2
levels (gt250pmolL) were excluded from the analysis given these samples are
likely to have been taken outside of early follicular phase of menstrual cycle
In patients with repeated FSH measurements measurements conducted on the
same day as first AMH were selected If the patient did not have FSH
measurement on the day of AMH sampling the measurement with the closest
date to first AMH sample was selected
Measurement of AFC
Measurement of AFC is conducted in patients referred for assisted
conception during their initial work up Our department uses a stringent
protocol for the assessment of AFC and qualified radiographers who have
undergone specific training on measurement of AFC The methodology
consists of counting of all antral follicles measuring 2-6mm in longitudinal and
transverse cross sections of both ovaries using transvaginal ultrasound
scanning at early follicular phase (Day 0-5) of the menstrual cycle The AFC
measurement with the closest date to first AMH sample was selected
Data collection
Data was extracted from electronic clinical data management systems
and from information held in written hospital notes for each patient Data on
AMH and FSH measurements were obtained from the Biochemistry
Department and validated by checking the results documented in the hospital
case notes of randomly selected 50 patients against the results obtained from
electronic clinical data management system (Clinical Workstation) finding
100 concordance Information on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
175
surgery were obtained from the hospital case notes The ethnicity of the
patients was established using a patient questionnaire and data were extracted
from the hospital database for the patient demographics (PAS)
Definitions and groups
First the datasets were merged using a unique patient identifier (hospital
number) Validation of the merger using additional patient identifiers (NHS
number name date of birth) revealed existence of duplicate hospital numbers
in patients transferred from secondary care infertility services of our hospital to
IVF Department We established that in our datasets combination of the
patientrsquos first name surname and date of birth in a continuous string variable
could be used as a unique identifier Hence we used this identifier to merge all
datasets achieving a robust merger of all independent datasets into a combined
final dataset Following creation of an anonymised a unique study number for
each patient all patient identifiers were dropped and the anonymised
combined dataset was used for the analysis
Body mass index (BMI) of patients was categorized using standard NHS
cut-off reference ranges Underweight (lt185) Normal (185-249)
Overweight (25-299) and Obese (30-40) (The Office for National Statistics
2011) Causes of infertility were established by searching the hospital notes
including the referral letters clinical notes and letters generated following clinic
consultations Patients with history of bilateral tubal block which was
confirmed by laparoscopic dye test and patients with history of bilateral
salpingectomy were categorized as having severe tubal factor infertility
Patients with unilateral tubal patency or unilateral salpingectomy were
categorized as having mild tubal factor infertility Severe male factor infertility
was defined as azoospermia or severe oligospermia (lt1mln sperm sample)
Patients with abnormal sperm count but do not meet above criteria were
classified as having mild male factor infertility
Patients with reproductive surgery were categorized as having history of
salpingectomy cystectomy for endometrioma cystectomy for ovarian cysts
other than endometrioma or unilateral salpingo-oopherectomy First
measurement of AMH AFC and FSH following surgery was selected for the
study
176
Statistical analysis
A multivariable regression model that included age ethnicity BMI
endometriosis presence of endometrioma the causes of infertility tubal and
ovarian surgery was fitted for each of the ovarian reserve markers AMH AFC
and FSH Difference between the groups were considered significant at
p005 Preliminary analysis of AMH AFC and FSH indicated that
logarithmically transformed values with a quadratic age term provided adequate
fits The precise age on the day measurement of each of the marker of ovarian
reserve (AMH AFC and FSH) was included in the model as a quadratic
function following centering to 30 years of age
Interactions between all explanatory variables were tested at a
significance level of 001 We observed significant interaction between BMI
and other covariates This may be due to biological complexity in the
relationship of BMI and other factors (eg ethnicity) in determination of
ovarian reserve However given data on BMI was not available in considerable
number of patients the observed interactions may be due to limitation of our
dataset Therefore in order to assist in interpretation of the results analyses
with and without BMI in the models were conducted
RESULTS
In total 3179 patients were included in the study The AMH
measurements of 66 women were excluded due to haemolysed samples or
delay in processing the samples leaving 3113 women for analysis 1934 of
patients had measurement of AFC and 2580 had FSH samples that met
inclusion criteria The mean age AMH AFC and FSH of patients were
328plusmn45 173plusmn148 139plusmn62 80plusmn75 respectively There were 138 women
who had unilateral or bilateral salpingectomy 36 women with history of
unilateral salpingo-oopherectomy 41 women with history of cystectomy for
ovarian cysts that other than endometrioma and 40 women had cystectomy for
endometrioma (Table 1) The results of regression analysis on the effect of
reproductive surgery on AMH AFC and FSH measurements are shown in
Table 2
The analysis did not find any significant differences in AMH (9
p=033) AFC (-2 p=059) and FSH (-14 p=021) measurements in
women with history of salpingectomy compared to women without history of
177
surgery and we did not observe marked change in the estimates in a smaller
subset where BMI was included in the model (Table 2)
Women with history of unilateral salpingo-oopherectomy were found
to have significantly lower AMH (-54 p=0001) and AFC (-28 p=034)
and increased FSH (14 p=006) measurements where effect on AMH
reached the level of statistical significance Similarly the analysis of the model
that included BMI showed significantly lower AMH and AFC and higher FSH
measurements in surgery group where both AMH and FSH analysis were
statistically significant (Table 2)
The study did not find a significant association between previous
history of ovarian cystectomy that was for disease other than endometrioma
and measurement of AMH (7 p=062) AFC (13 p=018) or FSH (11
p=016) which did not change noticeably following adding BMI in the model
(Table 2)
The analysis of the effect of ovarian cystectomy for endometrioma
showed that women with history of surgery had around 66 lower AMH
(p=0002) measurements The effect of surgery for endometrioma was not
significant in assessment of AFC (14 p=022) and FSH (10 p=028)
However in the model with BMI association of the surgery with both AMH (-
64 p=0005) and FSH (24 p=0015) were found to be significant (Table
2)
DISUCUSSION
Salpingectomy
The blood supply to human ovaries is maintained by the direct branches
of aorta ovarian arteries which form anastomoses with ovarian and tubal
branch of uterine arteries in mesovarium and mesosalpynx In salpingectomy
often tubal branches of uterine arteries are excised alongside mesosalpynx and
hence it is believed disruption to blood supply to ovaries may lead to a
reduction of ovarian reserve However in our study we did not observe an
appreciable association between salpingectomy and any of the biomarkers of
ovarian reserve suggesting this surgery does not appreciably affect ovarian
reserve These findings are supported by study that assessed the effect of tubal
178
dissection to AMH AFC FSH levels (n=49) using longitudinal data (Erkan et
al 2012) There were no differences between preoperative and 3 month
postoperative measurements with median AMH (15 vs 14 p=007) AFC
(8437 vs 7941 p=009) FSH (76 21 vs 7721 p=010) da Silva et al
assessed the effect of tubal ligation (n=52) in longer term postoperative period
(1 year) and reported that median AMH (143 IQR 063-262 vs and 130 IQR
053-285 p=023) and mean AFC ( 8 IQR 5-14 vs 11 IQR 7-15 p=012)
measurements did not change significantly Our results and on other published
evidence suggest that salpingectomy or tubal division does not have an
adverse effect to ovarian reserve
Unilateral salpingo-oopherectomy
Although salpingo-oopherectomy is rare in women of reproductive age
significant ovarian pathologies and acute diseases such as ovarian torsion may
necessitate unilateral salpingo-oopherectomy There is a plausible causative
relationship between this surgery and ovarian reserve although to our
knowledge there is no previous published evidence We found that women
with a history of unilateral salpingo-oopherectomy have significantly lower
AMH (-54) and higher FSH (13) measurements suggesting the surgery has
considerable negative impact to ovarian reserve Important clinical question in
this clinical scenario is ldquoDo these patients have comparable reproductive
lifespan or experience accelerated loss of oocytes resulting premature loss of
fertilityrdquo as this would allow appropriate pre-operative counseling of patients
regarding long term effect of the surgery to fertility and age at menopause
Considering our data had relatively small number of patients with a history of
salpingo-oopherectomy we were not able to obtain reliable estimates on age-
related decline of ovarian reserve in this study We suggest that studies with
larger number of patients preferably using longitudinal data should address
this research question
Ovarian cystectomy
In women with a history of ovarian cystectomy for ovarian cysts other
than those due to endometrioma we did not observe any significant
association between the surgery and markers of ovarian reserve However
women that had ovarian cystectomy for endometrioma appear to have
179
significantly lower AMH (-66) measurements compared to those without
history of surgery
During the last few years a number of studies have assessed the effect of
cystectomy on AMH levels in patients with endometrioma (Chang et al 2010
Erkan et al 2010 Lee et al 2011) The studies have been summarised by a
recent systematic review which concluded that cystectomy results in damage
to ovarian reserve (Somigliana et al 2012) Further studies evaluated the
mechanism of damage and these suggest that coagulation for purpose of
hemostasis as well as stripping of the cyst wall may cause direct damage to
ovarian reserve Sonmezer et al compared the effect of diathermy coagulation
(n=15) for hemostasis compared to use of hemostatic matrix (n=13) in a
randomized controlled trial and reported that use of diathermy coagulation is
associated with significantly lower AMH measurements (164 plusmn 093 vs 272 plusmn
149 ngmL) in the first postoperative month
Similarly stripping of the cyst wall also appears to have detrimental
effect of ovarian reserve due to inadvertent removal of ovarian tissue (Donnez
et al 1996) Using histological data Roman et al demonstrated that normal
ovarian tissue was removed in 97 specimens of surgically removed
endometriomata (Roman et al 2010) Furthermore it appears that ovarian
cortex containing endometrioma appears to have significantly reduced density
compared to normal ovarian cortex and therefore loss of oocyte containing
normal ovarian cortex may be unavoidable in cystectomy for endometrioma
(Sanchez et al 2014) Matsuzaki et al conducted histological assessment of
cystectomy specimens and found that normal ovarian tissue adjacent to cyst
wall was found in 58 (71121) of patients with endometrioma whereas
normal ovarian tissue was excised in 54 (356) following cystectomy for
other benign cyst (Matsuzaki et al 2008) Similarly in our study women with a
history of cystectomy for endometrioma had significantly lower AMH
measurements whilst those had cystectomy for other benign cysts do not
appear to have lower AMH measurements In view of our findings and other
published research evidence it seems clear that cystectomy for endometrioma
results in significant reduction in ovarian reserve and women undergoing
surgery should be counseled regarding the adverse effect of surgery
180
Strengths and Limitations
The published studies have used longitudinal data comparing biomarkers
before and after cystectomy and provide reliable estimates on the effect of the
intervention on ovarian reserve However data on the effect of salpingectomy
and unilateral salpingoophorectomy is lacking In addition to reevaluation of
the effect of cystectomy this is study has assessed the impact of salpingectomy
and unilateral salpingoophorectomy on the markers of ovarian reserve In
contrast to published studies this study employed analysis of cross sectional
data Given a robust adjustment for all relevant factors has been conducted
our analysis of the cross sectional data should provide reliable estimates of the
effects of various intervention on the markers of ovarian reserve Furthermore
the effect of surgery on all the main biomarkers of ovarian reserve has been
assessed which improves our understanding of the clinical value of each test in
the assessment of patients with history of tubal or ovarian surgery In addition
the analyses adjusted for other relevant factors that may affect ovarian reserve
In patients with history of cystectomy for endometrioma we estimated
independent effects of pathology and surgery providing important data for
preoperative counseling It is important to note that the study evaluated The
effect of surgery using retrospective data which has limitations due variation in
recording of surgical history and missing data In addition given BMI results
for around one third of patients were not available we were not able to fully
explore the effect of BMI However data on the analyses with and without
BMI in the model have been provided to evaluate the effect of this factor The
study employed the data obtained using first generation DSL AMH assay
which is no longer in use However the paper describes the effects of the
interventions in percentage terms and therefore the results are interpretable in
any AMH assay measurement
Important to note although the effects are significant in population level
there is considerable variation between individuals which is evident from the
fact there is overlap between median and interquartile ranges of the groups
(Figure 1) This indicates that clinicians should exercise caution in predicting
the effect of surgery to ovarian reserve of individual patients Nevertheless
given I used a robust methodology for data extraction and conducted careful
analysis I think that the study provides fairly reliable estimates on the effect of
surgery to ovarian reserve
181
CONCLUSION
This multivariable regression analysis of retrospectively collected cross-
sectional data suggests that neither salpingectomy nor ovarian cystectomy for
cysts other than endometrioma has an appreciable effect on ovarian reserve
determined by AMH AFC and FSH In contrast salpingoophorectomy and
ovarian cystectomy for endometrioma have a significant detrimental impact to
ovarian reserve On the basis of findings of this study and other published
studies women undergoing reproductive should be counseled with regards to
the effect of the surgery on their ovarian reserve
182
References
Biacchiardi CP Piane LD Camanni M Deltetto F Delpiano EM Marchino GL et al Laparoscopic stripping of endometriomas negatively affects ovarian follicular reserve even if performed by experienced surgeons Reprod Biomed Online 201123740ndash6 Chang HJ Han SH Lee JR Jee BC Lee BI Suh CS et al Impact of laparoscopic cystectomy on ovarian reserve serial changes of serum anti-Mullerian hormone levels Fertil Steril 201094343ndash9 Dogan E Ulukus EC Okyay E Ertugrul C Saygili U Koyuncuoglu M Retrospective analysis of follicle loss after laparoscopic excision of endometrioma compared with benign nonendometriotic ovarian cysts Int J Gynaecol Obstet 2011114124ndash7 Ercan CM Sakinci M Duru NK Alanbay I Karasahin KE Baser I (2010) Antimullerian hormone levels after laparoscopic endometrioma stripping surgery Gynecol Endocrinol 201026468ndash72 Ercan CM Duru NK Karasahin KE Coksuer H Dede M Baser I (2011) Ultrasonographic evaluation and anti-mullerian hormone levels after laparoscopic stripping of unilateral endometriomas Eur J Obstet Gynecol Reprod Biol 2011158280ndash4 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hachisuga T Kawarabayashi T Histopathological analysis of laparoscopically treated ovarian endometriotic cysts with special reference to loss of follicles Hum Reprod 200217432ndash5 Hirokawa W Iwase A Goto M Takikawa S Nagatomo Y Nakahara T et al The post-operative decline in serum anti-Mullerian hormone correlates with the bilaterality and severity of endometriosis Hum Reprod 201126904ndash10 Hwu YM Wu FS Li SH Sun FJ Lin MH Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reprod Biol Endocrinol 2011980 Iwase A Hirokawa W Goto M Takikawa S Nagatomo Y Nakahara T et al Serum anti-Mullerian hormone level is a useful marker for evaluating the impact of laparoscopic cystectomy on ovarian reserve Fertil Steril 201094 2846ndash9 Kitajima M Khan KN Hiraki K Inoue T Fujishita A Masuzaki H Changes in serum anti-Mullerian hormone levels may predict damage to residual normal ovarian tissue after laparoscopic surgery for women with ovarian endometrioma Fertil Steril 2011952589ndash91e1 Kitajima M Defr_ere S Dolmans MM Colette S Squifflet J van
183
Langendonckt A et al Endometriomas as a possible cause of reduced ovarian reserve in women with endometriosis Fertil Steril 201196685ndash91 Lee DY Young Kim N Jae Kim M Yoon BK Choi D Effects of laparoscopic surgery on serum anti-Meuroullerian hormone levels in reproductive-aged women with endometrioma Gynecol Endocrinol 201127733ndash6 Matsouzaki S Houlle C Darcha S Pouly JL Mage G Canis M Analysis of risk factors for the removal of normal ovarian tissue during laparoscopic cystectomy for ovarian endometriosis Hum Reprod 2009 241402ndash1406 Muzii L Bianchi A Croc_e C Manci N Panici PB Laparoscopic excision of ovarian cysts is the stripping technique a tissue-sparing procedure Fertil Steril 200277609ndash14 Office for National Statistics (ONS) Social Trends 41 Health 2011 Roman H Tarta O Pura I Opris I Bourdel N Marpeau L et al Direct proportional relationship between endometrioma size and ovarian parenchyma inadvertently removed during cystectomy and its implication on the management of enlarged endometriomas Hum Reprod 201025 1428ndash32 Romualdi D Franco Zannoni G Lanzone A Selvaggi L Tagliaferri V Gaetano Vellone V et al Follicular loss in endoscopic surgery for ovarian endometriosis quantitative and qualitative observations Fertil Steril 201196374ndash8
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642 Sanchez A P Viganograve P Somigliana E Panina-Bordignon P Vercellini and Candiani M The distinguishing cellular and molecular features of the endometriotic ovarian cyst from pathophysiology to the potential endometrioma-mediated damage to the ovary Hum Reprod Update (MarchApril 2014)
Shi J Leng J Cui Q Lang J Follicle loss after laparoscopic treatment of ovarian endometriotic cysts Int J Gynaecol Obstet 2011115277ndash81 Tsolakidis D Pados G Vavilis D Athanatos D Tsalikis T Giannakou A et al The impact on ovarian reserve after laparoscopic ovarian cystectomy versus three-stage management in patients with endometriomas a prospective randomized study Fertil Steril 20109471ndash7 Vicino M Scioscia M Resta L Marzullo A Ceci O Selvaggi LE Fibrotic tissue in the endometrioma capsule surgical and physiopathologic considerations from histologic findings Fertil Steril 200991(4 Suppl)1326ndash8
184
Figure 1 Box plots of AMH by various groups Upper panel shows the raw data and the lower panel the AMH measurement (in pmolL) adjusted for age ethnicity BMI causes of infertility endometriosis endometrioma and surgery Groups (left to right) 1) Endometrioma without history of cystectomy (endoma-no surg) 2) Cystectomy for endometrioma (endoma+surg) 3) Endometriosis without endometrioma (endsisonly) 4) Without endometriosis or any surgery (No end+no surg) 5) Oopherectomy (oe) 6) Cystectomy for cyst other than those for endometrioma (other cyst) 7) Salpingectomy (se)
185
Table1 Distribution of patients
BMI excluded
BMI Included
Age AMH AFC FSH AMH AFC
FSH
Mean (SD) N Mean n Mean (SD) N Mean (SD) n n N
Non-surgery 328plusmn45 2880 175plusmn150 18100 139plusmn63 23770 79plusmn72 1976 15830 17880
Oophorectomy 324plusmn50 36 106plusmn84 2 115plusmn77 34 118plusmn230 25 2 23
Salpingectomy 331plusmn42 138 154plusmn119 91 13plusmn43 122 82plusmn 123 121 84 27
Cystectomy Other 336plusmn42 41 168plusmn132 18 148plusmn50 29 122plusmn249 27 15 20
Cystectomy Endometrioma
327plusmn51 40 119plusmn140 17 137plusmn41 37 89plusmn56 23 10 22
186
Table 2 Multivariable regression analysis Adjusted for age ethnicity causes of infertility endometriosis (without endometrioma) endometrioma and reproductive surgery
BMI(+)
BMI(-)
N
Coeff
95 CI
P
N
Coeff
95 CI
P
Oophorectomy
AMH 2128 -0779 -1135 -0422 00005 3049 -0540 -0868 -0213 0001
AFC 1697 -0278 -0848 0292 0340 1946 -0280 -0857 0298 0342
FSH 1929 0266 0110 0422 0001 2546 0139 -0006 0284 0060
Salpingectomy
AMH 2128 0067 -0118 0252 0476 2128 0094 -0097 0285 0333
AFC 1697 -0027 -0128 0075 0605 1697 -0027 -0126 0072 0595
FSH 1929 -0085 -0167 -0004 0041 1929 -0056 -0143 0032 0210
Cystectomy Other
AMH 2128 0102 -0230 0433 0548 2128 0075 -0226 0376 0626
AFC 1697 0102 -0107 0311 0339 1697 0130 -0064 0323 0189
FSH 1929 0134 -0028 0297 0106 1929 0110 -0044 0265 0161
Cystectomy Endometrioma
AMH 2128 -0647 -1100 -0194 0005 2128 -0667 -1081 -0252 0002
AFC 1697 0115 -0172 0402 0433 1697 0144 -0089 0376 0225
FSH 1929 0243 0047 0439 0015 1929 0103 -0084 0290 0281
187
ASSESSMENT OF DETERMINANTS OF OOCYTE
NUMBER USING RETROSPECTIVE DATA ON
IVF CYCLES AND EXPLORATIVE STUDY OF
THE POTENTIAL FOR OPTIMIZATION OF AMH-
TAILORED STRATIFICATION OF CONTROLLED
OVARIAN HYPERSTIMULATION
Oybek Rustamov
Cheryl Fitzgerald Stephen A Roberts
6
188
Title
Assessment of determinants of oocyte number using large retrospective
data on IVF cycles and explorative study of the potential for
optimization of AMH-tailored stratification of controlled ovarian
stimulation
Authors
Oybek Rustamova Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Centre for Biostatistics Institute of Population Health Manchester
Academic Health Science Centre (MAHSC) University of Manchester
Manchester M13 9PL UK
Word count 7520
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgement
Authors would like to thank Dr Monica Krishnan (Foundation Trainee
Manchester Royal Infirmary) for her assistance in data extraction We would
also like to thank colleagues Dr Greg Horne (Senior Clinical Embryologist)
Ann Hinchliffe (Clinical Biochemistry Department) and Helen Shackleton
(Information Operations Manager) for their help in obtaining datasets for the
study
189
Declaration of authorsrsquo roles
OR prepared the study protocol prepared the dataset conducted statistical
analysis and prepared all versions of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed to the
discussion and reviewed all versions of the manuscript
190
ABSTRACT
Objectives
1) To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield
2) To explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Design
Retrospective cross sectional study using multivariable regression analysis
First the effect of a set of plausible factors that may affect the outcomes have
been established including assessment of the effect of age AMH AFC causes
of infertility attempt of IVFICSI cycle COH protocol changes
gonadotrophin preparations operator for oocyte recovery pituitary
desensitisation regime and initial daily dose of gonadotrophins Then the
regression models that examined the effect of gonadotrophin dose and regime
categories on total and mature oocyte numbers have been developed
Setting
Tertiary referral centre for management of infertility St Maryrsquos Hospital
Central Manchester University Hospitals NHS Foundation Trust
Participants
Women without ultrasound features of polycystic ovaries who underwent
IVFICSI cycle using pituitary desensitisation with GnRH long agonist or
GnRH antagonist regimes and had previous measurement of AMH with the
DSL assay In total of 1847 IVF or ICSI cycles of 1428 patients met the
inclusion criteria for the study AMH measurements of all cycles and AFC
measurements for 1671 cycles (n=1289 patients) were available In the analysis
of total oocytes 1653 cycles were included and the analysis of metaphase II
oocytes comprised of 1101 ICSI cycles
Interventions
None (observational study)
191
Main outcome measures
Total oocyte number Metaphase II oocyte number
Results
After adjustment for all the above factors age remained a negative predictor of
oocyte yield whereas we observed a gradual and significant increase in oocyte
number with increasing AMH and AFC values suggesting all these markers
display an independent association with oocyte yield
Compared to 1st IVF cycles those with 2nd (8 p=001) and particularly 3rd
attempt (24 p=0001) had considerably higher total oocytes The effect of
attempt on mature oocyte yield was not significant (p=045) Similarly there
was significant between-operator variability in total oocyte number when
oocyte recovery practitioners were compared (p=00005) However the effect
of oocyte recovery practitioner on mature oocyte yield did not reach statistical
significance (p=0058) Comparison of the effect of gonadotrophin type
showed that rFSH was associated with higher total oocyte yield compared to
that of HMG (p=0008) although the numbers of mature oocytes were not
significantly different between the groups (p=026)
After adjustment for all above factors compared to a reference group (Agonist
with 75-150 IU hMGrFSH) none of the regime and dose categories provided
higher total oocyte yield and Antagonist with 75-150 IU hMGrFSH (-36
p=00005) provided significantly less total oocyte With regards to the mature
oocyte yield Antagonist with 187-250 IU rFSHhMG (43 p=005) and
Antagonist 375 IU rFSHhMG (47 p=002) were associated with
significantly higher oocyte number compared to that of above reference group
This implies that compared to long Agonist down regulation Antagonist
regime is associated with higher mature oocyte yield
Following adjustment for all above variables we did not observe significant
increase in oocyte number with increasing gonadotrophin dose categories
192
Conclusions
Given there was no expected increase in oocyte number with increasing
gonadotrophin dose categories we believe there may not be significant direct
dose-response effect Consequently strict protocols for tailoring the initial
dose of gonadotrophins may not necessarily improve ovarian performance in
IVF treatment It is important to note our COS protocols instructed the use
of cycle monitoring with ultrasound follicle tracking and oestradiol levels and
corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
Key Words
Ovarian reserve AMH AFC IVF Controlled ovarian stimulation AMH-
tailored ovarian stimulation Individualisation of ovarian stimulation
193
INTRODUCTION
According to the HFEA around 12 of IVF cycles in the UK are
cancelled due to poor or excessive ovarian response in the UK which
highlights the importance of the provision of optimal ovarian stimulation in
improving the outcomes (Kurinczuk et al 2010) Traditionally patientrsquos age and
basal FSH measurements were used for the assessment of ovarian reserve with
subsequent tailoring of the initial dose of gonadotrophins and regime for
pituitary desensitisation for controlled ovarian stimulation in IVF Studies on
the prognostic value of markers of ovarian reserve show that AMH and AFC
are the best predictors of ovarian response in cycles of IVF (Broer et al 2011)
Furthermore unlike most other markers AMH has potential discriminatory
power due to significantly higher between-patient (CV 94) variability
compared to its within-patient (CV 28) variation (Rustamov et al 2011)
which allows stratification of patients into various degrees of (eg low normal
high) ovarian reserve Consequently development of optimal ovarian
stimulation protocol for each band of ovarian reserve using AMH may be
feasible
Controlled ovarian stimulation (COS) based on tailoring the pituitary
desensitisation and initial dose of gonadotrophins to AMH measurements is
known under various names individualisation of ovarian stimulation AMH-
tailored stratification of COS personalization of IVF are the most commonly
used This strategy is believed to be effective and has been widely
recommended (Nelson et al 2013 Dewailly et al 2014 La Marca et al 2014)
Although AMH based assessment of ovarian reserve with pituitary down
regulation in patients with extremes of ovarian reserve may improve the
outcomes of ovarian response compared to conventional ovarian stimulation
protocols (Nelson et al 2009 Yates et al 2011) there is no robust data on
AMH-tailored individualisation of ovarian stimulation To establish
individualisation of ovarian stimulation the studies should ideally assess
various pituitary desensitisation regimes and initial doses of gonadotrophins in
patients across the full range of ovarian reserve For instance in AMH-tailored
individualisation of pituitary desensitisation regime studies should evaluate the
effect of both GnRH Agonist and GnRH Antagonist regimes for the groups
for each band of AMH levels (eg low normal high) necessitating 6
comparison groups (Figure 1) In individualisation of the initial dose of
194
gonadotrophins the groups of each band of AMH should be treated with the
range of doses of gonadotrophins (eg low moderate high dose) which
requires 9 treatment groups (Figure 2) Consequently to evaluate the
individualisation of both the stimulation regime and the initial dose of
gonadotrophin across the full range of AMH measurements in a single study
ideally 18 comparison groups are needed Indeed the study should have a large
enough sample to adjust for the confounders and obtain sufficient power for
the estimates of each treatment group In addition assessment of ovarian
reserve should be based on reliable AMH measurements with minimal sample-
to-sample variation which appears to be an issue at present (Rustamov et al
2013) Finally evidence on AMH-tailored individualisation of ovarian
stimulation should ideally be based on randomized controlled trials given in
this context AMH is being used as a therapeutic intervention At present there
is no single RCT that assessed AMH-tailored individualisation of ovarian
stimulation and most quoted research evidence appear to have been based on
two retrospective studies (Nelson et al 2009 Yates et al 2011) Both studies
display a number of methodological issues including small sample size and
centre-dependent or time-dependent selection of cohorts Therefore the role
of confounding factors on the obtained estimates of these studies is unclear
The first study on AMH-tailored individualisation ovarian stimulation
compared outcomes of the cohorts who had IVF cycles in two different IVF
centers (Nelson et al 2009) In this case control study the patients in the 1st
centre (n=370) had minimal tailoring of dose of gonadotrophins and were
offered mainly GnRH agonist regime for pituitary desensitisation except
patients with very low AMH (lt10pmolL) who had GnRH antagonist regime
In patients undergoing treatment in the 2nd centre (n=168) the daily dose of
the gonadotrophins was tailored on the basis of AMH levels and GnRH
antagonist based protocol employed for women with low (1-5 pmolL) and
high (gt15 pmolL) AMH levels whereas patients with normal (5-15 pmolL)
AMH levels had standard long GnRH agonist regimen In addition the
patients with very low AMH (lt10 pmolL) had modified natural cycle IVF
treatment in 2nd centre The study reported that the group that had significant
tailoring of both mode and degree of stimulation to AMH levels (2nd centre)
had higher pregnancy rate and less cycle cancellation However given the
methodological weaknesses the findings of the study ought to be interpreted
with caution First the study compared the outcomes of small number of
195
patients who had treatment in two different centers suggesting that differences
in the outcomes may be due to variation in the characteristics of patient
populations andor performance of two different centers Moreover both
cohorts had some degree of tailoring of pituitary desensitisation regimens as
well as the daily dose of gonadotrophins to AMH levels suggesting estimation
of the effect of AMH tailoring to the outcome of treatment may not be
reliable
A subsequent study attempted to address the above issues by assessing a
somewhat larger number of IVF cycles from the same fertility centre (Yates et
al 2011) The study compared IVF outcomes of the cohorts that underwent
ovarian stimulation using chronological age and serum FSH (n=346) with
women that had AMH-tailored (n=423) treatment cycles (Yates et al 2011)
The study found that the group that had AMH-tailored ovarian stimulation
had significantly higher pregnancy rate less cycle cancellation due to poor or
excessive ovarian response and had significantly lower treatment costs
However this study also has appreciable weaknesses given that it was based
on retrospective data that compared outcomes of treatment cycles that took
place over two year period During this period apart from introduction of
AMH-tailored stimulation protocols other new interventions were introduced
particularly in the steps involved in embryo culture Although the outcomes of
the ovarian response to stimulation could have mainly been due to
performance of the stimulation protocols downstream outcomes such as
clinical pregnancy rate may be associated with the introduction of new
interventions in embryo culture techniques Nevertheless the study
demonstrated that tailoring of ovarian stimulation protocol to AMH levels
could reduce the incidence of cycle cancellation OHSS and the cost of
treatment supporting the need for more robust studies on the use of AMH in
the individualisation of ovarian stimulation in IVF
It appears despite a lack of good quality evidence that AMH-tailored
individualisation has been widely advocated and has been introduced in clinical
practice in a number of fertility units In the absence of good quality evidence
we decided to obtain more reliable estimates on the feasibility of AMH-tailored
ovarian stimulation using more robust methodology Availability of the data on
a large cohort of patients with AMH measurements who subsequently
underwent IVF treatment cycles in a single centre may allow us to obtain more
reliable estimates on the effectiveness of AMH-tailored COS Furthermore due
196
to changes on COS protocol combination of various regime and initial dose of
gonadotrophin were used for patients in each band of ovarian reserve This
may facilitate development of predictive models for both regime and dose for
the whole range of AMH measurements In addition as a part of the study we
decided to establish the role of patient and treatment related factors in
determination of ovarian response in cycle of IVF I believe that
understanding the effect of various factors on ovarian performance in COS
will improve the methodology of the study and can be used as a guide for
identification of confounders in future studies The first step in such an
analysis is to develop a statistical model to describe the relationship between
ovarian response and patient and treatment factors This can then be utilized
to explore the effects of treatment on outcome and potentially to allow optimal
treatments to be identified for given patient characteristics and ovarian reserve
METHODS
Objective
The objectives of the study were 1) to determine the effect of age AMH
AFC causes of infertility and treatment interventions on oocyte yield and 2) to
explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Population
Women of 21-43 years of age undergoing ovarian stimulation for
IVFICSI treatment using their own eggs at the Reproductive Medicine
Department of St Maryrsquos Hospital Manchester from 1st October 2008 to 8th
August 2012 were included Patients with previous AMH measurements using
DSL assay were included and patients that had AMH measurement with only
Gen II assay were excluded given the observed issues with this assay
(Rustamov et al 2012) The patients with ultrasound features of PCO previous
history of salpingectomy ovarian cystectomy andor unilateral
salpingoophorectomy have been excluded from the analysis Similarly cycles
with ovarian stimulation other than GnRH agonist long down regulation or
Short GnRH antagonist cycles were not included in the study
197
Dataset
The dataset for the study was prepared using a protocol for the data
extraction management linking and validation which is described in Chapter
4 In short first the data contained in clinical data management systems were
obtained on patient demography AMH measurements and IVF treatment
cycles Then data not available in electronic format were collected from the
patient case notes which includes causes of infertility previous history of
reproductive surgery AFC and folliculogram for monitoring of ovarian
stimulation Each dataset was downloaded in original Excel format into Stata
12 Data Management and Statistics Software (StataCorp LP Texas USA) and
analysis datasets were prepared in Stata format All IVF cycles commenced
during the study period were identified and the combined study dataset was
created by linking all datasets in cycle level using the anonymised patient
identifiers and the dates of interventions All steps of data handling have been
recorded using Stata Do files to ensure reproducibility and provide a record of
the data management process
Categorization of diagnosis
Patients with history of unilateral tubal occlusion or unilateral
salpingectomy were categorized as mild tubal factor infertility and patients with
blocked tubes bilaterally or with history of bilateral salpingectomy were
allocated to severe tubal disease Severe male factor infertility was defined if
the partner had azoospermia surgical sperm extraction or severe oligospermia
which necessitated Multiple Ejaculation Resuspension and Centrifugation test
(MERC) for assisted conception Mild male factor was defined as abnormal
sperm count that do not above meet criteria for severe male infertility
Diagnosis of endometriosis was based on a previous history of endometriosis
confirmed using Laparoscopy Diagnosis of endometrioma was established
using transvaginal ultrasound scan prior to IVF treatment In couples without a
definite cause for infertility following investigation the diagnosis was
categorized as unexplained Women with features of polycystic ovaries on
transvaginal ultrasound were categorized as PCO and excluded from analyses
198
Measurement of AMH and AFC
AMH measurements were performed by the in-house laboratory Clinical
Assay Laboratory of Central Manchester NHS Foundation Trust and the
procedure for sample handling and analysis was based on the manufacturerrsquos
recommendations Venous blood samples were taken without regard to the day
of womenrsquos menstrual cycle and serum samples were separated within two
hours of venipuncture Samples were frozen at -20C until analysed in batches
using the enzymatically amplified two-site immunoassay (DSL Active
MISAMH ELISA Diagnostic Systems Laboratories Webster Texas) The
intra-assay coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and
29 (at 56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and
49 (at 56pmoll) Haemolysed samples were not included in the study In
patients with repeated AMH the measurement closest to their IVF treatment
cycle was selected The working range of the assay was up to 100pmolL and a
minimum detection limit was 063pmolLThe results with minimum detection
limit were coded as 50 of the minimum detection limit (031 pmolL) and
the test results that are higher than the assay ranges were coded as 150 of the
maximum range (150 pmolL)
In our department the measurement of AFC is conducted as part of
initial clinical investigation before first consultation with clinicians and prior to
IVF cycle Qualified radiographers performed the assessment of AFC during
early follicular phase (Day 0-5) of menstrual cycle The methodology of
measurement of AFC consisted of the counting of all antral follicles measuring
2-6mm in longitudinal and transverse cross sections of both ovaries using
transvaginal ultrasound scan The AFC closest to the IVF cycle was selected
for the analysis
Description of COS Protocols
On the basis of their AMH measurement patients were stratified into
the treatment bands for ovarian stimulation using COS protocols During the
study two different COS protocols were used in our centre and in addition
three minor modifications were made in the 2nd protocol Time periods AMH
bands down regulation regimes initial dose of gonadotrophins and adjustment
of daily dose of gonadotrophins of the protocols are described in Table 1
Similarly the management of excessive ovarian response was tailored to
199
pretreatment AMH measurements although mainly based on the results of
oestradiol and scan monitoring the cycle stimulation (Table 2) Assessment of
transvaginal ultrasound guided follicle tracking and serum oestradiol levels in
specific days of the stimulation were used for monitoring of COS (Table 2)
The criteria for the cycle cancellation for poor ovarian response were same
across all protocols fewer than 3 follicles gt15mm in size on Day 10 of ovarian
stimulation
In patients undergoing their first IVF cycle AMH measurement
obtained at the initial assessment was used for determination of which band of
COS the patient would be allocated In the patients with repeated IVF cycles
AMH measurements were obtained prior to each IVF cycle unless a last
measurement performed within 12 months of period was available During the
study period two different assay methods for measurement of AMH was used
in our centre DSL Assay (1 October 2008- 16 November 2010) and Gen II
Assay (17 November 2010- 8 August 2012) Correspondingly during the study
period two different COS Protocols were used 1st Protocol (1 October 2008-
31 December 2010) and 2nd Protocol (1 January 2011-8 August 2012)
Consequently allocation into the ovarian reserve bands of the patients of 1st
protocol were based on DSL assay samples whereas the stratification of
patients of 2nd protocol was based either on DSL assay or Gen II assay
samples Specifically the patients with recent DSL measurements (lt12 months
old) who had IVF treatment during the period of 2nd Protocol had
stratification on the basis of their DSL measurements In these patients in
order to obtain equivalent Gen II value the DSL result was multiplied by 14
in accordance with the manufacturerrsquos recommendation at the time In the
patients without previous or recent (lt12 months old) DSL measurements
stratification into ovarian reserve bands was achieved using their most recent
Gen II measurements Therefore DSL measurements presented in this study
may or may not have been used for formulation of the treatment strategies for
individual patients In fact in this study DSL measurements have been
included in order to understand the role of AMH in determination of ovarian
response in IVF cycles rather than an evaluation of AMH-tailored COS
protocols In addition to introduction of 2nd protocol further modifications
were made to the protocol and therefore 2nd protocol comprised of 4 different
versions (Table 1-2) These changes in the protocols allowed us to compare the
effect of the various modifications to COS protocols on oocyte yield
200
Pituitary desensitisation regimes
Selection of pituitary desensitisation regime was based on the patientrsquos
AMH according to the COH protocol at the time of commencement of IVF
cycle (Table 1) Long agonist regime involved daily subcutaneous injection of
250g or 500 g of the GnRH agonist Buseralin acetate (Supercur Sanofi
Aventis Ltd Surrey UK) from the mid-luteal phase (Day 21) of preceding
menstrual cycle which continued throughout ovarian stimulation Women
treated with Antagonist regime had daily subcutaneous administration of
GnRH antagonist Ganirelex (Orgalutran Organon Laboratories Ltd
Cambridge UK) from Day 4 post-stimulation until the day of HCGGnRH
agonist trigger Ovarian stimulation was achieved by injection of daily dose of
hMG Menopuir (Ferring Pharmaceuticals UK) or rFSH Gonal F (Merck
Serono) as per AMH-tailored protocols (Table 1) Oocyte maturation was
triggered using 5000 international units of HCG (Pregnyl Organon
Laboratories Ltd Cambridge UK) and the criteria for timing of HCG
injection was consistent across all protocols one (or more) leading follicle
measuring gt18mm and two (or more) follicle gt17mm
Oocyte collection
Oocyte collection was conducted 34-36 hours following injection of
HCG for follicle maturation An Ultrasound Guided Oocyte Recovery (USOR)
was conducted by experienced clinicians under sedation The names of
practitioners were anonymised and the practitioner with the largest number of
oocyte recovery was categorized as a reference group Practitioners with a
small number (lt10) of oocyte collection were pooled (group J) If the cycle
was cancelled before oocyte recovery it was categorized under the practitioner
who was on-call for oocyte recovery session on the day of cycle cancellation
In cycles with pre-USOR cancellation for excessive ovarian response
total oocyte number was coded as 27 and Metaphase II oocyte number was
coded as 19 This was based on mean oocyte number in the patients who had
post-USOR cancellation for excessive ovarian response or OHSS
Quantitative assessment of total oocytes were conducted immediately
post-USOR by an embryologist In patients undergoing ICSI the assessment
of the quality of oocytes were conducted 4-6 hours post-USOR and the
201
oocytes assessed as in Metaphase II stage (MII) of maturation were categorized
as mature oocytes
Statistical analysis
The total number of collected oocytes in all cycles and the number of
mature oocytes in the subset of ICSI cycles were used as outcome measures
for the study Oocyte was selected as the primary outcome measure for
assessment of ovarian performance as this provides an objective measure
which is largely determined by effectiveness of ovarian stimulation regimens
In contrast downstream measures such as clinical pregnancy and live birth are
influenced by factors related to management gametes and embryos
Statistical analysis was conducted using multivariable regression models
and the process of model building included following steps 1) Analyses of
distribution of the groups and variables 2) Univariate analysis to establish the
factors that likely to affect total oocyte number 3) Evaluation of
representation of continuous variables 4) Analysis of interaction between
explanatory variables 5) Sensitivity analysis
First the distribution of patients the ovarian reserve markers
interventions and the outcomes were explored using cross tabulation
histograms Box Whisker and scatter plots Then in order to establish the
factors that likely to affect the oocyte number univariate analyses of Age
AMH AFC PCO status attempt of IVFICSI ethnicity BMI protocol
regime USOR practitioner and initial dose of gonadotrophins were conducted
Following this all these explanatory variables were run as part of initial
multivariable regression model Adjustment for confounders related to the
modifications of the protocols and unknown time-dependent changes
conducted by inclusion of the COS protocol categories in the regression
model
Evaluation of representation of oocyte number Age AMH AFC initial
dose of gonadotrophins were conducted by establishing best fit on the basis of
Akaike and Bayesian Information Criteria In addition interpretability of the
data and clinical applicability of the results (eg cut off ranges) were used as a
guide for selection of optimal representation Given the oocyte number was
not normally distributed it was represented in logarithmic scale (log(oocyte
number+5) To establish best representation for AMH AFC and initial dose
202
the models in following scales were run for each variable Linear quadratic
cubic 4th order polynomial linear (log) quadratic (log) cubic (log) 4th order
polynomial (log) cut-off ranges according to distribution Age adjustment in
quadratic scale following centering it to 30 years of age was found to provide
the most parsimonious representation AMH was found to be best represented
using following cut-off ranges 0-3 4-5 6-8 9-10 11-12 13-15 16-18 19-22
23-28 and 29-200 The best representation for AFC was found to be cut-off
ranges of 0-7 8-910-1112-14 15-19 20-24 and 25-100 Initial dose of
gonadotrophins were categorized as following 75-150IU 187-250IU 300IU
375IU 450IU
Subsequently interactions between explanatory variables were tested at
significance level of plt001 which revealed there were significant interaction
between PCO status and other covariables Given these interactions were
found to be complex and not easily computable we decided to restrict the
regression analysis to the non-PCO group We observed significant interaction
between regime and initial dose and therefore these variables were fitted with
interaction term in the model Finally sensitivity analyses of final regression
models were conducted Significance of the results was interpreted using p
value (lt005) effect size and clinical significance For assessment of feasibility
of individualization of stimulation regime and initial dose visual representation
of data was achieved using plots for observed and fitted values (Figure 1-4)
RESULTS
Description of data
A total of 1847 IVF or ICSI cycles of 1428 patients met inclusion criteria for
the study AMH measurements of all cycles and AFC measurements for 1671
cycles (n=1289 patients) were available In the analysis of total oocytes 1653
cycles were included and the analysis of MII oocytes comprised of 1101 ICSI
cycles
Mean AMH was found to be 178 (125) mean AFC was 142 56
mean number of total oocytes was 101 64 and mean number of mature
oocytes was 74 53 The distribution of the cycles according to patient
characteristics and interventions is shown in Tables 3
203
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
Table 4a and 4b show that there was a significant negative association of
oocyte yield with age and oocyte number following adjustment for AMH
AFC causes of infertility attempt of IVFICSI cycle USOR practitioner COS
protocol pituitary desensitisation regime type of gonadotrophin preparation
and initial daily dose of gonadotrophins (Table 4a) With each increase of age
by 1 year we observed approximately a 3 reduction in total oocyte
(p=00005) and a 2 decrease in mature oocyte number (p=0006) which was
independent of age and other covariables
In the analysis of AMH there was significant gradual increase in total
oocyte as well as mature oocyte number with increasing AMH following
adjustment for all covariables (Figure 1 and 2) Compared to an AMH range of
0-3 pmolL there was increase of 25 in the range of 4-5 pmolL (p=007)
36 in 6-8 pmolL (p=0008) 60 in 9-10 pmolL (p=00005) 65 in 11-12
pmolL (p=00005) 77 in 13-15 pmolL (p=00005) 83 in 16-18 pmolL
(p=00005) 80 in 19-22 pmolL (p=00005) 95 in 23-28 pmolL
(p=00005) and 112 in the range of 29-150 pmolL (p=00005) in total
oocyte number (Table 4a) Similar but less marked increase in MII oocyte
number was observed with increasing AMH
The data on AFC also showed that there was gradual increase in total
oocyte number with increasing AFC following adjustment of all covariables
(Table 4a) Compared to an AFC of 0-7 there was increase of 14 in the
range of 10-11 (p=003) 22 in AFC of 12-14 (p=0001) 26 in AFC of 15-
19 (p=00005) 34 in AFC of 20-24 (p=00005) and 40 in AFC of gt25
(p=0005) However there was no increase in total oocyte number in AFC
range of 8-9 compared to that of 0-7 AFC-related Increase in MII oocytes was
less marked compared to that of total oocytes (Table 4a)
Causes of infertility
We did not observe any significant associations between the causes of
infertility and number of retrieved oocytes However women diagnosed with
unexplained infertility appear to have marginally higher (10 p=002) total
number of oocytes compared to women whose causes of infertility were
204
known Diagnosis of severe tubal (-37 p=019) and severe male (-37
p=035) factor infertility was found to be associated with lower number of MII
oocytes compared to other causes of infertility However neither of these
parameters reached statistical significance Similarly there was no significant
association between oocyte number and diagnosis of endometriosis with or
without endometriomata compared to women that were not diagnosed with
the disease (Table 4a)
Attempt
Analysis of total number of oocytes showed that women who had their
2nd attempt of IVFICSI cycle had slightly higher (85 p=001) and those
that had their 3rd or 4th attempt of treatment had significantly higher total
oocyte yield (24 p=0001) compared to women undergoing their 1st attempt
of IVFICSI cycle (Table 4a) Similarly overall effect of attempt on total
oocyte yield was significant (p=0001)
However we did not observe any association between the attempt and
MII oocyte number in the analysis of the subset of ICSI cycles (p=045)
USOR practitioner COS protocol and gonadotrophin preparation
There was a significant association (p=00005) between total oocyte yield
with USOR practitioner (Table 4b) However the association of USOR
practitioner with MII oocyte number did not reach statistical significance
(p=0058)
We observed significant association between the COS protocols in the
analysis of total number of oocytes 1st version of 2nd Protocol (-18
p=00005) 2nd amp 3rd versions of 2nd Protocol (-14 p=005) and 4th version of
2nd Protocol (-24 p=0009) provided significantly lower number of total
oocytes compared to 1st Protocol However the effect of the COS Protocol
changes to MII oocyte number was not significant (p=024)
Compared to hMG ovarian stimulation using rFSH provided 13
higher total oocytes (p=0008) In the analysis of Metaphase II oocytes there
was no significant difference in oocyte yield between hMG and rFSH (026)
205
Regime and Initial dose of gonadotrophins
The regression analyses of the regimes for pituitary desensitisation and
initial dose categories were conducted in comparison to the reference group
(Agonist with 75-150IU hMGrFSH) IVFICSI cycles where Antagonist
with 75-100IU of hMGrFSH (-36 p=00005) was used provided
significantly lower total oocyte yield whereas cycles with Agonist and 300IU
hMGrFSH (15 p=005) provided marginally higher total oocyte number
In the analysis of MII oocytes cycles using Antagonist with 187-250IU
of hMGrFSH (43 p=005) Agonist with 300IU of hMGrFSH (25
p=016) and Antagonist with 375IU hMGrFSH (47 p=002) yielded higher
number of oocytes Use of Agonist with 375IU hMGrFSH (-18 p=05) and
Agonist with 450IU of hMGrFSH (-28 p=02) was associated with lower
mature oocyte number although the analysis did not reach statistical
significance
AMH-tailored individualization of COS
The overall effect of initial gonadotrophin dose to total oocyte yield
was found to be significant (plt0001) However other than the lowest dose
category with Antagonist regime the analysis did not show any consistent
dose-response effect on total oocyte number with increasing gonadotrophin
dose (Table 4b Figure 3a Figure 3b Figure 4a and Figure 4b)
In the analysis of MII compared to reference group of 75-150 IU of
initial daily gonadotrophins we observed increased oocyte yield in the
categories of 187-250 IU (43 p=005) and 375 IU (47 p=002) of
gonadotrophins However both of these groups had Antagonist regime for
pituitary desensitisation compared to that of Agonist in the reference group
and therefore the observed effect may be related to the regime of COS rather
than daily dose of gonadotrophins
206
DISCUSSION
In this study we explored the effect of age AMH AFC causes of
infertility attempt of IVF ICSI treatment and interventions of COS on
ovarian performance using a retrospective data on large cohort of IVF ICSI
cycles of non-PCO patients To our knowledge this is largest study to have
conducted a detailed analysis of the effect of AMH and AFC on ovarian
performance in IVFICSI cycles The study utilized a dataset that was
prepared using a robust protocol for data extraction and handling Similarly
the statistical analysis was based on a systematic exploration of the effect of all
relevant factors followed by adjustment for all relevant factors and finally
careful analysis
With regards to the outcome measures the quantitative response of
ovaries were measured using total collected oocytes in IVFICSI cycles and
the MII oocyte number in the subset of ICSI cycles were used as a
measurement of quantitative response of ovaries to COS Arguably oocyte
number is the best outcome measure for determination of ovarian response to
COS given it is mainly determined by patientrsquos true ovarian reserve the quality
of assessment of ovarian reserve and treatment strategies for ovarian
stimulation In contrast downstream outcomes such as clinical pregnancy and
live birth are subject to additional clinical and interventional factors which may
not always be possible to adjust for using retrospective data Indeed large
observational studies suggest that achieving optimal ovarian response is one of
the most important determinants of success of IVFICSI cycles and
recommend to use oocyte number as a surrogate marker for live birth (Sunkara
et al 2011) It appears around 10-15 total oocytes or 3-4 mature oocytes
provide optimal chance for a one live birth in IVFICSI cycles (Sunkara et al
2011 Stoop et al 2012) Therefore oocyte number appears to be most useful
marker for assessment of ovarian response to COS as well as in prediction of
live birth in cycles of IVFICSI
207
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
After adjusting for AMH AFC the patient characteristics and above
mentioned treatment interventions age remained as an independent predictor
of ovarian response to COS Our data showed approximately 3 (p=00005)
decrease in total oocyte and 2 (p=0006) reduction in mature oocyte number
with increase of age by factor of 1 year (Figure 3b and Figure 4b)
Interestingly the effect of AMH was also found to predict oocyte yield
independently of age with an effect actually more pronounced compared to
that of age After adjusting for age and all other factors there was gradual
increase in total oocyte number with increasing AMH which were both
clinically (25-110) and statistically (p=007-p=00005) significant (Table 4a)
We observed a largely similar effect of AMH in the analysis of mature
oocytes It is important to note that due to the issues with Gen II AMH assay
(Rustamov et al 2012) in this study we included only measurements obtained
with the DSL assay Consequently presented cut-off ranges may not be
applicable with current assay methods We suggest that future studies should
revisit the optimality of the cut-off ranges once a reliable assay method has
been established
Similarly after adjusting for all factors the effect of AFC on total
oocytes remained significant (14-40 plt003) However the effect of AFC
appears to be less marked compared to AMH It is important to note that the
AFC assessment in this study is based on the measurement of 2-6mm antral
follicles using two-dimensional transvaginal ultrasound scan The cut-off
ranges may not be applicable in centers where AFC measurement is obtained
using different criteria
Our analysis suggests that age AMH and AFC are independent
determinants of total and MII oocyte number in IVFICSI cycles and can be
used as predictors of ovarian performance irrespective of patient and treatment
characteristics However assessment of oocyte number is the quantitative
response of ovaries to COS and may not necessarily reflect qualitative
outcome
208
Causes Endometriosis Endometrioma
The causes of infertility do not appear to make a significant contribution
in determining total oocyte number after controlling for age AMH AFC the
attempt and treatment interventions Although in the analysis of MII oocytes
we observed reduced oocyte yield in women with severe tubal (-37) and
severe male (-37) infertility this was not statistically significant The analysis
of MII oocytes only included the subset of ICSI cycles consisting of women
with male factor infertility Therefore the effect of severe male factor infertility
may have been more marked in this model
We did not observe a significant difference in total or MII oocyte
number in women with a history of endometriosis with or without
endometriomata Current understanding of the effect of endometriosis in the
outcomes of IVF treatment suggests that the disease has detrimental effect on
IVF outcomes (Barnhart et al 2007 Barnhart et al 2002) However some argue
that no association is observed if the analysis conducted using proper
adjustment for all relevant confounders (Surrey 2013) Our data suggests that
after adjustment for all relevant factors there is no measurable association with
endometriosis (with or without endometriomata) and oocyte number Some
suggest that using ultra-long down regulation using depot GnRH analogue up
tp 3-6 months prior to ovarian stimulation improves ovarian performance in
patients with endometriomata Our dataset did not have information on
pituitary desensitisation prior IVF treatment cycles and we are therefore unable
to assess the effect of this intervention
Attempt
Our study found that 2nd and 3rd cycles were associated with 8
(p=001) and 24 (p=0001) higher total oocytes compared to that of 1st IVF
cycle However the effect of the attempt on MII oocytes was not significant
In our centre only patients with a previously unsuccessful IVF treatment are
offered subsequent cycles and therefore compared to the patients with
repeated attempts the group with first cycle may be expected to have better
oocyte yield However when controlled for all relevant confounders including
adjustment of treatment interventions 1st IVF cycle does not appear to provide
better oocyte yield In keeping with our findings a recent study demonstrated
independence of attempts of IVF cycles in terms of outcomes (Roberts SA and
209
Stylianou C 2012) Increased total oocyte yield with progressed attempts is
likely to be due to the adjustment of COS on the basis of information on the
ovarian response in previous cycles It is important to note that in this study
we assessed oocyte yield as the outcome measure and this may not necessarily
translate into live birth which is desired outcome for the couples Therefore
availability of data on the attempt-dependency of live birth in IVF cycles is
important and we suggest future studies should explore it
USOR practitioner
To our knowledge this is the first study that explored the effect of an
oocyte recovery practitioner on oocyte yield adjusting for all relevant
confounders We observed a considerable operator-dependent effect on total
oocyte yield which may be due to a variation of patients across the days of the
week (p=00005) The practitioners were allocated to the sessions of oocyte
recovery using a specific rota template according to the day of the week Given
in our centre we do not conduct oocyte recovery at weekends there may be
day-dependent variation in selection of patients For instance the patients who
are likely to have maturation of leading follicles during the weekend may have
been scheduled slightly earlier Similarly the patients with confirmed
maturation of leading follicles whose oocyte recovery would have fallen on
weekends may have been scheduled after the weekend allowing maturation of
additional follicles Therefore practitioners conducting the sessions of oocyte
recovery in extremes of weekdays may not necessarily have similar patients
compared to that of other days which may have introduced some bias in
estimating the outcomes of individual practitioners Nevertheless given the
statistical analysis adjusted for age ovarian reserve and treatment interventions
we think there is considerable true between-operator variability on total oocyte
number We suggest that future studies should assess it further by including
adjustment for follicle number and size on the day of HCG
Interestingly overall effect of the operator did not reach statistical
significance in the analysis of MII oocytes in ICSI subset (p=0058) This may
suggest irrespective of total oocyte yield aspiration of only follicles of larger
than a certain size provides oocytes with potential for fertilization
210
COS Protocol
Controlled ovarian hyperstimulation in IVF is conducted using a pre-
defined protocol which contains the policy on selection of regime for pituitary
desensitisation the initial daily dose of gonadotrophins the monitoring of
ovarian response the adjustment of daily dose of gonadotrophins the policy
for cancellation due to poor or excessive ovarian response and criteria for
HCG trigger for final maturation of oocytes Determination of the optimal
treatment regime and the initial dose of gonadotrophins for each patient is
frequently achieved by stratification of patients into various bands of ovarian
reserve on the basis of the assessment of ovarian reserve The assessment of
ovarian reserve prior to IVF cycle is performed using biomarkers which usually
consist of one or combination of following Age AMH AFC and FSH In our
centre stratification of patients into the bands of ovarian reserve was
determined on the basis of the patientrsquos AMH measurements For instance the
patients deemed to have lower ovarian reserve were allocated to the treatment
band with higher daily dose of gonadotrophins and vice versa (Table 1)
The study found that the 2nd protocol was associated with 14-24 lower
total oocyte yield compared to the 1stprotocol The differences in the
interventions between the protocols are described in Table 1 and Table2
Compared to the 1st protocol the 2nd protocol had a) some patients allocated
to COS bands using Gen II assay measurements which later was found to
provide inaccurate measurements b) more AMH cut-off bands for COS
bands c) strict monitoring of ovarian response and corresponding adjustment
of daily dose of gonadotrophins and d) strict criteria for cycle cancellation for
excessive response Therefore our data suggests that the COS protocols with
broader AMH cut-off bands with less strict criteria for adjustment of daily
gonadotrophins may provide higher oocyte yield However given it is
retrospective analysis the limitation of the study should be recognized and we
recommend more robust prospective studies on optimization of AMH tailored
protocols should be conducted
Gonadotrophin type
The study showed that rFSH was associated with higher total oocyte
number (13 p=0008) Interestingly analysis of MII oocyte showed a larger
confidence interval and did not reach statistical significance suggesting the
211
effect of rFSH was not a strong determinant of mature oocytes Perhaps
observation of higher total oocytes in rFSH cycles compared to that of HMG
and yet comparable mature oocyte number in the study suggest that regardless
of total oocyte yield only follicles with a potential for maturation will achieve a
stage of metaphase II
Ovarian stimulation in cycles for IVF is largely achieved by two different
analogues of follicle stimulating hormone human menopausal gonadotrophin
(hMG) and recombinant follicle stimulating hormone r(FSH) Although
purified hMG contains more luteinising hormone compared to rFSH which is
believed to assist endometrial maturation and improve odds of implantation in
cycles of IVF Furthermore the LH component of hMG is believed to assist in
maturation of oocyte with subsequent improvement in live birth On the other
hand historically rFSH was believed to have less batch-to-batch variation
compared to that of HMG which allows administration of more precise daily
dose of gonadotrophins To date a number of studies have been published
comparing these two forms of gonadotrophin preparations which provide
conflicting findings However systematic review that compared of the effect of
these types of gonadotrophins on live birth rate suggests that there is no
significant difference on live birth rate (van Wely et al 2011) This supports our
findings on that irrespective of total oocyte yield clinically useful mature
oocyte number is comparable between the groups
Regime and dose of gonadotrophins
The study found that compared to the reference group (Agonist 75-
150IU) none of the combination of the regime and gonadotrophin dose
provided a higher total oocyte yield Women that were in Antagonist regime
group with an initial daily dose of 75-150 IU gonadotrophins produced
approximately 36 fewer total oocytes (p=00005) However comparison of
MII oocytes of these groups did not reach statistical significance and the effect
size was much smaller (-19 p=023) This and reference groups represent the
patients with high ovarian reserve who had milder ovarian stimulation because
of risk of excessive ovarian response and OHSS Lower total oocyte yield and
comparable mature oocyte number in the Antagonist regime may explain why
this regime is reported to be associated with reduction in the risk of OHSS and
212
yet comparable live birth in patients with high ovarian reserve (Yates et al
2012)
In the analysis of MII oocytes Antagonist with 187-250 IU of
gonadotrophin and Antagonist with 375 IU of gonadotrophin provided around
43 (p=005) and 47 (p=002) more oocytes compared to that of the
reference group (Agonist 75-150 IU) Interestingly total oocytes of these
groups were comparable to that of reference group suggesting that using
Antagonist protocol may be associated with improvement in oocyte
maturation compared to Long Agonist regime Perhaps in addition to the
effect of exogenous HCG endogenous LH may play role in oocyte maturation
in IVFICSI cycles and shorter desensitisation of pituitary using Antagonist
regime may allow secretion of LH during COS in lower quantities
AMH-tailored individualisation of COS
Given that we did not observe a significant dose-dependent effect on
oocyte number we were not able to develop AMH or AFC tailored
individualisation protocols for COS Although the initial dose of
gonadotrophin is believed to be one of the main determinants of oocyte yield
our study suggests that the association between these variables is weak
Consequently strict protocols for tailoring the initial dose of
gonadotrophins may not necessarily improve ovarian performance in IVF
treatment It is important to note that our COS protocols recommended close
monitoring of ovarian response and corresponding dose adjustment starting
from 3rd day of COS which may have masked the effect of initial dose
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
213
Strengths of the study
Here we presented the largest study on assessment of the role of patient
and treatment related factors on oocyte yield and exploration of optimization
of AMH-tailored COS using a validated dataset Statistical analysis included
systematic assessment of the effect possible confounders on measured
outcome including of age AMH AFC causes of infertility attempt of IVF
treatment USOR practitioner type of gonadotrophin pituitary desensitisation
regime and initial dose of gonadotrophins On the basis of above analysis a
robust multivariable regression models for assessment of the effect all above
factors on total and mature oocyte number have been developed
Prior to conducting this study previous projects explored the
performance of AMH assay methods The studies found that Gen II assay may
yield highly non-reproducible measurements compared to that of DSL assay
(Rustamov et al 2012a) Therefore in this study only DSL AMH assay
measurements were included Furthermore previous projects (Chapter 5 and 6)
explored the effect of various patient related factors on AMH AFC and FSH
measurements and found that some of the factors had measurable impact on
ovarian reserve These findings were used in establishing which patient related
factors ought to be explored in the building of regression models for this
study However the DSL assay is no longer available and most clinics are
mainly using Gen II AMH assay in formulation of COS in IVF Given its
observed instability AMH-tailoring based on Gen II samples may lead to
erroneous allocation of patients to the band that is significantly inconsistent
with patientrsquos ovarian reserve Subsequently this may result in the extremes of
ovarian response to COS including severe OHSS and cycle cancellation
Weaknesses of the study
The main weakness of the study is that the analysis is based on
retrospectively collected data The methodology included an extensive
exploration for possible confounders and adjustment for the ones that were
found to be significant However there are may be unmeasured factors that
that might have affected the estimates In addition the study included only
patients that did not have PCO appearance on ultrasound scan The analysis in
all patients showed that interaction of PCO status with other covariables was
complex which could introduce bias in estimation of the effects of other
214
factors Therefore analyses of the groups with and without PCO were run
separately Subsequently results of non-PCO group was presented in the thesis
given it had the largest number of cycles Compared to non-PCO analysis we
did not observe significant difference in the results of PCO model
The study assessed ovarian response using oocyte yield only Other
outcomes of ovarian response such as duration of ovarian stimulation total
dose of gonadotrophins cycle cancellation due to poor or excessive ovarian
response and OHSS have not been analysed Therefore it is important to
interpret the findings of this study in the context of ovarian response
determined by oocyte yield Specifically the study should not be used to
interpret cycle cancellation for excessive ovarian response As described in the
methodology of the study the oocyte number in the cycles with cancellation of
oocyte recovery due to excessive response were recoded with comparable
values with cycles that were cancelled following oocyte recovery for OHSS
Given the main desired outcome of IVF treatment is live birth the
overall success of a treatment cycle should reflect this outcome measure This
study does not assess the effect of above factors to overall success of IVF
treatment However the study provides a robust data on research methodology
in assessment of IVF outcomes which can assist in the assessment of other
outcome measures in future studies
SUMMARY
After adjustment for all the above factors age remained a negative
predictor of oocyte yield whereas we observed a gradual and significant
increase in oocyte number with increasing AMH and AFC values suggesting
all these markers display an independent association with oocyte yield IVF
attempt oocyte recovery practitioner type of gonadotrophin were found to
have significant effect on total oocyte yield However the effect of these
factors on mature oocyte number did not reach statistical significance Whilst
total oocyte number was comparable between pituitary desensitisation regimes
GnRH antagonist cycles were found to provide significantly higher mature
oocytes compared to that of long GnRH agonist regime
In terms of the effect of initial dose on oocyte yield following
adjustment for all above variables we did not observe significant increase in
215
oocyte number with increasing gonadotrophin dose categories Therefore
strict protocols for tailoring the initial dose of gonadotrophins may not
necessarily improve ovarian performance in IVF treatment However further
time series regression analysis with full parameters of cycle monitoring and the
dose adjustments in the model should be conducted in order to ascertain the
role of AMH in tailoring the dose of gonadotrophins in cycles of IVF
This study demonstrates complexity of the factors that determine
ovarian response in IVF cycles Therefore assessment of AMH-tailored
individualisation of ovarian stimulation should be based on a robust
methodology preferably using a large randomized controlled trial
Furthermore measurement of AMH ought to be based on a reliable assay
method which is currently not available In the meantime the limitations of
available evidence on AMH-tailored individualisation of ovarian stimulation
should be taken into account in the management of patients
216
References
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Barnhart K Dunsmoor-Su R Coutifaris C Effect of endometriosis on in vitro fertilization Fertil Steril 2002771148ndash55 Dechaud H Dechanet C Brunet C et al Endometriosis and in vitro fertilization a review Gynecol Endocrinol 200925717ndash21 Dewailly D Andersen CY Balen A Broekmans F Dilaver N Fanchin R Griesinger G Kelsey TW La Marca A Lambalk C Mason H Nelson SM Visser JA Wallace WH Anderson RA The physiology and clinical utility of anti-Mullerian hormone in women Hum Reprod Update 2014 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A and Sunkara S K Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers from theory to practice Human Reproduction Update Vol20 No1 pp 124ndash140 2014
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867-75 Nelson SM Biomarkers of ovarian response current and future applications Fertil Steril 201399963ndash969
Roberts SA Stylianou C The non-independence of treatment outcomes from repeat IVF cycles estimates and consequences Hum Reprod 2012 Feb27(2)436-43
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum
217
Reprod 2012a273085-3091
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071 Stoop D Ermini B Polyzos NP Haentjens P De Vos M Verheyen G and Devroey P Reproductive potential of a metaphase II oocyte retrieved after ovarian stimulation an analysis of 23 354 ICSI cycles Human Reproduction 2012 Vol27 No7 pp 2030ndash2035 2012 Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011 261768ndash1774 Sunkara SK Coomarasamy A Faris R Braude P Khalaf Y Effectiveness of the GnRH agonist long GnRH agonist short and GnRH antagonist regimens in poor responders undergoing IVF treatment a three arm randomised controlled trial (ESHRE) 2013London UK SurreyES Endometriosis and Assisted Reproductive Technologies Maximizing Outcomes Semin Reprod Med 201331154ndash163 van Wely M1 Kwan I Burt AL Thomas J Vail A Van der Veen F Al-Inany HG Recombinant versus urinary gonadotrophin for ovarian stimulation in assisted reproductive technology cycles Cochrane Database Syst Rev 2011 Feb 16(2)CD005354
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
218
Figure 1 Study groups for assessment of Individualisation of pituitary desensitisation regime
Individualisation of pituitary desensitisation regimens can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high ovarian reserve
Individualisation of COS Regime
Low AMH
(eg DSL assay
22-157 pmolL)
GnRH
Antagonist
GnRH
Agonist
Normal AMH
(eg DSL assay
158-288pmolL)
GnRH
Antagonist
GnRH
Agonist
High AMH
(eg DSL assay
gt288 pmolL)
GnRH
Antagonist
GnRH
Agonist
219
Fiure 2 Study groups for assessment of individualisation of initial gonadotrophin dose
Individualisation of daily dose of gonadotrophins can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high
ovarian reserve
Individualisation
Gonadotrophin
Dose
Low AMH
(eg DSL assay 22-157 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
Normal AMH
(eg DSL assay158-288pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
High AMH
(eg DSL assay gt288 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-375 IU)
Low Dose
(eg HMG 75-150 IU)
220
Table 1 AMH-tailored stratification protocols for regime starting dose of hMGrFSH and adjusting daily dose of gonadotrophins (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (V1) (01 Jan 2011-30 Apr 2011)
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Initial dose (Day 1-3) 1) lt22 AMH (DSL) Exclude 2) 22-156 AMH (DSL) Antagonist 300 hMG 3) 157-285 AMH (DSL) Long Agonist 200 rFSH225 hMG 4) gt286 AMH (DSL) Antagonist 150 hMG
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 375 hMG 3) 11-21 AMH (Gen II) Long Agonist 300 hMG 4) 22-30 AMH (Gen II) Long Agonist 225 hMG 5) 31-39 AMH (Gen II) Long Agonist 150 hMG 6) 40-67 AMH (Gen II) without PCO Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCO Long Agonist 125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Long Agonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Long Agonist 1125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 450 hMG 2) 3-10 AMH (Gen II) Long Agonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 rFSH 8) gt67 AMH (Gen II) Antagonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 300 rFSH 2) 3-10 AMH (Gen II) Long Agonist 225 rFSH 3) 11-21 AMH (Gen II) Long Agonist 1875 rFSH 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 hMG 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 hMG 8) gt67 AMH (Gen II) Antagonist 1125 hMG
Dose adjustment No or minimum change on daily dose of gonadotrophin
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
221
Table 2 AMH-tailored stratification protocols for management of suspected excessive response (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (v1) (01 Jan 2011-30 Apr 2011)
amp
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Coasting for excessive response on day 8
Oestradiol gt20000 pgml 30-40 follicles larger than 10mm or Oestradiol gt18000 pgml
30-40 follicles larger than 12mm
No coasting
Coasting for excessive response once follicle maturation meets criteria
Oestradiol gt20000 pgml
30-40 follicles larger than 10mm
25-40 follicles larger than 10mm
25-30 follicles larger than 15mm
Cancellation for excessive response
Day 8 or thereafter Oestradiol lgt20000 pgml and symptoms of OHSS after gt3 days of coasting
Day 8 or thereafter More than 40 follicles larger than 10mm
Day 10 or thereafter More than 40 follicles larger than 15mm
Day 8 or thereafter Cancel only if symptoms of OHSS
222
Table 3 Distribution of patient characteristics and interventions
In total 1847 cycles included in the study
n
Causes
Unexplained 591 32
Mild tubal 325 176
Severe tubal 37 2
Mild male 589 3189
Severe male 18 097
Endometriosis 91 493
Endometrioma 47 28
Attempt
1 1346 7287
2 406 2198
3 91 493
4 4 022
USOR practitioner
A 570 317
B 412 2291
C 147 818
D 15 083
E 153 851
F 86 478
G 118 656
H 136 756
I 141 784
J 20 111
Protocol
1 1265 6849
2 (v1) 399 216
2 (v2ampv3) 79 428
2 (v4) 104 563
FSH preparation
HMG 1594 87
rFSH 237 13
Regime
Long Agonist 820 444
Antagonist 1027 556
Initial dose
75-150IU 298 1617
187-250IU 483 2621
300IU 914 4959
375IU 60 326
450IU 88 477
223
Table 4a Results of multivariable regression analysis for total and MII oocytes
Total oocytes (n=1653) Metaphase II oocytes (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
Age -0031 -004 -002 00005 -0021 -004 -001 0006
age2 -0002 000 000 0047 -0002 -001 000 0206
AMH categories (Ref0-3 pmolL) 00005 00005
4-5 pmolL 0254 -003 054 0078 -0073 -054 040 0761
6-8 pmolL 0368 010 064 0008 0250 -019 069 0267
9-10 pmolL 0605 034 087 00005 0474 004 091 0034
11-12 pmolL 0651 039 091 00005 0305 -016 077 0198
13-15 pmolL 0779 051 104 00005 0372 -008 083 0109
16-18 pmolL 0836 057 111 00005 0655 018 113 0007
19-22 pmolL 0803 051 109 00005 0381 -013 089 0142
23-28 pmolL 0954 067 123 00005 0832 034 132 0001
29-200 pmolL 1126 084 141 00005 0872 035 139 0001
AFC categories (Ref 0-7) 00005 0008
8-9 -0039 -018 010 0589 0001 -024 024 0992
10-11 0145 001 028 0037 0185 -005 042 0119
12-14 0223 009 036 0001 0254 002 049 0031
15-19 0263 013 040 00005 0113 -013 036 0362
20-24 0344 017 052 00005 0456 013 078 0006
25-100 0405 021 060 00005 0455 009 082 0015
Causes of infertility
Unexplained 0103 002 019 0021 0090 -010 028 0354
Mild tubal -0012 -010 008 0797 -0098 -029 009 0307
Severe tubal -0066 -030 017 0579 -0371 -093 019 0194
Mild male 0014 -007 009 0729 0135 -002 029 009
Severe male -0074 -055 040 0758 -0377 -117 042 0351
Endometriosis -0108 -026 005 0169 -0139 -041 013 0314
Endometrioma -0016 -018 015 0843 0043 -035 044 083
Attempt (Ref 1st) 0001 045
2nd 0085 002 015 0016 0080 -006 022 0274
3rd4th attempt 0243 010 039 0001 0116 -014 037 0367
224
Table 4b Results of multivariable regression analysis for total and MII oocytes Continuation of Table 4a)
Total oocyte (n=1653) Metaphase II oocyte (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
USOR Practitioner (Ref A) 00005 0058
B -0009 -009 007 0823 -0129 -031 005 0153
C 0104 -003 024 0129 0111 -012 034 0348
D -0260 -059 007 0125 -0287 -108 051 0478
E -0297 -044 -016 0 -0246 -048 -001 0043
F -0173 -032 -003 0017 -0367 -072 -001 0043
G -0213 -039 -003 002 -0311 -061 -001 0044
H -0007 -012 011 0909 0022 -020 025 0849
I -0149 -025 -004 0005 -0082 -030 014 0462
J -0549 -095 -015 0007 -0408 -095 014 0143
Protocol (Ref 1st) 00003 024
2nd (v1) -0186 -027 -010 0 -0066 -024 010 0449
2nd (v2ampv3) -0140 -028 000 0056 0175 -007 042 0156
2nd (v4) -0244 -043 -006 0009 0002 -031 031 0989
Gonadotrophin (Ref HMG)
rFSH 0137 004 024 0008 0119 -009 033 0262
Dose amp Regime (RefAgonist 75-150IU) 00005 00052
Antagonist 75-150IU -0364 -053 -020 0 -0199 -051 011 0203
Agonist 187-250IU 0104 -003 024 0139 0028 -031 036 0869
Antagonist 187-250IU 0124 -006 030 0176 0436 -002 089 0059
Agonist 300IU 0151 -001 031 0059 0258 -011 062 0165
Antagonist 300IU 0003 -016 017 0968 0143 -022 050 0433
Agonist 375IU 0072 -023 037 0639 -0185 -086 049 0591
Antagonist 375IU 0124 -011 035 0291 0478 005 090 0028
Agonist 450IU -0129 -041 015 037 -0285 -080 023 0278
Antagonist 450IU -0207 -048 006 0134 0046 -041 051 0843
Intercept 1342 102 166 0 0993 043 155 0001
225
Figure 3a Total oocytes
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fit0
Non-PCO
226
Figure 3b Total oocytes
Plots show the raw data as dots Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 stimulation with HMG USOR practitioner-A none of the specific causes of infertility
25 30 35 40
12
510
20
Age
To
tal E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
To
tal E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
To
tal E
gg
s
AFC
fit0
Non-PCO
227
Figure 4a Metaphase II oocytes (ICSI subset)
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fitm0
Non-PCO
228
Figure 4b Metaphase II oocytes (ICSI subset)
Plots show raw data as dot Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 simulation with HMG USOR practitioner-A None of the specific causes of infertility
25 30 35 40
12
510
20
Age
Ma
ture
IC
SI E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
Ma
ture
IC
SI E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
Ma
ture
IC
SI E
gg
s
AFC
fitm0
Non-PCO
229
GENERAL SUMMARY
7
230
GENERAL SUMMARY
Anti-Muumlllerian hormone a dimeric glycoprotein secreted from granulosa cells
of growing ovarian follicles appears to play a central role in the regulation of
oocyte recruitment and folliculogenesis (Durlinger et al 2002)
Serum anti-Muumlllerian hormone concentration has been found to be one of
the best predictors of ovarian performance in IVF treatment (van Rooij et al
2002 Broer et al 2011) Therefore an evaluation of the role of AMH in assisted
conception has been of great interest and consequently a considerable body of
research work has been performed during last two decades Most published
studies with varying methodological quality have suggested that AMH is one
of the most reliable predictors of ovarian performance in IVF treatment cycles
Consequently many fertility centers have introduced measurement of AMH for
the assessment of ovarian reserve and as a tool for formulation of treatment
strategies for controlled ovarian hyperstimulation in assisted conception
However the studies described in this thesis suggest that some assumptions on
the clinical value of AMH particularly reliability of AMH assay methods and
the role of AMH-tailored individualisation of daily dose of gonadotrophins in
IVF were not based on robust data
For the purpose of this thesis I conducted a comprehensive review of the
published literature on the biology of ovarian reserve the role of AMH in
female reproduction the assay methods and clinical application of AMH in
assisted conception (Chapter 1) I established that a) published work on
sampling variability of AMH measurements and comparability of various assay
methods provide conflicting results b) data on the effect of ethnicity BMI
reproductive pathology and surgery is scarce and c) good quality data on
individualisation of AMH-tailored controlled ovarian hyperstimulation in IVF
is lacking Consequently I decided to conduct a series of studies that directed
towards an improvement of the scientific evidence in these areas of research
Our previous work on within-patient variability of the first generation DSL
assay samples showed that AMH measurements may exhibit considerable (CV
28) sample-to-sample variability (Rustamov et al 2011) In view of this it was
decided to evaluate the validity of newly introduced Gen II assay (Chapter
21) In order to achieve adequately powered results all available AMH
samples of women of 20-46 years of age who had investigation for infertility at
231
secondary and tertiary care divisions of St Maryrsquos Hospital during the study
period were selected for the study According to the manufacturerrsquos
recommendation haemolysed AMH samples may provide erroneous results
and therefore women with haemolysed samples were excluded from the
analysis Inclusion of all women during the study period was also important in
reducing the risk of selection bias particularly in this study which compared
historical and current AMH assay Given the referral criteria of patients did not
change throughout the study period I could confidently report that observed
comparison between DSL and Gen II samples were the reflection of true
differences of the assay methods It is important to note that validity and
performance of a new test should ideally be compared to a reliable ldquogold
standardrdquo test However to date there appears to be no gold standard test in
measurement of AMH and hence an evaluation of the performance of assay
methods can be chllanging Given the lack of a gold standard I decided to
assess the quality of the new test in comparison to what was considered the
most reliable test available at that time accepting that such a comparison may
have limitations Previously two AMH assays (DSL and IOT) were in use and
there is no research evidence on the superiority of one assay over other
Therefore in this study the new Gen II assay was compared to the DSL assay
method which was previously available in our clinic
Once I prepared a robust and validated dataset the quality of Gen II assay
was evaluated by taking following steps of investigation First within-patient
between-sample variability of AMH measurements of Gen II assay samples
were obtained and compared to that of DSL assay samples Then the validity
of the manufacturer recommended between-assay conversion factor was
evaluated by comparing the Gen II assay sample measurements to that of DSL
assay method using both cross-sectional and longitudinal datasets The stability
of the Gen II assay samples was assessed by examining a) stability of the
samples in room temperature b) the linearity of dilution of the samples c)
comparing the standard assay preparation method to that of an equivalent
method and d) stability of samples during storage in frozen condition
Worryingly the study found that the Gen II AMH assay which was
reported to be more reliable than previous assays gave significantly higher
sampling variability (CV 59) compared to that of DSL samples (CV 28)
This significant variation in between repeated measurements of Gen II samples
indicated that there might be a profound fault in the assay method The
232
comparison of the assay methods using a large cohort of clinical samples
suggested that Gen II assay provided 40 lower measurements compared to
that of DSL contradicting the manufacturerrsquos reported 40 higher
measurements (Kumar et al 2011) These discrepancies in the sampling
variability and assay-method comparability suggested that Gen II assay samples
may lack stability which had not been observed previously
When different assays are available for a particular analyte it is critical that
the comparability of results is established and reliable conversion factors or
calibration curves are determined The study demonstrated that the difference
between the previously recommended (Kumar et al 2011 Wallace et al 2011)
conversion factor and the conversion formula obtained in this study was as
high as 60-80 All three studies followed the manufacturersrsquo
recommendations as supplied in the kit insert In terms of the study design
and analysis previous studies assessed the within-sample difference between
the two assays considered this involved the thawing of samples splitting into
two different aliquots and analysis of each aliquot with a different assay In
contrast I conducted between-sample comparison of historical DSL
measurements to that of Gen II using cross sectional and longitudinal
population based analyses The laboratory based within-sample conversion
formula should be reproducible in population based between-sample
comparison particularly in longitudinal analysis Observed discrepancies in the
conversion factors again suggested that AMH samples may suffer from pre-
analytical instability
Thus in collaboration with the scientific team of the Clinical Assay
Laboratory of our hospital we investigated the stability of Gen II assay
samples The studies on sample storage and preparation confirmed the Gen II
assay samples exhibited considerable instability under the storage and
processing conditions recommended by the manufacturer It was suggested
that Gen II samples remain stable when stored in unfrozen conditions up to 7
days and many IVF clinics adopted the practice of shipping unfrozen AMH
samples to centralized laboratories for processing and analysis (Kumar et al
2010 Nelson and La Marca 2011) This study demonstrated that storage of
unfrozen samples can affect obtained results considerably Evaluation of the
stability of samples (n=48) at room temperature found that in the majority of
samples AMH levels in serum increased progressively during 7 days of storage
with an overall increase as high as 58 Contrary to the manufacturerrsquos report
233
even storage of samples in frozen condition (-20 ordmC) does not ensure the
stability of the samples Storage at -20ordmC for 5 days increased AMH levels by
23 compared to fresh samples Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample In contrary the study showed that Gen II samples exhibit
considerable increase with the dilution Pre dilution of serum prior to assay
gave AMH levels up to twice that found in the corresponding neat sample
Similarly pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave overall 72 higher readings compared to sequential
addition These experiments confirmed that Gen II assay methodology was
completely flawed and routine clinical samples were likely to provide highly
erroneous results which could lead to adverse clinical consequences in
patients
To evaluate the robustness of our data I validated the study on the
variability of Gen II samples using external data (Chapter 22) Assessment of
samples obtained from different patient population and different assay-
laboratory found that within-patient between-sample variability of Gen II
AMH measurements were similar to that of my study (CV 62) This
confirmed that Gen II assay sampling variability was independent of
population or laboratory and specific to the assay-method
Findings of this series of studies suggested that the use of Gen II
measurements might have considerable clinical implications particularly when
used as a marker for triaging patient to ovarian stimulation regimens in cycles
of IVF In order to obtain equivalent clinical cut-off ranges for Gen II
samples previously used DSL assay based guidance ranges were recommended
to be increased by 40 However my study found that Gen II assay may
actually provide 20-40 lower measurements compared to that of DSL which
might led to allocation of patients to inappropriate treatment regimens Given
that using the above conversion formula may underestimate ovarian reserve by
60-80 the patients may inadvertently be given significantly higher dose of
gonadotrophins than appropriate in the individual IVF treatment cycles This
can increase the patientrsquos risk of excessive ovarian response resulting in
cancellation of IVF cycles andor severe ovarian hyperstimulation syndrome
(OHSS) In addition significant variation of Gen II assay sample
measurements (CV 59) may also lead to inconsistency in allocation of
patients to appropriate cut off ranges Indeed this was demonstrated by a
234
recent study which found that 7 out of 12 patients moved from one cut-off
range to another when Gen II assay was used for AMH measurements
(Hadlow et al 2013) Therefore we suggested that Gen II assay samples should
not be used in allocating patients to ovarian stimulation regimens
Immediate steps were taken to report these findings to the manufacturer
scientists clinicians and the quality assessment agencies The findings of the
study were presented at the annual meetings of European Society of Human
Reproduction and Embryology as well as British Fertility Society The study
was also published in Human Reproduction which generated an important debate
on the validity of Gen II assay measurements Further independent studies by
other research groups and re-evaluation of the assay by the manufacturer have
confirmed our results (Han et al 2013) This led to recognition of the issues of
the Gen II assay by the manufacturer and consequent modification of the assay
method (King 2012) Subsequent evaluation of Gen II assay by the Medicines
and Healthcare Products Regulatory Agency (MHRA) and the National
External Quality Assessment Service (NEQAS) have confirmed the above
findings As a result the Human Fertility and Embryology Authority have
circulated a field safety notice with the regards to the pitfalls of the AMH Gen
II assay We informed National Institute for Health and Care Excellence
(NICE) of the problems of AMH measurements and urged it to review its
current recommendation on the use of AMH in the investigation and
treatment of infertility With regards to the impact of this work it is important
to note that AMH is widely used in fertility clinics around the world and Gen
II assay is the only commercially available kit for the measurement of AMH in
most countries Consequently this study has made a direct significant impact
in the improving safety and effectiveness of fertility investigation and
treatment around the world However further studies are required to
determine the cause of the instability In addition the validity of the modified
protocol for Gen II assay and other new AMH assays need to be evaluated In
the meantime caution should be exercised in the interpretation of Gen II
AMH measurements
Studies above established that invalid commercial AMH assay was
introduced for clinical use without full and independent validation Regretfully
the issues with the assay were not identified early enough to prevent
widespread use of this faulty test in clinical management of patients around the
world In order to avoid above failures and improve reliability of future AMH
235
assays I recommend following steps should be taken 1) International
standards for the evaluation of validity of existing and future AMH assays
should be developed 2) Independent research groups should evaluate validity
of AMH assays before introduction of the test for clinical application 3)
Validity and performance of already introduced AMH assays ought to be
evaluated by independent research groups periodically to ensure timely
detection of the deterioration in the quality of the test
In view of the observed issues with AMH measurements we conducted
a critical appraisal of the published research on the previous and current assay
methods that reported AMH measurement variability assay method
comparison and sample stability (Chapter 3) Following a systematic search
for all published studies on the evaluation of performance of historic and
current AMH assays ten sample stability studies 17 intrainter-cycle variability
studies and 14 assay method comparability studies were identified Previously
most studies reported that variability of AMH in serum was very small and
suggested a random single measurement provides an accurate assessment of
circulating AMH in serum Therefore using a random AMH measurement for
assessment of ovarian reserve has become a routine practice It appears that
both in reporting particularly in its interpretation the term ldquoAMH variabilityrdquo
was used too broadly and had a various meanings Reviewing all published
studies that used term ldquoAMH variabilityrdquo I identified that the term was used in
interpretation of four distinct outcomes for measurement of variability of
AMH in serum 1) circadian 2) within the menstrual cycle 3) between
menstrual cycles and 4) between repeated samples without consideration of the
day of menstrual cycle In order to delineate the reported variability of AMH
for each outcome I divided the variability studies into four separate groups
and reviewed each study within its appropriate group The review found that
most studies were based on small sample sizes and did not report the
methodology for sample processing and analysis fully The studies also appear
to refer to their outcomes as biological variability of AMH without taking into
account the variability arising due to errors in its measurement More
importantly the review demonstrated that there is clinically significant
variability between AMH measurements in repeated samples which was
reported to be markedly higher with currently used Gen II assay compared to
that of historic DSL and IOT assays
236
Appraisal of assay method comparability found that despite using the
standard manufacturer protocols for the sample analysis the studies have
generated strikingly different between-assay conversion factors The studies
comparing first generation AMH assays (DSL vs IOT) reported conversion
factors ranging from five-fold higher with the IOT assay compared to both
assays giving equivalent AMH concentrations Similarly studies comparing first
and second-generation assays (DSL vs Gen II or IOT vs Gen II) derived
conflicting conclusions The apparent disparity in results of the assay
comparison studies implies that AMH reference ranges and guidance ranges
for IVF treatment which have been established using one assay cannot be
reliably used with another assay method without full and independent
validation Similarly caution is required when comparing the outcomes of
research studies using different AMH assay methods Correspondingly the
review of studies on sample stability revealed conflicting reports on the
stability of AMH under normal storage and processing conditions which was
reported to be a more significant issue with the Gen II assay Similarly there
was considerable discrepancy in the reported results on the linearity of dilution
of AMH samples particularly in Gen II studies In view of above findings we
concluded that AMH in serum may exhibit pre-analytical instability which may
vary with assay method Therefore robust international standards for the
development and validation of AMH assays are required
Although AMH assays have been in clinical use for more than a decade
this appears to be first published review that examined the studies on the
performance of AMH assay methods Indeed a number of review articles
comparing clinical performance of AMH test to other markers of ovarian
reserve have been published (Broer et al 2009 Broer et al 2011b La Marca et
al 2009) Reviewing observational studies the articles concluded that AMH
measurement was one of the most robust methods of assessment of ovarian
reserve However there appears to be no review article that specifically
evaluated the validity of the AMH assay methods suggesting AMH assay
methods were assumed to be reliable despite the lack of robust data on the
validity of assay methods
Reassuringly the report of instability of the Gen II assay samples has
generated significant research interest directed towards understanding the
causes of the issue As a result several hypotheses have been proposed and are
undergoing testing by various research groups For instance in the work
237
described here it was proposed that AMH molecule may undergo proteolytic
changes under certain storage and processing conditions exposing additional
antibody binding sites (Rustamov et al 2012a) The manufacturer of the assay
suggested that the sample instability is due to the presence of complement
interference (King 2012) More recent studies have reported the presence of
another form of AMH molecule pro-AMH in the serum may be the source of
erroneous measurements (Pankhurst et al 2014) Furthermore this study
demonstrated that Gen II assay detects both AMH and pro-AMH suggesting
that the mechanism of sample instability may be more complex than previously
thought It is indeed important to continue the quest to determine the cause of
the sample instability in order to develop reliable method for measurement of
AMH in future In the meantime clinicians should exercise caution when using
AMH measurements in the formulation of treatment strategies for individual
patients
Using a robust protocol for extraction of data and preparation of
datasets I have built a large validated research database (Chapter 4) Utilizing
the clinical electronic data management systems and case notes of patients I
have prepared a validated dataset that will enable study of ovarian reserve in a
wide context including a) assessment of ovarian reserve b) evaluation of the
performance of the biomarkers c) study individualization of ovarian
stimulation in IVF d) association of biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryos live birth) The database has been
used to address research questions posed in chapter 5 and chapter 6 of this
thesis In addition it can be utilized for future studies on assessment of ovarian
reserve and IVF treatment interventions
Both formation and decline of ovarian reserve appears to be largely
determined by genetic factors although at present data on genetic markers are
scarce (Shuh-Huerta et al 2012) Therefore availability of data on clinically
measurable determinants of ovarian reserve is important Consequently I
explored the role of ethnicity BMI endometriosis causes of infertility and
reproductive surgery to ovarian reserve using AMH AFC and FSH
measurements of a large cohort of infertile patients (Chapter 51)
Multivariable regression analysis of data on the non-PCO cohort showed the
association between ethnicity and the markers of ovarian reserve is weak In
contrast I observed a clinically significant association between BMI and
ovarian reserve obese women were found to have higher AMH and lower
238
FSH measurements compared to those of non-obese With regard to the role
of the causes of infertility I did not observe a significant association between
the markers of ovarian reserve and subsets diagnosed with unexplained or
tubal factor infertility In contrast those diagnosed with male factor infertility
had significantly higher AMH and lower FSH measurements which increased
with the severity of the disease In conclusion the study demonstrated that
some of the above factors have a significant impact on above biomarkers of
ovarian reserve and therefore I suggest future studies on ovarian reserve
should include adjustment for the effects these factors
The study showed that in the absence of endometrioma endometriosis
was not found to have a strong association with markers of ovarian reserve
compared to those without the disease Interestingly women with an
endometrioma had significantly higher AMH measurements than those
without endometriosis This is the first study that has reported increased
AMH in serum in the presence of endometrioma Interestingly recent studies
have demonstrated that AMH and its receptor are expressed in tissue samples
obtained from ovarian endometriosis (Wang et al 2009 Carelli et al 2014) It
appears that AMH inhibits growth of both epithelial and stromal cells
(Signorille et al 2014) I believe these intriguing findings warrant further
research on the role of AMH in the pathophysiology of endometriosis With
regards to assessment of ovarian reserve AMH may not reflect ovarian reserve
in the presence of endometrioma and therefore caution should be exercised
With respect to reproductive surgery I conducted a study to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease (Chapter 52) Multivariable regression analysis of the
cross-sectional data showed that salpingo-ophorectomy and ovarian
cystectomy for endometrioma have a significant detrimental impact on ovarian
reserve as estimated by AMH AFC and FSH In contrast neither
salpingectomy nor ovarian cystectomy for cysts other than endometrioma was
found to have appreciable effects on the markers of ovarian reserve I suggest
that women undergoing surgery should be counseled regarding the potential
impact of surgical interventions to their fertility However there was
appreciable overlap between the interquartile ranges of the comparison groups
This suggests that although the effects are significant at a population level
there is considerable variation between individuals Therefore clinicians should
239
exercise caution in predicting the effect of surgery on ovarian reserve of
individual patients
Published studies on the prognostic value of AMH in assisted
conception suggested there is a strong correlation between AMH and extremes
of ovarian response in cycles of IVF (Nelson et al 2007 Nardo et al 2007)
Later case control studies showed that tailoring the daily dose of
gonadotrophins to individual patientrsquos AMH levels and pituitary
desensitisation with GnRH antagonist in patients with the extremes of ovarian
reserve improved the outcomes of IVF treatment (Nelson et al 2009 Yates et
al 2012) However these studies displayed a number of methodological issues
largely due to retrospective analysis small sample size and centre-dependent or
time-dependent selection of cohorts Therefore the role of confounding
factors on the obtained estimates of these studies is unclear Ideally clinical
application of these treatment interventions should be based on research
evidence based on large randomized controlled trials In the absence of
controlled trials I decided to obtain best available estimates on the role of
AMH in individualisation of controlled ovarian stimulation using a robust
methodology in my large cohort of treatment cycles (Chapter 6) Oocyte yield
was used as the outcome measure given it is mainly determined by the
effectiveness of treatment strategies for ovarian stimulation which is the
question the study has addressed In contrast downstream outcomes such as
clinical pregnancy and live birth are subject to additional clinical and
interventional factors The study developed multivariable regression models of
total oocyte yield in all included IVF ICSI cycles (n=1653) and Metaphase II
oocytes of the ICSI subset (n=1101) to measure ovarian response to COH In
view of the significant interaction of PCO status with other variables I
restricted the analysis to non-PCO patients First in order to identify the
confounders I established the effect of a set of plausible factors that may affect
the outcomes including assessment of the effect of age AMH AFC causes of
infertility attempt of IVFICSI cycle COH protocol changes gonadotrophin
preparations operator for oocyte recovery pituitary desensitisation regime and
initial daily dose of gonadotrophins Then I developed the regression models
that examined the effect of gonadotrophin dose and regime categories on total
and mature oocyte numbers
240
The study found that after adjustment for all the above factors age
remained a negative predictor of oocyte yield whereas I observed a gradual
and significant increase in oocyte number with increasing AMH and AFC
values suggesting all these markers display an independent association with
oocyte yield Interestingly after adjustment for all above variables in non-PCO
patients I did not observe the expected increase in oocyte number with
increasing gonadotrophin dose categories beyond the very lowest doses This
suggests that there may not be a significant direct dose-response effect and
consequently strict protocols for tailoring the initial dose of gonadotrophins
may not necessarily optimize ovarian performance in IVF treatment It is
important to note our COH protocols utilized extensive cycle monitoring
using ultrasound follicle tracking and measurement of serum oestradiol levels
with corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not demonstrate a significant
impact on oocyte yield Nevertheless further longitudinal regression analysis
including full time course parameters of cycle monitoring and the dose
adjustments in the model should be conducted in order to ascertain the role of
AMH in tailoring the dose of gonadotrophins in cycles of IVF Moreover the
role of AMH on downstream outcomes of IVF cycles particularly on live
birth should be examined in this dataset Now equipped with a better
understanding of the research methodology and a robust database I am
planning to visit these research questions in future work
Although clinical biomarkers have improved the assessment of ovarian
reserve there remains a significant limitation in their performance in terms of
accurate estimation of ovarian reserve Given that ovarian reserve is believed
to be largely determined genetically recent large Genome-Wide Association
Studies (GWASs) have focused on the identification of genetic markers of
ovarian aging A meta-analysis of these 22 studies identified four genes with
nonsynonymous SNPs as being significantly associated with an age at
menopause (Stolk et al 2012 He et al 2012) However these SNPs were found
to account for only 25-41 of association of the age at menopause
Furthermore studies in mice and humans have identified more than 400 genes
that are involved in ovarian development and function (Wood et al 2013)
Given this genetic heterogeneity it is unlikely that a single genetic determinant
241
of ovarian reserve will be identified In addition epigenetic noncoding RNAs
and gene regulatory regions may play an important role in determination of
ovarian reserve which is yet to be fully explored (Bernstein et al 2012) Indeed
further large scale studies for ascertainment of genetic markers of ovarian
reserve are needed However current biomarkers including AMH appear to
remain as the most useful tests for the assessment of ovarian reserve in the
foreseeable future and further efforts to improve the performance of these
tests are therefore important
In summary some of the assumptions on performance of AMH
measurements particularly Gen II assay appear to have been based on weak
research evidence Similarly there are significant methodological limitations in
the published studies on AMH-tailored individualisation of controlled ovarian
hyperstimulation in IVF I believe the studies described in this thesis have
revealed instability of Gen II assay samples and raised awareness of the pitfalls
of AMH measurements These studies have also demonstrated the effect of
clinically measurable factors on ovarian reserve and provided data on the effect
of AMH other patient characteristics and treatment interventions on oocyte
yield in cycles of IVF Furthermore a robust database and statistical models
have been developed which can be used in future studies on ovarian reserve
and IVF treatment interventions I believe the work presented here has
provided a better understanding of the performance of AMH as an
investigative tool and its role in management of infertile women and provided
resource for future work in this area
242
References Bernstein BE Birney E Dunham I Green ED Gunter C Snyder M ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome Nature 2012 489(7414)57ndash74 [PubMed 22955616] Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Broer SL Doacutelleman M Opmeer BC Fauser BC Mol BW Broekmans FJ AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 Jan-Feb 17(1)46-54 Epub 2010 Jul 28 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 20141011353ndash8
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013 May 99(6)1791-7 Han X Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Human ReproductionJun2013 Vol 28 Issue suppl_1 He C Murabito JM Genome-wide association studies of age at menarche and age at natural menopause Mol Cell Endocrinol 2012
King D URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian
243
response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Pankhurst M Chong Y H and McLennan ISEnzyme-linked immunosorbent assay measurements of antimeuroullerian hormone (AMH) in human blood are a composite of the uncleaved and bioactive cleaved forms of AMH Fertility and Sterility2014
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Stolk L Perry JR Chasman DI et al Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways Nat Genet 2012 44(3)260ndash268
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH
244
and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wood M and Rajkovic A Genomic Markers of Ovarian Reserve Semin Reprod Med 2013 31(6) 399ndash415
245
Authors and affiliations
Stephen A Roberts PhD
Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL United Kingdom
Cheryl Fitzgerald MD
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester M13 0JH
United Kingdom
Philip W Pemberton MSc
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Alexander Smith PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Luciano G Nardo MD
Reproductive Medicine and Gynaecology Unit GyneHealth
Manchester M3 4DN United Kingdom
Allen P Yates PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Monica Krishnan MBChB
Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL United Kingdom
246
Acknowledgments
First and foremost I would like to thank my supervisors Dr Stephen A
Roberts and Dr Cheryl Fitzgerald I am indebted to you for introducing me
into the world of science showing its wonders and guiding me through its
terrains Without your 247 advise and support none of these projects would
have been possible Thank you
I would also like to thank other members of our team Dr Philip W
Pemberton Dr Luciano G Nardo Dr Alexander Smith Dr Allen P Yates and
Monica Krishnan It has been exciting and fun to be a part of the Manchester
AMH Group
I am grateful for the support and friendship of all secretaries nurses
embryologists and consultants of IVF Department at St Maryrsquos Hospital I
would like to express my special thanks to Professor Daniel Brison for his
advice on the projects and providing a great opportunity for research I would
like to express my gratitude to Dr Greg Horne Senior Embryologist for his
patience in taking me through tons of IVF data It was a privilege to be part of
this team
Indeed without support of my wife Zilola Navruzova I could not have
completed my MD programme Thank you for being there for me through
thick and thin of life You are love of my life Your optimism can make
anything possible Your sense of humor and kindness brightened my long
research hours after on-call shifts Only because of your enthusiasm we could
juggle work research and family And thanks for pretending that AMH is
interesting
My children Firuza Sitora and Timur You are most great kids Always stay
cool and funny like this Sorry for not taking you to holiday during my never-
ending research during last year Hope I havenrsquot put you off doing research in
future You get lots of conference holidays after research
247
I canrsquot thank enough my mother Karomat Rajobova and father Dr Sohib
Rustamov Your love kindness and wisdom have always been inspiration and a
guide in my life I always strive to follow your example albeit impossible to
achieve
My brother Ulugbek Rustamov thank your selfless support As always you
have been my guide and strength during these three years My friends Odil
Nizomov Dr Rohit Arora Tarek Sharif and Sabiha Sharif I am grateful for
your friendship and support during my MD Programme
248
I would like to dedicate this thesis to my mother father my wife and
children
Shu Doctorlik Dissertaciysini
Onam (Karomat Rajabova)
Dadam (Dr Sohib Rustamov)
Turmush Urtogim (Zilola Navruzova)
Farzandlarim (Firuza Sohibova Sitora Sohibova
Timur Rustamov) ga bagishlayman
Sizlar mani kuzimni nuri sizlar
Yaratgandan sizlarga mustahkam sogliq va quvonch tilayman
_______________________
Oybek
31 March 2014 Manchester United Kingdom
4
DECLARATION
No portion of the work referred to in the thesis has been submitted in support
of an application for another degree or qualification of this or any other
university or other institute of learning
COPYRIGHT STATEMENT
i The author of this thesis (including any appendices andor schedules to this
thesis) owns certain copyright or related rights in it (the ldquoCopyrightrdquo) and she
has given The University of Manchester certain rights to use such Copyright
including for administrative purposes
ii Copies of this thesis either in full or in extracts and whether in hard or
electronic copy may be made only in accordance with the Copyright Designs
and Patents Act 1988 (as amended) and regulations issued under it or where
appropriate in accordance with licensing agreements which the University has
from time to time This page must form part of any such copies made
iii The ownership of certain Copyright patents designs trade marks and
other intellectual property (the ldquoIntellectual Propertyrdquo) and any reproductions
of copyright works in the thesis for example graphs and tables
(ldquoReproductionsrdquo) which may be described in this thesis may not be owned
by the author and may be owned by third parties Such Intellectual Property
and Reproductions cannot and must not be made available for use without the
prior written permission of the owner(s) of the relevant Intellectual Property
andor Reproductions
iv Further information on the conditions under which disclosure publication
and commercialisation of this thesis the Copyright and any Intellectual
Property andor Reproductions described in it may take place is available in
the University IP Policy (see
httpdocumentsmanchesteracukDocuInfoaspxDocID=487) in any
relevant Thesis restriction declarations deposited in the University Library The
University Libraryrsquos regulations (see
httpwwwmanchesteracuklibraryaboutusregulations) and in The
Universityrsquos policy on Presentation of Theses
5
PUBLICATIONS ARISING FROM THE THESIS
Journal Articles
1 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton
The measurement of Anti-Muumlllerian hormone a critical appraisal
The Journal of Clinical Endocrinology amp Metabolism 2014 Mar99(3)723-32
2A Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large
cohort of subjects suggests sample instability Human Reproduction 2012 Oct
27(10) 3085-91
2B Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Human Reproduction Dec2012 Vol 27 Issue 12 p3641
6
Conference presentations
1 O Rustamov S Roberts C Fitzgerald
Ovarian endometrioma is associated with increased AMH levels
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2014 Munich
Poster Presentation
2 O Rustamov M Krishnan R Mathur S Roberts C Fitzgerald
The effect of BMI to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral presentation Dr O Rustamov
3 M Krishnan O Rustamov R Mathur S Roberts C Fitzgerald
The effect of the ethnicity to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral Presentation Dr M Krishnan
4 O Rustamov M Krishnan S Roberts C Fitzgerald
Reproductive surgery and ovarian reserve
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr O Rustamov
5 C Fitzgerald O Rustamov P Pemberton A Smith A Yates M Krishnan
R Russell L Nardo SRoberts
AMH assays A review of the literature on assay method comparability
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr C Fitzgerald
6 M Krishnan O Rustamov R Russell C Fitzgerald S Roberts
The role of the ethnicity and the body weight in determination of AMH levels
in infertile women
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
7
Poster presentation
7 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
AMH Gen II assay - can we believe the measurements
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
8 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Old and new AMH assays Can we rely on current conversion factor
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
9 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Random AMH measurement is not reproducible
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
10 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
The reproducibility of serum Anti-Muumlllerian hormone AMH Gen II assay
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2012 Istanbul
Oral Presentation Dr O Rustamov
8
GENERAL INTRODUCTION
AND LITERATURE REVIEW
1
9
CONTENTS I LITERATURE REVIEWhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10 GENERAL BACKGROUNDhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10
1 OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip12 11 Primordial Follicle Assemblyhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip13 12 Oocyte recruitmenthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip14 13 Theory of neo-oogenesishelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip15 2 MARKERS OF OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 21 Ovarian reserve markers with limited clinical valuehelliphelliphelliphelliphellip16 213 Inhibin Bhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 214 Basal oestradiolhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 215 Dynamic testshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 216 Ovarian volumehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 22 Ovarian reserve markers in routine clinical usehelliphelliphelliphelliphelliphelliphellip18 221 Chronological agehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 222 Basal FSHhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 223 Antral follicle counthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip19 3 ANTI-MUumlLLERIAN HORMONEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 31 Biology of anti-Muumlllerian hormonehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 311 The role of AMH in the ovaryhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip21 312 AMH in women with polycystic ovary syndromehelliphelliphelliphelliphellip22 32 AMH Assayhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip23 33 Variability of AMH measurementshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip24 34 Role of AMH in assessment of ovarian reservehelliphelliphelliphelliphelliphellip25 341 Prediction of poor and excessive ovarian response in IVFhelliphellip25 342 Prediction of live birth in cycles of IVFhelliphelliphelliphelliphelliphelliphelliphelliphellip26
3 5 Role of AMH in ovarian stimulation for cycles of IVFhelliphelliphelliphellip26
4 MULTIVARIATE TESTShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip27
5 SUMMARYhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip28 II GENERAL INTRODUCTIONhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip29 REFERENCEShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip31
10
I LITERATURE REVIEW GENERAL BACKGROUND
Infertility is a disease of the reproductive system defined by the failure to
achieve a pregnancy after 12 months of regular unprotected sexual intercourse
although the criteria for the duration vary between different countries (NICE
2013) Worldwide prevalence of infertility estimated to be around 724 million
couples and around 40 million of those seek medical care (Hull et al 1985) In
the UK 15 couples present with infertility with an annual incidence of 12
couples per 1000 general population (Scott et al 2009) The main causes of
infertility are tubal disease ovulatory disorders male factor and poor ovarian
reserve In a third of couples the cause of failure to achieve pregnancy is not
established which is known as unexplained infertility (NICE 2013) Effective
treatment options include improving lifestyle factors medical andor surgical
treatment of underlying pathology induction of ovulation and Assisted
Reproductive Technology (ART) Assisted Reproduction consist of
intrauterine insemination (IUI) and in vitro fertilisation (IVF) cycles with or
without introcytoplasmic sperm injection (ICSI) as well as treatment involving
donated gametes It is estimated that 75 of infertile couples presenting at
primary care centres in the UK are referred to fertility specialists based at
secondary or tertiary care centres and nearly 50 of those are subsequently
offered IVFICSI treatment (Scott et al 2009) This is supported by figures of
Human Fertility and Embryology Authority (HFEA) which indicates more
than 50000 IVF treatment cycles are performed in the UK annually (HFEA
2008)
An IVF treatment cycle involves a) pituitary down regulation b)
controlled ovarian stimulation c) oocyte recovery c) in vitro fertilisation of eggs
with sperm d) transfer of resulting embryo(s) back to uterus and c) luteal
phase support (NICE 2013) Prevention of premature surge of luteinising
hormone during controlled ovarian stimulation (COS) is achieved by pituitary
down regulation using either preparations of gonadotrophin releasing hormone
agonist which is widely known as ldquoAgonist cyclerdquo or gonadotrophin releasing
hormone antagonist which is known ldquoAntagonist cyclerdquo (Figure 1 and 2)
Controlled ovarian stimulation involves administration of gonadotrophins to
encourage the development of supernumerary preovulatory follicles followed
by administration of exogenous human chorionic gonadotropin (hCG) or
11
recombinant luteinising hormone (rLH) to assist in maturation of oocytes 34-
36 hours prior to egg collection which is usually conducted with guidance of
transvaginal ultrasound scanning Subject to sperm parameters the fertilisation
of oocytes is conducted by in vitro insemination or intracytoplasmic sperm
injection The resulting embryo(s) are cultured under strict laboratory
conditions and undergo regular qualitative and quantitative assessments before
transferring the best quality embryo(s) back into uterus during its cleavage
(Day 2 or Day 3) or blastocyst (Day 5 or Day 6) stage of development In
natural menstrual cycles under the influence of HCG progesterone secreted
by the ovarian corpus luteum ensures proliferative changes in the endometrium
providing the optimal environment for implantation of embryo(s) (van der
Linden et al 2011) However in IVF treatment cycles owing to pituitary down
regulation and lack of HCG progesterone levels are not in sufficiently high
concentration to ensure an adequate endometrial receptivity and therefore
exogenous analogues of this hormone is administered following transfer of
embryo(s) This is called ldquoluteal phase supportrdquo and in patients with viable
pregnancy usually lasts till 12th week of gestation when placenta starts
producing progesterone in sufficient quantities (van der Linden et al 2011)
In IVF programmes the ldquosuccessrdquo of the treatment often defined as
achieving a live birth following IVF cycle and expressed using Live Birth Rate
(LBR) In general success in IVF predominantly determined by womanrsquos age
cause(s) of infertility ovarian reserve previous reproductive history and
lifestyle factors (NICE 2013 Taylor 2003 Lintsen et al 2005) However
effectiveness of medical interventions as well as the quality of care play
important role in determining the outcome of IVF treatment This is evident
from significant variation in live birth rates among fertility clinics given for
instance in the UK LBR for women younger than 35 years of age after IVF
cycles varies from 15 to 61 (HFEA 2008 HFEA 2007) The provision of
effective interventions in both clinical and laboratory aspects of the care
appears to be the key in achieving high success rates Identification of patients
with sufficient ovarian reserve who benefit from IVF cycles followed by
providing optimal ovarian stimulation regimens may be useful in improving the
outcomes of IVF programmes According to HFEA data around 12 of IVF
cycles are cancelled due to poor or excessive ovarian response (Kurinczuk et al
2010) Availability of reliable markers for assessment of ovarian reserve and
tailoring ovarian stimulation regimens to the need of each individual patient
12
may improve selection of patients with sufficient ovarian reserve and reduce
the rate of cycle cancellation consequently improving the success of IVF
cycles (Yates et al 2011)
Assessment of ovarian reserve can be achieved using various biomarkers
and four of those are currently used by most clinics womanrsquos chronological
age (Age) serum follicle stimulating hormone (FSH) antral follicle count
(AFC) and serum anti-Muumlllerian hormone (AMH) More recently AMH has
been a focus of interest given it is the only available endocrine marker that is
suitable for direct assessment of the activity of ovarian follicles in their non-
cyclical stage development providing a window to FSH independent phase of
follicular recruitment Furthermore it appears to be reliable biomarker for a)
both the assessment of ovarian reserve and the optimisation of ovarian
stimulation regimens (Yates et al 2011 La Marca et al 2009) b) screening and
diagnosis of polycystic ovarian syndrome (PCOS) (Cook et al 2002) c)
monitoring of disease activity in women with a history of granulosa cell
tumours (Lane et al 1999) d) prediction of the age of diminished fertility and
the menopause (van Disseldorp et al 2008 Broer et al 2011) and finally (e)
assessment of the long term effect of chemotherapy on ovarian reserve
(Anderson 2011)
In this review I first discuss current knowledge on factors that
determine ovarian reserve including the formation and loss of oocyte pool
Then characteristics of the markers of ovarian reserve are reviewed Finally I
examine current understanding of biology of anti-Muumlllerian hormone and its
role in management of infertility
1 OVARIAN RESERVE
It is important to recognize that there is no universal definition for the
term ldquoovarian reserverdquo and the term can have various meanings depending on
the context in which it is used For instance the scientific literature describing
the biology of ovarian reserve usually refers to ldquothe total number of remaining
oocytes in the ovaries which consists of the number of resting primordial
follicles and growing primary pre-antral and antral folliclesrdquo (Gleicher et al
2011) In contrast the use of the term in the context of clinical studies may
refer to ldquoclinically measurable ovarian reserve established using available
biomarkers of ovarian reserverdquo For the purpose of clarity in this thesis the
13
term ldquoovarian reserverdquo refers to clinically measurable ovarian reserve whilst
true biological ovarian reserve will be termed ldquobiological ovarian reserverdquo
Recent studies have demonstrated that ovarian reserve is highly variable
between women due to the variation in the size of initial ovarian reserve at
birth as well as the rate of loss of ovarian reserve thereafter (Wallace et al
2010) Interestingly the rate of oocyte loss appears to be mainly determined by
the initial ovarian reserve which is believed to be facilitated by most potent
ovarian growth factor anti-Muumlllerian hormone Similarly the size of the initial
ovarian reserve is mainly underpinned by the rate of primordial follicle
assembly in the embryo which is also regulated by AMH Both primordial
follicle assembly and the rate of oocyte loss appear to be primarily under the
influence of genetic factors although developmental and environmental factors
are also believed to play a role (Nilsson et al 2010 Shuh-Huerta et al 2012)
11 Primordial follicle assembly
The process of assembly of primordial follicles in the female embryo
spans from the early embryonic to the early postnatal period and formation of
primordial follicles consists of following stages 1) primordial germ cell (PGC)
2) oogonia 3) primary oocyte and 4) primordial follicle In the human female
fetus around a hundred cells that differentiated from extra-embryonic
ectoderm form early PGCs on the yolk sac and migrate via hindgut to gonadal
ridges during 4th - 6th weeks of gestation (MC et al 1953 Donovan 1998) Once
arrived to the gonadal ridges these cells are called primary oogonia which
consequently undergo several rounds of mitotic division during 6th - 28th weeks
of gestation Interestingly the numbers of oogonia reach as high as six million
during its highest rate of mitotic division at around 20 weeks of gestation
Following the last round of mitotic division oogonia enter meiosis which
marks their new stage of development-primary oocyte Formation of
primordial follicles starts as early as at 8th week of gestation and is characterised
by meiosis of primary oocyte that arrest in diplotyne stage and surrounding of
the oocyte by somatic granulosa cell (Baker et al 1963 Maheshwari and Fowler
2010) Indeed the primordial follicle is the cardinal unit of the biological
ovarian reserve and therefore the rate of formation of primordial follicles is the
main determinant of initial biological ovarian reserve at birth
Interestingly the process of loss of oogonia and oocytes which is also
one of the main determinants of the initial ovarian reserve takes place
14
throughout the period of follicle assembly The formation of the granulosa cell
layer around the oocyte prevents the oocyte from subsequent atresia The
oocyte enveloped in a single layer of granulosa cells which is also known as
primordial follicle remains quiescent until recruitment of the follicle for
growth which may not take place for a number of decades after the formation
of a particular primordial follicle (Skinner 2005 Maheshwari and Fowler 2010)
12 Oocyte recruitment
Follicle growth in women consists of two stages a) the initial non-cyclical
recruitment of primordial follicles and the formation of a primary and a pre-
antral follicles and b) cyclical development of antral follicles with subsequent
selection of usually a single dominant follicle The initial recruitment of
primordial follicles is continuous non-cyclical process that starts as early as
from 18-20 weeks of gestation and lasts till the depletion of follicle pool which
later results in the menopause (McGee and Hsueh 2000) Transformation of
flat granulosa cells into cuboidal cells increases the diameter of the oocyte and
the formation of zona pellicuda completes the stage of formation of a primary
follicle During pre-antral stage oocytes increase in diameter and mitotic
division of granulose cells create a new layer of cells-theca cells The
mechanism of initial recruitment of oocytes is not well understood but it is
clear that the process is independent of influence of pituitary gonadotrophins
and appears to be governed by the genetically pre-programmed interaction of
the oocyte with local growth factors the most important of which appears to
be anti-Muumlllerian hormone and cytokines (McGee and Hsueh 2000)
The cyclical phase of development of oocytes is characterised by the
transformation of secondary follicle into antral follicle and subsequent growth
of antral follicles into pre-ovulatory stages In general the process of cyclic
recruitment starts from puberty under the influence of rising levels of pituitary
follicular stimulating hormone (FSH) During the antral stage oocyte increases
in size even further and the formation of a fluid filled space in follicle is
observed Under the influence of FSH luteinising hormone (LH) and local
growth factorsselection of a single dominant follicle occurs which followsby an
ovulation (McGee and Hsueh 2000)
Oocyte loss is a continuous process and occurs due to atresia of oocytes
during primary secondary and antral stages of development The rate of
oocyte loss appears to increase until the age of around 14 and declines
15
thereafter until the age of the menopause when around 1000 primordial
follicles remain (Hansen et al 2008 Oktem and Oktayl 2008) Furthermore by
the age of 30 years the average age at which women of western societies plan
to start a family around 90 of initial primordial follicles are lost which
illustrates that formation and maintenance of ovarian reserve is wasteful
process in humans (ONS 2012 Wallace and Kelsey 2010) As mentioned
above there is a wide individual variation in both sizes of initial primordial
follicular pool and the rate of oocyte loss which explains variation in the
reproductive lifespan in women Evidently the number of primordial follicles
at birth ranges between around 35000 to 25 million per ovary and similarly
the rate of oocyte loss during its peak at 14 years of age may range between
100 to 7500 primordial follicles per month which is believed to be inversely
proportional to initial size of primordial follicle pool (Wallace and Kelsey
2010)
13 Theory of neo-oogenesis
The traditional view of oogenesis states that the process of the creation
and the mitotic division of oogonia with subsequent formation of primordial
follicles takes place only during embryonic and foetal life (Zuckerman 1951)
According to this central theory of mammalian reproductive biology females
are born with a certain number of germ cells that is gradually lost but not
renewed during postnatal period However Johnson et al have recently
challenged this view and reported that adult mammalian ovary may possesses
mitotically active germ cells that continuously replenish the primordial follicle
pool (Johnson et al 2004) The group reported that ovaries of juvenile and
young adult mice contained large ovoid cells which resemble germ cells of
foetal mouse ovaries Interestingly immunohistochemical staining for a gene
which is expressed exclusively in germ cells have been reported to have
confirmed that these large ovoid cells were of germline lineage Furthermore
application of a mitotic germ cell toxicant busulphan appeared to have
eliminated primordial follicle reserve by early adulthood but did not induce
atresia suggesting the presence of proliferative germ cells in postnatal mouse
ovary (Johnson et al 2004 Bazer 2004) The study has generated enormous
amount of interest as well as debate among reproductive biologists (Notarianni
2011) Some other groups have also reported an evidence of postnatal
oogenesis (Pacchiarott et al 2010 Zou et al 2009 Bukovsky et al 2004)) while
16
others do not support the theory (Bristol-Gould et al 2006 Byskov et al 2005
Begum et al 2008) Furthermore some authors argued that adult mouse
germline stem cells exist and remain quiescent in physiologic conditions and
neo-oogenesis occurs only in response to ovotoxic damage (Tilly et al 2007 De
Felici 2010) Although consensus has yet to emerge to date there is no
conclusive evidence on validity of theory of neo-oogenesis
2 MARKERS FOR ASSESMENT OF OVARIAN RESERVE
Biological ovarian reserve is defined as the number of primordial and
growing follicles left in the ovary at any given time and therefore only
counting the number of primordial follicles by histological assessment can
accurately determine ovarian reserve which is clearly not feasible in clinical
setting However ovarian reserve can be estimated using various biomarkers
dynamic clinical tests and implied from the outcomes of ART cycles
Although a wide range of clinical (age ovarian response in previous IVF
cycles) biochemical (basal FSH Inhibin B basal oestradiol AMH) ultrasound
(ovarian volume antral follicle count (AFC)) and dynamic (clomiphene
challenge test exogenous FSH ovarian reserve test GnRH analogue
stimulating test) tests of ovarian reserve exist only a few of the markers are
reliable and practical enough to be of use in routine clinical practice In this
chapter first I discuss the research evidence on the assessment of the markers
andor tests of ovarian reserve that have limited clinical value Then I
evaluated more reliable markers that are in routine clinical use Age FSH
AFC and combination of these markers in multivariable tests Finally I
conducted detailed review of biology of AMH and the role AMH measurement
in the management of infertility
21 Ovarian reserve markers with limited clinical value
211 Inhibin B
Inhibins are members of TGFβ family and expressed in granulosa cells
of growing follicles Principal role of inhibins is thought to be the negative
feedback regulation of pituitary FSH secretion and therefore the serum level of
circulating hormone is believed to reflect the state of folliculogenesis
17
Consequently several groups have studied the role of serum Inhibin β in the
assessment of ovarian reserve Although initial reports were encouraging
(Seifer et al 1997) more robust studies demonstrated that serum Inhibin β was
less reliable than chronological age or basal FSH (Creus et al 2000 Urbancsek
2005) The systematic review of nine studies demonstrated that accuracy of the
Inhibin β test for predicting poor ovarian response and non-pregnancy in IVF
cycles was modest even at a very low threshold level (Broekmans et al 2006)
Therefore it is recommended that inhibin β at best can be used as only
screening test in the fertility centers where other more reliable markers are not
available (Broekmans et al 2006)
212 Basal oestradiol
Some studies suggested that elevated basal oestradiol levels indicate low
ovarian reserve and are associated with poor fertility prognosis (Johannes et al
1998 Licciardi and Rosenwaks 1995) Johannes et al demonstrated basal
oestradiol in conjunction with serum FSH is more reliable than serum FSH
alone in prediction of cycle cancellation due to the poor response in IVF cycles
(Johannes et al 1998) However there are no published data on the comparison
of basal oestradiol to more reliable markers such as AMH or antral follicle
count (AFC) Moreover a recent systematic review has demonstrated that
basal oestradiol has very low predictive value for poor response and has no
discriminatory power for accuracy of non-pregnancy prediction (Broekmans et
al 2006)
213 Dynamic tests of ovarian reserve
The dynamic tests of ovarian reserve are based on assessment of ovarian
response by measuring serum FSH and oestradiol levels following
administration of exogenous stimulation The following tests are reported in
literature Clomiphene Citrate Challenge Test (CCCT) Exogenous FSH
Ovarian Reserve Test (EFORT) and GnRH agonist stimulation test A recent
systematic review and meta-analysis on the accuracy of these tests showed that
none of them can adequately predict poor response or non-pregnancy in IVF
cycles and therefore are not recommended for use in routine clinical practice
(Maheshwari et al 2009)
18
214 Ovarian volume
There is some evidence that increased age is associated with decreased
ovarian volume and women with smaller ovaries are more likely to have
cancellation of their IVF cycles due to poor ovarian response (Syrop et al 1995
Syrop et al 1999 Templeton 1995) However a meta-analysis of the published
studies on the accuracy of ovarian volume as a predictor of poor response and
non-pregnancy in IVF cycles failed to demonstrate clinical usefulness of the
test and suggested the test is not reliable enough for use in a routine clinical
practice (Broekmans et al 2006)
22 Ovarian reserve markers in routine clinical use
221 Chronological age
Owing to the biological age-related decline of the quantity and arguably
the quality of oocytes the chronological age can be used as a marker of ovarian
reserve Studies have demonstrated that ovarian reserve (Wallace and Kelsey
2010 Kelsey 2011) natural fecundity (Islam et al 1989 and outcomes of ART
(Templeton et al 1996 van Kooij et al 1996) decline significantly from age of
35 when it is believed the ovarian reserve undergoes accelerated decline
Although there is a strong association between chronological age and reduction
in fertility evidently there is a significant variation in age-related ovarian
reserve indicating chronological age alone may not be sufficient to estimate the
individual womanrsquos ovarian reserve reliably (Broekmans et al 2006)
222 Basal FSH
Basal FSH was one of the first endocrine markers introduced in ART
programs and is still utilized in many fertility clinics albeit in conjunction with
other markers which are considered more reliable (Creus et al 2000) Secretion
of FSH is largely governed by the negative feedback effect of steroid
hormones primarily oestradiol and inhibins which are expressed in granulosa
cells of growing ovarian follicles Consequently decreased or diminished
recruitment of ovarian follicles is associated increased serum FSH
measurements and high particularly very high basal FSH reading is considered
as a good marker of very low or diminished ovarian reserve (Abdalla et al
2006) However unlike some other markers FSH measurements do not
appear to have discriminatory power for categorisation of patients to various
19
bands of ovarian reserve Given between-patient variability FSH measurement
(CV 30) is similar to its within-patient variability (27) stratification of
patients to various ranges of ovarian reserve does not appear to be feasible
(Rustamov et al 2011) Indeed a recent systematic review of 37 studies on the
prediction of poor response and non-pregnancy in IVF cycle has concluded
that basal FSH is an adequate test at very high threshold levels and therefore
has limited value in modern ART programs (Broekmans et al 2006)
223 Antral follicle count
Antral follicle count estimation involves ultrasound assessment of
ovaries between 2nd and 4th day of menstrual period and counting ldquofolliclesrdquo
which corresponds to antral stage of folliculogenesis (Broekmans et al 2010)
The test provides direct quantitative assessment of growing follicles and is
known as one of the most reliable markers of ovarian reserve (Broekmans et al
2006) AFC measurement has been reported as having a similar sensitivity and
specificity to AMH in prediction of poor and excessive ovarian response in
IVF cycles (Broekmans et al 2006 Broer et al 2010 Jayaprakasan et al 2010)
Given AFC measurement is available instantly and allows patients to be
counseled immediately the test eliminates the need for an additional patient
visit prior to IVF cycle However AFC is normally performed only in the early
follicular phase of the menstrual cycle given most published data on
measurement of AFC are based on studies that assessed antral follicles during
this stage of the cycle (Broekmans et al 2010a) Interestingly more recent
studies suggest that variability of AFC during menstrual cycle is small
particularly when follicles between 2-6mm are counted and therefore
assessment of AFC without account for the day of menstrual cycle may be
feasible (Deb et al 2013)
One of the main drawbacks of AFC is that the cut off levels for size of
counted follicles remains to be standardised (Broekmans 2010b) Initially
follicles of 2-10mm were introduced as the range for AFC and many studies
were based on this cut off Later counting follicles of 2-6mm was reported to
provide most accurate assessment of ovarian reserve (Jayaprakasan et al 2010b
Haadsma et al 2007) and therefore some newer studies are based on AFC
measurements that used this criterion Consequently direct comparison of the
outcomes of various studies on assessment of AFC requires careful analysis
20
3 ANTI-MUumlLLERIAN HORMONE
31 Biology of Anti-Muumlllerian hormone
AMH is a member of transforming growth factor β superfamily which
was discovered by Jost et al in 1947 and was initially known for its is role in
regression of Muumlllerian ducts in sex differentiation of the male embryo In
women AMH is believed to be solely produced by ovaries and expressed in
granulosa cells of growing follicles of 2-6 mm in size which corresponds to
primary pre-antral and early antral stage of follicular development Although
there has been a report of expression of AMH in endometrial cells to date
there is no other published evidence that supports this finding (Wang et al
2009) Indeed studies that evaluated half-life of AMH in serum have
demonstrated that in women who had bilateral salpingo-oopherectomy AMH
becomes undetectable within 3-5 days of following surgery suggesting ovaries
are the only source of secretion of AMH in appreciable quantity (La Marca et
al 2005b) Anti-Muumlllerian hormone is a dimeric glycoprotein which is
composed of a long N-terminus and short C-terminus and was believed to be
secreted in serum only in this dimeric form (AMH-N C)
Like other members of TGF-β family which includes inhibins activins
bone morphogenic proteins (BMPs) and growth and differentiation factors
(Massague et al 1990) AMH binds to two type of serinethreonine kinase
receptors referred to as type I and type II In order to activate AMH signaling
pathway both receptors have to form a heteromeric complex When AMH
binds to the type II (AMHR-II) receptor (Massague et al 2000) this will
phosphorylate and activate a type I receptor (ALK2 -3 andor -6) which
subsequently activates the SMAD pathway through phosphorylation of
SMAD 1 5 andor 8 These activated SMADs interact with SMAD4 and
translocate to the nucleus regulating the expression of different genes
inhibiting the recruitment of primordial follicles and reducing FSH sensitivity
in growing follicles In addition AMH receptors as well as the other members
of TGF-β family can activate MAPK and PI3KAKT pathways
Studies on AMHR II-deficient male mice demonstrated lack of
regression of Muumlllerian ducts suggesting that type II receptor is essential in
AMH signaling (Mishina et al 1996) Similarly Type I receptors which includes
three members of activin receptor-like kinase (ALK2 ALK3 and ALK6) also
appear to play an important role in the regression of Muumlllerian ducts although
21
the role of ALK 6 in AMH signaling appears not to be crucial (Visser 2003
Clarke et al 2001) The signal transduction pathway of AMH in the ovary is
largely not understood In postnatal mice ovary AMHR-II receptor was
expressed in both granulosa and theca cells of pre-antral and antral follicles
(Visser 2003) AMH type I receptors ALK 2 and ALK 3 is expressed in foetal
as well as adult mouse ovary while ALK 6 is expressed in only adult ovary
(Visser 2003)
311 The role of AMH in the ovary
In the mammalian ovary the role of AMH appears to be one of a
regulation of size of the primordial follicle pool by its inhibitory effect on the
formation as well as the growth of primordial follicles (Nilsson et al 2011) In
the embryonic mouse ovary AMH inhibits the initiation of the assembly of
follicles when the process of apoptosis of the majority of oocytes is observed
(Nilsson et al 2011) Consequently AMH reduces the rate of oocyte loss
which plays an important role in the determination of the size of initial follicle
pool Similarly in the adult mouse ovary AMH plays a central role in
maintaining the follicle pool AMH inhibits both the processes of the initial
(non-cyclical) recruitment of primordial follicles and subsequent FSH-
dependent cyclical growth of antral follicles (Figure 3) Inhibition of the initial
recruitment of a new cohort of follicles is believed to be achieved by a
paracrine negative feedback effect of the rising levels of AMH secreted from
already recruited growing follicles (Durlinger et al 1999) Durlinger et al
compared the complete follicle population of AMHnull mice and wild type
mice of different ages of 25 days 4 months old and 13 months old and found
that the ovaries of 25 day and 4 months old AMHnull females contained
significantly higher number of growing pre-antral and antral follicles but
significantly fewer primordial follicles compared to wild-type females
(Durlinger et al 1999) Interestingly almost no primordial follicles were
detected in 13 months old AMHnull mice ovaries suggesting AMH is a potent
inhibitor of the recruitment of primordial follicles and in the absence of AMH
ovaries undergo premature depletion of primordial follicles due to an
accelerated recruitment Subsequent study conducted by the group
demonstrated that in addition to its inhibitory effect to the resting follicles
AMH also suppresses the development of the growing follicles (Durlinger et al
2001 Durlinger et al 2002 Themmen 2005) It appears that AMH inhibits
22
FSH-induced follicle growth by reducing the sensitivity of growing follicles to
FSH which has been confirmed by in vivo as well as in vitro studies (Durlinger
et al 1999 Durlinger et al 2001) In the initial study the group observed that
despite lower levels of serum FSH concentration ovaries of AMHnull mice
contained more growing follicles than that of their wild-type littermates which
has been supported by the findings of subsequent in vitro study (Durlinger et al
1999) Addition of AMH to the culture inhibited FSH-induced follicle growth
of pre-antral mouse follicles due to reduction in granulosa cell proliferation
(Durlinger et al 2001)
In the human embryo the expression of AMH commences in the late
foetal life and can be detected only from 36 weeks of gestation (Rajpert-De et
al 1999 Lee et al 1996) Following a small decline in first two years of life
AMH levels gradually increase to peak at (mean 5 ngml) around age of 24
years In line with the pattern of oocyte loss serum hormone levels gradually
decline with increasing age and become undetectable around 5 years prior to
menopause (Kelsey et al 2011 Nelson et al 2011)
It has been suggested that anti-Muumlllerian hormone plays a central role in
determining the pace of recruitment of primordial follicles hence maintaining
the primordial follicle pool of postnatal mammalian ovary Consequently a
reduction in the concentration of circulating AMH signals the exhaustion of
the primordial follicle pool and the decline of ovarian function
312 AMH in women with polycystic ovary syndrome
Polycystic ovary syndrome (PCOS) endocrine abnormality characterised
by increased ovarian androgen secretion infrequent ovulation and the
appearance of ldquopolycysticrdquo ovaries on ultrasound scan (Dunaif 1997 Homburg
et al 1993) It is the commonest endocrine abnormality in women of
reproductive age and affects around 15-20 of women PCOS is also one of
the main causes of anovulation and subsequent sub-fertility (Webber et al
2003) Although the role of anti-Muumlllerian hormone in the development of
PCOS is not fully understood it is becoming increasingly evident that the
hormone plays an important role in its pathogenesis (Pehlivanov et al 2011)
There is a strong association between serum AMH levels and PCOS and it
appears that women diagnosed with PCOS have two to three fold higher
serum AMH concentration compared to normo-ovulatory women (Cook et al
2002 Pigny et al 2003) Similarly women with PCOS are found to have
23
significantly higher number antral follicles Interestingly the expression of
AMH in granulosa cells of follicles were found to be 75 times higher in women
with PCOS compared to those without a the disease suggesting increased
serum AMH in PCOS may be due to increased secretion of hormone per
follicle rather than due to an increased number of antral follicles (Pellat et al
2007) High AMH concentrations may act as the main facilitator of abnormal
folliculogenesis in PCOS given the follicles appear to arrest when they reach
an antral stage (2-6mm) of development (Rajpert-De et al 1999) Indeed the
studies of Durlinger et al have demonstrated that AMH inhibits selection of
dominant follicle when follicles reach antral stage of development (Durlinger et
al 2001) Serum AMH levels appear to decrease with treatment of PCOS
which may play important role in restoration of ovulatory cycles Studies have
reported a significant reduction in serum concentration of AMH following
treatment of PCOS with metformin and laparoscopic ovarian diathermy (Falbo
et al 2010 Amer et al 2009 Elmashad 2011) Similarly reduction of BMI
following intensified endurance exercise training for treatment of PCOS may
also lead to a significant reduction in serum AMH levels (Moran et al 2011)
This suggests that there is strong association between serum concentration of
AMH and abnormal folliculogenesis in PCOS and therefore understanding the
molecular mechanisms of this interaction should be one of the priorities of
future research
32 AMH Assays
Enzyme-linked immunosorbent assay specific for measurement of anti-
Muumlllerian hormone was first developed in 1990 and was recognised as a
significant step in the assessment of ovarian reserve (Hudson et al 1990)
Subsequently a number of non-commercial immunoassays were developed
which were mainly used in research settings (Lee et al 1996) Later Diagnostic
Systems Ltd (DSL) and Immunotech Beckman Coulter Ltd (IOT) introduced
two commercial immunoassays for the routine clinical assessment of ovarian
reserve which are known as ldquofirst generation AMH assaysrdquo (Nelson and La
Marca 2011) These assays employed two different antibodies against AMH
and used different standards for calibration providing non-comparable
measurements (Nelson and La Marca 2011) Consequently several studies
attempted to develop a reliable between-assay conversion factor which
interestingly revealed from five-fold higher with the IOT assay to assay
24
equivalence causing significant impact to reliability of AMH measurements and
interpretation of research findings (Hehenkamp et al 2006 Freour et al 2007
Bersinger et al 2007 Taieb et al 2008 Lee et al 2011)
Later the manufacturer of IOT assay (Beckmann Coulter Ltd)
consolidated the manufacturer of the DSL assay (Diagnostic Systems
Laboratories Inc) and introduced a new assay ldquoGen II AMH assayrdquo which is
only available commercial immunoassay in most countries including the UK
AMH Gen II assay was developed using the antibodies derived from first
generation DSL assay and calibrated using the standards used for IOT assay
and was believed to be considerably more stable compared to the first
generation immunoassays providing more reliable measurements (Kumar et al
2010 Nelson and La Marca 2011) The manufacturer as well as initial external
validation study recommended when compared to old DSL assay AMH Gen
II assay provides around 40 higher measurements and therefore previously
reported DSL-based clinical cut-off levels for estimation of ovarian reserve
should be increased by 40 in order to use Gen II-based AMH results (Kumar
et al 2010 Wallace et al 2011 Nelson and La Marca 2011)
33 Variability of AMH measurements
It is generally believed that AMH values do not change throughout the
menstrual cycle and early studies reported that variation in AMH
measurements between repeated measurements of same patient was negligible
(van Disseldorp et al 2010 La Marca 2010) On the basis of these studies
sampling at a random time in the menstrual cycle was introduced as a method
for measurement of AMH in routine clinical practice However the
methodologies of some of these studies do not appear to be robust enough to
reliably estimate sample-to-sample variability of AMH which is mainly due to
small sample sizes (Rustamov et al 2011) Consequently in a recent study we
assessed sample-to-sample variability of AMH using DSL assay and found that
within-subject coefficient of variation (CV) of AMH between samples were as
high as 28 which cannot be attributed to any patient or cycle characteristics
(Rustamov et al 2011) Although there is no consensus in the causes of this
observed variability in AMH measurements we believe it is largely attributable
to instability of AMH samples given initial recruitment of primordial follicles
and growth of AMH producing pre-antral and antral follicles are continuous
process and therefore the true biological variation between samples is unlikely
25
to be high However given the importance of establishing true variability of
AMH in both understanding of the biology of hormone and clinical
application of the test future studies should be conducted to establish the
source of variability in the clinical samples
3 4 The role of AMH in the assessment of ovarian reserve
341 Prediction of poor and excessive ovarian response in cycles of
IVF
A number of studies have assessed the role of AMH in the prediction of
poor ovarian response in IVF cycles using first generation AMH assays and
found that AMH and AFC were the best predictors of poor ovarian response
compared to other markers of ovarian reserve Nardo et al showed that the
predictive value of AMH in receiver operating characteristic curve (ROC)
analysis was similar to (AUC 088) that of AFC (AUC 081) and found that
AMH cut offs of gt375 ngmL and lt10 ngmL would have modest
sensitivity and specificity in predicting the extremes of response (Nardo et al
2009) These findings were largely supported by subsequent prospective studies
and a systematic review (Nelson et al 2007 Jayaprakasan et al 2010 Broer et al
2011) Similarly comparison of chronological age basal FSH ovarian volume
AFC and AMH found that only AMH (AUC 090) and AFC (AUC 093) were
reliable predictors of poor ovarian response in cycles of IVF Subsequent
combination of the effect of AMH and AFC using multivariable regression
analysis did not improve the level of prediction of poor ovarian response
significantly (AUC 094) suggesting both AMH and AFC can be used as
independent markers (Jayaprakasan et al 2010)
Similarly most studies agree that AMH and AFC are the best predictors
of excessive ovarian response and ovarian hyperstimulation syndrome (OHSS)
compared to other clinical endocrine and ultrasound markers (Nardo et al
2009 Nelson et al 2007) Broer et al compared these two tests in systematic
review of 14 studies and reported that the summary estimates of the sensitivity
and the specificity for AMH were 82 and 76 respectively and for AFC 82
and 80 respectively (Broer et al 2011) Consequently the study concluded
that AMH and AFC were equally predictive and the difference in the predictive
value between the tests was not statistically significant
26
342 Prediction of live birth rate (LBR) in cycles of IVF
Lee at al reported that AMH and chronological age were more accurate
than basal FSH AFC BMI and causes of infertility in the prediction of live
birth rate (Lee et al 2009) Similarly La Marca et al suggested that odds of live
birth could be reliably predicted using AMH (La Marca et al 2010b) although
subsequent review of the study questioned strength of the evidence (Loh and
Maheshwari 2011)
A study conducted by Nelson et al found that higher AMH levels had
stronger association with increased live birth rate compared to age and FSH
(Nelson et al 2007) However the study also suggested that this association
was mainly confined in the women with low AMH levels and there was no
additional increase in live birth in women with AMH levels of higher than 710
pmolL This may suggest that achieving a live birth may be under the
influence of number of other factors and that markers of ovarian reserve alone
may not be able predict this outcome reliably
35 The role of AMH in individualisation of ovarian stimulation in
IVF cycles
Prediction of ovarian response to the stimulation of ovaries in cycles of
IVF plays an important role in the counseling of couples undergoing treatment
programmes and hence many clinical studies on AMH have focused on the
prognostic value of AMH measurements However data on using AMH as a
tool for improving the clinical outcomes in IVF cycles appear to be lacking
considering AMH may be useful tool in tailoring treatment strategies to an
individual patientrsquos ovarian reserve Unlike most other markers AMH has
discriminatory power in determining various degrees of ovarian reserve due to
significantly higher between patient (CV 94) variability compared to its
within-patient (CV 28) variation (Rustamov et al 2011) which allows
stratification of patients into various degrees of (eg low normal high) ovarian
reserve Subsequently most optimal ovarian stimulation protocol may be
established for each band of ovarian reserve Consequently reference ranges
on the basis of distribution of AMH in infertile women were developed which
were subsequently adopted by fertility clinics for a tailoring the mode of
27
ovarian stimulation and daily dose of gonadotrophins in IVF (The Doctors
Laboratory 2008 However currently available clinical reference ranges are
based on the first generation DSL assay and may not be reliably convertible to
currently available Gen II assay measurements (Wallace et al 2011) Indeed the
findings of the studies on comparability of the first generation AMH assays
suggest that establishing a reliable between assay conversion factor between
AMH assays may not be straightforward Furthermore the reference ranges
appear to reflect the distribution of AMH measurements within a specific
population and may therefore not be directly applicable for the prediction of
response to ovarian stimulation in IVF patients (The Doctors Laboratory
2008)
More importantly despite lack of good quality evidence on the
effectiveness of AMH-tailored ovarian stimulation protocols a number of
fertility clinics appear to have introduced various AMH-based COH protocols
in their IVF programs At present research evidence on AMH-tailored
ovarian stimulation in IVF is largely based on two retrospective studies
(Nelson et al 2009 Yates et al 2012) Both of these studies display considerable
methodological limitations including small sample size and centre-related or
period-related selection of their cohorts In this context AMH is used as a tool
for therapeutic intervention and therefore the research evidence should ideally
be derived from randomised controlled trials However recruitment of large
enough patients in IVF setting may take considerable time and resources In
the meantime given AMH-tailored ovarian stimulation has already been
introduced in clinical practice and there is urgent need for more reliable data
the studies with a larger cohorts and robust methodology should assess the role
of AMH in individualisation of ovarian stimulation in IVF treatment cycles
4 Multivariate models of assessment of ovarian reserve
In view of the fact there is not a single marker of ovarian reserve that
can accurately predict ovarian response various models for combination of
multiple ovarian markers have been developed (Verhagen et al 2008) A
number of studies reported that multivariate models are better predictors of
poor ovarian response in IVF compared to a single marker (Bancsi et al 2002
Balasch et al 1996 Creus et al 2000 Durmusoglu et al 2004) However a meta-
analysis showed that when compared to a single marker (AFC) multivariate
28
model has a similar accuracy in terms of prediction of poor ovarian response
(Verhagen et al 2008) In contrast a more recent study demonstrated that
multivariate score was superior to chronological age basal FSH or AFC alone
in predicting likelihood of poor ovarian response and clinical pregnancy
(Younis et al 2010) However the study did not include one of the most
reliable markers AMH in either arm necessitating further assessment of the
role of combined tests which include all reliable biomarkers
4 SUMMARY
During the last two decades a significant leap has been taken towards
understanding the biology of anti-Muumlllerian hormone and its role in female
reproduction (Durlinger et al 2002 Themmen et al 2005) Availability of
commercial AMH assays has resulted in significant increase in interest in the
role of the measurement of serum AMH in the assessment of ovarian reserve
which has been followed by the introduction of the test into routine clinical
practice (Nelson et al 2011) However more recent studies suggest that current
methodologies for the measurement of AMH may provide significant sampling
variability (Rustamov et al 2011) Furthermore the studies that compared first
generation commercial assay methods appear to provide non-reproducible
results suggesting there may be underlying issues with assay methodologies
(Lee et al 2011) Similarly despite lack of sufficient evidence in the role of
AMH in individualisation of ovarian stimulation protocols in IVF AMH-
tailored IVF protocols have been introduced in routine clinical practice of
many fertility clinics around the world
Consequently it appears that clinical application of AMH test has
surpassed the research evidence in some aspects of fertility treatment and
therefore future projects should be directed toward areas where gaps in
research evidence exist On the basis of the review of literature we believe that
evaluation of the performance of assay methods understanding the role of
AMH in assessment ovarian reserve and establishing its role in
individualisation of ovarian stimulation protocols should be research priority
29
II GENERAL INTRODUCTION
On the basis of the review of published literature I have identified that
the following areas of research on the clinical application of AMH in the
management of infertility requires further investigation 1) Within-patient
variability of measurement of AMH using Gen II assay method 2)
Establishment of clinically measurable determinants of AMH levels and 3) The
role of AMH in individualisation of ovarian stimulation in IVF treatment
cycles
In our previous study we estimated that there was significant sample-to-
sample variation (CV 28) in AMH measurements when the first generation
DSL assay was used (Rustamov et al 2011) The source of variability is likely to
be related to the assay method given that biological within-cycle variation of
AMH is believed to be small (La Marca et al 2006) Therefore assessment of
sample-to-sample variability of AMH using the newly introduced Gen II assay
which is believed to be significantly more stable and sensitive compared to that
of DSL assay should enable us to establish the measurement related variability
of AMH Furthermore given I am planning to use data from both DSL and
Gen II assays I need to establish between-assay conversion factor for these
assays using data on clinical samples
There appears to be a lack of good quality data on the effect of
ethnicity BMI causes of infertility reproductive history and reproductive
surgery on ovarian reserve Therefore I am planning to ascertain the role of
above factors on determination of ovarian reserve by analysing AMH
measurements of a large cohort of patients
There is a strong correlation between AMH and ovarian performance
in IVF treatment when conventional ovarian stimulation using GnRH agonist
regimens with a standard daily dose of gonadotrophins are used (Nelson et al
2007 Nardo et al 2007) Furthermore studies suggest tailoring the ovarian
stimulation protocols to AMH measurement may improve ovarian
performance and subsequently the success of IVF treatment (Nelson et al
2011 Yates et al 2012) However given methodologies of the published
studies the effectiveness of currently proposed AMH-tailored ovarian
stimulation protocols remains unknown Therefore I am planning to develop
individualised ovarian stimulation protocols by establishing the most optimal
mode of pituitary down regulation and starting dose of gonadotrophins for
30
each AMH cut-off bands using a robust research methodology However
development of individualised ovarian stimulation protocols on the basis of
retrospective data requires a reliable and validated database containing a large
number of observations In the IVF Department of St Maryrsquos Hospital we
have data on a large number of patients who underwent ovarian stimulation
following the introduction of AMH However the data on various aspects of
investigation and treatment of patients is stored in different clinical data
management systems and may not be easily linkable In addition it appears that
data on certain important variables (eg causes of infertility AFC) are available
only in the hospital records necessitating searching for data from the hospital
records of each patient Consequently I designed a project for building a
research database which will have comprehensive and validated datasets that
are necessary for investigation of the research questions of the MD
programme
In conclusion I am planning to conduct a series of studies to improve
the understanding of the role of AMH in the management of women with
infertility Specifically I am intending to evaluate 1) sample-to-sample variability
of Gen II AMH measurements 2) conversion factor between DSL and Gen II
assays in clinical samples 3) the effect of ethnicity BMI causes of infertility
endometriosis reproductive history and reproductive surgery to ovarian
reserve and explore AMH-tailored individualisation of ovarian stimulation in
IVF cycles
31
References
Abbeel E The Istanbul consensus workshop on embryo assessment proceedings of an expert meeting Human reproduction 2011 26 p 1270-83 Abdalla HT M Y Repeated testing of basal FSH levels has no predictive value for IVF outcome in women with elevated basal FSH Human reproduction 2006 21(1) p 171-4 Amer SA LT Ledger WL The value of measuring anti-Mullerian hormone in women with polycystic ovary syndrome undergoing laparoscopic ovarian diathermy Human reproduction 2009 24 p 2760-6 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343 Balaban B BD Calderoacuten G Catt J Conaghan J Cowan L Ebner T Gardner D Hardarson T Lundin K Cristina Magli M Mortimer D Mortimer S Munneacute S Royere D Scott L Smitz J Thornhill A van Blerkom J Van den Baker A quantitative and cytological study of germ cells in human ovaries Proc R Soc Lond B Biol Sci 1963 158 p 417-433 Balasch J CM Fabregues F Carmona F Casamitjana R Ascaso and VJ C Inhibin follicle-stimulating hormone and age as predictors of ovarian response in in vitro fertilization cycles stimulated with gonadotropin-releasing hormone agonist-gonadotropin treatment Am J Obstet Gynecol 1996 175 p 1226-1230 Bancsi LF BF Eiijekemans MJ at al Predictors of poor ovarian response in in vitro fertilisation a prospective study comparing basal markers of ovarian reserve Fertility and Sterility 2002 77 p 328-336 Bazer FW Strong science challenges conventional wisdom new perspectives on ovarian biology Reprod Biol Endocrinol 2004 2 p 28 Begum S VE Papaioannou and RG Gosden The oocyte population is not renewed in transplanted or irradiated adult ovaries Hum Reprod 2008 23(10) p 2326-30
Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175 Bristol-Gould SK et al Fate of the initial follicle pool empirical and mathematical evidence supporting its sufficiency for adult fertility Dev Biol 2006 298(1) p 149-54 Broekmans FJ et al A systematic review of tests predicting ovarian reserve and IVF outcome Hum Reprod Update 2006 12(6) p 685-718
32
Broekmans Frank J M de Ziegler Dominique Howles Colin M Gougeon Alain Trew Geoffrey and Olivennes Francois The antral follicle count practical recommendations for better standardization Fertility and Sterility 2010 94 p 1044-51 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011 Aug96(8)2532-9
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Bukovsky A et al Origin of germ cells and formation of new primary follicles in adult human ovaries Reprod Biol Endocrinol 2004 2 p 20 Byskov AG et al Eggs forever Differentiation 2005 73(9-10) p 438-46 Clarke TR et al Mullerian inhibiting substance signaling uses a bone morphogenetic protein (BMP)-like pathway mediated by ALK2 and induces SMAD6 expression Mol Endocrinol 2001 15(6) p 946-59
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146 Creus M PJ Faacutebregues F Vidal E Carmona F Casamitjana R and BJ Vanrell JA Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-2346 Creus M PaJ Fabregues F Vidal E Carmona F Casamitjana R et al Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-6 Cook CL SY Brenner AG et al Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertility and Sterility 2002 77 p 141-6 Deb S Campbell B K Clewis JS Pincott-Allen C and Raine-Fenning NJ Intracycle variation in number of antral follicles stratified by size and in endocrine markers of ovarian reserve in women with normal ovulatory menstrual cycles Ultrasound Obstet Gynecol 2013 41 216ndash222 De Felici M Germ stem cells in the mammalian adult ovary considerations by a fan of the primordial germ cells 2010 Mol Hum Reprod 16(9) p 632-6 Donovan PJ (1998) The germ cell ndash the mother of all stem cells Int J Dev Biol 42 1043ndash50 Dunaif A Insulin resistance and the polycystic ovary syndrome mechanism adn implications for pathogenesis Endocr Rev 1997 18 p 774-800
33
Durlinger AL et al Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 1999 140(12) p 5789-96 Durlinger AL et al Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 2001 142(11) p 4891-9 Durlinger AL JA Visser and AP Themmen Regulation of ovarian function the role of anti-Mullerian hormone Reproduction 2002 124(5) p 601-9 Durmusoglu F EK Yoruk P Erenus M Combining day 7 follicle count with the basal antral follicle count improves the prediction of ovarian response Fertility and Sterility 2004 81 p 1073-78 Ebner T et al Basal level of anti-Mullerian hormone is associated with oocyte quality in stimulated cycles Hum Reprod 2006 21(8) p 2022-6 Elmashad AI Impact of laparoscopic ovarian drilling on anti-Muumlllerian hormone levels and ovarian stromal blood flow using three-dimensional power Doppler in women with anovulatory polycystic ovary syndrome Fertility and Sterility 2011 95 p 2342-6 Falbo A RM Russo T DEttore A Tolino A Zullo F Orio F Palomba S Serum and follicular anti-Mullerian hormone levels in women with polycystic ovary syndrome (PCOS) under metformin J Ovarian Resere 2010 Jul p 16 Fanchin R et al Anti-Mullerian hormone concentrations in the follicular fluid of the preovulatory follicle are predictive of the implantation potential of the ensuing embryo obtained by in vitro fertilization J Clin Endocrinol Metab 2007 92(5) p 1796-802 Fasouliotis SJ A Simon and N Laufer Evaluation and treatment of low responders in assisted reproductive technology a challenge to meet J Assist Reprod Genet 2000 17(7) p 357-73 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164 Gleicher N A Weghofer and DH Barad Defining ovarian reserve to better understand ovarian aging Reprod Biol Endocrinol 9 p 23 Haadsma ML BA Groen H Roeloffzen EM Groenewoud ER Heineman MJ et al The number of small antral follicles (2ndash6 mm) determines the outcome of endocrine ovarian reserve tests in a subfertile population Human reproduction 2007 22 p 1925-31 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699ndash708
34
Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hazout A et al Serum antimullerian hormonemullerian-inhibiting substance appears to be a more discriminatory marker of assisted reproductive technology outcome than follicle-stimulating hormone inhibin B or estradiol Fertil Steril 2004 82(5) p 1323-9
Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 HFEA Fertility Figures 2005 2007 HFEA HFEA Fertility Facts and Figures 2008 HFEA 2010 Homburg R BD Levy T Feldberg D Ashkenazi J Ben-Rafael Z In vitro fertilisation and embryo transfer for the treatment of infertility associated with polycystic ovary syndrome Fertility and Sterility 1993 60 p 858-863 Hudson PL et al An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 1990 70(1) p 16-22 Hull MG GC Kelly NJ et al Population study of causes treatment and outcome of infertility Br Med J Clin Res Ed 1985 291 p 1693-1697 Islam MN and MM Islam Biological and behavioural determinants of fertility in Bangladesh 1975-1989 Asia Pac Popul J 1993 8(1) p 3-18 Jayaprakasan K et al A prospective comparative analysis of anti-Mullerian hormone inhibin-B and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation(2010a) Fertil Steril 2010 93(3) p 855-64 Jayaprakasan et al (2010b) The cohort of antral follicles measuring 2ndash6 mmreflects the quantitative status of ovarian reserve as assessed by serum levels of anti-Mullerian hormone and response to controlled ovarian stimulation Fertil Steril_ 2010941775ndash81 Johannes L H Evers MD Peronneke Slaats MS Jolande A Land MD John C M Dumoulin PhD and Gerard A J Dunselman MD Elevated Levels of Basal Estradiol-17β Predict Poor Response in Patients with Normal Basal Levels of Follicle-Stimulating Hormone Undergoing In Vitro Fertilization Fertility and Sterility 1998(69) p 1010-4 Johnson J et al Germline stem cells and follicular renewal in the postnatal mammalian ovary Nature 2004 428(6979) p 145-50 Kelsey TW et al A validated model of serum anti-mullerian hormone from conception to menopause PLoS One 2011 6(7) p e22024
35
Kumar A et al Development of a second generation anti-Mullerian hormone (AMH) ELISA J Immunol Methods 362(1-2) p 51-9 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A De Leo V Giulini S Orvieto R Malmusi S Giannella L Volpe A Anti-Mullerian hormone in premenopausal women and after spontaneous or surgically induced menopause J Soc Gynecol Investig 2005b12545-548 La Marca A et al Normal serum concentrations of anti-Mullerian hormone in women with regular menstrual cycles (2010a) Reprod Biomed Online 2010 21(4) p 463-9 La Marca A et al Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction (2010b) Reprod Biomed Online 2010 22(4) p 341-9 La Marca A et al Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART) Hum Reprod Update 16(2) p 113-30 La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351 Lee MM et al Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 1996 81(2) p 571-6 Lee TH et al Impact of female age and male infertility on ovarian reserve markers to predict outcome of assisted reproduction technology cycles Reprod Biol Endocrinol 2009 7 p 100
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604 Licciardi FL LH Rosenwaks Z Day 3 estradiol serum concentrations as prognosticators of ovarian stimulation response and pregnancy outcome in patients undergoing in vitro fertilization Fertility and Sterility 1995 64 p 991-4 Lie Fong S et al Anti-Mullerian hormone a marker for oocyte quantity oocyte quality and embryo quality Reprod Biomed Online 2008 16(5) p 664-70 Lintsen AM et al Effects of subfertility cause smoking and body weight on the success rate of IVF Hum Reprod 2005 20(7) p 1867-75 Maheshwari A and PA Fowler Primordial follicular assembly in humans--
36
revisited Zygote 2008 16(4) p 285-96 Maheshwari A et al Dynamic tests of ovarian reserve a systematic review of diagnostic accuracy Reprod Biomed Online 2009 18(5) p 717-34 Massague J et al TGF-beta receptors and TGF-beta binding proteoglycans recent progress in identifying their functional properties Ann N Y Acad Sci 1990 593 p 59-72 Massague J and YG Chen Controlling TGF-beta signaling Genes Dev 2000 14(6) p 627-44 Mc KD HA Adams EC Danziger S Histochemical observations on the germ cells of human embryos Anat Rec 1953 2 p 201-219 McGee EA and AJ Hsueh Initial and cyclic recruitment of ovarian follicles Endocr Rev 2000 21(2) p 200-14 Mishina Y et al Genetic analysis of the Mullerian-inhibiting substance signal transduction pathway in mammalian sexual differentiation Genes Dev 1996 10(20) p 2577-87 Moran LJ HC Hutchinson SK Stepto NK Strauss BJ Teede HJ Exercise decreases anti-Mullerian horomone in anovulatory overweight women with polycystic ovary syndrome-A pilot study Horm Metab Res 2011 October Nardo LG et al Circulating basal anti-Mullerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 92(5) p 1586-93 Nelson SM RW Yates and R Fleming Serum anti-Mullerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007 22(9) p 2414-21 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867 Nelson SM and A La Marca The journey from the old to the new AMH assay how to avoid getting lost in the values 2011 Reprod Biomed Online Nelson SM et al External validation of nomogram for the decline in serum anti-Mullerian hormone in women a population study of 15834 infertility patients Reprod Biomed Online 2011 23(2) p 204-6 NICE Assessment and treatment for people with fertility problems NICE Guidelines 2013 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS ONE 5(7) e11637 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS
37
ONE 2010 5(7) 11637 Nilsson EE et al Inhibitory actions of Anti-Mullerian Hormone (AMH) on ovarian primordial follicle assembly PLoS One 2011 6(5) p e20087 Notarianni E Reinterpretation of evidence advanced for neo-oogenesis in mammals in terms of a finite oocyte reserve 2011 J Ovarian Res 4(1) p 1 Office of National Statistics 2012 1 2011 Live Births in England and Wales by Characteristics of Mother Oktem O and B Urman Understanding follicle growth in vivo Hum Reprod 25(12) p 2944-54 Oktem O and K Oktay The ovary anatomy and function throughout human life Ann N Y Acad Sci 2008 1127 p 1-9 Ottosen LD et al Pregnancy prediction models and eSET criteria for IVF patients--do we need more information J Assist Reprod Genet 2007 24(1) p 29-36 Pacchiarotti J et al Differentiation potential of germ line stem cells derived from the postnatal mouse ovary Differentiation 2010 79(3) p 159-70 Paternot G WA Thonon F Vansteenbrugge A Willemen D Devroe J Debrock S DHooghe TM Spiessens C Intra- and interobserver analysis in the morphological assessment of early stage embryos during an IVF procedure a multicentre study Reprod Biol Endocrinol 2011 9 p 127 Pehlivanov B OM Anti-Muumlllerian hormone in women with polycystic ovary syndrome Folia Medica 2011 53 p 5-10 Pellat L HL Brincat M et al Granulosa cell production of anti-Muumlllerian hormone is increased in polycystic ovaries J Clin Endocrinol Metab 2007 92 p 240-5 Pigny P ME Robert Y et al Elevated serum level of anti-Mullerian hormone in patients with polycystic ovary syndrome relationship to the ovarian follicle excess and the follicular arrest J Clin Endocrinol Metab 2003 88 p 5957-62 Porter RN et al Induction of ovulation for in-vitro fertilisation using buserelin and gonadotropins Lancet 1984 2(8414) p 1284-5 Rajpert-De Meyts E et al Expression of anti-Mullerian hormone during normal and pathological gonadal development association with differentiation of Sertoli and granulosa cells J Clin Endocrinol Metab 1999 84(10) p 3836-44
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-
38
Scott Wilkes Murdoch Alison DC and Greg Rubin Epidimiology and management of infertility a poppulation-based study in UK primary care Family Practice 2009 26 p 269-274 Seifer DB L-MG Hogan JW Gardiner AC Blaza AS Berk CA Day 3 serum inhibin-B is predictive of assisted reproductive technologies outcome Fertility and Sterility 1997 67 p 110-4 Skinner MK (2005) Regulation of primordial follicle assembly and development Hum Reprod Update 11 461ndash71 Syrop CH et al Ovarian volume may predict assisted reproductive outcomes better than follicle stimulating hormone concentration on day 3 Hum Reprod 1999 14(7) p 1752-6 Syrop CH A Willhoite and BJ Van Voorhis Ovarian volume a novel outcome predictor for assisted reproduction Fertil Steril 1995 64(6) p 1167-71 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Taylor A ABC of subfertility Making a diagnosis Br Med J Clin Res Ed 2003 327 p 799-801 Templeton A JK Morris and W Parslow Factors that affect outcome of in-vitro fertilisation treatment Lancet 1996 348(9039) p 1402-6 Templeton A Infertility-epidemiology aetiology and effective management Health Bull (Edinb) 1995 53(5) p 294-8 TDL test update AMH Stability Hormones and OCPs The Doctors Laboratory Guide 2008 page 29 Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34) p 18-21 Tilly JL and J Johnson Recent arguments against germ cell renewal in the adult human ovary is an absence of marker gene expression really acceptable evidence of an absence of oogenesis Cell Cycle 2007 6(8) p 879-83 Urbancsek J Use of serum inhibin B levels at the start of ovarian stimulation and at oocyte pickup in the prediction of assisted reproduction treatment outcome Fertility and Sterility 2005 83(2) p 341-348 van der Linden M BK Farquhar C Kremer JAM Metwally M Luteal phase support for assisted reproduction cycles (Review) Cochrane Library 2011 October
39
van Disseldorp J et al Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2011 25(1) p 221-7 van Kooij RJ et al Age-dependent decrease in embryo implantation rate after in vitro fertilization Fertil Steril 1996 66(5) p 769-75 van Rooij IA et al Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002 17(12) p 3065-71 Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539 van Disseldorp J Kwee CBL J Looman CWN Eijkemans MJC and FJ Broekmans Comparison of inter- and intra-cycle variability of anti-Muuml llerian hormone and antral follicle counts Human reproduction 2010 25 p 221-227 Verberg MF et al Predictors of low response to mild ovarian stimulation initiated on cycle day 5 for IVF Hum Reprod 2007 22(7) p 1919-24 Verhagen TE et al The accuracy of multivariate models predicting ovarian reserve and pregnancy after in vitro fertilization a meta-analysis Hum Reprod Update 2008 14(2) p 95-100 Visser JA AMH signaling from receptor to target gene Mol Cell Endocrinol 2003 211(1-2) p 65-73 Wallace WH and TW Kelsey Human ovarian reserve from conception to the menopause PLoS One 5(1) p e8772 Wallace AM et al A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 2011 48(Pt 4) p 370-3 Webber L J SS Stark J Trew G H Margara R Hardy K Franks S Formation and early development of follicles in the polycystic ovary Lancet 2003 362(September) p 1017-1021
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362 Younis JS et al A simple multivariate score could predict ovarian reserve as well as pregnancy rate in infertile women Fertil Steril 2010 94(2) p 655-61 Zou K et al Production of offspring from a germline stem cell line derived from neonatal ovaries Nat Cell Biol 2009 11(5) p 631-6 Zuckerman The number of oocytes in the mature ovary Recent Prog Horm Res 1951 6(63-108)
Figure 1 Schematic representation of a long GnRH agonist cycle
In a long agonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH agonist preparations starting from mid-luteal phase of the preceding menstrual cycle till the day of administration of HCG
Cycle Started
Menstrual Period
Daily GnRH agonist
From mid-luteal phase
Daily GnRH agonist
Menstrual
Period
Daily GnRH agonist
amp
Daily hMG
Day 2-10
HCG
USOR
amp
ET
41
Figure 2 Schematic representation of GnRH antagonist cycle
In an antagonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH antagonist preparations starting from the 5th day of IVF cycle till the day of administration of HCG Therefore an ldquoAntagonistrdquo cycle is significantly shorter than an ldquoAgonistrdquo cycle
Cycle Started
Menstrual Period
Daily GnRH antagonist
(Day 5-10)
amp
Daily hMG
(Day 2-10)
HCG
USOR
amp
ET
42
Figure 3 The role of AMH in regulation of oocyte recruitment and folliculogenesis
It appears that AMH plays an important role in a) the recruitment of primordial follicles and b) the selection of a dominant follicle from a cohort of antral follicles AMH is believed to be the main regulator of ovarian reserve which is achieved by its paracrine negative feedback effect to resting primordial follicles (Durlinger et al 1999) AMH was found to play an important role
in the regulation of the selection of a dominant follicle by inhibition of the FSH-induced follicle growth (Durlinger et al 2001)
EVALUATION OF THE GEN II AMH ASSAY BETWEEN-SAMPLE VARIABILITY AND
ASSAY-METHOD COMPARABILITY
2
44
ANTI-MUumlLLERIAN HORMONE SERUM LEVELS AND REPRODUCIBILITY
IN A LARGE COHORT OF SUBJECTS SUGGEST
SAMPLE INSTABILITY
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G
Nardo Philip W Pemberton
Human Reproduction 2012a 273085-3091
21
45
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large
cohort of subjects suggest sample instability
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester Foundation Trust
Manchester M13 9WL UK
c Health Sciences - Methodology Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
d School of Medicine University of Manchester Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Corresponding author
Oybek Rustamov MRCOG
Research Fellow in Reproductive Medicine
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
E-mail oybekrustamovcmftnhsuk oybek_rustamovyahoocouk
Word count 3909
Conflicts of Interest There are no potential conflicts of interest
Acknowledgement of financial support
Dr Steve Roberts is supported by the NIHR Manchester Biomedical Research Centre
46
Declaration of authorsrsquo roles
OR led on clinical aspects of this study with responsibility for collation of the
clinical database and the analysis of the clinical data OR prepared the first
draft of the clinical work and was involved in preparation of the whole paper
and submission of the final manuscript CF and LGN contributed to clinical
data analysis draft preparation and approval of the final manuscript MK was
involved in clinical data collation and approval of the final draft PWP was the
laboratory lead responsible for all of the laboratory based experiments and for
the routine analysis of clinical samples PWP prepared the first draft of the
laboratory work and was involved in the preparation of the whole paper and
submission of the final manuscript AS suggested the sample stability studies
and was involved in discussion draft preparation and approval of the final
manuscript APY was involved in some of the routine clinical analyses and
progression of drafts to approval of the final manuscript SAR was involved in
clinical study design oversaw the statistical analysis and progression of drafts
through to approval of the final manuscript OR and PWP should be
considered as joint first authors
47
ABSTRACT
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large cohort of
subjects suggest sample instability
Study question
What is the variability of anti-muumlllerian hormone (AMH) concentration in
repeat samples from the same individual when using the Gen II assay and how
do values compare to Gen I (DSL) assay results
Summary answer
Both AMH assays displayed appreciable variability which can be explained by
sample instability
What is known already
AMH is the primary predictor of ovarian performance and is used to tailor
gonadatrophin dosage in cycles of IVFICSI and in other routine clinical
settings A robust reproducible and sensitive method for AMH analysis is of
paramount importance The Beckman Coulter Gen II ELISA for AMH was
introduced to replace earlier DSL and Immunotech assays The performance
of the Gen II assay has not previously been studied in a clinical setting
Study design size and duration
For AMH concentration study we studied an unselected group of 5007
women referred for fertility problems between 1st September 2008 to 25th
October 2011 AMH was measured initially using the DSL AMH ELISA and
subsequently using the Gen II assay AMH values in the two populations were
compared using a regression model in log(AMH) with a quadratic adjustment
for age Additionally women (n=330) in whom AMH had been determined in
different samples using both the DSL and Gen II assays (paired samples)
identified and the difference in AMH levels between the DSL and Gen II
assays was estimated using the age adjusted regression analysis
In AMH variability study 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay and 87 women had repeated AMH
determinations using the Gen II assay (n=177 samples) were identified A
mixed effects model in log (AMH) was utilised to estimate the sample-to-
48
sample (within-subject) coefficients of variation of AMH adjusting for age
Laboratory experiments including sample stability at room temperature
linearity of dilution and storage conditions used anonymised samples
Main results and the role of chance
In clinical practice Gen II AMH values were ~20 lower than those
generated using the DSL assay instead of the 40 increase predicted by the kit
manufacturer Both assays displayed high within-subject variability (Gen II
assay CV=59 DSL assay CV=32) In the laboratory AMH levels in serum
from 48 subjects incubated at RT for up to 7 days increased progressively in
the majority of samples (58 increase overall) Pre dilution of serum prior to
assay gave AMH levels up to twice that found in the corresponding neat
sample Pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave higher readings (72 overall) compared to sequential
addition Storage at -20ordmC for 5 days increased AMH levels by 23 compared
to fresh samples The statistical significance of results was assessed where
appropriate
Limitations reasons for caution
The analysis of AMH levels is a retrospective study and therefore we cannot
entirely rule out the existence of differences in referral practices or changes in
the two populations
Wider implications of the findings
Our data suggests that AMH may not be stable under some storage or assay
conditions and that this may be more pronounced with the Gen II assay The
published conversion factors between the Gen II and DSL assays appear to be
inappropriate for routine clinical practice Further studies are urgently required
to confirm our observations and to determine the cause of the apparent
instability In the meantime caution should be exercised in the interpretation
of AMH levels in the clinical setting
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA DSL Active MIS AMH ELISA sample stability
49
INTRODUCTION
AMH in women is secreted by the granulosa cells of pre-antral and small
antral follicles (Vigier et al 1984 Themmen 2005) and circulating levels reflect
the ovarian pool from which follicles can be recruited (Loh amp Maheshwari
2011) Measurement of AMH has become of paramount significance in clinical
practice in IVF units to assign candidates to the most suitable controlled
ovarian hyperstimulation protocol and its level is used to predict poor or
excessive ovarian response (Nelson et al 2007 Nardo et al 2009 Yates et al
2011) It is also of increasing importance in (a) prediction of live birth rate in
IVF cycles (La Marca et al 2011) (b) screeningdiagnosis of polycystic ovarian
syndrome (Cook et al 2002) (c) follow up of women with a history of
granulosa cell tumours (Lane et al 1999) (d) prediction of the age of onset of
infertility due to the menopause (van Disseldorp et al 2008 Broer et al 2011)
and finally (e) assessment of the long term effect of chemotherapy on fertility
(Anderson 2011)
Following development of the first laboratory AMH assay in 1990
(Hudson et al 1990 Lee et al 1996) first generation commercially available
immunoassays were introduced by Diagnostic Systems Ltd (DSL) and
Immunotech Ltd (IOT) These assays used different antibodies and standards
(Nelson amp La Marca 2011) and the resulting AMH concentrations obtained
using the IOT assay were found to be higher than those produced using the
DSL assay by most but not all authors (Freour et al 2007Taieb et al 2008 Lee
et al 2011) The AMH Gen II Assay (Beckman-Coulter Ltd) replaced both of
these assays using the DSL Gen I antibody with the IOT standards AMH
values obtained using this kit were predicted to correlate with but be higher
than those using the old DSL kit (Kumar et al 2010 Nelson amp La Marca
2011) This was confirmed (Wallace et al 2011) with the AMH Gen II assay
giving values approximately 40 higher than the DSL assay The
recommended conversion factor of 14 (AMH Gen II = DSL x 14) was also
applied to the DSL reference ranges but this recommendation does not appear
to have been independently validated
It is generally accepted that serum AMH concentrations are highly
reproducible within and across several menstrual cycles and therefore a single
blood sampling for AMH measurement has been accepted as routine practice
50
(Hehenkamp et al 2006 La Marca et al 2006 Tsepelidis et al 2007) However
we recently challenged this view and reported significant sample-to-sample
variation in AMH levels using the DSL assay in women who had repeated
measurements 28 difference between samples taken from the same patient
with a median time between sampling of 26 months and taking no account of
menstrual cycle (Rustamov et al 2011) Although we could not explain the
cause of this variability we speculated that it might be due to true biological
variation in secretion of AMH or due to post-sampling pre-analytical
instability of the specimen
Given the widespread adoption of AMH in Clinical Units it is critical
that the sources of variability in any AMH assay are understood and quantified
This paper presents the results of clinical and laboratory studies on routine
clinical samples using the new AMH Gen II assay specifically comparing assay
values with the older DSL assay assessing between sample variability and
investigating analytical and pre-analytical factors affecting AMH measurement
METHODS
Study population
Samples were obtained from women of 20-46 years of age attending for
investigation of infertility requiring AMH assessment at the secondary
(Gynecology Department) and tertiary (Reproductive Medicine Department)
care divisions of St Maryrsquos Hospital Manchester from 1st September 2008 to
25th October 2011 Samples which were lipaemic or haemolysed and samples
not frozen within 2 hours of venepuncture were excluded from the study
Anonymised samples from this pool of patients were used for stability studies
after routine AMH measurements had been completed The full dataset
comprised AMH results on 5868 samples from 5007 women meeting the
inclusion criteria Additionally we identified women in whom AMH had been
determined in different samples using both the DSL and Gen II assays (paired
samples from 330 women)
51
Sample processing
Collection and handling of all AMH samples was conducted according
to the standards set out by the manufacturers and did not vary between the
different assays Serum samples were transported immediately to the
Department of Clinical Biochemistry based in the same hospital and
separated within 2 hours of venepuncture using the Modular Pre-Analytics
Evo (Roche Diagnostics Burgess Hill West Sussex UK) Samples were frozen
in aliquots at -20C until analysis normally within one week of receipt The
laboratory participates in the pilot National external quality assessment scheme
(UKNEQAS) for AMH in Edinburgh and performance has been satisfactory
AMH analysis
All AMH assays were carried out strictly according to the protocols
provided by the manufacturer and sample collection and storage also
conformed to these recommendations All AMH samples were analysed in
duplicate and the mean of the two replicates was reported as the final result
1) The DSL AMH assay The enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) was used for measurement of AMH prior to 17th
November 2010 The working range of the assay was up to 100pmolL with a
minimum detection limit of 063pmolL The intra-assay coefficient of
variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at 56pmoll) The
inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at 56pmoll)
2) The Beckman Coulter Gen II assay After 17th November 2010
AMH was measured using the enzymatically amplified two-site immunoassay
(AMH Gen II ELISA Beckman Coulter Inc Brea CA USA) The working
range of the assay is up to 150pmolL with a minimum detection limit of
057pmolL The intra-assay CV (n=16) is 292 (at 18pmoll) and 203 (at
60pmoll) The inter-assay coefficient of variation (n=28) is 357 (at
18pmoll) and 364 (at 60pmoll)
Sample Stability Studies
(1) Stability of AMH in serum at room temperature (RT) serum samples
(n = 48) were allowed to thaw and then left at RT for one week At 0 1 2 4
and 7 days 100microl aliquots were removed and immediately stored at -80 ordmC in
52
2ml screw-capped polypropylene tubes (Alpha Laboratories Eastleigh UK)
Two freezethaw cycles had no effect on AMH concentration (results not
shown) Samples from individual subjects were analysed for AMH on the same
GenII microtitre plate to eliminate inter-assay variability Results were
expressed as a percentage of the day 0 value
(2) Linearity of Dilution 100microl fresh serum (n = 9) was added to 100microl
AMH Gen II sample diluent incubated for 30min at RT and the mixture
analysed using the standard GenII assay procedure
(3) Comparison between the Standard Assay method and an equivalent
procedure in the standard GenII ELISA assay method the first steps involve
the addition of calibrators controls or serum samples to microtitration wells
coated with anti-AMH antibody Assay buffer is then added to each well As a
comparison serum and assay buffer were mixed in a separate tube incubated
for 10min at RT and then added in exactly the same volume and proportions
to the microtitre plate Thereafter the assay was performed using the standard
protocol
(4) Stability of AMH during storage fresh serum samples (n = 8)
analysed on the day of reception were compared with aliquots from the same
samples that had been frozen for 5 days either in polystyrene tubes at -20degC or
polypropylene tubes at -80degC
Statistical Analysis
Data analysis was performed using the Stata 12 analytical package
(StataCorp Texas USA) Data management and analysis of clinical data was
conducted by one of the researchers (OR) and verified independently by
another member of the research team (SR) using different statistical software
(R statistical environment) Approval for the use of the data was obtained from
the Local Research Ethics Committee (UK-NHS 10H101522) The age-
related relationship of the DSL and Gen II assays to AMH was visualised using
scatter plots and quadratic fit on a logarithmic scale (Nelson et al 2011) The
age adjusted regression analysis of paired samples was used to estimate the
difference in AMH levels between the DSL and Gen II assays A mixed effects
model in log (AMH) was utilised to estimate the sample-to-sample (within-
subject) coefficients of variation of AMH levels in women who had repeated
53
measurements within a 1 year period from the patientrsquos first AMH sample
adjusting for age as above In the sample stability studies percentage changes
are expressed as mean plusmn SEM In the stability of AMH in serum at RT study a
paired t-test determined the level of significance between baseline and
subsequent days
RESULTS
Population studies and variability
AMH concentration
Table 1 summarizes the results of AMH determinations in our
population of women attending the IVF Clinic prior to the 17th November
2010 (using the DSL assay) and after that date (using the Gen II assay) A
second analysis compares AMH levels in women who had AMH measured
using both assays at different times Results were consistent with lower serum
levels of AMH observed when samples were analysed using the Gen II assay
compared to the DSL assay Figure 1 shows the correlation of AMH with age
for the unselected groups After adjustment for age the total cohorts showed
Gen II giving AMH values 34 lower than those for DSL Analysis restricted
to patients with AMH determinations using both assays gave an age-adjusted
difference of 21
AMH variability
During the study period 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay with 295 patients having two samples 17
three samples and one five samples The median time between samples was 51
months Eighty seven women had repeated AMH determinations using the
Gen II assay (n=177 samples) with 84 women having two samples and 3
having three samples The median interval between repeat samples was 32
months Both assays exhibit high sample-to-sample variability (CV) this was
32 in the DSL assay group (our previous finding (Rustamov et al 2011) in a
smaller group was 28) variability in the Gen II assay group was much higher
(59)
54
Table 1 Median and inter-quartile range for the two assays in the
different datasets along with the mean difference from an age-
adjusted regression model expressed as a percentage
DSL Gen II
difference ()
n age AMH (pmoll
)
n Age
AMH (pmoll
)
all data
3934
33 (29 36)
147 (78250
)
1934 33 (29 36)
112 (45 216)
-335 (-395 to -
275)
paired sample
s
330 32 (29 36)
149 (74 247)
330 34 (30 37)
110 (56 209)
-214 (-362 to -64)
Figure 1 Unselected AMH values from DSL (circles) and Gen II
(triangles) assays as a function of age Lines show the regression
fits of log(AMH) against a quadratic function of age solid lines
Gen II broken lined DSL
20 25 30 35 40 45
Age
AM
H [p
mo
lL
]
DSLGen II
11
01
00
55
Sample stability studies
(1) Stability of AMH in serum at room temperature
AMH levels in 11 of the 48 individuals remained relatively unchanged
giving values within plusmn10 of the original activity over the period of a week
and one patient had an undetectable AMH at all time points The remaining 36
serum samples had AMH values that increased progressively with time In the
47 samples with detectable AMH levels increased significantly (plt0001) for
each time interval compared to baseline the increase at day 7 being 1584 plusmn 76
(Figure 2)
Figure 2 Stability of AMH in serum at RT
Results at each time interval are expressed as a percentage of the patientrsquos AMH concentration at day 0 Means plusmn SEM are indicated
56
(2) Linearity of Dilution
In a group of nine anonymised samples proportionality with two-fold
sample dilution does not hold and on average there is a 574 plusmn 123 increase
in the apparent AMH concentration on dilution compared to neat sample (see
table 2a) Two samples which gave the highest increases were diluted further It
was apparent that after the anomalous doubling of AMH concentration on
initial two-fold dilution subsequent dilutions gave a much more proportional
result (see Table 2b) Linearity of dilution was maintained only in samples that
showed no initial increase on two-fold dilution
Table 2a Proportionality with two-fold dilution of serum
AMH (pmoll)
sample no neat serum x2 dilution recovery
1 1105 2294 2076 2 4941 9900 2004 3 415 483 1164 4 923 1122 1216 5 2801 3066 1091 6 362 628 1734 7 2739 3962 1447 8 553 1034 1870 9 1849 2892 1564
Table 2b Linearity with multiple dilution of serum
AMH (pmoll)
sample no dilution Measured expected recovery ()
1 x1 1105 1105 100 x2 1147 5525 2076 x4 5532 2763 2002 x7 3072 1579 1946 x10 2145 1105 1941
2 x1 4941 4941 100
x2 4950 2471 2003 x4 2286 1235 1851 x7 1228 706 1739 x10 857 494 1735
57
(3) Comparison between the Standard Assay method and an equivalent
procedure Serum samples that had been pre-mixed with buffer prior to
addition gave on average 718 plusmn 48 higher readings than those added
sequentially using the standard procedure (see table 3)
Table 3 Comparison between equivalent ELISA procedures
AMH (pmoll)
sample no A B BA ()
1 1466 2284 1558 2 839 1642 1957 3 3151 6446 2046 4 1244 2014 1619 5 1393 2276 1634 6 701 1246 1777 7 778 1358 1746 8 1693 3298 1948 9 955 1793 1877 10 2849 5437 1908
11 1365 2062 1511 12 1773 2868 1617 13 1468 2429 1655 14 1499 2115 1411 15 249 357 1434 16 1284 2289 1783
A = 20microl serum added directly to the plate followed by 100microl assay buffer
B = 60microl serum + 300microl assay buffer mixed amp incubated at RT for 10min 120microl mixture added to the plate
(4) Stability of AMH during storage AMH levels in samples stored at -20degC
showed an average increase of 225 plusmn 111 over 5 days compared with fresh
values while those samples stored at -80degC showed no change (18 plusmn 31)
(see Table 4)
Table 4 Stability of AMH in serum on storage
AMH (pmoll)
sample no
fresh -20ordmC PS -80ordmC PP
1 1241 1551 1312 2 4217 7542 4508 3 1193 1712 1239 4 1042 1282 1228 5 956 905 879 6 1902 2601 1884 7 2402 2016 2362 8 145 137 132
PS = polystyrene LP4 tube PP = polypropylene 2ml tube
58
DISCUSSION
This publication arose from two initially separate pieces of work in the
Clinical IVF Unit at St Maryrsquos Hospital and in the Specialist Assay Laboratory
at Central Manchester Foundation Trust The IVF Unit had become
concerned with their observed increase in variation in AMH values and
consequently with the reliability of their AMH-tailored treatment guidance
The Laboratory wished to establish whether the practice of sending samples in
the post (which has been adopted by many laboratories rather than frozen as
specified by Beckman) was viable It soon became clear that these anomalies
observed in clinical practice might be explained by a marked degree of sample
instability seen in the Laboratory which had not previously been reported and
which may or may not have been an issue with previous AMH assays
The data contained in this paper represents the largest retrospective
study on the variability of the DSL assay and the first study on the variability
of the Gen II assay Early studies reported insignificant variation between
repeated AMH measurements suggesting that a single AMH measurement
may be sufficient in assessment of ovarian reserve (La Marca et al 2006
Tsepelidis et al 2007) However these recommendations have been challenged
by a number of groups (Lahlou et al 2006 Wunder et al 2008 Rustamov et al
2011) The current study in a large cohort of patients has demonstrated
substantial sample-to-sample variation in AMH levels using the DSL assay and
an even larger variability using the Gen II assay We suggest that this variability
may be due to sample instability related to specimen processing given that a)
AMH is produced non-cyclically and true biological variation is believed to be
small (Fanchin et al 2005 van Disseldorp et al 2009) and b) the intra-and inter
assay variation in our laboratory for both the DSL and Gen II assays is small
(lt50) suggesting that the observed variation is not due to poor analytical
technique
The population data presented in this paper also suggests that in routine
clinical use the Gen II assay provides AMH results which are 20-40 lower
compared to those measured using the DSL assay This is in contrast to
validation studies for the Gen II assay which showed that this assay gave AMH
values ~40 higher than those found with the DSL assay (Kumar et al 2010
Preissner et al 2010 Wallace et al 2011)
59
All samples in this retrospective study were subject to the same handling
procedures and analyzed by the same laboratory the two populations were
comparable with the same local referral criteria for investigation of infertility
and we are unaware of any other alterations in practice which might produce
such a large effect on AMH we cannot rule out the possibility of other
changes in the population being assayed that were coincident in time with the
assay change However any such change would have to be coincident and
produce a 50 decrease in observed AMH levels to explain our findings We
did note a weak trend towards decreasing AMH over calendar time assuming a
linear trend in the analysis implies that AMH values might be 12 (2-22)
lower when the Gen II assay was being used compared to the Gen I assay
This suggests that the age adjusted analysis of repeat samples on individuals
showing a 21 decrease in AMH with the Gen II assay is currently the best
estimate of the assay difference
This is the first study to compare AMH assays in a routine clinical setting
in a large group of subjects and as such is likely to reflect the true nature of the
relationship between AMH measured by two different ELISA kits and avoids
some of the issues in other published studies Previous laboratory studies have
compared AMH assays in aliquots from the same sample which only provides
data on the within-sample relationship between the two assays (Kumar et al
2010 Preissner et al 2010 Wallace et al 2011) Although it is difficult to give a
definitive explanation for the discrepancy between the previously published
studies (on within-sample relationships) and this study (on between-sample
relationships) we suggest that it may be due to degradation of the specimen in
one (or both) of the assays If AMH in serum is unstable under certain storage
and handling conditions this might result in differing values being generated
because of differential sensitivity of the two assays to degradation products
Unfortunately we cannot suggest which step of sample handling might have
caused this discrepancy since the published studies did not provide detailed
information
The present study used samples which were frozen very soon after
phlebotomy and analysed shortly thereafter hopefully minimising storage
effects The most striking change followed incubation over a period of 7 days
at RT this showed a substantial increase in AMH levels rather than the
expected decline Previously Kumar et al (2010) had shown that the average
variation between fresh serum samples and those stored for seven days to be
60
approximately 4 at 2-8ordmC and lt1 at -20ordmC but presented no data on RT
stability Zhao et al (2007) reported that AMH values were likely to differ by
lt20 in samples incubated at RT for 2 days compared to those frozen
immediately
Several supplementary experiments were performed in order to
investigate this observed increase in AMH when samples were incubated at
RT These included (1) addition of the detergent Tween-20 to assay buffer to
disclose potential antibody-binding sites on the AMH molecule (2) the
removal of heterophilic antibodies from serum using PEG precipitation or
heterophilic blocking tubes None of these approaches affected AMH levels
significantly (results not shown)
Examination of the data presented here shows that in some samples
AMH levels tend towards twice those expected while results greater than that
only occur in two outliers found in Figure 2 The AMH molecule is made up
of two identical 72kDA monomers which are covalently bound (Wilson et al
1993 di Clemente et al 2010) During cytoplasmic transit each monomer is
cleaved to generate 110-kDa N-terminal and 25-kDa C-terminal homodimers
which remain associated in a noncovalent complex The C-terminal
homodimer binds to the receptor but in contrast to other TGF-β superfamily
members AMH is thought to require the N-terminal domain to potentiate this
binding to achieve full bioactivity of the C-terminal domain After activation of
the receptor the N-terminal homodimer is released (Wilson et al 1993) One
possible explanation for our findings is that the N-and C-terminal
homodimers dissociate gradually under certain storage conditions and that
either the two resulting N- and C-terminal components bind to the ELISA
plate or a second binding site on the antigen is exposed by the dissociation
effectively doubling the concentration of AMH It has been shown (di
Clemente et al 2010) that no dissociation occurs once the complex is bound to
immobilised AMH antibodies The observation that in some of our samples
there was no change after one week at RT might be explained by the
supposition that in those samples AMH is already fully dissociated A mixture
of dissociated and complex forms in the same sample would therefore
account for the observed recoveries between 100 and 200 in the
experiments presented in this paper Rapid sample processing and storage of
the resulting serum in a different tube type at -80ordmC might slow down this
breakdown process
61
The change in ionic strength or pH that occurs on dilution also seems to
have the same effect in increasing apparent AMH levels and again may be due
to dissociation or exposure of a second binding site Our results contradict
those reported by Kumar et al (2010) who showed that serum samples in the
range of 36-93pmoll of AMH when diluted in Gen II sample diluent showed
linear results across the dynamic range of the assay with average recoveries on
dilution close to 100 This might be explained if Kumarrsquos samples were
already dissociated before dilution Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample but our results do not seem to support this
These findings have significant clinical relevance given the widespread
use of AMH as the primary tool for assessment of ovarian reserve and as a
marker for tailoring the dose of gonadotrophins in cycles of IVFICSI As no
guideline studies have been published using the new Gen II assay some ART
centres have adopted modified treatment ldquocut off levelsrdquo for ovarian
stimulation programs based on the old DSL assay based ldquocut off levelsrdquo
multiplied by a conversion factor of 14 (Nelson et al 2007 Nelson et al 2009
Wallace et al 2011) The data presented in this paper suggest that this approach
could result in patients being allocated to the wrong ovarian reserve group
Poor performance of the Gen II assay in terms of sample-to-sample variability
(up to 59) could also lead to unreliable allocation to treatment protocols It
is a matter of some urgency therefore that any possible anomalies in the
estimation of AMH using the Gen II assay be thoroughly investigated and that
this work should be repeated in other centres
62
References
Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146
di Clemente N Jamin SP Lugovskoy A Carmillo P Ehrenfels C Picard JY Whitty A Josso N Pepinsky RB Cate RL Processing of anti-mullerian hormone regulates receptor activation by a mechanism distinct from TGF-beta Mol Endocrinol 2010242193-2206
Freour T Mirallie S Bach-Ngohou K Denis M Barriere P and Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
Fanchin R Taieb J Mendez Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Mullerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 2005 20923-927
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
La Marca A Nelson SM Sighinolfi G Manno M Baraldi E Roli L Xella S Marsella T Tagliasacchi D DAmico R Volpe A Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction Reprod Biomed Online 2011 22341-349
Lahlou N Chabbert-Buffet E Gainer E Roger M Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-5
63
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
Lee MM Donahoe PK Hasegawa T Silverman B Crist GB Best S Hasegawa Y Noto RA Schoenfeld D MacLaughlin DT Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 199681571-576
Loh JS Maheshwari A Anti-Mullerian hormone--is it a crystal ball for predicting ovarian ageing Hum Reprod 2011262925-2932
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 201195736-741
Preissner CM MD Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Taieb J Coussieu C Guibourdenche J Picard JY and di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34)18-21
Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormoneconcentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
van Disseldorp J Lambalk CB Kwee J Looman CWN Eijkemans MJC Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2010 25 221-227
64
Vigier B Picard JY Tran D Legeai L Josso N Production of anti-Mullerian hormone another homology between Sertoli and granulosa cells Endocrinology 19841141315-1320
Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-MuSllerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
Wilson CA Di Clemente N Ehrenfels C Pepinsky RB Josso NVigier B Cate RL Muumlllerian inhibiting substance requires its N-terminal domain for maintenance of biological activity a novel finding within the transforming growth-factor-beta superfamily Mol Endocrinol 19937247ndash257
Wunder DM Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrualcycle in reproductive age women Fertil Steril 200889927-933
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 2007 88S17
65
AMH GEN II ASSAY A VALIDATION STUDY OF
OBSERVED VARIABILITY BETWEEN REPEATED
AMH MEASUREMENTS
Oybek Rustamov Richard Russell
Cheryl Fitzgerald Stephen Troup Stephen A Roberts
22
66
Title
AMH Gen II assay A validation study of observed variability between
repeated AMH measurements
Authors
Oybek Rustamov 1 Richard Russell2 Cheryl Fitzgerald1 Stephen Troup2
Stephen A Roberts3
Institutions
1Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospitals NHS Foundation Trust Manchester
M13 9WL UK
2Hewitt Fertility Centre Liverpool Womenrsquos NHS Foundation Trust Hospital
Crown Street Liverpool L8 7SS
3 Centre for Biostatistics Institute of Population Health University of
Manchester Manchester M13 9PL UK
Word count 1782
Conflict of interest Authors have nothing to disclose
Acknowledgment
The authors would like to thank the Biomedical Andrology Laboratory team at
the Hewitt Fertility Centre for their assistance
67
Declaration of authorsrsquo roles
OR coordinated the study conducted the statistical analysis and prepared first
draft of the manuscript RR extracted data prepared the dataset assisted in
preparation of first draft of manuscript CF ST and SR involved in study
design oversaw statistical analysis contributed to the discussion and
preparation of the final version of the manuscript
68
ABSTRACT
Objective
To study the within patient sample-to-sample variability of AMH levels using
the Gen II assay reproduced in an independent population and laboratory
Design Retrospective cohort analysis
SettingTertiary referral IVF Unit in the United Kingdom
Patients Women being investigated for sub-fertility
Interventions
Retrospective measurements were obtained from women who had AMH
measurements using Gen II assay during routine investigation for infertility at a
tertiary referral unit during a 1-year period The patients who had repeated
AMH measurements were identified and within-patient coefficient of variation
(CV) calculated using a mixed effects model with quadratic adjustment for age
Main Outcome Measures
The within-patient coefficient of variation (CV) calculated using a random
effects model with quadratic adjustment for age
Results
There was in total of 76 samples from 38 women with repeated AMH
measurements during the study period The within-patient sample-to-sample
variation (CV) was found to be 62
Conclusions
The study has confirmed that even when samples are processed promptly and
strictly in accordance with the manufacturers instructions substantial
variability exists between repeated samples Thus caution is recommended in
the use of these newer assays to guide treatment decisions Further work is
required to understand the underlying cause of this variability
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA AMH ELISA sample variability
69
INTRODUCTION
Anti-Muumlllerian hormone is a dimeric glycoprotein that is produced by
the granulosa cells of pre-antral and early antral follicles and has been found to
be the primary regulator of oocyte recruitment and folliculogenesis (Durlinger
et al 1999 Durlinger et al 2001) Strong correlation between AMH levels and
primordial follicle count (Hansen et al 2011) and hence a reflection of ovarian
response has promised a valuable tool in the reproductive specialistsrsquo armory
The development of commercially available AMH immunoassay assay kits has
heralded the widespread introduction and routine usage of AMH assessment in
the clinical setting Several studies have demonstrated that AMH serves as a
good predictor of ovarian response to gonadotrophin stimulation during IVF
treatment (van Rooij et al 2002 Nelson et al 2007 Nardo et al 2009) AMH
testing has also been shown to identify patients at risk of excessive ovarian
response and ovarian hyperstimulation syndrome (Yates et al 2011) with
consequent reduction in per cycle treatment costs by adopting an antagonist
approach during controlled ovarian stimulation Sensitivity and specificity of
AMH in detecting extremes of response has been shown to be comparable to
antral follicle count without the apparent technical limitations of the latter
(Broer et al 2009 Broer et al 2011)
It is stated that the sample-to-sample variation of AMH concentration in
individual women is small and therefore a single AMH measurement has been
recommended as standard practice (La Marca et al 2006 Hehenkamp et al
2006) However recent studies based on data from a single centre recently
published in Human Reproduction found that larger variability between
repeated samples exists which is particularly profound when currently
available second generation AMH assay (AMH Gen II ELISA Beckman
Coulter Inc Brea CA USA) is used (Rustamov et al 2012a Rustamov et al
2012b Rustamov et al 2011)
The trial team had 2 objectives firstly to assess whether the controversial
findings from the above study (Rustamov et al 2012a) were reproducible when
performed in the data based on the samples from a different laboratory with
differing populations If our study reached similar conclusions concerns
regarding the AMH Gen II assay and or manufacturers recommendations on
handling and sampling processes would be validated Alternatively if non-
70
similar findings were reported the laboratory performance in the initial study
ought to be questioned Secondly and more importantly if the repeat samples
are found to be within acceptable parameters then the current clinical standard
of a single random AMH measurement in patients is appropriate If the results
of repeated samples are significantly different following adjustment for age it
would suggest that AMH measurement is not a true estimation of the patientrsquos
ovarian reserve
In view of clinical and research implications of these findings we
undertook to replicate the variability study in a second fertility centre The
authors wish to note that Beckman Coulter recently issued a worldwide STOP
SHIP order on all AMH Gen II Elisa assay kits until further notice due to
manufacturing and quality issues
MATERIALS AND METHODS
Population
Women had serum AMH measurements using Gen II AMH assay from
15 April 2011 to 25 May 2012 for investigation of infertility at the Hewitt
Fertility Centre in the Liverpool Womens NHS Foundation Trust Hospital
tertiary referral unit were identified using the Biochemistry Laboratory AMH
samples database and all women within age range of 20-46 years were included
in the study The main reasons for repeating the samples were a) obtaining up-
to-date assessment of ovarian reserve b) patient request and c) for formulation
of a treatment strategy prior to repeat IVF cycles
Institutional Review Board approval was granted by the Audit
Department Liverpool Womenrsquos NHS Foundation Trust Hospital
Assay procedure
Samples were transported immediately to the in-house laboratory of
Liverpool Womenrsquos Hospital for the processing and analysis The serum was
separated within 8 hours from venipuncture and frozen at -50C until analyzed
71
in batches The sample preparation and assay methodology strictly followed
the manufacturers guidelines The AMH analysis of laboratory is regularly
monitored by external quality assessment scheme (UKNEQAS) and
performance has been satisfactory
The samples were analyzed using enzymatically amplified two-site
immunoassay (AMH Gen II ELISA Beckman Coulter Inc Brea CA USA)
The intra-assay CV was 521 and inter-assay CV (n=9) was 276 (low
controls) and 657 (high controls) The working range of the assay was
150pmolL and the minimum detection limit was 057pmolL
The main difference in the assay preparation in this study is that the
samples were processed within 8 hours whilst the samples in the previous
study were processed within 2 hours (Rustamov 2012a) Importantly the kit
insert of Gen II AMH assay does not state any maximum duration of storage
of unprocessed samples or any constraints on the transportation of
unprocessed samples Therefore there appears to be considerable variation in
practice of sample processing between clinics which ranges from processing
samples immediately to shipping unfrozen whole samples to long distances
Statistical analysis
The dataset was obtained from the Biomedical Andrology Laboratory
of the hospital and anonymised by one of the researchers (RR) Data
management and analysis of the anonymised data followed the same
procedures as the previous study (13) and were performed using Stata 12
Statistical Package (StataCorp Texas USA) Approval for data management
analysis and publication was obtained from the Research and Development
Department of Liverpool Womenrsquos Hospital
Between and within-subject sample-to-sample coefficient of variability
(CV) as well as the intra correlation coefficient (ICC) was estimated using a
mixed effects model in log (AMH) with quadratic adjustment for age AMH
levels of the samples that fell below minimum detection limit of the assay
(lt057 pmolL) were arbitrarily assigned a value of 031 pmolL in line with
the previous analysis (Rustamov et al 2012a)
72
RESULTS
During the study period in total of 1719 women had AMH
measurements using Gen II assay Thirty-eight women had repeated AMH
measurements with a total number of 76 repeat samples (Figure 1) The
median age of the women was 318 (IQR 304-364) The median AMH level
was 52pmolL (IQR 15-114) The median interval between samples was 93
days (IQR 49-164) with range of 6-375 days Age-adjusted regression analysis
of samples of these women showed that within-patient sample-to-sample
coefficient of variation (CV) of AMH measurements was 62 while between-
patient CV was 125 An age adjusted intra-correlation coefficient was 079
Figure 1 The repeated AMH measurements by date lines join the
repeats from the same patients (AMH in pmolL)
73
DISCUSSION
A number of studies have recently been published that have expressed
concerns regarding the stability and reproducibility of AMH results Whilst
technical issues regarding reproducibility between assays were known more
recently the reproducibility of results regarding the current Gen II assay has
raised significant concern (Rustamov et al 2012a Rustamov et al 2012b
Rustamov et al 2011) Proponents of the assay have proposed that poor
sample handling and preparation are responsible for these observed concerns
(Nelson et al 2013) Several studies have observed the stability of samples at
room temperature Kumar et al (Kumar et al 2010) observed a 4 variation in
results after 7 days storage compared with those samples analysed immediately
These results were consistent with studies by Fleming and Nelson who also
reported no change in AMH concentration over a period of several days
(Fleming et al 2012) However Rustamov et al reported a measured AMH
increase of 58 in samples stored at room temperature over a seven day
period (Rustamov et al 2012a) Similar concerns were raised regarding the
appropriate freezing process whilst samples frozen at -20C demonstrated
variation in results of between 6 and 22 (Durlinger et al 1999 Rustamov et al
2012a) freezing at -80C obviated a significant variation in assay results (Al-
Qahtani et al 2005 Rustamov et al 2012a) Several studies initially reported
good linearity of dilution (Kumar et al 2012 Preissner et al 2010 Fleming et al
2012) which was contradicted by reports that demonstrated poor linearity in
dilution when fresh samples were utilized (Rustamov et al 2012a) This study
suggested a tendency of AMH results to double with dilution More recently
Beckman Coulter issued a warning on their Gen II AMH ELISA kits that the
dilution of sample may give an erroneous result confirming non linearity of
dilution (King Dave 2012)
A number of studies have looked at the variability of AMH in repeated
samples without account to the menstrual cycle utilizing different assays
Dorgan et al in analyzing DSL samples frozen for prolonged periods
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median-sample interval of one year (Dorgan et al 2012)
Rustamov et al presented a larger series of 186 infertile patients with a median
between-sample interval of 26 months and a CV of 28 in DSL samples
74
(ICC 091 95 CI 090-093(Rustamov et al 2011) In a follow-up study
utilizing the Gen II assay in a group of 84 infertile patients the coefficient
variation of repeated results was 59 (ICC of 084 95 CI 079-090) a
substantial increase in the observed variability of the studies reporting for the
DSL assay (Rustamov et al 2012a) The most recent study to cast doubt on
current practice suggested that repeated measurement of AMH using Gen II
assay resulted in a within-subject variability of 80 (CV) (Hadlow et al 2012)
As a result 7 out of 12 women were subsequently reclassified according to their
originally predicted ovarian response Our study outlined above involving 76
samples from 38 infertile patients demonstrated a within-patient sample-to-
sample coefficient of variation (CV) of AMH measurements was 62
Overall these results suggest that there is significant within patient
variability that may be more pronounced in the Gen II assay Whilst biological
variation has been demonstrated to play a part within this the appreciative
effects of sample handling storage and freezing play a significant part in the
results and it may be that the Gen II assays may be more susceptible to these
changes This study has confirmed that there is significant within-patient
sample-to-sample variability in AMH measurements when the Gen II AMH
assay is used which is not confined to a single population or laboratory It is
important to note that the samples reported by both Rustamov et al 2012
and this study were processed and analyzed strictly according to
manufacturerrsquos recommendations in their respective local laboratories without
external transportation (Rustamov et al 2012a) Therefore it seems reasonable
to suggest that AMH results from other centers and laboratories are likely to
display similar significant sampling variability
Reproducibility of AMH measurements is of paramount importance
given that a single random AMH measurement is used for triaging patients
unsuitable for proceeding with IVFICSI and determining the dose of
gonadotrophins for ovarian stimulation for those patients who proceed with
treatment Similarly other clinical applications of AMH such as an assessment
of the effect of chemotherapy to fertility and follow up of women with history
of granulosa cell tumors also rely on accurate measurement of circulating
hormone levels The present work confirms the high between-sample within-
patient variability The recent warning from Beckman Coulter utilizing their
Gen II ELISA assay kits may give an erroneous result with dilution of samples
further questions the stability of the assay (King David 2012) Subsequently
75
the manufacturer recalled the assay kits due to issues with the instability of
samples and introduced modified protocol for preparation of Gen II assay
samples
Given there can be a substantial difference between two samples from
the same patient the use of such measurements for clinical decision-making
should be questioned and caution is advised
76
References
Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP and Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 2005 63267-273
Broer SL Dolleman M Opmeer BC Fauser BC Mol BW Broekmans FJM AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 20111746-54
Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL and Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
Fleming R and Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
Hadlow Narelle Longhurst Katherine McClements Allison Natalwala Jay Brown Suzanne J and Matson Phillip L Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response (Article in press) Fertil Steril 2012
Hansen KL Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170-5
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 King Dave URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L and Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593 6
77
Nelson S Biomarkers of ovarian response current and future applications Fertil and Steril 201399963-969
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton Reply Reproducibility of AMH Hum Reprod 2012b273641-3642
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Preissner CM Morbeck DE Gada RP and Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011261768-74
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse patient variability Fertil Steril 2011951185-118
78
THE MEASUREMENT OF ANTI-MUumlLLERIAN
HORMONE A CRITICAL APPRAISAL
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan
Luciano G Nardo Philip W Pemberton
The Journal of Clinical Endocrinology amp Metabolism
2014 Mar 99(3) 723-32
3
79
Title
The measurement of Anti-Muumlllerian hormone a critical appraisal
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK d Manchester Royal Infirmary Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Key terms
Anti-Muumlllerian hormone AMH Active MISAMH ELISA Diagnostic
Systems Laboratories AMHMIS ELISA Immunotech AMH Gen II assay
Beckman Coulter
Word Count 3947 (intro ndash general summary text only (no headings)
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
80
Declaration of authorsrsquo roles
The idea was developed during discussion between OR CF and SAR
OR conducted the initial appraisal of the studies prepared and revised the
manuscript SAR and CF contributed to the discussion and interpretation of
the studies and oversaw the revision of the manuscript PWP AY MK
and AS reviewed the data extraction and interpretation contributed to
the discussion of the studies and revision of the manuscript LGN
contributed to the discussion of the studies and revision of the manuscript
81
ABSTRACT
Context
Measurement of AMH is perceived as reliable but the literature reveals
discrepancies in reported within-subject variability and between-assay
conversion factors Recent studies suggest that AMH may be prone to pre-
analytical instability We therefore examined the published evidence on the
performance of current and historic AMH assays in terms of the assessment of
sample stability within-patient variability and comparability of the assay
methods
Evidence Acquisition
Studies (manuscripts or abstracts) measuring AMH published between
01011990 and 01082013 in peer-reviewed journals using appropriate
PubMedMedline searches
Evidence Synthesis
AMH levels in specimens left at room temperature for varying periods
increased by 20 in one study and almost 60 in another depending on
duration and the AMH assay used Even at -20degC increased AMH
concentrations were observed An increase over expected values of 20-30 or
57 respectively was observed following two-fold dilution in two linearity-of-
dilution studies but not in others Several studies investigating within-cycle
variability of AMH reported conflicting results although most studies suggest
variability of AMH within the menstrual cycle appears to be small However
between-sample variability without regard to menstrual cycle as well as within-
sample variation appears to be higher using the Gen II AMH assay than with
previous assays a fact now conceded by the kit manufacturer Studies
comparing first generation AMH assays with each other and with the Gen II
assay reported widely varying differences
Conclusions AMH may exhibit assay-specific pre-analytical instability
Robust protocols for the development and validation of commercial AMH
assays are required
82
INTORDUCTION
In the female AMH produced by granulosa cells of pre-antral and early
antral ovarian follicles regulates oocyte recruitment and folliculogenesis (1 2)
It can assess ovarian reserve (3-5) and guide gonadotrophin stimulation in
assisted reproduction technology (ART) (6) AMH is also used as a granulosa
cell tumour marker a marker of ovarian reserve post-chemotherapy (7 8) and
to predict age at menopause (910)
AMH immunoassays first developed by Hudson et al in 1990 (11) were
introduced commercially by Diagnostic Systems Laboratories (DSL) and
Immunotech (IOT) These assays were integrated into a second-generation
AMH assay GenII (12) by Beckman-Coulter but recent work suggests that this
new assay exhibits clinically important within-patient sample variability (13-
15) Beckman Coulter have recently confirmed this with a field safety notice
(FSN 20434-3) they cite without showing evidence for complement
interference as the problem
ldquoTruerdquo AMH variability comprises both biological and analytical
components (Figure 1) and given the varying antibody specificity and
sensitivity of different AMH assays then logically different kits will respond to
these components to varying degrees This review considers the published
literature on AMH measurement using previous and currently available assays
Potential sources of variation and their contribution to observed AMH
variability were identified
Review structure
This review has been divided into logical subgroups We first address the
stability of AMH at different storage temperatures then the effects of
freezethaw cycles and finally AMH variability in dilution studies Secondly
the within-person variability of AMH measurement is considered
encompassing intra- and inter-menstrual cycle variability and repeat sample
variability in general The final section covers AMH method comparisons
comparing older methods to each other and to the newer now prevalent
GenII method finishing with data on published guidance ranges concerning
the use of AMH in ART A general summary concludes the paper
83
Systematic review
The terms ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting
Substance and MIS were used to search the PubMedMedline MeSH
database between 1st January 1990 and 1st August 2013 for publications in
English commenting on AMH sample stability biological and sample-to-
sample variability or assay method comparison in human clinical or healthy
volunteer samples Titles andor abstracts of 1653 articles were screened to
yield the following eligible publications ten stability studies 17 intrainter-
cycle variability studies and 14 assay method comparability studies
Sample stability
Recent work has established that the GenII-measured AMH is
susceptible to significant preanalytical variability (13 14) not previously
acknowledged which may have influenced results in previous studies with this
assay
Stability of unfrozen samples
Five studies examined AMH stability in samples stored either at room or
fridge temperature (Table 1) (13 16-19) Al-Qahtani et al (16) assessing the
precursor of the DSL ELISA reported that ldquoimmunoreactivity survived the
storage of samples unfrozen for 4 daysrdquo but did not record storage
temperature or sample numbers Evaluating the GenII assay Kumar et al (18)
stored 10 samples at 2-8degC for up to a week and found an average 4
variation compared to samples analysed immediately However their
specimens originally reported as ldquofreshrdquo appear to have been kept cool and
transported overnight Fleming amp Nelson (19) reported no significant change
in the GenII-assayed AMH from 51 samples stored at 4degC Methodological
information was limited but interrogation of their data by Rustamov et al (14)
suggested that AMH levels rose by an average of 27 after 7 days storage
Zhao et al (17) reported a difference of less than 20 between DSL-assayed
AMH in 7 serum samples kept at 22degC for 48 hours when compared to
aliquots from the same samples frozen immediately at -20degC Rustamov et al
(13) measured AMH (GenII) daily in 48 serum samples at room temperature
for 7 days and observed an average 58 increase (from 0 to gt200) whilst
others (20) reported a 31 mean rise in GenII-assayed AMH in whole blood
84
after 90hrs at 20oC whereas serum AMH was virtually unchanged after
prolonged storage at 20oC
Sample stability at -20 o or -80oC and the effects of freezethaw
Rey et al (21) reported a significant increase in AMH (in-house assay)
in samples stored at -20degC for a few weeks attributing this to proteolysis
which could be stabilised with protease inhibitor (see discussion below)
Kumar et al (18) saw 6 variation between GenII-assayed AMH levels from
10 fresh and 10 frozen samples whilst Rustamov et al (13) observed a 22
increase in AMH (GenII) on re-analysis of 8 serum samples after 5 days
storage at -20degC These authors saw no AMH increase in serum stored at -80deg
C for the same period
Linearity of dilution
Six studies examined linearity of dilution on observed AMH
concentrations Long et al (22) recovered between 84 and 105 of the
expected AMH concentration (IOT n=3) AMH dilution curves parallel to
the standard curve were reported by others (16)Kumar et al (18) (n=4) and
Preissner et al (23) ) (n=7) reported GenII-assayed AMH recoveries from 95
to 104 and 96 respectively Sample handling information was limited in
some of these studies (16 23) Fleming amp Nelson (19) (GenII n=10) reported
variances of 8 using assay diluent and 5 using AMH-free serum following
2-fold dilution however interrogation of their data reveals an apparent
dilutional AMH increase of 20-30 in samples stored prior to dilution and
analysis Rustamov et al (13) (GenII n=9) in freshly collected serum observed
an average 57 increase in apparent AMH concentration following two-fold
dilution but with considerable variation
Discussion Sample stability
Sample stability can be a major analytical problem and detailed
examination suggests that previous evidence stating that commercially
measured AMH is stable in storage and exhibits linearity of dilution (12 16 18
19) is weak or conflicting
No study looking at room temperature storage on IOT-assayed AMH
was found and only one using DSL-assayed AMH which showed an increase
85
of less than 20 during storage (17) Studies using the GenII assay to
investigate the effect of storage on AMH variability at room temperature in
the fridge and at -200C reach differing conclusions ranging from stable to an
average 58 increase in measured levels It is important to note here that
sample preparation and storage prior to these experiments was different and
could account for the observed discrepancies The most stable storage
temperature for AMH in serum appears to be -80degC (13 16)
Linearity of dilution studies were also conflicting (13 18 19 23) those
reporting good linearity used samples transported or stored prior to baseline
analysis whereas dilution of fresh samples showed poor linearity In late 2012
Beckman Coulter accepted that the GenII assay did not exhibit linear dilution
and issued a warning on kits that samples should not be diluted They now
suggest that with the newly introduced pre-mixing protocol dilution should
not be a problem
This review highlights the fact that assumptions about AMH stability in
serum were based on a limited number of small studies often providing
limited methodological detail (impairing detailed assessment and comparison
with other studies) using samples stored or transported under unreported
conditions Furthermore conclusions derived using one particular AMH assay
have been applied to other commercial assays without independent validation
The available data suggests that dilution of samples andor storage or
transport in sub-optimal conditions can lead to an increase in apparent AMH
concentration The conditions under which this occurs in each particular AMH
assay are not yet clear and more work is required to understand the underlying
mechanisms Two alternative hypotheses have been proposed firstly that
AMH may undergo proteolytic change as postulated by Rey et al (21) or
conformational change as proposed by Rustamov et al (1314) during storage
resulting in ldquostabilisationrdquo of the molecule in a more immunoreactive form
secondly Beckman have postulated the presence of an interferent
(complement) which degrades on storage (Beckman Coulter field safety notice
FSN 20434-3)
A recent case report found that a falsely high AMH level was corrected
by the use of heterophylic antibody blocking tubes (24) but this does not
explain elevation of AMH on storage (13)
Whatever the mechanism responsible two solutions are available either
inhibit the process completely or force it to completion prior to analysis
86
Rustamov et al (13) and Han et al (15) both suggest pre-dilution of samples to
force the process a protocol now adopted by Beckman Coulter in their revised
GenII assay protocol Any solution must be robustly and independently
validated both experimentally and clinically prior to introduction in clinical
practice Fresh optimal ranges for interpretation of AMH levels in ART will be
needed and the validity of studies carried out using unreported storage
conditions may have to be re-evaluated
Within-person variability
The biological components of AMH variability such as circadian and
interintra-cycle variability have been extensively studied (Table 2 amp
Supplementary table 1)
Circadian variation
Bungum et al (25) evaluated circadian variability measuring AMH
(IOT) two hourly over 24hrs within day 2ndash6 of the menstrual cycle in younger
(20-30 years) and older (35-45 years) women Within-individual CVs of 23
(range 10-230) in the younger group and 68 (range 17-147) in the older
group were observed
Variability within the menstrual cycle
Cook et al (26) observed significant (12) variation in mean AMH (in-
house) levels in 20 healthy women throughout different phases of the
menstrual cycle Intra-cycle variability of IOT-assayed AMH was reported in
three publications (27-29) In two sequential samples were stored at -20degC
until analysis (27 28) Streuli et al (29) did not report on storage La Marca et
al (27) saw no difference in mean follicular phase AMH levels (days 2 4 and 6)
in untreated spontaneous menstrual cycles from 24 women This group went
on to report a small insignificant change (14) in within-group AMH
variability throughout the whole menstrual cycle in 12 healthy women
However this analysis does not appear to allow for correlations within same-
patient samples Streuli et al (29) studied intra-cycle variation of AMH
throughout two menstrual cycles in 10 healthy women and also reported no
significant changes (lt5)
87
The DSL assay was used in eight studies assessing intra-cycle variability
(30-37) Four studied sample storage at -20deg C (30323437) and two studied
samples storage at -80degC (3335) No sample storage data was given in two
publications (31 36) Hehenkamp et al (30) assessed within-subject variation
of AMH in 44 healthy women throughout two consecutive menstrual cycles
and reported an intra-cycle variation of 174 Lahlou et al (31) reported a
ldquodiphasicrdquo pattern of AMH with a significant decrease in levels during the LH
surge from 10 women at various cycle phases Tsepelidis et al (32) reported a
mean intra-cycle coefficient of variation of 14 comparing group mean AMH
levels in 20 women during various stages of the menstrual cycle Wunder et al
(33) reported an intra-cycle variability of around 30 in 36 healthy women
sampling on alternate days They saw a marked fall around ovulation which
might have been missed with less frequent sampling intervals as in other
studies Sowers et al (35) studied within-cycle variability in 20 healthy women
but did not compute an overall estimate instead they selected subgroups of
low and high AMH and reported significant within-cycle variability for women
with high AMH but not those with low AMH - an analysis that has been
questioned (38 39) Robertson et al (36) subgrouped mean AMH levels in 61
women observing that AMH levels were stable in women of reproductive age
and ovulatory women in late reproductive age whilst AMH in other women in
late reproductive age was much more variable Using the data from
Hehenkamp et al (30) van Disseldorp et al (34) calculated intra-class
correlation (ICC) and reported a within-cycle variability of 13 although this
was not clearly defined Using the same data Overbeek et al (37) analyzed the
absolute intra-individual difference in younger (38 years) and older (gt38
years) women This study concluded that the AMH concentration was more
variable in younger women (081059 gL) compared to older women
(031029 gL) during the menstrual cycle (P=0001) thus a single AMH
measurement may be unreliable A recent study using the GenII assay
reported 20 intra-cycle variability in AMH measurements in women (n=12)
with regular ovulatory cycles (40) All the reports considered have findings
consistent with a modest true systematic variability of 10-20 in the level of
AMH in circulation during the menstrual cycle Whilst there have been
suggestions that this variability may differ between subgroups of women these
88
have been based on post-hoc subgroup analyses and there is no convincing
evidence for such subgroups (38)
Variability between menstrual cycles
Three studies (Supplementary table 1) evaluated AMH variability in
samples taken during the early follicular phase of consecutive menstrual cycles
(102941) and three studies have reported on the variability of AMH in repeat
samples from the same patient taken with no regard to the menstrual cycle
(134243) One study employed an in-house assay (41) one study used the
IOT assay (29) three studies used the DSL assay (10 42 43) and one study
(13) used the GenII assay In four infertile women Fanchin et al (41) assessed
the early follicular phase AMH (in-house) variability across three consecutive
menstrual cycles they concluded that inter-sample AMH variability was
characterised by an ICC of 089 (95 CI 083-094) Streuli et al (29)
calculated a between-sample coefficient of variation of 285 in AMH (IOT)
in 10 healthy women In 77 infertile women van Disseldorp et al (10) found
an inter-cycle AMH (DSL) variability of 11 In summary these studies
suggest that the overall inter-cycle variability of AMH ranges from 11 (DSL)
to 28 (IOT) this figure will include both biological and measurement-related
variability
Variability between repeat samples
Variability between repeat samples without regard to menstrual cycle
phase was examined in three studies (Supplementary table 1) In a group of 20
women using samples frozen for prolonged periods Dorgan et al (42)
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median between-sample interval of one year In a larger series
of 186 infertile women Rustamov et al (43) (DSL) found a CV of 28
between repeated samples with a median between-sample interval of 26
months (ICC 091 95 CI 090-093) Rustamov et al (13) found that the
coefficient of variation of repeated GenII-assayed AMH in a group of 84
infertile women was 59 (ICC of 084 95 CI 079-090) substantially higher
than that reported using the DSL assay Similarly a recent study by Hadlow et
al (40) found a within-subject GenII-assayed AMH variability of 80 As a
89
result 5 of the 12 women studied crossed clinical cut-off levels following
repeated measurements
Discussion Within-patient variability
Evidence suggests that repeated measurement of AMH can result in
clinically important variability particularly when using the GenII assay This
questions the assumption that a single AMH measurement is acceptable in
guiding individual treatment strategies in ART
The observed concentration of any analyte measured in a blood
(serum) sample is a function of its ldquotruerdquo concentration and the influence of a
number of other factors (Figure 1) Studies examining the variability of AMH
by repeated measurement of the hormone will therefore reflect both true
biological variation and measurement-related variability introduced by sample
handling andor processing Thus within-sample inter-assay variability used as
an indicator of assay performance may not reflect true measurement-related
variability between samples since it does not take into account the contribution
from pre-analytical variability Measurement-related between-sample variability
can be established in part using blood samples taken simultaneously (to avoid
biological variability) from a group of subjects although even this does not
reflect the full variability in sample processing and storage inherent in real
clinical measurement
Since AMH is only produced by steadily growing ovarian follicles it is
plausible to predict a small true biological variability in serum reflected in the
modest 1-20 variability found within the menstrual cycle In contrast it
appears that the magnitude of measurement-related variability of AMH is more
significant a) within-sample inter-assay variation can be as high as 13 b)
different assays display substantially different variability and c) AMH appears
to be unstable under certain conditions of sample handling and storage (Table
1) Consequently any modest variation in true biological AMH concentration
may be overshadowed by a larger measurement-related variability and careful
experimental designs are required to characterise such differences In general
the reported variability in published studies should be regarded as a measure of
total sample-to-sample variability ie the sum of biological and measurement-
related variability (Figure 1)
90
In repeat samples the available evidence confirms that there is a
significant level of within-patient variability between measurements which is
assay-dependent greater than the estimates of within cycle variability and
therefore likely to be predominantly measurement-related Evidence from
several sources suggests that the effects of sample handling storage and
freezing differ between commercial assays and that the newer GenII assay may
be more susceptible to these changes under clinical conditions When it has
been established that the modified protocol for the GenII assay can produce
reproducible results independent of storage conditions then it will be
necessary to re-examine intra and inter cycle variability of AMH
Assay method comparability
AMH assay comparisons have either used same sample aliquots or
used population-based data with repeat samples Study population
characteristics sample handling inter-method conversion formulae and results
from these comparisons are summarised in Table 3 AMH levels were almost
universally compared using a laboratory based within-sample design The
Rustamov et al study (13) was population-based comparing AMH results in
two different samples from the same patient at different time points using 2
different assays
IOT vs DSL
Table 3 summarises 8 large studies (17 29 30 44-48) that compared the
DSL and IOT AMH assays They demonstrate strikingly different conversion
factors from five-fold higher with the IOT assay to assay equivalence Most
studies carried out both analyses at the same time to avoid analytical variation
(Figure 1) However this does mean that samples were batched and frozen at -
18degC to -80degC prior to analysis which as already outlined may influence pre-
analytical variability and contribute to the observed discrepancies in conversion
factors
IOT vs GenII
Three studies have compared the IOT and Gen II assays (Table 3)
Kumar (18) reported that both assays gave identical AMH concentrations
However Li et al (48) found that the IOT assay produced AMH values 38
91
lower than the Gen II assay whilst Pigny et al (49) found levels that were 2-fold
lower
DSL vs GenII
Four studies analysed same-sample aliquots using the DSL and GenII
assays either simultaneously or sequentially (33 48 50 51) Only Li et al (48)
gave details of sample handling (Table 3) All four studies found that AMH
values that were 35 ndash 50 lower using the DSL compared to the GenII assay
Rustamov et al (13) carried out a between-sample comparison of the assays
measuring AMH in fresh or briefly stored clinical samples from the same
women at different times with values adjusted for patient age (Table 3) In
contrast to within-sample comparisons this study found that the DSL assay gave
results on average 21 higher than with the GenII assay Whilst this
comparison is open to other bias it does reflect the full range of variability
present in clinical samples and avoids issues associated with longer term
sample storage
Discussion Assay method comparability
It is critical for across-method comparison of clinical studies that
reliable conversion factors for AMH are established In-house assays aside
three commercially available AMH ELISAs have been widely available (IOT
DSL and GenII) and the literature demonstrates considerable diversity in
reported conversion factors between first-generation assays (DSL vs IOT)
and between first and second-generation immunoassays (DSLIOT vs GenII)
Although most studies appear to follow manufacturersrsquo protocols
detailed methodological information is sometimes lacking The assessment of
within-sample difference between the two assays involved thawing of a single
sample and simultaneous analysis of two aliquots with each assay Both
aliquots experience the same pre-analytical sample-handling and processing
conditions therefore the results should be reproducible provided the AMH
samples are stable during the post-thaw analytical stage and the study
populations are comparable However this review has identified significant
discrepancies between studies perhaps due to either significant instability of
the sample or significant variation in assay performance Studies comparing
AMH levels measured using different assays in populations during routine
92
clinical use have also come to differing conclusions (13 51) Given the study
designs that workers have used to try to ensure that samples are comparable
the finding of significant discrepancies in the observed conversion factors
between assays is consistent with the proposal that AMH is subject to
instability during the pre-analytical stage of sample handling This coupled
with any differential sensitivity and specificity between these commercial
assays could give rise to the observed results ie some assays are more
sensitive than others to pre analytical effects
AMH guidance in ART
AMH guidance ranges to assess ovarian reserve (52) or subsequent
response to treatment (53 54) have been published The Doctors Laboratory
using the DSL assay advised the following ranges for ovarian reserve (lt
057pmolL-undetectable 057-21 pmolL-very low 22-157 pmolL-low
158-286 pmolL-satisfactory 287-485pmolL-optimal gt485pmolL-very
high) ranges that supposedly increased by 40 on changing to the GenII assay
(51) More recently other authors have attempted to correlate AMH levels with
subsequent birth rates Brodin et al (53) using the DSL assay observed that
higher birth rates were seen in women with an AMH level gt 21 pmolL and
low birth rates were seen in women who had AMH levels lt 143 pmolL In
the UK the National Institute for Health and Care Excellence (NICE) have
recently issued guidance on AMH levels in the assessment of ovarian reserve in
the new clinical guideline on Fertility (54) They advise that an AMH level of le
54 pmolL would indicate a low response to subsequent treatment and an
AMH ge 250 pmolL indicates a possible high response Although not
specifically stated interrogation of the guideline suggests that these levels have
been obtained using the DSL assay which is no longer available in the UK
As discussed above the initial study of comparability between the DSL
and GenII assays reported that GenII generated values 40 higher compared
to the DSL assay clinics were therefore recommended to increase their
treatment guidance ranges accordingly (51) However a more recent study
using fresh samples found that the original GenII assay may actually give
values which are 20-30 lower suggesting that following the above
recommendation may lead to allocation of patients to inappropriate treatment
groups (13) The apparent disparity in assay comparison studies implies that
93
AMH reference ranges and guidance ranges for IVF treatment which have
been established using one assay cannot be reliably used with another assay
method without full independent validation Similarly caution is required
when comparing the outcomes of research studies using different AMH assay
methods
General Summary
Recent publications have suggested that GenII-assayed AMH is
susceptible to pre-analytical change leading to significant variability in
determined AMH concentration an observation now accepted by the kit
manufacturer However this review suggests that all AMH assays may display a
differential response to pre-analytical proteolysis conformational changes of
the AMH dimer or presence of interfering substances The existence of
appreciable sample-to-sample variability and substantial discrepancies in
between-assay conversion factors suggests that sample instability may have
been an issue with previous AMH assays but appears to be more pronounced
with the currently available GenII immunoassay The observed discrepancies
may be explicable in terms of changes in AMH or assay performance that are
dependent on sample handling transport and storage conditions factors
under-reported in the literature We strongly recommend that future studies on
AMH should explicitly report on how samples are collected processed and
stored If it can be clearly demonstrated that the new GenII protocol drives
this process to completion in all samples ensuring stability then a re-
examination of reference and guidance ranges for AMH interpretation will be
necessary There is a clear need for an international reference standard for
AMH and for robust independent evaluation of commercial assays in routine
clinical samples with well-defined sample handling and processing protocols
These issues of sample instability and lack of reliable inter-assay comparability
data should be taken into account in the interpretation of available research
evidence and the application of AMH measurement in clinical practice
94
References
1 Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
2 Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
3 van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
4 Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
5 Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009921586-1593 6 Yates AP Rustamov O Roberts SA Lim HYN Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353ndash2362
7 Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-55
8 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
9 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
10 van Disseldorp J Lambalk CB Kwee J Looman CW Eijkemans MJ Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Muumlllerian hormone and antral follicle counts Hum Reprod 201025221-227
11 Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
95
12 Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642
15 Han X McShane M Sahertian R White C Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Hum Reprod 201328 (suppl 1)i76-i78 (abstract)
16 Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 200563267-273
17 Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 200788S17 (abstract)
18 Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
19 Fleming R Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
20 Fleming R Fairbairn C Blaney C Lucas D Gaudoin M Stability of AMH measurement in blood and avoidance of proteolytic changes Reprod Biomed Online 201326130-132
21 Rey R Lordereau-Richard I Carel JC Barbet P Cate RL Roger M Chaussain JL Josso N Anti-Mullerian hormone and testosterone serum levels are inversely related during normal and precocious pubertal development J Clin Endocrinol Metab 199377 1220ndash1226
22 Long WQ Ranchin V Pautier P Belville C Denizot P Cailla H Lhomme C Picard JY Bidart JM Rey R Detection of minimal levels of serum anti-Mullerian hormone during follow-up of patients with ovarian granulosa cell tumor by means of a highly sensitive enzyme-linked immunosorbent assay J Clin Endocrinol Metab 200085540ndash544
23 Preissner CM Morbeck DE Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54 (abstract)
24 Cappy H Pigny P Leroy-Billiard M Dewailly D Catteau‐Jonard S Falsely elevated serum antimuumlllerian hormone level in a context of heterophilic
96
interference Fertil Steril 2013991729-1732
25 Bungum L Jacobsson AK Roseacuten F Becker C Yding Andersen C Guumlner N Giwercman A Circadian variation in concentration of anti-Mullerian hormone in regularly menstruating females relation to age gonadotrophin and sex steroid levels Hum Reprod 201126678ndash684
26 Cook CL Siow Y Taylor S Fallat ME Serum muumlllerian-inhibiting substance levels during normal menstrual cycles Fertil Steril 200073859-861
27 La Marca A Malmusi S Giulini S Tamaro LF Orvieto R Levratti P Volpe A Anti-Muumlllerian hormone plasma levels in spontaneous menstrual cycle and during treatment with FSH to induce ovulation Hum Reprod 2004192738-2741
28 La Marca A Stabile G Carduccio Artenisio A Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash310729 Streuli I Fraisse T Chapron C Bijaoui G Bischof P de Ziegler D Clinical uses of anti-Mullerian hormone assays pitfalls and promises Fertil Steril 200991226-230
30 Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
31 Lahlou N Chabbert-Buffet N Gainer E Roger M Bouchard P Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11 (abstract)
32 Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
33 Wunder DM Bersinger NA Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrual cycle in reproductive age women Fertil Steril 200889927-933
34 van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormone concentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
35 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 2010 941482-1486
36 Robertson DM Hale GE Fraser IS Hughes CL Burger HG Changes in serum antimuumlllerian hormone levels across the ovulatory menstrual cycle in late reproductive age Menopause 201118521-524
37 Overbeek A Broekmans FJ Hehenkamp WJ Wijdeveld ME van
97
Disseldorp J van Dulmen-den Broeder E Lambalk CB Intra-cycle fluctuations of anti-Mullerian hormone in normal women with a regular cycle a re-analysis Reprod Biomed Online 201224664ndash 669
38 Roberts SA Variability in anti-Mullerian hormone levels a comment on Sowers et al ldquoAnti-Mullerian hormone and inhibin B variability during normal menstrual cyclesrdquo Fertil Steril 201094e59
39 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Reply of the authors Variability in anti-Muumlllerian hormone levels a comment on Sowers et al Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 201094e60
40 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013991791-1797
41 Fanchin R Taieb J Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Muumlllerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 200520923-927
42 Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
43 Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
44 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
45 Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175
46 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
47 Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
48 Li HW Ng EH Wong BP Anderson RA Ho PC Yeung WS Correlation between three assay systems for anti-Mullerian hormone (AMH)
98
determination J Assist Reprod Genet 2012291443-1446
49 Pigny P Dassonneville A Catteau-Jonard S Decanter C Dewailly D Comparative analysis of two-widely used immunoassays for the measurement of serum AMH in women Hum Reprod 2013 28i311-316 (abstract)
50 Gada R Hughes P Amols M Amols M Preissner C Morbeck D Coddington C Validation and comparison of AMH serum levels using the original active MISAMH ELISA to the new active AMH Gen II ELISA Fertil Steril 201195S23 (abstract)
51 Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
52 The Doctors Laboratory Lab Report newsletter ndash Winter 20072008 ndash AMH
53 Brodin T Hadziosmanovic N Berglund L Olovsson M Holte J Antimullerian hormone levels are strongly associated with live birth rates after assisted reproduction J Clin Endocrinol Metab 201398(3)1107-1104
54 National Institute for Care and Health Excellence NICE clinical guideline CG156 Fertility
99
Figure 1 Biological and analytical variability of AMH
100
Table 1 AMH assay validation effect of sample storage conditions freshthaw cycles and linearity of dilution
Study Assay Method Result
Rey et al (21) in-house effect of Long-term storage at -20C (n=4) AMH levels in archival samples were 230 higher than original value
Long et al (22) IOT linearity up to 16-fold dilution (n=3) observed AMH was 84-105 of expected AMH
Al-Qahtani et al (16) in-house a freezethaw stability storage unfrozen for 4 days
b linearity up to 32-fold dilution (n=6)
a immuno-reactivity survived both multiple freeze-thaw cycles and storage unfrozen for 4 days b dilution curves were parallel to the standard curve
Zhao et al (17) DSL
serum frozen immediately at -20C compared to
aliquots stored at 4C or 22C for up to 2 days (n=7) AMH levels increased by 1 at 4C and 9 at 22C after 2 days compared to sample frozen immediately
Kumar et al (18) Gen II
a serum or plasma stored at 2-8C or -20C for up to 7 days (n = 20) b serum or plasma underwent up to three freezethaw cycles (n=20) c linearity of dilution (n=4)
a AMH levels were stable for up to 7 days at 2-8C or -20C
b AMH increased by 15 in serum and by 5 in plasma after 3 cycles c linear results obtained across the dynamic range of the assay
Preissner et al (23) Gen II linearity of dilution (n=7) average agreement with expected result was 97
Rustamov et al (13) Gen II
a stability at RT for up to 7 days (n=48)
b storage for 5 days at -20C or -80C compared to fresh sample (n=8) c linearity on 2-fold dilution (n=9)
a AMH levels increased by an average of 58 over 7 days
b AMH levels increased by 23 at -20C but were unchanged at -80C c AMH levels were on average 157 higher than expected
Fleming amp Nelson (19) Gen II a serum stored at 4C for 7 days (n=48) b linearity of dilution (n=10)
a AMH levels increased by an average of 27 b AMH was 28 amp 33 higher on 2-fold amp 4-fold dilution resp
Fleming et al (20) Gen II
a whole blood stored for up to 90 hours at 4C (n=32) or 20C (n=21)
b serum stored for 5 days at 20C and 2 days at 4C (n=13)
a AMH increased by 11 at 4C and by 31 at 20C b only 1 increase in AMH compared to original value
Han et al (15) Gen II
serum from non-pregnant (n=13) or early pregnant (n=7) women
stored at RT -20C or -80C for up to 7 days
In non-pregnant women AMH increased by 26 after 7 days at RT but was
unchanged at -20C or -80C
In pregnant women AMH increased by 50 at RT and 27 at -80C after 48 hours
101
Table 2 Intra-cycle variability of AMH Study
Subjects
a cycles b day sampled
Assay
a storage b freezethaw c measurement
Result
Authorsrsquo Conclusion
Cook et al (26)
healthy age 22-35 regular cycle (n=20)
a 1 cycle b day 23 LH surge LH surge +7 d
in-house
a -80C b once c inter-assay variation eliminated
day 3 AMH = 14 09ngml
mid cycle AMH = 17 11ngmL
mid luteal AMH = 14 09ngmL
Fluctuations significant (plt0008) AMH may have a regulatory role in folliculogenesis
La Marca et al (27)
healthy age 21-36
regular cycle (n=24)
a follicular phase b alternate days
IOT
a -20C
b once
AMH did not change from days 2 to 6 in spontaneous cycles but decreased progressively in FSH-treated cycles
AMH levels did not change significantly during follicular phase of the menstrual cycle
La Marca et al (28)
healthy age18-24
regular cycle (n=12)
a 1 cycle b alternate days day 0 = day of LH surge
IOT
a -20C
b once
low mean AMH = 3411ngmL (day 14)
high mean AMH =3913ngmL (day 12)
AMH levels did not change significantly throughout menstrual cycle
Lahlou et al (31)
placebo-treated (n=12)
a 1 cycle
b every 3 days
DSL
NR 7 days pre LH surge AMH = 26
32pmolL peak AMH = 191 35pmolL 10 days post LH surge
AMH = 254 43pmolL
AMH levels exhibited a diphasic pattern with levels declining significantly (plt005) during the LH surge
Hehenkamp et al (30)
healthy
fertile regular cycle (n=44)
a 2 cycles
b AMH measured at each of 7 cycle phases
DSL a -20C a sine pattern fitted to AMH data was not significant (p=040) b72 repeat AMH values fell within the same quintile 28 in adjacent quintile
AMH shows no consistent fluctuation through the cycle compared to FSH LH amp E2
van Disseldorp et al (10)
data from Hehenkamp et al (30)
Intra-cycle within-subject variation of AMH was only 13 compared to 31-34 for AFC (dependent on follicle size)
AMH displays less intra-cycle variability than AFC
Overbeek et al (37)
data from Hehenkamp et al (30)
Fluctuations were larger than 05microgL in one cycle in significantly (p = 0001) more women in the younger group than the older one
AMH can fluctuate substantially in younger women during menstrual cycle so a single measurement could be unreliable
102
Tsepelidis
et al (32)
healthy age 18-35 regular cycles (n=20)
a 1 cycle b days 3 7 10-16 18 21 amp 25
DSL
a -20C
b once
Within-cycle differences not significant (p=0408)
AMH levels do not vary during the menstrual cycle
Wunder et al (33)
healthy
age 20-32 regular cycles (n=36)
a 1 cycle
b alternate days
DSL
a -80C
AMH levels were statistically higher in the late follicular phase than at the time of ovulation (p= 0019) or in the early luteal phases (plt00001)
AMH levels vary significantly during the menstrual cycle
Streuli
et al (29)
healthy mean age=241 regular cycles
(n=10)
a 1 cycle b before (LH
-10-5-2-1) and after LH surge (LH +1+2+10)
IOT
a -18C
AMH levels were statistically lower during the early luteal phase compared to early follicular phase (p=0016) and late luteal phase levels (p=002)
In clinical practice AMH can be measured at any time during the menstrual cycle
Sowers et al
(35)
healthy age 30-40 regular cycles
(n=20)
a 1 cycle b daily
DSL
a -80C
b once c simultaneous
Higher AMH levels with significant variation between days 2-7 in the ldquoyounger ovaryrdquo Low AMH levels with little variation in the ldquoaging ovaryrdquo
AMH varies across the menstrual cycle in the ldquoyounger ovaryrdquo
Robertson et al (36)
a age 21-35 regular cycles
(n=43) b age 45-55
variable cycles (n=18)
a 1 cycle + initial stages of succeeding cycle b three times weekly
DSL
NR No intracycle variation in AMH level was found in women in mid reproductive life or in 33 women with regular cycles in late reproductive age In the remaining cycles there was a significant (plt001) two-fold decrease in AMH in 11 cycles and a significant (plt001) 42-fold increase between the follicular amp luteal phases
When AMH levels are substantially reduced they become less reliable markers of ovarian reserve
Hadlow
et al (40)
age 29-43 regular cycles non-PCOS
(n=12)
a 1 cycle b 5-9 samples per subject
Gen II a -20C within 4 hours of sampling b once
c simultaneous
712 women could be reclassified depending on when AMH was measured during the cycle 212 crossed cut-offs predicting hyperstimulation
AMH cycles varied during menstrual cycle and clinical classification of the ovarian response was altered
103
Table 3 Variability in AMH levels between menstrual cycles
Study
Subjects
a cycles b day sampled
Assay
Storage
Result
Authorsrsquo Conclusion
Fanchin et al (41)
infertile
age 25-40 regular cycles
(n=47)
a 3 cycles
b day 3
in-house
(Long et al 2000)
-80C
AMH showed significantly
higher reproducibility than inhibin B (plt003) E2 (plt00001) FSH (plt001) and early AFC (plt00001)
AMH showed improved cycle-to-cycle consistency compared to other markers of ovarian follicular status
Streuli
et al (29)
healthy mean age = 241 regular cycles
(n=10)
a 2 cycles b before (LH -10-5-2-1) and
after LH surge (LH +1+2+10)
IOT
-18C Inter-cycle variability of 285
AMH fluctuations during the cycle were smaller than or equal to the variability between two cycles
van Disseldorp et al (10)
infertile median age =33
PCOS excluded (n=77)
a average 373 cycles b day 3
DSL
-80C
AMH showed a within-subject variability of 11 compared to 27 for AFC
AMH demonstrated less individual inter-cycle variability than AFC
Dorgan
et al (42)
blood donors age 36-44 collected 1977-1981 (n=20)
two samples collected during the same menstrual cycle phase at least 1yr apart
DSL
-70C
between-subject variance in AMH of 219 was large compared to the within-subject variance of 031
AMH was relatively stable over 1 year in pre-menopausal women
Rustamov et al (36)
infertile women age 22-41
(n=186)
random sampling median interval = 26 months
DSL
-70C
within-subject CV for AMH was 28 compared to 27 for FSH
AMH showed significant sample-to-sample variation
Rustamov et al (13)
infertile women age 20-46
(n=87)
random sampling median interval = 51 months
Gen II
-20C
within-subject CV for AMH was 59
AMH demonstrated a large sample-to-sample variation
104
Table 4 Within-subject comparison between AMH methods Study
Assays
Subjects
Simultaneous Analysis
Regression
Summary
Freour et al (44) DSL vs IOT 69 infertile women age 22-40
Yes IOT = 401 x DSL + 098 (microgL) (Deming regression)
DSL = 22 IOT (plt00001)
Hehenkamp et al (30) DSL vs IOT 82 healthy women NR DSL= 0495 x IOT - 003 DSL = 495 IOT
Bersinger et al (45) a DSL vs IOT
b DSL vs IOT
a 11 infertile women
b 55 infertile women
a yes
b no
a DSL= 0180 x IOT
b DSL= 0325 x IOT + 0733
a DSL = 18 IOT
b DSL= 33 IOT
Zhao et al (17) DSL vs IOT 38 donors NR IOT = 15 x DSL + 07 (ngml) DSL = 66 IOT
Taieb et al (46) DSL vs IOT 104 samples NR DSL = 104 x IOT - 149 DSL = 96 IOT
Streuli et al (29) DSL vs IOT 153 normal and infertile No IOT = 107 x DSL - 029 DSL = IOT
Kumar et al (18) IOT vs Gen II 60 female 60 male volunteers NR IOT =10 Gen II IOT=Gen II
Gada et al (50) DSL vs Gen II 42 women NR NR DSL = 63 Gen II
Preissner et al (23) DSL vs Gen II 206 samples NR Gen II = 153 x DSL - 077 DSL = 66 Gen II
Lee et al (47) DSL vs IOT 172 infertile women Yes IOT = 1102 x DSL - 0042 DSL = IOT
Wallace et al (51) DSL vs Gen II 271 women NR Gen II = 140 x DSL - 062 DSL = 71 Gen II
Li et al (48) a DSL vs IOT b DSL vs Gen II c IOT vs Gen II
56 women with PCOS or sub-fertility Yes a IOT = 097 x DSL -296 b Gen II = 133 x DSL - 417 c Gen II = 138 x IOT - 068
a DSL = IOT b DSL = 67 Gen II c IOT = 62 Gen II
Rustamov et al (13) DSL vs Gen II female IVF patients (n=330)
median of 2yr between samples
No NR
DSL = 127 Gen II
(age-adjusted)
Pigny et al (49) IOT vs Gen II 59 women 32 controls 27 with PCOS Yes NR IOT = 200 Gen II
105
Appendix I Flow-chart of the search for publications Database search for sample stability measurement variability and assay-method comparability was conducted simultaneously using the MeSH database of PubMedMedline using the search terms of ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting Substance and MIS which identified n=1653 studies on AMH The initial step of identification involved screening of articles by reading titles andor abstracts Further search involved identification of studies from the reference sections of the initially identified studies
Database Search
n=1653
Sample
Stability
Screening Titles
n=6
Further Search
n=4
Total
n=10
Measurment Variability
Screening Titles
n=14
Further Search
n=3
Total
n=17
Method comparability
Screening Titles
n=10
Further Search
n=4
Total
n=14
106
EXTRACTION PREPARATION AND
COLLATION OF DATASETS FOR THE
ASSESSMENT OF THE ROLE OF THE MARKERS
OF OVARIAN RESERVE IN FEMALE
REPRODUCTION AND IVF TREATMENT
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
Research Database
4
107
Title
Extraction preparation and collation of datasets for the assessment of
the role of the markers of ovarian reserve in female reproduction and
IVF treatment
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
NHS Research Ethics Approval
North West Research Ethics Committee (10H101522)
Word count 5088
Grants or fellowships
No funding was sought for this study
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
108
Declaration of authorsrsquo roles
OR prepared the protocol extracted data from electronic sources and hospital
notes prepared datasets and prepared all versions of the chapter MK assisted
in collection of data from hospital notes SR and CF oversaw and supervised
preparation the protocol extraction of data preparation of datasets and
reviewed the chapter
109
CONTENTS I PROTOCOL Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip110
Methodshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Objectiveshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Inclusion Criteriahelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip114 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 RH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 AFC datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Folliculogram datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Data managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data cleaning and codinghelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118 Merging datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data security and storagehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip119 II RESULTS Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip120 Data extraction and managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 RH AFC and Folliculogram datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 Merging Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip124 Conclusionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip125
110
I PROTOCOL
INTRODUCTION
The aim of the project is to create a series of reliable and validated
datasets which contain all relevant data on the ovarian reserve markers (AMH
AFC FSH) ethnicity BMI reproductive history causes of infertility IVF
treatment parameters for patients that meet inclusion criteria as described
below The datasets will be used for the subsequent research projects of the
MD programme and future research studies on ovarian reserve
Most data can be obtained from following existing clinical electronic
records a) Patient Administration System (PAS) b) Biochemistry Department
data management system c) the hospital database for surgical procedures and
d) AMH dataset and e) ACUBase IVF data management system Following
obtaining original datasets from the administrators of the data management
systems in their original Excel format the datasets will be converted into Stata
format and ldquopreparedrdquo by a) checking and recoding spurious data
transforming the dates from string to numeric format which will be consistent
across all datasets (Day Month Year) and stored in Stata format under
following names ldquoDemographyrdquo ldquoBiochemistryrdquo ldquoAMHrdquo ldquoSurgeryrdquo ldquoIVFrdquo
ldquoFETrdquo ldquoEmbryologyrdquo Copies of original datasets will be kept in the
password-protected and encrypted computer located in the Clinical Records
Room of Reproductive Medicine Department Central Manchester University
Hospitals NHS Foundation Trust which is maintained by IT department of
the Trust (Figure 1)
Data not available in electronic format will be collected from the hospital
records of each patient by researchers Dr Oybek Rustamov and Dr Monica
Krishnan and entered into following datasets Reproductive history (RH)
antral follicle count (AFC) and Folliculogram The hospital notes of all
included patients will be hand-searched The datasets will be transferred to
Stata and each step of data preparation will be recorded using Stata Do files
and the files will be stored under the filenames of ldquoHistoryrdquo ldquoAFCrdquo
Folliculogramrdquo in Stata format In order to ensure the robustness of the data
and for the purpose of validation of the datasets electronic scanned copies of
all available reports of pelvic ultrasound assessments for AFC and
folliculograms will be obtained and stored in the password-protected and
111
encrypted computer located in the Clinical Records Room of Reproductive
Medicine Department Ethics approval for collection of data has already been
obtained (UK-NHS 10H101522)
The datasets will be merged and datasets for each research project with
all available data nested with IVF cycles nested within patients will be created
METHODS
Objectives
The aim of the project is to build a robust database which can reliably
used for the following purposes
1 To estimate the effect of ethnicity BMI endometriosis and the causes
of infertility on ovarian reserve using cross sectional data (Chapter 51)
2 To estimate the effect of salpingectomy ovarian cystectomy and
unilateral salpingo-oopherectomy on ovarian reserve using cross
sectional data (Chapter 52)
3 To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield (Chapter 6)
4 To explore the potential for optimization of AMH-tailored
individualisation of ovarian stimulation using retrospective data
(Chapter 6)
Inclusion criteria
In order to capture the populations for all three studies the database will
have broad inclusion criteria All women from 20 to 50 years of age referred to
Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust will be included if a) they were referred for
management of infertility or fertility preservation and b) had AMH
measurement during the period from 1 September 2008 till 16 November
2011
112
Datasets
PAS dataset
The dataset contains information on the hospital number surname first
name date of birth and the ethnicity of all patients referred to Reproductive
Medicine Department CMFT (Table 1) The data are originally entered during
registration of the patient for clinical care by administrative staff of
Gynaecology and Reproductive Medicine Departments The dataset will be
obtained from the administrators of the Information Unit
The dataset will be obtained in Excel format and transferred into Stata
12 Data Management and Statistical Software The date values (referral date
and date of birth) will be converted into numeric variable using ldquoDate Month
Yearrdquo format (DMY) Ethnicity will be coded using numeric variables in
alphabetical order as pre-specified in the Table 2a
Biochemistry dataset
The dataset contains all blood test results specimen numbers the names
of the tests and the date of sampling of women who had assays for follicle
stimulating hormone (FSH) oestradiol (E2) luteinizing hormone (LH) and
AMH during the study period (Table 1) Data entries were conducted by the
clinical scientists the technicians and the members of administrative team of
the Biochemistry Department The dataset will be obtained from an
administrator of the database
The date of sampling and analyses will be converted to the numeric
ldquoDMYrdquo format The specimen number will be kept unaltered in the string
variable format and used to link the tests that were taken in the same sample
tube The name of the test will be kept as described in the original format
ldquoAMHrdquo ldquoFSHrdquo ldquoLH and ldquoOestrdquo In the original dataset the samples sent
from Reproductive Medicine Department are coded as ldquoIVFrdquo which will be
kept unaltered and the remaining observations will be divided into
ldquoGynaecology Departmentrdquo ldquoNon-IVFGynaecologyrdquo and ldquoUnknownrdquo
categories using the code of referred ward and the names of the consultants
The test results will be converted into numeric format and the results with
minimum detection limit will be coded as 50 of the minimum detection limit
as follows AMH ldquolt061rdquo= 031 pmolL FSH ldquolt05rdquo= 025 mlUml LH
113
ldquolt05rdquo=025 mlUml Oest ldquolt50rdquo=025 pgml The test results that are
higher than the assay ranges will be set to 150 of the maximum range
Interpretation of serum FSH results in conjunction with serum
oestradiol levels is important in establishing true early follicular phase hormone
levels The test results are believed to be inaccurate if serum oestradiol levels
higher than 250pmolL at the time of sampling and therefore a new variable
for FSH results with only serum FSH observations that meet above criteria will
be created and used subsequently All ambiguous data will be checked using
electronic pathology data management system Clinical Work Station (CWS)
Surgery dataset
The electronic dataset will be obtained from Information Department
in Excel format The dataset created using clinical coding software and data
entry conducted during patient treatment episodes by theatre nursing and
medical staff In order to evaluate effect of past reproductive surgery to
ovarian reserve all patients had ovarian cystectomy drainage of ovarian cyst
salpingectomy salpingo-oopherectomy during 1 January 2000-16 November
2011 at Central Manchester University Hospitals NHS Foundation Trust will
be included in the dataset The dataset contains following variables hospital
number surname first name date of birth date of operation name of
operation laterality of operation and name of surgeon
The final dataset will be stored in Stata dta format (Figure 1) The
dataset will be used to validate data on reproductive surgery that was collected
from hospital records in the RH dataset
AMH dataset
The dataset contains the AMH results the dates of sampling the dates
of analyses and the assay generation (DSL or Gen II) for all patients included
in the study (Table 1) The dataset will be obtained from the senior clinical
scientist Dr Philip Pemberton Specialist Assay Laboratory who is responsible
for the data entry and updating of the dataset
There are two separate primary Excel based AMH data files 1) DSL
dataset and 2) Gen II dataset The datasets will be transferred to Stata 12
software separately and following preparation of the datasets which logged
using Stata Do file Stata versions of the data files will be stored under ldquoDSLrdquo
114
and ldquoGen2rdquo names Then the files will be combined by appending ldquoDSLrdquo to
ldquoGen2rdquo in order to create a new combined ldquoAMHrdquo dataset The date variables
the sample date the assay date and the date of birth will be converted into
numeric ldquoDMYrdquo format The samples sent from other NHS trusts and private
clinics will be excluded from the dataset alongside the records from male
patients and the patients outside of the age range of 20-50 years of age The
manufacturers of the assays suggest that haemolysed and partly haemolysed
samples may provide inaccurate test readings Therefore a new variable
without these samples will be created and used in the analyses for all studies
All the ambiguous data will be checked and verified using duplicate datasets
obtained from Biochemistry dataset and the hospital records of the patients
IVF dataset
The IVF dataset will be downloaded from ACUBase Data management
system in original Excel format and contains detailed information on causes of
infertility sperm parameters treatment interventions assessment of oocyte
quantity and quality assessment of embryo quantity and quality and the
outcomes of treatment cycles (Table 1)Data entry to ACUBase was
performed by members of administrative nursing embryology and medical
staff of the Reproductive Medicine Department at the point of care This is
only electronic data management system for ART cycles and used for
monitoring of the clinical performance of the department by internal and
external quality assessment agencies and regulators (eg HFEA CQC)
Therefore the quality of data entry for the main indicators of the performance
of IVFICSI programs (the treatment procedures the outcomes of the cycles
and assessment of embryos) should be fairly accurate
Table 2b describes the coding of the treatment outcomes and the
practitioners of ICSI the ultrasound-guided oocyte retrieval (USOR) and the
embryo transfer (ET) procedures
In addition to the main patient identifier (Hospital Number) this dataset
contains in-built cycle identifier (IVF Reference Number) which will be used
to link the original IVF cycles to corresponding Frozen Embryo Transfer
(FET) cycles and the embryos originating from the index cycle using ldquoFETrdquo
and ldquoEmbryordquo datasets respectively
115
FET dataset
The dataset provides information on the quality and the quantity of
transferred embryos the date of embryo transfer and the outcome of the cycle
in frozen embryo transfer cycles (Table 1) Primary data entry was performed
by the members of the clinical embryology team during the treatment of
patients and will be downloaded from ACUBase by Dr O Rustamov
Together with ldquoIVFrdquo dataset it can be used to study cumulative live birth rate
(LBR) of index cycles The treatment outcomes as well as ICSI USOR and ET
practitioners will be converted to numeric variables using the codes which are
shown in Table 2b The dataset can be linked to the index fresh IVF cycles as
well as to embryos of FET cycles using the IVF Reference number
Embryology dataset
The dataset has comprehensive information on the quality and the
quantity of embryos on each day of their culturing including embryos that
were cryopreserved and those that were discarded (Table 1) The dataset also
includes patient identifiers (name date of birth IVF reference number) and
the dates of embryo transfer The primary data entry into this dataset was
conducted by the members of clinical embryology team during the clinical
episodes and will be downloaded from ACUBase by Dr O Rustamov The
dataset can be linked to index fresh IVF cycle and FET cycles using IVF
Reference numbers of corresponding datasets
RH dataset
This dataset will be created and data entry will be conducted during the
search of the hospital notes Following identification of included patients using
AMH dataset Excel electronic data collection file will be created The hospital
notes of each patient will be searched for by systematically checking all filed
hospital records in Clinical Records Room of Reproductive Medicine
Department by the order of their hospital number Further search for missing
notes will be conducted by checking all hospital notes located in the offices of
nurses doctors and secretaries Electronic hospital notes filed in Medisec
Digital Dictation Database will be used for data extraction for the patients
whose hospital notes were not located
116
All available diagnosis will be recorded under the following columns 1)
female referral diagnosis 2) male referral diagnosis 3) female initial clinic
diagnosis 4) female final clinic diagnosis 5) diagnosis prior 2nd IVF cycle 6)
diagnosis prior 3rd IVF cycle Furthermore other relevant information on
pathology of reproductive system will be documented For instance all possible
iatrogenic causes of poor ovarian reserve (eg oophorectomy ovarian
cystectomy salpingectomy chemotherapy and radiotherapy) will be recorded
In order to establish the existence of polycystic ovary syndrome (PCOS) the
history of oligomenorrhea amenorrhea and diagnosis of polycystic ovaries
(PCO) on pelvic ultrasound scan will be collected and used in conjunction with
serum LH levels of Biochemistry dataset (Table 1)
Male infertility will be defined as ldquosevere male factorrdquo if the sperm
parameters were low enough to meet criteria (lt05 mlnml or retrograde
ejaculation) for Multiple Ejaculation Resuspension and Centrifugation test
(MERC) as part of investigation for infertility A variable for patients
diagnosed with azoospermia will be created and the diagnosis will be recorded
The patients diagnosed with male factor infertility but with the sperm
parameters that did not reach criteria for MERC will be diagnosed with ldquomild
male factorrdquo infertility Patients diagnosed with ldquosevererdquo andor ldquostage IVrdquo
andor ldquostage IIIrdquo endometriosis will be categorized as ldquosevere
endometriosisrdquo while patients diagnosed with mild or moderate endometriosis
will be coded as ldquomild endometriosisrdquo group In diagnosing the tubal factor
infertility only patients with history of bilateral salpingectomy and the patients
with evidence of bilateral tubal blockage on a laparoscopy and dye test will be
diagnosed as ldquosevere tubal factorrdquo The patients with history of unilateral
salpingectomy unilateral tubal block in laparoscopy and dye test or
unilateralbilateral tubal block on hysterosalpingogram will be categorized as
ldquomild tubal factorrdquo infertility Diagnosis of polycystic ovarian syndrome
(PCOS) will be based in Rotterdam criteria existence of two of the following
features 1) oligo- or anovulation 2) clinical andor biochemical signs of
hyperandrgoenism 3) polycystic ovaries Referral for fertility preservation will
be defined as ldquoreferral for consideration of obtaining oocytes orand embryos
andor sperm prior to chemotherapy radiotherapy or surgical management of
a malignant diseaserdquo The length of infertility will be recorded as per proforma
of initial consultation for the patients attended initial clinic appointment
following introduction of serum AMH test 1 September 2008 For patients
117
attended initial consultation prior to introduction of AMH test the length of
infertility will be documented as per the initial clinic proforma plus years till the
patientrsquos first AMH test The patientrsquos body mass index (BMI) documented at
initial assessment will used for patients who had assessment after introduction
of AMH test 1 September 2008 whereas the most up to date BMI result is
collected for the patients seen prior to this date
AFC dataset
Data will be extracted from the hospital notes The data on the
assessment of AFC will be obtained from the pelvic ultrasound scan reports
The date of assessment the AFC in each ovary the name of sonographer will
be recorded (Table 1) Furthermore other relevant ultrasound findings such
as ovarian cyst hydrosalpynx and submucous uterine fibroids will also be
entered in the dataset To permit data validation scanned copies of ultrasound
scan report of each AFC investigation will be stored in PDF format in the
computer that located in the Clinical Notes Room
The department uses a stringent methodology for the assessment of
AFC which consist of counting of all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
The ultrasound assessments are conducted by qualified sonographers who use
the same methodology for the measurement of AFC However it is well
known that the counting of antral follicles may be prone to significant inter-
operator variability Therefore the name of sonographers will be recorded
during primary data collection and coded (Table 2a) so that the estimates of
within- and between-operator variability can be obtained if necessary
Folliculogram dataset
Although most data on IVFICSI cycles are available in ldquoIVFrdquo dataset
certain important data on IVF treatment are recorded only in the hard copy
IVF folliculograms Consequently data on ultrasound follicle tracking the
reasons for changing the doses of stimulation drugs are only available in the
folliculograms Furthermore the length of ldquothe coastingrdquo and the causes for
cycle cancellation are usually recorded in both folliculograms and ldquoIVFrdquo
dataset which can be used to validate accuracy of ldquoIVFrdquo dataset Therefore
118
these data will be collected using the folliculograms that filed in the hospital
notes and the scanned copies of each folliculograms will be stored in the
computer located Clinical Records Room for data validation purposes (Table
1)
The number of follicles on Day 8 and Day 10 ultrasound scans will be
recorded according to the size of the follicles 10-16mm and 17mm
Numeric variables for the follicle numbers will be created and used for
assessment of ovarian response in IVF cycles
Data management
Data cleaning and coding
All datasets will be obtained in Excel format and transferred in the
original unaltered condition into Stata 12 data management and statistical
package (Stata 12 StataCorp Texas USA) and all steps of the data cleaning
and the coding will be recorded using Stata Do files to create audit trails of the
data management process Both original Excel and cleaned Stata versions of
data files will be stored in computer that is located in Clinical Records Room at
Reproductive Medicine Department Uniformity of hospital numbers in all
datasets will be achieved by converting a) leading lower case prefixes ldquosrdquo to
upper case ldquoSrdquo b) dropping suffixes ldquozrdquo and ldquoZrdquo and c) dropping all leading
zeros in the second part of the hospital number (eg ldquos1000235Zrdquo
=rdquoS10235rdquo) The coding of the datasets is shown in the Table 2a and the
Table 2b All ambiguous data will be checked using electronic data
management systems (eg CWS Medisec) and hospital notes
Merging the datasets
The datasets will be structured as such that the data files can be used
independently or merged at a) patient or b) IVF cycle levels using the patient
identifier cycle identifier and date variables (Figure 1) This allows analysis of
outcomes of both ldquoFresh IVF cyclesrdquo and study the cumulative outcomes of
Fresh IVF and Frozen Embryo Transfer cycles originating form index IVF
cycles
Each dataset will contain two main patient identifiers and patient
number (Patient ID) which will be used for linking the datasets in a patient
119
level At the initial stages of the data management the hospital numbers will be
used as the main patient identifier The accuracy of the hospital numbers in
each dataset will be validated using PAS dataset by checking patient surname
first name and date of birth
Following methodology will be used to add study numbers into each
dataset First all dataset will be merged in a wide format using the hospital
numbers which creates Master Datasets for each of the research projects Then
an accuracy of the merger will be checked using DOB surname and first name
Once the dataset is validated several copies of the Patient ID variable will be
created and distributed to each dataset Finally the datasets will be separated
and stored as independent datasets alongside Master Datasets for each research
projects
ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo datasets contain cycle specific IVF
reference numbers which were allocated during the clinical episodes on
ACUBase Using IVF reference number new ID variable (Cycle ID) will be
created and allocated to all datasets using closest observation prior to the IVF
cycle in Master Research Dataset Consequently by using cycle reference
number all patient and cycle related data can be linked in the IVF FET cycle
and embryo level
Data security and storage
The encrypted and password protected hospital computer will be used to
process the data until all the patient identifiers have been removed and the
datasets have been anonymised Once the Master Research Datasets are
validated and research team is satisfied with the quality of the data the dataset
will be anonymised by dropping variables for following patient identifiers
hospital number surname first name date of birth and IVF reference number
The study number and the cycle reference numbers will be used as a patient
and a cycle identifiers and only this anonymised dataset will be used for
statistical analysis A copy of non-anonymised dataset will be stored in the
computer located in Clinical Records Room for data verification and a
reference purposes The datasets will be stored within IVF unit for the
duration of the research projects of the MD programme The necessity of
storage of the datasets and measures of data security will be reviewed every
three years thereafter
120
II RESULTS
INTRODUCTION
According to the protocol all women from 20 to 50 years of age referred
to Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust for management of infertility or fertility
preservation and had AMH measurement during the period from 1 September
2008 till 16 November 2011 have been included in the database In total of
4506 patients met the inclusion criteria with 3381 patients in DSL AMH
assay group and 1125 patients Gen II assay group The following datasets
have been extracted from the clinical electronic data management systems
ldquoPASrdquordquo Biochemistryrdquo ldquoSurgeryrdquo ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo Data
extraction from the paper-based hospital records of 3681 patients (n=3130
DSL and n=551 Gen II) were performed by two researchers Dr ORustamov
(n=2801) and Dr M Krishnan (n=880) In addition data collection using
Medisec Digital Dictation Software for the notes that were not located in DSL
group (n=251 patients) was completed by Dr O Rustamov In view of the
issues with validity of Gen II assay measurements which were observed in the
earlier study of the MD Programme (Chapter 2 AMH variability and assay
method comparison) I decided to base subsequent work for the last three
projects (Chapter 5-7) of the MD programme only on DSL assay
measurements and not to include samples based on Gen II AMH Assay
Therefore I decided not to collect data from the hospital notes for the patients
that had AMH measurements using exclusively Gen II Assay where the notes
were not found during the first round of data collection (n=575)
As a result in DSL group all datasets for 3130 patients were completed
and all but AFC and Folliculogram datasets were completed for 251 (Figure 2)
In Gen II group all datasets were completed for 551 patients and all but RH
AFC and Folliculogram datasets were obtained for 575 patients (Figure 2)
As described above the studies of the last three projects (Chapter 5-7)
are based on DSL assay which is no longer in clinical use The review of
literature presented in Chapter 3 suggests that DSL assay appears to have
provided the most reproducible measurements of AMH compared to that of
other assays Therefore AMH measured using DSL assay is perhaps most
121
reliable in terms addressing the research questions In all three chapters
estimates of the effect sizes are provided in percentage terms and therefore the
results are convertible to any AMH assay
Datasets
Demography dataset
The dataset was obtained from Mr Peter Hoyle Senior Data Analyst of
Information Unit CMFT on 16 October 2012 The dataset includes all patients
referred to Reproductive Medicine Department between 1 January 2006 and 31
August 2012 and contains 5573 patients I created a dataset ldquoDemographyrdquo in
Stata format using the steps of data cleaning coding and management as per
protocol The audit trial of the data management was created using Stata Do
file (Figure 1)
Biochemistry dataset
The biochemistry data file was obtained from Dr Alexander Smith
Senior Clinical Scientist Biochemistry Department on 24 January 2011 The
dataset contains the results of all serum AMH FSH LH and E2 samples
conducted from 01 September 2008 to 31 December 2010 The dataset was in
Excel format that consisted of two datasheets 1) Biochemistry 2008-2009 and
2) Biochemistry 2010 The datasheets transferred to Stata 12 in original
unaltered condition and a single Stata ldquoBiochemistryrdquo dataset was created by
combining datasheets by appending them to each other The dataset contains
in total of 78415 blood results of 11574 patients with 6643 AMH 19175 FSH
28677 LH and 23920 E2 results A wide format of the dataset was prepared by
transferring all blood results of each patient to a single row A variable which
indicates valid FSH results was created by coding FSH results as missing if
corresponding E2 levels were higher than 250 pmolL The audit trial of the
data management was created using a Stata Do file
Surgery dataset
Data management was conducted according to the protocol In total
dataset contained 2096 operations in 1787 patients Data on all operations on
122
Fallopian tubes (eg salpingectomy salpingostomy) and ovaries (eg
cystectomy drainage of cyst) at Central Manchester NHS Foundation Trust
from 1 January 2000 to 16 January 2011 are available in the dataset The
dataset will be used to validate the data on history of reproductive surgery of
Reproductive History dataset
AMH dataset
Both AMH datasets were received from Dr Philip Pemberton Senior
Clinical Scientist of the Specialist Assay Laboratory on 13 January 2012 and
transferred to Stata 12 software in the original format All steps of the data
cleaning and the management were recorded using Stata Do file
There were 3381 patients in DSL dataset and 1125 patients in Gen II
dataset Cleaning and coding of the datasets were achieved using the
methodology described in above protocol and new AMH dataset has been
created
IVF dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov on
08 October 2012 and following importing the dataset into Stata 12 in original
format dataset was prepared according to the protocol The dataset contains all
IVFICSI cycles that took place between 01 January 2004 and 01 October
2012 including the cycles of women who acted as egg donors and egg
recipients There were in total of 4323 patients who had 5737 IVFICSI cycles
with 4123 IVFICSI cycles using own eggs 10 embryo storage 40 oocyte
donation 7 oocyte storage 55 oocyte recipient cycles The dataset has
anonymised unique patient (Patient ID) and cycle identifiers (Cycle ID) and
therefore can be linked to all other datasets including all FET cycles and
embryos originated from the index IVF cycle
FET dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov
in Excel format on 20 October 2012 and transferred to Stata 12 Software in
the original condition The data managed as per above protocol and each step
of the process of preparation of the dataset was recorded in Stata Do file The
dataset comprised of all FET cycles (n= 3709) of all women (n=1991)
123
conducted between 01 January 2004 and 01 October 2010 and the Stata
version of ldquoFETrdquo dataset contains complete data on number of thawed
cleaved discarded and research embryos for all patients The dataset contains
unique patient identifier (Patient N) and unique cycle identifiers (Cycle N) and
therefore can be linked to all datasets in patient and cycle levels including index
IVF cycle and embryos
Embryology dataset
The Excel dataset was downloaded from ACUBase by Dr Oybek
Rustamov on 20 October 2012 and transferred into Stata 12 Software in
unaltered condition The data was managed according to the above protocol
The dataset has details of all 65535 (n=4305 women) embryos that were
created between 01 January 2004 and 01 October 2012 The dataset contains
complete data on quantity and the assessment of embryo quality which
includes grading of number evenness and defragmentation of the cells for
each day of culturing of the embryos Furthermore the destination of each
embryo (eg transferred cryopreserved discarded and donated) and the
outcomes of cycles for transferred embryos are available in the dataset Given
that the Embryology dataset has the unique patient as well as the cycle
identifiers this dataset is nested within patients and IVF cycles Consequently
each embryo can be linked to patient index Fresh IVF cycle and subsequent
FET cycles
Reproductive History AFC and Folliculogram datasets
The hospital notes of all patients (n=4506) were searched during the
period of 1 April 2012 to 15 October 2012 for collection of data for
Reproductive history AFC and Folliculogram datasets as per protocol All case
noted filed in the Clinical Records Room the Nurses Room the Doctors
Room and the Secretaries Room of Reproductive Medicine Department were
searched and relevant notes were pulled and searched for data All ultrasound
scan reports containing data on AFC and all IVFICSI folliculograms of
patients were scanned and electronic copy of scanned documents were stored
in the password protected NHS computer located in the Clinical Records
Room
124
The first round of data gathering achieved following result In DSL
dataset there were in total of 3381 patients with 3130 patients had complete
data extraction from their hospital notes and hospital records of 251 patients
were not found There were in total of 1126 patients in Gen II dataset 551 of
whom had complete data extraction from their hospital records and the case
notes of 575 patients were not located (Figure 2) The main reason for
ldquomissing case notesrdquo was found to be the use of hospital records by clinical
laboratory and administrative members of staff at the time of data collection in
patients undergoing investigation and treatment
In the meantime the results of our previous research study indicated that
Gen II samples provide erroneous results (Chapter II) and therefore we
decided to use only data from the patients in DSL group Data on reproductive
history for the remaining patients in the DSL group (n=251) with missing
hospital records were collected using digital clinic letters stored in Medisec
Digital Dictation Software (Medisec Software UK) The data file that
contained combined datasets of reproductive history and AFC was transferred
to Stata 12 in original condition and data management was conducted
according to the protocol All steps of data management was recorded using
Stata do file for audit trail and to ensure reproducibility of the management of
the data Similarly the management of Folliculogram dataset was achieved
using the procedures described in the protocol and all steps of data
management was logged using Stata Do file As result of above data collection
and management I created three Stata datasets ldquoRHrdquo (reproductive history)
ldquoAFCrdquo and ldquoFolliculogramrdquo
Merging Datasets
First the datasets were merged using a unique patient identifier (hospital
number) as per protocol Validation of the merger using additional patient
identifiers (NHS number name date of birth) revealed existence of duplicate
hospital numbers in patients transferred from secondary care infertility services
to IVF Department of Central Manchester University Hospitals NHS
Foundation Trust I established that in the datasets the combination of the
patientrsquos first name surname and date of birth in a single string variable could
be used as a unique identifier Hence I used this identifier to merge all
datasets achieving a robust merger of all independent datasets into combined
125
final Master Datasets for each of the research projects Following the creation
of an anonymised unique patient identifier (Patient ID) for each patient and
anonymised unique cycle identifier (Cycle ID) for each IVF cycle all patient
identifiers (eg surname forename hospital number IVF ref number) were
dropped (Figure 1) The anonymised independent datasets (eg AMH AFC
IVF etc) and anonymised Master Datasets were stored as per protocol
Subsequently these anonymised datasets were used for the statistical analyses
of the research projects The original unanonymised data files were stored in
two password protected NHS hospital computers in the Clinical Records
Room and Doctors Room of Reproductive Medicine Department and
archived according to the Trust policies thereafter Only members of clinical
staff have access to the computers and only nominated clinical members of the
research group who have specific approval can have access to unanomysed
Fully anonymised datasets have been made available to other members of the
research team with the stipulation that the datasets are stored on secure
password protected servers or fully encrypted computers Fully anonymised
datasets may in the future be shared with other researchers following
consideration of the request by the person responsible for the datasets (Dr
Cheryl Fitzgerald) and appropriate ethical and data protection approval
CONCLUSION
Following extraction and management of the data I have built
comprehensive validated datasets which will enable to study ovarian reserve in
a wide context including a) assessment of ovarian reserve b) evaluation of the
performance of ovarian biomarkers c) study individualization of ovarian
stimulation in IVF d) association of the biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryo live birth) The database will be used
to address the research questions posed in the subsequent chapters of this
thesis and beyond that for future studies on the assessment of ovarian reserve
and IVF treatment
126
Figure 1 Data and program files Datasets and programme files created in preparation of the research datasets File names and types are provided in the brackets
127
Table 1a Available vriables The
available identifiers variables and the source of data for following datasets Ethnicity RH AMH AFC Biochemistry OHSS Folliculogram
Datasets
Clinical ID
Study ID
Variables
Source
Demography Hospital N Surname
First name DOB
Patient ID
Ethnicity Information Department
(PAS)
RH
(Reproductive History)
Hospital N Surname
First name DOB
Patient ID
1 Diagnosis Referral Female Referral Male
Clinic Female Clinic Male
Post Cycle 1 Post cycle 2 Post cycle 3
2 Iatrogenic causes of loss of ovarian reserve Ovarian surgery tubal surgery chemotherapy radiotherapy
3 BMI 4 PCOS (PCO oligomenorrhea amenorrhea hirsutism)
Hospital Records
Surgery Hospital N Surname
First name DOB
Patient ID Date
Procedure Date Operator
Information Department
AMH Hospital N Surname
First name DOB
Patient ID Date
Date of sample Date of assay AMH level Assay generation AMH dataset of Specialist Assay
Lab
AFC Hospital N Surname
First name DOB
Patient ID Date
AFC (up to six AFC scans)
Left ovary Right ovary Date of Scan Sonographer Comments (Ovarian cyst hydrosalpynx fibroid poorly visualized etc)
Hospital Records
Biochemistry Hospital N Surname
First name DOB
Patient ID Date
Oestradiol (Date of sample Date of assay serum level) FSH (Date of sample Date of assay serum level)
LH (Date of sample Date of assay serum level)
Biochemistry Electronic
Database
Folliculogram Hospital N Surname
First name DOB
Patient ID Date
Folliculogram (up to 3 cycles) Date (1st day of ovarian stimulation)
Day 8( 10-16mm) Day 8 (gt17mm) Day 10 (10-16mm) Day 8 (gt17mm)
Comments (Day of HCG OHSS Cancellation Ovarian cyst Hydrosalpynx Coasting etc)
Hospital Records
128
Table 1b Available variables The available identifiers variables and the source of data for IVF dataset
Datasets Clinical ID Study Variables Source
IVF Hospital N Surname First name DOB PCT code
Patient ID Cycle ID Date
GENERAL
Attempt Type Protocol DaysStim InitDose Outcome OutcomeDt Age PartnerAge EggCollect TreatDate ETransfer Add_Drug1 Add_Drug2 Add_Drug3 Add_Drug4 Add_Drug5 Add_Drug6 Add_Drug7 EGG RECOVERY SNumber Follicles TotEgg EggNumber
FERTILISATION IVFEgg IVFCleaved ICSICleaved Cleaved PN2 IVFPN2 ICSI2PN ICSICl ICSIEgg ICSIFPN IVFFPN IVFTransfer ICSITransfer IVFLysed ICSILysed IVFMetII IVFMetI IVFAtretic IVFAbnormal IVFEmptyZona IVFG_Vesicle ICSIMetII ICSIMetI ICSIAtretic ICSIAbnormal ICSIEmptyZona ICSIG_Vesicle
OUTCOME
sacs Hearts Preg ICSIPract STORAGE Frozen IVFFroz ICSIFroz SpermSource SortKeySTAR HISTORY cat_tubal cat_OvFail cat_UtProb cat_unex cat_ MF cat_Meno cat_Genetic cat_endo cat_anov cat_noMale Inf_Since MaleInf
CoupleInf Preg24Wk MiscTOP Ectopic LiveBirth FSH AMH Emb_Recip Surrogate Sperm_Recip StoreEggs EggThaw Treat_Reason IgnoreKPI EMBRYOLOGY
D1LteClCells1 D1LteClCells2 D2Cells2 D2Cells3 D2Cells4 D2Even2 D2Even3 D2Even4 D2Frag2 D2Frag3 D2Frag
SPERM Conc_Init MotA MotB Conc_ Prep MotAP MotBP SemenSource SemenAnalysis STIMULATION BMI TotDose GonadUsed Incubator ICSIRigg AMHBand DHEA EGG
Egg_Recip Own_Eggs Altruistic_D
ACUBASE Electronic Database
129
Table 1c Available variables
The available identifiers variables and the source of the data for FET and Embryo datasets
Datasets Clinical ID Study ID
Variables
Source
FER
Hospital N Surname First name
Patient ID Cycle ID Date
GENERAL treatdate transfer ETDate
OUTCOME preg IUP Outcome OutcomeDt
EMBRYOLOGY
Thawed Survived Cleaved Discarded Research
STORAGE NumStored DtCreated
CLINICIAN ETClinician ETEmbryologist OrigCycle
ACUBASE Electronic Database
Embryo
Hospital N Surname First name DOB
Patient ID Cycle ID Date
GENERAL TreatDate Injected Destination
CELLS CellsD1 CellsD2_AM CellsD2_PM CellsD3_AM CellsD3_PM
EVENNES EvenD2_AM EvenD2_PM EvenD3_AM EvenD3_PM
FRAGMENT FragD1 FragD2_AM FragD2_PM FragD3_AM FragD3_PM
OUTCOMES ICSIPract Maturity PosPreg Hearts SpermSource Age
ACUBASE Electronic Database
130
Table 2a Coding
The codes used to convert ethnicity and diagnosis variables from string to numeric format in PAS and RH datasets
131
Table 2b Coding
The codes used to convert treatment outcomes from string to numeric format in IVF and FET datasets
Datasets Codes for outcomes
IVF
FET
ldquoBiochemical Pregnancyrdquo=1 ldquoCancel (other)rdquo=2
ldquoCancel Hyperstimulationrdquo=3 ldquoCancel Poor responserdquo=4
ldquoCancelled no sperm on day of ECrdquo=5 ldquoCONVERTED IVF TO IUIrdquo=6
ldquoDelayed Miscarriagerdquo=7 ldquoDonatedrdquo=8 ldquoEctopicrdquo=9
ldquoEgg donationrdquo=10 ldquoEmbryos for storagerdquo=11
ldquoEmpty Sacrdquo=12 ldquoFailed Fertilisationrdquo=13
ldquoFor donationrdquo=14 ldquoFreeze Allrdquo=15
ldquoFreeze All (OHSS)rdquo=16 ldquoFreeze All (Other)rdquo=17
ldquoLate Miscarriagerdquo=18 ldquolost to contactrdquo=19
ldquolost to follow uprdquo=19 ldquoNo Eggsrdquo=20
ldquoNo Spermrdquo=21 ldquoNo Normal Embryosrdquo=22
ldquoNot Pregnantrdquo=23 ldquoOngoing Singletonrdquo=24
ldquoOngoing Twinrdquo=25 ldquoPositive hCGrdquo=26
ldquoSingleton Birth=27rdquo ldquoTwin Birthrdquo=28
ldquoTriplet Birthrdquo=29 ldquoStill Birthrdquo=30The
132
Figure 2 Data collection from hospital records
Completeness of data collection from hospital records for RH AFC and Folliculogram datasets
All
patients
DSL
(n=3381)
All Datasets
Complete
n=3130
AFC and Folliculogram
not complete
n=251
Gen II
(n=1126)
All Datasets
Complete
n=551
RH AFC Follicologram
not complete
n=575
133
Table 3 Results Datasets and observation
Summary of the number of patients observations IVFFET cycles and data entry period for all datasets
Datasets Patients Observations Cycles Period
AMH DSL 3381Gen II 1126
DSL-3913 DSL 01 Sep 2008-15 Nov 2010 Gen II 16 Nov 2010-16 Nov 2011
Demography 5573 01 Jan 2006-31 Aug 2012
Biochemistry 11754 Total 78415 6643-AMH 19175-FSH 28677-LH 23920-E2
01 Sep 2008-31 Dec 2010
RH DSL-3381 DSL-3381 01 Sep 2008-01 Oct 2012
Surgery 1787
2096 01 Jan 2000-16 Nov 2011
AFC DSL 2411 DSL Total 4174 Single measurement2411 Repeats 2-1250 3-370 4-105 5-25 6-7 7-1
01 Sep 2008-01 Oct 2012
Folliculogram 1736 2183
01 Sep 2008-01 Oct 2012
IVFICSI 4324 - Total 5737 own eggs-4123 oocyte recipients-55 oocyte donors-40 Embryo storage-10 oocyte storage-7
01 Jan 2004-01 Oct 2012
FET 1991 - 3709
01 Jan 2004-01 Oct 2012
Embryology
4305 65535 embryos - 01 Jan 2004-01 Oct 2012
134
Figure 3 Merging datasets
The process of merging datasets in patient and cycle levels using patient date and cycle IDs
135
ASSESSMENT OF DETERMINANTS OF
ANTI-MUumlLLERIAN HORMONE IN INFERTILE
WOMEN
5
136
THE EFFECT OF ETHNICITY BMI
ENDOMETRIOSIS AND THE CAUSES OF
INFERTILITY ON OVARIAN RESERVE
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
To be submitted to Fertility and Sterility
51
137
Title
The effect of ethnicity BMI endometriosis and the causes of infertility
on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK c Centre for Biostatistics
Institute of Population Health Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Word count 4715
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
138
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all version
of the manuscript MK assisted in data extraction contributed in discussion
and the review of the manuscript SR and CF oversaw and supervised
preparation of dataset statistical analysis contributed in discussion and
reviewed all versions of the manuscript
139
ABSTRACT
Objective
To estimate the effect of ethnicity BMI endometriosis and the causes of
infertility on ovarian reserve
Design Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 2946 patients were included in the study of which 65 did not have
data on ethnicity leaving 2881 women in the sample
Interventions Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH serum basal FSH and basal AFC measurements
Results
Multivariable regression excluding BMI showed that woman of Black ethnicity
and the group defined as ldquoOther ethnicityrdquo had significantly lower AMH
measurements when compared to that of White (-25 p=0013 and -19
p=0047) and overall ethnicity was a significant predictor of AMH (p=0007)
However inclusion of BMI in the model reduced these effects and the overall
effect of ethnicity did not reach statistical significance (p=008) AFC was
significantly reduced in Pakistani and women of ldquoOther ethnicitiesrdquo although
the effect sizes were small (10-14) and the overall effect of ethnicity was
significant in both models (p=004 and p=003) None of the groups showed a
statistically significant difference in FSH although women of ldquoOther Asianrdquo
ethnicity appear to have lower FSH measurements (12) which was close to
statistical significance (-12 p=007)
140
Obese women had higher AMH measurements (16 p=0035) compared to
that with normal BMI and the overall effect of the BMI was significant
(p=003) In the analysis of the effect of BMI to AFC measurements we did
not observe differences that were statistically significant However FSH results
showed that there is a modest association between BMI and FSH with both
overweight and obese women having significantly lower FSH measurements
compared to lean women (-5 p=0003 and -10 p=0003)
In the absence of endometrioma endometriosis was associated with lower
AMH measurements although this did not reach statistical significance
Neither AFC nor FSH was significantly different in the endometriosis group
compared to those without endometriosis In contrast we observed around
31 higher AMH levels in the patients with at least one endometrioma
(p=0034) although this did not reach statistical significance (21 p=01) in
the smaller subset after adjustment for BMI AFC and FSH did not show any
statistically significant association with endometrioma
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to the ones who did not have
unexplained infertility except the analysis that did not include BMI as a
covariate which found a weakly positive correlation (10 p=003) Similarly
the estimation of the effect of the diagnosis of unexplained infertility to AFC
as well as FSH showed that there were weak association between the markers
and diagnosis of unexplained infertility
There was no significant difference in AMH AFC and FSH measurements of
women with mild and severe tubal infertility in the models which included all
covariates except the analysis of FSH and mild tubal factor where we found
weakly negative correlation between these variables
Women diagnosed with male factor infertility had significantly higher AMH
and lower FSH measurements the effect sizes of which were directly
proportional to the severity of the diagnosis In the analysis of AFC we did not
found significant difference in the measurements between patients with male
factor infertility and to that of non-male factor
141
Conclusions
Ethnicity does not appear to play a major role in determination of ovarian
reserve as measured by AMH AFC and FSH whereas there is a significant
positive association with BMI and these markers of ovarian reserve Women
with endometriosis appear to have lower AMH whilst patients with
endometrioma have significantly higher AMH and lower FSH measurements
The study showed that the association between markers of ovarian reserve and
unexplained infertility as well as tubal disease is weak In contrast women
diagnosed with male factor infertility have higher ovarian reserve
Key Words
Ovarian reserve AMH AFC FSH ethnicity BMI infertility endometriosis
endometrioma
142
INTRODUCTION
The ovarian reserve consists of a total number of resting primordial and
growing oocytes which appears to be determined by the initial oocyte pool at
birth and the age-related decline in the oocyte number (Hansen et al 2008
Wallace and Kelsey 2010) Both of these factors appear to be largely
predetermined genetically although certain environmental socioeconomic and
medical factors likely to play a role in the rate of the decline (Schuh-Huerta et
al 2012b Kim et al 2013 Dolleman et al 2013) The understanding of the
formation and the loss of ovarian reserve have been improved greatly due to
recently published data on the histological assessment of ovarian reserve
(Hansen et al 2008) Furthermore the use of the biomarkers has enabled the
evaluation of ovarian reserve in larger population-based samples Biomarkers
such as AMH and AFC can only assess the measurement of growing pre-antral
and early antral follicle activity However some studies suggest that there is a
close correlation between the measurements of these markers and the number
of resting primordial follicles (Hansen et al 2011)
Studies on age related decline of AMH and AFC have played important
roles in understanding the decline of ovarian reserve although most of the
data have been derived from heterogeneous population without full account
for characteristics of individual patients (Nelson et al 2011 Seifer et al 2011
Shebl et al 2011) These studies have demonstrated that there is a significant
between-subject variation in ovarian reserve beyond that due to chronological
age (Kelsey et al 2011) More recent studies reported interesting findings on
the role of demographic anthropometric and clinical factors in the
determination of ovarian reserve Although these studies have employed
better-described samples some have small sample sizes and lack power for the
estimation of the effect of these factors Consequently studies on large and
well-characterised populations are necessary for evaluation of the determinants
of ovarian aging as well as to provide normative data for the individualisation
of the assessment of ovarian reserve
There have been reports of measurable disparities in the reproductive
aging and reproductive endocrinology between various ethnicities For
instance according to a large prospective study White Black and Hispanic
women reported higher rates of premature ovarian failure compared to
143
Chinese and Japanese (Luborsky et al 2002) In contrast the prevalence of
PCOS which is associated with higher ovarian reserve has been reported to be
significantly lower in Chinese (22) compared to British (8) women
(Michelmore et al 1999 Chen et al 2002) Although these disparities may
partially be due to the differences in the local diagnostic criteria it is plausible
to believe that the ethnicity may play a role in the determination of the
reproductive aging With regard to the effect of ethnicity to the markers of
ovarian reserve Seifer et al found that African American and Hispanic women
have lower AMH levels compared to White (Seifer et al 2009) In contrast
Randolph et al reported that African American women had significantly higher
ovarian reserve compared to that of White when determined by FSH
measurements (Randolph et al 2003) These studies indicate that ethnicity may
play a role in the determination of ovarian reserve and therefore warrants
further investigation which should include other major ethnic groups
Body weight appears to be closely associated with human female
reproduction which is evident by its effect on the natural fecundity as well as
the success of the assisted conception treatment cycles (Maheshwari et al
2007) Indeed the relationship of increased body mass index (BMI) and PCOS
is well described although the mechanism of this is not yet fully understood
Consequently a number of recent studies have assessed the effect of BMI to
the various aspects of reproductive endocrinology including ovarian reserve
Studies on the influence of BMI on the markers of ovarian reserve have
provided conflicting results probably due to the limited statistical power in
most of these studies and the difficulties encountered in properly accounting
for confounding factors such as age ethnicity and medical diagnosis (Buyuk et
al 2011 Freeman et al 2007 Su et al 2008 Seifer et al 2008 Sahmay et al 2012
Skalba et al 2011) Therefore there is a need for studies with large datasets and
good adjustment for confounding factors
We therefore designed and undertook a study to estimate the effect of
ethnicity BMI endometriosis and causes of infertility on ovarian reserve as
measured by AMH AFC and FSH using a robust dataset from a large cohort
of patients referred for infertility investigation and treatment in a single centre
144
METHODS
Objectives
The objectives of the study were to assess the role of the ethnicity BMI
and endometriosis and the causes of infertility on ovarian reserve as assessed
by the biomarkers AMH AFC and FSH using a large clinical data obtained
retrospectively
Sample
All women between 20 to 45 years of age referred to the Womenrsquos
Outpatient Department (WOP) and the Reproductive Medicine Department
(RMD) of Central Manchester University Hospitals NHS Foundation Trust for
management of infertility from 1 September 2008 to 16 November 2010 and
who had the measurement of AMH using DSL assay (DSL Active MISAMH
ELISA Diagnostic Systems Laboratories Webster Texas) were included in
this study Patients referred for fertility preservation (eg prior to or after the
treatment of a malignant disorder) and patients with a history of tubal or
ovarian surgery (salpingectomy ovarian cystectomy salpingo-oopherectomy)
and patients diagnosed with polycystic ovaries on ultrasound were excluded
The sample size was determined on pragmatic grounds and represents all
available patients meeting the inclusion criteria
Measurement of AMH
All patients referred to RMD had a measurement of AMH prior to
management of their infertility whereas the patients seen at WOP had AMH
measurements if they had a clinical indication for an assessment of ovarian
reserve
Blood samples for the measurement of AMH were taken at an initial
patient visit without regard to the day of the menstrual cycle and transported
to the in-house Biochemistry Laboratory All samples were processed and
analysed strictly according to the assay kit insert provided by the manufacturer
Serum samples were separated within two hours from venipuncture and frozen
at -20C until analysed in batches using the enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
145
100pmolL with a minimum detection limit of 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first
measurement was selected for this study
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the in-house
Biochemistry Laboratory within two hours of venipuncture for sample
processing and analysis Serum FSH levels were measured using specific
immunoassay kits (Cobas Roche Diagnostics Mannheim Germany) for use
on an autoanalyser platform (Roche Modular Analytics E170 Roche USA)
The intra-assay and inter-assay CVs were 60 and 68 respectively FSH
measurements in samples with high E2 levels (gt250) were defined as non-
representative of early follicular phase and were not included in this study
Where patients had repeated FSH measurements the measurement with the
closest date to that of AMH measurement was used
Measurement of AFC
Measurement of AFC was conducted in all patients undergoing assisted
conception The department uses a stringent protocol for the assessment of
AFC which consists of counting all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
Fully qualified sonographers conducted the ultrasound assessments Where
patients had repeated AFC measurements the AFC closest to the date of the
AMH measurement was used
Data collection
Data was extracted from hospital electronic clinical data management
systems and from written hospital notes of each patient AMH and FSH
measurements were obtained from the Biochemistry Department of the
hospital and validated by checking results of randomly selected 50 patients
146
against the results available in electronic clinical data management system
(Clinical Workstation) Data on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
surgery were gathered from the hospital case notes Data on the ethnicity was
obtained from the hospitalrsquos administrative database (PAS) The datasets were
merged using a unique patient identifier (hospital number) and the validity of
the linkage was validated using other patient identifiers (NHS number
patientrsquos name and date of birth)
Definitions and groups
In our hospital the ethnicity of the patient is established using a patient
questionnaire based on the UK census classification The body mass index
(BMI) of patients was categorised using NHS UK cut-off reference ranges
Underweight (lt185) Normal (185-249) Overweight (25-299) and Obese
(30-40) Causes of infertility were established by searching hospital records
including referral letters clinical entries and the letters generated following
initial and follow up clinic consultations Patients with a history of bilateral
tubal block which was confirmed by laparoscopy and dye test and patients
with a history of bilateral salpingectomy were categorised as having severe
tubal factor infertility Patients with unilateral tubal patency or unilateral
salpingectomy were categorised as having mild tubal factor infertility Patientrsquos
with laparoscopic diagnosis of stage III and Stage IV endometriosis (AFS)
were categorised as diagnosed with severe endometriosis whilst patients with
Stage I and Stage II endometriosis were allocated to group of mild
endometriosis Severe male factor infertility was defined as azoospermia or
severe oligospermia which necessitated Multiple Ejaculation Resuspension and
Centrifugation test (MERC) for assisted conception The criteria for MERC
were a) sperm count of lt05 mlnml or b) retrograde ejaculation Patients with
abnormal sperm count but who did not meet above criteria were classified as
mild male factor infertility
Statistical analysis
Firstly univariate analyses of the effect of age ethnicity BMI
endometriosis with and without endometrioma causes of infertility and
duration of infertility were conducted using two sample t test Then a
147
multivariate linear regression model that included age ethnicity BMI
endometriosis presence of endometrioma and the causes of infertility was
specified for the analyses of the effect of these factors to AMH AFC and
FSH Logarithmically transformed values were used for the statistical analysis
of AMH AFC and FSH The precise age on the day measurement of each of
the marker of ovarian reserve (AMH AFC and FSH) was used and age
adjustment utilised a quadratic function following centring to 30 years of age
Differences between the groups were considered significant at p005
Interactions between all explanatory variables were tested at a significance level
of plt001 In order to estimate the effect of BMI we fitted two different
models with a) BMI not included and b) BMI included in the model
Duration of infertility did not show any clinical or statistically significant
differences for any of the markers and therefore this variable was not included
in the models
RESULTS
In total 2946 patients were included in the study of whom 2880 of these
patient had valid AMH measurements 1810 had measurement of AFC and
2377 had FSH samples The mean and median age of patients were 328 (45)
and 332 (295 365) respectively and the distribution of patients according to
age categories ethnicity BMI endometriosis and the causes of infertility is
shown in the Table 1 The summary statistics for the markers of ovarian
reserve were as follows AMH mean 175 (501) median 142 (76-232) AFC
mean 139 (63) median 13 (10-17) and FSH mean 79 (72) median 7 (58-85)
As expected chronological age was found to be a significant determinant of all
markers of ovarian reserve We observed in average 5 decline in AMH levels
2 decline in AFC and 1 increase in FSH measurements per year (Table 2-
4)
Out of 2946 patients 2021 had data on BMI measurements and in 925
BMI was not available Table 5 describes age AMH AFC and FSH according
to the availability of data on BMI Distribution of patients by their ethnicity
and an availability of data on BMI is provided in Table 6 Similarly patient
distribution by diagnosis and availability of data on BMI can be found in Table
7
148
Ethnicity
The multivariable regression excluding BMI (Table 2) showed that
woman of Black ethnicity and the group defined as ldquoOther ethnicityrdquo had
significantly lower AMH measurements when compared to that of White (-25
p=0013 and -19 p=0047) and the overall ethnicity was a significant
predictor of AMH (p=0007) However inclusion of BMI in the model
reduced these effects and none of the groups had a statistically significant
difference in AMH levels compared to that of White and the overall effect of
ethnicity did not reach statistical significance (p=008)
AFC was significantly reduced in Pakistani and women of ldquoOther
ethnicitiesrdquo (Table 3) although the effect sizes were small (10-14) and the
overall effect of ethnicity was significant in the models with and without BMI
(p=004 and p=003) None of the groups showed statistically significant
differences in FSH (Table 4) although women of ldquoOther Asianrdquo ethnicity
appear to have lower FSH measurements (12) which was close to the level of
statistical significance (-12 p=007)
BMI
Obese women had 16 higher measurements of AMH (p=0035) and
overall effect of the BMI was significant (p=003) No interaction were
detected between BMI and ethnicity causes of infertility or diagnosis of
endometriosis suggesting that effect of BMI was independent of these factors
(Table 2)
In the analysis of the effect of BMI on AFC measurements we did not
observe any differences that were statistically significant (Table 3) However
FSH results showed that there is a modest association between BMI and FSH
with both overweight (Table 4) and obese women having significantly lower
FSH measurements compared to lean women (-5 p=0003 and -10
p=0003)
Endometriosis
In the absence of endometrioma endometriosis was associated with
lower AMH measurements although this did not reach statistical significance
149
(Table 2) Neither AFC nor FSH was significantly different in the
endometriosis group compared to those without endometriosis (Table 3-4)
In contrast we observed around 31 higher AMH levels in the patients
with endometrioma (p=0034) although this reduced to 21 and did not reach
statistical significance (p=010) in the smaller subset after adjustment for BMI
(Table 2) AFC and FSH did not show any statistically significant association
with endometrioma (Table 3-4)
Causes of Infertility
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to those with diagnosis
except the analysis that did not include BMI as a covariate which found a
weakly positive correlation (10 p=003) Similarly the estimation of the
effect of a diagnosis of unexplained infertility on AFC as well as FSH showed
that there were weak association between the markers and a diagnosis of
unexplained infertility (Table 2-4)
There were no significant differences in AMH AFC and FSH in women
with mild and severe tubal infertility in the models which included all
covariates other than weakly negative correlation between FSH and mild tubal
factor (Table 2-4)
Women diagnosed with male factor infertility had significantly higher
AMH and lower FSH measurements the effect sizes of which increased with
the severity of the diagnosis We did not find any significant difference in AFC
between patients with and without male factor infertility (Table 2-4)
DISCUSSION
This is first study investigating the effect of demographic
anthropometric and clinical factors on all three markers of ovarian reserve
using a large cohort of women of reproductive age Furthermore the statistical
analysis adjusted for relevant covariables using multivariable linear regression
models
150
Ethnicity
Our study found that amongst the main British ethnic groups the
effect of ethnicity on ovarian reserve measured using AMH AFC and FSH is
fairly weak and can be accounted for by differences in BMI between the
ethnic groups Recently studies have been published on the relationship of
ethnicity and markers of ovarian reserve all of which compared North
American populations One study which assessed a relatively small number of
women (n=102) at late reproductive age did not find a difference in AMH
levels between White and African American Women OR 123 (056 271
P=070) (Freeman et al 2007) In contrast Seifer et al reported that Black
(n=462) women had around 25 lower AMH measurements (P=0037)
compared to that of White (n=122) (Seifer et al 2009) which is not consistent
with our findings The main differences of this study compared to our study
were a) a majority were HIV infected women b) women were older (median
375 years of age) c) the analysis did not control for possible confounders
related to PCO reproductive pathology and surgery Furthermore unlike our
results the study did not find a correlation between BMI and AMH levels
Similarly Shuh-Huerta and colleagues reported that African American women
(n=200) had significantly lower AMH levels (P=000074) compared to that of
White (n=232) Mean AMH 22817 pmolL and 301+15 pmolL
respectively (Shuh-Huerta et al 2012b) Although the group used very stringent
selection of patients and statistical analysis BMI was not included in the
regression model Indeed our analysis without BMI in the model found similar
results (Table 2) But controlling for BMI has revealed no significant difference
in the AMH levels between White and Black ethnic groups
With regard to AFC measurements Shuh Huerta et al reported no
difference in the follicle counts between White (n=245) and African American
(n=202) women which supports our findings (Shuh-Huerta et al 2012b)
Again similar to our results the authors reported that FSH results of these
ethnic groups provided comparable results (Shuh-Huerta et al 2012a)
Although our results do not support some of previously published data
in view of above arguments we believe the ethnicity does not appear to play a
major role in determination of ovarian reserve However in view of the
discrepant findings of the currently available studies we suggest further studies
151
in large and diverse cohorts should be carried out in order to fully understand
the role of ethnicity
BMI
Our results show that BMI has direct correlation with AMH and AFC
and negative correlation with FSH suggesting women with increased BMI are
likely to have higher ovarian reserve The effect of this association was
significant in the analysis of AMH and FSH obese women appear to have
approximately 16 higher AMH and 10 lower FSH measurements when
compared to women with normal BMI Although the difference in AFC
measurements did not reach statistical significance there was direct correlation
between AFC and BMI
Published data on the effect of BMI to AMH levels provide conflicting
results compared to our study given the studies reported either no association
(Buyuk et al 2011 Freeman et al 2007 Su et al 2008) or a negative correlation
between these factors (Seifer et al 2008 Sahmay et al 2012 Skalba et al 2011)
However most of these studies assessed peremenopausal women or that of
late reproductive age Indeed the studies evaluated the effect of BMI to AMH
measurements in women of reproductive age demonstrated that lower AMH
levels in obese women were due to age rather than increased BMI (La Marca
et al 2012 Streuli et al 2012) Furthermore most of these studies either
employed univariate analysis or multivariate regression models that did not
included all relevant explanatory factors In addition these studies had
significantly smaller numbers of samples ranging from 10 to 809 compared to
our study (n=1953) Indeed other large study (n=3302) with multivariate
analysis supports our findings on the effect of BMI on ovarian reserve
indicating obese women have significantly lower FSH levels (Randolph et al
2004)
Endometriosis
Here we present data on the measurement of all three main markers of
ovarian reserve in women with endometriosis We observed that women with
endometriosis without endometrioma did not have significantly different
AMH AFC or FSH measurements compared to women that do not have this
pathology Intriguingly women who had endometriosis with endometriomata
152
tended to have higher AMH levels Given the data was collected
retrospectively we did not have full information on laparoscopic staging of
endometriosis in all patients and therefore an analysis according to severity or
staging of endometriosis was not feasible However the analysis controlled for
the important variables mentioned above and importantly only included the
patients without previous history of ovarian surgery We therefore we believe
the study provides fairly robust data on relationship of endometriosis and the
markers of ovarian reserve
Although it is generally believed that endometriosis has a damaging
effect on ovarian reserve published literature provides conflicting views
ranging from no correlation between these factors to a significant negative
effect of endometriosis As mentioned above most studies were small and
used matched comparison of patients with endometriosis to control group
using retrospectively collected data Carvalho et al compared women with
endometriosis (n=27) and to that of male factor infertility (n=50) and reported
there was no difference in basal AMH and AFC levels whilst FSH levels of
women with endometriosis was lower Another small study which used similar
methodology where an endometriosis group (n=17) was compared to patients
with tubal factor infertility (n=17) reported opposite results suggesting
endometriosis was associated with lower AMH measurements and there was
no correlation between the pathology and FSH or AFC (Lemos et al 2007)
Shebl et al compared AMH results of women with endometriosis (n=153) to a
matched group that did not have the pathology (n=306) and reported that
women with mild endometriosis had similar AMH levels whereas the group
with severe endometriosis had significantly lower AMH compared to the
control group (Shebl et al 2009) Although using well-matched control groups
is a robust study design direct comparison of the two groups without
controlling for other important covariables may result in inaccurate results
Indeed the study that used multivariate regression analysis was able to
demonstrate that there are number of factors that can affect AMH results and
indeed following controlling for these factors there was no difference between
AMH results of women with endometriosis compared to that of without
disease (Streuli et al 2012) In view of above considerations we believe the
effect of endometriosis to ovarian reserve is poorly understood and warrants
further investigation
153
Regarding the effect of endometrioma on AMH levels current evidence
is conflicting Using univariate analysis without controlling for confounders
Uncu et al reported that women with endometrioma (n=30) had significantly
lower AMH and AFC measurements compared to control (n=30) women
(Uncu et al 2013) Similarly Hwu et al reported that women with
endometrioma (n=141) had significantly lower AMH measurements compared
to that of without pathology (n=1323) pathology (Hwu et al 2013) However
the study population appears to have a disproportionately higher number of
women with history of previous and current history of endometrioma
(3191642) compared to any published studies and therefore the study may
have been subject of selection bias
Kim et al reported lower AMH measurements in women with
endometrioma (n=102) compared to control group (102) meanplusmnSEM
29plusmn03 ngmL_vs 33plusmn03_ngmL although this did not reach statistical
significance (P=028)
In our view the most robust data on measurement of AMH in women
with endometriosis was published by Streuli et al which compared AMH levels
of 313 women with laparoscopically and histologically confirmed
endometriosis to 413 women without pathology (Streuli et al 2009) The group
with endometriosis consisted of women with superficial peritoneal
endometriosis (n=35) deep infiltrating endometriosis (n=183) and ovarian
endometrioma (n=95) and relevant factors such as age parity smoking and
previous ovarian surgery were adjusted for using multivariate regression
analysis In keeping with our findings women with endometriosis did not have
lower AMH levels except for patients with previous history of surgery for
endometrioma Most interestingly the findings of Streuili et al and this study
suggest that women with ovarian endometrioma do not have low AMH levels
In contrast according to our data the presence of endometrioma may be
associated with a significant increase in serum AMH levels Given that an
endometrioma is believed to cause significant damage to ovarian stroma this is
an interesting finding Increased AMH levels in the presence of endometrioma
may be due to acceleration in the rate of recruitment of primordial follicles
andor increased expression of AMH in granulosa cells Furthermore
increased AMH levels in these patients may be due to expressions of AMH in
endometriotic cells A study by Wang et al suggested that AMH is secreted by
human endometrial cells in-vitro (Wang et al 2009) This was the first report of
154
non-ovarian secretion of AMH and suggested that AMH may play important
role in regulation of the function of the human endometrium Subsequently
the findings of Wang et al were independently confirmed by two different
groups Carrarelli et al assessed expression of AMH and AMH type II receptor
(AMHRII) in specimens of endometrium obtained by hysteroscopy from
patients with endometriosis (n=55) and from healthy (n=45) controls
(Carrarelli et al 2014) The study also assessed specimens from patients with
ovarian endometriosis (n=29) and deep peritoneal endometriosis (n=26) The
study found that both AMH and AMHRII were expressed in endometrium
Interestingly ectopic endometrium obtained from patients with endometriosis
had significantly higher AMH and AMHRII levels compared to that of healthy
individuals Furthermore the specimens collected from ovarian and deep
endometriosis had highest AMH and AMHII mRNA expression These
findings confirm that AMH as well as AMHRII are expressed in human
endometrium and AMH may play a role in pathophysiology of endometriosis
A further study conducted by Signorile et al also confirmed expression of
AMH and AMHRII in human endometriosis glands Furthermore the study
found that treatment of endometriosis cells with AMH resulted in a decrease in
cell growth suggesting that AMH may inhibit the growth of endometriotic
cells This suggests that further studies to understand the role of AMH in
pathophysiology of endometriosis are warranted
Causes of infertility
Unlike the above-mentioned factors the association of the various
causes of infertility and the markers of ovarian reserve are poorly studied
Therefore our study appears to provide only available data on AMH AFC and
FSH levels in women with three main causes of infertility unexplained tubal
and male factor
In our study AMH levels of women with unexplained infertility did not
differ from those with a diagnosis Similarly the effect of a diagnosis on AFC
and FSH measurements were weak Women with unexplained infertility do not
have any significant pathology that can account for their infertility However
understanding the role of ovarian reserve in these patients is important Our
study suggests that women with unexplained infertility have comparable AMH
levels to other infertile women
155
We did not find any significant differences in AMH AFC or FSH
measurements of women diagnosed with tubal factor infertility compared to
infertile women without tubal disease Pelvic inflammatory disease and
endometriosis are well known causes of tubal pathology and our regression
model has controlled for the effect of endometriosis in these analyses Our
results suggest that despite having damaging effect to the tubes pelvic
infection does not reduce ovarian reserve
In contrast our analyses showed that women with mild and severe male
factor infertility have significantly increased AMH and lower FSH
measurements which demonstrates that these women have better ovarian
reserve compared to general infertility population
Strengths and Limitations of the study
The study is based on retrospectively collected data and therefore was
subject to the issues related to this methodology However we believe that we
have overcome most problems and improved the validity of our results by
using a robust methodology for data collection large sample size and careful
analysis We included all women who presented during the study period and
met the inclusion criteria of the study Importantly women with previous
history of PCO chemotherapy radiotherapy tubal surgery or ovarian surgery
have been excluded from the study given these factors may have significant
acute impact on ovarian reserve effect of which may be difficult to control for
The analysis showed an interaction between BMI and ethnicity which
could not be explored fully due to missing data on BMI (Tables 6) Therefore
analyses with and without BMI in models have been performed (Tables 2-4)
and the distribution of patients according to availability of data on BMI has
been obtained (Tables 5-7) I suggest further studies with sufficient data should
explore this interaction
I was not able to establish the patients that meet Rotterdam criteria for
diagnosis of PCOS given data on menstrual history and biochemical
assessment of androgenemia were not available Therefore ultrasound
diagnosis of PCO was used to categories patients with polycystic ovaries and
all patients with PCO were excluded from analysis
It is important to note that measurement of AMH using Gen II assay may
provide erroneous results (Rustamov et al 2012a) Therefore only samples
156
obtained using DSL assay have been included in the study The DSL assay
appears to provide more reproducible results than the Gen II assay (Rustamov
et al 2011 and Rustamov et al 2012a) and therefore we believe the estimates
in this study reflect the relationship between circulating AMH and the above
factors
SUMMARY
Our data suggests that there is no strong association between ethnicity
and AMH AFC or FSH whilst women with increased BMI appear to have
higher ovarian reserve There was no evidence of reduced ovarian reserve in
women with endometriosis who do not have a previous history of ovarian
surgery In contrast women with current history of endometrioma may have
higher AMH levels which warrants further investigation Women with a
history of unexplained infertility do not appear to have reduced ovarian
reserve as measured with AMH AFC and FSH compared to general infertile
women Similarly women with tubal factor infertility have comparable ovarian
reserve with women who do not have tubal disease In contrast women with
male factor infertility have significantly higher ovarian reserve compared to
infertile women who do not have male factor infertility
This study has elucidated the effect of demographic anthropometric and
clinical factors on all commonly used markers of ovarian reserve and
demonstrated that some of these factors have significant impact on ovarian
reserve
157
References Buyuk E Seifer DB Illions E Grazi RV and Lieman H Elevated body mass index is associated with lower serum anti-mullerian hormone levels in infertile women with diminished ovarian reserve but not with normal ovarian reserve Fertility and Sterility_ Vol 95 No 7 June 2011 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 2014 1011353ndash8 de Carvalho BR Rosa-e-Silva AC Rosa-e-Silva JC dos Reis RM Ferriani RA de Saacute MFIncreased basal FSH levels as predictors of low-quality follicles in infertile women with endometriosis International Journal of Gynecology and Obstetrics 110 (2010) 208ndash212 Doacutelleman M Verschuren W M M Eijkemans M J C Dolle M E T Jansen E H J M Broekmans F J M and van der Schouw Y T Reproductive and Lifestyle Determinants of Anti-Mullerian Hormone in a Large Population-based Study J Clin Endocrinol Metab May 2013 98(5) 2106ndash2115 Freeman EW Gracia CR Sammel MD Lin H Lim LC Strauss JF 3rd Association of anti-mullerian hormone levels with obesity in late reproductive-age women Fertil Steril 2007 87101-6 Halawaty S ElKattan E Azab H ElGhamry N Al-Inany H Effect of obesity on parameters of ovarian reserve in premenopausal women J Obstet Gynaecol Can 2010 32687ndash690 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699-708 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 2011 95170ndash5 Hwu Y Wu FS Li S Sun F Lin M and Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reproductive Biology and Endocrinology 2011 980 Kelsey TW Wright P Nelson SM Anderson RA Wallace WHB (2011) A Validated Model of Serum Anti-Muumlllerian Hormone from Conception to Menopause PLoS ONE 6(7) e22024 Kim MJ Byung Chul Jee Chang Suk Suh and Kim SH Preoperative Serum Anti-Mullerian Hormone Level in Women with Ovarian Endometrioma and Mature Cystic Teratoma Yonsei Med J Volume 54 Number 4 July 2013 La Marca A Sighinolfi G Papaleo E Cagnacci A Volpe A et al (2013) Prediction of Age at Menopause from Assessment of Ovarian Reserve May Be
158
Improved by Using Body Mass Index and Smoking Status PLoS ONE 8(3) e57005 Lemos NA Arbo E Scalco R Weiler E Rosa V Cunha-Filho JS Decreased anti-Muumlllerian hormone and altered ovarian follicular cohort in infertile patients with mildminimal endometriosis Fertil Steril 2008 May 89(5)1064-8 Luborsky JL Meyer P Sowers MF Gold EB Santoro N Premature menopause in a multi-ethnic population study of the menopause transition Hum Reprod 200218199-206 Maheshwari A Stofberg L Bhattacharya S Effect of overweight and obesity on assisted reproductive technologymdasha systematic review Hum Reprod Update 200713433ndash44 Michelmore K Balen A Dunger D Vessey M Polycystic ovaries and associated clinical and biochemical features in young women Clin Endocrinol (Oxf) 199951779-86 Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 95736-741 e731-7332011 Chen X Yang D Mo Y Li L Chen Y Huang Y Prevalence of polycystic ovary syndrome in unselected women from southern China Eur J Obstet Gynecol Reprod Biol 2008 13959-64 Randolph JF Sowers M Gold EB Mohr BA Luborsky J Santoro M et al Reproductive hormones in early menopausal transition relationship to ethnicity body size and menopausalstatus J Clin Endocrinol Metab Apr2003 88(4)1516ndash1522 [PubMed 12679432] Sahmay S Usta T Erel CT Imamoğlu M Kuuk M Atakul N Seyisoğlu H Is there any correlation between amh and obesity in premenopausal women Arch Gynecol Obstet 2012 Sep 286(3) 661-5 Seifer DB Baker VL and Leader B Age-specific serum anti-Meuroullerian hormone values for 17120 women presenting to fertility centers within the United States Fertility and Sterility_ Vol 95 No 2 February 2011 Seifer DB Golub ET Lambert-Messerlian G Benning L Anastos K Watts H Cohen MH Karim R Young MA Minkoff H and Greenblatt RM Variations in Serum Mullerian Inhibiting Substance Between White Black and Hispanic Women Fertil Steril 2009 November 92(5) 1674ndash1678 Shebl O Ebner T Sir A Schreier-Lechner E Mayer RB Tews GSommergruber M Age-related distribution of basal serum AMH level in women of reproductive age and a presumably healthy cohort Fertil Steril 2011 95 832ndash834
159
Shebl O Ebner T Sommergruber M Sir A Tews G Anti muellerian hormone serum levels in women with endometriosis a case-control study Gynecol Endocrinol 200925713-6 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities Hum Genet (2012b) 1311709ndash1724 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Skałba P Cygal A Madej P Dabkowska-Huc A Sikora J Martirosian G Romanik M Olszanecka-Glinianowicz M Is the plasma anti-Mullerian hormone (AMH) level associated with body weight and metabolic and hormonal disturbances in women with and without polycystic ovary syndrome European Journal of Obstetrics amp Gynecology and Reproductive Biology 158 (2011) 254ndash259 Streuli I de Ziegler D Gayet V Santulli P Bijaoui G de Mouzon J and Chapron C In women with endometriosis anti-Mullerian hormone levels are decreased only in those with previous endometrioma surgery Human Reproduction Vol27 No11 pp 3294ndash3303 2012 Su IH Sammel MD Freeman EW Lin H DeBlasis T Gracia C Body size affects measures of ovarian reserve in late reproductive age women Menopause 2008 15(5) 857ndash861 Uncu G Kasapoglu I Ozerkan K Seyhan A Oral Yilmaztepe A Ata B Prospective assessment of the impact of endometriomas and their removal on ovarian reserve and determinants of the rate of decline in ovarian reserve Hum Reprod 2013 Aug 28(8) 2140-5 Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wallace WHB Kelsey TW (2010) Human Ovarian Reserve from Conception to the Menopause PLoS ONE 5(1) e87
160
Table 1 Distribution of patients
AMH AFC FSH
n Mean (SD) n Mean (SD) n Mean (SD)
All 2880 175150 1810 13972 2377 7972
Ethnicity
White (Reference) 1833 169139 1222 13959 1556 7966
Other White 137 172131 85 14480 107 7637
Black 93 202208 43 16098 73 104135
Indian 108 216169 69 14360 94 7127
Other Asian 46 194157 30 14560 41 6717
Pakistani 276 201164 166 14375 232 81124
Other ethnic 103 158130 63 12448 83 7640
Not disclosed 220 170152 114 13161 157 7937
Data not available 64 183251 18 11952 34 8956
Patients with BMI
Normal (Reference) 1110 172137 917 13861 1011 7844
Underweight 38 179136 30 13947 38 7751
Overweight 679 168134 546 13763 620 7544
Obese 149 220209 90 14167 119 7142
Data not available 904 177163 227 14967 589 88123
Diagnosis
Unexplained 894 156120 667 13354 801 7632
Mild tubal 411 172158 284 13771 370 7530
Severe tubal 40 12685 27 13658 38 7827
Mild male 779 181134 538 14058 668 7342
Severe male 356 198135 197 14661 208 6818
Endometriosis ndash endometrioma 141 137108 91 13658 122 8341
Endometriosis + endometrioma 46 196159 15 14449 42 7123
161
Table 2 Regression models for AMH
AMH (Log)
BMI included
n=1952
BMI excluded
n=2816
Β 95 CI P β 95 CI P
Age -0057 -0069 -0045 00001 -0056 -0067 -0046 00001
age2 -0003 -0005 -0001 00001 -0004 -0006 -0003 00001
Ethnicity 00812 00079
Other White -0046 -0226 0133 0611 0038 -0131 0208 0658
Black 0209 -0038 0457 0097 -0259 -0464 -0054 0013
Indian 0032 -0164 0228 0749 0119 -0071 0310 022
Other Asian 0292 -0014 0598 0061 0250 -0037 0537 0088
Pakistani -0116 -0251 0017 0089 -0100 -0226 0025 0118
Other ethnic -0174 -0390 0041 0113 -0197 -0392 -0002 0047
Not disclosed -0002 -0162 0157 0977 -0104 -0241 0033 0138
BMI 00374
Underweight -0107 -0394 0179 0462
Overweight -0058 -0143 0025 017
Obese 0165 00119 0318 0035
Diagnosis
Unexplained 0039 -0073 0152 0492 0105 0007 0204 0035
Mild tubal 0089 -0033 0212 0153 0113 -000009 0226 005
Severe tubal -0168 -0463 0126 0264 -0133 -0444 0177 0401
Mild male 0118 0009 0227 0033 0180 0084 0275 00001
Severe male 0245 0096 0395 0001 0287 0162 0412 00001
Endometriosis -0136 -0311 0037 0124 -0152 -0324 0018 0081
Endometrioma 0217 -0068 0503 0136 0314 0023 0606 0034
_cons 2731 2616 2847 0 2658 2567 2750 0
162
Table 3 Regression models for AFC
AFC (Log)
BMI Included
n=1589
BMI Excluded
n=1810
Β 95 CI P Β 95 CI P
Age -0028 -0035 -0021 0 -0027 -0033 -0021 0
age2 000009 -00009 0001 086 000007 -00008 0001 0885
Ethnicity 00265 00383
Other White -0024 -0119 0070 0614 0003 -0087 0094 0942
Black 0093 -0037 0224 0162 0049 -0075 0175 0436
Indian -0042 -0148 0064 0438 -0035 -0136 0065 0492
Other Asian 0037 -0125 0200 0651 0037 -0114 0189 0626
Pakistani -0095 -0166 -0024 0008 -0083 -0151 -0015 0016
Other ethnic -0142 -0253 -0031 0012 -0132 -0237 -0027 0013
Not disclosed -0008 -0094 0078 0853 -0067 -0148 0012 0098
BMI 07713
Underweight -0040 -0190 0109 0599
Overweight -0018 -0062 0024 0398
Obese 0012 -0077 0103 0779
Diagnosis
Unexplained -0071 -0131 -0011 0019 -0065 -0121 -0009 0021
Mild tubal -0047 -0112 0017 0151 -0060 -0121 00003 0051
Severe tubal -0110 -0267 0045 0164 -0141 -0294 0010 0069
Mild male -0037 -0095 0020 0201 -0027 -0081 0025 0307
Severe male 0007 -0071 0086 0853 -0021 -0093 0050 0563
Endometriosis -0019 -0114 0076 0691 -0004 -0096 0087 0922
Endometrioma -0079 -0215 0055 0248 -0106 -0231 0019 0097
_cons 2694 2632 2755 0 2691 2636 2745 0
163
Table 4 Regression models for FSH
FSH (Log)
BMI Included
n=1772
BMI Excluded n=2343
Β 95 CI P Β 95 CI P
age 0009 0003 0014 0001 0009 0004 0014 00001
age2 00009 00001 0001 0019 0001 00003 0001 0003
Ethnicity 04415 03329
Other White 0034 -0046 0114 0403 -0017 -0099 0065 0685
Black 0043 -0065 0153 043 0068 -0030 0167 0175
Indian -0010 -0097 0076 0808 -0070 -0157 0017 0116
Other Asian -0119 -0250 0011 0074 -0104 -0234 0026 0117
Pakistani -0031 -0089 0026 029 -0014 -0073 0045 064
Other ethnic 0031 -0062 0125 0508 -0002 -0095 0090 0962
Not disclosed 0022 -0049 0093 0541 0026 -0042 0095 045
BMI 00017
Underweight -0070 -0189 0048 0246
Overweight -0055 -0091 -0018 0003
Obese -0106 -0176 -0036 0003
Diagnosis
Unexplained -0055 -0104 -0006 0028 -0055 -0101 -0009 0018
Mild tubal -0052 -0105 000008 005 -0050 -0103 0001 0056
Severe tubal 0004 -0118 0127 0943 0016 -0120 0154 0809
Mild male -0084 -0132 -0037 00001 -0071 -0116 -0026 0002
Severe male -0127 -0196 -0059 00001 -0102 -0168 -0036 0002
Endometriosis 0035 -0039 0111 0353 0044 -0034 0124 0268
Endometrioma -0074 -0196 0047 0229 -0056 -0186 0074 0402
_cons 1999 1948 2049 0 1958 1915 2002 0
164
Table 5 Distribution of patient characteristics by availability of data on BMI The number of observations and mean (SD) of the markers of ovarian reserve (Age AMH AFC and FSH) described according to an availability of data on BMI
BMI (+)
BMI (-) Total
n Mean (SD) n Mean (SD) n Mean (SD)
Age 1976 32944 904 32750 2880 32946
AMH 1976 175144 904 178164 2880 176150
AFC 1583 13862 227 14968 1810 14063
FSH 1788 7744 589 88123 2377 8073
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
165
Table 6 Distribution of ethnicity by availability of data on BMI Distribution of the number of observations by ethnicity and availability of data on BMI
AMH AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
BMI (+) BMI (-) Total
White 1308 525 1833 1070 152 1222 1201 355 1556
Other White 97 40 137 76 9 85 83 24 107
Black 50 43 93 39 4 43 44 29 73
Indian 81 27 108 60 9 69 70 24 94
Other Asian 32 14 46 25 5 30 30 11 41
Pakistani 193 83 276 148 18 166 177 55 232
Other ethnic 66 37 103 55 8 63 60 23 83
Not disclosed 125 95 220 95 19 114 107 50 157
Data not available 24 40 64 15 3 18 16 18 34
Total 1976 904 2880 1583 227 1810 1788 589 2377
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
166
Table 7 Distribution of diagnosis by availability of data on BMI Distribution of number of observations in each diagnosis group tabulated by availability of data on BMI
AMH
AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
Unexplained 730 164 894 611 56 667 672 129 801
Mild tubal 319 92 411 258 26 284 298 72 370
Severe tubal 36 4 40 26 1 27 36 2 38
Mild male 567 212 779 461 77 538 525 143 668
Severe male 196 160 356 161 36 197 153 55 208
Endometriosis ndash endometrioma 112 29 141 83 8 91 101 21 122
Endometriosis + endometrioma 38 8 46 38 8 46 36 6 42
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
167
THE EFFECT OF SALPINGECTOMY
OVARIAN CYSTECTOMY AND UNILATERAL
SALPINGOOPHERECTOMY ON OVARIAN
RESERVE
Oybek Rustamov Monica Krishnan
Stephen A Roberts Cheryl Fitzgerald
To be submitted to Gynecological Surgery
52
168
Title
Effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Stephen A Robertsc Cheryl Fitzgeralda
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable Word count 2904
Acknowledgement
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
169
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all
versions of the manuscript MK assisted in data extraction contributed in
discussion and the review of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed in discussion
and reviewed all versions of the manuscript
170
ABSTRACT
Objective
To estimate the effect of salpingectomy ovarian cystectomy and unilateral
salpingo-oopherectomy on ovarian reserve
Design
Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 3179 patients were included in the study The AMH measurements
of 66 women were excluded due to haemolysed samples or delay in processing
the samples leaving 3113 women for analysis There were 138 women who
had unilateral or bilateral salpingectomy 36 women with history of unilateral
salpingo-oopherectomy 41 women with history of cystectomy for ovarian
cysts that other than endometrioma and 40 women had cystectomy for
endometrioma
Interventions
Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH basal serum FSH and basal AFC measurements
Results
The analysis did not find any significant differences in AMH (9 p=033)
AFC (-2 p=059) and FSH (-14 p=021) measurements between women
with a history of salpingectomy and those without history of surgery Women
with history of unilateral salpingo-oopherectomy were found to have
significantly lower AMH (-54 p=0001) and AFC (-28 p=034) and
increased FSH (14 p=006) The study did not find any significant
171
association between a previous history of ovarian cystectomy that was for
conditions other than endometrioma and AMH (7 p=062) AFC (13
p=018) or FSH (11 p=016) The analysis of the effect of ovarian
cystectomy for endometrioma showed that women with history of surgery had
around 66 lower AMH (p=0002) Surgery for endometrioma did not
significantly affect AFC (14 p=022) or FSH (10 p=028)
Conclusions
Salpingo-oopherectomy and ovarian cystectomy for endometrioma have a
significant detrimental impact on ovarian reserve Neither salpingectomy nor
ovarian cystectomy for cysts other than endometrioma has an appreciable
effect on ovarian reserve
Key Words
Salpingectomy Ovarian cystectomy Salpingo-oopherectomy ovarian reserve
AMH AFC FSH
172
INTRODUCTION
Human ovarian reserve is determined by the size of oocyte pool at birth
and decline in the oocyte numbers thereafter Both of these processes are
largely under the influence of genetic factors and to date no effective
interventions are available to improve physiological ovarian reserve (Shuh-
Huerta et al 2012) However various other environmental pathological and
iatrogenic factors appear to play a role in the determination of ovarian reserve
and consequently it may be influenced either directly or indirectly Evidently
the use of chemotherapeutic agents certain radio-therapeutic modalities and
surgical interventions that damage ovarian parenchyma can cause substantial
damage to ovarian reserve (Nielsen et al 2013 Somigliana et al 2012)
Estimation of the effect of each of these interventions is of paramount
importance in ascertainment of lesser ootoxic treatment modalities and safer
surgical methods
Age is the main determinant of the number of non-growing follicles
accounting for 84 of its variation and used as marker of ovarian reserve
(Hansen et al 2008) However biomarkers that allow direct assessment of the
dynamics of growing follicles AMH and AFC may provide more accurate
estimation of ovarian reserve Although these markers only reflect
folliculogenesis of already recruited growing follicles there appears to be a
good correlation between their measurements and histologically determined
total ovarian reserve (Hansen et al 2011) Thus the biomarkers can effectively
be utilized for estimation of the effect of above adverse factors on the
primordial oocyte pool
Surgical interventions that lead to disruption of the blood supply to
ovaries or involve direct damage to ovarian tissue may be expected to lead to a
reduction in the primordial follicle pool Indeed a number of studies have
reported an association between surgical interventions to ovaries and reduction
in ovarian reserve (Somigliana et al 2012) However given both underlying
disease and surgery may affect ovarian reserve disentanglement of the
individual effects of these factors may be challenging and requires robust
research methodology Here we present a study that intended to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease
173
METHODS
The effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve were studied using serum AMH AFC and
FSH measurements in a large cross sectional study
Population
All women between the ages of 20 to 45 who were referred to the
Womenrsquos Outpatient Department (WOP) and the Reproductive Medicine
Department (RMD) of Central Manchester University Hospitals NHS
Foundation Trust for management of infertility between 1 September 2008
and 16 November 2010 and had an AMH measurement using the DSL assay
(DSL Active MISAMH ELISA Diagnostic Systems Laboratories Webster
Texas) were included We excluded patients referred for fertility preservation
(eg prior to or after treatment for a malignant disorder) and those with a
diagnosis of polycystic ovaries (PCO) on transvaginal ultrasound scan which
was defined as volume of one or both ovaries more than 10ml Patients with
haemolysed AMH andor FSH samples were not included in the analysis of
these markers Non-smoking is an essential criteria for investigation prior to
assisted conception and therefore to our best knowledge our population
consisted of non-smokers
Measurement of AMH
Blood samples for AMH were taken without regard to the day of
womenrsquos menstrual cycle and serum samples were separated within two hours
of venipuncture in the Biochemistry laboratory of our hospital All samples
were processed strictly according to the manufacturerrsquos recommendations and
frozen at -20C until analysed in batches using the enzymatically amplified two-
site immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
100pmolL and a minimum detection limit was 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first AMH of
the patients were selected
174
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the Biochemistry
Laboratory within two hours of venipuncture for sample processing and
analysis Specific immunoassay kits (Cobas Roche Diagnostics Mannheim
Germany) and an autoanalyser platform was used (Roche Modular Analytics
E170 Roche USA) for analysis of FSH The intra-assay CV was 60 and
inter-assay CV was 68 The FSH measurements in the samples with high E2
levels (gt250pmolL) were excluded from the analysis given these samples are
likely to have been taken outside of early follicular phase of menstrual cycle
In patients with repeated FSH measurements measurements conducted on the
same day as first AMH were selected If the patient did not have FSH
measurement on the day of AMH sampling the measurement with the closest
date to first AMH sample was selected
Measurement of AFC
Measurement of AFC is conducted in patients referred for assisted
conception during their initial work up Our department uses a stringent
protocol for the assessment of AFC and qualified radiographers who have
undergone specific training on measurement of AFC The methodology
consists of counting of all antral follicles measuring 2-6mm in longitudinal and
transverse cross sections of both ovaries using transvaginal ultrasound
scanning at early follicular phase (Day 0-5) of the menstrual cycle The AFC
measurement with the closest date to first AMH sample was selected
Data collection
Data was extracted from electronic clinical data management systems
and from information held in written hospital notes for each patient Data on
AMH and FSH measurements were obtained from the Biochemistry
Department and validated by checking the results documented in the hospital
case notes of randomly selected 50 patients against the results obtained from
electronic clinical data management system (Clinical Workstation) finding
100 concordance Information on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
175
surgery were obtained from the hospital case notes The ethnicity of the
patients was established using a patient questionnaire and data were extracted
from the hospital database for the patient demographics (PAS)
Definitions and groups
First the datasets were merged using a unique patient identifier (hospital
number) Validation of the merger using additional patient identifiers (NHS
number name date of birth) revealed existence of duplicate hospital numbers
in patients transferred from secondary care infertility services of our hospital to
IVF Department We established that in our datasets combination of the
patientrsquos first name surname and date of birth in a continuous string variable
could be used as a unique identifier Hence we used this identifier to merge all
datasets achieving a robust merger of all independent datasets into a combined
final dataset Following creation of an anonymised a unique study number for
each patient all patient identifiers were dropped and the anonymised
combined dataset was used for the analysis
Body mass index (BMI) of patients was categorized using standard NHS
cut-off reference ranges Underweight (lt185) Normal (185-249)
Overweight (25-299) and Obese (30-40) (The Office for National Statistics
2011) Causes of infertility were established by searching the hospital notes
including the referral letters clinical notes and letters generated following clinic
consultations Patients with history of bilateral tubal block which was
confirmed by laparoscopic dye test and patients with history of bilateral
salpingectomy were categorized as having severe tubal factor infertility
Patients with unilateral tubal patency or unilateral salpingectomy were
categorized as having mild tubal factor infertility Severe male factor infertility
was defined as azoospermia or severe oligospermia (lt1mln sperm sample)
Patients with abnormal sperm count but do not meet above criteria were
classified as having mild male factor infertility
Patients with reproductive surgery were categorized as having history of
salpingectomy cystectomy for endometrioma cystectomy for ovarian cysts
other than endometrioma or unilateral salpingo-oopherectomy First
measurement of AMH AFC and FSH following surgery was selected for the
study
176
Statistical analysis
A multivariable regression model that included age ethnicity BMI
endometriosis presence of endometrioma the causes of infertility tubal and
ovarian surgery was fitted for each of the ovarian reserve markers AMH AFC
and FSH Difference between the groups were considered significant at
p005 Preliminary analysis of AMH AFC and FSH indicated that
logarithmically transformed values with a quadratic age term provided adequate
fits The precise age on the day measurement of each of the marker of ovarian
reserve (AMH AFC and FSH) was included in the model as a quadratic
function following centering to 30 years of age
Interactions between all explanatory variables were tested at a
significance level of 001 We observed significant interaction between BMI
and other covariates This may be due to biological complexity in the
relationship of BMI and other factors (eg ethnicity) in determination of
ovarian reserve However given data on BMI was not available in considerable
number of patients the observed interactions may be due to limitation of our
dataset Therefore in order to assist in interpretation of the results analyses
with and without BMI in the models were conducted
RESULTS
In total 3179 patients were included in the study The AMH
measurements of 66 women were excluded due to haemolysed samples or
delay in processing the samples leaving 3113 women for analysis 1934 of
patients had measurement of AFC and 2580 had FSH samples that met
inclusion criteria The mean age AMH AFC and FSH of patients were
328plusmn45 173plusmn148 139plusmn62 80plusmn75 respectively There were 138 women
who had unilateral or bilateral salpingectomy 36 women with history of
unilateral salpingo-oopherectomy 41 women with history of cystectomy for
ovarian cysts that other than endometrioma and 40 women had cystectomy for
endometrioma (Table 1) The results of regression analysis on the effect of
reproductive surgery on AMH AFC and FSH measurements are shown in
Table 2
The analysis did not find any significant differences in AMH (9
p=033) AFC (-2 p=059) and FSH (-14 p=021) measurements in
women with history of salpingectomy compared to women without history of
177
surgery and we did not observe marked change in the estimates in a smaller
subset where BMI was included in the model (Table 2)
Women with history of unilateral salpingo-oopherectomy were found
to have significantly lower AMH (-54 p=0001) and AFC (-28 p=034)
and increased FSH (14 p=006) measurements where effect on AMH
reached the level of statistical significance Similarly the analysis of the model
that included BMI showed significantly lower AMH and AFC and higher FSH
measurements in surgery group where both AMH and FSH analysis were
statistically significant (Table 2)
The study did not find a significant association between previous
history of ovarian cystectomy that was for disease other than endometrioma
and measurement of AMH (7 p=062) AFC (13 p=018) or FSH (11
p=016) which did not change noticeably following adding BMI in the model
(Table 2)
The analysis of the effect of ovarian cystectomy for endometrioma
showed that women with history of surgery had around 66 lower AMH
(p=0002) measurements The effect of surgery for endometrioma was not
significant in assessment of AFC (14 p=022) and FSH (10 p=028)
However in the model with BMI association of the surgery with both AMH (-
64 p=0005) and FSH (24 p=0015) were found to be significant (Table
2)
DISUCUSSION
Salpingectomy
The blood supply to human ovaries is maintained by the direct branches
of aorta ovarian arteries which form anastomoses with ovarian and tubal
branch of uterine arteries in mesovarium and mesosalpynx In salpingectomy
often tubal branches of uterine arteries are excised alongside mesosalpynx and
hence it is believed disruption to blood supply to ovaries may lead to a
reduction of ovarian reserve However in our study we did not observe an
appreciable association between salpingectomy and any of the biomarkers of
ovarian reserve suggesting this surgery does not appreciably affect ovarian
reserve These findings are supported by study that assessed the effect of tubal
178
dissection to AMH AFC FSH levels (n=49) using longitudinal data (Erkan et
al 2012) There were no differences between preoperative and 3 month
postoperative measurements with median AMH (15 vs 14 p=007) AFC
(8437 vs 7941 p=009) FSH (76 21 vs 7721 p=010) da Silva et al
assessed the effect of tubal ligation (n=52) in longer term postoperative period
(1 year) and reported that median AMH (143 IQR 063-262 vs and 130 IQR
053-285 p=023) and mean AFC ( 8 IQR 5-14 vs 11 IQR 7-15 p=012)
measurements did not change significantly Our results and on other published
evidence suggest that salpingectomy or tubal division does not have an
adverse effect to ovarian reserve
Unilateral salpingo-oopherectomy
Although salpingo-oopherectomy is rare in women of reproductive age
significant ovarian pathologies and acute diseases such as ovarian torsion may
necessitate unilateral salpingo-oopherectomy There is a plausible causative
relationship between this surgery and ovarian reserve although to our
knowledge there is no previous published evidence We found that women
with a history of unilateral salpingo-oopherectomy have significantly lower
AMH (-54) and higher FSH (13) measurements suggesting the surgery has
considerable negative impact to ovarian reserve Important clinical question in
this clinical scenario is ldquoDo these patients have comparable reproductive
lifespan or experience accelerated loss of oocytes resulting premature loss of
fertilityrdquo as this would allow appropriate pre-operative counseling of patients
regarding long term effect of the surgery to fertility and age at menopause
Considering our data had relatively small number of patients with a history of
salpingo-oopherectomy we were not able to obtain reliable estimates on age-
related decline of ovarian reserve in this study We suggest that studies with
larger number of patients preferably using longitudinal data should address
this research question
Ovarian cystectomy
In women with a history of ovarian cystectomy for ovarian cysts other
than those due to endometrioma we did not observe any significant
association between the surgery and markers of ovarian reserve However
women that had ovarian cystectomy for endometrioma appear to have
179
significantly lower AMH (-66) measurements compared to those without
history of surgery
During the last few years a number of studies have assessed the effect of
cystectomy on AMH levels in patients with endometrioma (Chang et al 2010
Erkan et al 2010 Lee et al 2011) The studies have been summarised by a
recent systematic review which concluded that cystectomy results in damage
to ovarian reserve (Somigliana et al 2012) Further studies evaluated the
mechanism of damage and these suggest that coagulation for purpose of
hemostasis as well as stripping of the cyst wall may cause direct damage to
ovarian reserve Sonmezer et al compared the effect of diathermy coagulation
(n=15) for hemostasis compared to use of hemostatic matrix (n=13) in a
randomized controlled trial and reported that use of diathermy coagulation is
associated with significantly lower AMH measurements (164 plusmn 093 vs 272 plusmn
149 ngmL) in the first postoperative month
Similarly stripping of the cyst wall also appears to have detrimental
effect of ovarian reserve due to inadvertent removal of ovarian tissue (Donnez
et al 1996) Using histological data Roman et al demonstrated that normal
ovarian tissue was removed in 97 specimens of surgically removed
endometriomata (Roman et al 2010) Furthermore it appears that ovarian
cortex containing endometrioma appears to have significantly reduced density
compared to normal ovarian cortex and therefore loss of oocyte containing
normal ovarian cortex may be unavoidable in cystectomy for endometrioma
(Sanchez et al 2014) Matsuzaki et al conducted histological assessment of
cystectomy specimens and found that normal ovarian tissue adjacent to cyst
wall was found in 58 (71121) of patients with endometrioma whereas
normal ovarian tissue was excised in 54 (356) following cystectomy for
other benign cyst (Matsuzaki et al 2008) Similarly in our study women with a
history of cystectomy for endometrioma had significantly lower AMH
measurements whilst those had cystectomy for other benign cysts do not
appear to have lower AMH measurements In view of our findings and other
published research evidence it seems clear that cystectomy for endometrioma
results in significant reduction in ovarian reserve and women undergoing
surgery should be counseled regarding the adverse effect of surgery
180
Strengths and Limitations
The published studies have used longitudinal data comparing biomarkers
before and after cystectomy and provide reliable estimates on the effect of the
intervention on ovarian reserve However data on the effect of salpingectomy
and unilateral salpingoophorectomy is lacking In addition to reevaluation of
the effect of cystectomy this is study has assessed the impact of salpingectomy
and unilateral salpingoophorectomy on the markers of ovarian reserve In
contrast to published studies this study employed analysis of cross sectional
data Given a robust adjustment for all relevant factors has been conducted
our analysis of the cross sectional data should provide reliable estimates of the
effects of various intervention on the markers of ovarian reserve Furthermore
the effect of surgery on all the main biomarkers of ovarian reserve has been
assessed which improves our understanding of the clinical value of each test in
the assessment of patients with history of tubal or ovarian surgery In addition
the analyses adjusted for other relevant factors that may affect ovarian reserve
In patients with history of cystectomy for endometrioma we estimated
independent effects of pathology and surgery providing important data for
preoperative counseling It is important to note that the study evaluated The
effect of surgery using retrospective data which has limitations due variation in
recording of surgical history and missing data In addition given BMI results
for around one third of patients were not available we were not able to fully
explore the effect of BMI However data on the analyses with and without
BMI in the model have been provided to evaluate the effect of this factor The
study employed the data obtained using first generation DSL AMH assay
which is no longer in use However the paper describes the effects of the
interventions in percentage terms and therefore the results are interpretable in
any AMH assay measurement
Important to note although the effects are significant in population level
there is considerable variation between individuals which is evident from the
fact there is overlap between median and interquartile ranges of the groups
(Figure 1) This indicates that clinicians should exercise caution in predicting
the effect of surgery to ovarian reserve of individual patients Nevertheless
given I used a robust methodology for data extraction and conducted careful
analysis I think that the study provides fairly reliable estimates on the effect of
surgery to ovarian reserve
181
CONCLUSION
This multivariable regression analysis of retrospectively collected cross-
sectional data suggests that neither salpingectomy nor ovarian cystectomy for
cysts other than endometrioma has an appreciable effect on ovarian reserve
determined by AMH AFC and FSH In contrast salpingoophorectomy and
ovarian cystectomy for endometrioma have a significant detrimental impact to
ovarian reserve On the basis of findings of this study and other published
studies women undergoing reproductive should be counseled with regards to
the effect of the surgery on their ovarian reserve
182
References
Biacchiardi CP Piane LD Camanni M Deltetto F Delpiano EM Marchino GL et al Laparoscopic stripping of endometriomas negatively affects ovarian follicular reserve even if performed by experienced surgeons Reprod Biomed Online 201123740ndash6 Chang HJ Han SH Lee JR Jee BC Lee BI Suh CS et al Impact of laparoscopic cystectomy on ovarian reserve serial changes of serum anti-Mullerian hormone levels Fertil Steril 201094343ndash9 Dogan E Ulukus EC Okyay E Ertugrul C Saygili U Koyuncuoglu M Retrospective analysis of follicle loss after laparoscopic excision of endometrioma compared with benign nonendometriotic ovarian cysts Int J Gynaecol Obstet 2011114124ndash7 Ercan CM Sakinci M Duru NK Alanbay I Karasahin KE Baser I (2010) Antimullerian hormone levels after laparoscopic endometrioma stripping surgery Gynecol Endocrinol 201026468ndash72 Ercan CM Duru NK Karasahin KE Coksuer H Dede M Baser I (2011) Ultrasonographic evaluation and anti-mullerian hormone levels after laparoscopic stripping of unilateral endometriomas Eur J Obstet Gynecol Reprod Biol 2011158280ndash4 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hachisuga T Kawarabayashi T Histopathological analysis of laparoscopically treated ovarian endometriotic cysts with special reference to loss of follicles Hum Reprod 200217432ndash5 Hirokawa W Iwase A Goto M Takikawa S Nagatomo Y Nakahara T et al The post-operative decline in serum anti-Mullerian hormone correlates with the bilaterality and severity of endometriosis Hum Reprod 201126904ndash10 Hwu YM Wu FS Li SH Sun FJ Lin MH Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reprod Biol Endocrinol 2011980 Iwase A Hirokawa W Goto M Takikawa S Nagatomo Y Nakahara T et al Serum anti-Mullerian hormone level is a useful marker for evaluating the impact of laparoscopic cystectomy on ovarian reserve Fertil Steril 201094 2846ndash9 Kitajima M Khan KN Hiraki K Inoue T Fujishita A Masuzaki H Changes in serum anti-Mullerian hormone levels may predict damage to residual normal ovarian tissue after laparoscopic surgery for women with ovarian endometrioma Fertil Steril 2011952589ndash91e1 Kitajima M Defr_ere S Dolmans MM Colette S Squifflet J van
183
Langendonckt A et al Endometriomas as a possible cause of reduced ovarian reserve in women with endometriosis Fertil Steril 201196685ndash91 Lee DY Young Kim N Jae Kim M Yoon BK Choi D Effects of laparoscopic surgery on serum anti-Meuroullerian hormone levels in reproductive-aged women with endometrioma Gynecol Endocrinol 201127733ndash6 Matsouzaki S Houlle C Darcha S Pouly JL Mage G Canis M Analysis of risk factors for the removal of normal ovarian tissue during laparoscopic cystectomy for ovarian endometriosis Hum Reprod 2009 241402ndash1406 Muzii L Bianchi A Croc_e C Manci N Panici PB Laparoscopic excision of ovarian cysts is the stripping technique a tissue-sparing procedure Fertil Steril 200277609ndash14 Office for National Statistics (ONS) Social Trends 41 Health 2011 Roman H Tarta O Pura I Opris I Bourdel N Marpeau L et al Direct proportional relationship between endometrioma size and ovarian parenchyma inadvertently removed during cystectomy and its implication on the management of enlarged endometriomas Hum Reprod 201025 1428ndash32 Romualdi D Franco Zannoni G Lanzone A Selvaggi L Tagliaferri V Gaetano Vellone V et al Follicular loss in endoscopic surgery for ovarian endometriosis quantitative and qualitative observations Fertil Steril 201196374ndash8
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642 Sanchez A P Viganograve P Somigliana E Panina-Bordignon P Vercellini and Candiani M The distinguishing cellular and molecular features of the endometriotic ovarian cyst from pathophysiology to the potential endometrioma-mediated damage to the ovary Hum Reprod Update (MarchApril 2014)
Shi J Leng J Cui Q Lang J Follicle loss after laparoscopic treatment of ovarian endometriotic cysts Int J Gynaecol Obstet 2011115277ndash81 Tsolakidis D Pados G Vavilis D Athanatos D Tsalikis T Giannakou A et al The impact on ovarian reserve after laparoscopic ovarian cystectomy versus three-stage management in patients with endometriomas a prospective randomized study Fertil Steril 20109471ndash7 Vicino M Scioscia M Resta L Marzullo A Ceci O Selvaggi LE Fibrotic tissue in the endometrioma capsule surgical and physiopathologic considerations from histologic findings Fertil Steril 200991(4 Suppl)1326ndash8
184
Figure 1 Box plots of AMH by various groups Upper panel shows the raw data and the lower panel the AMH measurement (in pmolL) adjusted for age ethnicity BMI causes of infertility endometriosis endometrioma and surgery Groups (left to right) 1) Endometrioma without history of cystectomy (endoma-no surg) 2) Cystectomy for endometrioma (endoma+surg) 3) Endometriosis without endometrioma (endsisonly) 4) Without endometriosis or any surgery (No end+no surg) 5) Oopherectomy (oe) 6) Cystectomy for cyst other than those for endometrioma (other cyst) 7) Salpingectomy (se)
185
Table1 Distribution of patients
BMI excluded
BMI Included
Age AMH AFC FSH AMH AFC
FSH
Mean (SD) N Mean n Mean (SD) N Mean (SD) n n N
Non-surgery 328plusmn45 2880 175plusmn150 18100 139plusmn63 23770 79plusmn72 1976 15830 17880
Oophorectomy 324plusmn50 36 106plusmn84 2 115plusmn77 34 118plusmn230 25 2 23
Salpingectomy 331plusmn42 138 154plusmn119 91 13plusmn43 122 82plusmn 123 121 84 27
Cystectomy Other 336plusmn42 41 168plusmn132 18 148plusmn50 29 122plusmn249 27 15 20
Cystectomy Endometrioma
327plusmn51 40 119plusmn140 17 137plusmn41 37 89plusmn56 23 10 22
186
Table 2 Multivariable regression analysis Adjusted for age ethnicity causes of infertility endometriosis (without endometrioma) endometrioma and reproductive surgery
BMI(+)
BMI(-)
N
Coeff
95 CI
P
N
Coeff
95 CI
P
Oophorectomy
AMH 2128 -0779 -1135 -0422 00005 3049 -0540 -0868 -0213 0001
AFC 1697 -0278 -0848 0292 0340 1946 -0280 -0857 0298 0342
FSH 1929 0266 0110 0422 0001 2546 0139 -0006 0284 0060
Salpingectomy
AMH 2128 0067 -0118 0252 0476 2128 0094 -0097 0285 0333
AFC 1697 -0027 -0128 0075 0605 1697 -0027 -0126 0072 0595
FSH 1929 -0085 -0167 -0004 0041 1929 -0056 -0143 0032 0210
Cystectomy Other
AMH 2128 0102 -0230 0433 0548 2128 0075 -0226 0376 0626
AFC 1697 0102 -0107 0311 0339 1697 0130 -0064 0323 0189
FSH 1929 0134 -0028 0297 0106 1929 0110 -0044 0265 0161
Cystectomy Endometrioma
AMH 2128 -0647 -1100 -0194 0005 2128 -0667 -1081 -0252 0002
AFC 1697 0115 -0172 0402 0433 1697 0144 -0089 0376 0225
FSH 1929 0243 0047 0439 0015 1929 0103 -0084 0290 0281
187
ASSESSMENT OF DETERMINANTS OF OOCYTE
NUMBER USING RETROSPECTIVE DATA ON
IVF CYCLES AND EXPLORATIVE STUDY OF
THE POTENTIAL FOR OPTIMIZATION OF AMH-
TAILORED STRATIFICATION OF CONTROLLED
OVARIAN HYPERSTIMULATION
Oybek Rustamov
Cheryl Fitzgerald Stephen A Roberts
6
188
Title
Assessment of determinants of oocyte number using large retrospective
data on IVF cycles and explorative study of the potential for
optimization of AMH-tailored stratification of controlled ovarian
stimulation
Authors
Oybek Rustamova Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Centre for Biostatistics Institute of Population Health Manchester
Academic Health Science Centre (MAHSC) University of Manchester
Manchester M13 9PL UK
Word count 7520
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgement
Authors would like to thank Dr Monica Krishnan (Foundation Trainee
Manchester Royal Infirmary) for her assistance in data extraction We would
also like to thank colleagues Dr Greg Horne (Senior Clinical Embryologist)
Ann Hinchliffe (Clinical Biochemistry Department) and Helen Shackleton
(Information Operations Manager) for their help in obtaining datasets for the
study
189
Declaration of authorsrsquo roles
OR prepared the study protocol prepared the dataset conducted statistical
analysis and prepared all versions of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed to the
discussion and reviewed all versions of the manuscript
190
ABSTRACT
Objectives
1) To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield
2) To explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Design
Retrospective cross sectional study using multivariable regression analysis
First the effect of a set of plausible factors that may affect the outcomes have
been established including assessment of the effect of age AMH AFC causes
of infertility attempt of IVFICSI cycle COH protocol changes
gonadotrophin preparations operator for oocyte recovery pituitary
desensitisation regime and initial daily dose of gonadotrophins Then the
regression models that examined the effect of gonadotrophin dose and regime
categories on total and mature oocyte numbers have been developed
Setting
Tertiary referral centre for management of infertility St Maryrsquos Hospital
Central Manchester University Hospitals NHS Foundation Trust
Participants
Women without ultrasound features of polycystic ovaries who underwent
IVFICSI cycle using pituitary desensitisation with GnRH long agonist or
GnRH antagonist regimes and had previous measurement of AMH with the
DSL assay In total of 1847 IVF or ICSI cycles of 1428 patients met the
inclusion criteria for the study AMH measurements of all cycles and AFC
measurements for 1671 cycles (n=1289 patients) were available In the analysis
of total oocytes 1653 cycles were included and the analysis of metaphase II
oocytes comprised of 1101 ICSI cycles
Interventions
None (observational study)
191
Main outcome measures
Total oocyte number Metaphase II oocyte number
Results
After adjustment for all the above factors age remained a negative predictor of
oocyte yield whereas we observed a gradual and significant increase in oocyte
number with increasing AMH and AFC values suggesting all these markers
display an independent association with oocyte yield
Compared to 1st IVF cycles those with 2nd (8 p=001) and particularly 3rd
attempt (24 p=0001) had considerably higher total oocytes The effect of
attempt on mature oocyte yield was not significant (p=045) Similarly there
was significant between-operator variability in total oocyte number when
oocyte recovery practitioners were compared (p=00005) However the effect
of oocyte recovery practitioner on mature oocyte yield did not reach statistical
significance (p=0058) Comparison of the effect of gonadotrophin type
showed that rFSH was associated with higher total oocyte yield compared to
that of HMG (p=0008) although the numbers of mature oocytes were not
significantly different between the groups (p=026)
After adjustment for all above factors compared to a reference group (Agonist
with 75-150 IU hMGrFSH) none of the regime and dose categories provided
higher total oocyte yield and Antagonist with 75-150 IU hMGrFSH (-36
p=00005) provided significantly less total oocyte With regards to the mature
oocyte yield Antagonist with 187-250 IU rFSHhMG (43 p=005) and
Antagonist 375 IU rFSHhMG (47 p=002) were associated with
significantly higher oocyte number compared to that of above reference group
This implies that compared to long Agonist down regulation Antagonist
regime is associated with higher mature oocyte yield
Following adjustment for all above variables we did not observe significant
increase in oocyte number with increasing gonadotrophin dose categories
192
Conclusions
Given there was no expected increase in oocyte number with increasing
gonadotrophin dose categories we believe there may not be significant direct
dose-response effect Consequently strict protocols for tailoring the initial
dose of gonadotrophins may not necessarily improve ovarian performance in
IVF treatment It is important to note our COS protocols instructed the use
of cycle monitoring with ultrasound follicle tracking and oestradiol levels and
corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
Key Words
Ovarian reserve AMH AFC IVF Controlled ovarian stimulation AMH-
tailored ovarian stimulation Individualisation of ovarian stimulation
193
INTRODUCTION
According to the HFEA around 12 of IVF cycles in the UK are
cancelled due to poor or excessive ovarian response in the UK which
highlights the importance of the provision of optimal ovarian stimulation in
improving the outcomes (Kurinczuk et al 2010) Traditionally patientrsquos age and
basal FSH measurements were used for the assessment of ovarian reserve with
subsequent tailoring of the initial dose of gonadotrophins and regime for
pituitary desensitisation for controlled ovarian stimulation in IVF Studies on
the prognostic value of markers of ovarian reserve show that AMH and AFC
are the best predictors of ovarian response in cycles of IVF (Broer et al 2011)
Furthermore unlike most other markers AMH has potential discriminatory
power due to significantly higher between-patient (CV 94) variability
compared to its within-patient (CV 28) variation (Rustamov et al 2011)
which allows stratification of patients into various degrees of (eg low normal
high) ovarian reserve Consequently development of optimal ovarian
stimulation protocol for each band of ovarian reserve using AMH may be
feasible
Controlled ovarian stimulation (COS) based on tailoring the pituitary
desensitisation and initial dose of gonadotrophins to AMH measurements is
known under various names individualisation of ovarian stimulation AMH-
tailored stratification of COS personalization of IVF are the most commonly
used This strategy is believed to be effective and has been widely
recommended (Nelson et al 2013 Dewailly et al 2014 La Marca et al 2014)
Although AMH based assessment of ovarian reserve with pituitary down
regulation in patients with extremes of ovarian reserve may improve the
outcomes of ovarian response compared to conventional ovarian stimulation
protocols (Nelson et al 2009 Yates et al 2011) there is no robust data on
AMH-tailored individualisation of ovarian stimulation To establish
individualisation of ovarian stimulation the studies should ideally assess
various pituitary desensitisation regimes and initial doses of gonadotrophins in
patients across the full range of ovarian reserve For instance in AMH-tailored
individualisation of pituitary desensitisation regime studies should evaluate the
effect of both GnRH Agonist and GnRH Antagonist regimes for the groups
for each band of AMH levels (eg low normal high) necessitating 6
comparison groups (Figure 1) In individualisation of the initial dose of
194
gonadotrophins the groups of each band of AMH should be treated with the
range of doses of gonadotrophins (eg low moderate high dose) which
requires 9 treatment groups (Figure 2) Consequently to evaluate the
individualisation of both the stimulation regime and the initial dose of
gonadotrophin across the full range of AMH measurements in a single study
ideally 18 comparison groups are needed Indeed the study should have a large
enough sample to adjust for the confounders and obtain sufficient power for
the estimates of each treatment group In addition assessment of ovarian
reserve should be based on reliable AMH measurements with minimal sample-
to-sample variation which appears to be an issue at present (Rustamov et al
2013) Finally evidence on AMH-tailored individualisation of ovarian
stimulation should ideally be based on randomized controlled trials given in
this context AMH is being used as a therapeutic intervention At present there
is no single RCT that assessed AMH-tailored individualisation of ovarian
stimulation and most quoted research evidence appear to have been based on
two retrospective studies (Nelson et al 2009 Yates et al 2011) Both studies
display a number of methodological issues including small sample size and
centre-dependent or time-dependent selection of cohorts Therefore the role
of confounding factors on the obtained estimates of these studies is unclear
The first study on AMH-tailored individualisation ovarian stimulation
compared outcomes of the cohorts who had IVF cycles in two different IVF
centers (Nelson et al 2009) In this case control study the patients in the 1st
centre (n=370) had minimal tailoring of dose of gonadotrophins and were
offered mainly GnRH agonist regime for pituitary desensitisation except
patients with very low AMH (lt10pmolL) who had GnRH antagonist regime
In patients undergoing treatment in the 2nd centre (n=168) the daily dose of
the gonadotrophins was tailored on the basis of AMH levels and GnRH
antagonist based protocol employed for women with low (1-5 pmolL) and
high (gt15 pmolL) AMH levels whereas patients with normal (5-15 pmolL)
AMH levels had standard long GnRH agonist regimen In addition the
patients with very low AMH (lt10 pmolL) had modified natural cycle IVF
treatment in 2nd centre The study reported that the group that had significant
tailoring of both mode and degree of stimulation to AMH levels (2nd centre)
had higher pregnancy rate and less cycle cancellation However given the
methodological weaknesses the findings of the study ought to be interpreted
with caution First the study compared the outcomes of small number of
195
patients who had treatment in two different centers suggesting that differences
in the outcomes may be due to variation in the characteristics of patient
populations andor performance of two different centers Moreover both
cohorts had some degree of tailoring of pituitary desensitisation regimens as
well as the daily dose of gonadotrophins to AMH levels suggesting estimation
of the effect of AMH tailoring to the outcome of treatment may not be
reliable
A subsequent study attempted to address the above issues by assessing a
somewhat larger number of IVF cycles from the same fertility centre (Yates et
al 2011) The study compared IVF outcomes of the cohorts that underwent
ovarian stimulation using chronological age and serum FSH (n=346) with
women that had AMH-tailored (n=423) treatment cycles (Yates et al 2011)
The study found that the group that had AMH-tailored ovarian stimulation
had significantly higher pregnancy rate less cycle cancellation due to poor or
excessive ovarian response and had significantly lower treatment costs
However this study also has appreciable weaknesses given that it was based
on retrospective data that compared outcomes of treatment cycles that took
place over two year period During this period apart from introduction of
AMH-tailored stimulation protocols other new interventions were introduced
particularly in the steps involved in embryo culture Although the outcomes of
the ovarian response to stimulation could have mainly been due to
performance of the stimulation protocols downstream outcomes such as
clinical pregnancy rate may be associated with the introduction of new
interventions in embryo culture techniques Nevertheless the study
demonstrated that tailoring of ovarian stimulation protocol to AMH levels
could reduce the incidence of cycle cancellation OHSS and the cost of
treatment supporting the need for more robust studies on the use of AMH in
the individualisation of ovarian stimulation in IVF
It appears despite a lack of good quality evidence that AMH-tailored
individualisation has been widely advocated and has been introduced in clinical
practice in a number of fertility units In the absence of good quality evidence
we decided to obtain more reliable estimates on the feasibility of AMH-tailored
ovarian stimulation using more robust methodology Availability of the data on
a large cohort of patients with AMH measurements who subsequently
underwent IVF treatment cycles in a single centre may allow us to obtain more
reliable estimates on the effectiveness of AMH-tailored COS Furthermore due
196
to changes on COS protocol combination of various regime and initial dose of
gonadotrophin were used for patients in each band of ovarian reserve This
may facilitate development of predictive models for both regime and dose for
the whole range of AMH measurements In addition as a part of the study we
decided to establish the role of patient and treatment related factors in
determination of ovarian response in cycle of IVF I believe that
understanding the effect of various factors on ovarian performance in COS
will improve the methodology of the study and can be used as a guide for
identification of confounders in future studies The first step in such an
analysis is to develop a statistical model to describe the relationship between
ovarian response and patient and treatment factors This can then be utilized
to explore the effects of treatment on outcome and potentially to allow optimal
treatments to be identified for given patient characteristics and ovarian reserve
METHODS
Objective
The objectives of the study were 1) to determine the effect of age AMH
AFC causes of infertility and treatment interventions on oocyte yield and 2) to
explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Population
Women of 21-43 years of age undergoing ovarian stimulation for
IVFICSI treatment using their own eggs at the Reproductive Medicine
Department of St Maryrsquos Hospital Manchester from 1st October 2008 to 8th
August 2012 were included Patients with previous AMH measurements using
DSL assay were included and patients that had AMH measurement with only
Gen II assay were excluded given the observed issues with this assay
(Rustamov et al 2012) The patients with ultrasound features of PCO previous
history of salpingectomy ovarian cystectomy andor unilateral
salpingoophorectomy have been excluded from the analysis Similarly cycles
with ovarian stimulation other than GnRH agonist long down regulation or
Short GnRH antagonist cycles were not included in the study
197
Dataset
The dataset for the study was prepared using a protocol for the data
extraction management linking and validation which is described in Chapter
4 In short first the data contained in clinical data management systems were
obtained on patient demography AMH measurements and IVF treatment
cycles Then data not available in electronic format were collected from the
patient case notes which includes causes of infertility previous history of
reproductive surgery AFC and folliculogram for monitoring of ovarian
stimulation Each dataset was downloaded in original Excel format into Stata
12 Data Management and Statistics Software (StataCorp LP Texas USA) and
analysis datasets were prepared in Stata format All IVF cycles commenced
during the study period were identified and the combined study dataset was
created by linking all datasets in cycle level using the anonymised patient
identifiers and the dates of interventions All steps of data handling have been
recorded using Stata Do files to ensure reproducibility and provide a record of
the data management process
Categorization of diagnosis
Patients with history of unilateral tubal occlusion or unilateral
salpingectomy were categorized as mild tubal factor infertility and patients with
blocked tubes bilaterally or with history of bilateral salpingectomy were
allocated to severe tubal disease Severe male factor infertility was defined if
the partner had azoospermia surgical sperm extraction or severe oligospermia
which necessitated Multiple Ejaculation Resuspension and Centrifugation test
(MERC) for assisted conception Mild male factor was defined as abnormal
sperm count that do not above meet criteria for severe male infertility
Diagnosis of endometriosis was based on a previous history of endometriosis
confirmed using Laparoscopy Diagnosis of endometrioma was established
using transvaginal ultrasound scan prior to IVF treatment In couples without a
definite cause for infertility following investigation the diagnosis was
categorized as unexplained Women with features of polycystic ovaries on
transvaginal ultrasound were categorized as PCO and excluded from analyses
198
Measurement of AMH and AFC
AMH measurements were performed by the in-house laboratory Clinical
Assay Laboratory of Central Manchester NHS Foundation Trust and the
procedure for sample handling and analysis was based on the manufacturerrsquos
recommendations Venous blood samples were taken without regard to the day
of womenrsquos menstrual cycle and serum samples were separated within two
hours of venipuncture Samples were frozen at -20C until analysed in batches
using the enzymatically amplified two-site immunoassay (DSL Active
MISAMH ELISA Diagnostic Systems Laboratories Webster Texas) The
intra-assay coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and
29 (at 56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and
49 (at 56pmoll) Haemolysed samples were not included in the study In
patients with repeated AMH the measurement closest to their IVF treatment
cycle was selected The working range of the assay was up to 100pmolL and a
minimum detection limit was 063pmolLThe results with minimum detection
limit were coded as 50 of the minimum detection limit (031 pmolL) and
the test results that are higher than the assay ranges were coded as 150 of the
maximum range (150 pmolL)
In our department the measurement of AFC is conducted as part of
initial clinical investigation before first consultation with clinicians and prior to
IVF cycle Qualified radiographers performed the assessment of AFC during
early follicular phase (Day 0-5) of menstrual cycle The methodology of
measurement of AFC consisted of the counting of all antral follicles measuring
2-6mm in longitudinal and transverse cross sections of both ovaries using
transvaginal ultrasound scan The AFC closest to the IVF cycle was selected
for the analysis
Description of COS Protocols
On the basis of their AMH measurement patients were stratified into
the treatment bands for ovarian stimulation using COS protocols During the
study two different COS protocols were used in our centre and in addition
three minor modifications were made in the 2nd protocol Time periods AMH
bands down regulation regimes initial dose of gonadotrophins and adjustment
of daily dose of gonadotrophins of the protocols are described in Table 1
Similarly the management of excessive ovarian response was tailored to
199
pretreatment AMH measurements although mainly based on the results of
oestradiol and scan monitoring the cycle stimulation (Table 2) Assessment of
transvaginal ultrasound guided follicle tracking and serum oestradiol levels in
specific days of the stimulation were used for monitoring of COS (Table 2)
The criteria for the cycle cancellation for poor ovarian response were same
across all protocols fewer than 3 follicles gt15mm in size on Day 10 of ovarian
stimulation
In patients undergoing their first IVF cycle AMH measurement
obtained at the initial assessment was used for determination of which band of
COS the patient would be allocated In the patients with repeated IVF cycles
AMH measurements were obtained prior to each IVF cycle unless a last
measurement performed within 12 months of period was available During the
study period two different assay methods for measurement of AMH was used
in our centre DSL Assay (1 October 2008- 16 November 2010) and Gen II
Assay (17 November 2010- 8 August 2012) Correspondingly during the study
period two different COS Protocols were used 1st Protocol (1 October 2008-
31 December 2010) and 2nd Protocol (1 January 2011-8 August 2012)
Consequently allocation into the ovarian reserve bands of the patients of 1st
protocol were based on DSL assay samples whereas the stratification of
patients of 2nd protocol was based either on DSL assay or Gen II assay
samples Specifically the patients with recent DSL measurements (lt12 months
old) who had IVF treatment during the period of 2nd Protocol had
stratification on the basis of their DSL measurements In these patients in
order to obtain equivalent Gen II value the DSL result was multiplied by 14
in accordance with the manufacturerrsquos recommendation at the time In the
patients without previous or recent (lt12 months old) DSL measurements
stratification into ovarian reserve bands was achieved using their most recent
Gen II measurements Therefore DSL measurements presented in this study
may or may not have been used for formulation of the treatment strategies for
individual patients In fact in this study DSL measurements have been
included in order to understand the role of AMH in determination of ovarian
response in IVF cycles rather than an evaluation of AMH-tailored COS
protocols In addition to introduction of 2nd protocol further modifications
were made to the protocol and therefore 2nd protocol comprised of 4 different
versions (Table 1-2) These changes in the protocols allowed us to compare the
effect of the various modifications to COS protocols on oocyte yield
200
Pituitary desensitisation regimes
Selection of pituitary desensitisation regime was based on the patientrsquos
AMH according to the COH protocol at the time of commencement of IVF
cycle (Table 1) Long agonist regime involved daily subcutaneous injection of
250g or 500 g of the GnRH agonist Buseralin acetate (Supercur Sanofi
Aventis Ltd Surrey UK) from the mid-luteal phase (Day 21) of preceding
menstrual cycle which continued throughout ovarian stimulation Women
treated with Antagonist regime had daily subcutaneous administration of
GnRH antagonist Ganirelex (Orgalutran Organon Laboratories Ltd
Cambridge UK) from Day 4 post-stimulation until the day of HCGGnRH
agonist trigger Ovarian stimulation was achieved by injection of daily dose of
hMG Menopuir (Ferring Pharmaceuticals UK) or rFSH Gonal F (Merck
Serono) as per AMH-tailored protocols (Table 1) Oocyte maturation was
triggered using 5000 international units of HCG (Pregnyl Organon
Laboratories Ltd Cambridge UK) and the criteria for timing of HCG
injection was consistent across all protocols one (or more) leading follicle
measuring gt18mm and two (or more) follicle gt17mm
Oocyte collection
Oocyte collection was conducted 34-36 hours following injection of
HCG for follicle maturation An Ultrasound Guided Oocyte Recovery (USOR)
was conducted by experienced clinicians under sedation The names of
practitioners were anonymised and the practitioner with the largest number of
oocyte recovery was categorized as a reference group Practitioners with a
small number (lt10) of oocyte collection were pooled (group J) If the cycle
was cancelled before oocyte recovery it was categorized under the practitioner
who was on-call for oocyte recovery session on the day of cycle cancellation
In cycles with pre-USOR cancellation for excessive ovarian response
total oocyte number was coded as 27 and Metaphase II oocyte number was
coded as 19 This was based on mean oocyte number in the patients who had
post-USOR cancellation for excessive ovarian response or OHSS
Quantitative assessment of total oocytes were conducted immediately
post-USOR by an embryologist In patients undergoing ICSI the assessment
of the quality of oocytes were conducted 4-6 hours post-USOR and the
201
oocytes assessed as in Metaphase II stage (MII) of maturation were categorized
as mature oocytes
Statistical analysis
The total number of collected oocytes in all cycles and the number of
mature oocytes in the subset of ICSI cycles were used as outcome measures
for the study Oocyte was selected as the primary outcome measure for
assessment of ovarian performance as this provides an objective measure
which is largely determined by effectiveness of ovarian stimulation regimens
In contrast downstream measures such as clinical pregnancy and live birth are
influenced by factors related to management gametes and embryos
Statistical analysis was conducted using multivariable regression models
and the process of model building included following steps 1) Analyses of
distribution of the groups and variables 2) Univariate analysis to establish the
factors that likely to affect total oocyte number 3) Evaluation of
representation of continuous variables 4) Analysis of interaction between
explanatory variables 5) Sensitivity analysis
First the distribution of patients the ovarian reserve markers
interventions and the outcomes were explored using cross tabulation
histograms Box Whisker and scatter plots Then in order to establish the
factors that likely to affect the oocyte number univariate analyses of Age
AMH AFC PCO status attempt of IVFICSI ethnicity BMI protocol
regime USOR practitioner and initial dose of gonadotrophins were conducted
Following this all these explanatory variables were run as part of initial
multivariable regression model Adjustment for confounders related to the
modifications of the protocols and unknown time-dependent changes
conducted by inclusion of the COS protocol categories in the regression
model
Evaluation of representation of oocyte number Age AMH AFC initial
dose of gonadotrophins were conducted by establishing best fit on the basis of
Akaike and Bayesian Information Criteria In addition interpretability of the
data and clinical applicability of the results (eg cut off ranges) were used as a
guide for selection of optimal representation Given the oocyte number was
not normally distributed it was represented in logarithmic scale (log(oocyte
number+5) To establish best representation for AMH AFC and initial dose
202
the models in following scales were run for each variable Linear quadratic
cubic 4th order polynomial linear (log) quadratic (log) cubic (log) 4th order
polynomial (log) cut-off ranges according to distribution Age adjustment in
quadratic scale following centering it to 30 years of age was found to provide
the most parsimonious representation AMH was found to be best represented
using following cut-off ranges 0-3 4-5 6-8 9-10 11-12 13-15 16-18 19-22
23-28 and 29-200 The best representation for AFC was found to be cut-off
ranges of 0-7 8-910-1112-14 15-19 20-24 and 25-100 Initial dose of
gonadotrophins were categorized as following 75-150IU 187-250IU 300IU
375IU 450IU
Subsequently interactions between explanatory variables were tested at
significance level of plt001 which revealed there were significant interaction
between PCO status and other covariables Given these interactions were
found to be complex and not easily computable we decided to restrict the
regression analysis to the non-PCO group We observed significant interaction
between regime and initial dose and therefore these variables were fitted with
interaction term in the model Finally sensitivity analyses of final regression
models were conducted Significance of the results was interpreted using p
value (lt005) effect size and clinical significance For assessment of feasibility
of individualization of stimulation regime and initial dose visual representation
of data was achieved using plots for observed and fitted values (Figure 1-4)
RESULTS
Description of data
A total of 1847 IVF or ICSI cycles of 1428 patients met inclusion criteria for
the study AMH measurements of all cycles and AFC measurements for 1671
cycles (n=1289 patients) were available In the analysis of total oocytes 1653
cycles were included and the analysis of MII oocytes comprised of 1101 ICSI
cycles
Mean AMH was found to be 178 (125) mean AFC was 142 56
mean number of total oocytes was 101 64 and mean number of mature
oocytes was 74 53 The distribution of the cycles according to patient
characteristics and interventions is shown in Tables 3
203
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
Table 4a and 4b show that there was a significant negative association of
oocyte yield with age and oocyte number following adjustment for AMH
AFC causes of infertility attempt of IVFICSI cycle USOR practitioner COS
protocol pituitary desensitisation regime type of gonadotrophin preparation
and initial daily dose of gonadotrophins (Table 4a) With each increase of age
by 1 year we observed approximately a 3 reduction in total oocyte
(p=00005) and a 2 decrease in mature oocyte number (p=0006) which was
independent of age and other covariables
In the analysis of AMH there was significant gradual increase in total
oocyte as well as mature oocyte number with increasing AMH following
adjustment for all covariables (Figure 1 and 2) Compared to an AMH range of
0-3 pmolL there was increase of 25 in the range of 4-5 pmolL (p=007)
36 in 6-8 pmolL (p=0008) 60 in 9-10 pmolL (p=00005) 65 in 11-12
pmolL (p=00005) 77 in 13-15 pmolL (p=00005) 83 in 16-18 pmolL
(p=00005) 80 in 19-22 pmolL (p=00005) 95 in 23-28 pmolL
(p=00005) and 112 in the range of 29-150 pmolL (p=00005) in total
oocyte number (Table 4a) Similar but less marked increase in MII oocyte
number was observed with increasing AMH
The data on AFC also showed that there was gradual increase in total
oocyte number with increasing AFC following adjustment of all covariables
(Table 4a) Compared to an AFC of 0-7 there was increase of 14 in the
range of 10-11 (p=003) 22 in AFC of 12-14 (p=0001) 26 in AFC of 15-
19 (p=00005) 34 in AFC of 20-24 (p=00005) and 40 in AFC of gt25
(p=0005) However there was no increase in total oocyte number in AFC
range of 8-9 compared to that of 0-7 AFC-related Increase in MII oocytes was
less marked compared to that of total oocytes (Table 4a)
Causes of infertility
We did not observe any significant associations between the causes of
infertility and number of retrieved oocytes However women diagnosed with
unexplained infertility appear to have marginally higher (10 p=002) total
number of oocytes compared to women whose causes of infertility were
204
known Diagnosis of severe tubal (-37 p=019) and severe male (-37
p=035) factor infertility was found to be associated with lower number of MII
oocytes compared to other causes of infertility However neither of these
parameters reached statistical significance Similarly there was no significant
association between oocyte number and diagnosis of endometriosis with or
without endometriomata compared to women that were not diagnosed with
the disease (Table 4a)
Attempt
Analysis of total number of oocytes showed that women who had their
2nd attempt of IVFICSI cycle had slightly higher (85 p=001) and those
that had their 3rd or 4th attempt of treatment had significantly higher total
oocyte yield (24 p=0001) compared to women undergoing their 1st attempt
of IVFICSI cycle (Table 4a) Similarly overall effect of attempt on total
oocyte yield was significant (p=0001)
However we did not observe any association between the attempt and
MII oocyte number in the analysis of the subset of ICSI cycles (p=045)
USOR practitioner COS protocol and gonadotrophin preparation
There was a significant association (p=00005) between total oocyte yield
with USOR practitioner (Table 4b) However the association of USOR
practitioner with MII oocyte number did not reach statistical significance
(p=0058)
We observed significant association between the COS protocols in the
analysis of total number of oocytes 1st version of 2nd Protocol (-18
p=00005) 2nd amp 3rd versions of 2nd Protocol (-14 p=005) and 4th version of
2nd Protocol (-24 p=0009) provided significantly lower number of total
oocytes compared to 1st Protocol However the effect of the COS Protocol
changes to MII oocyte number was not significant (p=024)
Compared to hMG ovarian stimulation using rFSH provided 13
higher total oocytes (p=0008) In the analysis of Metaphase II oocytes there
was no significant difference in oocyte yield between hMG and rFSH (026)
205
Regime and Initial dose of gonadotrophins
The regression analyses of the regimes for pituitary desensitisation and
initial dose categories were conducted in comparison to the reference group
(Agonist with 75-150IU hMGrFSH) IVFICSI cycles where Antagonist
with 75-100IU of hMGrFSH (-36 p=00005) was used provided
significantly lower total oocyte yield whereas cycles with Agonist and 300IU
hMGrFSH (15 p=005) provided marginally higher total oocyte number
In the analysis of MII oocytes cycles using Antagonist with 187-250IU
of hMGrFSH (43 p=005) Agonist with 300IU of hMGrFSH (25
p=016) and Antagonist with 375IU hMGrFSH (47 p=002) yielded higher
number of oocytes Use of Agonist with 375IU hMGrFSH (-18 p=05) and
Agonist with 450IU of hMGrFSH (-28 p=02) was associated with lower
mature oocyte number although the analysis did not reach statistical
significance
AMH-tailored individualization of COS
The overall effect of initial gonadotrophin dose to total oocyte yield
was found to be significant (plt0001) However other than the lowest dose
category with Antagonist regime the analysis did not show any consistent
dose-response effect on total oocyte number with increasing gonadotrophin
dose (Table 4b Figure 3a Figure 3b Figure 4a and Figure 4b)
In the analysis of MII compared to reference group of 75-150 IU of
initial daily gonadotrophins we observed increased oocyte yield in the
categories of 187-250 IU (43 p=005) and 375 IU (47 p=002) of
gonadotrophins However both of these groups had Antagonist regime for
pituitary desensitisation compared to that of Agonist in the reference group
and therefore the observed effect may be related to the regime of COS rather
than daily dose of gonadotrophins
206
DISCUSSION
In this study we explored the effect of age AMH AFC causes of
infertility attempt of IVF ICSI treatment and interventions of COS on
ovarian performance using a retrospective data on large cohort of IVF ICSI
cycles of non-PCO patients To our knowledge this is largest study to have
conducted a detailed analysis of the effect of AMH and AFC on ovarian
performance in IVFICSI cycles The study utilized a dataset that was
prepared using a robust protocol for data extraction and handling Similarly
the statistical analysis was based on a systematic exploration of the effect of all
relevant factors followed by adjustment for all relevant factors and finally
careful analysis
With regards to the outcome measures the quantitative response of
ovaries were measured using total collected oocytes in IVFICSI cycles and
the MII oocyte number in the subset of ICSI cycles were used as a
measurement of quantitative response of ovaries to COS Arguably oocyte
number is the best outcome measure for determination of ovarian response to
COS given it is mainly determined by patientrsquos true ovarian reserve the quality
of assessment of ovarian reserve and treatment strategies for ovarian
stimulation In contrast downstream outcomes such as clinical pregnancy and
live birth are subject to additional clinical and interventional factors which may
not always be possible to adjust for using retrospective data Indeed large
observational studies suggest that achieving optimal ovarian response is one of
the most important determinants of success of IVFICSI cycles and
recommend to use oocyte number as a surrogate marker for live birth (Sunkara
et al 2011) It appears around 10-15 total oocytes or 3-4 mature oocytes
provide optimal chance for a one live birth in IVFICSI cycles (Sunkara et al
2011 Stoop et al 2012) Therefore oocyte number appears to be most useful
marker for assessment of ovarian response to COS as well as in prediction of
live birth in cycles of IVFICSI
207
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
After adjusting for AMH AFC the patient characteristics and above
mentioned treatment interventions age remained as an independent predictor
of ovarian response to COS Our data showed approximately 3 (p=00005)
decrease in total oocyte and 2 (p=0006) reduction in mature oocyte number
with increase of age by factor of 1 year (Figure 3b and Figure 4b)
Interestingly the effect of AMH was also found to predict oocyte yield
independently of age with an effect actually more pronounced compared to
that of age After adjusting for age and all other factors there was gradual
increase in total oocyte number with increasing AMH which were both
clinically (25-110) and statistically (p=007-p=00005) significant (Table 4a)
We observed a largely similar effect of AMH in the analysis of mature
oocytes It is important to note that due to the issues with Gen II AMH assay
(Rustamov et al 2012) in this study we included only measurements obtained
with the DSL assay Consequently presented cut-off ranges may not be
applicable with current assay methods We suggest that future studies should
revisit the optimality of the cut-off ranges once a reliable assay method has
been established
Similarly after adjusting for all factors the effect of AFC on total
oocytes remained significant (14-40 plt003) However the effect of AFC
appears to be less marked compared to AMH It is important to note that the
AFC assessment in this study is based on the measurement of 2-6mm antral
follicles using two-dimensional transvaginal ultrasound scan The cut-off
ranges may not be applicable in centers where AFC measurement is obtained
using different criteria
Our analysis suggests that age AMH and AFC are independent
determinants of total and MII oocyte number in IVFICSI cycles and can be
used as predictors of ovarian performance irrespective of patient and treatment
characteristics However assessment of oocyte number is the quantitative
response of ovaries to COS and may not necessarily reflect qualitative
outcome
208
Causes Endometriosis Endometrioma
The causes of infertility do not appear to make a significant contribution
in determining total oocyte number after controlling for age AMH AFC the
attempt and treatment interventions Although in the analysis of MII oocytes
we observed reduced oocyte yield in women with severe tubal (-37) and
severe male (-37) infertility this was not statistically significant The analysis
of MII oocytes only included the subset of ICSI cycles consisting of women
with male factor infertility Therefore the effect of severe male factor infertility
may have been more marked in this model
We did not observe a significant difference in total or MII oocyte
number in women with a history of endometriosis with or without
endometriomata Current understanding of the effect of endometriosis in the
outcomes of IVF treatment suggests that the disease has detrimental effect on
IVF outcomes (Barnhart et al 2007 Barnhart et al 2002) However some argue
that no association is observed if the analysis conducted using proper
adjustment for all relevant confounders (Surrey 2013) Our data suggests that
after adjustment for all relevant factors there is no measurable association with
endometriosis (with or without endometriomata) and oocyte number Some
suggest that using ultra-long down regulation using depot GnRH analogue up
tp 3-6 months prior to ovarian stimulation improves ovarian performance in
patients with endometriomata Our dataset did not have information on
pituitary desensitisation prior IVF treatment cycles and we are therefore unable
to assess the effect of this intervention
Attempt
Our study found that 2nd and 3rd cycles were associated with 8
(p=001) and 24 (p=0001) higher total oocytes compared to that of 1st IVF
cycle However the effect of the attempt on MII oocytes was not significant
In our centre only patients with a previously unsuccessful IVF treatment are
offered subsequent cycles and therefore compared to the patients with
repeated attempts the group with first cycle may be expected to have better
oocyte yield However when controlled for all relevant confounders including
adjustment of treatment interventions 1st IVF cycle does not appear to provide
better oocyte yield In keeping with our findings a recent study demonstrated
independence of attempts of IVF cycles in terms of outcomes (Roberts SA and
209
Stylianou C 2012) Increased total oocyte yield with progressed attempts is
likely to be due to the adjustment of COS on the basis of information on the
ovarian response in previous cycles It is important to note that in this study
we assessed oocyte yield as the outcome measure and this may not necessarily
translate into live birth which is desired outcome for the couples Therefore
availability of data on the attempt-dependency of live birth in IVF cycles is
important and we suggest future studies should explore it
USOR practitioner
To our knowledge this is the first study that explored the effect of an
oocyte recovery practitioner on oocyte yield adjusting for all relevant
confounders We observed a considerable operator-dependent effect on total
oocyte yield which may be due to a variation of patients across the days of the
week (p=00005) The practitioners were allocated to the sessions of oocyte
recovery using a specific rota template according to the day of the week Given
in our centre we do not conduct oocyte recovery at weekends there may be
day-dependent variation in selection of patients For instance the patients who
are likely to have maturation of leading follicles during the weekend may have
been scheduled slightly earlier Similarly the patients with confirmed
maturation of leading follicles whose oocyte recovery would have fallen on
weekends may have been scheduled after the weekend allowing maturation of
additional follicles Therefore practitioners conducting the sessions of oocyte
recovery in extremes of weekdays may not necessarily have similar patients
compared to that of other days which may have introduced some bias in
estimating the outcomes of individual practitioners Nevertheless given the
statistical analysis adjusted for age ovarian reserve and treatment interventions
we think there is considerable true between-operator variability on total oocyte
number We suggest that future studies should assess it further by including
adjustment for follicle number and size on the day of HCG
Interestingly overall effect of the operator did not reach statistical
significance in the analysis of MII oocytes in ICSI subset (p=0058) This may
suggest irrespective of total oocyte yield aspiration of only follicles of larger
than a certain size provides oocytes with potential for fertilization
210
COS Protocol
Controlled ovarian hyperstimulation in IVF is conducted using a pre-
defined protocol which contains the policy on selection of regime for pituitary
desensitisation the initial daily dose of gonadotrophins the monitoring of
ovarian response the adjustment of daily dose of gonadotrophins the policy
for cancellation due to poor or excessive ovarian response and criteria for
HCG trigger for final maturation of oocytes Determination of the optimal
treatment regime and the initial dose of gonadotrophins for each patient is
frequently achieved by stratification of patients into various bands of ovarian
reserve on the basis of the assessment of ovarian reserve The assessment of
ovarian reserve prior to IVF cycle is performed using biomarkers which usually
consist of one or combination of following Age AMH AFC and FSH In our
centre stratification of patients into the bands of ovarian reserve was
determined on the basis of the patientrsquos AMH measurements For instance the
patients deemed to have lower ovarian reserve were allocated to the treatment
band with higher daily dose of gonadotrophins and vice versa (Table 1)
The study found that the 2nd protocol was associated with 14-24 lower
total oocyte yield compared to the 1stprotocol The differences in the
interventions between the protocols are described in Table 1 and Table2
Compared to the 1st protocol the 2nd protocol had a) some patients allocated
to COS bands using Gen II assay measurements which later was found to
provide inaccurate measurements b) more AMH cut-off bands for COS
bands c) strict monitoring of ovarian response and corresponding adjustment
of daily dose of gonadotrophins and d) strict criteria for cycle cancellation for
excessive response Therefore our data suggests that the COS protocols with
broader AMH cut-off bands with less strict criteria for adjustment of daily
gonadotrophins may provide higher oocyte yield However given it is
retrospective analysis the limitation of the study should be recognized and we
recommend more robust prospective studies on optimization of AMH tailored
protocols should be conducted
Gonadotrophin type
The study showed that rFSH was associated with higher total oocyte
number (13 p=0008) Interestingly analysis of MII oocyte showed a larger
confidence interval and did not reach statistical significance suggesting the
211
effect of rFSH was not a strong determinant of mature oocytes Perhaps
observation of higher total oocytes in rFSH cycles compared to that of HMG
and yet comparable mature oocyte number in the study suggest that regardless
of total oocyte yield only follicles with a potential for maturation will achieve a
stage of metaphase II
Ovarian stimulation in cycles for IVF is largely achieved by two different
analogues of follicle stimulating hormone human menopausal gonadotrophin
(hMG) and recombinant follicle stimulating hormone r(FSH) Although
purified hMG contains more luteinising hormone compared to rFSH which is
believed to assist endometrial maturation and improve odds of implantation in
cycles of IVF Furthermore the LH component of hMG is believed to assist in
maturation of oocyte with subsequent improvement in live birth On the other
hand historically rFSH was believed to have less batch-to-batch variation
compared to that of HMG which allows administration of more precise daily
dose of gonadotrophins To date a number of studies have been published
comparing these two forms of gonadotrophin preparations which provide
conflicting findings However systematic review that compared of the effect of
these types of gonadotrophins on live birth rate suggests that there is no
significant difference on live birth rate (van Wely et al 2011) This supports our
findings on that irrespective of total oocyte yield clinically useful mature
oocyte number is comparable between the groups
Regime and dose of gonadotrophins
The study found that compared to the reference group (Agonist 75-
150IU) none of the combination of the regime and gonadotrophin dose
provided a higher total oocyte yield Women that were in Antagonist regime
group with an initial daily dose of 75-150 IU gonadotrophins produced
approximately 36 fewer total oocytes (p=00005) However comparison of
MII oocytes of these groups did not reach statistical significance and the effect
size was much smaller (-19 p=023) This and reference groups represent the
patients with high ovarian reserve who had milder ovarian stimulation because
of risk of excessive ovarian response and OHSS Lower total oocyte yield and
comparable mature oocyte number in the Antagonist regime may explain why
this regime is reported to be associated with reduction in the risk of OHSS and
212
yet comparable live birth in patients with high ovarian reserve (Yates et al
2012)
In the analysis of MII oocytes Antagonist with 187-250 IU of
gonadotrophin and Antagonist with 375 IU of gonadotrophin provided around
43 (p=005) and 47 (p=002) more oocytes compared to that of the
reference group (Agonist 75-150 IU) Interestingly total oocytes of these
groups were comparable to that of reference group suggesting that using
Antagonist protocol may be associated with improvement in oocyte
maturation compared to Long Agonist regime Perhaps in addition to the
effect of exogenous HCG endogenous LH may play role in oocyte maturation
in IVFICSI cycles and shorter desensitisation of pituitary using Antagonist
regime may allow secretion of LH during COS in lower quantities
AMH-tailored individualisation of COS
Given that we did not observe a significant dose-dependent effect on
oocyte number we were not able to develop AMH or AFC tailored
individualisation protocols for COS Although the initial dose of
gonadotrophin is believed to be one of the main determinants of oocyte yield
our study suggests that the association between these variables is weak
Consequently strict protocols for tailoring the initial dose of
gonadotrophins may not necessarily improve ovarian performance in IVF
treatment It is important to note that our COS protocols recommended close
monitoring of ovarian response and corresponding dose adjustment starting
from 3rd day of COS which may have masked the effect of initial dose
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
213
Strengths of the study
Here we presented the largest study on assessment of the role of patient
and treatment related factors on oocyte yield and exploration of optimization
of AMH-tailored COS using a validated dataset Statistical analysis included
systematic assessment of the effect possible confounders on measured
outcome including of age AMH AFC causes of infertility attempt of IVF
treatment USOR practitioner type of gonadotrophin pituitary desensitisation
regime and initial dose of gonadotrophins On the basis of above analysis a
robust multivariable regression models for assessment of the effect all above
factors on total and mature oocyte number have been developed
Prior to conducting this study previous projects explored the
performance of AMH assay methods The studies found that Gen II assay may
yield highly non-reproducible measurements compared to that of DSL assay
(Rustamov et al 2012a) Therefore in this study only DSL AMH assay
measurements were included Furthermore previous projects (Chapter 5 and 6)
explored the effect of various patient related factors on AMH AFC and FSH
measurements and found that some of the factors had measurable impact on
ovarian reserve These findings were used in establishing which patient related
factors ought to be explored in the building of regression models for this
study However the DSL assay is no longer available and most clinics are
mainly using Gen II AMH assay in formulation of COS in IVF Given its
observed instability AMH-tailoring based on Gen II samples may lead to
erroneous allocation of patients to the band that is significantly inconsistent
with patientrsquos ovarian reserve Subsequently this may result in the extremes of
ovarian response to COS including severe OHSS and cycle cancellation
Weaknesses of the study
The main weakness of the study is that the analysis is based on
retrospectively collected data The methodology included an extensive
exploration for possible confounders and adjustment for the ones that were
found to be significant However there are may be unmeasured factors that
that might have affected the estimates In addition the study included only
patients that did not have PCO appearance on ultrasound scan The analysis in
all patients showed that interaction of PCO status with other covariables was
complex which could introduce bias in estimation of the effects of other
214
factors Therefore analyses of the groups with and without PCO were run
separately Subsequently results of non-PCO group was presented in the thesis
given it had the largest number of cycles Compared to non-PCO analysis we
did not observe significant difference in the results of PCO model
The study assessed ovarian response using oocyte yield only Other
outcomes of ovarian response such as duration of ovarian stimulation total
dose of gonadotrophins cycle cancellation due to poor or excessive ovarian
response and OHSS have not been analysed Therefore it is important to
interpret the findings of this study in the context of ovarian response
determined by oocyte yield Specifically the study should not be used to
interpret cycle cancellation for excessive ovarian response As described in the
methodology of the study the oocyte number in the cycles with cancellation of
oocyte recovery due to excessive response were recoded with comparable
values with cycles that were cancelled following oocyte recovery for OHSS
Given the main desired outcome of IVF treatment is live birth the
overall success of a treatment cycle should reflect this outcome measure This
study does not assess the effect of above factors to overall success of IVF
treatment However the study provides a robust data on research methodology
in assessment of IVF outcomes which can assist in the assessment of other
outcome measures in future studies
SUMMARY
After adjustment for all the above factors age remained a negative
predictor of oocyte yield whereas we observed a gradual and significant
increase in oocyte number with increasing AMH and AFC values suggesting
all these markers display an independent association with oocyte yield IVF
attempt oocyte recovery practitioner type of gonadotrophin were found to
have significant effect on total oocyte yield However the effect of these
factors on mature oocyte number did not reach statistical significance Whilst
total oocyte number was comparable between pituitary desensitisation regimes
GnRH antagonist cycles were found to provide significantly higher mature
oocytes compared to that of long GnRH agonist regime
In terms of the effect of initial dose on oocyte yield following
adjustment for all above variables we did not observe significant increase in
215
oocyte number with increasing gonadotrophin dose categories Therefore
strict protocols for tailoring the initial dose of gonadotrophins may not
necessarily improve ovarian performance in IVF treatment However further
time series regression analysis with full parameters of cycle monitoring and the
dose adjustments in the model should be conducted in order to ascertain the
role of AMH in tailoring the dose of gonadotrophins in cycles of IVF
This study demonstrates complexity of the factors that determine
ovarian response in IVF cycles Therefore assessment of AMH-tailored
individualisation of ovarian stimulation should be based on a robust
methodology preferably using a large randomized controlled trial
Furthermore measurement of AMH ought to be based on a reliable assay
method which is currently not available In the meantime the limitations of
available evidence on AMH-tailored individualisation of ovarian stimulation
should be taken into account in the management of patients
216
References
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Barnhart K Dunsmoor-Su R Coutifaris C Effect of endometriosis on in vitro fertilization Fertil Steril 2002771148ndash55 Dechaud H Dechanet C Brunet C et al Endometriosis and in vitro fertilization a review Gynecol Endocrinol 200925717ndash21 Dewailly D Andersen CY Balen A Broekmans F Dilaver N Fanchin R Griesinger G Kelsey TW La Marca A Lambalk C Mason H Nelson SM Visser JA Wallace WH Anderson RA The physiology and clinical utility of anti-Mullerian hormone in women Hum Reprod Update 2014 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A and Sunkara S K Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers from theory to practice Human Reproduction Update Vol20 No1 pp 124ndash140 2014
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867-75 Nelson SM Biomarkers of ovarian response current and future applications Fertil Steril 201399963ndash969
Roberts SA Stylianou C The non-independence of treatment outcomes from repeat IVF cycles estimates and consequences Hum Reprod 2012 Feb27(2)436-43
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum
217
Reprod 2012a273085-3091
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071 Stoop D Ermini B Polyzos NP Haentjens P De Vos M Verheyen G and Devroey P Reproductive potential of a metaphase II oocyte retrieved after ovarian stimulation an analysis of 23 354 ICSI cycles Human Reproduction 2012 Vol27 No7 pp 2030ndash2035 2012 Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011 261768ndash1774 Sunkara SK Coomarasamy A Faris R Braude P Khalaf Y Effectiveness of the GnRH agonist long GnRH agonist short and GnRH antagonist regimens in poor responders undergoing IVF treatment a three arm randomised controlled trial (ESHRE) 2013London UK SurreyES Endometriosis and Assisted Reproductive Technologies Maximizing Outcomes Semin Reprod Med 201331154ndash163 van Wely M1 Kwan I Burt AL Thomas J Vail A Van der Veen F Al-Inany HG Recombinant versus urinary gonadotrophin for ovarian stimulation in assisted reproductive technology cycles Cochrane Database Syst Rev 2011 Feb 16(2)CD005354
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
218
Figure 1 Study groups for assessment of Individualisation of pituitary desensitisation regime
Individualisation of pituitary desensitisation regimens can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high ovarian reserve
Individualisation of COS Regime
Low AMH
(eg DSL assay
22-157 pmolL)
GnRH
Antagonist
GnRH
Agonist
Normal AMH
(eg DSL assay
158-288pmolL)
GnRH
Antagonist
GnRH
Agonist
High AMH
(eg DSL assay
gt288 pmolL)
GnRH
Antagonist
GnRH
Agonist
219
Fiure 2 Study groups for assessment of individualisation of initial gonadotrophin dose
Individualisation of daily dose of gonadotrophins can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high
ovarian reserve
Individualisation
Gonadotrophin
Dose
Low AMH
(eg DSL assay 22-157 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
Normal AMH
(eg DSL assay158-288pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
High AMH
(eg DSL assay gt288 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-375 IU)
Low Dose
(eg HMG 75-150 IU)
220
Table 1 AMH-tailored stratification protocols for regime starting dose of hMGrFSH and adjusting daily dose of gonadotrophins (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (V1) (01 Jan 2011-30 Apr 2011)
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Initial dose (Day 1-3) 1) lt22 AMH (DSL) Exclude 2) 22-156 AMH (DSL) Antagonist 300 hMG 3) 157-285 AMH (DSL) Long Agonist 200 rFSH225 hMG 4) gt286 AMH (DSL) Antagonist 150 hMG
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 375 hMG 3) 11-21 AMH (Gen II) Long Agonist 300 hMG 4) 22-30 AMH (Gen II) Long Agonist 225 hMG 5) 31-39 AMH (Gen II) Long Agonist 150 hMG 6) 40-67 AMH (Gen II) without PCO Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCO Long Agonist 125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Long Agonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Long Agonist 1125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 450 hMG 2) 3-10 AMH (Gen II) Long Agonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 rFSH 8) gt67 AMH (Gen II) Antagonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 300 rFSH 2) 3-10 AMH (Gen II) Long Agonist 225 rFSH 3) 11-21 AMH (Gen II) Long Agonist 1875 rFSH 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 hMG 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 hMG 8) gt67 AMH (Gen II) Antagonist 1125 hMG
Dose adjustment No or minimum change on daily dose of gonadotrophin
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
221
Table 2 AMH-tailored stratification protocols for management of suspected excessive response (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (v1) (01 Jan 2011-30 Apr 2011)
amp
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Coasting for excessive response on day 8
Oestradiol gt20000 pgml 30-40 follicles larger than 10mm or Oestradiol gt18000 pgml
30-40 follicles larger than 12mm
No coasting
Coasting for excessive response once follicle maturation meets criteria
Oestradiol gt20000 pgml
30-40 follicles larger than 10mm
25-40 follicles larger than 10mm
25-30 follicles larger than 15mm
Cancellation for excessive response
Day 8 or thereafter Oestradiol lgt20000 pgml and symptoms of OHSS after gt3 days of coasting
Day 8 or thereafter More than 40 follicles larger than 10mm
Day 10 or thereafter More than 40 follicles larger than 15mm
Day 8 or thereafter Cancel only if symptoms of OHSS
222
Table 3 Distribution of patient characteristics and interventions
In total 1847 cycles included in the study
n
Causes
Unexplained 591 32
Mild tubal 325 176
Severe tubal 37 2
Mild male 589 3189
Severe male 18 097
Endometriosis 91 493
Endometrioma 47 28
Attempt
1 1346 7287
2 406 2198
3 91 493
4 4 022
USOR practitioner
A 570 317
B 412 2291
C 147 818
D 15 083
E 153 851
F 86 478
G 118 656
H 136 756
I 141 784
J 20 111
Protocol
1 1265 6849
2 (v1) 399 216
2 (v2ampv3) 79 428
2 (v4) 104 563
FSH preparation
HMG 1594 87
rFSH 237 13
Regime
Long Agonist 820 444
Antagonist 1027 556
Initial dose
75-150IU 298 1617
187-250IU 483 2621
300IU 914 4959
375IU 60 326
450IU 88 477
223
Table 4a Results of multivariable regression analysis for total and MII oocytes
Total oocytes (n=1653) Metaphase II oocytes (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
Age -0031 -004 -002 00005 -0021 -004 -001 0006
age2 -0002 000 000 0047 -0002 -001 000 0206
AMH categories (Ref0-3 pmolL) 00005 00005
4-5 pmolL 0254 -003 054 0078 -0073 -054 040 0761
6-8 pmolL 0368 010 064 0008 0250 -019 069 0267
9-10 pmolL 0605 034 087 00005 0474 004 091 0034
11-12 pmolL 0651 039 091 00005 0305 -016 077 0198
13-15 pmolL 0779 051 104 00005 0372 -008 083 0109
16-18 pmolL 0836 057 111 00005 0655 018 113 0007
19-22 pmolL 0803 051 109 00005 0381 -013 089 0142
23-28 pmolL 0954 067 123 00005 0832 034 132 0001
29-200 pmolL 1126 084 141 00005 0872 035 139 0001
AFC categories (Ref 0-7) 00005 0008
8-9 -0039 -018 010 0589 0001 -024 024 0992
10-11 0145 001 028 0037 0185 -005 042 0119
12-14 0223 009 036 0001 0254 002 049 0031
15-19 0263 013 040 00005 0113 -013 036 0362
20-24 0344 017 052 00005 0456 013 078 0006
25-100 0405 021 060 00005 0455 009 082 0015
Causes of infertility
Unexplained 0103 002 019 0021 0090 -010 028 0354
Mild tubal -0012 -010 008 0797 -0098 -029 009 0307
Severe tubal -0066 -030 017 0579 -0371 -093 019 0194
Mild male 0014 -007 009 0729 0135 -002 029 009
Severe male -0074 -055 040 0758 -0377 -117 042 0351
Endometriosis -0108 -026 005 0169 -0139 -041 013 0314
Endometrioma -0016 -018 015 0843 0043 -035 044 083
Attempt (Ref 1st) 0001 045
2nd 0085 002 015 0016 0080 -006 022 0274
3rd4th attempt 0243 010 039 0001 0116 -014 037 0367
224
Table 4b Results of multivariable regression analysis for total and MII oocytes Continuation of Table 4a)
Total oocyte (n=1653) Metaphase II oocyte (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
USOR Practitioner (Ref A) 00005 0058
B -0009 -009 007 0823 -0129 -031 005 0153
C 0104 -003 024 0129 0111 -012 034 0348
D -0260 -059 007 0125 -0287 -108 051 0478
E -0297 -044 -016 0 -0246 -048 -001 0043
F -0173 -032 -003 0017 -0367 -072 -001 0043
G -0213 -039 -003 002 -0311 -061 -001 0044
H -0007 -012 011 0909 0022 -020 025 0849
I -0149 -025 -004 0005 -0082 -030 014 0462
J -0549 -095 -015 0007 -0408 -095 014 0143
Protocol (Ref 1st) 00003 024
2nd (v1) -0186 -027 -010 0 -0066 -024 010 0449
2nd (v2ampv3) -0140 -028 000 0056 0175 -007 042 0156
2nd (v4) -0244 -043 -006 0009 0002 -031 031 0989
Gonadotrophin (Ref HMG)
rFSH 0137 004 024 0008 0119 -009 033 0262
Dose amp Regime (RefAgonist 75-150IU) 00005 00052
Antagonist 75-150IU -0364 -053 -020 0 -0199 -051 011 0203
Agonist 187-250IU 0104 -003 024 0139 0028 -031 036 0869
Antagonist 187-250IU 0124 -006 030 0176 0436 -002 089 0059
Agonist 300IU 0151 -001 031 0059 0258 -011 062 0165
Antagonist 300IU 0003 -016 017 0968 0143 -022 050 0433
Agonist 375IU 0072 -023 037 0639 -0185 -086 049 0591
Antagonist 375IU 0124 -011 035 0291 0478 005 090 0028
Agonist 450IU -0129 -041 015 037 -0285 -080 023 0278
Antagonist 450IU -0207 -048 006 0134 0046 -041 051 0843
Intercept 1342 102 166 0 0993 043 155 0001
225
Figure 3a Total oocytes
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fit0
Non-PCO
226
Figure 3b Total oocytes
Plots show the raw data as dots Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 stimulation with HMG USOR practitioner-A none of the specific causes of infertility
25 30 35 40
12
510
20
Age
To
tal E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
To
tal E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
To
tal E
gg
s
AFC
fit0
Non-PCO
227
Figure 4a Metaphase II oocytes (ICSI subset)
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fitm0
Non-PCO
228
Figure 4b Metaphase II oocytes (ICSI subset)
Plots show raw data as dot Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 simulation with HMG USOR practitioner-A None of the specific causes of infertility
25 30 35 40
12
510
20
Age
Ma
ture
IC
SI E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
Ma
ture
IC
SI E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
Ma
ture
IC
SI E
gg
s
AFC
fitm0
Non-PCO
229
GENERAL SUMMARY
7
230
GENERAL SUMMARY
Anti-Muumlllerian hormone a dimeric glycoprotein secreted from granulosa cells
of growing ovarian follicles appears to play a central role in the regulation of
oocyte recruitment and folliculogenesis (Durlinger et al 2002)
Serum anti-Muumlllerian hormone concentration has been found to be one of
the best predictors of ovarian performance in IVF treatment (van Rooij et al
2002 Broer et al 2011) Therefore an evaluation of the role of AMH in assisted
conception has been of great interest and consequently a considerable body of
research work has been performed during last two decades Most published
studies with varying methodological quality have suggested that AMH is one
of the most reliable predictors of ovarian performance in IVF treatment cycles
Consequently many fertility centers have introduced measurement of AMH for
the assessment of ovarian reserve and as a tool for formulation of treatment
strategies for controlled ovarian hyperstimulation in assisted conception
However the studies described in this thesis suggest that some assumptions on
the clinical value of AMH particularly reliability of AMH assay methods and
the role of AMH-tailored individualisation of daily dose of gonadotrophins in
IVF were not based on robust data
For the purpose of this thesis I conducted a comprehensive review of the
published literature on the biology of ovarian reserve the role of AMH in
female reproduction the assay methods and clinical application of AMH in
assisted conception (Chapter 1) I established that a) published work on
sampling variability of AMH measurements and comparability of various assay
methods provide conflicting results b) data on the effect of ethnicity BMI
reproductive pathology and surgery is scarce and c) good quality data on
individualisation of AMH-tailored controlled ovarian hyperstimulation in IVF
is lacking Consequently I decided to conduct a series of studies that directed
towards an improvement of the scientific evidence in these areas of research
Our previous work on within-patient variability of the first generation DSL
assay samples showed that AMH measurements may exhibit considerable (CV
28) sample-to-sample variability (Rustamov et al 2011) In view of this it was
decided to evaluate the validity of newly introduced Gen II assay (Chapter
21) In order to achieve adequately powered results all available AMH
samples of women of 20-46 years of age who had investigation for infertility at
231
secondary and tertiary care divisions of St Maryrsquos Hospital during the study
period were selected for the study According to the manufacturerrsquos
recommendation haemolysed AMH samples may provide erroneous results
and therefore women with haemolysed samples were excluded from the
analysis Inclusion of all women during the study period was also important in
reducing the risk of selection bias particularly in this study which compared
historical and current AMH assay Given the referral criteria of patients did not
change throughout the study period I could confidently report that observed
comparison between DSL and Gen II samples were the reflection of true
differences of the assay methods It is important to note that validity and
performance of a new test should ideally be compared to a reliable ldquogold
standardrdquo test However to date there appears to be no gold standard test in
measurement of AMH and hence an evaluation of the performance of assay
methods can be chllanging Given the lack of a gold standard I decided to
assess the quality of the new test in comparison to what was considered the
most reliable test available at that time accepting that such a comparison may
have limitations Previously two AMH assays (DSL and IOT) were in use and
there is no research evidence on the superiority of one assay over other
Therefore in this study the new Gen II assay was compared to the DSL assay
method which was previously available in our clinic
Once I prepared a robust and validated dataset the quality of Gen II assay
was evaluated by taking following steps of investigation First within-patient
between-sample variability of AMH measurements of Gen II assay samples
were obtained and compared to that of DSL assay samples Then the validity
of the manufacturer recommended between-assay conversion factor was
evaluated by comparing the Gen II assay sample measurements to that of DSL
assay method using both cross-sectional and longitudinal datasets The stability
of the Gen II assay samples was assessed by examining a) stability of the
samples in room temperature b) the linearity of dilution of the samples c)
comparing the standard assay preparation method to that of an equivalent
method and d) stability of samples during storage in frozen condition
Worryingly the study found that the Gen II AMH assay which was
reported to be more reliable than previous assays gave significantly higher
sampling variability (CV 59) compared to that of DSL samples (CV 28)
This significant variation in between repeated measurements of Gen II samples
indicated that there might be a profound fault in the assay method The
232
comparison of the assay methods using a large cohort of clinical samples
suggested that Gen II assay provided 40 lower measurements compared to
that of DSL contradicting the manufacturerrsquos reported 40 higher
measurements (Kumar et al 2011) These discrepancies in the sampling
variability and assay-method comparability suggested that Gen II assay samples
may lack stability which had not been observed previously
When different assays are available for a particular analyte it is critical that
the comparability of results is established and reliable conversion factors or
calibration curves are determined The study demonstrated that the difference
between the previously recommended (Kumar et al 2011 Wallace et al 2011)
conversion factor and the conversion formula obtained in this study was as
high as 60-80 All three studies followed the manufacturersrsquo
recommendations as supplied in the kit insert In terms of the study design
and analysis previous studies assessed the within-sample difference between
the two assays considered this involved the thawing of samples splitting into
two different aliquots and analysis of each aliquot with a different assay In
contrast I conducted between-sample comparison of historical DSL
measurements to that of Gen II using cross sectional and longitudinal
population based analyses The laboratory based within-sample conversion
formula should be reproducible in population based between-sample
comparison particularly in longitudinal analysis Observed discrepancies in the
conversion factors again suggested that AMH samples may suffer from pre-
analytical instability
Thus in collaboration with the scientific team of the Clinical Assay
Laboratory of our hospital we investigated the stability of Gen II assay
samples The studies on sample storage and preparation confirmed the Gen II
assay samples exhibited considerable instability under the storage and
processing conditions recommended by the manufacturer It was suggested
that Gen II samples remain stable when stored in unfrozen conditions up to 7
days and many IVF clinics adopted the practice of shipping unfrozen AMH
samples to centralized laboratories for processing and analysis (Kumar et al
2010 Nelson and La Marca 2011) This study demonstrated that storage of
unfrozen samples can affect obtained results considerably Evaluation of the
stability of samples (n=48) at room temperature found that in the majority of
samples AMH levels in serum increased progressively during 7 days of storage
with an overall increase as high as 58 Contrary to the manufacturerrsquos report
233
even storage of samples in frozen condition (-20 ordmC) does not ensure the
stability of the samples Storage at -20ordmC for 5 days increased AMH levels by
23 compared to fresh samples Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample In contrary the study showed that Gen II samples exhibit
considerable increase with the dilution Pre dilution of serum prior to assay
gave AMH levels up to twice that found in the corresponding neat sample
Similarly pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave overall 72 higher readings compared to sequential
addition These experiments confirmed that Gen II assay methodology was
completely flawed and routine clinical samples were likely to provide highly
erroneous results which could lead to adverse clinical consequences in
patients
To evaluate the robustness of our data I validated the study on the
variability of Gen II samples using external data (Chapter 22) Assessment of
samples obtained from different patient population and different assay-
laboratory found that within-patient between-sample variability of Gen II
AMH measurements were similar to that of my study (CV 62) This
confirmed that Gen II assay sampling variability was independent of
population or laboratory and specific to the assay-method
Findings of this series of studies suggested that the use of Gen II
measurements might have considerable clinical implications particularly when
used as a marker for triaging patient to ovarian stimulation regimens in cycles
of IVF In order to obtain equivalent clinical cut-off ranges for Gen II
samples previously used DSL assay based guidance ranges were recommended
to be increased by 40 However my study found that Gen II assay may
actually provide 20-40 lower measurements compared to that of DSL which
might led to allocation of patients to inappropriate treatment regimens Given
that using the above conversion formula may underestimate ovarian reserve by
60-80 the patients may inadvertently be given significantly higher dose of
gonadotrophins than appropriate in the individual IVF treatment cycles This
can increase the patientrsquos risk of excessive ovarian response resulting in
cancellation of IVF cycles andor severe ovarian hyperstimulation syndrome
(OHSS) In addition significant variation of Gen II assay sample
measurements (CV 59) may also lead to inconsistency in allocation of
patients to appropriate cut off ranges Indeed this was demonstrated by a
234
recent study which found that 7 out of 12 patients moved from one cut-off
range to another when Gen II assay was used for AMH measurements
(Hadlow et al 2013) Therefore we suggested that Gen II assay samples should
not be used in allocating patients to ovarian stimulation regimens
Immediate steps were taken to report these findings to the manufacturer
scientists clinicians and the quality assessment agencies The findings of the
study were presented at the annual meetings of European Society of Human
Reproduction and Embryology as well as British Fertility Society The study
was also published in Human Reproduction which generated an important debate
on the validity of Gen II assay measurements Further independent studies by
other research groups and re-evaluation of the assay by the manufacturer have
confirmed our results (Han et al 2013) This led to recognition of the issues of
the Gen II assay by the manufacturer and consequent modification of the assay
method (King 2012) Subsequent evaluation of Gen II assay by the Medicines
and Healthcare Products Regulatory Agency (MHRA) and the National
External Quality Assessment Service (NEQAS) have confirmed the above
findings As a result the Human Fertility and Embryology Authority have
circulated a field safety notice with the regards to the pitfalls of the AMH Gen
II assay We informed National Institute for Health and Care Excellence
(NICE) of the problems of AMH measurements and urged it to review its
current recommendation on the use of AMH in the investigation and
treatment of infertility With regards to the impact of this work it is important
to note that AMH is widely used in fertility clinics around the world and Gen
II assay is the only commercially available kit for the measurement of AMH in
most countries Consequently this study has made a direct significant impact
in the improving safety and effectiveness of fertility investigation and
treatment around the world However further studies are required to
determine the cause of the instability In addition the validity of the modified
protocol for Gen II assay and other new AMH assays need to be evaluated In
the meantime caution should be exercised in the interpretation of Gen II
AMH measurements
Studies above established that invalid commercial AMH assay was
introduced for clinical use without full and independent validation Regretfully
the issues with the assay were not identified early enough to prevent
widespread use of this faulty test in clinical management of patients around the
world In order to avoid above failures and improve reliability of future AMH
235
assays I recommend following steps should be taken 1) International
standards for the evaluation of validity of existing and future AMH assays
should be developed 2) Independent research groups should evaluate validity
of AMH assays before introduction of the test for clinical application 3)
Validity and performance of already introduced AMH assays ought to be
evaluated by independent research groups periodically to ensure timely
detection of the deterioration in the quality of the test
In view of the observed issues with AMH measurements we conducted
a critical appraisal of the published research on the previous and current assay
methods that reported AMH measurement variability assay method
comparison and sample stability (Chapter 3) Following a systematic search
for all published studies on the evaluation of performance of historic and
current AMH assays ten sample stability studies 17 intrainter-cycle variability
studies and 14 assay method comparability studies were identified Previously
most studies reported that variability of AMH in serum was very small and
suggested a random single measurement provides an accurate assessment of
circulating AMH in serum Therefore using a random AMH measurement for
assessment of ovarian reserve has become a routine practice It appears that
both in reporting particularly in its interpretation the term ldquoAMH variabilityrdquo
was used too broadly and had a various meanings Reviewing all published
studies that used term ldquoAMH variabilityrdquo I identified that the term was used in
interpretation of four distinct outcomes for measurement of variability of
AMH in serum 1) circadian 2) within the menstrual cycle 3) between
menstrual cycles and 4) between repeated samples without consideration of the
day of menstrual cycle In order to delineate the reported variability of AMH
for each outcome I divided the variability studies into four separate groups
and reviewed each study within its appropriate group The review found that
most studies were based on small sample sizes and did not report the
methodology for sample processing and analysis fully The studies also appear
to refer to their outcomes as biological variability of AMH without taking into
account the variability arising due to errors in its measurement More
importantly the review demonstrated that there is clinically significant
variability between AMH measurements in repeated samples which was
reported to be markedly higher with currently used Gen II assay compared to
that of historic DSL and IOT assays
236
Appraisal of assay method comparability found that despite using the
standard manufacturer protocols for the sample analysis the studies have
generated strikingly different between-assay conversion factors The studies
comparing first generation AMH assays (DSL vs IOT) reported conversion
factors ranging from five-fold higher with the IOT assay compared to both
assays giving equivalent AMH concentrations Similarly studies comparing first
and second-generation assays (DSL vs Gen II or IOT vs Gen II) derived
conflicting conclusions The apparent disparity in results of the assay
comparison studies implies that AMH reference ranges and guidance ranges
for IVF treatment which have been established using one assay cannot be
reliably used with another assay method without full and independent
validation Similarly caution is required when comparing the outcomes of
research studies using different AMH assay methods Correspondingly the
review of studies on sample stability revealed conflicting reports on the
stability of AMH under normal storage and processing conditions which was
reported to be a more significant issue with the Gen II assay Similarly there
was considerable discrepancy in the reported results on the linearity of dilution
of AMH samples particularly in Gen II studies In view of above findings we
concluded that AMH in serum may exhibit pre-analytical instability which may
vary with assay method Therefore robust international standards for the
development and validation of AMH assays are required
Although AMH assays have been in clinical use for more than a decade
this appears to be first published review that examined the studies on the
performance of AMH assay methods Indeed a number of review articles
comparing clinical performance of AMH test to other markers of ovarian
reserve have been published (Broer et al 2009 Broer et al 2011b La Marca et
al 2009) Reviewing observational studies the articles concluded that AMH
measurement was one of the most robust methods of assessment of ovarian
reserve However there appears to be no review article that specifically
evaluated the validity of the AMH assay methods suggesting AMH assay
methods were assumed to be reliable despite the lack of robust data on the
validity of assay methods
Reassuringly the report of instability of the Gen II assay samples has
generated significant research interest directed towards understanding the
causes of the issue As a result several hypotheses have been proposed and are
undergoing testing by various research groups For instance in the work
237
described here it was proposed that AMH molecule may undergo proteolytic
changes under certain storage and processing conditions exposing additional
antibody binding sites (Rustamov et al 2012a) The manufacturer of the assay
suggested that the sample instability is due to the presence of complement
interference (King 2012) More recent studies have reported the presence of
another form of AMH molecule pro-AMH in the serum may be the source of
erroneous measurements (Pankhurst et al 2014) Furthermore this study
demonstrated that Gen II assay detects both AMH and pro-AMH suggesting
that the mechanism of sample instability may be more complex than previously
thought It is indeed important to continue the quest to determine the cause of
the sample instability in order to develop reliable method for measurement of
AMH in future In the meantime clinicians should exercise caution when using
AMH measurements in the formulation of treatment strategies for individual
patients
Using a robust protocol for extraction of data and preparation of
datasets I have built a large validated research database (Chapter 4) Utilizing
the clinical electronic data management systems and case notes of patients I
have prepared a validated dataset that will enable study of ovarian reserve in a
wide context including a) assessment of ovarian reserve b) evaluation of the
performance of the biomarkers c) study individualization of ovarian
stimulation in IVF d) association of biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryos live birth) The database has been
used to address research questions posed in chapter 5 and chapter 6 of this
thesis In addition it can be utilized for future studies on assessment of ovarian
reserve and IVF treatment interventions
Both formation and decline of ovarian reserve appears to be largely
determined by genetic factors although at present data on genetic markers are
scarce (Shuh-Huerta et al 2012) Therefore availability of data on clinically
measurable determinants of ovarian reserve is important Consequently I
explored the role of ethnicity BMI endometriosis causes of infertility and
reproductive surgery to ovarian reserve using AMH AFC and FSH
measurements of a large cohort of infertile patients (Chapter 51)
Multivariable regression analysis of data on the non-PCO cohort showed the
association between ethnicity and the markers of ovarian reserve is weak In
contrast I observed a clinically significant association between BMI and
ovarian reserve obese women were found to have higher AMH and lower
238
FSH measurements compared to those of non-obese With regard to the role
of the causes of infertility I did not observe a significant association between
the markers of ovarian reserve and subsets diagnosed with unexplained or
tubal factor infertility In contrast those diagnosed with male factor infertility
had significantly higher AMH and lower FSH measurements which increased
with the severity of the disease In conclusion the study demonstrated that
some of the above factors have a significant impact on above biomarkers of
ovarian reserve and therefore I suggest future studies on ovarian reserve
should include adjustment for the effects these factors
The study showed that in the absence of endometrioma endometriosis
was not found to have a strong association with markers of ovarian reserve
compared to those without the disease Interestingly women with an
endometrioma had significantly higher AMH measurements than those
without endometriosis This is the first study that has reported increased
AMH in serum in the presence of endometrioma Interestingly recent studies
have demonstrated that AMH and its receptor are expressed in tissue samples
obtained from ovarian endometriosis (Wang et al 2009 Carelli et al 2014) It
appears that AMH inhibits growth of both epithelial and stromal cells
(Signorille et al 2014) I believe these intriguing findings warrant further
research on the role of AMH in the pathophysiology of endometriosis With
regards to assessment of ovarian reserve AMH may not reflect ovarian reserve
in the presence of endometrioma and therefore caution should be exercised
With respect to reproductive surgery I conducted a study to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease (Chapter 52) Multivariable regression analysis of the
cross-sectional data showed that salpingo-ophorectomy and ovarian
cystectomy for endometrioma have a significant detrimental impact on ovarian
reserve as estimated by AMH AFC and FSH In contrast neither
salpingectomy nor ovarian cystectomy for cysts other than endometrioma was
found to have appreciable effects on the markers of ovarian reserve I suggest
that women undergoing surgery should be counseled regarding the potential
impact of surgical interventions to their fertility However there was
appreciable overlap between the interquartile ranges of the comparison groups
This suggests that although the effects are significant at a population level
there is considerable variation between individuals Therefore clinicians should
239
exercise caution in predicting the effect of surgery on ovarian reserve of
individual patients
Published studies on the prognostic value of AMH in assisted
conception suggested there is a strong correlation between AMH and extremes
of ovarian response in cycles of IVF (Nelson et al 2007 Nardo et al 2007)
Later case control studies showed that tailoring the daily dose of
gonadotrophins to individual patientrsquos AMH levels and pituitary
desensitisation with GnRH antagonist in patients with the extremes of ovarian
reserve improved the outcomes of IVF treatment (Nelson et al 2009 Yates et
al 2012) However these studies displayed a number of methodological issues
largely due to retrospective analysis small sample size and centre-dependent or
time-dependent selection of cohorts Therefore the role of confounding
factors on the obtained estimates of these studies is unclear Ideally clinical
application of these treatment interventions should be based on research
evidence based on large randomized controlled trials In the absence of
controlled trials I decided to obtain best available estimates on the role of
AMH in individualisation of controlled ovarian stimulation using a robust
methodology in my large cohort of treatment cycles (Chapter 6) Oocyte yield
was used as the outcome measure given it is mainly determined by the
effectiveness of treatment strategies for ovarian stimulation which is the
question the study has addressed In contrast downstream outcomes such as
clinical pregnancy and live birth are subject to additional clinical and
interventional factors The study developed multivariable regression models of
total oocyte yield in all included IVF ICSI cycles (n=1653) and Metaphase II
oocytes of the ICSI subset (n=1101) to measure ovarian response to COH In
view of the significant interaction of PCO status with other variables I
restricted the analysis to non-PCO patients First in order to identify the
confounders I established the effect of a set of plausible factors that may affect
the outcomes including assessment of the effect of age AMH AFC causes of
infertility attempt of IVFICSI cycle COH protocol changes gonadotrophin
preparations operator for oocyte recovery pituitary desensitisation regime and
initial daily dose of gonadotrophins Then I developed the regression models
that examined the effect of gonadotrophin dose and regime categories on total
and mature oocyte numbers
240
The study found that after adjustment for all the above factors age
remained a negative predictor of oocyte yield whereas I observed a gradual
and significant increase in oocyte number with increasing AMH and AFC
values suggesting all these markers display an independent association with
oocyte yield Interestingly after adjustment for all above variables in non-PCO
patients I did not observe the expected increase in oocyte number with
increasing gonadotrophin dose categories beyond the very lowest doses This
suggests that there may not be a significant direct dose-response effect and
consequently strict protocols for tailoring the initial dose of gonadotrophins
may not necessarily optimize ovarian performance in IVF treatment It is
important to note our COH protocols utilized extensive cycle monitoring
using ultrasound follicle tracking and measurement of serum oestradiol levels
with corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not demonstrate a significant
impact on oocyte yield Nevertheless further longitudinal regression analysis
including full time course parameters of cycle monitoring and the dose
adjustments in the model should be conducted in order to ascertain the role of
AMH in tailoring the dose of gonadotrophins in cycles of IVF Moreover the
role of AMH on downstream outcomes of IVF cycles particularly on live
birth should be examined in this dataset Now equipped with a better
understanding of the research methodology and a robust database I am
planning to visit these research questions in future work
Although clinical biomarkers have improved the assessment of ovarian
reserve there remains a significant limitation in their performance in terms of
accurate estimation of ovarian reserve Given that ovarian reserve is believed
to be largely determined genetically recent large Genome-Wide Association
Studies (GWASs) have focused on the identification of genetic markers of
ovarian aging A meta-analysis of these 22 studies identified four genes with
nonsynonymous SNPs as being significantly associated with an age at
menopause (Stolk et al 2012 He et al 2012) However these SNPs were found
to account for only 25-41 of association of the age at menopause
Furthermore studies in mice and humans have identified more than 400 genes
that are involved in ovarian development and function (Wood et al 2013)
Given this genetic heterogeneity it is unlikely that a single genetic determinant
241
of ovarian reserve will be identified In addition epigenetic noncoding RNAs
and gene regulatory regions may play an important role in determination of
ovarian reserve which is yet to be fully explored (Bernstein et al 2012) Indeed
further large scale studies for ascertainment of genetic markers of ovarian
reserve are needed However current biomarkers including AMH appear to
remain as the most useful tests for the assessment of ovarian reserve in the
foreseeable future and further efforts to improve the performance of these
tests are therefore important
In summary some of the assumptions on performance of AMH
measurements particularly Gen II assay appear to have been based on weak
research evidence Similarly there are significant methodological limitations in
the published studies on AMH-tailored individualisation of controlled ovarian
hyperstimulation in IVF I believe the studies described in this thesis have
revealed instability of Gen II assay samples and raised awareness of the pitfalls
of AMH measurements These studies have also demonstrated the effect of
clinically measurable factors on ovarian reserve and provided data on the effect
of AMH other patient characteristics and treatment interventions on oocyte
yield in cycles of IVF Furthermore a robust database and statistical models
have been developed which can be used in future studies on ovarian reserve
and IVF treatment interventions I believe the work presented here has
provided a better understanding of the performance of AMH as an
investigative tool and its role in management of infertile women and provided
resource for future work in this area
242
References Bernstein BE Birney E Dunham I Green ED Gunter C Snyder M ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome Nature 2012 489(7414)57ndash74 [PubMed 22955616] Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Broer SL Doacutelleman M Opmeer BC Fauser BC Mol BW Broekmans FJ AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 Jan-Feb 17(1)46-54 Epub 2010 Jul 28 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 20141011353ndash8
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013 May 99(6)1791-7 Han X Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Human ReproductionJun2013 Vol 28 Issue suppl_1 He C Murabito JM Genome-wide association studies of age at menarche and age at natural menopause Mol Cell Endocrinol 2012
King D URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian
243
response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Pankhurst M Chong Y H and McLennan ISEnzyme-linked immunosorbent assay measurements of antimeuroullerian hormone (AMH) in human blood are a composite of the uncleaved and bioactive cleaved forms of AMH Fertility and Sterility2014
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Stolk L Perry JR Chasman DI et al Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways Nat Genet 2012 44(3)260ndash268
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH
244
and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wood M and Rajkovic A Genomic Markers of Ovarian Reserve Semin Reprod Med 2013 31(6) 399ndash415
245
Authors and affiliations
Stephen A Roberts PhD
Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL United Kingdom
Cheryl Fitzgerald MD
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester M13 0JH
United Kingdom
Philip W Pemberton MSc
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Alexander Smith PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Luciano G Nardo MD
Reproductive Medicine and Gynaecology Unit GyneHealth
Manchester M3 4DN United Kingdom
Allen P Yates PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Monica Krishnan MBChB
Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL United Kingdom
246
Acknowledgments
First and foremost I would like to thank my supervisors Dr Stephen A
Roberts and Dr Cheryl Fitzgerald I am indebted to you for introducing me
into the world of science showing its wonders and guiding me through its
terrains Without your 247 advise and support none of these projects would
have been possible Thank you
I would also like to thank other members of our team Dr Philip W
Pemberton Dr Luciano G Nardo Dr Alexander Smith Dr Allen P Yates and
Monica Krishnan It has been exciting and fun to be a part of the Manchester
AMH Group
I am grateful for the support and friendship of all secretaries nurses
embryologists and consultants of IVF Department at St Maryrsquos Hospital I
would like to express my special thanks to Professor Daniel Brison for his
advice on the projects and providing a great opportunity for research I would
like to express my gratitude to Dr Greg Horne Senior Embryologist for his
patience in taking me through tons of IVF data It was a privilege to be part of
this team
Indeed without support of my wife Zilola Navruzova I could not have
completed my MD programme Thank you for being there for me through
thick and thin of life You are love of my life Your optimism can make
anything possible Your sense of humor and kindness brightened my long
research hours after on-call shifts Only because of your enthusiasm we could
juggle work research and family And thanks for pretending that AMH is
interesting
My children Firuza Sitora and Timur You are most great kids Always stay
cool and funny like this Sorry for not taking you to holiday during my never-
ending research during last year Hope I havenrsquot put you off doing research in
future You get lots of conference holidays after research
247
I canrsquot thank enough my mother Karomat Rajobova and father Dr Sohib
Rustamov Your love kindness and wisdom have always been inspiration and a
guide in my life I always strive to follow your example albeit impossible to
achieve
My brother Ulugbek Rustamov thank your selfless support As always you
have been my guide and strength during these three years My friends Odil
Nizomov Dr Rohit Arora Tarek Sharif and Sabiha Sharif I am grateful for
your friendship and support during my MD Programme
248
I would like to dedicate this thesis to my mother father my wife and
children
Shu Doctorlik Dissertaciysini
Onam (Karomat Rajabova)
Dadam (Dr Sohib Rustamov)
Turmush Urtogim (Zilola Navruzova)
Farzandlarim (Firuza Sohibova Sitora Sohibova
Timur Rustamov) ga bagishlayman
Sizlar mani kuzimni nuri sizlar
Yaratgandan sizlarga mustahkam sogliq va quvonch tilayman
_______________________
Oybek
31 March 2014 Manchester United Kingdom
5
PUBLICATIONS ARISING FROM THE THESIS
Journal Articles
1 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton
The measurement of Anti-Muumlllerian hormone a critical appraisal
The Journal of Clinical Endocrinology amp Metabolism 2014 Mar99(3)723-32
2A Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large
cohort of subjects suggests sample instability Human Reproduction 2012 Oct
27(10) 3085-91
2B Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton Human Reproduction Dec2012 Vol 27 Issue 12 p3641
6
Conference presentations
1 O Rustamov S Roberts C Fitzgerald
Ovarian endometrioma is associated with increased AMH levels
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2014 Munich
Poster Presentation
2 O Rustamov M Krishnan R Mathur S Roberts C Fitzgerald
The effect of BMI to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral presentation Dr O Rustamov
3 M Krishnan O Rustamov R Mathur S Roberts C Fitzgerald
The effect of the ethnicity to the ovarian reserve
Annual Meeting of British Fertility Society January 2014 Sheffield
Oral Presentation Dr M Krishnan
4 O Rustamov M Krishnan S Roberts C Fitzgerald
Reproductive surgery and ovarian reserve
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr O Rustamov
5 C Fitzgerald O Rustamov P Pemberton A Smith A Yates M Krishnan
R Russell L Nardo SRoberts
AMH assays A review of the literature on assay method comparability
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
Oral presentation Dr C Fitzgerald
6 M Krishnan O Rustamov R Russell C Fitzgerald S Roberts
The role of the ethnicity and the body weight in determination of AMH levels
in infertile women
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2013 London
7
Poster presentation
7 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
AMH Gen II assay - can we believe the measurements
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
8 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Old and new AMH assays Can we rely on current conversion factor
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
9 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
Random AMH measurement is not reproducible
8th Biennial Conference of UK Fertility Societies January 2013 Liverpool
Poster presentation
10 Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates
Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W
Pemberton
The reproducibility of serum Anti-Muumlllerian hormone AMH Gen II assay
Annual Meeting of European Society of Human Reproduction and
Embryology (ESHRE) July 2012 Istanbul
Oral Presentation Dr O Rustamov
8
GENERAL INTRODUCTION
AND LITERATURE REVIEW
1
9
CONTENTS I LITERATURE REVIEWhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10 GENERAL BACKGROUNDhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip10
1 OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip12 11 Primordial Follicle Assemblyhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip13 12 Oocyte recruitmenthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip14 13 Theory of neo-oogenesishelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip15 2 MARKERS OF OVARIAN RESERVEhelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 21 Ovarian reserve markers with limited clinical valuehelliphelliphelliphelliphellip16 213 Inhibin Bhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip16 214 Basal oestradiolhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 215 Dynamic testshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip17 216 Ovarian volumehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 22 Ovarian reserve markers in routine clinical usehelliphelliphelliphelliphelliphelliphellip18 221 Chronological agehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 222 Basal FSHhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18 223 Antral follicle counthelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip19 3 ANTI-MUumlLLERIAN HORMONEhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 31 Biology of anti-Muumlllerian hormonehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip20 311 The role of AMH in the ovaryhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip21 312 AMH in women with polycystic ovary syndromehelliphelliphelliphelliphellip22 32 AMH Assayhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip23 33 Variability of AMH measurementshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip24 34 Role of AMH in assessment of ovarian reservehelliphelliphelliphelliphelliphellip25 341 Prediction of poor and excessive ovarian response in IVFhelliphellip25 342 Prediction of live birth in cycles of IVFhelliphelliphelliphelliphelliphelliphelliphelliphellip26
3 5 Role of AMH in ovarian stimulation for cycles of IVFhelliphelliphelliphellip26
4 MULTIVARIATE TESTShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip27
5 SUMMARYhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip28 II GENERAL INTRODUCTIONhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip29 REFERENCEShelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip31
10
I LITERATURE REVIEW GENERAL BACKGROUND
Infertility is a disease of the reproductive system defined by the failure to
achieve a pregnancy after 12 months of regular unprotected sexual intercourse
although the criteria for the duration vary between different countries (NICE
2013) Worldwide prevalence of infertility estimated to be around 724 million
couples and around 40 million of those seek medical care (Hull et al 1985) In
the UK 15 couples present with infertility with an annual incidence of 12
couples per 1000 general population (Scott et al 2009) The main causes of
infertility are tubal disease ovulatory disorders male factor and poor ovarian
reserve In a third of couples the cause of failure to achieve pregnancy is not
established which is known as unexplained infertility (NICE 2013) Effective
treatment options include improving lifestyle factors medical andor surgical
treatment of underlying pathology induction of ovulation and Assisted
Reproductive Technology (ART) Assisted Reproduction consist of
intrauterine insemination (IUI) and in vitro fertilisation (IVF) cycles with or
without introcytoplasmic sperm injection (ICSI) as well as treatment involving
donated gametes It is estimated that 75 of infertile couples presenting at
primary care centres in the UK are referred to fertility specialists based at
secondary or tertiary care centres and nearly 50 of those are subsequently
offered IVFICSI treatment (Scott et al 2009) This is supported by figures of
Human Fertility and Embryology Authority (HFEA) which indicates more
than 50000 IVF treatment cycles are performed in the UK annually (HFEA
2008)
An IVF treatment cycle involves a) pituitary down regulation b)
controlled ovarian stimulation c) oocyte recovery c) in vitro fertilisation of eggs
with sperm d) transfer of resulting embryo(s) back to uterus and c) luteal
phase support (NICE 2013) Prevention of premature surge of luteinising
hormone during controlled ovarian stimulation (COS) is achieved by pituitary
down regulation using either preparations of gonadotrophin releasing hormone
agonist which is widely known as ldquoAgonist cyclerdquo or gonadotrophin releasing
hormone antagonist which is known ldquoAntagonist cyclerdquo (Figure 1 and 2)
Controlled ovarian stimulation involves administration of gonadotrophins to
encourage the development of supernumerary preovulatory follicles followed
by administration of exogenous human chorionic gonadotropin (hCG) or
11
recombinant luteinising hormone (rLH) to assist in maturation of oocytes 34-
36 hours prior to egg collection which is usually conducted with guidance of
transvaginal ultrasound scanning Subject to sperm parameters the fertilisation
of oocytes is conducted by in vitro insemination or intracytoplasmic sperm
injection The resulting embryo(s) are cultured under strict laboratory
conditions and undergo regular qualitative and quantitative assessments before
transferring the best quality embryo(s) back into uterus during its cleavage
(Day 2 or Day 3) or blastocyst (Day 5 or Day 6) stage of development In
natural menstrual cycles under the influence of HCG progesterone secreted
by the ovarian corpus luteum ensures proliferative changes in the endometrium
providing the optimal environment for implantation of embryo(s) (van der
Linden et al 2011) However in IVF treatment cycles owing to pituitary down
regulation and lack of HCG progesterone levels are not in sufficiently high
concentration to ensure an adequate endometrial receptivity and therefore
exogenous analogues of this hormone is administered following transfer of
embryo(s) This is called ldquoluteal phase supportrdquo and in patients with viable
pregnancy usually lasts till 12th week of gestation when placenta starts
producing progesterone in sufficient quantities (van der Linden et al 2011)
In IVF programmes the ldquosuccessrdquo of the treatment often defined as
achieving a live birth following IVF cycle and expressed using Live Birth Rate
(LBR) In general success in IVF predominantly determined by womanrsquos age
cause(s) of infertility ovarian reserve previous reproductive history and
lifestyle factors (NICE 2013 Taylor 2003 Lintsen et al 2005) However
effectiveness of medical interventions as well as the quality of care play
important role in determining the outcome of IVF treatment This is evident
from significant variation in live birth rates among fertility clinics given for
instance in the UK LBR for women younger than 35 years of age after IVF
cycles varies from 15 to 61 (HFEA 2008 HFEA 2007) The provision of
effective interventions in both clinical and laboratory aspects of the care
appears to be the key in achieving high success rates Identification of patients
with sufficient ovarian reserve who benefit from IVF cycles followed by
providing optimal ovarian stimulation regimens may be useful in improving the
outcomes of IVF programmes According to HFEA data around 12 of IVF
cycles are cancelled due to poor or excessive ovarian response (Kurinczuk et al
2010) Availability of reliable markers for assessment of ovarian reserve and
tailoring ovarian stimulation regimens to the need of each individual patient
12
may improve selection of patients with sufficient ovarian reserve and reduce
the rate of cycle cancellation consequently improving the success of IVF
cycles (Yates et al 2011)
Assessment of ovarian reserve can be achieved using various biomarkers
and four of those are currently used by most clinics womanrsquos chronological
age (Age) serum follicle stimulating hormone (FSH) antral follicle count
(AFC) and serum anti-Muumlllerian hormone (AMH) More recently AMH has
been a focus of interest given it is the only available endocrine marker that is
suitable for direct assessment of the activity of ovarian follicles in their non-
cyclical stage development providing a window to FSH independent phase of
follicular recruitment Furthermore it appears to be reliable biomarker for a)
both the assessment of ovarian reserve and the optimisation of ovarian
stimulation regimens (Yates et al 2011 La Marca et al 2009) b) screening and
diagnosis of polycystic ovarian syndrome (PCOS) (Cook et al 2002) c)
monitoring of disease activity in women with a history of granulosa cell
tumours (Lane et al 1999) d) prediction of the age of diminished fertility and
the menopause (van Disseldorp et al 2008 Broer et al 2011) and finally (e)
assessment of the long term effect of chemotherapy on ovarian reserve
(Anderson 2011)
In this review I first discuss current knowledge on factors that
determine ovarian reserve including the formation and loss of oocyte pool
Then characteristics of the markers of ovarian reserve are reviewed Finally I
examine current understanding of biology of anti-Muumlllerian hormone and its
role in management of infertility
1 OVARIAN RESERVE
It is important to recognize that there is no universal definition for the
term ldquoovarian reserverdquo and the term can have various meanings depending on
the context in which it is used For instance the scientific literature describing
the biology of ovarian reserve usually refers to ldquothe total number of remaining
oocytes in the ovaries which consists of the number of resting primordial
follicles and growing primary pre-antral and antral folliclesrdquo (Gleicher et al
2011) In contrast the use of the term in the context of clinical studies may
refer to ldquoclinically measurable ovarian reserve established using available
biomarkers of ovarian reserverdquo For the purpose of clarity in this thesis the
13
term ldquoovarian reserverdquo refers to clinically measurable ovarian reserve whilst
true biological ovarian reserve will be termed ldquobiological ovarian reserverdquo
Recent studies have demonstrated that ovarian reserve is highly variable
between women due to the variation in the size of initial ovarian reserve at
birth as well as the rate of loss of ovarian reserve thereafter (Wallace et al
2010) Interestingly the rate of oocyte loss appears to be mainly determined by
the initial ovarian reserve which is believed to be facilitated by most potent
ovarian growth factor anti-Muumlllerian hormone Similarly the size of the initial
ovarian reserve is mainly underpinned by the rate of primordial follicle
assembly in the embryo which is also regulated by AMH Both primordial
follicle assembly and the rate of oocyte loss appear to be primarily under the
influence of genetic factors although developmental and environmental factors
are also believed to play a role (Nilsson et al 2010 Shuh-Huerta et al 2012)
11 Primordial follicle assembly
The process of assembly of primordial follicles in the female embryo
spans from the early embryonic to the early postnatal period and formation of
primordial follicles consists of following stages 1) primordial germ cell (PGC)
2) oogonia 3) primary oocyte and 4) primordial follicle In the human female
fetus around a hundred cells that differentiated from extra-embryonic
ectoderm form early PGCs on the yolk sac and migrate via hindgut to gonadal
ridges during 4th - 6th weeks of gestation (MC et al 1953 Donovan 1998) Once
arrived to the gonadal ridges these cells are called primary oogonia which
consequently undergo several rounds of mitotic division during 6th - 28th weeks
of gestation Interestingly the numbers of oogonia reach as high as six million
during its highest rate of mitotic division at around 20 weeks of gestation
Following the last round of mitotic division oogonia enter meiosis which
marks their new stage of development-primary oocyte Formation of
primordial follicles starts as early as at 8th week of gestation and is characterised
by meiosis of primary oocyte that arrest in diplotyne stage and surrounding of
the oocyte by somatic granulosa cell (Baker et al 1963 Maheshwari and Fowler
2010) Indeed the primordial follicle is the cardinal unit of the biological
ovarian reserve and therefore the rate of formation of primordial follicles is the
main determinant of initial biological ovarian reserve at birth
Interestingly the process of loss of oogonia and oocytes which is also
one of the main determinants of the initial ovarian reserve takes place
14
throughout the period of follicle assembly The formation of the granulosa cell
layer around the oocyte prevents the oocyte from subsequent atresia The
oocyte enveloped in a single layer of granulosa cells which is also known as
primordial follicle remains quiescent until recruitment of the follicle for
growth which may not take place for a number of decades after the formation
of a particular primordial follicle (Skinner 2005 Maheshwari and Fowler 2010)
12 Oocyte recruitment
Follicle growth in women consists of two stages a) the initial non-cyclical
recruitment of primordial follicles and the formation of a primary and a pre-
antral follicles and b) cyclical development of antral follicles with subsequent
selection of usually a single dominant follicle The initial recruitment of
primordial follicles is continuous non-cyclical process that starts as early as
from 18-20 weeks of gestation and lasts till the depletion of follicle pool which
later results in the menopause (McGee and Hsueh 2000) Transformation of
flat granulosa cells into cuboidal cells increases the diameter of the oocyte and
the formation of zona pellicuda completes the stage of formation of a primary
follicle During pre-antral stage oocytes increase in diameter and mitotic
division of granulose cells create a new layer of cells-theca cells The
mechanism of initial recruitment of oocytes is not well understood but it is
clear that the process is independent of influence of pituitary gonadotrophins
and appears to be governed by the genetically pre-programmed interaction of
the oocyte with local growth factors the most important of which appears to
be anti-Muumlllerian hormone and cytokines (McGee and Hsueh 2000)
The cyclical phase of development of oocytes is characterised by the
transformation of secondary follicle into antral follicle and subsequent growth
of antral follicles into pre-ovulatory stages In general the process of cyclic
recruitment starts from puberty under the influence of rising levels of pituitary
follicular stimulating hormone (FSH) During the antral stage oocyte increases
in size even further and the formation of a fluid filled space in follicle is
observed Under the influence of FSH luteinising hormone (LH) and local
growth factorsselection of a single dominant follicle occurs which followsby an
ovulation (McGee and Hsueh 2000)
Oocyte loss is a continuous process and occurs due to atresia of oocytes
during primary secondary and antral stages of development The rate of
oocyte loss appears to increase until the age of around 14 and declines
15
thereafter until the age of the menopause when around 1000 primordial
follicles remain (Hansen et al 2008 Oktem and Oktayl 2008) Furthermore by
the age of 30 years the average age at which women of western societies plan
to start a family around 90 of initial primordial follicles are lost which
illustrates that formation and maintenance of ovarian reserve is wasteful
process in humans (ONS 2012 Wallace and Kelsey 2010) As mentioned
above there is a wide individual variation in both sizes of initial primordial
follicular pool and the rate of oocyte loss which explains variation in the
reproductive lifespan in women Evidently the number of primordial follicles
at birth ranges between around 35000 to 25 million per ovary and similarly
the rate of oocyte loss during its peak at 14 years of age may range between
100 to 7500 primordial follicles per month which is believed to be inversely
proportional to initial size of primordial follicle pool (Wallace and Kelsey
2010)
13 Theory of neo-oogenesis
The traditional view of oogenesis states that the process of the creation
and the mitotic division of oogonia with subsequent formation of primordial
follicles takes place only during embryonic and foetal life (Zuckerman 1951)
According to this central theory of mammalian reproductive biology females
are born with a certain number of germ cells that is gradually lost but not
renewed during postnatal period However Johnson et al have recently
challenged this view and reported that adult mammalian ovary may possesses
mitotically active germ cells that continuously replenish the primordial follicle
pool (Johnson et al 2004) The group reported that ovaries of juvenile and
young adult mice contained large ovoid cells which resemble germ cells of
foetal mouse ovaries Interestingly immunohistochemical staining for a gene
which is expressed exclusively in germ cells have been reported to have
confirmed that these large ovoid cells were of germline lineage Furthermore
application of a mitotic germ cell toxicant busulphan appeared to have
eliminated primordial follicle reserve by early adulthood but did not induce
atresia suggesting the presence of proliferative germ cells in postnatal mouse
ovary (Johnson et al 2004 Bazer 2004) The study has generated enormous
amount of interest as well as debate among reproductive biologists (Notarianni
2011) Some other groups have also reported an evidence of postnatal
oogenesis (Pacchiarott et al 2010 Zou et al 2009 Bukovsky et al 2004)) while
16
others do not support the theory (Bristol-Gould et al 2006 Byskov et al 2005
Begum et al 2008) Furthermore some authors argued that adult mouse
germline stem cells exist and remain quiescent in physiologic conditions and
neo-oogenesis occurs only in response to ovotoxic damage (Tilly et al 2007 De
Felici 2010) Although consensus has yet to emerge to date there is no
conclusive evidence on validity of theory of neo-oogenesis
2 MARKERS FOR ASSESMENT OF OVARIAN RESERVE
Biological ovarian reserve is defined as the number of primordial and
growing follicles left in the ovary at any given time and therefore only
counting the number of primordial follicles by histological assessment can
accurately determine ovarian reserve which is clearly not feasible in clinical
setting However ovarian reserve can be estimated using various biomarkers
dynamic clinical tests and implied from the outcomes of ART cycles
Although a wide range of clinical (age ovarian response in previous IVF
cycles) biochemical (basal FSH Inhibin B basal oestradiol AMH) ultrasound
(ovarian volume antral follicle count (AFC)) and dynamic (clomiphene
challenge test exogenous FSH ovarian reserve test GnRH analogue
stimulating test) tests of ovarian reserve exist only a few of the markers are
reliable and practical enough to be of use in routine clinical practice In this
chapter first I discuss the research evidence on the assessment of the markers
andor tests of ovarian reserve that have limited clinical value Then I
evaluated more reliable markers that are in routine clinical use Age FSH
AFC and combination of these markers in multivariable tests Finally I
conducted detailed review of biology of AMH and the role AMH measurement
in the management of infertility
21 Ovarian reserve markers with limited clinical value
211 Inhibin B
Inhibins are members of TGFβ family and expressed in granulosa cells
of growing follicles Principal role of inhibins is thought to be the negative
feedback regulation of pituitary FSH secretion and therefore the serum level of
circulating hormone is believed to reflect the state of folliculogenesis
17
Consequently several groups have studied the role of serum Inhibin β in the
assessment of ovarian reserve Although initial reports were encouraging
(Seifer et al 1997) more robust studies demonstrated that serum Inhibin β was
less reliable than chronological age or basal FSH (Creus et al 2000 Urbancsek
2005) The systematic review of nine studies demonstrated that accuracy of the
Inhibin β test for predicting poor ovarian response and non-pregnancy in IVF
cycles was modest even at a very low threshold level (Broekmans et al 2006)
Therefore it is recommended that inhibin β at best can be used as only
screening test in the fertility centers where other more reliable markers are not
available (Broekmans et al 2006)
212 Basal oestradiol
Some studies suggested that elevated basal oestradiol levels indicate low
ovarian reserve and are associated with poor fertility prognosis (Johannes et al
1998 Licciardi and Rosenwaks 1995) Johannes et al demonstrated basal
oestradiol in conjunction with serum FSH is more reliable than serum FSH
alone in prediction of cycle cancellation due to the poor response in IVF cycles
(Johannes et al 1998) However there are no published data on the comparison
of basal oestradiol to more reliable markers such as AMH or antral follicle
count (AFC) Moreover a recent systematic review has demonstrated that
basal oestradiol has very low predictive value for poor response and has no
discriminatory power for accuracy of non-pregnancy prediction (Broekmans et
al 2006)
213 Dynamic tests of ovarian reserve
The dynamic tests of ovarian reserve are based on assessment of ovarian
response by measuring serum FSH and oestradiol levels following
administration of exogenous stimulation The following tests are reported in
literature Clomiphene Citrate Challenge Test (CCCT) Exogenous FSH
Ovarian Reserve Test (EFORT) and GnRH agonist stimulation test A recent
systematic review and meta-analysis on the accuracy of these tests showed that
none of them can adequately predict poor response or non-pregnancy in IVF
cycles and therefore are not recommended for use in routine clinical practice
(Maheshwari et al 2009)
18
214 Ovarian volume
There is some evidence that increased age is associated with decreased
ovarian volume and women with smaller ovaries are more likely to have
cancellation of their IVF cycles due to poor ovarian response (Syrop et al 1995
Syrop et al 1999 Templeton 1995) However a meta-analysis of the published
studies on the accuracy of ovarian volume as a predictor of poor response and
non-pregnancy in IVF cycles failed to demonstrate clinical usefulness of the
test and suggested the test is not reliable enough for use in a routine clinical
practice (Broekmans et al 2006)
22 Ovarian reserve markers in routine clinical use
221 Chronological age
Owing to the biological age-related decline of the quantity and arguably
the quality of oocytes the chronological age can be used as a marker of ovarian
reserve Studies have demonstrated that ovarian reserve (Wallace and Kelsey
2010 Kelsey 2011) natural fecundity (Islam et al 1989 and outcomes of ART
(Templeton et al 1996 van Kooij et al 1996) decline significantly from age of
35 when it is believed the ovarian reserve undergoes accelerated decline
Although there is a strong association between chronological age and reduction
in fertility evidently there is a significant variation in age-related ovarian
reserve indicating chronological age alone may not be sufficient to estimate the
individual womanrsquos ovarian reserve reliably (Broekmans et al 2006)
222 Basal FSH
Basal FSH was one of the first endocrine markers introduced in ART
programs and is still utilized in many fertility clinics albeit in conjunction with
other markers which are considered more reliable (Creus et al 2000) Secretion
of FSH is largely governed by the negative feedback effect of steroid
hormones primarily oestradiol and inhibins which are expressed in granulosa
cells of growing ovarian follicles Consequently decreased or diminished
recruitment of ovarian follicles is associated increased serum FSH
measurements and high particularly very high basal FSH reading is considered
as a good marker of very low or diminished ovarian reserve (Abdalla et al
2006) However unlike some other markers FSH measurements do not
appear to have discriminatory power for categorisation of patients to various
19
bands of ovarian reserve Given between-patient variability FSH measurement
(CV 30) is similar to its within-patient variability (27) stratification of
patients to various ranges of ovarian reserve does not appear to be feasible
(Rustamov et al 2011) Indeed a recent systematic review of 37 studies on the
prediction of poor response and non-pregnancy in IVF cycle has concluded
that basal FSH is an adequate test at very high threshold levels and therefore
has limited value in modern ART programs (Broekmans et al 2006)
223 Antral follicle count
Antral follicle count estimation involves ultrasound assessment of
ovaries between 2nd and 4th day of menstrual period and counting ldquofolliclesrdquo
which corresponds to antral stage of folliculogenesis (Broekmans et al 2010)
The test provides direct quantitative assessment of growing follicles and is
known as one of the most reliable markers of ovarian reserve (Broekmans et al
2006) AFC measurement has been reported as having a similar sensitivity and
specificity to AMH in prediction of poor and excessive ovarian response in
IVF cycles (Broekmans et al 2006 Broer et al 2010 Jayaprakasan et al 2010)
Given AFC measurement is available instantly and allows patients to be
counseled immediately the test eliminates the need for an additional patient
visit prior to IVF cycle However AFC is normally performed only in the early
follicular phase of the menstrual cycle given most published data on
measurement of AFC are based on studies that assessed antral follicles during
this stage of the cycle (Broekmans et al 2010a) Interestingly more recent
studies suggest that variability of AFC during menstrual cycle is small
particularly when follicles between 2-6mm are counted and therefore
assessment of AFC without account for the day of menstrual cycle may be
feasible (Deb et al 2013)
One of the main drawbacks of AFC is that the cut off levels for size of
counted follicles remains to be standardised (Broekmans 2010b) Initially
follicles of 2-10mm were introduced as the range for AFC and many studies
were based on this cut off Later counting follicles of 2-6mm was reported to
provide most accurate assessment of ovarian reserve (Jayaprakasan et al 2010b
Haadsma et al 2007) and therefore some newer studies are based on AFC
measurements that used this criterion Consequently direct comparison of the
outcomes of various studies on assessment of AFC requires careful analysis
20
3 ANTI-MUumlLLERIAN HORMONE
31 Biology of Anti-Muumlllerian hormone
AMH is a member of transforming growth factor β superfamily which
was discovered by Jost et al in 1947 and was initially known for its is role in
regression of Muumlllerian ducts in sex differentiation of the male embryo In
women AMH is believed to be solely produced by ovaries and expressed in
granulosa cells of growing follicles of 2-6 mm in size which corresponds to
primary pre-antral and early antral stage of follicular development Although
there has been a report of expression of AMH in endometrial cells to date
there is no other published evidence that supports this finding (Wang et al
2009) Indeed studies that evaluated half-life of AMH in serum have
demonstrated that in women who had bilateral salpingo-oopherectomy AMH
becomes undetectable within 3-5 days of following surgery suggesting ovaries
are the only source of secretion of AMH in appreciable quantity (La Marca et
al 2005b) Anti-Muumlllerian hormone is a dimeric glycoprotein which is
composed of a long N-terminus and short C-terminus and was believed to be
secreted in serum only in this dimeric form (AMH-N C)
Like other members of TGF-β family which includes inhibins activins
bone morphogenic proteins (BMPs) and growth and differentiation factors
(Massague et al 1990) AMH binds to two type of serinethreonine kinase
receptors referred to as type I and type II In order to activate AMH signaling
pathway both receptors have to form a heteromeric complex When AMH
binds to the type II (AMHR-II) receptor (Massague et al 2000) this will
phosphorylate and activate a type I receptor (ALK2 -3 andor -6) which
subsequently activates the SMAD pathway through phosphorylation of
SMAD 1 5 andor 8 These activated SMADs interact with SMAD4 and
translocate to the nucleus regulating the expression of different genes
inhibiting the recruitment of primordial follicles and reducing FSH sensitivity
in growing follicles In addition AMH receptors as well as the other members
of TGF-β family can activate MAPK and PI3KAKT pathways
Studies on AMHR II-deficient male mice demonstrated lack of
regression of Muumlllerian ducts suggesting that type II receptor is essential in
AMH signaling (Mishina et al 1996) Similarly Type I receptors which includes
three members of activin receptor-like kinase (ALK2 ALK3 and ALK6) also
appear to play an important role in the regression of Muumlllerian ducts although
21
the role of ALK 6 in AMH signaling appears not to be crucial (Visser 2003
Clarke et al 2001) The signal transduction pathway of AMH in the ovary is
largely not understood In postnatal mice ovary AMHR-II receptor was
expressed in both granulosa and theca cells of pre-antral and antral follicles
(Visser 2003) AMH type I receptors ALK 2 and ALK 3 is expressed in foetal
as well as adult mouse ovary while ALK 6 is expressed in only adult ovary
(Visser 2003)
311 The role of AMH in the ovary
In the mammalian ovary the role of AMH appears to be one of a
regulation of size of the primordial follicle pool by its inhibitory effect on the
formation as well as the growth of primordial follicles (Nilsson et al 2011) In
the embryonic mouse ovary AMH inhibits the initiation of the assembly of
follicles when the process of apoptosis of the majority of oocytes is observed
(Nilsson et al 2011) Consequently AMH reduces the rate of oocyte loss
which plays an important role in the determination of the size of initial follicle
pool Similarly in the adult mouse ovary AMH plays a central role in
maintaining the follicle pool AMH inhibits both the processes of the initial
(non-cyclical) recruitment of primordial follicles and subsequent FSH-
dependent cyclical growth of antral follicles (Figure 3) Inhibition of the initial
recruitment of a new cohort of follicles is believed to be achieved by a
paracrine negative feedback effect of the rising levels of AMH secreted from
already recruited growing follicles (Durlinger et al 1999) Durlinger et al
compared the complete follicle population of AMHnull mice and wild type
mice of different ages of 25 days 4 months old and 13 months old and found
that the ovaries of 25 day and 4 months old AMHnull females contained
significantly higher number of growing pre-antral and antral follicles but
significantly fewer primordial follicles compared to wild-type females
(Durlinger et al 1999) Interestingly almost no primordial follicles were
detected in 13 months old AMHnull mice ovaries suggesting AMH is a potent
inhibitor of the recruitment of primordial follicles and in the absence of AMH
ovaries undergo premature depletion of primordial follicles due to an
accelerated recruitment Subsequent study conducted by the group
demonstrated that in addition to its inhibitory effect to the resting follicles
AMH also suppresses the development of the growing follicles (Durlinger et al
2001 Durlinger et al 2002 Themmen 2005) It appears that AMH inhibits
22
FSH-induced follicle growth by reducing the sensitivity of growing follicles to
FSH which has been confirmed by in vivo as well as in vitro studies (Durlinger
et al 1999 Durlinger et al 2001) In the initial study the group observed that
despite lower levels of serum FSH concentration ovaries of AMHnull mice
contained more growing follicles than that of their wild-type littermates which
has been supported by the findings of subsequent in vitro study (Durlinger et al
1999) Addition of AMH to the culture inhibited FSH-induced follicle growth
of pre-antral mouse follicles due to reduction in granulosa cell proliferation
(Durlinger et al 2001)
In the human embryo the expression of AMH commences in the late
foetal life and can be detected only from 36 weeks of gestation (Rajpert-De et
al 1999 Lee et al 1996) Following a small decline in first two years of life
AMH levels gradually increase to peak at (mean 5 ngml) around age of 24
years In line with the pattern of oocyte loss serum hormone levels gradually
decline with increasing age and become undetectable around 5 years prior to
menopause (Kelsey et al 2011 Nelson et al 2011)
It has been suggested that anti-Muumlllerian hormone plays a central role in
determining the pace of recruitment of primordial follicles hence maintaining
the primordial follicle pool of postnatal mammalian ovary Consequently a
reduction in the concentration of circulating AMH signals the exhaustion of
the primordial follicle pool and the decline of ovarian function
312 AMH in women with polycystic ovary syndrome
Polycystic ovary syndrome (PCOS) endocrine abnormality characterised
by increased ovarian androgen secretion infrequent ovulation and the
appearance of ldquopolycysticrdquo ovaries on ultrasound scan (Dunaif 1997 Homburg
et al 1993) It is the commonest endocrine abnormality in women of
reproductive age and affects around 15-20 of women PCOS is also one of
the main causes of anovulation and subsequent sub-fertility (Webber et al
2003) Although the role of anti-Muumlllerian hormone in the development of
PCOS is not fully understood it is becoming increasingly evident that the
hormone plays an important role in its pathogenesis (Pehlivanov et al 2011)
There is a strong association between serum AMH levels and PCOS and it
appears that women diagnosed with PCOS have two to three fold higher
serum AMH concentration compared to normo-ovulatory women (Cook et al
2002 Pigny et al 2003) Similarly women with PCOS are found to have
23
significantly higher number antral follicles Interestingly the expression of
AMH in granulosa cells of follicles were found to be 75 times higher in women
with PCOS compared to those without a the disease suggesting increased
serum AMH in PCOS may be due to increased secretion of hormone per
follicle rather than due to an increased number of antral follicles (Pellat et al
2007) High AMH concentrations may act as the main facilitator of abnormal
folliculogenesis in PCOS given the follicles appear to arrest when they reach
an antral stage (2-6mm) of development (Rajpert-De et al 1999) Indeed the
studies of Durlinger et al have demonstrated that AMH inhibits selection of
dominant follicle when follicles reach antral stage of development (Durlinger et
al 2001) Serum AMH levels appear to decrease with treatment of PCOS
which may play important role in restoration of ovulatory cycles Studies have
reported a significant reduction in serum concentration of AMH following
treatment of PCOS with metformin and laparoscopic ovarian diathermy (Falbo
et al 2010 Amer et al 2009 Elmashad 2011) Similarly reduction of BMI
following intensified endurance exercise training for treatment of PCOS may
also lead to a significant reduction in serum AMH levels (Moran et al 2011)
This suggests that there is strong association between serum concentration of
AMH and abnormal folliculogenesis in PCOS and therefore understanding the
molecular mechanisms of this interaction should be one of the priorities of
future research
32 AMH Assays
Enzyme-linked immunosorbent assay specific for measurement of anti-
Muumlllerian hormone was first developed in 1990 and was recognised as a
significant step in the assessment of ovarian reserve (Hudson et al 1990)
Subsequently a number of non-commercial immunoassays were developed
which were mainly used in research settings (Lee et al 1996) Later Diagnostic
Systems Ltd (DSL) and Immunotech Beckman Coulter Ltd (IOT) introduced
two commercial immunoassays for the routine clinical assessment of ovarian
reserve which are known as ldquofirst generation AMH assaysrdquo (Nelson and La
Marca 2011) These assays employed two different antibodies against AMH
and used different standards for calibration providing non-comparable
measurements (Nelson and La Marca 2011) Consequently several studies
attempted to develop a reliable between-assay conversion factor which
interestingly revealed from five-fold higher with the IOT assay to assay
24
equivalence causing significant impact to reliability of AMH measurements and
interpretation of research findings (Hehenkamp et al 2006 Freour et al 2007
Bersinger et al 2007 Taieb et al 2008 Lee et al 2011)
Later the manufacturer of IOT assay (Beckmann Coulter Ltd)
consolidated the manufacturer of the DSL assay (Diagnostic Systems
Laboratories Inc) and introduced a new assay ldquoGen II AMH assayrdquo which is
only available commercial immunoassay in most countries including the UK
AMH Gen II assay was developed using the antibodies derived from first
generation DSL assay and calibrated using the standards used for IOT assay
and was believed to be considerably more stable compared to the first
generation immunoassays providing more reliable measurements (Kumar et al
2010 Nelson and La Marca 2011) The manufacturer as well as initial external
validation study recommended when compared to old DSL assay AMH Gen
II assay provides around 40 higher measurements and therefore previously
reported DSL-based clinical cut-off levels for estimation of ovarian reserve
should be increased by 40 in order to use Gen II-based AMH results (Kumar
et al 2010 Wallace et al 2011 Nelson and La Marca 2011)
33 Variability of AMH measurements
It is generally believed that AMH values do not change throughout the
menstrual cycle and early studies reported that variation in AMH
measurements between repeated measurements of same patient was negligible
(van Disseldorp et al 2010 La Marca 2010) On the basis of these studies
sampling at a random time in the menstrual cycle was introduced as a method
for measurement of AMH in routine clinical practice However the
methodologies of some of these studies do not appear to be robust enough to
reliably estimate sample-to-sample variability of AMH which is mainly due to
small sample sizes (Rustamov et al 2011) Consequently in a recent study we
assessed sample-to-sample variability of AMH using DSL assay and found that
within-subject coefficient of variation (CV) of AMH between samples were as
high as 28 which cannot be attributed to any patient or cycle characteristics
(Rustamov et al 2011) Although there is no consensus in the causes of this
observed variability in AMH measurements we believe it is largely attributable
to instability of AMH samples given initial recruitment of primordial follicles
and growth of AMH producing pre-antral and antral follicles are continuous
process and therefore the true biological variation between samples is unlikely
25
to be high However given the importance of establishing true variability of
AMH in both understanding of the biology of hormone and clinical
application of the test future studies should be conducted to establish the
source of variability in the clinical samples
3 4 The role of AMH in the assessment of ovarian reserve
341 Prediction of poor and excessive ovarian response in cycles of
IVF
A number of studies have assessed the role of AMH in the prediction of
poor ovarian response in IVF cycles using first generation AMH assays and
found that AMH and AFC were the best predictors of poor ovarian response
compared to other markers of ovarian reserve Nardo et al showed that the
predictive value of AMH in receiver operating characteristic curve (ROC)
analysis was similar to (AUC 088) that of AFC (AUC 081) and found that
AMH cut offs of gt375 ngmL and lt10 ngmL would have modest
sensitivity and specificity in predicting the extremes of response (Nardo et al
2009) These findings were largely supported by subsequent prospective studies
and a systematic review (Nelson et al 2007 Jayaprakasan et al 2010 Broer et al
2011) Similarly comparison of chronological age basal FSH ovarian volume
AFC and AMH found that only AMH (AUC 090) and AFC (AUC 093) were
reliable predictors of poor ovarian response in cycles of IVF Subsequent
combination of the effect of AMH and AFC using multivariable regression
analysis did not improve the level of prediction of poor ovarian response
significantly (AUC 094) suggesting both AMH and AFC can be used as
independent markers (Jayaprakasan et al 2010)
Similarly most studies agree that AMH and AFC are the best predictors
of excessive ovarian response and ovarian hyperstimulation syndrome (OHSS)
compared to other clinical endocrine and ultrasound markers (Nardo et al
2009 Nelson et al 2007) Broer et al compared these two tests in systematic
review of 14 studies and reported that the summary estimates of the sensitivity
and the specificity for AMH were 82 and 76 respectively and for AFC 82
and 80 respectively (Broer et al 2011) Consequently the study concluded
that AMH and AFC were equally predictive and the difference in the predictive
value between the tests was not statistically significant
26
342 Prediction of live birth rate (LBR) in cycles of IVF
Lee at al reported that AMH and chronological age were more accurate
than basal FSH AFC BMI and causes of infertility in the prediction of live
birth rate (Lee et al 2009) Similarly La Marca et al suggested that odds of live
birth could be reliably predicted using AMH (La Marca et al 2010b) although
subsequent review of the study questioned strength of the evidence (Loh and
Maheshwari 2011)
A study conducted by Nelson et al found that higher AMH levels had
stronger association with increased live birth rate compared to age and FSH
(Nelson et al 2007) However the study also suggested that this association
was mainly confined in the women with low AMH levels and there was no
additional increase in live birth in women with AMH levels of higher than 710
pmolL This may suggest that achieving a live birth may be under the
influence of number of other factors and that markers of ovarian reserve alone
may not be able predict this outcome reliably
35 The role of AMH in individualisation of ovarian stimulation in
IVF cycles
Prediction of ovarian response to the stimulation of ovaries in cycles of
IVF plays an important role in the counseling of couples undergoing treatment
programmes and hence many clinical studies on AMH have focused on the
prognostic value of AMH measurements However data on using AMH as a
tool for improving the clinical outcomes in IVF cycles appear to be lacking
considering AMH may be useful tool in tailoring treatment strategies to an
individual patientrsquos ovarian reserve Unlike most other markers AMH has
discriminatory power in determining various degrees of ovarian reserve due to
significantly higher between patient (CV 94) variability compared to its
within-patient (CV 28) variation (Rustamov et al 2011) which allows
stratification of patients into various degrees of (eg low normal high) ovarian
reserve Subsequently most optimal ovarian stimulation protocol may be
established for each band of ovarian reserve Consequently reference ranges
on the basis of distribution of AMH in infertile women were developed which
were subsequently adopted by fertility clinics for a tailoring the mode of
27
ovarian stimulation and daily dose of gonadotrophins in IVF (The Doctors
Laboratory 2008 However currently available clinical reference ranges are
based on the first generation DSL assay and may not be reliably convertible to
currently available Gen II assay measurements (Wallace et al 2011) Indeed the
findings of the studies on comparability of the first generation AMH assays
suggest that establishing a reliable between assay conversion factor between
AMH assays may not be straightforward Furthermore the reference ranges
appear to reflect the distribution of AMH measurements within a specific
population and may therefore not be directly applicable for the prediction of
response to ovarian stimulation in IVF patients (The Doctors Laboratory
2008)
More importantly despite lack of good quality evidence on the
effectiveness of AMH-tailored ovarian stimulation protocols a number of
fertility clinics appear to have introduced various AMH-based COH protocols
in their IVF programs At present research evidence on AMH-tailored
ovarian stimulation in IVF is largely based on two retrospective studies
(Nelson et al 2009 Yates et al 2012) Both of these studies display considerable
methodological limitations including small sample size and centre-related or
period-related selection of their cohorts In this context AMH is used as a tool
for therapeutic intervention and therefore the research evidence should ideally
be derived from randomised controlled trials However recruitment of large
enough patients in IVF setting may take considerable time and resources In
the meantime given AMH-tailored ovarian stimulation has already been
introduced in clinical practice and there is urgent need for more reliable data
the studies with a larger cohorts and robust methodology should assess the role
of AMH in individualisation of ovarian stimulation in IVF treatment cycles
4 Multivariate models of assessment of ovarian reserve
In view of the fact there is not a single marker of ovarian reserve that
can accurately predict ovarian response various models for combination of
multiple ovarian markers have been developed (Verhagen et al 2008) A
number of studies reported that multivariate models are better predictors of
poor ovarian response in IVF compared to a single marker (Bancsi et al 2002
Balasch et al 1996 Creus et al 2000 Durmusoglu et al 2004) However a meta-
analysis showed that when compared to a single marker (AFC) multivariate
28
model has a similar accuracy in terms of prediction of poor ovarian response
(Verhagen et al 2008) In contrast a more recent study demonstrated that
multivariate score was superior to chronological age basal FSH or AFC alone
in predicting likelihood of poor ovarian response and clinical pregnancy
(Younis et al 2010) However the study did not include one of the most
reliable markers AMH in either arm necessitating further assessment of the
role of combined tests which include all reliable biomarkers
4 SUMMARY
During the last two decades a significant leap has been taken towards
understanding the biology of anti-Muumlllerian hormone and its role in female
reproduction (Durlinger et al 2002 Themmen et al 2005) Availability of
commercial AMH assays has resulted in significant increase in interest in the
role of the measurement of serum AMH in the assessment of ovarian reserve
which has been followed by the introduction of the test into routine clinical
practice (Nelson et al 2011) However more recent studies suggest that current
methodologies for the measurement of AMH may provide significant sampling
variability (Rustamov et al 2011) Furthermore the studies that compared first
generation commercial assay methods appear to provide non-reproducible
results suggesting there may be underlying issues with assay methodologies
(Lee et al 2011) Similarly despite lack of sufficient evidence in the role of
AMH in individualisation of ovarian stimulation protocols in IVF AMH-
tailored IVF protocols have been introduced in routine clinical practice of
many fertility clinics around the world
Consequently it appears that clinical application of AMH test has
surpassed the research evidence in some aspects of fertility treatment and
therefore future projects should be directed toward areas where gaps in
research evidence exist On the basis of the review of literature we believe that
evaluation of the performance of assay methods understanding the role of
AMH in assessment ovarian reserve and establishing its role in
individualisation of ovarian stimulation protocols should be research priority
29
II GENERAL INTRODUCTION
On the basis of the review of published literature I have identified that
the following areas of research on the clinical application of AMH in the
management of infertility requires further investigation 1) Within-patient
variability of measurement of AMH using Gen II assay method 2)
Establishment of clinically measurable determinants of AMH levels and 3) The
role of AMH in individualisation of ovarian stimulation in IVF treatment
cycles
In our previous study we estimated that there was significant sample-to-
sample variation (CV 28) in AMH measurements when the first generation
DSL assay was used (Rustamov et al 2011) The source of variability is likely to
be related to the assay method given that biological within-cycle variation of
AMH is believed to be small (La Marca et al 2006) Therefore assessment of
sample-to-sample variability of AMH using the newly introduced Gen II assay
which is believed to be significantly more stable and sensitive compared to that
of DSL assay should enable us to establish the measurement related variability
of AMH Furthermore given I am planning to use data from both DSL and
Gen II assays I need to establish between-assay conversion factor for these
assays using data on clinical samples
There appears to be a lack of good quality data on the effect of
ethnicity BMI causes of infertility reproductive history and reproductive
surgery on ovarian reserve Therefore I am planning to ascertain the role of
above factors on determination of ovarian reserve by analysing AMH
measurements of a large cohort of patients
There is a strong correlation between AMH and ovarian performance
in IVF treatment when conventional ovarian stimulation using GnRH agonist
regimens with a standard daily dose of gonadotrophins are used (Nelson et al
2007 Nardo et al 2007) Furthermore studies suggest tailoring the ovarian
stimulation protocols to AMH measurement may improve ovarian
performance and subsequently the success of IVF treatment (Nelson et al
2011 Yates et al 2012) However given methodologies of the published
studies the effectiveness of currently proposed AMH-tailored ovarian
stimulation protocols remains unknown Therefore I am planning to develop
individualised ovarian stimulation protocols by establishing the most optimal
mode of pituitary down regulation and starting dose of gonadotrophins for
30
each AMH cut-off bands using a robust research methodology However
development of individualised ovarian stimulation protocols on the basis of
retrospective data requires a reliable and validated database containing a large
number of observations In the IVF Department of St Maryrsquos Hospital we
have data on a large number of patients who underwent ovarian stimulation
following the introduction of AMH However the data on various aspects of
investigation and treatment of patients is stored in different clinical data
management systems and may not be easily linkable In addition it appears that
data on certain important variables (eg causes of infertility AFC) are available
only in the hospital records necessitating searching for data from the hospital
records of each patient Consequently I designed a project for building a
research database which will have comprehensive and validated datasets that
are necessary for investigation of the research questions of the MD
programme
In conclusion I am planning to conduct a series of studies to improve
the understanding of the role of AMH in the management of women with
infertility Specifically I am intending to evaluate 1) sample-to-sample variability
of Gen II AMH measurements 2) conversion factor between DSL and Gen II
assays in clinical samples 3) the effect of ethnicity BMI causes of infertility
endometriosis reproductive history and reproductive surgery to ovarian
reserve and explore AMH-tailored individualisation of ovarian stimulation in
IVF cycles
31
References
Abbeel E The Istanbul consensus workshop on embryo assessment proceedings of an expert meeting Human reproduction 2011 26 p 1270-83 Abdalla HT M Y Repeated testing of basal FSH levels has no predictive value for IVF outcome in women with elevated basal FSH Human reproduction 2006 21(1) p 171-4 Amer SA LT Ledger WL The value of measuring anti-Mullerian hormone in women with polycystic ovary syndrome undergoing laparoscopic ovarian diathermy Human reproduction 2009 24 p 2760-6 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343 Balaban B BD Calderoacuten G Catt J Conaghan J Cowan L Ebner T Gardner D Hardarson T Lundin K Cristina Magli M Mortimer D Mortimer S Munneacute S Royere D Scott L Smitz J Thornhill A van Blerkom J Van den Baker A quantitative and cytological study of germ cells in human ovaries Proc R Soc Lond B Biol Sci 1963 158 p 417-433 Balasch J CM Fabregues F Carmona F Casamitjana R Ascaso and VJ C Inhibin follicle-stimulating hormone and age as predictors of ovarian response in in vitro fertilization cycles stimulated with gonadotropin-releasing hormone agonist-gonadotropin treatment Am J Obstet Gynecol 1996 175 p 1226-1230 Bancsi LF BF Eiijekemans MJ at al Predictors of poor ovarian response in in vitro fertilisation a prospective study comparing basal markers of ovarian reserve Fertility and Sterility 2002 77 p 328-336 Bazer FW Strong science challenges conventional wisdom new perspectives on ovarian biology Reprod Biol Endocrinol 2004 2 p 28 Begum S VE Papaioannou and RG Gosden The oocyte population is not renewed in transplanted or irradiated adult ovaries Hum Reprod 2008 23(10) p 2326-30
Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175 Bristol-Gould SK et al Fate of the initial follicle pool empirical and mathematical evidence supporting its sufficiency for adult fertility Dev Biol 2006 298(1) p 149-54 Broekmans FJ et al A systematic review of tests predicting ovarian reserve and IVF outcome Hum Reprod Update 2006 12(6) p 685-718
32
Broekmans Frank J M de Ziegler Dominique Howles Colin M Gougeon Alain Trew Geoffrey and Olivennes Francois The antral follicle count practical recommendations for better standardization Fertility and Sterility 2010 94 p 1044-51 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011 Aug96(8)2532-9
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Bukovsky A et al Origin of germ cells and formation of new primary follicles in adult human ovaries Reprod Biol Endocrinol 2004 2 p 20 Byskov AG et al Eggs forever Differentiation 2005 73(9-10) p 438-46 Clarke TR et al Mullerian inhibiting substance signaling uses a bone morphogenetic protein (BMP)-like pathway mediated by ALK2 and induces SMAD6 expression Mol Endocrinol 2001 15(6) p 946-59
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146 Creus M PJ Faacutebregues F Vidal E Carmona F Casamitjana R and BJ Vanrell JA Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-2346 Creus M PaJ Fabregues F Vidal E Carmona F Casamitjana R et al Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome Human reproduction 2000 15 p 2341-6 Cook CL SY Brenner AG et al Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertility and Sterility 2002 77 p 141-6 Deb S Campbell B K Clewis JS Pincott-Allen C and Raine-Fenning NJ Intracycle variation in number of antral follicles stratified by size and in endocrine markers of ovarian reserve in women with normal ovulatory menstrual cycles Ultrasound Obstet Gynecol 2013 41 216ndash222 De Felici M Germ stem cells in the mammalian adult ovary considerations by a fan of the primordial germ cells 2010 Mol Hum Reprod 16(9) p 632-6 Donovan PJ (1998) The germ cell ndash the mother of all stem cells Int J Dev Biol 42 1043ndash50 Dunaif A Insulin resistance and the polycystic ovary syndrome mechanism adn implications for pathogenesis Endocr Rev 1997 18 p 774-800
33
Durlinger AL et al Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 1999 140(12) p 5789-96 Durlinger AL et al Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 2001 142(11) p 4891-9 Durlinger AL JA Visser and AP Themmen Regulation of ovarian function the role of anti-Mullerian hormone Reproduction 2002 124(5) p 601-9 Durmusoglu F EK Yoruk P Erenus M Combining day 7 follicle count with the basal antral follicle count improves the prediction of ovarian response Fertility and Sterility 2004 81 p 1073-78 Ebner T et al Basal level of anti-Mullerian hormone is associated with oocyte quality in stimulated cycles Hum Reprod 2006 21(8) p 2022-6 Elmashad AI Impact of laparoscopic ovarian drilling on anti-Muumlllerian hormone levels and ovarian stromal blood flow using three-dimensional power Doppler in women with anovulatory polycystic ovary syndrome Fertility and Sterility 2011 95 p 2342-6 Falbo A RM Russo T DEttore A Tolino A Zullo F Orio F Palomba S Serum and follicular anti-Mullerian hormone levels in women with polycystic ovary syndrome (PCOS) under metformin J Ovarian Resere 2010 Jul p 16 Fanchin R et al Anti-Mullerian hormone concentrations in the follicular fluid of the preovulatory follicle are predictive of the implantation potential of the ensuing embryo obtained by in vitro fertilization J Clin Endocrinol Metab 2007 92(5) p 1796-802 Fasouliotis SJ A Simon and N Laufer Evaluation and treatment of low responders in assisted reproductive technology a challenge to meet J Assist Reprod Genet 2000 17(7) p 357-73 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164 Gleicher N A Weghofer and DH Barad Defining ovarian reserve to better understand ovarian aging Reprod Biol Endocrinol 9 p 23 Haadsma ML BA Groen H Roeloffzen EM Groenewoud ER Heineman MJ et al The number of small antral follicles (2ndash6 mm) determines the outcome of endocrine ovarian reserve tests in a subfertile population Human reproduction 2007 22 p 1925-31 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699ndash708
34
Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hazout A et al Serum antimullerian hormonemullerian-inhibiting substance appears to be a more discriminatory marker of assisted reproductive technology outcome than follicle-stimulating hormone inhibin B or estradiol Fertil Steril 2004 82(5) p 1323-9
Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 HFEA Fertility Figures 2005 2007 HFEA HFEA Fertility Facts and Figures 2008 HFEA 2010 Homburg R BD Levy T Feldberg D Ashkenazi J Ben-Rafael Z In vitro fertilisation and embryo transfer for the treatment of infertility associated with polycystic ovary syndrome Fertility and Sterility 1993 60 p 858-863 Hudson PL et al An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 1990 70(1) p 16-22 Hull MG GC Kelly NJ et al Population study of causes treatment and outcome of infertility Br Med J Clin Res Ed 1985 291 p 1693-1697 Islam MN and MM Islam Biological and behavioural determinants of fertility in Bangladesh 1975-1989 Asia Pac Popul J 1993 8(1) p 3-18 Jayaprakasan K et al A prospective comparative analysis of anti-Mullerian hormone inhibin-B and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation(2010a) Fertil Steril 2010 93(3) p 855-64 Jayaprakasan et al (2010b) The cohort of antral follicles measuring 2ndash6 mmreflects the quantitative status of ovarian reserve as assessed by serum levels of anti-Mullerian hormone and response to controlled ovarian stimulation Fertil Steril_ 2010941775ndash81 Johannes L H Evers MD Peronneke Slaats MS Jolande A Land MD John C M Dumoulin PhD and Gerard A J Dunselman MD Elevated Levels of Basal Estradiol-17β Predict Poor Response in Patients with Normal Basal Levels of Follicle-Stimulating Hormone Undergoing In Vitro Fertilization Fertility and Sterility 1998(69) p 1010-4 Johnson J et al Germline stem cells and follicular renewal in the postnatal mammalian ovary Nature 2004 428(6979) p 145-50 Kelsey TW et al A validated model of serum anti-mullerian hormone from conception to menopause PLoS One 2011 6(7) p e22024
35
Kumar A et al Development of a second generation anti-Mullerian hormone (AMH) ELISA J Immunol Methods 362(1-2) p 51-9 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A De Leo V Giulini S Orvieto R Malmusi S Giannella L Volpe A Anti-Mullerian hormone in premenopausal women and after spontaneous or surgically induced menopause J Soc Gynecol Investig 2005b12545-548 La Marca A et al Normal serum concentrations of anti-Mullerian hormone in women with regular menstrual cycles (2010a) Reprod Biomed Online 2010 21(4) p 463-9 La Marca A et al Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction (2010b) Reprod Biomed Online 2010 22(4) p 341-9 La Marca A et al Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART) Hum Reprod Update 16(2) p 113-30 La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351 Lee MM et al Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 1996 81(2) p 571-6 Lee TH et al Impact of female age and male infertility on ovarian reserve markers to predict outcome of assisted reproduction technology cycles Reprod Biol Endocrinol 2009 7 p 100
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604 Licciardi FL LH Rosenwaks Z Day 3 estradiol serum concentrations as prognosticators of ovarian stimulation response and pregnancy outcome in patients undergoing in vitro fertilization Fertility and Sterility 1995 64 p 991-4 Lie Fong S et al Anti-Mullerian hormone a marker for oocyte quantity oocyte quality and embryo quality Reprod Biomed Online 2008 16(5) p 664-70 Lintsen AM et al Effects of subfertility cause smoking and body weight on the success rate of IVF Hum Reprod 2005 20(7) p 1867-75 Maheshwari A and PA Fowler Primordial follicular assembly in humans--
36
revisited Zygote 2008 16(4) p 285-96 Maheshwari A et al Dynamic tests of ovarian reserve a systematic review of diagnostic accuracy Reprod Biomed Online 2009 18(5) p 717-34 Massague J et al TGF-beta receptors and TGF-beta binding proteoglycans recent progress in identifying their functional properties Ann N Y Acad Sci 1990 593 p 59-72 Massague J and YG Chen Controlling TGF-beta signaling Genes Dev 2000 14(6) p 627-44 Mc KD HA Adams EC Danziger S Histochemical observations on the germ cells of human embryos Anat Rec 1953 2 p 201-219 McGee EA and AJ Hsueh Initial and cyclic recruitment of ovarian follicles Endocr Rev 2000 21(2) p 200-14 Mishina Y et al Genetic analysis of the Mullerian-inhibiting substance signal transduction pathway in mammalian sexual differentiation Genes Dev 1996 10(20) p 2577-87 Moran LJ HC Hutchinson SK Stepto NK Strauss BJ Teede HJ Exercise decreases anti-Mullerian horomone in anovulatory overweight women with polycystic ovary syndrome-A pilot study Horm Metab Res 2011 October Nardo LG et al Circulating basal anti-Mullerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 92(5) p 1586-93 Nelson SM RW Yates and R Fleming Serum anti-Mullerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007 22(9) p 2414-21 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867 Nelson SM and A La Marca The journey from the old to the new AMH assay how to avoid getting lost in the values 2011 Reprod Biomed Online Nelson SM et al External validation of nomogram for the decline in serum anti-Mullerian hormone in women a population study of 15834 infertility patients Reprod Biomed Online 2011 23(2) p 204-6 NICE Assessment and treatment for people with fertility problems NICE Guidelines 2013 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS ONE 5(7) e11637 Nilsson EE Savenkova MI Schindler R Zhang B Schadt EE et al (2010) Gene Bionetwork Analysis of Ovarian Primordial Follicle Development PLoS
37
ONE 2010 5(7) 11637 Nilsson EE et al Inhibitory actions of Anti-Mullerian Hormone (AMH) on ovarian primordial follicle assembly PLoS One 2011 6(5) p e20087 Notarianni E Reinterpretation of evidence advanced for neo-oogenesis in mammals in terms of a finite oocyte reserve 2011 J Ovarian Res 4(1) p 1 Office of National Statistics 2012 1 2011 Live Births in England and Wales by Characteristics of Mother Oktem O and B Urman Understanding follicle growth in vivo Hum Reprod 25(12) p 2944-54 Oktem O and K Oktay The ovary anatomy and function throughout human life Ann N Y Acad Sci 2008 1127 p 1-9 Ottosen LD et al Pregnancy prediction models and eSET criteria for IVF patients--do we need more information J Assist Reprod Genet 2007 24(1) p 29-36 Pacchiarotti J et al Differentiation potential of germ line stem cells derived from the postnatal mouse ovary Differentiation 2010 79(3) p 159-70 Paternot G WA Thonon F Vansteenbrugge A Willemen D Devroe J Debrock S DHooghe TM Spiessens C Intra- and interobserver analysis in the morphological assessment of early stage embryos during an IVF procedure a multicentre study Reprod Biol Endocrinol 2011 9 p 127 Pehlivanov B OM Anti-Muumlllerian hormone in women with polycystic ovary syndrome Folia Medica 2011 53 p 5-10 Pellat L HL Brincat M et al Granulosa cell production of anti-Muumlllerian hormone is increased in polycystic ovaries J Clin Endocrinol Metab 2007 92 p 240-5 Pigny P ME Robert Y et al Elevated serum level of anti-Mullerian hormone in patients with polycystic ovary syndrome relationship to the ovarian follicle excess and the follicular arrest J Clin Endocrinol Metab 2003 88 p 5957-62 Porter RN et al Induction of ovulation for in-vitro fertilisation using buserelin and gonadotropins Lancet 1984 2(8414) p 1284-5 Rajpert-De Meyts E et al Expression of anti-Mullerian hormone during normal and pathological gonadal development association with differentiation of Sertoli and granulosa cells J Clin Endocrinol Metab 1999 84(10) p 3836-44
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-
38
Scott Wilkes Murdoch Alison DC and Greg Rubin Epidimiology and management of infertility a poppulation-based study in UK primary care Family Practice 2009 26 p 269-274 Seifer DB L-MG Hogan JW Gardiner AC Blaza AS Berk CA Day 3 serum inhibin-B is predictive of assisted reproductive technologies outcome Fertility and Sterility 1997 67 p 110-4 Skinner MK (2005) Regulation of primordial follicle assembly and development Hum Reprod Update 11 461ndash71 Syrop CH et al Ovarian volume may predict assisted reproductive outcomes better than follicle stimulating hormone concentration on day 3 Hum Reprod 1999 14(7) p 1752-6 Syrop CH A Willhoite and BJ Van Voorhis Ovarian volume a novel outcome predictor for assisted reproduction Fertil Steril 1995 64(6) p 1167-71 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Taylor A ABC of subfertility Making a diagnosis Br Med J Clin Res Ed 2003 327 p 799-801 Templeton A JK Morris and W Parslow Factors that affect outcome of in-vitro fertilisation treatment Lancet 1996 348(9039) p 1402-6 Templeton A Infertility-epidemiology aetiology and effective management Health Bull (Edinb) 1995 53(5) p 294-8 TDL test update AMH Stability Hormones and OCPs The Doctors Laboratory Guide 2008 page 29 Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34) p 18-21 Tilly JL and J Johnson Recent arguments against germ cell renewal in the adult human ovary is an absence of marker gene expression really acceptable evidence of an absence of oogenesis Cell Cycle 2007 6(8) p 879-83 Urbancsek J Use of serum inhibin B levels at the start of ovarian stimulation and at oocyte pickup in the prediction of assisted reproduction treatment outcome Fertility and Sterility 2005 83(2) p 341-348 van der Linden M BK Farquhar C Kremer JAM Metwally M Luteal phase support for assisted reproduction cycles (Review) Cochrane Library 2011 October
39
van Disseldorp J et al Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2011 25(1) p 221-7 van Kooij RJ et al Age-dependent decrease in embryo implantation rate after in vitro fertilization Fertil Steril 1996 66(5) p 769-75 van Rooij IA et al Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002 17(12) p 3065-71 Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539 van Disseldorp J Kwee CBL J Looman CWN Eijkemans MJC and FJ Broekmans Comparison of inter- and intra-cycle variability of anti-Muuml llerian hormone and antral follicle counts Human reproduction 2010 25 p 221-227 Verberg MF et al Predictors of low response to mild ovarian stimulation initiated on cycle day 5 for IVF Hum Reprod 2007 22(7) p 1919-24 Verhagen TE et al The accuracy of multivariate models predicting ovarian reserve and pregnancy after in vitro fertilization a meta-analysis Hum Reprod Update 2008 14(2) p 95-100 Visser JA AMH signaling from receptor to target gene Mol Cell Endocrinol 2003 211(1-2) p 65-73 Wallace WH and TW Kelsey Human ovarian reserve from conception to the menopause PLoS One 5(1) p e8772 Wallace AM et al A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 2011 48(Pt 4) p 370-3 Webber L J SS Stark J Trew G H Margara R Hardy K Franks S Formation and early development of follicles in the polycystic ovary Lancet 2003 362(September) p 1017-1021
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362 Younis JS et al A simple multivariate score could predict ovarian reserve as well as pregnancy rate in infertile women Fertil Steril 2010 94(2) p 655-61 Zou K et al Production of offspring from a germline stem cell line derived from neonatal ovaries Nat Cell Biol 2009 11(5) p 631-6 Zuckerman The number of oocytes in the mature ovary Recent Prog Horm Res 1951 6(63-108)
Figure 1 Schematic representation of a long GnRH agonist cycle
In a long agonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH agonist preparations starting from mid-luteal phase of the preceding menstrual cycle till the day of administration of HCG
Cycle Started
Menstrual Period
Daily GnRH agonist
From mid-luteal phase
Daily GnRH agonist
Menstrual
Period
Daily GnRH agonist
amp
Daily hMG
Day 2-10
HCG
USOR
amp
ET
41
Figure 2 Schematic representation of GnRH antagonist cycle
In an antagonist cycle pituitary down regulation is achieved by administration of a daily dose of GnRH antagonist preparations starting from the 5th day of IVF cycle till the day of administration of HCG Therefore an ldquoAntagonistrdquo cycle is significantly shorter than an ldquoAgonistrdquo cycle
Cycle Started
Menstrual Period
Daily GnRH antagonist
(Day 5-10)
amp
Daily hMG
(Day 2-10)
HCG
USOR
amp
ET
42
Figure 3 The role of AMH in regulation of oocyte recruitment and folliculogenesis
It appears that AMH plays an important role in a) the recruitment of primordial follicles and b) the selection of a dominant follicle from a cohort of antral follicles AMH is believed to be the main regulator of ovarian reserve which is achieved by its paracrine negative feedback effect to resting primordial follicles (Durlinger et al 1999) AMH was found to play an important role
in the regulation of the selection of a dominant follicle by inhibition of the FSH-induced follicle growth (Durlinger et al 2001)
EVALUATION OF THE GEN II AMH ASSAY BETWEEN-SAMPLE VARIABILITY AND
ASSAY-METHOD COMPARABILITY
2
44
ANTI-MUumlLLERIAN HORMONE SERUM LEVELS AND REPRODUCIBILITY
IN A LARGE COHORT OF SUBJECTS SUGGEST
SAMPLE INSTABILITY
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G
Nardo Philip W Pemberton
Human Reproduction 2012a 273085-3091
21
45
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large
cohort of subjects suggest sample instability
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester Foundation Trust
Manchester M13 9WL UK
c Health Sciences - Methodology Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
d School of Medicine University of Manchester Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Corresponding author
Oybek Rustamov MRCOG
Research Fellow in Reproductive Medicine
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester Foundation Trust Manchester M13 0JH UK
E-mail oybekrustamovcmftnhsuk oybek_rustamovyahoocouk
Word count 3909
Conflicts of Interest There are no potential conflicts of interest
Acknowledgement of financial support
Dr Steve Roberts is supported by the NIHR Manchester Biomedical Research Centre
46
Declaration of authorsrsquo roles
OR led on clinical aspects of this study with responsibility for collation of the
clinical database and the analysis of the clinical data OR prepared the first
draft of the clinical work and was involved in preparation of the whole paper
and submission of the final manuscript CF and LGN contributed to clinical
data analysis draft preparation and approval of the final manuscript MK was
involved in clinical data collation and approval of the final draft PWP was the
laboratory lead responsible for all of the laboratory based experiments and for
the routine analysis of clinical samples PWP prepared the first draft of the
laboratory work and was involved in the preparation of the whole paper and
submission of the final manuscript AS suggested the sample stability studies
and was involved in discussion draft preparation and approval of the final
manuscript APY was involved in some of the routine clinical analyses and
progression of drafts to approval of the final manuscript SAR was involved in
clinical study design oversaw the statistical analysis and progression of drafts
through to approval of the final manuscript OR and PWP should be
considered as joint first authors
47
ABSTRACT
Title
Anti-Muumlllerian hormone serum levels and reproducibility in a large cohort of
subjects suggest sample instability
Study question
What is the variability of anti-muumlllerian hormone (AMH) concentration in
repeat samples from the same individual when using the Gen II assay and how
do values compare to Gen I (DSL) assay results
Summary answer
Both AMH assays displayed appreciable variability which can be explained by
sample instability
What is known already
AMH is the primary predictor of ovarian performance and is used to tailor
gonadatrophin dosage in cycles of IVFICSI and in other routine clinical
settings A robust reproducible and sensitive method for AMH analysis is of
paramount importance The Beckman Coulter Gen II ELISA for AMH was
introduced to replace earlier DSL and Immunotech assays The performance
of the Gen II assay has not previously been studied in a clinical setting
Study design size and duration
For AMH concentration study we studied an unselected group of 5007
women referred for fertility problems between 1st September 2008 to 25th
October 2011 AMH was measured initially using the DSL AMH ELISA and
subsequently using the Gen II assay AMH values in the two populations were
compared using a regression model in log(AMH) with a quadratic adjustment
for age Additionally women (n=330) in whom AMH had been determined in
different samples using both the DSL and Gen II assays (paired samples)
identified and the difference in AMH levels between the DSL and Gen II
assays was estimated using the age adjusted regression analysis
In AMH variability study 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay and 87 women had repeated AMH
determinations using the Gen II assay (n=177 samples) were identified A
mixed effects model in log (AMH) was utilised to estimate the sample-to-
48
sample (within-subject) coefficients of variation of AMH adjusting for age
Laboratory experiments including sample stability at room temperature
linearity of dilution and storage conditions used anonymised samples
Main results and the role of chance
In clinical practice Gen II AMH values were ~20 lower than those
generated using the DSL assay instead of the 40 increase predicted by the kit
manufacturer Both assays displayed high within-subject variability (Gen II
assay CV=59 DSL assay CV=32) In the laboratory AMH levels in serum
from 48 subjects incubated at RT for up to 7 days increased progressively in
the majority of samples (58 increase overall) Pre dilution of serum prior to
assay gave AMH levels up to twice that found in the corresponding neat
sample Pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave higher readings (72 overall) compared to sequential
addition Storage at -20ordmC for 5 days increased AMH levels by 23 compared
to fresh samples The statistical significance of results was assessed where
appropriate
Limitations reasons for caution
The analysis of AMH levels is a retrospective study and therefore we cannot
entirely rule out the existence of differences in referral practices or changes in
the two populations
Wider implications of the findings
Our data suggests that AMH may not be stable under some storage or assay
conditions and that this may be more pronounced with the Gen II assay The
published conversion factors between the Gen II and DSL assays appear to be
inappropriate for routine clinical practice Further studies are urgently required
to confirm our observations and to determine the cause of the apparent
instability In the meantime caution should be exercised in the interpretation
of AMH levels in the clinical setting
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA DSL Active MIS AMH ELISA sample stability
49
INTRODUCTION
AMH in women is secreted by the granulosa cells of pre-antral and small
antral follicles (Vigier et al 1984 Themmen 2005) and circulating levels reflect
the ovarian pool from which follicles can be recruited (Loh amp Maheshwari
2011) Measurement of AMH has become of paramount significance in clinical
practice in IVF units to assign candidates to the most suitable controlled
ovarian hyperstimulation protocol and its level is used to predict poor or
excessive ovarian response (Nelson et al 2007 Nardo et al 2009 Yates et al
2011) It is also of increasing importance in (a) prediction of live birth rate in
IVF cycles (La Marca et al 2011) (b) screeningdiagnosis of polycystic ovarian
syndrome (Cook et al 2002) (c) follow up of women with a history of
granulosa cell tumours (Lane et al 1999) (d) prediction of the age of onset of
infertility due to the menopause (van Disseldorp et al 2008 Broer et al 2011)
and finally (e) assessment of the long term effect of chemotherapy on fertility
(Anderson 2011)
Following development of the first laboratory AMH assay in 1990
(Hudson et al 1990 Lee et al 1996) first generation commercially available
immunoassays were introduced by Diagnostic Systems Ltd (DSL) and
Immunotech Ltd (IOT) These assays used different antibodies and standards
(Nelson amp La Marca 2011) and the resulting AMH concentrations obtained
using the IOT assay were found to be higher than those produced using the
DSL assay by most but not all authors (Freour et al 2007Taieb et al 2008 Lee
et al 2011) The AMH Gen II Assay (Beckman-Coulter Ltd) replaced both of
these assays using the DSL Gen I antibody with the IOT standards AMH
values obtained using this kit were predicted to correlate with but be higher
than those using the old DSL kit (Kumar et al 2010 Nelson amp La Marca
2011) This was confirmed (Wallace et al 2011) with the AMH Gen II assay
giving values approximately 40 higher than the DSL assay The
recommended conversion factor of 14 (AMH Gen II = DSL x 14) was also
applied to the DSL reference ranges but this recommendation does not appear
to have been independently validated
It is generally accepted that serum AMH concentrations are highly
reproducible within and across several menstrual cycles and therefore a single
blood sampling for AMH measurement has been accepted as routine practice
50
(Hehenkamp et al 2006 La Marca et al 2006 Tsepelidis et al 2007) However
we recently challenged this view and reported significant sample-to-sample
variation in AMH levels using the DSL assay in women who had repeated
measurements 28 difference between samples taken from the same patient
with a median time between sampling of 26 months and taking no account of
menstrual cycle (Rustamov et al 2011) Although we could not explain the
cause of this variability we speculated that it might be due to true biological
variation in secretion of AMH or due to post-sampling pre-analytical
instability of the specimen
Given the widespread adoption of AMH in Clinical Units it is critical
that the sources of variability in any AMH assay are understood and quantified
This paper presents the results of clinical and laboratory studies on routine
clinical samples using the new AMH Gen II assay specifically comparing assay
values with the older DSL assay assessing between sample variability and
investigating analytical and pre-analytical factors affecting AMH measurement
METHODS
Study population
Samples were obtained from women of 20-46 years of age attending for
investigation of infertility requiring AMH assessment at the secondary
(Gynecology Department) and tertiary (Reproductive Medicine Department)
care divisions of St Maryrsquos Hospital Manchester from 1st September 2008 to
25th October 2011 Samples which were lipaemic or haemolysed and samples
not frozen within 2 hours of venepuncture were excluded from the study
Anonymised samples from this pool of patients were used for stability studies
after routine AMH measurements had been completed The full dataset
comprised AMH results on 5868 samples from 5007 women meeting the
inclusion criteria Additionally we identified women in whom AMH had been
determined in different samples using both the DSL and Gen II assays (paired
samples from 330 women)
51
Sample processing
Collection and handling of all AMH samples was conducted according
to the standards set out by the manufacturers and did not vary between the
different assays Serum samples were transported immediately to the
Department of Clinical Biochemistry based in the same hospital and
separated within 2 hours of venepuncture using the Modular Pre-Analytics
Evo (Roche Diagnostics Burgess Hill West Sussex UK) Samples were frozen
in aliquots at -20C until analysis normally within one week of receipt The
laboratory participates in the pilot National external quality assessment scheme
(UKNEQAS) for AMH in Edinburgh and performance has been satisfactory
AMH analysis
All AMH assays were carried out strictly according to the protocols
provided by the manufacturer and sample collection and storage also
conformed to these recommendations All AMH samples were analysed in
duplicate and the mean of the two replicates was reported as the final result
1) The DSL AMH assay The enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) was used for measurement of AMH prior to 17th
November 2010 The working range of the assay was up to 100pmolL with a
minimum detection limit of 063pmolL The intra-assay coefficient of
variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at 56pmoll) The
inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at 56pmoll)
2) The Beckman Coulter Gen II assay After 17th November 2010
AMH was measured using the enzymatically amplified two-site immunoassay
(AMH Gen II ELISA Beckman Coulter Inc Brea CA USA) The working
range of the assay is up to 150pmolL with a minimum detection limit of
057pmolL The intra-assay CV (n=16) is 292 (at 18pmoll) and 203 (at
60pmoll) The inter-assay coefficient of variation (n=28) is 357 (at
18pmoll) and 364 (at 60pmoll)
Sample Stability Studies
(1) Stability of AMH in serum at room temperature (RT) serum samples
(n = 48) were allowed to thaw and then left at RT for one week At 0 1 2 4
and 7 days 100microl aliquots were removed and immediately stored at -80 ordmC in
52
2ml screw-capped polypropylene tubes (Alpha Laboratories Eastleigh UK)
Two freezethaw cycles had no effect on AMH concentration (results not
shown) Samples from individual subjects were analysed for AMH on the same
GenII microtitre plate to eliminate inter-assay variability Results were
expressed as a percentage of the day 0 value
(2) Linearity of Dilution 100microl fresh serum (n = 9) was added to 100microl
AMH Gen II sample diluent incubated for 30min at RT and the mixture
analysed using the standard GenII assay procedure
(3) Comparison between the Standard Assay method and an equivalent
procedure in the standard GenII ELISA assay method the first steps involve
the addition of calibrators controls or serum samples to microtitration wells
coated with anti-AMH antibody Assay buffer is then added to each well As a
comparison serum and assay buffer were mixed in a separate tube incubated
for 10min at RT and then added in exactly the same volume and proportions
to the microtitre plate Thereafter the assay was performed using the standard
protocol
(4) Stability of AMH during storage fresh serum samples (n = 8)
analysed on the day of reception were compared with aliquots from the same
samples that had been frozen for 5 days either in polystyrene tubes at -20degC or
polypropylene tubes at -80degC
Statistical Analysis
Data analysis was performed using the Stata 12 analytical package
(StataCorp Texas USA) Data management and analysis of clinical data was
conducted by one of the researchers (OR) and verified independently by
another member of the research team (SR) using different statistical software
(R statistical environment) Approval for the use of the data was obtained from
the Local Research Ethics Committee (UK-NHS 10H101522) The age-
related relationship of the DSL and Gen II assays to AMH was visualised using
scatter plots and quadratic fit on a logarithmic scale (Nelson et al 2011) The
age adjusted regression analysis of paired samples was used to estimate the
difference in AMH levels between the DSL and Gen II assays A mixed effects
model in log (AMH) was utilised to estimate the sample-to-sample (within-
subject) coefficients of variation of AMH levels in women who had repeated
53
measurements within a 1 year period from the patientrsquos first AMH sample
adjusting for age as above In the sample stability studies percentage changes
are expressed as mean plusmn SEM In the stability of AMH in serum at RT study a
paired t-test determined the level of significance between baseline and
subsequent days
RESULTS
Population studies and variability
AMH concentration
Table 1 summarizes the results of AMH determinations in our
population of women attending the IVF Clinic prior to the 17th November
2010 (using the DSL assay) and after that date (using the Gen II assay) A
second analysis compares AMH levels in women who had AMH measured
using both assays at different times Results were consistent with lower serum
levels of AMH observed when samples were analysed using the Gen II assay
compared to the DSL assay Figure 1 shows the correlation of AMH with age
for the unselected groups After adjustment for age the total cohorts showed
Gen II giving AMH values 34 lower than those for DSL Analysis restricted
to patients with AMH determinations using both assays gave an age-adjusted
difference of 21
AMH variability
During the study period 313 women had repeated AMH determinations
(n=646 samples) using the DSL assay with 295 patients having two samples 17
three samples and one five samples The median time between samples was 51
months Eighty seven women had repeated AMH determinations using the
Gen II assay (n=177 samples) with 84 women having two samples and 3
having three samples The median interval between repeat samples was 32
months Both assays exhibit high sample-to-sample variability (CV) this was
32 in the DSL assay group (our previous finding (Rustamov et al 2011) in a
smaller group was 28) variability in the Gen II assay group was much higher
(59)
54
Table 1 Median and inter-quartile range for the two assays in the
different datasets along with the mean difference from an age-
adjusted regression model expressed as a percentage
DSL Gen II
difference ()
n age AMH (pmoll
)
n Age
AMH (pmoll
)
all data
3934
33 (29 36)
147 (78250
)
1934 33 (29 36)
112 (45 216)
-335 (-395 to -
275)
paired sample
s
330 32 (29 36)
149 (74 247)
330 34 (30 37)
110 (56 209)
-214 (-362 to -64)
Figure 1 Unselected AMH values from DSL (circles) and Gen II
(triangles) assays as a function of age Lines show the regression
fits of log(AMH) against a quadratic function of age solid lines
Gen II broken lined DSL
20 25 30 35 40 45
Age
AM
H [p
mo
lL
]
DSLGen II
11
01
00
55
Sample stability studies
(1) Stability of AMH in serum at room temperature
AMH levels in 11 of the 48 individuals remained relatively unchanged
giving values within plusmn10 of the original activity over the period of a week
and one patient had an undetectable AMH at all time points The remaining 36
serum samples had AMH values that increased progressively with time In the
47 samples with detectable AMH levels increased significantly (plt0001) for
each time interval compared to baseline the increase at day 7 being 1584 plusmn 76
(Figure 2)
Figure 2 Stability of AMH in serum at RT
Results at each time interval are expressed as a percentage of the patientrsquos AMH concentration at day 0 Means plusmn SEM are indicated
56
(2) Linearity of Dilution
In a group of nine anonymised samples proportionality with two-fold
sample dilution does not hold and on average there is a 574 plusmn 123 increase
in the apparent AMH concentration on dilution compared to neat sample (see
table 2a) Two samples which gave the highest increases were diluted further It
was apparent that after the anomalous doubling of AMH concentration on
initial two-fold dilution subsequent dilutions gave a much more proportional
result (see Table 2b) Linearity of dilution was maintained only in samples that
showed no initial increase on two-fold dilution
Table 2a Proportionality with two-fold dilution of serum
AMH (pmoll)
sample no neat serum x2 dilution recovery
1 1105 2294 2076 2 4941 9900 2004 3 415 483 1164 4 923 1122 1216 5 2801 3066 1091 6 362 628 1734 7 2739 3962 1447 8 553 1034 1870 9 1849 2892 1564
Table 2b Linearity with multiple dilution of serum
AMH (pmoll)
sample no dilution Measured expected recovery ()
1 x1 1105 1105 100 x2 1147 5525 2076 x4 5532 2763 2002 x7 3072 1579 1946 x10 2145 1105 1941
2 x1 4941 4941 100
x2 4950 2471 2003 x4 2286 1235 1851 x7 1228 706 1739 x10 857 494 1735
57
(3) Comparison between the Standard Assay method and an equivalent
procedure Serum samples that had been pre-mixed with buffer prior to
addition gave on average 718 plusmn 48 higher readings than those added
sequentially using the standard procedure (see table 3)
Table 3 Comparison between equivalent ELISA procedures
AMH (pmoll)
sample no A B BA ()
1 1466 2284 1558 2 839 1642 1957 3 3151 6446 2046 4 1244 2014 1619 5 1393 2276 1634 6 701 1246 1777 7 778 1358 1746 8 1693 3298 1948 9 955 1793 1877 10 2849 5437 1908
11 1365 2062 1511 12 1773 2868 1617 13 1468 2429 1655 14 1499 2115 1411 15 249 357 1434 16 1284 2289 1783
A = 20microl serum added directly to the plate followed by 100microl assay buffer
B = 60microl serum + 300microl assay buffer mixed amp incubated at RT for 10min 120microl mixture added to the plate
(4) Stability of AMH during storage AMH levels in samples stored at -20degC
showed an average increase of 225 plusmn 111 over 5 days compared with fresh
values while those samples stored at -80degC showed no change (18 plusmn 31)
(see Table 4)
Table 4 Stability of AMH in serum on storage
AMH (pmoll)
sample no
fresh -20ordmC PS -80ordmC PP
1 1241 1551 1312 2 4217 7542 4508 3 1193 1712 1239 4 1042 1282 1228 5 956 905 879 6 1902 2601 1884 7 2402 2016 2362 8 145 137 132
PS = polystyrene LP4 tube PP = polypropylene 2ml tube
58
DISCUSSION
This publication arose from two initially separate pieces of work in the
Clinical IVF Unit at St Maryrsquos Hospital and in the Specialist Assay Laboratory
at Central Manchester Foundation Trust The IVF Unit had become
concerned with their observed increase in variation in AMH values and
consequently with the reliability of their AMH-tailored treatment guidance
The Laboratory wished to establish whether the practice of sending samples in
the post (which has been adopted by many laboratories rather than frozen as
specified by Beckman) was viable It soon became clear that these anomalies
observed in clinical practice might be explained by a marked degree of sample
instability seen in the Laboratory which had not previously been reported and
which may or may not have been an issue with previous AMH assays
The data contained in this paper represents the largest retrospective
study on the variability of the DSL assay and the first study on the variability
of the Gen II assay Early studies reported insignificant variation between
repeated AMH measurements suggesting that a single AMH measurement
may be sufficient in assessment of ovarian reserve (La Marca et al 2006
Tsepelidis et al 2007) However these recommendations have been challenged
by a number of groups (Lahlou et al 2006 Wunder et al 2008 Rustamov et al
2011) The current study in a large cohort of patients has demonstrated
substantial sample-to-sample variation in AMH levels using the DSL assay and
an even larger variability using the Gen II assay We suggest that this variability
may be due to sample instability related to specimen processing given that a)
AMH is produced non-cyclically and true biological variation is believed to be
small (Fanchin et al 2005 van Disseldorp et al 2009) and b) the intra-and inter
assay variation in our laboratory for both the DSL and Gen II assays is small
(lt50) suggesting that the observed variation is not due to poor analytical
technique
The population data presented in this paper also suggests that in routine
clinical use the Gen II assay provides AMH results which are 20-40 lower
compared to those measured using the DSL assay This is in contrast to
validation studies for the Gen II assay which showed that this assay gave AMH
values ~40 higher than those found with the DSL assay (Kumar et al 2010
Preissner et al 2010 Wallace et al 2011)
59
All samples in this retrospective study were subject to the same handling
procedures and analyzed by the same laboratory the two populations were
comparable with the same local referral criteria for investigation of infertility
and we are unaware of any other alterations in practice which might produce
such a large effect on AMH we cannot rule out the possibility of other
changes in the population being assayed that were coincident in time with the
assay change However any such change would have to be coincident and
produce a 50 decrease in observed AMH levels to explain our findings We
did note a weak trend towards decreasing AMH over calendar time assuming a
linear trend in the analysis implies that AMH values might be 12 (2-22)
lower when the Gen II assay was being used compared to the Gen I assay
This suggests that the age adjusted analysis of repeat samples on individuals
showing a 21 decrease in AMH with the Gen II assay is currently the best
estimate of the assay difference
This is the first study to compare AMH assays in a routine clinical setting
in a large group of subjects and as such is likely to reflect the true nature of the
relationship between AMH measured by two different ELISA kits and avoids
some of the issues in other published studies Previous laboratory studies have
compared AMH assays in aliquots from the same sample which only provides
data on the within-sample relationship between the two assays (Kumar et al
2010 Preissner et al 2010 Wallace et al 2011) Although it is difficult to give a
definitive explanation for the discrepancy between the previously published
studies (on within-sample relationships) and this study (on between-sample
relationships) we suggest that it may be due to degradation of the specimen in
one (or both) of the assays If AMH in serum is unstable under certain storage
and handling conditions this might result in differing values being generated
because of differential sensitivity of the two assays to degradation products
Unfortunately we cannot suggest which step of sample handling might have
caused this discrepancy since the published studies did not provide detailed
information
The present study used samples which were frozen very soon after
phlebotomy and analysed shortly thereafter hopefully minimising storage
effects The most striking change followed incubation over a period of 7 days
at RT this showed a substantial increase in AMH levels rather than the
expected decline Previously Kumar et al (2010) had shown that the average
variation between fresh serum samples and those stored for seven days to be
60
approximately 4 at 2-8ordmC and lt1 at -20ordmC but presented no data on RT
stability Zhao et al (2007) reported that AMH values were likely to differ by
lt20 in samples incubated at RT for 2 days compared to those frozen
immediately
Several supplementary experiments were performed in order to
investigate this observed increase in AMH when samples were incubated at
RT These included (1) addition of the detergent Tween-20 to assay buffer to
disclose potential antibody-binding sites on the AMH molecule (2) the
removal of heterophilic antibodies from serum using PEG precipitation or
heterophilic blocking tubes None of these approaches affected AMH levels
significantly (results not shown)
Examination of the data presented here shows that in some samples
AMH levels tend towards twice those expected while results greater than that
only occur in two outliers found in Figure 2 The AMH molecule is made up
of two identical 72kDA monomers which are covalently bound (Wilson et al
1993 di Clemente et al 2010) During cytoplasmic transit each monomer is
cleaved to generate 110-kDa N-terminal and 25-kDa C-terminal homodimers
which remain associated in a noncovalent complex The C-terminal
homodimer binds to the receptor but in contrast to other TGF-β superfamily
members AMH is thought to require the N-terminal domain to potentiate this
binding to achieve full bioactivity of the C-terminal domain After activation of
the receptor the N-terminal homodimer is released (Wilson et al 1993) One
possible explanation for our findings is that the N-and C-terminal
homodimers dissociate gradually under certain storage conditions and that
either the two resulting N- and C-terminal components bind to the ELISA
plate or a second binding site on the antigen is exposed by the dissociation
effectively doubling the concentration of AMH It has been shown (di
Clemente et al 2010) that no dissociation occurs once the complex is bound to
immobilised AMH antibodies The observation that in some of our samples
there was no change after one week at RT might be explained by the
supposition that in those samples AMH is already fully dissociated A mixture
of dissociated and complex forms in the same sample would therefore
account for the observed recoveries between 100 and 200 in the
experiments presented in this paper Rapid sample processing and storage of
the resulting serum in a different tube type at -80ordmC might slow down this
breakdown process
61
The change in ionic strength or pH that occurs on dilution also seems to
have the same effect in increasing apparent AMH levels and again may be due
to dissociation or exposure of a second binding site Our results contradict
those reported by Kumar et al (2010) who showed that serum samples in the
range of 36-93pmoll of AMH when diluted in Gen II sample diluent showed
linear results across the dynamic range of the assay with average recoveries on
dilution close to 100 This might be explained if Kumarrsquos samples were
already dissociated before dilution Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample but our results do not seem to support this
These findings have significant clinical relevance given the widespread
use of AMH as the primary tool for assessment of ovarian reserve and as a
marker for tailoring the dose of gonadotrophins in cycles of IVFICSI As no
guideline studies have been published using the new Gen II assay some ART
centres have adopted modified treatment ldquocut off levelsrdquo for ovarian
stimulation programs based on the old DSL assay based ldquocut off levelsrdquo
multiplied by a conversion factor of 14 (Nelson et al 2007 Nelson et al 2009
Wallace et al 2011) The data presented in this paper suggest that this approach
could result in patients being allocated to the wrong ovarian reserve group
Poor performance of the Gen II assay in terms of sample-to-sample variability
(up to 59) could also lead to unreliable allocation to treatment protocols It
is a matter of some urgency therefore that any possible anomalies in the
estimation of AMH using the Gen II assay be thoroughly investigated and that
this work should be repeated in other centres
62
References
Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH Te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
Cook CL Siow Y Brenner AG Fallat ME Relationship between serum Mullerian inhibiting substance and other reproductive hormones in untreated women in PCOS and normal women Fertil Steril 200277141-146
di Clemente N Jamin SP Lugovskoy A Carmillo P Ehrenfels C Picard JY Whitty A Josso N Pepinsky RB Cate RL Processing of anti-mullerian hormone regulates receptor activation by a mechanism distinct from TGF-beta Mol Endocrinol 2010242193-2206
Freour T Mirallie S Bach-Ngohou K Denis M Barriere P and Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
Fanchin R Taieb J Mendez Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Mullerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 2005 20923-927
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
La Marca A Nelson SM Sighinolfi G Manno M Baraldi E Roli L Xella S Marsella T Tagliasacchi D DAmico R Volpe A Anti-Mullerian hormone-based prediction model for a live birth in assisted reproduction Reprod Biomed Online 2011 22341-349
Lahlou N Chabbert-Buffet E Gainer E Roger M Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11
Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-5
63
Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
Lee MM Donahoe PK Hasegawa T Silverman B Crist GB Best S Hasegawa Y Noto RA Schoenfeld D MacLaughlin DT Mullerian inhibiting substance in humans normal levels from infancy to adulthood J Clin Endocrinol Metab 199681571-576
Loh JS Maheshwari A Anti-Mullerian hormone--is it a crystal ball for predicting ovarian ageing Hum Reprod 2011262925-2932
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 201195736-741
Preissner CM MD Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Taieb J Coussieu C Guibourdenche J Picard JY and di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
Themmen AP Anti-Mullerian hormone its role in follicular growth initiation and survival and as an ovarian reserve marker J Natl Cancer Inst Monogr 2005(34)18-21
Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormoneconcentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
van Disseldorp J Lambalk CB Kwee J Looman CWN Eijkemans MJC Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Mullerian hormone and antral follicle counts Hum Reprod 2010 25 221-227
64
Vigier B Picard JY Tran D Legeai L Josso N Production of anti-Mullerian hormone another homology between Sertoli and granulosa cells Endocrinology 19841141315-1320
Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-MuSllerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
Wilson CA Di Clemente N Ehrenfels C Pepinsky RB Josso NVigier B Cate RL Muumlllerian inhibiting substance requires its N-terminal domain for maintenance of biological activity a novel finding within the transforming growth-factor-beta superfamily Mol Endocrinol 19937247ndash257
Wunder DM Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrualcycle in reproductive age women Fertil Steril 200889927-933
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 2007 88S17
65
AMH GEN II ASSAY A VALIDATION STUDY OF
OBSERVED VARIABILITY BETWEEN REPEATED
AMH MEASUREMENTS
Oybek Rustamov Richard Russell
Cheryl Fitzgerald Stephen Troup Stephen A Roberts
22
66
Title
AMH Gen II assay A validation study of observed variability between
repeated AMH measurements
Authors
Oybek Rustamov 1 Richard Russell2 Cheryl Fitzgerald1 Stephen Troup2
Stephen A Roberts3
Institutions
1Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospitals NHS Foundation Trust Manchester
M13 9WL UK
2Hewitt Fertility Centre Liverpool Womenrsquos NHS Foundation Trust Hospital
Crown Street Liverpool L8 7SS
3 Centre for Biostatistics Institute of Population Health University of
Manchester Manchester M13 9PL UK
Word count 1782
Conflict of interest Authors have nothing to disclose
Acknowledgment
The authors would like to thank the Biomedical Andrology Laboratory team at
the Hewitt Fertility Centre for their assistance
67
Declaration of authorsrsquo roles
OR coordinated the study conducted the statistical analysis and prepared first
draft of the manuscript RR extracted data prepared the dataset assisted in
preparation of first draft of manuscript CF ST and SR involved in study
design oversaw statistical analysis contributed to the discussion and
preparation of the final version of the manuscript
68
ABSTRACT
Objective
To study the within patient sample-to-sample variability of AMH levels using
the Gen II assay reproduced in an independent population and laboratory
Design Retrospective cohort analysis
SettingTertiary referral IVF Unit in the United Kingdom
Patients Women being investigated for sub-fertility
Interventions
Retrospective measurements were obtained from women who had AMH
measurements using Gen II assay during routine investigation for infertility at a
tertiary referral unit during a 1-year period The patients who had repeated
AMH measurements were identified and within-patient coefficient of variation
(CV) calculated using a mixed effects model with quadratic adjustment for age
Main Outcome Measures
The within-patient coefficient of variation (CV) calculated using a random
effects model with quadratic adjustment for age
Results
There was in total of 76 samples from 38 women with repeated AMH
measurements during the study period The within-patient sample-to-sample
variation (CV) was found to be 62
Conclusions
The study has confirmed that even when samples are processed promptly and
strictly in accordance with the manufacturers instructions substantial
variability exists between repeated samples Thus caution is recommended in
the use of these newer assays to guide treatment decisions Further work is
required to understand the underlying cause of this variability
Key Words
Anti-Muumlllerian hormone Muumlllerian inhibitory substance AMH AMH Gen II
ELISA AMH ELISA sample variability
69
INTRODUCTION
Anti-Muumlllerian hormone is a dimeric glycoprotein that is produced by
the granulosa cells of pre-antral and early antral follicles and has been found to
be the primary regulator of oocyte recruitment and folliculogenesis (Durlinger
et al 1999 Durlinger et al 2001) Strong correlation between AMH levels and
primordial follicle count (Hansen et al 2011) and hence a reflection of ovarian
response has promised a valuable tool in the reproductive specialistsrsquo armory
The development of commercially available AMH immunoassay assay kits has
heralded the widespread introduction and routine usage of AMH assessment in
the clinical setting Several studies have demonstrated that AMH serves as a
good predictor of ovarian response to gonadotrophin stimulation during IVF
treatment (van Rooij et al 2002 Nelson et al 2007 Nardo et al 2009) AMH
testing has also been shown to identify patients at risk of excessive ovarian
response and ovarian hyperstimulation syndrome (Yates et al 2011) with
consequent reduction in per cycle treatment costs by adopting an antagonist
approach during controlled ovarian stimulation Sensitivity and specificity of
AMH in detecting extremes of response has been shown to be comparable to
antral follicle count without the apparent technical limitations of the latter
(Broer et al 2009 Broer et al 2011)
It is stated that the sample-to-sample variation of AMH concentration in
individual women is small and therefore a single AMH measurement has been
recommended as standard practice (La Marca et al 2006 Hehenkamp et al
2006) However recent studies based on data from a single centre recently
published in Human Reproduction found that larger variability between
repeated samples exists which is particularly profound when currently
available second generation AMH assay (AMH Gen II ELISA Beckman
Coulter Inc Brea CA USA) is used (Rustamov et al 2012a Rustamov et al
2012b Rustamov et al 2011)
The trial team had 2 objectives firstly to assess whether the controversial
findings from the above study (Rustamov et al 2012a) were reproducible when
performed in the data based on the samples from a different laboratory with
differing populations If our study reached similar conclusions concerns
regarding the AMH Gen II assay and or manufacturers recommendations on
handling and sampling processes would be validated Alternatively if non-
70
similar findings were reported the laboratory performance in the initial study
ought to be questioned Secondly and more importantly if the repeat samples
are found to be within acceptable parameters then the current clinical standard
of a single random AMH measurement in patients is appropriate If the results
of repeated samples are significantly different following adjustment for age it
would suggest that AMH measurement is not a true estimation of the patientrsquos
ovarian reserve
In view of clinical and research implications of these findings we
undertook to replicate the variability study in a second fertility centre The
authors wish to note that Beckman Coulter recently issued a worldwide STOP
SHIP order on all AMH Gen II Elisa assay kits until further notice due to
manufacturing and quality issues
MATERIALS AND METHODS
Population
Women had serum AMH measurements using Gen II AMH assay from
15 April 2011 to 25 May 2012 for investigation of infertility at the Hewitt
Fertility Centre in the Liverpool Womens NHS Foundation Trust Hospital
tertiary referral unit were identified using the Biochemistry Laboratory AMH
samples database and all women within age range of 20-46 years were included
in the study The main reasons for repeating the samples were a) obtaining up-
to-date assessment of ovarian reserve b) patient request and c) for formulation
of a treatment strategy prior to repeat IVF cycles
Institutional Review Board approval was granted by the Audit
Department Liverpool Womenrsquos NHS Foundation Trust Hospital
Assay procedure
Samples were transported immediately to the in-house laboratory of
Liverpool Womenrsquos Hospital for the processing and analysis The serum was
separated within 8 hours from venipuncture and frozen at -50C until analyzed
71
in batches The sample preparation and assay methodology strictly followed
the manufacturers guidelines The AMH analysis of laboratory is regularly
monitored by external quality assessment scheme (UKNEQAS) and
performance has been satisfactory
The samples were analyzed using enzymatically amplified two-site
immunoassay (AMH Gen II ELISA Beckman Coulter Inc Brea CA USA)
The intra-assay CV was 521 and inter-assay CV (n=9) was 276 (low
controls) and 657 (high controls) The working range of the assay was
150pmolL and the minimum detection limit was 057pmolL
The main difference in the assay preparation in this study is that the
samples were processed within 8 hours whilst the samples in the previous
study were processed within 2 hours (Rustamov 2012a) Importantly the kit
insert of Gen II AMH assay does not state any maximum duration of storage
of unprocessed samples or any constraints on the transportation of
unprocessed samples Therefore there appears to be considerable variation in
practice of sample processing between clinics which ranges from processing
samples immediately to shipping unfrozen whole samples to long distances
Statistical analysis
The dataset was obtained from the Biomedical Andrology Laboratory
of the hospital and anonymised by one of the researchers (RR) Data
management and analysis of the anonymised data followed the same
procedures as the previous study (13) and were performed using Stata 12
Statistical Package (StataCorp Texas USA) Approval for data management
analysis and publication was obtained from the Research and Development
Department of Liverpool Womenrsquos Hospital
Between and within-subject sample-to-sample coefficient of variability
(CV) as well as the intra correlation coefficient (ICC) was estimated using a
mixed effects model in log (AMH) with quadratic adjustment for age AMH
levels of the samples that fell below minimum detection limit of the assay
(lt057 pmolL) were arbitrarily assigned a value of 031 pmolL in line with
the previous analysis (Rustamov et al 2012a)
72
RESULTS
During the study period in total of 1719 women had AMH
measurements using Gen II assay Thirty-eight women had repeated AMH
measurements with a total number of 76 repeat samples (Figure 1) The
median age of the women was 318 (IQR 304-364) The median AMH level
was 52pmolL (IQR 15-114) The median interval between samples was 93
days (IQR 49-164) with range of 6-375 days Age-adjusted regression analysis
of samples of these women showed that within-patient sample-to-sample
coefficient of variation (CV) of AMH measurements was 62 while between-
patient CV was 125 An age adjusted intra-correlation coefficient was 079
Figure 1 The repeated AMH measurements by date lines join the
repeats from the same patients (AMH in pmolL)
73
DISCUSSION
A number of studies have recently been published that have expressed
concerns regarding the stability and reproducibility of AMH results Whilst
technical issues regarding reproducibility between assays were known more
recently the reproducibility of results regarding the current Gen II assay has
raised significant concern (Rustamov et al 2012a Rustamov et al 2012b
Rustamov et al 2011) Proponents of the assay have proposed that poor
sample handling and preparation are responsible for these observed concerns
(Nelson et al 2013) Several studies have observed the stability of samples at
room temperature Kumar et al (Kumar et al 2010) observed a 4 variation in
results after 7 days storage compared with those samples analysed immediately
These results were consistent with studies by Fleming and Nelson who also
reported no change in AMH concentration over a period of several days
(Fleming et al 2012) However Rustamov et al reported a measured AMH
increase of 58 in samples stored at room temperature over a seven day
period (Rustamov et al 2012a) Similar concerns were raised regarding the
appropriate freezing process whilst samples frozen at -20C demonstrated
variation in results of between 6 and 22 (Durlinger et al 1999 Rustamov et al
2012a) freezing at -80C obviated a significant variation in assay results (Al-
Qahtani et al 2005 Rustamov et al 2012a) Several studies initially reported
good linearity of dilution (Kumar et al 2012 Preissner et al 2010 Fleming et al
2012) which was contradicted by reports that demonstrated poor linearity in
dilution when fresh samples were utilized (Rustamov et al 2012a) This study
suggested a tendency of AMH results to double with dilution More recently
Beckman Coulter issued a warning on their Gen II AMH ELISA kits that the
dilution of sample may give an erroneous result confirming non linearity of
dilution (King Dave 2012)
A number of studies have looked at the variability of AMH in repeated
samples without account to the menstrual cycle utilizing different assays
Dorgan et al in analyzing DSL samples frozen for prolonged periods
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median-sample interval of one year (Dorgan et al 2012)
Rustamov et al presented a larger series of 186 infertile patients with a median
between-sample interval of 26 months and a CV of 28 in DSL samples
74
(ICC 091 95 CI 090-093(Rustamov et al 2011) In a follow-up study
utilizing the Gen II assay in a group of 84 infertile patients the coefficient
variation of repeated results was 59 (ICC of 084 95 CI 079-090) a
substantial increase in the observed variability of the studies reporting for the
DSL assay (Rustamov et al 2012a) The most recent study to cast doubt on
current practice suggested that repeated measurement of AMH using Gen II
assay resulted in a within-subject variability of 80 (CV) (Hadlow et al 2012)
As a result 7 out of 12 women were subsequently reclassified according to their
originally predicted ovarian response Our study outlined above involving 76
samples from 38 infertile patients demonstrated a within-patient sample-to-
sample coefficient of variation (CV) of AMH measurements was 62
Overall these results suggest that there is significant within patient
variability that may be more pronounced in the Gen II assay Whilst biological
variation has been demonstrated to play a part within this the appreciative
effects of sample handling storage and freezing play a significant part in the
results and it may be that the Gen II assays may be more susceptible to these
changes This study has confirmed that there is significant within-patient
sample-to-sample variability in AMH measurements when the Gen II AMH
assay is used which is not confined to a single population or laboratory It is
important to note that the samples reported by both Rustamov et al 2012
and this study were processed and analyzed strictly according to
manufacturerrsquos recommendations in their respective local laboratories without
external transportation (Rustamov et al 2012a) Therefore it seems reasonable
to suggest that AMH results from other centers and laboratories are likely to
display similar significant sampling variability
Reproducibility of AMH measurements is of paramount importance
given that a single random AMH measurement is used for triaging patients
unsuitable for proceeding with IVFICSI and determining the dose of
gonadotrophins for ovarian stimulation for those patients who proceed with
treatment Similarly other clinical applications of AMH such as an assessment
of the effect of chemotherapy to fertility and follow up of women with history
of granulosa cell tumors also rely on accurate measurement of circulating
hormone levels The present work confirms the high between-sample within-
patient variability The recent warning from Beckman Coulter utilizing their
Gen II ELISA assay kits may give an erroneous result with dilution of samples
further questions the stability of the assay (King David 2012) Subsequently
75
the manufacturer recalled the assay kits due to issues with the instability of
samples and introduced modified protocol for preparation of Gen II assay
samples
Given there can be a substantial difference between two samples from
the same patient the use of such measurements for clinical decision-making
should be questioned and caution is advised
76
References
Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP and Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 2005 63267-273
Broer SL Dolleman M Opmeer BC Fauser BC Mol BW Broekmans FJM AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 20111746-54
Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL and Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
Fleming R and Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
Hadlow Narelle Longhurst Katherine McClements Allison Natalwala Jay Brown Suzanne J and Matson Phillip L Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response (Article in press) Fertil Steril 2012
Hansen KL Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170-5
Hehenkamp WJ Looman CW Themmen AP de Jong FH Te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063 King Dave URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L and Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A Stabile G Artenisio AC Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash3107
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593 6
77
Nelson S Biomarkers of ovarian response current and future applications Fertil and Steril 201399963-969
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo and Philip W Pemberton Anti-Muumlllerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091
Oybek Rustamov Alexander Smith Stephen A Roberts Allen P Yates Cheryl Fitzgerald Monica Krishnan Luciano G Nardo Philip W Pemberton Reply Reproducibility of AMH Hum Reprod 2012b273641-3642
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Preissner CM Morbeck DE Gada RP and Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54
Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011261768-74
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse patient variability Fertil Steril 2011951185-118
78
THE MEASUREMENT OF ANTI-MUumlLLERIAN
HORMONE A CRITICAL APPRAISAL
Oybek Rustamov Alexander Smith Stephen A Roberts
Allen P Yates Cheryl Fitzgerald Monica Krishnan
Luciano G Nardo Philip W Pemberton
The Journal of Clinical Endocrinology amp Metabolism
2014 Mar 99(3) 723-32
3
79
Title
The measurement of Anti-Muumlllerian hormone a critical appraisal
Authors
Oybek Rustamova Alexander Smithb Stephen A Robertsc Allen P Yatesb
Cheryl Fitzgeralda Monica Krishnand Luciano G Nardoe Philip W
Pembertonb
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Department of Clinical Biochemistry Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK d Manchester Royal Infirmary Central Manchester University
Hospitals NHS Foundation Trust Manchester M13 9WL UK
e Reproductive Medicine and Gynaecology Unit GyneHealth Manchester M3
4DN UK
Key terms
Anti-Muumlllerian hormone AMH Active MISAMH ELISA Diagnostic
Systems Laboratories AMHMIS ELISA Immunotech AMH Gen II assay
Beckman Coulter
Word Count 3947 (intro ndash general summary text only (no headings)
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
80
Declaration of authorsrsquo roles
The idea was developed during discussion between OR CF and SAR
OR conducted the initial appraisal of the studies prepared and revised the
manuscript SAR and CF contributed to the discussion and interpretation of
the studies and oversaw the revision of the manuscript PWP AY MK
and AS reviewed the data extraction and interpretation contributed to
the discussion of the studies and revision of the manuscript LGN
contributed to the discussion of the studies and revision of the manuscript
81
ABSTRACT
Context
Measurement of AMH is perceived as reliable but the literature reveals
discrepancies in reported within-subject variability and between-assay
conversion factors Recent studies suggest that AMH may be prone to pre-
analytical instability We therefore examined the published evidence on the
performance of current and historic AMH assays in terms of the assessment of
sample stability within-patient variability and comparability of the assay
methods
Evidence Acquisition
Studies (manuscripts or abstracts) measuring AMH published between
01011990 and 01082013 in peer-reviewed journals using appropriate
PubMedMedline searches
Evidence Synthesis
AMH levels in specimens left at room temperature for varying periods
increased by 20 in one study and almost 60 in another depending on
duration and the AMH assay used Even at -20degC increased AMH
concentrations were observed An increase over expected values of 20-30 or
57 respectively was observed following two-fold dilution in two linearity-of-
dilution studies but not in others Several studies investigating within-cycle
variability of AMH reported conflicting results although most studies suggest
variability of AMH within the menstrual cycle appears to be small However
between-sample variability without regard to menstrual cycle as well as within-
sample variation appears to be higher using the Gen II AMH assay than with
previous assays a fact now conceded by the kit manufacturer Studies
comparing first generation AMH assays with each other and with the Gen II
assay reported widely varying differences
Conclusions AMH may exhibit assay-specific pre-analytical instability
Robust protocols for the development and validation of commercial AMH
assays are required
82
INTORDUCTION
In the female AMH produced by granulosa cells of pre-antral and early
antral ovarian follicles regulates oocyte recruitment and folliculogenesis (1 2)
It can assess ovarian reserve (3-5) and guide gonadotrophin stimulation in
assisted reproduction technology (ART) (6) AMH is also used as a granulosa
cell tumour marker a marker of ovarian reserve post-chemotherapy (7 8) and
to predict age at menopause (910)
AMH immunoassays first developed by Hudson et al in 1990 (11) were
introduced commercially by Diagnostic Systems Laboratories (DSL) and
Immunotech (IOT) These assays were integrated into a second-generation
AMH assay GenII (12) by Beckman-Coulter but recent work suggests that this
new assay exhibits clinically important within-patient sample variability (13-
15) Beckman Coulter have recently confirmed this with a field safety notice
(FSN 20434-3) they cite without showing evidence for complement
interference as the problem
ldquoTruerdquo AMH variability comprises both biological and analytical
components (Figure 1) and given the varying antibody specificity and
sensitivity of different AMH assays then logically different kits will respond to
these components to varying degrees This review considers the published
literature on AMH measurement using previous and currently available assays
Potential sources of variation and their contribution to observed AMH
variability were identified
Review structure
This review has been divided into logical subgroups We first address the
stability of AMH at different storage temperatures then the effects of
freezethaw cycles and finally AMH variability in dilution studies Secondly
the within-person variability of AMH measurement is considered
encompassing intra- and inter-menstrual cycle variability and repeat sample
variability in general The final section covers AMH method comparisons
comparing older methods to each other and to the newer now prevalent
GenII method finishing with data on published guidance ranges concerning
the use of AMH in ART A general summary concludes the paper
83
Systematic review
The terms ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting
Substance and MIS were used to search the PubMedMedline MeSH
database between 1st January 1990 and 1st August 2013 for publications in
English commenting on AMH sample stability biological and sample-to-
sample variability or assay method comparison in human clinical or healthy
volunteer samples Titles andor abstracts of 1653 articles were screened to
yield the following eligible publications ten stability studies 17 intrainter-
cycle variability studies and 14 assay method comparability studies
Sample stability
Recent work has established that the GenII-measured AMH is
susceptible to significant preanalytical variability (13 14) not previously
acknowledged which may have influenced results in previous studies with this
assay
Stability of unfrozen samples
Five studies examined AMH stability in samples stored either at room or
fridge temperature (Table 1) (13 16-19) Al-Qahtani et al (16) assessing the
precursor of the DSL ELISA reported that ldquoimmunoreactivity survived the
storage of samples unfrozen for 4 daysrdquo but did not record storage
temperature or sample numbers Evaluating the GenII assay Kumar et al (18)
stored 10 samples at 2-8degC for up to a week and found an average 4
variation compared to samples analysed immediately However their
specimens originally reported as ldquofreshrdquo appear to have been kept cool and
transported overnight Fleming amp Nelson (19) reported no significant change
in the GenII-assayed AMH from 51 samples stored at 4degC Methodological
information was limited but interrogation of their data by Rustamov et al (14)
suggested that AMH levels rose by an average of 27 after 7 days storage
Zhao et al (17) reported a difference of less than 20 between DSL-assayed
AMH in 7 serum samples kept at 22degC for 48 hours when compared to
aliquots from the same samples frozen immediately at -20degC Rustamov et al
(13) measured AMH (GenII) daily in 48 serum samples at room temperature
for 7 days and observed an average 58 increase (from 0 to gt200) whilst
others (20) reported a 31 mean rise in GenII-assayed AMH in whole blood
84
after 90hrs at 20oC whereas serum AMH was virtually unchanged after
prolonged storage at 20oC
Sample stability at -20 o or -80oC and the effects of freezethaw
Rey et al (21) reported a significant increase in AMH (in-house assay)
in samples stored at -20degC for a few weeks attributing this to proteolysis
which could be stabilised with protease inhibitor (see discussion below)
Kumar et al (18) saw 6 variation between GenII-assayed AMH levels from
10 fresh and 10 frozen samples whilst Rustamov et al (13) observed a 22
increase in AMH (GenII) on re-analysis of 8 serum samples after 5 days
storage at -20degC These authors saw no AMH increase in serum stored at -80deg
C for the same period
Linearity of dilution
Six studies examined linearity of dilution on observed AMH
concentrations Long et al (22) recovered between 84 and 105 of the
expected AMH concentration (IOT n=3) AMH dilution curves parallel to
the standard curve were reported by others (16)Kumar et al (18) (n=4) and
Preissner et al (23) ) (n=7) reported GenII-assayed AMH recoveries from 95
to 104 and 96 respectively Sample handling information was limited in
some of these studies (16 23) Fleming amp Nelson (19) (GenII n=10) reported
variances of 8 using assay diluent and 5 using AMH-free serum following
2-fold dilution however interrogation of their data reveals an apparent
dilutional AMH increase of 20-30 in samples stored prior to dilution and
analysis Rustamov et al (13) (GenII n=9) in freshly collected serum observed
an average 57 increase in apparent AMH concentration following two-fold
dilution but with considerable variation
Discussion Sample stability
Sample stability can be a major analytical problem and detailed
examination suggests that previous evidence stating that commercially
measured AMH is stable in storage and exhibits linearity of dilution (12 16 18
19) is weak or conflicting
No study looking at room temperature storage on IOT-assayed AMH
was found and only one using DSL-assayed AMH which showed an increase
85
of less than 20 during storage (17) Studies using the GenII assay to
investigate the effect of storage on AMH variability at room temperature in
the fridge and at -200C reach differing conclusions ranging from stable to an
average 58 increase in measured levels It is important to note here that
sample preparation and storage prior to these experiments was different and
could account for the observed discrepancies The most stable storage
temperature for AMH in serum appears to be -80degC (13 16)
Linearity of dilution studies were also conflicting (13 18 19 23) those
reporting good linearity used samples transported or stored prior to baseline
analysis whereas dilution of fresh samples showed poor linearity In late 2012
Beckman Coulter accepted that the GenII assay did not exhibit linear dilution
and issued a warning on kits that samples should not be diluted They now
suggest that with the newly introduced pre-mixing protocol dilution should
not be a problem
This review highlights the fact that assumptions about AMH stability in
serum were based on a limited number of small studies often providing
limited methodological detail (impairing detailed assessment and comparison
with other studies) using samples stored or transported under unreported
conditions Furthermore conclusions derived using one particular AMH assay
have been applied to other commercial assays without independent validation
The available data suggests that dilution of samples andor storage or
transport in sub-optimal conditions can lead to an increase in apparent AMH
concentration The conditions under which this occurs in each particular AMH
assay are not yet clear and more work is required to understand the underlying
mechanisms Two alternative hypotheses have been proposed firstly that
AMH may undergo proteolytic change as postulated by Rey et al (21) or
conformational change as proposed by Rustamov et al (1314) during storage
resulting in ldquostabilisationrdquo of the molecule in a more immunoreactive form
secondly Beckman have postulated the presence of an interferent
(complement) which degrades on storage (Beckman Coulter field safety notice
FSN 20434-3)
A recent case report found that a falsely high AMH level was corrected
by the use of heterophylic antibody blocking tubes (24) but this does not
explain elevation of AMH on storage (13)
Whatever the mechanism responsible two solutions are available either
inhibit the process completely or force it to completion prior to analysis
86
Rustamov et al (13) and Han et al (15) both suggest pre-dilution of samples to
force the process a protocol now adopted by Beckman Coulter in their revised
GenII assay protocol Any solution must be robustly and independently
validated both experimentally and clinically prior to introduction in clinical
practice Fresh optimal ranges for interpretation of AMH levels in ART will be
needed and the validity of studies carried out using unreported storage
conditions may have to be re-evaluated
Within-person variability
The biological components of AMH variability such as circadian and
interintra-cycle variability have been extensively studied (Table 2 amp
Supplementary table 1)
Circadian variation
Bungum et al (25) evaluated circadian variability measuring AMH
(IOT) two hourly over 24hrs within day 2ndash6 of the menstrual cycle in younger
(20-30 years) and older (35-45 years) women Within-individual CVs of 23
(range 10-230) in the younger group and 68 (range 17-147) in the older
group were observed
Variability within the menstrual cycle
Cook et al (26) observed significant (12) variation in mean AMH (in-
house) levels in 20 healthy women throughout different phases of the
menstrual cycle Intra-cycle variability of IOT-assayed AMH was reported in
three publications (27-29) In two sequential samples were stored at -20degC
until analysis (27 28) Streuli et al (29) did not report on storage La Marca et
al (27) saw no difference in mean follicular phase AMH levels (days 2 4 and 6)
in untreated spontaneous menstrual cycles from 24 women This group went
on to report a small insignificant change (14) in within-group AMH
variability throughout the whole menstrual cycle in 12 healthy women
However this analysis does not appear to allow for correlations within same-
patient samples Streuli et al (29) studied intra-cycle variation of AMH
throughout two menstrual cycles in 10 healthy women and also reported no
significant changes (lt5)
87
The DSL assay was used in eight studies assessing intra-cycle variability
(30-37) Four studied sample storage at -20deg C (30323437) and two studied
samples storage at -80degC (3335) No sample storage data was given in two
publications (31 36) Hehenkamp et al (30) assessed within-subject variation
of AMH in 44 healthy women throughout two consecutive menstrual cycles
and reported an intra-cycle variation of 174 Lahlou et al (31) reported a
ldquodiphasicrdquo pattern of AMH with a significant decrease in levels during the LH
surge from 10 women at various cycle phases Tsepelidis et al (32) reported a
mean intra-cycle coefficient of variation of 14 comparing group mean AMH
levels in 20 women during various stages of the menstrual cycle Wunder et al
(33) reported an intra-cycle variability of around 30 in 36 healthy women
sampling on alternate days They saw a marked fall around ovulation which
might have been missed with less frequent sampling intervals as in other
studies Sowers et al (35) studied within-cycle variability in 20 healthy women
but did not compute an overall estimate instead they selected subgroups of
low and high AMH and reported significant within-cycle variability for women
with high AMH but not those with low AMH - an analysis that has been
questioned (38 39) Robertson et al (36) subgrouped mean AMH levels in 61
women observing that AMH levels were stable in women of reproductive age
and ovulatory women in late reproductive age whilst AMH in other women in
late reproductive age was much more variable Using the data from
Hehenkamp et al (30) van Disseldorp et al (34) calculated intra-class
correlation (ICC) and reported a within-cycle variability of 13 although this
was not clearly defined Using the same data Overbeek et al (37) analyzed the
absolute intra-individual difference in younger (38 years) and older (gt38
years) women This study concluded that the AMH concentration was more
variable in younger women (081059 gL) compared to older women
(031029 gL) during the menstrual cycle (P=0001) thus a single AMH
measurement may be unreliable A recent study using the GenII assay
reported 20 intra-cycle variability in AMH measurements in women (n=12)
with regular ovulatory cycles (40) All the reports considered have findings
consistent with a modest true systematic variability of 10-20 in the level of
AMH in circulation during the menstrual cycle Whilst there have been
suggestions that this variability may differ between subgroups of women these
88
have been based on post-hoc subgroup analyses and there is no convincing
evidence for such subgroups (38)
Variability between menstrual cycles
Three studies (Supplementary table 1) evaluated AMH variability in
samples taken during the early follicular phase of consecutive menstrual cycles
(102941) and three studies have reported on the variability of AMH in repeat
samples from the same patient taken with no regard to the menstrual cycle
(134243) One study employed an in-house assay (41) one study used the
IOT assay (29) three studies used the DSL assay (10 42 43) and one study
(13) used the GenII assay In four infertile women Fanchin et al (41) assessed
the early follicular phase AMH (in-house) variability across three consecutive
menstrual cycles they concluded that inter-sample AMH variability was
characterised by an ICC of 089 (95 CI 083-094) Streuli et al (29)
calculated a between-sample coefficient of variation of 285 in AMH (IOT)
in 10 healthy women In 77 infertile women van Disseldorp et al (10) found
an inter-cycle AMH (DSL) variability of 11 In summary these studies
suggest that the overall inter-cycle variability of AMH ranges from 11 (DSL)
to 28 (IOT) this figure will include both biological and measurement-related
variability
Variability between repeat samples
Variability between repeat samples without regard to menstrual cycle
phase was examined in three studies (Supplementary table 1) In a group of 20
women using samples frozen for prolonged periods Dorgan et al (42)
demonstrated a variability of 31 (ICC 078 95 CI 060-095) between two
samples with a median between-sample interval of one year In a larger series
of 186 infertile women Rustamov et al (43) (DSL) found a CV of 28
between repeated samples with a median between-sample interval of 26
months (ICC 091 95 CI 090-093) Rustamov et al (13) found that the
coefficient of variation of repeated GenII-assayed AMH in a group of 84
infertile women was 59 (ICC of 084 95 CI 079-090) substantially higher
than that reported using the DSL assay Similarly a recent study by Hadlow et
al (40) found a within-subject GenII-assayed AMH variability of 80 As a
89
result 5 of the 12 women studied crossed clinical cut-off levels following
repeated measurements
Discussion Within-patient variability
Evidence suggests that repeated measurement of AMH can result in
clinically important variability particularly when using the GenII assay This
questions the assumption that a single AMH measurement is acceptable in
guiding individual treatment strategies in ART
The observed concentration of any analyte measured in a blood
(serum) sample is a function of its ldquotruerdquo concentration and the influence of a
number of other factors (Figure 1) Studies examining the variability of AMH
by repeated measurement of the hormone will therefore reflect both true
biological variation and measurement-related variability introduced by sample
handling andor processing Thus within-sample inter-assay variability used as
an indicator of assay performance may not reflect true measurement-related
variability between samples since it does not take into account the contribution
from pre-analytical variability Measurement-related between-sample variability
can be established in part using blood samples taken simultaneously (to avoid
biological variability) from a group of subjects although even this does not
reflect the full variability in sample processing and storage inherent in real
clinical measurement
Since AMH is only produced by steadily growing ovarian follicles it is
plausible to predict a small true biological variability in serum reflected in the
modest 1-20 variability found within the menstrual cycle In contrast it
appears that the magnitude of measurement-related variability of AMH is more
significant a) within-sample inter-assay variation can be as high as 13 b)
different assays display substantially different variability and c) AMH appears
to be unstable under certain conditions of sample handling and storage (Table
1) Consequently any modest variation in true biological AMH concentration
may be overshadowed by a larger measurement-related variability and careful
experimental designs are required to characterise such differences In general
the reported variability in published studies should be regarded as a measure of
total sample-to-sample variability ie the sum of biological and measurement-
related variability (Figure 1)
90
In repeat samples the available evidence confirms that there is a
significant level of within-patient variability between measurements which is
assay-dependent greater than the estimates of within cycle variability and
therefore likely to be predominantly measurement-related Evidence from
several sources suggests that the effects of sample handling storage and
freezing differ between commercial assays and that the newer GenII assay may
be more susceptible to these changes under clinical conditions When it has
been established that the modified protocol for the GenII assay can produce
reproducible results independent of storage conditions then it will be
necessary to re-examine intra and inter cycle variability of AMH
Assay method comparability
AMH assay comparisons have either used same sample aliquots or
used population-based data with repeat samples Study population
characteristics sample handling inter-method conversion formulae and results
from these comparisons are summarised in Table 3 AMH levels were almost
universally compared using a laboratory based within-sample design The
Rustamov et al study (13) was population-based comparing AMH results in
two different samples from the same patient at different time points using 2
different assays
IOT vs DSL
Table 3 summarises 8 large studies (17 29 30 44-48) that compared the
DSL and IOT AMH assays They demonstrate strikingly different conversion
factors from five-fold higher with the IOT assay to assay equivalence Most
studies carried out both analyses at the same time to avoid analytical variation
(Figure 1) However this does mean that samples were batched and frozen at -
18degC to -80degC prior to analysis which as already outlined may influence pre-
analytical variability and contribute to the observed discrepancies in conversion
factors
IOT vs GenII
Three studies have compared the IOT and Gen II assays (Table 3)
Kumar (18) reported that both assays gave identical AMH concentrations
However Li et al (48) found that the IOT assay produced AMH values 38
91
lower than the Gen II assay whilst Pigny et al (49) found levels that were 2-fold
lower
DSL vs GenII
Four studies analysed same-sample aliquots using the DSL and GenII
assays either simultaneously or sequentially (33 48 50 51) Only Li et al (48)
gave details of sample handling (Table 3) All four studies found that AMH
values that were 35 ndash 50 lower using the DSL compared to the GenII assay
Rustamov et al (13) carried out a between-sample comparison of the assays
measuring AMH in fresh or briefly stored clinical samples from the same
women at different times with values adjusted for patient age (Table 3) In
contrast to within-sample comparisons this study found that the DSL assay gave
results on average 21 higher than with the GenII assay Whilst this
comparison is open to other bias it does reflect the full range of variability
present in clinical samples and avoids issues associated with longer term
sample storage
Discussion Assay method comparability
It is critical for across-method comparison of clinical studies that
reliable conversion factors for AMH are established In-house assays aside
three commercially available AMH ELISAs have been widely available (IOT
DSL and GenII) and the literature demonstrates considerable diversity in
reported conversion factors between first-generation assays (DSL vs IOT)
and between first and second-generation immunoassays (DSLIOT vs GenII)
Although most studies appear to follow manufacturersrsquo protocols
detailed methodological information is sometimes lacking The assessment of
within-sample difference between the two assays involved thawing of a single
sample and simultaneous analysis of two aliquots with each assay Both
aliquots experience the same pre-analytical sample-handling and processing
conditions therefore the results should be reproducible provided the AMH
samples are stable during the post-thaw analytical stage and the study
populations are comparable However this review has identified significant
discrepancies between studies perhaps due to either significant instability of
the sample or significant variation in assay performance Studies comparing
AMH levels measured using different assays in populations during routine
92
clinical use have also come to differing conclusions (13 51) Given the study
designs that workers have used to try to ensure that samples are comparable
the finding of significant discrepancies in the observed conversion factors
between assays is consistent with the proposal that AMH is subject to
instability during the pre-analytical stage of sample handling This coupled
with any differential sensitivity and specificity between these commercial
assays could give rise to the observed results ie some assays are more
sensitive than others to pre analytical effects
AMH guidance in ART
AMH guidance ranges to assess ovarian reserve (52) or subsequent
response to treatment (53 54) have been published The Doctors Laboratory
using the DSL assay advised the following ranges for ovarian reserve (lt
057pmolL-undetectable 057-21 pmolL-very low 22-157 pmolL-low
158-286 pmolL-satisfactory 287-485pmolL-optimal gt485pmolL-very
high) ranges that supposedly increased by 40 on changing to the GenII assay
(51) More recently other authors have attempted to correlate AMH levels with
subsequent birth rates Brodin et al (53) using the DSL assay observed that
higher birth rates were seen in women with an AMH level gt 21 pmolL and
low birth rates were seen in women who had AMH levels lt 143 pmolL In
the UK the National Institute for Health and Care Excellence (NICE) have
recently issued guidance on AMH levels in the assessment of ovarian reserve in
the new clinical guideline on Fertility (54) They advise that an AMH level of le
54 pmolL would indicate a low response to subsequent treatment and an
AMH ge 250 pmolL indicates a possible high response Although not
specifically stated interrogation of the guideline suggests that these levels have
been obtained using the DSL assay which is no longer available in the UK
As discussed above the initial study of comparability between the DSL
and GenII assays reported that GenII generated values 40 higher compared
to the DSL assay clinics were therefore recommended to increase their
treatment guidance ranges accordingly (51) However a more recent study
using fresh samples found that the original GenII assay may actually give
values which are 20-30 lower suggesting that following the above
recommendation may lead to allocation of patients to inappropriate treatment
groups (13) The apparent disparity in assay comparison studies implies that
93
AMH reference ranges and guidance ranges for IVF treatment which have
been established using one assay cannot be reliably used with another assay
method without full independent validation Similarly caution is required
when comparing the outcomes of research studies using different AMH assay
methods
General Summary
Recent publications have suggested that GenII-assayed AMH is
susceptible to pre-analytical change leading to significant variability in
determined AMH concentration an observation now accepted by the kit
manufacturer However this review suggests that all AMH assays may display a
differential response to pre-analytical proteolysis conformational changes of
the AMH dimer or presence of interfering substances The existence of
appreciable sample-to-sample variability and substantial discrepancies in
between-assay conversion factors suggests that sample instability may have
been an issue with previous AMH assays but appears to be more pronounced
with the currently available GenII immunoassay The observed discrepancies
may be explicable in terms of changes in AMH or assay performance that are
dependent on sample handling transport and storage conditions factors
under-reported in the literature We strongly recommend that future studies on
AMH should explicitly report on how samples are collected processed and
stored If it can be clearly demonstrated that the new GenII protocol drives
this process to completion in all samples ensuring stability then a re-
examination of reference and guidance ranges for AMH interpretation will be
necessary There is a clear need for an international reference standard for
AMH and for robust independent evaluation of commercial assays in routine
clinical samples with well-defined sample handling and processing protocols
These issues of sample instability and lack of reliable inter-assay comparability
data should be taken into account in the interpretation of available research
evidence and the application of AMH measurement in clinical practice
94
References
1 Durlinger AL Kramer P Karels B de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Control of primordial follicle recruitment by anti-Mullerian hormone in the mouse ovary Endocrinology 19991405789ndash5796
2 Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899
3 van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
4 Nelson SM Yates RW Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
5 Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009921586-1593 6 Yates AP Rustamov O Roberts SA Lim HYN Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353ndash2362
7 Lane AH Lee MM Fuller AF Jr Kehas DJ Donahoe PK MacLaughlin DT Diagnostic utility of Mullerian inhibiting substance determination in patients with primary and recurrent granulosa cell tumors Gynecol Oncol 19997351-55
8 Anderson RA Pretreatment serum anti-Mullerian hormone predicts long-term ovarian function and bone mass after chemotherapy for early breast cancer J Clin Endocrinol Metab 2011961336-1343
9 Broer SL Eijkemans MJ Scheffer GJ van Rooij IA de Vet A Themmen AP Laven JS de Jong FH te Velde ER Fauser BC Broekmans FJ Anti-mullerian hormone predicts menopause a long-term follow-up study in normoovulatory women J Clin Endocrinol Metab 2011962532-2539
10 van Disseldorp J Lambalk CB Kwee J Looman CW Eijkemans MJ Fauser BC Broekmans FJ Comparison of inter- and intra-cycle variability of anti-Muumlllerian hormone and antral follicle counts Hum Reprod 201025221-227
11 Hudson PL Dougas I Donahoe PK Cate RL Epstein J Pepinsky RB MacLaughlin DT An immunoassay to detect human mullerian inhibiting substance in males and females during normal development J Clin Endocrinol Metab 19907016-22
95
12 Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642
15 Han X McShane M Sahertian R White C Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Hum Reprod 201328 (suppl 1)i76-i78 (abstract)
16 Al-Qahtani A Muttukrishna S Appasamy M Johns J Cranfield M Visser JA Themmen AP Groome NP Development of a sensitive enzyme immunoassay for anti-Mullerian hormone and the evaluation of potential clinical applications in males and females Clin Endoc 200563267-273
17 Zhao J Ng SY Rivnay B Leader BS Serum Anti-Mullerian hormone (AMH) measurement inter-assay agreement and temperature stability Fertil Steril 200788S17 (abstract)
18 Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
19 Fleming R Nelson SM Reproducibility of AMH Hum Reprod 2012273639-3641
20 Fleming R Fairbairn C Blaney C Lucas D Gaudoin M Stability of AMH measurement in blood and avoidance of proteolytic changes Reprod Biomed Online 201326130-132
21 Rey R Lordereau-Richard I Carel JC Barbet P Cate RL Roger M Chaussain JL Josso N Anti-Mullerian hormone and testosterone serum levels are inversely related during normal and precocious pubertal development J Clin Endocrinol Metab 199377 1220ndash1226
22 Long WQ Ranchin V Pautier P Belville C Denizot P Cailla H Lhomme C Picard JY Bidart JM Rey R Detection of minimal levels of serum anti-Mullerian hormone during follow-up of patients with ovarian granulosa cell tumor by means of a highly sensitive enzyme-linked immunosorbent assay J Clin Endocrinol Metab 200085540ndash544
23 Preissner CM Morbeck DE Gada RP Grebe SK Validation of a second generation assay for anti-Mullerian hormone Clin Chem 201056A54 (abstract)
24 Cappy H Pigny P Leroy-Billiard M Dewailly D Catteau‐Jonard S Falsely elevated serum antimuumlllerian hormone level in a context of heterophilic
96
interference Fertil Steril 2013991729-1732
25 Bungum L Jacobsson AK Roseacuten F Becker C Yding Andersen C Guumlner N Giwercman A Circadian variation in concentration of anti-Mullerian hormone in regularly menstruating females relation to age gonadotrophin and sex steroid levels Hum Reprod 201126678ndash684
26 Cook CL Siow Y Taylor S Fallat ME Serum muumlllerian-inhibiting substance levels during normal menstrual cycles Fertil Steril 200073859-861
27 La Marca A Malmusi S Giulini S Tamaro LF Orvieto R Levratti P Volpe A Anti-Muumlllerian hormone plasma levels in spontaneous menstrual cycle and during treatment with FSH to induce ovulation Hum Reprod 2004192738-2741
28 La Marca A Stabile G Carduccio Artenisio A Volpe A Serum anti-Mullerian hormone throughout the human menstrual cycle Hum Reprod 2006213103ndash310729 Streuli I Fraisse T Chapron C Bijaoui G Bischof P de Ziegler D Clinical uses of anti-Mullerian hormone assays pitfalls and promises Fertil Steril 200991226-230
30 Hehenkamp WJ Looman CW Themmen AP de Jong FH te Velde ER Broekmans FJ Anti-Mullerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation J Clin Endocrinol Metab 2006914057-4063
31 Lahlou N Chabbert-Buffet N Gainer E Roger M Bouchard P Diphasic pattern of anti-Mullerian hormone (AMH) in ovulatory cycles as evidenced by means of ultra-sensitive assay new insights into ovarian function Fertil Steril 200686S11 (abstract)
32 Tsepelidis S Devreker F Demeestere I Flahaut A Gervy C Englert Y Stable serum levels of anti-Mullerian hormone during the menstrual cycle a prospective study in normo-ovulatory women Hum Reprod 2007221837ndash1840
33 Wunder DM Bersinger NA Yared M Kretschmer R Birkhauser MH Statistically significant changes of anti-Mullerian hormone and inhibin levels during the physiologic menstrual cycle in reproductive age women Fertil Steril 200889927-933
34 van Disseldorp J Faddy MJ Themmen AP de Jong FH Peeters PH van der Schouw YT Broekmans FJ Relationship of serum antimullerian hormone concentration to age at menopause J Clin Endocrinol Metab 2008932129-2134
35 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 2010 941482-1486
36 Robertson DM Hale GE Fraser IS Hughes CL Burger HG Changes in serum antimuumlllerian hormone levels across the ovulatory menstrual cycle in late reproductive age Menopause 201118521-524
37 Overbeek A Broekmans FJ Hehenkamp WJ Wijdeveld ME van
97
Disseldorp J van Dulmen-den Broeder E Lambalk CB Intra-cycle fluctuations of anti-Mullerian hormone in normal women with a regular cycle a re-analysis Reprod Biomed Online 201224664ndash 669
38 Roberts SA Variability in anti-Mullerian hormone levels a comment on Sowers et al ldquoAnti-Mullerian hormone and inhibin B variability during normal menstrual cyclesrdquo Fertil Steril 201094e59
39 Sowers M McConnell D Gast K Zheng H Nan B McCarthy JD Randolph JF Reply of the authors Variability in anti-Muumlllerian hormone levels a comment on Sowers et al Anti-Muumlllerian hormone and inhibin B variability during normal menstrual cycles Fertil Steril 201094e60
40 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013991791-1797
41 Fanchin R Taieb J Lozano DH Ducot B Frydman R Bouyer J High reproducibility of serum anti-Muumlllerian hormone measurements suggests a multi-staged follicular secretion and strengthens its role in the assessment of ovarian follicular status Hum Reprod 200520923-927
42 Dorgan JF Spittle CS Egleston BL Shaw CM Kahle LL Brinton LA Assay reproducibility and within-person variation of Mullerian inhibiting substance Fertil Steril 201094301-304
43 Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
44 Freour T Mirallie S Bach-Ngohou K Denis M Barriere P Masson D Measurement of serum anti-Mullerian hormone by Beckman Coulter ELISA and DSL ELISA comparison and relevance in assisted reproduction technology (ART) Clin Chim Acta 2007375162-164
45 Bersinger NA Wunder D Birkhaumluser MH Guibourdenche J Measurement of anti-mullerian hormone by Beckman Coulter ELISA and DSL ELISA in assisted reproduction differences between serum and follicular fluid Clin Chim Acta 2007384174ndash175
46 Taieb J Belville C Coussieu C Guibourdenche J Picard JY di Clemente N Two immunoassays for antimullerian hormone measurement analytical and clinical performances Ann Biol Clin (Paris) 200866537-547
47 Lee JR Kim SH Jee BC Suh CS Kim KC Moon SY Antimullerian hormone as a predictor of controlled ovarian hyperstimulation outcome comparison of two commercial immunoassay kits Fertil Steril 2011952602-2604
48 Li HW Ng EH Wong BP Anderson RA Ho PC Yeung WS Correlation between three assay systems for anti-Mullerian hormone (AMH)
98
determination J Assist Reprod Genet 2012291443-1446
49 Pigny P Dassonneville A Catteau-Jonard S Decanter C Dewailly D Comparative analysis of two-widely used immunoassays for the measurement of serum AMH in women Hum Reprod 2013 28i311-316 (abstract)
50 Gada R Hughes P Amols M Amols M Preissner C Morbeck D Coddington C Validation and comparison of AMH serum levels using the original active MISAMH ELISA to the new active AMH Gen II ELISA Fertil Steril 201195S23 (abstract)
51 Wallace AM Faye SA Fleming R Nelson SM A multicentre evaluation of the new Beckman Coulter anti-Mullerian hormone immunoassay (AMH Gen II) Ann Clin Biochem 201148370-373
52 The Doctors Laboratory Lab Report newsletter ndash Winter 20072008 ndash AMH
53 Brodin T Hadziosmanovic N Berglund L Olovsson M Holte J Antimullerian hormone levels are strongly associated with live birth rates after assisted reproduction J Clin Endocrinol Metab 201398(3)1107-1104
54 National Institute for Care and Health Excellence NICE clinical guideline CG156 Fertility
99
Figure 1 Biological and analytical variability of AMH
100
Table 1 AMH assay validation effect of sample storage conditions freshthaw cycles and linearity of dilution
Study Assay Method Result
Rey et al (21) in-house effect of Long-term storage at -20C (n=4) AMH levels in archival samples were 230 higher than original value
Long et al (22) IOT linearity up to 16-fold dilution (n=3) observed AMH was 84-105 of expected AMH
Al-Qahtani et al (16) in-house a freezethaw stability storage unfrozen for 4 days
b linearity up to 32-fold dilution (n=6)
a immuno-reactivity survived both multiple freeze-thaw cycles and storage unfrozen for 4 days b dilution curves were parallel to the standard curve
Zhao et al (17) DSL
serum frozen immediately at -20C compared to
aliquots stored at 4C or 22C for up to 2 days (n=7) AMH levels increased by 1 at 4C and 9 at 22C after 2 days compared to sample frozen immediately
Kumar et al (18) Gen II
a serum or plasma stored at 2-8C or -20C for up to 7 days (n = 20) b serum or plasma underwent up to three freezethaw cycles (n=20) c linearity of dilution (n=4)
a AMH levels were stable for up to 7 days at 2-8C or -20C
b AMH increased by 15 in serum and by 5 in plasma after 3 cycles c linear results obtained across the dynamic range of the assay
Preissner et al (23) Gen II linearity of dilution (n=7) average agreement with expected result was 97
Rustamov et al (13) Gen II
a stability at RT for up to 7 days (n=48)
b storage for 5 days at -20C or -80C compared to fresh sample (n=8) c linearity on 2-fold dilution (n=9)
a AMH levels increased by an average of 58 over 7 days
b AMH levels increased by 23 at -20C but were unchanged at -80C c AMH levels were on average 157 higher than expected
Fleming amp Nelson (19) Gen II a serum stored at 4C for 7 days (n=48) b linearity of dilution (n=10)
a AMH levels increased by an average of 27 b AMH was 28 amp 33 higher on 2-fold amp 4-fold dilution resp
Fleming et al (20) Gen II
a whole blood stored for up to 90 hours at 4C (n=32) or 20C (n=21)
b serum stored for 5 days at 20C and 2 days at 4C (n=13)
a AMH increased by 11 at 4C and by 31 at 20C b only 1 increase in AMH compared to original value
Han et al (15) Gen II
serum from non-pregnant (n=13) or early pregnant (n=7) women
stored at RT -20C or -80C for up to 7 days
In non-pregnant women AMH increased by 26 after 7 days at RT but was
unchanged at -20C or -80C
In pregnant women AMH increased by 50 at RT and 27 at -80C after 48 hours
101
Table 2 Intra-cycle variability of AMH Study
Subjects
a cycles b day sampled
Assay
a storage b freezethaw c measurement
Result
Authorsrsquo Conclusion
Cook et al (26)
healthy age 22-35 regular cycle (n=20)
a 1 cycle b day 23 LH surge LH surge +7 d
in-house
a -80C b once c inter-assay variation eliminated
day 3 AMH = 14 09ngml
mid cycle AMH = 17 11ngmL
mid luteal AMH = 14 09ngmL
Fluctuations significant (plt0008) AMH may have a regulatory role in folliculogenesis
La Marca et al (27)
healthy age 21-36
regular cycle (n=24)
a follicular phase b alternate days
IOT
a -20C
b once
AMH did not change from days 2 to 6 in spontaneous cycles but decreased progressively in FSH-treated cycles
AMH levels did not change significantly during follicular phase of the menstrual cycle
La Marca et al (28)
healthy age18-24
regular cycle (n=12)
a 1 cycle b alternate days day 0 = day of LH surge
IOT
a -20C
b once
low mean AMH = 3411ngmL (day 14)
high mean AMH =3913ngmL (day 12)
AMH levels did not change significantly throughout menstrual cycle
Lahlou et al (31)
placebo-treated (n=12)
a 1 cycle
b every 3 days
DSL
NR 7 days pre LH surge AMH = 26
32pmolL peak AMH = 191 35pmolL 10 days post LH surge
AMH = 254 43pmolL
AMH levels exhibited a diphasic pattern with levels declining significantly (plt005) during the LH surge
Hehenkamp et al (30)
healthy
fertile regular cycle (n=44)
a 2 cycles
b AMH measured at each of 7 cycle phases
DSL a -20C a sine pattern fitted to AMH data was not significant (p=040) b72 repeat AMH values fell within the same quintile 28 in adjacent quintile
AMH shows no consistent fluctuation through the cycle compared to FSH LH amp E2
van Disseldorp et al (10)
data from Hehenkamp et al (30)
Intra-cycle within-subject variation of AMH was only 13 compared to 31-34 for AFC (dependent on follicle size)
AMH displays less intra-cycle variability than AFC
Overbeek et al (37)
data from Hehenkamp et al (30)
Fluctuations were larger than 05microgL in one cycle in significantly (p = 0001) more women in the younger group than the older one
AMH can fluctuate substantially in younger women during menstrual cycle so a single measurement could be unreliable
102
Tsepelidis
et al (32)
healthy age 18-35 regular cycles (n=20)
a 1 cycle b days 3 7 10-16 18 21 amp 25
DSL
a -20C
b once
Within-cycle differences not significant (p=0408)
AMH levels do not vary during the menstrual cycle
Wunder et al (33)
healthy
age 20-32 regular cycles (n=36)
a 1 cycle
b alternate days
DSL
a -80C
AMH levels were statistically higher in the late follicular phase than at the time of ovulation (p= 0019) or in the early luteal phases (plt00001)
AMH levels vary significantly during the menstrual cycle
Streuli
et al (29)
healthy mean age=241 regular cycles
(n=10)
a 1 cycle b before (LH
-10-5-2-1) and after LH surge (LH +1+2+10)
IOT
a -18C
AMH levels were statistically lower during the early luteal phase compared to early follicular phase (p=0016) and late luteal phase levels (p=002)
In clinical practice AMH can be measured at any time during the menstrual cycle
Sowers et al
(35)
healthy age 30-40 regular cycles
(n=20)
a 1 cycle b daily
DSL
a -80C
b once c simultaneous
Higher AMH levels with significant variation between days 2-7 in the ldquoyounger ovaryrdquo Low AMH levels with little variation in the ldquoaging ovaryrdquo
AMH varies across the menstrual cycle in the ldquoyounger ovaryrdquo
Robertson et al (36)
a age 21-35 regular cycles
(n=43) b age 45-55
variable cycles (n=18)
a 1 cycle + initial stages of succeeding cycle b three times weekly
DSL
NR No intracycle variation in AMH level was found in women in mid reproductive life or in 33 women with regular cycles in late reproductive age In the remaining cycles there was a significant (plt001) two-fold decrease in AMH in 11 cycles and a significant (plt001) 42-fold increase between the follicular amp luteal phases
When AMH levels are substantially reduced they become less reliable markers of ovarian reserve
Hadlow
et al (40)
age 29-43 regular cycles non-PCOS
(n=12)
a 1 cycle b 5-9 samples per subject
Gen II a -20C within 4 hours of sampling b once
c simultaneous
712 women could be reclassified depending on when AMH was measured during the cycle 212 crossed cut-offs predicting hyperstimulation
AMH cycles varied during menstrual cycle and clinical classification of the ovarian response was altered
103
Table 3 Variability in AMH levels between menstrual cycles
Study
Subjects
a cycles b day sampled
Assay
Storage
Result
Authorsrsquo Conclusion
Fanchin et al (41)
infertile
age 25-40 regular cycles
(n=47)
a 3 cycles
b day 3
in-house
(Long et al 2000)
-80C
AMH showed significantly
higher reproducibility than inhibin B (plt003) E2 (plt00001) FSH (plt001) and early AFC (plt00001)
AMH showed improved cycle-to-cycle consistency compared to other markers of ovarian follicular status
Streuli
et al (29)
healthy mean age = 241 regular cycles
(n=10)
a 2 cycles b before (LH -10-5-2-1) and
after LH surge (LH +1+2+10)
IOT
-18C Inter-cycle variability of 285
AMH fluctuations during the cycle were smaller than or equal to the variability between two cycles
van Disseldorp et al (10)
infertile median age =33
PCOS excluded (n=77)
a average 373 cycles b day 3
DSL
-80C
AMH showed a within-subject variability of 11 compared to 27 for AFC
AMH demonstrated less individual inter-cycle variability than AFC
Dorgan
et al (42)
blood donors age 36-44 collected 1977-1981 (n=20)
two samples collected during the same menstrual cycle phase at least 1yr apart
DSL
-70C
between-subject variance in AMH of 219 was large compared to the within-subject variance of 031
AMH was relatively stable over 1 year in pre-menopausal women
Rustamov et al (36)
infertile women age 22-41
(n=186)
random sampling median interval = 26 months
DSL
-70C
within-subject CV for AMH was 28 compared to 27 for FSH
AMH showed significant sample-to-sample variation
Rustamov et al (13)
infertile women age 20-46
(n=87)
random sampling median interval = 51 months
Gen II
-20C
within-subject CV for AMH was 59
AMH demonstrated a large sample-to-sample variation
104
Table 4 Within-subject comparison between AMH methods Study
Assays
Subjects
Simultaneous Analysis
Regression
Summary
Freour et al (44) DSL vs IOT 69 infertile women age 22-40
Yes IOT = 401 x DSL + 098 (microgL) (Deming regression)
DSL = 22 IOT (plt00001)
Hehenkamp et al (30) DSL vs IOT 82 healthy women NR DSL= 0495 x IOT - 003 DSL = 495 IOT
Bersinger et al (45) a DSL vs IOT
b DSL vs IOT
a 11 infertile women
b 55 infertile women
a yes
b no
a DSL= 0180 x IOT
b DSL= 0325 x IOT + 0733
a DSL = 18 IOT
b DSL= 33 IOT
Zhao et al (17) DSL vs IOT 38 donors NR IOT = 15 x DSL + 07 (ngml) DSL = 66 IOT
Taieb et al (46) DSL vs IOT 104 samples NR DSL = 104 x IOT - 149 DSL = 96 IOT
Streuli et al (29) DSL vs IOT 153 normal and infertile No IOT = 107 x DSL - 029 DSL = IOT
Kumar et al (18) IOT vs Gen II 60 female 60 male volunteers NR IOT =10 Gen II IOT=Gen II
Gada et al (50) DSL vs Gen II 42 women NR NR DSL = 63 Gen II
Preissner et al (23) DSL vs Gen II 206 samples NR Gen II = 153 x DSL - 077 DSL = 66 Gen II
Lee et al (47) DSL vs IOT 172 infertile women Yes IOT = 1102 x DSL - 0042 DSL = IOT
Wallace et al (51) DSL vs Gen II 271 women NR Gen II = 140 x DSL - 062 DSL = 71 Gen II
Li et al (48) a DSL vs IOT b DSL vs Gen II c IOT vs Gen II
56 women with PCOS or sub-fertility Yes a IOT = 097 x DSL -296 b Gen II = 133 x DSL - 417 c Gen II = 138 x IOT - 068
a DSL = IOT b DSL = 67 Gen II c IOT = 62 Gen II
Rustamov et al (13) DSL vs Gen II female IVF patients (n=330)
median of 2yr between samples
No NR
DSL = 127 Gen II
(age-adjusted)
Pigny et al (49) IOT vs Gen II 59 women 32 controls 27 with PCOS Yes NR IOT = 200 Gen II
105
Appendix I Flow-chart of the search for publications Database search for sample stability measurement variability and assay-method comparability was conducted simultaneously using the MeSH database of PubMedMedline using the search terms of ldquoanti-Muumlllerian hormonerdquo AMH Muumlllerian Inhibiting Substance and MIS which identified n=1653 studies on AMH The initial step of identification involved screening of articles by reading titles andor abstracts Further search involved identification of studies from the reference sections of the initially identified studies
Database Search
n=1653
Sample
Stability
Screening Titles
n=6
Further Search
n=4
Total
n=10
Measurment Variability
Screening Titles
n=14
Further Search
n=3
Total
n=17
Method comparability
Screening Titles
n=10
Further Search
n=4
Total
n=14
106
EXTRACTION PREPARATION AND
COLLATION OF DATASETS FOR THE
ASSESSMENT OF THE ROLE OF THE MARKERS
OF OVARIAN RESERVE IN FEMALE
REPRODUCTION AND IVF TREATMENT
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
Research Database
4
107
Title
Extraction preparation and collation of datasets for the assessment of
the role of the markers of ovarian reserve in female reproduction and
IVF treatment
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
NHS Research Ethics Approval
North West Research Ethics Committee (10H101522)
Word count 5088
Grants or fellowships
No funding was sought for this study
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
108
Declaration of authorsrsquo roles
OR prepared the protocol extracted data from electronic sources and hospital
notes prepared datasets and prepared all versions of the chapter MK assisted
in collection of data from hospital notes SR and CF oversaw and supervised
preparation the protocol extraction of data preparation of datasets and
reviewed the chapter
109
CONTENTS I PROTOCOL Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip110
Methodshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Objectiveshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Inclusion Criteriahelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip111 Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip112 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip113 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip114 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 RH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip115 AFC datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Folliculogram datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip117 Data managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data cleaning and codinghelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118 Merging datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip118
Data security and storagehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip119 II RESULTS Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip120 Data extraction and managementhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Demography datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Biochemistry datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 Surgery datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip121 AMH datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 IVF datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 FET datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip122 Embryology datasethelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 RH AFC and Folliculogram datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip123 Merging Datasetshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip124 Conclusionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip125
110
I PROTOCOL
INTRODUCTION
The aim of the project is to create a series of reliable and validated
datasets which contain all relevant data on the ovarian reserve markers (AMH
AFC FSH) ethnicity BMI reproductive history causes of infertility IVF
treatment parameters for patients that meet inclusion criteria as described
below The datasets will be used for the subsequent research projects of the
MD programme and future research studies on ovarian reserve
Most data can be obtained from following existing clinical electronic
records a) Patient Administration System (PAS) b) Biochemistry Department
data management system c) the hospital database for surgical procedures and
d) AMH dataset and e) ACUBase IVF data management system Following
obtaining original datasets from the administrators of the data management
systems in their original Excel format the datasets will be converted into Stata
format and ldquopreparedrdquo by a) checking and recoding spurious data
transforming the dates from string to numeric format which will be consistent
across all datasets (Day Month Year) and stored in Stata format under
following names ldquoDemographyrdquo ldquoBiochemistryrdquo ldquoAMHrdquo ldquoSurgeryrdquo ldquoIVFrdquo
ldquoFETrdquo ldquoEmbryologyrdquo Copies of original datasets will be kept in the
password-protected and encrypted computer located in the Clinical Records
Room of Reproductive Medicine Department Central Manchester University
Hospitals NHS Foundation Trust which is maintained by IT department of
the Trust (Figure 1)
Data not available in electronic format will be collected from the hospital
records of each patient by researchers Dr Oybek Rustamov and Dr Monica
Krishnan and entered into following datasets Reproductive history (RH)
antral follicle count (AFC) and Folliculogram The hospital notes of all
included patients will be hand-searched The datasets will be transferred to
Stata and each step of data preparation will be recorded using Stata Do files
and the files will be stored under the filenames of ldquoHistoryrdquo ldquoAFCrdquo
Folliculogramrdquo in Stata format In order to ensure the robustness of the data
and for the purpose of validation of the datasets electronic scanned copies of
all available reports of pelvic ultrasound assessments for AFC and
folliculograms will be obtained and stored in the password-protected and
111
encrypted computer located in the Clinical Records Room of Reproductive
Medicine Department Ethics approval for collection of data has already been
obtained (UK-NHS 10H101522)
The datasets will be merged and datasets for each research project with
all available data nested with IVF cycles nested within patients will be created
METHODS
Objectives
The aim of the project is to build a robust database which can reliably
used for the following purposes
1 To estimate the effect of ethnicity BMI endometriosis and the causes
of infertility on ovarian reserve using cross sectional data (Chapter 51)
2 To estimate the effect of salpingectomy ovarian cystectomy and
unilateral salpingo-oopherectomy on ovarian reserve using cross
sectional data (Chapter 52)
3 To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield (Chapter 6)
4 To explore the potential for optimization of AMH-tailored
individualisation of ovarian stimulation using retrospective data
(Chapter 6)
Inclusion criteria
In order to capture the populations for all three studies the database will
have broad inclusion criteria All women from 20 to 50 years of age referred to
Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust will be included if a) they were referred for
management of infertility or fertility preservation and b) had AMH
measurement during the period from 1 September 2008 till 16 November
2011
112
Datasets
PAS dataset
The dataset contains information on the hospital number surname first
name date of birth and the ethnicity of all patients referred to Reproductive
Medicine Department CMFT (Table 1) The data are originally entered during
registration of the patient for clinical care by administrative staff of
Gynaecology and Reproductive Medicine Departments The dataset will be
obtained from the administrators of the Information Unit
The dataset will be obtained in Excel format and transferred into Stata
12 Data Management and Statistical Software The date values (referral date
and date of birth) will be converted into numeric variable using ldquoDate Month
Yearrdquo format (DMY) Ethnicity will be coded using numeric variables in
alphabetical order as pre-specified in the Table 2a
Biochemistry dataset
The dataset contains all blood test results specimen numbers the names
of the tests and the date of sampling of women who had assays for follicle
stimulating hormone (FSH) oestradiol (E2) luteinizing hormone (LH) and
AMH during the study period (Table 1) Data entries were conducted by the
clinical scientists the technicians and the members of administrative team of
the Biochemistry Department The dataset will be obtained from an
administrator of the database
The date of sampling and analyses will be converted to the numeric
ldquoDMYrdquo format The specimen number will be kept unaltered in the string
variable format and used to link the tests that were taken in the same sample
tube The name of the test will be kept as described in the original format
ldquoAMHrdquo ldquoFSHrdquo ldquoLH and ldquoOestrdquo In the original dataset the samples sent
from Reproductive Medicine Department are coded as ldquoIVFrdquo which will be
kept unaltered and the remaining observations will be divided into
ldquoGynaecology Departmentrdquo ldquoNon-IVFGynaecologyrdquo and ldquoUnknownrdquo
categories using the code of referred ward and the names of the consultants
The test results will be converted into numeric format and the results with
minimum detection limit will be coded as 50 of the minimum detection limit
as follows AMH ldquolt061rdquo= 031 pmolL FSH ldquolt05rdquo= 025 mlUml LH
113
ldquolt05rdquo=025 mlUml Oest ldquolt50rdquo=025 pgml The test results that are
higher than the assay ranges will be set to 150 of the maximum range
Interpretation of serum FSH results in conjunction with serum
oestradiol levels is important in establishing true early follicular phase hormone
levels The test results are believed to be inaccurate if serum oestradiol levels
higher than 250pmolL at the time of sampling and therefore a new variable
for FSH results with only serum FSH observations that meet above criteria will
be created and used subsequently All ambiguous data will be checked using
electronic pathology data management system Clinical Work Station (CWS)
Surgery dataset
The electronic dataset will be obtained from Information Department
in Excel format The dataset created using clinical coding software and data
entry conducted during patient treatment episodes by theatre nursing and
medical staff In order to evaluate effect of past reproductive surgery to
ovarian reserve all patients had ovarian cystectomy drainage of ovarian cyst
salpingectomy salpingo-oopherectomy during 1 January 2000-16 November
2011 at Central Manchester University Hospitals NHS Foundation Trust will
be included in the dataset The dataset contains following variables hospital
number surname first name date of birth date of operation name of
operation laterality of operation and name of surgeon
The final dataset will be stored in Stata dta format (Figure 1) The
dataset will be used to validate data on reproductive surgery that was collected
from hospital records in the RH dataset
AMH dataset
The dataset contains the AMH results the dates of sampling the dates
of analyses and the assay generation (DSL or Gen II) for all patients included
in the study (Table 1) The dataset will be obtained from the senior clinical
scientist Dr Philip Pemberton Specialist Assay Laboratory who is responsible
for the data entry and updating of the dataset
There are two separate primary Excel based AMH data files 1) DSL
dataset and 2) Gen II dataset The datasets will be transferred to Stata 12
software separately and following preparation of the datasets which logged
using Stata Do file Stata versions of the data files will be stored under ldquoDSLrdquo
114
and ldquoGen2rdquo names Then the files will be combined by appending ldquoDSLrdquo to
ldquoGen2rdquo in order to create a new combined ldquoAMHrdquo dataset The date variables
the sample date the assay date and the date of birth will be converted into
numeric ldquoDMYrdquo format The samples sent from other NHS trusts and private
clinics will be excluded from the dataset alongside the records from male
patients and the patients outside of the age range of 20-50 years of age The
manufacturers of the assays suggest that haemolysed and partly haemolysed
samples may provide inaccurate test readings Therefore a new variable
without these samples will be created and used in the analyses for all studies
All the ambiguous data will be checked and verified using duplicate datasets
obtained from Biochemistry dataset and the hospital records of the patients
IVF dataset
The IVF dataset will be downloaded from ACUBase Data management
system in original Excel format and contains detailed information on causes of
infertility sperm parameters treatment interventions assessment of oocyte
quantity and quality assessment of embryo quantity and quality and the
outcomes of treatment cycles (Table 1)Data entry to ACUBase was
performed by members of administrative nursing embryology and medical
staff of the Reproductive Medicine Department at the point of care This is
only electronic data management system for ART cycles and used for
monitoring of the clinical performance of the department by internal and
external quality assessment agencies and regulators (eg HFEA CQC)
Therefore the quality of data entry for the main indicators of the performance
of IVFICSI programs (the treatment procedures the outcomes of the cycles
and assessment of embryos) should be fairly accurate
Table 2b describes the coding of the treatment outcomes and the
practitioners of ICSI the ultrasound-guided oocyte retrieval (USOR) and the
embryo transfer (ET) procedures
In addition to the main patient identifier (Hospital Number) this dataset
contains in-built cycle identifier (IVF Reference Number) which will be used
to link the original IVF cycles to corresponding Frozen Embryo Transfer
(FET) cycles and the embryos originating from the index cycle using ldquoFETrdquo
and ldquoEmbryordquo datasets respectively
115
FET dataset
The dataset provides information on the quality and the quantity of
transferred embryos the date of embryo transfer and the outcome of the cycle
in frozen embryo transfer cycles (Table 1) Primary data entry was performed
by the members of the clinical embryology team during the treatment of
patients and will be downloaded from ACUBase by Dr O Rustamov
Together with ldquoIVFrdquo dataset it can be used to study cumulative live birth rate
(LBR) of index cycles The treatment outcomes as well as ICSI USOR and ET
practitioners will be converted to numeric variables using the codes which are
shown in Table 2b The dataset can be linked to the index fresh IVF cycles as
well as to embryos of FET cycles using the IVF Reference number
Embryology dataset
The dataset has comprehensive information on the quality and the
quantity of embryos on each day of their culturing including embryos that
were cryopreserved and those that were discarded (Table 1) The dataset also
includes patient identifiers (name date of birth IVF reference number) and
the dates of embryo transfer The primary data entry into this dataset was
conducted by the members of clinical embryology team during the clinical
episodes and will be downloaded from ACUBase by Dr O Rustamov The
dataset can be linked to index fresh IVF cycle and FET cycles using IVF
Reference numbers of corresponding datasets
RH dataset
This dataset will be created and data entry will be conducted during the
search of the hospital notes Following identification of included patients using
AMH dataset Excel electronic data collection file will be created The hospital
notes of each patient will be searched for by systematically checking all filed
hospital records in Clinical Records Room of Reproductive Medicine
Department by the order of their hospital number Further search for missing
notes will be conducted by checking all hospital notes located in the offices of
nurses doctors and secretaries Electronic hospital notes filed in Medisec
Digital Dictation Database will be used for data extraction for the patients
whose hospital notes were not located
116
All available diagnosis will be recorded under the following columns 1)
female referral diagnosis 2) male referral diagnosis 3) female initial clinic
diagnosis 4) female final clinic diagnosis 5) diagnosis prior 2nd IVF cycle 6)
diagnosis prior 3rd IVF cycle Furthermore other relevant information on
pathology of reproductive system will be documented For instance all possible
iatrogenic causes of poor ovarian reserve (eg oophorectomy ovarian
cystectomy salpingectomy chemotherapy and radiotherapy) will be recorded
In order to establish the existence of polycystic ovary syndrome (PCOS) the
history of oligomenorrhea amenorrhea and diagnosis of polycystic ovaries
(PCO) on pelvic ultrasound scan will be collected and used in conjunction with
serum LH levels of Biochemistry dataset (Table 1)
Male infertility will be defined as ldquosevere male factorrdquo if the sperm
parameters were low enough to meet criteria (lt05 mlnml or retrograde
ejaculation) for Multiple Ejaculation Resuspension and Centrifugation test
(MERC) as part of investigation for infertility A variable for patients
diagnosed with azoospermia will be created and the diagnosis will be recorded
The patients diagnosed with male factor infertility but with the sperm
parameters that did not reach criteria for MERC will be diagnosed with ldquomild
male factorrdquo infertility Patients diagnosed with ldquosevererdquo andor ldquostage IVrdquo
andor ldquostage IIIrdquo endometriosis will be categorized as ldquosevere
endometriosisrdquo while patients diagnosed with mild or moderate endometriosis
will be coded as ldquomild endometriosisrdquo group In diagnosing the tubal factor
infertility only patients with history of bilateral salpingectomy and the patients
with evidence of bilateral tubal blockage on a laparoscopy and dye test will be
diagnosed as ldquosevere tubal factorrdquo The patients with history of unilateral
salpingectomy unilateral tubal block in laparoscopy and dye test or
unilateralbilateral tubal block on hysterosalpingogram will be categorized as
ldquomild tubal factorrdquo infertility Diagnosis of polycystic ovarian syndrome
(PCOS) will be based in Rotterdam criteria existence of two of the following
features 1) oligo- or anovulation 2) clinical andor biochemical signs of
hyperandrgoenism 3) polycystic ovaries Referral for fertility preservation will
be defined as ldquoreferral for consideration of obtaining oocytes orand embryos
andor sperm prior to chemotherapy radiotherapy or surgical management of
a malignant diseaserdquo The length of infertility will be recorded as per proforma
of initial consultation for the patients attended initial clinic appointment
following introduction of serum AMH test 1 September 2008 For patients
117
attended initial consultation prior to introduction of AMH test the length of
infertility will be documented as per the initial clinic proforma plus years till the
patientrsquos first AMH test The patientrsquos body mass index (BMI) documented at
initial assessment will used for patients who had assessment after introduction
of AMH test 1 September 2008 whereas the most up to date BMI result is
collected for the patients seen prior to this date
AFC dataset
Data will be extracted from the hospital notes The data on the
assessment of AFC will be obtained from the pelvic ultrasound scan reports
The date of assessment the AFC in each ovary the name of sonographer will
be recorded (Table 1) Furthermore other relevant ultrasound findings such
as ovarian cyst hydrosalpynx and submucous uterine fibroids will also be
entered in the dataset To permit data validation scanned copies of ultrasound
scan report of each AFC investigation will be stored in PDF format in the
computer that located in the Clinical Notes Room
The department uses a stringent methodology for the assessment of
AFC which consist of counting of all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
The ultrasound assessments are conducted by qualified sonographers who use
the same methodology for the measurement of AFC However it is well
known that the counting of antral follicles may be prone to significant inter-
operator variability Therefore the name of sonographers will be recorded
during primary data collection and coded (Table 2a) so that the estimates of
within- and between-operator variability can be obtained if necessary
Folliculogram dataset
Although most data on IVFICSI cycles are available in ldquoIVFrdquo dataset
certain important data on IVF treatment are recorded only in the hard copy
IVF folliculograms Consequently data on ultrasound follicle tracking the
reasons for changing the doses of stimulation drugs are only available in the
folliculograms Furthermore the length of ldquothe coastingrdquo and the causes for
cycle cancellation are usually recorded in both folliculograms and ldquoIVFrdquo
dataset which can be used to validate accuracy of ldquoIVFrdquo dataset Therefore
118
these data will be collected using the folliculograms that filed in the hospital
notes and the scanned copies of each folliculograms will be stored in the
computer located Clinical Records Room for data validation purposes (Table
1)
The number of follicles on Day 8 and Day 10 ultrasound scans will be
recorded according to the size of the follicles 10-16mm and 17mm
Numeric variables for the follicle numbers will be created and used for
assessment of ovarian response in IVF cycles
Data management
Data cleaning and coding
All datasets will be obtained in Excel format and transferred in the
original unaltered condition into Stata 12 data management and statistical
package (Stata 12 StataCorp Texas USA) and all steps of the data cleaning
and the coding will be recorded using Stata Do files to create audit trails of the
data management process Both original Excel and cleaned Stata versions of
data files will be stored in computer that is located in Clinical Records Room at
Reproductive Medicine Department Uniformity of hospital numbers in all
datasets will be achieved by converting a) leading lower case prefixes ldquosrdquo to
upper case ldquoSrdquo b) dropping suffixes ldquozrdquo and ldquoZrdquo and c) dropping all leading
zeros in the second part of the hospital number (eg ldquos1000235Zrdquo
=rdquoS10235rdquo) The coding of the datasets is shown in the Table 2a and the
Table 2b All ambiguous data will be checked using electronic data
management systems (eg CWS Medisec) and hospital notes
Merging the datasets
The datasets will be structured as such that the data files can be used
independently or merged at a) patient or b) IVF cycle levels using the patient
identifier cycle identifier and date variables (Figure 1) This allows analysis of
outcomes of both ldquoFresh IVF cyclesrdquo and study the cumulative outcomes of
Fresh IVF and Frozen Embryo Transfer cycles originating form index IVF
cycles
Each dataset will contain two main patient identifiers and patient
number (Patient ID) which will be used for linking the datasets in a patient
119
level At the initial stages of the data management the hospital numbers will be
used as the main patient identifier The accuracy of the hospital numbers in
each dataset will be validated using PAS dataset by checking patient surname
first name and date of birth
Following methodology will be used to add study numbers into each
dataset First all dataset will be merged in a wide format using the hospital
numbers which creates Master Datasets for each of the research projects Then
an accuracy of the merger will be checked using DOB surname and first name
Once the dataset is validated several copies of the Patient ID variable will be
created and distributed to each dataset Finally the datasets will be separated
and stored as independent datasets alongside Master Datasets for each research
projects
ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo datasets contain cycle specific IVF
reference numbers which were allocated during the clinical episodes on
ACUBase Using IVF reference number new ID variable (Cycle ID) will be
created and allocated to all datasets using closest observation prior to the IVF
cycle in Master Research Dataset Consequently by using cycle reference
number all patient and cycle related data can be linked in the IVF FET cycle
and embryo level
Data security and storage
The encrypted and password protected hospital computer will be used to
process the data until all the patient identifiers have been removed and the
datasets have been anonymised Once the Master Research Datasets are
validated and research team is satisfied with the quality of the data the dataset
will be anonymised by dropping variables for following patient identifiers
hospital number surname first name date of birth and IVF reference number
The study number and the cycle reference numbers will be used as a patient
and a cycle identifiers and only this anonymised dataset will be used for
statistical analysis A copy of non-anonymised dataset will be stored in the
computer located in Clinical Records Room for data verification and a
reference purposes The datasets will be stored within IVF unit for the
duration of the research projects of the MD programme The necessity of
storage of the datasets and measures of data security will be reviewed every
three years thereafter
120
II RESULTS
INTRODUCTION
According to the protocol all women from 20 to 50 years of age referred
to Reproductive Medicine Department of Central Manchester University
Hospitals NHS Foundation Trust for management of infertility or fertility
preservation and had AMH measurement during the period from 1 September
2008 till 16 November 2011 have been included in the database In total of
4506 patients met the inclusion criteria with 3381 patients in DSL AMH
assay group and 1125 patients Gen II assay group The following datasets
have been extracted from the clinical electronic data management systems
ldquoPASrdquordquo Biochemistryrdquo ldquoSurgeryrdquo ldquoIVFrdquo ldquoFETrdquo and ldquoEmbryologyrdquo Data
extraction from the paper-based hospital records of 3681 patients (n=3130
DSL and n=551 Gen II) were performed by two researchers Dr ORustamov
(n=2801) and Dr M Krishnan (n=880) In addition data collection using
Medisec Digital Dictation Software for the notes that were not located in DSL
group (n=251 patients) was completed by Dr O Rustamov In view of the
issues with validity of Gen II assay measurements which were observed in the
earlier study of the MD Programme (Chapter 2 AMH variability and assay
method comparison) I decided to base subsequent work for the last three
projects (Chapter 5-7) of the MD programme only on DSL assay
measurements and not to include samples based on Gen II AMH Assay
Therefore I decided not to collect data from the hospital notes for the patients
that had AMH measurements using exclusively Gen II Assay where the notes
were not found during the first round of data collection (n=575)
As a result in DSL group all datasets for 3130 patients were completed
and all but AFC and Folliculogram datasets were completed for 251 (Figure 2)
In Gen II group all datasets were completed for 551 patients and all but RH
AFC and Folliculogram datasets were obtained for 575 patients (Figure 2)
As described above the studies of the last three projects (Chapter 5-7)
are based on DSL assay which is no longer in clinical use The review of
literature presented in Chapter 3 suggests that DSL assay appears to have
provided the most reproducible measurements of AMH compared to that of
other assays Therefore AMH measured using DSL assay is perhaps most
121
reliable in terms addressing the research questions In all three chapters
estimates of the effect sizes are provided in percentage terms and therefore the
results are convertible to any AMH assay
Datasets
Demography dataset
The dataset was obtained from Mr Peter Hoyle Senior Data Analyst of
Information Unit CMFT on 16 October 2012 The dataset includes all patients
referred to Reproductive Medicine Department between 1 January 2006 and 31
August 2012 and contains 5573 patients I created a dataset ldquoDemographyrdquo in
Stata format using the steps of data cleaning coding and management as per
protocol The audit trial of the data management was created using Stata Do
file (Figure 1)
Biochemistry dataset
The biochemistry data file was obtained from Dr Alexander Smith
Senior Clinical Scientist Biochemistry Department on 24 January 2011 The
dataset contains the results of all serum AMH FSH LH and E2 samples
conducted from 01 September 2008 to 31 December 2010 The dataset was in
Excel format that consisted of two datasheets 1) Biochemistry 2008-2009 and
2) Biochemistry 2010 The datasheets transferred to Stata 12 in original
unaltered condition and a single Stata ldquoBiochemistryrdquo dataset was created by
combining datasheets by appending them to each other The dataset contains
in total of 78415 blood results of 11574 patients with 6643 AMH 19175 FSH
28677 LH and 23920 E2 results A wide format of the dataset was prepared by
transferring all blood results of each patient to a single row A variable which
indicates valid FSH results was created by coding FSH results as missing if
corresponding E2 levels were higher than 250 pmolL The audit trial of the
data management was created using a Stata Do file
Surgery dataset
Data management was conducted according to the protocol In total
dataset contained 2096 operations in 1787 patients Data on all operations on
122
Fallopian tubes (eg salpingectomy salpingostomy) and ovaries (eg
cystectomy drainage of cyst) at Central Manchester NHS Foundation Trust
from 1 January 2000 to 16 January 2011 are available in the dataset The
dataset will be used to validate the data on history of reproductive surgery of
Reproductive History dataset
AMH dataset
Both AMH datasets were received from Dr Philip Pemberton Senior
Clinical Scientist of the Specialist Assay Laboratory on 13 January 2012 and
transferred to Stata 12 software in the original format All steps of the data
cleaning and the management were recorded using Stata Do file
There were 3381 patients in DSL dataset and 1125 patients in Gen II
dataset Cleaning and coding of the datasets were achieved using the
methodology described in above protocol and new AMH dataset has been
created
IVF dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov on
08 October 2012 and following importing the dataset into Stata 12 in original
format dataset was prepared according to the protocol The dataset contains all
IVFICSI cycles that took place between 01 January 2004 and 01 October
2012 including the cycles of women who acted as egg donors and egg
recipients There were in total of 4323 patients who had 5737 IVFICSI cycles
with 4123 IVFICSI cycles using own eggs 10 embryo storage 40 oocyte
donation 7 oocyte storage 55 oocyte recipient cycles The dataset has
anonymised unique patient (Patient ID) and cycle identifiers (Cycle ID) and
therefore can be linked to all other datasets including all FET cycles and
embryos originated from the index IVF cycle
FET dataset
The dataset was downloaded from ACUBase by Dr Oybek Rustamov
in Excel format on 20 October 2012 and transferred to Stata 12 Software in
the original condition The data managed as per above protocol and each step
of the process of preparation of the dataset was recorded in Stata Do file The
dataset comprised of all FET cycles (n= 3709) of all women (n=1991)
123
conducted between 01 January 2004 and 01 October 2010 and the Stata
version of ldquoFETrdquo dataset contains complete data on number of thawed
cleaved discarded and research embryos for all patients The dataset contains
unique patient identifier (Patient N) and unique cycle identifiers (Cycle N) and
therefore can be linked to all datasets in patient and cycle levels including index
IVF cycle and embryos
Embryology dataset
The Excel dataset was downloaded from ACUBase by Dr Oybek
Rustamov on 20 October 2012 and transferred into Stata 12 Software in
unaltered condition The data was managed according to the above protocol
The dataset has details of all 65535 (n=4305 women) embryos that were
created between 01 January 2004 and 01 October 2012 The dataset contains
complete data on quantity and the assessment of embryo quality which
includes grading of number evenness and defragmentation of the cells for
each day of culturing of the embryos Furthermore the destination of each
embryo (eg transferred cryopreserved discarded and donated) and the
outcomes of cycles for transferred embryos are available in the dataset Given
that the Embryology dataset has the unique patient as well as the cycle
identifiers this dataset is nested within patients and IVF cycles Consequently
each embryo can be linked to patient index Fresh IVF cycle and subsequent
FET cycles
Reproductive History AFC and Folliculogram datasets
The hospital notes of all patients (n=4506) were searched during the
period of 1 April 2012 to 15 October 2012 for collection of data for
Reproductive history AFC and Folliculogram datasets as per protocol All case
noted filed in the Clinical Records Room the Nurses Room the Doctors
Room and the Secretaries Room of Reproductive Medicine Department were
searched and relevant notes were pulled and searched for data All ultrasound
scan reports containing data on AFC and all IVFICSI folliculograms of
patients were scanned and electronic copy of scanned documents were stored
in the password protected NHS computer located in the Clinical Records
Room
124
The first round of data gathering achieved following result In DSL
dataset there were in total of 3381 patients with 3130 patients had complete
data extraction from their hospital notes and hospital records of 251 patients
were not found There were in total of 1126 patients in Gen II dataset 551 of
whom had complete data extraction from their hospital records and the case
notes of 575 patients were not located (Figure 2) The main reason for
ldquomissing case notesrdquo was found to be the use of hospital records by clinical
laboratory and administrative members of staff at the time of data collection in
patients undergoing investigation and treatment
In the meantime the results of our previous research study indicated that
Gen II samples provide erroneous results (Chapter II) and therefore we
decided to use only data from the patients in DSL group Data on reproductive
history for the remaining patients in the DSL group (n=251) with missing
hospital records were collected using digital clinic letters stored in Medisec
Digital Dictation Software (Medisec Software UK) The data file that
contained combined datasets of reproductive history and AFC was transferred
to Stata 12 in original condition and data management was conducted
according to the protocol All steps of data management was recorded using
Stata do file for audit trail and to ensure reproducibility of the management of
the data Similarly the management of Folliculogram dataset was achieved
using the procedures described in the protocol and all steps of data
management was logged using Stata Do file As result of above data collection
and management I created three Stata datasets ldquoRHrdquo (reproductive history)
ldquoAFCrdquo and ldquoFolliculogramrdquo
Merging Datasets
First the datasets were merged using a unique patient identifier (hospital
number) as per protocol Validation of the merger using additional patient
identifiers (NHS number name date of birth) revealed existence of duplicate
hospital numbers in patients transferred from secondary care infertility services
to IVF Department of Central Manchester University Hospitals NHS
Foundation Trust I established that in the datasets the combination of the
patientrsquos first name surname and date of birth in a single string variable could
be used as a unique identifier Hence I used this identifier to merge all
datasets achieving a robust merger of all independent datasets into combined
125
final Master Datasets for each of the research projects Following the creation
of an anonymised unique patient identifier (Patient ID) for each patient and
anonymised unique cycle identifier (Cycle ID) for each IVF cycle all patient
identifiers (eg surname forename hospital number IVF ref number) were
dropped (Figure 1) The anonymised independent datasets (eg AMH AFC
IVF etc) and anonymised Master Datasets were stored as per protocol
Subsequently these anonymised datasets were used for the statistical analyses
of the research projects The original unanonymised data files were stored in
two password protected NHS hospital computers in the Clinical Records
Room and Doctors Room of Reproductive Medicine Department and
archived according to the Trust policies thereafter Only members of clinical
staff have access to the computers and only nominated clinical members of the
research group who have specific approval can have access to unanomysed
Fully anonymised datasets have been made available to other members of the
research team with the stipulation that the datasets are stored on secure
password protected servers or fully encrypted computers Fully anonymised
datasets may in the future be shared with other researchers following
consideration of the request by the person responsible for the datasets (Dr
Cheryl Fitzgerald) and appropriate ethical and data protection approval
CONCLUSION
Following extraction and management of the data I have built
comprehensive validated datasets which will enable to study ovarian reserve in
a wide context including a) assessment of ovarian reserve b) evaluation of the
performance of ovarian biomarkers c) study individualization of ovarian
stimulation in IVF d) association of the biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryo live birth) The database will be used
to address the research questions posed in the subsequent chapters of this
thesis and beyond that for future studies on the assessment of ovarian reserve
and IVF treatment
126
Figure 1 Data and program files Datasets and programme files created in preparation of the research datasets File names and types are provided in the brackets
127
Table 1a Available vriables The
available identifiers variables and the source of data for following datasets Ethnicity RH AMH AFC Biochemistry OHSS Folliculogram
Datasets
Clinical ID
Study ID
Variables
Source
Demography Hospital N Surname
First name DOB
Patient ID
Ethnicity Information Department
(PAS)
RH
(Reproductive History)
Hospital N Surname
First name DOB
Patient ID
1 Diagnosis Referral Female Referral Male
Clinic Female Clinic Male
Post Cycle 1 Post cycle 2 Post cycle 3
2 Iatrogenic causes of loss of ovarian reserve Ovarian surgery tubal surgery chemotherapy radiotherapy
3 BMI 4 PCOS (PCO oligomenorrhea amenorrhea hirsutism)
Hospital Records
Surgery Hospital N Surname
First name DOB
Patient ID Date
Procedure Date Operator
Information Department
AMH Hospital N Surname
First name DOB
Patient ID Date
Date of sample Date of assay AMH level Assay generation AMH dataset of Specialist Assay
Lab
AFC Hospital N Surname
First name DOB
Patient ID Date
AFC (up to six AFC scans)
Left ovary Right ovary Date of Scan Sonographer Comments (Ovarian cyst hydrosalpynx fibroid poorly visualized etc)
Hospital Records
Biochemistry Hospital N Surname
First name DOB
Patient ID Date
Oestradiol (Date of sample Date of assay serum level) FSH (Date of sample Date of assay serum level)
LH (Date of sample Date of assay serum level)
Biochemistry Electronic
Database
Folliculogram Hospital N Surname
First name DOB
Patient ID Date
Folliculogram (up to 3 cycles) Date (1st day of ovarian stimulation)
Day 8( 10-16mm) Day 8 (gt17mm) Day 10 (10-16mm) Day 8 (gt17mm)
Comments (Day of HCG OHSS Cancellation Ovarian cyst Hydrosalpynx Coasting etc)
Hospital Records
128
Table 1b Available variables The available identifiers variables and the source of data for IVF dataset
Datasets Clinical ID Study Variables Source
IVF Hospital N Surname First name DOB PCT code
Patient ID Cycle ID Date
GENERAL
Attempt Type Protocol DaysStim InitDose Outcome OutcomeDt Age PartnerAge EggCollect TreatDate ETransfer Add_Drug1 Add_Drug2 Add_Drug3 Add_Drug4 Add_Drug5 Add_Drug6 Add_Drug7 EGG RECOVERY SNumber Follicles TotEgg EggNumber
FERTILISATION IVFEgg IVFCleaved ICSICleaved Cleaved PN2 IVFPN2 ICSI2PN ICSICl ICSIEgg ICSIFPN IVFFPN IVFTransfer ICSITransfer IVFLysed ICSILysed IVFMetII IVFMetI IVFAtretic IVFAbnormal IVFEmptyZona IVFG_Vesicle ICSIMetII ICSIMetI ICSIAtretic ICSIAbnormal ICSIEmptyZona ICSIG_Vesicle
OUTCOME
sacs Hearts Preg ICSIPract STORAGE Frozen IVFFroz ICSIFroz SpermSource SortKeySTAR HISTORY cat_tubal cat_OvFail cat_UtProb cat_unex cat_ MF cat_Meno cat_Genetic cat_endo cat_anov cat_noMale Inf_Since MaleInf
CoupleInf Preg24Wk MiscTOP Ectopic LiveBirth FSH AMH Emb_Recip Surrogate Sperm_Recip StoreEggs EggThaw Treat_Reason IgnoreKPI EMBRYOLOGY
D1LteClCells1 D1LteClCells2 D2Cells2 D2Cells3 D2Cells4 D2Even2 D2Even3 D2Even4 D2Frag2 D2Frag3 D2Frag
SPERM Conc_Init MotA MotB Conc_ Prep MotAP MotBP SemenSource SemenAnalysis STIMULATION BMI TotDose GonadUsed Incubator ICSIRigg AMHBand DHEA EGG
Egg_Recip Own_Eggs Altruistic_D
ACUBASE Electronic Database
129
Table 1c Available variables
The available identifiers variables and the source of the data for FET and Embryo datasets
Datasets Clinical ID Study ID
Variables
Source
FER
Hospital N Surname First name
Patient ID Cycle ID Date
GENERAL treatdate transfer ETDate
OUTCOME preg IUP Outcome OutcomeDt
EMBRYOLOGY
Thawed Survived Cleaved Discarded Research
STORAGE NumStored DtCreated
CLINICIAN ETClinician ETEmbryologist OrigCycle
ACUBASE Electronic Database
Embryo
Hospital N Surname First name DOB
Patient ID Cycle ID Date
GENERAL TreatDate Injected Destination
CELLS CellsD1 CellsD2_AM CellsD2_PM CellsD3_AM CellsD3_PM
EVENNES EvenD2_AM EvenD2_PM EvenD3_AM EvenD3_PM
FRAGMENT FragD1 FragD2_AM FragD2_PM FragD3_AM FragD3_PM
OUTCOMES ICSIPract Maturity PosPreg Hearts SpermSource Age
ACUBASE Electronic Database
130
Table 2a Coding
The codes used to convert ethnicity and diagnosis variables from string to numeric format in PAS and RH datasets
131
Table 2b Coding
The codes used to convert treatment outcomes from string to numeric format in IVF and FET datasets
Datasets Codes for outcomes
IVF
FET
ldquoBiochemical Pregnancyrdquo=1 ldquoCancel (other)rdquo=2
ldquoCancel Hyperstimulationrdquo=3 ldquoCancel Poor responserdquo=4
ldquoCancelled no sperm on day of ECrdquo=5 ldquoCONVERTED IVF TO IUIrdquo=6
ldquoDelayed Miscarriagerdquo=7 ldquoDonatedrdquo=8 ldquoEctopicrdquo=9
ldquoEgg donationrdquo=10 ldquoEmbryos for storagerdquo=11
ldquoEmpty Sacrdquo=12 ldquoFailed Fertilisationrdquo=13
ldquoFor donationrdquo=14 ldquoFreeze Allrdquo=15
ldquoFreeze All (OHSS)rdquo=16 ldquoFreeze All (Other)rdquo=17
ldquoLate Miscarriagerdquo=18 ldquolost to contactrdquo=19
ldquolost to follow uprdquo=19 ldquoNo Eggsrdquo=20
ldquoNo Spermrdquo=21 ldquoNo Normal Embryosrdquo=22
ldquoNot Pregnantrdquo=23 ldquoOngoing Singletonrdquo=24
ldquoOngoing Twinrdquo=25 ldquoPositive hCGrdquo=26
ldquoSingleton Birth=27rdquo ldquoTwin Birthrdquo=28
ldquoTriplet Birthrdquo=29 ldquoStill Birthrdquo=30The
132
Figure 2 Data collection from hospital records
Completeness of data collection from hospital records for RH AFC and Folliculogram datasets
All
patients
DSL
(n=3381)
All Datasets
Complete
n=3130
AFC and Folliculogram
not complete
n=251
Gen II
(n=1126)
All Datasets
Complete
n=551
RH AFC Follicologram
not complete
n=575
133
Table 3 Results Datasets and observation
Summary of the number of patients observations IVFFET cycles and data entry period for all datasets
Datasets Patients Observations Cycles Period
AMH DSL 3381Gen II 1126
DSL-3913 DSL 01 Sep 2008-15 Nov 2010 Gen II 16 Nov 2010-16 Nov 2011
Demography 5573 01 Jan 2006-31 Aug 2012
Biochemistry 11754 Total 78415 6643-AMH 19175-FSH 28677-LH 23920-E2
01 Sep 2008-31 Dec 2010
RH DSL-3381 DSL-3381 01 Sep 2008-01 Oct 2012
Surgery 1787
2096 01 Jan 2000-16 Nov 2011
AFC DSL 2411 DSL Total 4174 Single measurement2411 Repeats 2-1250 3-370 4-105 5-25 6-7 7-1
01 Sep 2008-01 Oct 2012
Folliculogram 1736 2183
01 Sep 2008-01 Oct 2012
IVFICSI 4324 - Total 5737 own eggs-4123 oocyte recipients-55 oocyte donors-40 Embryo storage-10 oocyte storage-7
01 Jan 2004-01 Oct 2012
FET 1991 - 3709
01 Jan 2004-01 Oct 2012
Embryology
4305 65535 embryos - 01 Jan 2004-01 Oct 2012
134
Figure 3 Merging datasets
The process of merging datasets in patient and cycle levels using patient date and cycle IDs
135
ASSESSMENT OF DETERMINANTS OF
ANTI-MUumlLLERIAN HORMONE IN INFERTILE
WOMEN
5
136
THE EFFECT OF ETHNICITY BMI
ENDOMETRIOSIS AND THE CAUSES OF
INFERTILITY ON OVARIAN RESERVE
Oybek Rustamov Monica Krishnan
Cheryl Fitzgerald Stephen A Roberts
To be submitted to Fertility and Sterility
51
137
Title
The effect of ethnicity BMI endometriosis and the causes of infertility
on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK c Centre for Biostatistics
Institute of Population Health Manchester Academic Health Science Centre
(MAHSC) University of Manchester Manchester M13 9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Word count 4715
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgements
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
138
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all version
of the manuscript MK assisted in data extraction contributed in discussion
and the review of the manuscript SR and CF oversaw and supervised
preparation of dataset statistical analysis contributed in discussion and
reviewed all versions of the manuscript
139
ABSTRACT
Objective
To estimate the effect of ethnicity BMI endometriosis and the causes of
infertility on ovarian reserve
Design Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 2946 patients were included in the study of which 65 did not have
data on ethnicity leaving 2881 women in the sample
Interventions Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH serum basal FSH and basal AFC measurements
Results
Multivariable regression excluding BMI showed that woman of Black ethnicity
and the group defined as ldquoOther ethnicityrdquo had significantly lower AMH
measurements when compared to that of White (-25 p=0013 and -19
p=0047) and overall ethnicity was a significant predictor of AMH (p=0007)
However inclusion of BMI in the model reduced these effects and the overall
effect of ethnicity did not reach statistical significance (p=008) AFC was
significantly reduced in Pakistani and women of ldquoOther ethnicitiesrdquo although
the effect sizes were small (10-14) and the overall effect of ethnicity was
significant in both models (p=004 and p=003) None of the groups showed a
statistically significant difference in FSH although women of ldquoOther Asianrdquo
ethnicity appear to have lower FSH measurements (12) which was close to
statistical significance (-12 p=007)
140
Obese women had higher AMH measurements (16 p=0035) compared to
that with normal BMI and the overall effect of the BMI was significant
(p=003) In the analysis of the effect of BMI to AFC measurements we did
not observe differences that were statistically significant However FSH results
showed that there is a modest association between BMI and FSH with both
overweight and obese women having significantly lower FSH measurements
compared to lean women (-5 p=0003 and -10 p=0003)
In the absence of endometrioma endometriosis was associated with lower
AMH measurements although this did not reach statistical significance
Neither AFC nor FSH was significantly different in the endometriosis group
compared to those without endometriosis In contrast we observed around
31 higher AMH levels in the patients with at least one endometrioma
(p=0034) although this did not reach statistical significance (21 p=01) in
the smaller subset after adjustment for BMI AFC and FSH did not show any
statistically significant association with endometrioma
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to the ones who did not have
unexplained infertility except the analysis that did not include BMI as a
covariate which found a weakly positive correlation (10 p=003) Similarly
the estimation of the effect of the diagnosis of unexplained infertility to AFC
as well as FSH showed that there were weak association between the markers
and diagnosis of unexplained infertility
There was no significant difference in AMH AFC and FSH measurements of
women with mild and severe tubal infertility in the models which included all
covariates except the analysis of FSH and mild tubal factor where we found
weakly negative correlation between these variables
Women diagnosed with male factor infertility had significantly higher AMH
and lower FSH measurements the effect sizes of which were directly
proportional to the severity of the diagnosis In the analysis of AFC we did not
found significant difference in the measurements between patients with male
factor infertility and to that of non-male factor
141
Conclusions
Ethnicity does not appear to play a major role in determination of ovarian
reserve as measured by AMH AFC and FSH whereas there is a significant
positive association with BMI and these markers of ovarian reserve Women
with endometriosis appear to have lower AMH whilst patients with
endometrioma have significantly higher AMH and lower FSH measurements
The study showed that the association between markers of ovarian reserve and
unexplained infertility as well as tubal disease is weak In contrast women
diagnosed with male factor infertility have higher ovarian reserve
Key Words
Ovarian reserve AMH AFC FSH ethnicity BMI infertility endometriosis
endometrioma
142
INTRODUCTION
The ovarian reserve consists of a total number of resting primordial and
growing oocytes which appears to be determined by the initial oocyte pool at
birth and the age-related decline in the oocyte number (Hansen et al 2008
Wallace and Kelsey 2010) Both of these factors appear to be largely
predetermined genetically although certain environmental socioeconomic and
medical factors likely to play a role in the rate of the decline (Schuh-Huerta et
al 2012b Kim et al 2013 Dolleman et al 2013) The understanding of the
formation and the loss of ovarian reserve have been improved greatly due to
recently published data on the histological assessment of ovarian reserve
(Hansen et al 2008) Furthermore the use of the biomarkers has enabled the
evaluation of ovarian reserve in larger population-based samples Biomarkers
such as AMH and AFC can only assess the measurement of growing pre-antral
and early antral follicle activity However some studies suggest that there is a
close correlation between the measurements of these markers and the number
of resting primordial follicles (Hansen et al 2011)
Studies on age related decline of AMH and AFC have played important
roles in understanding the decline of ovarian reserve although most of the
data have been derived from heterogeneous population without full account
for characteristics of individual patients (Nelson et al 2011 Seifer et al 2011
Shebl et al 2011) These studies have demonstrated that there is a significant
between-subject variation in ovarian reserve beyond that due to chronological
age (Kelsey et al 2011) More recent studies reported interesting findings on
the role of demographic anthropometric and clinical factors in the
determination of ovarian reserve Although these studies have employed
better-described samples some have small sample sizes and lack power for the
estimation of the effect of these factors Consequently studies on large and
well-characterised populations are necessary for evaluation of the determinants
of ovarian aging as well as to provide normative data for the individualisation
of the assessment of ovarian reserve
There have been reports of measurable disparities in the reproductive
aging and reproductive endocrinology between various ethnicities For
instance according to a large prospective study White Black and Hispanic
women reported higher rates of premature ovarian failure compared to
143
Chinese and Japanese (Luborsky et al 2002) In contrast the prevalence of
PCOS which is associated with higher ovarian reserve has been reported to be
significantly lower in Chinese (22) compared to British (8) women
(Michelmore et al 1999 Chen et al 2002) Although these disparities may
partially be due to the differences in the local diagnostic criteria it is plausible
to believe that the ethnicity may play a role in the determination of the
reproductive aging With regard to the effect of ethnicity to the markers of
ovarian reserve Seifer et al found that African American and Hispanic women
have lower AMH levels compared to White (Seifer et al 2009) In contrast
Randolph et al reported that African American women had significantly higher
ovarian reserve compared to that of White when determined by FSH
measurements (Randolph et al 2003) These studies indicate that ethnicity may
play a role in the determination of ovarian reserve and therefore warrants
further investigation which should include other major ethnic groups
Body weight appears to be closely associated with human female
reproduction which is evident by its effect on the natural fecundity as well as
the success of the assisted conception treatment cycles (Maheshwari et al
2007) Indeed the relationship of increased body mass index (BMI) and PCOS
is well described although the mechanism of this is not yet fully understood
Consequently a number of recent studies have assessed the effect of BMI to
the various aspects of reproductive endocrinology including ovarian reserve
Studies on the influence of BMI on the markers of ovarian reserve have
provided conflicting results probably due to the limited statistical power in
most of these studies and the difficulties encountered in properly accounting
for confounding factors such as age ethnicity and medical diagnosis (Buyuk et
al 2011 Freeman et al 2007 Su et al 2008 Seifer et al 2008 Sahmay et al 2012
Skalba et al 2011) Therefore there is a need for studies with large datasets and
good adjustment for confounding factors
We therefore designed and undertook a study to estimate the effect of
ethnicity BMI endometriosis and causes of infertility on ovarian reserve as
measured by AMH AFC and FSH using a robust dataset from a large cohort
of patients referred for infertility investigation and treatment in a single centre
144
METHODS
Objectives
The objectives of the study were to assess the role of the ethnicity BMI
and endometriosis and the causes of infertility on ovarian reserve as assessed
by the biomarkers AMH AFC and FSH using a large clinical data obtained
retrospectively
Sample
All women between 20 to 45 years of age referred to the Womenrsquos
Outpatient Department (WOP) and the Reproductive Medicine Department
(RMD) of Central Manchester University Hospitals NHS Foundation Trust for
management of infertility from 1 September 2008 to 16 November 2010 and
who had the measurement of AMH using DSL assay (DSL Active MISAMH
ELISA Diagnostic Systems Laboratories Webster Texas) were included in
this study Patients referred for fertility preservation (eg prior to or after the
treatment of a malignant disorder) and patients with a history of tubal or
ovarian surgery (salpingectomy ovarian cystectomy salpingo-oopherectomy)
and patients diagnosed with polycystic ovaries on ultrasound were excluded
The sample size was determined on pragmatic grounds and represents all
available patients meeting the inclusion criteria
Measurement of AMH
All patients referred to RMD had a measurement of AMH prior to
management of their infertility whereas the patients seen at WOP had AMH
measurements if they had a clinical indication for an assessment of ovarian
reserve
Blood samples for the measurement of AMH were taken at an initial
patient visit without regard to the day of the menstrual cycle and transported
to the in-house Biochemistry Laboratory All samples were processed and
analysed strictly according to the assay kit insert provided by the manufacturer
Serum samples were separated within two hours from venipuncture and frozen
at -20C until analysed in batches using the enzymatically amplified two-site
immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
145
100pmolL with a minimum detection limit of 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first
measurement was selected for this study
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the in-house
Biochemistry Laboratory within two hours of venipuncture for sample
processing and analysis Serum FSH levels were measured using specific
immunoassay kits (Cobas Roche Diagnostics Mannheim Germany) for use
on an autoanalyser platform (Roche Modular Analytics E170 Roche USA)
The intra-assay and inter-assay CVs were 60 and 68 respectively FSH
measurements in samples with high E2 levels (gt250) were defined as non-
representative of early follicular phase and were not included in this study
Where patients had repeated FSH measurements the measurement with the
closest date to that of AMH measurement was used
Measurement of AFC
Measurement of AFC was conducted in all patients undergoing assisted
conception The department uses a stringent protocol for the assessment of
AFC which consists of counting all antral follicles measuring 2-6mm in
longitudinal and transverse cross sections of both ovaries using transvaginal
ultrasound scanning at early follicular phase (Day 0-5) of the menstrual cycle
Fully qualified sonographers conducted the ultrasound assessments Where
patients had repeated AFC measurements the AFC closest to the date of the
AMH measurement was used
Data collection
Data was extracted from hospital electronic clinical data management
systems and from written hospital notes of each patient AMH and FSH
measurements were obtained from the Biochemistry Department of the
hospital and validated by checking results of randomly selected 50 patients
146
against the results available in electronic clinical data management system
(Clinical Workstation) Data on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
surgery were gathered from the hospital case notes Data on the ethnicity was
obtained from the hospitalrsquos administrative database (PAS) The datasets were
merged using a unique patient identifier (hospital number) and the validity of
the linkage was validated using other patient identifiers (NHS number
patientrsquos name and date of birth)
Definitions and groups
In our hospital the ethnicity of the patient is established using a patient
questionnaire based on the UK census classification The body mass index
(BMI) of patients was categorised using NHS UK cut-off reference ranges
Underweight (lt185) Normal (185-249) Overweight (25-299) and Obese
(30-40) Causes of infertility were established by searching hospital records
including referral letters clinical entries and the letters generated following
initial and follow up clinic consultations Patients with a history of bilateral
tubal block which was confirmed by laparoscopy and dye test and patients
with a history of bilateral salpingectomy were categorised as having severe
tubal factor infertility Patients with unilateral tubal patency or unilateral
salpingectomy were categorised as having mild tubal factor infertility Patientrsquos
with laparoscopic diagnosis of stage III and Stage IV endometriosis (AFS)
were categorised as diagnosed with severe endometriosis whilst patients with
Stage I and Stage II endometriosis were allocated to group of mild
endometriosis Severe male factor infertility was defined as azoospermia or
severe oligospermia which necessitated Multiple Ejaculation Resuspension and
Centrifugation test (MERC) for assisted conception The criteria for MERC
were a) sperm count of lt05 mlnml or b) retrograde ejaculation Patients with
abnormal sperm count but who did not meet above criteria were classified as
mild male factor infertility
Statistical analysis
Firstly univariate analyses of the effect of age ethnicity BMI
endometriosis with and without endometrioma causes of infertility and
duration of infertility were conducted using two sample t test Then a
147
multivariate linear regression model that included age ethnicity BMI
endometriosis presence of endometrioma and the causes of infertility was
specified for the analyses of the effect of these factors to AMH AFC and
FSH Logarithmically transformed values were used for the statistical analysis
of AMH AFC and FSH The precise age on the day measurement of each of
the marker of ovarian reserve (AMH AFC and FSH) was used and age
adjustment utilised a quadratic function following centring to 30 years of age
Differences between the groups were considered significant at p005
Interactions between all explanatory variables were tested at a significance level
of plt001 In order to estimate the effect of BMI we fitted two different
models with a) BMI not included and b) BMI included in the model
Duration of infertility did not show any clinical or statistically significant
differences for any of the markers and therefore this variable was not included
in the models
RESULTS
In total 2946 patients were included in the study of whom 2880 of these
patient had valid AMH measurements 1810 had measurement of AFC and
2377 had FSH samples The mean and median age of patients were 328 (45)
and 332 (295 365) respectively and the distribution of patients according to
age categories ethnicity BMI endometriosis and the causes of infertility is
shown in the Table 1 The summary statistics for the markers of ovarian
reserve were as follows AMH mean 175 (501) median 142 (76-232) AFC
mean 139 (63) median 13 (10-17) and FSH mean 79 (72) median 7 (58-85)
As expected chronological age was found to be a significant determinant of all
markers of ovarian reserve We observed in average 5 decline in AMH levels
2 decline in AFC and 1 increase in FSH measurements per year (Table 2-
4)
Out of 2946 patients 2021 had data on BMI measurements and in 925
BMI was not available Table 5 describes age AMH AFC and FSH according
to the availability of data on BMI Distribution of patients by their ethnicity
and an availability of data on BMI is provided in Table 6 Similarly patient
distribution by diagnosis and availability of data on BMI can be found in Table
7
148
Ethnicity
The multivariable regression excluding BMI (Table 2) showed that
woman of Black ethnicity and the group defined as ldquoOther ethnicityrdquo had
significantly lower AMH measurements when compared to that of White (-25
p=0013 and -19 p=0047) and the overall ethnicity was a significant
predictor of AMH (p=0007) However inclusion of BMI in the model
reduced these effects and none of the groups had a statistically significant
difference in AMH levels compared to that of White and the overall effect of
ethnicity did not reach statistical significance (p=008)
AFC was significantly reduced in Pakistani and women of ldquoOther
ethnicitiesrdquo (Table 3) although the effect sizes were small (10-14) and the
overall effect of ethnicity was significant in the models with and without BMI
(p=004 and p=003) None of the groups showed statistically significant
differences in FSH (Table 4) although women of ldquoOther Asianrdquo ethnicity
appear to have lower FSH measurements (12) which was close to the level of
statistical significance (-12 p=007)
BMI
Obese women had 16 higher measurements of AMH (p=0035) and
overall effect of the BMI was significant (p=003) No interaction were
detected between BMI and ethnicity causes of infertility or diagnosis of
endometriosis suggesting that effect of BMI was independent of these factors
(Table 2)
In the analysis of the effect of BMI on AFC measurements we did not
observe any differences that were statistically significant (Table 3) However
FSH results showed that there is a modest association between BMI and FSH
with both overweight (Table 4) and obese women having significantly lower
FSH measurements compared to lean women (-5 p=0003 and -10
p=0003)
Endometriosis
In the absence of endometrioma endometriosis was associated with
lower AMH measurements although this did not reach statistical significance
149
(Table 2) Neither AFC nor FSH was significantly different in the
endometriosis group compared to those without endometriosis (Table 3-4)
In contrast we observed around 31 higher AMH levels in the patients
with endometrioma (p=0034) although this reduced to 21 and did not reach
statistical significance (p=010) in the smaller subset after adjustment for BMI
(Table 2) AFC and FSH did not show any statistically significant association
with endometrioma (Table 3-4)
Causes of Infertility
There were no differences in the AMH measurements between patients
diagnosed with unexplained infertility compared to those with diagnosis
except the analysis that did not include BMI as a covariate which found a
weakly positive correlation (10 p=003) Similarly the estimation of the
effect of a diagnosis of unexplained infertility on AFC as well as FSH showed
that there were weak association between the markers and a diagnosis of
unexplained infertility (Table 2-4)
There were no significant differences in AMH AFC and FSH in women
with mild and severe tubal infertility in the models which included all
covariates other than weakly negative correlation between FSH and mild tubal
factor (Table 2-4)
Women diagnosed with male factor infertility had significantly higher
AMH and lower FSH measurements the effect sizes of which increased with
the severity of the diagnosis We did not find any significant difference in AFC
between patients with and without male factor infertility (Table 2-4)
DISCUSSION
This is first study investigating the effect of demographic
anthropometric and clinical factors on all three markers of ovarian reserve
using a large cohort of women of reproductive age Furthermore the statistical
analysis adjusted for relevant covariables using multivariable linear regression
models
150
Ethnicity
Our study found that amongst the main British ethnic groups the
effect of ethnicity on ovarian reserve measured using AMH AFC and FSH is
fairly weak and can be accounted for by differences in BMI between the
ethnic groups Recently studies have been published on the relationship of
ethnicity and markers of ovarian reserve all of which compared North
American populations One study which assessed a relatively small number of
women (n=102) at late reproductive age did not find a difference in AMH
levels between White and African American Women OR 123 (056 271
P=070) (Freeman et al 2007) In contrast Seifer et al reported that Black
(n=462) women had around 25 lower AMH measurements (P=0037)
compared to that of White (n=122) (Seifer et al 2009) which is not consistent
with our findings The main differences of this study compared to our study
were a) a majority were HIV infected women b) women were older (median
375 years of age) c) the analysis did not control for possible confounders
related to PCO reproductive pathology and surgery Furthermore unlike our
results the study did not find a correlation between BMI and AMH levels
Similarly Shuh-Huerta and colleagues reported that African American women
(n=200) had significantly lower AMH levels (P=000074) compared to that of
White (n=232) Mean AMH 22817 pmolL and 301+15 pmolL
respectively (Shuh-Huerta et al 2012b) Although the group used very stringent
selection of patients and statistical analysis BMI was not included in the
regression model Indeed our analysis without BMI in the model found similar
results (Table 2) But controlling for BMI has revealed no significant difference
in the AMH levels between White and Black ethnic groups
With regard to AFC measurements Shuh Huerta et al reported no
difference in the follicle counts between White (n=245) and African American
(n=202) women which supports our findings (Shuh-Huerta et al 2012b)
Again similar to our results the authors reported that FSH results of these
ethnic groups provided comparable results (Shuh-Huerta et al 2012a)
Although our results do not support some of previously published data
in view of above arguments we believe the ethnicity does not appear to play a
major role in determination of ovarian reserve However in view of the
discrepant findings of the currently available studies we suggest further studies
151
in large and diverse cohorts should be carried out in order to fully understand
the role of ethnicity
BMI
Our results show that BMI has direct correlation with AMH and AFC
and negative correlation with FSH suggesting women with increased BMI are
likely to have higher ovarian reserve The effect of this association was
significant in the analysis of AMH and FSH obese women appear to have
approximately 16 higher AMH and 10 lower FSH measurements when
compared to women with normal BMI Although the difference in AFC
measurements did not reach statistical significance there was direct correlation
between AFC and BMI
Published data on the effect of BMI to AMH levels provide conflicting
results compared to our study given the studies reported either no association
(Buyuk et al 2011 Freeman et al 2007 Su et al 2008) or a negative correlation
between these factors (Seifer et al 2008 Sahmay et al 2012 Skalba et al 2011)
However most of these studies assessed peremenopausal women or that of
late reproductive age Indeed the studies evaluated the effect of BMI to AMH
measurements in women of reproductive age demonstrated that lower AMH
levels in obese women were due to age rather than increased BMI (La Marca
et al 2012 Streuli et al 2012) Furthermore most of these studies either
employed univariate analysis or multivariate regression models that did not
included all relevant explanatory factors In addition these studies had
significantly smaller numbers of samples ranging from 10 to 809 compared to
our study (n=1953) Indeed other large study (n=3302) with multivariate
analysis supports our findings on the effect of BMI on ovarian reserve
indicating obese women have significantly lower FSH levels (Randolph et al
2004)
Endometriosis
Here we present data on the measurement of all three main markers of
ovarian reserve in women with endometriosis We observed that women with
endometriosis without endometrioma did not have significantly different
AMH AFC or FSH measurements compared to women that do not have this
pathology Intriguingly women who had endometriosis with endometriomata
152
tended to have higher AMH levels Given the data was collected
retrospectively we did not have full information on laparoscopic staging of
endometriosis in all patients and therefore an analysis according to severity or
staging of endometriosis was not feasible However the analysis controlled for
the important variables mentioned above and importantly only included the
patients without previous history of ovarian surgery We therefore we believe
the study provides fairly robust data on relationship of endometriosis and the
markers of ovarian reserve
Although it is generally believed that endometriosis has a damaging
effect on ovarian reserve published literature provides conflicting views
ranging from no correlation between these factors to a significant negative
effect of endometriosis As mentioned above most studies were small and
used matched comparison of patients with endometriosis to control group
using retrospectively collected data Carvalho et al compared women with
endometriosis (n=27) and to that of male factor infertility (n=50) and reported
there was no difference in basal AMH and AFC levels whilst FSH levels of
women with endometriosis was lower Another small study which used similar
methodology where an endometriosis group (n=17) was compared to patients
with tubal factor infertility (n=17) reported opposite results suggesting
endometriosis was associated with lower AMH measurements and there was
no correlation between the pathology and FSH or AFC (Lemos et al 2007)
Shebl et al compared AMH results of women with endometriosis (n=153) to a
matched group that did not have the pathology (n=306) and reported that
women with mild endometriosis had similar AMH levels whereas the group
with severe endometriosis had significantly lower AMH compared to the
control group (Shebl et al 2009) Although using well-matched control groups
is a robust study design direct comparison of the two groups without
controlling for other important covariables may result in inaccurate results
Indeed the study that used multivariate regression analysis was able to
demonstrate that there are number of factors that can affect AMH results and
indeed following controlling for these factors there was no difference between
AMH results of women with endometriosis compared to that of without
disease (Streuli et al 2012) In view of above considerations we believe the
effect of endometriosis to ovarian reserve is poorly understood and warrants
further investigation
153
Regarding the effect of endometrioma on AMH levels current evidence
is conflicting Using univariate analysis without controlling for confounders
Uncu et al reported that women with endometrioma (n=30) had significantly
lower AMH and AFC measurements compared to control (n=30) women
(Uncu et al 2013) Similarly Hwu et al reported that women with
endometrioma (n=141) had significantly lower AMH measurements compared
to that of without pathology (n=1323) pathology (Hwu et al 2013) However
the study population appears to have a disproportionately higher number of
women with history of previous and current history of endometrioma
(3191642) compared to any published studies and therefore the study may
have been subject of selection bias
Kim et al reported lower AMH measurements in women with
endometrioma (n=102) compared to control group (102) meanplusmnSEM
29plusmn03 ngmL_vs 33plusmn03_ngmL although this did not reach statistical
significance (P=028)
In our view the most robust data on measurement of AMH in women
with endometriosis was published by Streuli et al which compared AMH levels
of 313 women with laparoscopically and histologically confirmed
endometriosis to 413 women without pathology (Streuli et al 2009) The group
with endometriosis consisted of women with superficial peritoneal
endometriosis (n=35) deep infiltrating endometriosis (n=183) and ovarian
endometrioma (n=95) and relevant factors such as age parity smoking and
previous ovarian surgery were adjusted for using multivariate regression
analysis In keeping with our findings women with endometriosis did not have
lower AMH levels except for patients with previous history of surgery for
endometrioma Most interestingly the findings of Streuili et al and this study
suggest that women with ovarian endometrioma do not have low AMH levels
In contrast according to our data the presence of endometrioma may be
associated with a significant increase in serum AMH levels Given that an
endometrioma is believed to cause significant damage to ovarian stroma this is
an interesting finding Increased AMH levels in the presence of endometrioma
may be due to acceleration in the rate of recruitment of primordial follicles
andor increased expression of AMH in granulosa cells Furthermore
increased AMH levels in these patients may be due to expressions of AMH in
endometriotic cells A study by Wang et al suggested that AMH is secreted by
human endometrial cells in-vitro (Wang et al 2009) This was the first report of
154
non-ovarian secretion of AMH and suggested that AMH may play important
role in regulation of the function of the human endometrium Subsequently
the findings of Wang et al were independently confirmed by two different
groups Carrarelli et al assessed expression of AMH and AMH type II receptor
(AMHRII) in specimens of endometrium obtained by hysteroscopy from
patients with endometriosis (n=55) and from healthy (n=45) controls
(Carrarelli et al 2014) The study also assessed specimens from patients with
ovarian endometriosis (n=29) and deep peritoneal endometriosis (n=26) The
study found that both AMH and AMHRII were expressed in endometrium
Interestingly ectopic endometrium obtained from patients with endometriosis
had significantly higher AMH and AMHRII levels compared to that of healthy
individuals Furthermore the specimens collected from ovarian and deep
endometriosis had highest AMH and AMHII mRNA expression These
findings confirm that AMH as well as AMHRII are expressed in human
endometrium and AMH may play a role in pathophysiology of endometriosis
A further study conducted by Signorile et al also confirmed expression of
AMH and AMHRII in human endometriosis glands Furthermore the study
found that treatment of endometriosis cells with AMH resulted in a decrease in
cell growth suggesting that AMH may inhibit the growth of endometriotic
cells This suggests that further studies to understand the role of AMH in
pathophysiology of endometriosis are warranted
Causes of infertility
Unlike the above-mentioned factors the association of the various
causes of infertility and the markers of ovarian reserve are poorly studied
Therefore our study appears to provide only available data on AMH AFC and
FSH levels in women with three main causes of infertility unexplained tubal
and male factor
In our study AMH levels of women with unexplained infertility did not
differ from those with a diagnosis Similarly the effect of a diagnosis on AFC
and FSH measurements were weak Women with unexplained infertility do not
have any significant pathology that can account for their infertility However
understanding the role of ovarian reserve in these patients is important Our
study suggests that women with unexplained infertility have comparable AMH
levels to other infertile women
155
We did not find any significant differences in AMH AFC or FSH
measurements of women diagnosed with tubal factor infertility compared to
infertile women without tubal disease Pelvic inflammatory disease and
endometriosis are well known causes of tubal pathology and our regression
model has controlled for the effect of endometriosis in these analyses Our
results suggest that despite having damaging effect to the tubes pelvic
infection does not reduce ovarian reserve
In contrast our analyses showed that women with mild and severe male
factor infertility have significantly increased AMH and lower FSH
measurements which demonstrates that these women have better ovarian
reserve compared to general infertility population
Strengths and Limitations of the study
The study is based on retrospectively collected data and therefore was
subject to the issues related to this methodology However we believe that we
have overcome most problems and improved the validity of our results by
using a robust methodology for data collection large sample size and careful
analysis We included all women who presented during the study period and
met the inclusion criteria of the study Importantly women with previous
history of PCO chemotherapy radiotherapy tubal surgery or ovarian surgery
have been excluded from the study given these factors may have significant
acute impact on ovarian reserve effect of which may be difficult to control for
The analysis showed an interaction between BMI and ethnicity which
could not be explored fully due to missing data on BMI (Tables 6) Therefore
analyses with and without BMI in models have been performed (Tables 2-4)
and the distribution of patients according to availability of data on BMI has
been obtained (Tables 5-7) I suggest further studies with sufficient data should
explore this interaction
I was not able to establish the patients that meet Rotterdam criteria for
diagnosis of PCOS given data on menstrual history and biochemical
assessment of androgenemia were not available Therefore ultrasound
diagnosis of PCO was used to categories patients with polycystic ovaries and
all patients with PCO were excluded from analysis
It is important to note that measurement of AMH using Gen II assay may
provide erroneous results (Rustamov et al 2012a) Therefore only samples
156
obtained using DSL assay have been included in the study The DSL assay
appears to provide more reproducible results than the Gen II assay (Rustamov
et al 2011 and Rustamov et al 2012a) and therefore we believe the estimates
in this study reflect the relationship between circulating AMH and the above
factors
SUMMARY
Our data suggests that there is no strong association between ethnicity
and AMH AFC or FSH whilst women with increased BMI appear to have
higher ovarian reserve There was no evidence of reduced ovarian reserve in
women with endometriosis who do not have a previous history of ovarian
surgery In contrast women with current history of endometrioma may have
higher AMH levels which warrants further investigation Women with a
history of unexplained infertility do not appear to have reduced ovarian
reserve as measured with AMH AFC and FSH compared to general infertile
women Similarly women with tubal factor infertility have comparable ovarian
reserve with women who do not have tubal disease In contrast women with
male factor infertility have significantly higher ovarian reserve compared to
infertile women who do not have male factor infertility
This study has elucidated the effect of demographic anthropometric and
clinical factors on all commonly used markers of ovarian reserve and
demonstrated that some of these factors have significant impact on ovarian
reserve
157
References Buyuk E Seifer DB Illions E Grazi RV and Lieman H Elevated body mass index is associated with lower serum anti-mullerian hormone levels in infertile women with diminished ovarian reserve but not with normal ovarian reserve Fertility and Sterility_ Vol 95 No 7 June 2011 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 2014 1011353ndash8 de Carvalho BR Rosa-e-Silva AC Rosa-e-Silva JC dos Reis RM Ferriani RA de Saacute MFIncreased basal FSH levels as predictors of low-quality follicles in infertile women with endometriosis International Journal of Gynecology and Obstetrics 110 (2010) 208ndash212 Doacutelleman M Verschuren W M M Eijkemans M J C Dolle M E T Jansen E H J M Broekmans F J M and van der Schouw Y T Reproductive and Lifestyle Determinants of Anti-Mullerian Hormone in a Large Population-based Study J Clin Endocrinol Metab May 2013 98(5) 2106ndash2115 Freeman EW Gracia CR Sammel MD Lin H Lim LC Strauss JF 3rd Association of anti-mullerian hormone levels with obesity in late reproductive-age women Fertil Steril 2007 87101-6 Halawaty S ElKattan E Azab H ElGhamry N Al-Inany H Effect of obesity on parameters of ovarian reserve in premenopausal women J Obstet Gynaecol Can 2010 32687ndash690 Hansen KR Knowlton NS Thyer AC Charleston JS Soules MR Klein NA A new model of reproductive aging the decline in ovarian non-growing follicle number from birth to menopause Hum Reprod 200823699-708 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 2011 95170ndash5 Hwu Y Wu FS Li S Sun F Lin M and Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reproductive Biology and Endocrinology 2011 980 Kelsey TW Wright P Nelson SM Anderson RA Wallace WHB (2011) A Validated Model of Serum Anti-Muumlllerian Hormone from Conception to Menopause PLoS ONE 6(7) e22024 Kim MJ Byung Chul Jee Chang Suk Suh and Kim SH Preoperative Serum Anti-Mullerian Hormone Level in Women with Ovarian Endometrioma and Mature Cystic Teratoma Yonsei Med J Volume 54 Number 4 July 2013 La Marca A Sighinolfi G Papaleo E Cagnacci A Volpe A et al (2013) Prediction of Age at Menopause from Assessment of Ovarian Reserve May Be
158
Improved by Using Body Mass Index and Smoking Status PLoS ONE 8(3) e57005 Lemos NA Arbo E Scalco R Weiler E Rosa V Cunha-Filho JS Decreased anti-Muumlllerian hormone and altered ovarian follicular cohort in infertile patients with mildminimal endometriosis Fertil Steril 2008 May 89(5)1064-8 Luborsky JL Meyer P Sowers MF Gold EB Santoro N Premature menopause in a multi-ethnic population study of the menopause transition Hum Reprod 200218199-206 Maheshwari A Stofberg L Bhattacharya S Effect of overweight and obesity on assisted reproductive technologymdasha systematic review Hum Reprod Update 200713433ndash44 Michelmore K Balen A Dunger D Vessey M Polycystic ovaries and associated clinical and biochemical features in young women Clin Endocrinol (Oxf) 199951779-86 Nelson SM Messow MC Wallace AM Fleming R McConnachie A Nomogram for the decline in serum antimullerian hormone a population study of 9601 infertility patients Fertil Steril 95736-741 e731-7332011 Chen X Yang D Mo Y Li L Chen Y Huang Y Prevalence of polycystic ovary syndrome in unselected women from southern China Eur J Obstet Gynecol Reprod Biol 2008 13959-64 Randolph JF Sowers M Gold EB Mohr BA Luborsky J Santoro M et al Reproductive hormones in early menopausal transition relationship to ethnicity body size and menopausalstatus J Clin Endocrinol Metab Apr2003 88(4)1516ndash1522 [PubMed 12679432] Sahmay S Usta T Erel CT Imamoğlu M Kuuk M Atakul N Seyisoğlu H Is there any correlation between amh and obesity in premenopausal women Arch Gynecol Obstet 2012 Sep 286(3) 661-5 Seifer DB Baker VL and Leader B Age-specific serum anti-Meuroullerian hormone values for 17120 women presenting to fertility centers within the United States Fertility and Sterility_ Vol 95 No 2 February 2011 Seifer DB Golub ET Lambert-Messerlian G Benning L Anastos K Watts H Cohen MH Karim R Young MA Minkoff H and Greenblatt RM Variations in Serum Mullerian Inhibiting Substance Between White Black and Hispanic Women Fertil Steril 2009 November 92(5) 1674ndash1678 Shebl O Ebner T Sir A Schreier-Lechner E Mayer RB Tews GSommergruber M Age-related distribution of basal serum AMH level in women of reproductive age and a presumably healthy cohort Fertil Steril 2011 95 832ndash834
159
Shebl O Ebner T Sommergruber M Sir A Tews G Anti muellerian hormone serum levels in women with endometriosis a case-control study Gynecol Endocrinol 200925713-6 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities Hum Genet (2012b) 1311709ndash1724 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Skałba P Cygal A Madej P Dabkowska-Huc A Sikora J Martirosian G Romanik M Olszanecka-Glinianowicz M Is the plasma anti-Mullerian hormone (AMH) level associated with body weight and metabolic and hormonal disturbances in women with and without polycystic ovary syndrome European Journal of Obstetrics amp Gynecology and Reproductive Biology 158 (2011) 254ndash259 Streuli I de Ziegler D Gayet V Santulli P Bijaoui G de Mouzon J and Chapron C In women with endometriosis anti-Mullerian hormone levels are decreased only in those with previous endometrioma surgery Human Reproduction Vol27 No11 pp 3294ndash3303 2012 Su IH Sammel MD Freeman EW Lin H DeBlasis T Gracia C Body size affects measures of ovarian reserve in late reproductive age women Menopause 2008 15(5) 857ndash861 Uncu G Kasapoglu I Ozerkan K Seyhan A Oral Yilmaztepe A Ata B Prospective assessment of the impact of endometriomas and their removal on ovarian reserve and determinants of the rate of decline in ovarian reserve Hum Reprod 2013 Aug 28(8) 2140-5 Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wallace WHB Kelsey TW (2010) Human Ovarian Reserve from Conception to the Menopause PLoS ONE 5(1) e87
160
Table 1 Distribution of patients
AMH AFC FSH
n Mean (SD) n Mean (SD) n Mean (SD)
All 2880 175150 1810 13972 2377 7972
Ethnicity
White (Reference) 1833 169139 1222 13959 1556 7966
Other White 137 172131 85 14480 107 7637
Black 93 202208 43 16098 73 104135
Indian 108 216169 69 14360 94 7127
Other Asian 46 194157 30 14560 41 6717
Pakistani 276 201164 166 14375 232 81124
Other ethnic 103 158130 63 12448 83 7640
Not disclosed 220 170152 114 13161 157 7937
Data not available 64 183251 18 11952 34 8956
Patients with BMI
Normal (Reference) 1110 172137 917 13861 1011 7844
Underweight 38 179136 30 13947 38 7751
Overweight 679 168134 546 13763 620 7544
Obese 149 220209 90 14167 119 7142
Data not available 904 177163 227 14967 589 88123
Diagnosis
Unexplained 894 156120 667 13354 801 7632
Mild tubal 411 172158 284 13771 370 7530
Severe tubal 40 12685 27 13658 38 7827
Mild male 779 181134 538 14058 668 7342
Severe male 356 198135 197 14661 208 6818
Endometriosis ndash endometrioma 141 137108 91 13658 122 8341
Endometriosis + endometrioma 46 196159 15 14449 42 7123
161
Table 2 Regression models for AMH
AMH (Log)
BMI included
n=1952
BMI excluded
n=2816
Β 95 CI P β 95 CI P
Age -0057 -0069 -0045 00001 -0056 -0067 -0046 00001
age2 -0003 -0005 -0001 00001 -0004 -0006 -0003 00001
Ethnicity 00812 00079
Other White -0046 -0226 0133 0611 0038 -0131 0208 0658
Black 0209 -0038 0457 0097 -0259 -0464 -0054 0013
Indian 0032 -0164 0228 0749 0119 -0071 0310 022
Other Asian 0292 -0014 0598 0061 0250 -0037 0537 0088
Pakistani -0116 -0251 0017 0089 -0100 -0226 0025 0118
Other ethnic -0174 -0390 0041 0113 -0197 -0392 -0002 0047
Not disclosed -0002 -0162 0157 0977 -0104 -0241 0033 0138
BMI 00374
Underweight -0107 -0394 0179 0462
Overweight -0058 -0143 0025 017
Obese 0165 00119 0318 0035
Diagnosis
Unexplained 0039 -0073 0152 0492 0105 0007 0204 0035
Mild tubal 0089 -0033 0212 0153 0113 -000009 0226 005
Severe tubal -0168 -0463 0126 0264 -0133 -0444 0177 0401
Mild male 0118 0009 0227 0033 0180 0084 0275 00001
Severe male 0245 0096 0395 0001 0287 0162 0412 00001
Endometriosis -0136 -0311 0037 0124 -0152 -0324 0018 0081
Endometrioma 0217 -0068 0503 0136 0314 0023 0606 0034
_cons 2731 2616 2847 0 2658 2567 2750 0
162
Table 3 Regression models for AFC
AFC (Log)
BMI Included
n=1589
BMI Excluded
n=1810
Β 95 CI P Β 95 CI P
Age -0028 -0035 -0021 0 -0027 -0033 -0021 0
age2 000009 -00009 0001 086 000007 -00008 0001 0885
Ethnicity 00265 00383
Other White -0024 -0119 0070 0614 0003 -0087 0094 0942
Black 0093 -0037 0224 0162 0049 -0075 0175 0436
Indian -0042 -0148 0064 0438 -0035 -0136 0065 0492
Other Asian 0037 -0125 0200 0651 0037 -0114 0189 0626
Pakistani -0095 -0166 -0024 0008 -0083 -0151 -0015 0016
Other ethnic -0142 -0253 -0031 0012 -0132 -0237 -0027 0013
Not disclosed -0008 -0094 0078 0853 -0067 -0148 0012 0098
BMI 07713
Underweight -0040 -0190 0109 0599
Overweight -0018 -0062 0024 0398
Obese 0012 -0077 0103 0779
Diagnosis
Unexplained -0071 -0131 -0011 0019 -0065 -0121 -0009 0021
Mild tubal -0047 -0112 0017 0151 -0060 -0121 00003 0051
Severe tubal -0110 -0267 0045 0164 -0141 -0294 0010 0069
Mild male -0037 -0095 0020 0201 -0027 -0081 0025 0307
Severe male 0007 -0071 0086 0853 -0021 -0093 0050 0563
Endometriosis -0019 -0114 0076 0691 -0004 -0096 0087 0922
Endometrioma -0079 -0215 0055 0248 -0106 -0231 0019 0097
_cons 2694 2632 2755 0 2691 2636 2745 0
163
Table 4 Regression models for FSH
FSH (Log)
BMI Included
n=1772
BMI Excluded n=2343
Β 95 CI P Β 95 CI P
age 0009 0003 0014 0001 0009 0004 0014 00001
age2 00009 00001 0001 0019 0001 00003 0001 0003
Ethnicity 04415 03329
Other White 0034 -0046 0114 0403 -0017 -0099 0065 0685
Black 0043 -0065 0153 043 0068 -0030 0167 0175
Indian -0010 -0097 0076 0808 -0070 -0157 0017 0116
Other Asian -0119 -0250 0011 0074 -0104 -0234 0026 0117
Pakistani -0031 -0089 0026 029 -0014 -0073 0045 064
Other ethnic 0031 -0062 0125 0508 -0002 -0095 0090 0962
Not disclosed 0022 -0049 0093 0541 0026 -0042 0095 045
BMI 00017
Underweight -0070 -0189 0048 0246
Overweight -0055 -0091 -0018 0003
Obese -0106 -0176 -0036 0003
Diagnosis
Unexplained -0055 -0104 -0006 0028 -0055 -0101 -0009 0018
Mild tubal -0052 -0105 000008 005 -0050 -0103 0001 0056
Severe tubal 0004 -0118 0127 0943 0016 -0120 0154 0809
Mild male -0084 -0132 -0037 00001 -0071 -0116 -0026 0002
Severe male -0127 -0196 -0059 00001 -0102 -0168 -0036 0002
Endometriosis 0035 -0039 0111 0353 0044 -0034 0124 0268
Endometrioma -0074 -0196 0047 0229 -0056 -0186 0074 0402
_cons 1999 1948 2049 0 1958 1915 2002 0
164
Table 5 Distribution of patient characteristics by availability of data on BMI The number of observations and mean (SD) of the markers of ovarian reserve (Age AMH AFC and FSH) described according to an availability of data on BMI
BMI (+)
BMI (-) Total
n Mean (SD) n Mean (SD) n Mean (SD)
Age 1976 32944 904 32750 2880 32946
AMH 1976 175144 904 178164 2880 176150
AFC 1583 13862 227 14968 1810 14063
FSH 1788 7744 589 88123 2377 8073
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
165
Table 6 Distribution of ethnicity by availability of data on BMI Distribution of the number of observations by ethnicity and availability of data on BMI
AMH AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
BMI (+) BMI (-) Total
White 1308 525 1833 1070 152 1222 1201 355 1556
Other White 97 40 137 76 9 85 83 24 107
Black 50 43 93 39 4 43 44 29 73
Indian 81 27 108 60 9 69 70 24 94
Other Asian 32 14 46 25 5 30 30 11 41
Pakistani 193 83 276 148 18 166 177 55 232
Other ethnic 66 37 103 55 8 63 60 23 83
Not disclosed 125 95 220 95 19 114 107 50 157
Data not available 24 40 64 15 3 18 16 18 34
Total 1976 904 2880 1583 227 1810 1788 589 2377
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
166
Table 7 Distribution of diagnosis by availability of data on BMI Distribution of number of observations in each diagnosis group tabulated by availability of data on BMI
AMH
AFC
FSH
BMI (+) BMI (-) Total BMI (+) BMI (-) Total BMI (+) BMI (-)
Total
Unexplained 730 164 894 611 56 667 672 129 801
Mild tubal 319 92 411 258 26 284 298 72 370
Severe tubal 36 4 40 26 1 27 36 2 38
Mild male 567 212 779 461 77 538 525 143 668
Severe male 196 160 356 161 36 197 153 55 208
Endometriosis ndash endometrioma 112 29 141 83 8 91 101 21 122
Endometriosis + endometrioma 38 8 46 38 8 46 36 6 42
BMI (+) Record of BMI available BMI (-) Record of BMI not available Total All available observations
167
THE EFFECT OF SALPINGECTOMY
OVARIAN CYSTECTOMY AND UNILATERAL
SALPINGOOPHERECTOMY ON OVARIAN
RESERVE
Oybek Rustamov Monica Krishnan
Stephen A Roberts Cheryl Fitzgerald
To be submitted to Gynecological Surgery
52
168
Title
Effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve
Authors
Oybek Rustamova Monica Krishnanb Stephen A Robertsc Cheryl Fitzgeralda
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL UK
c Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL UK
Corresponding author amp reprint requests
Dr Oybek Rustamov Department of Reproductive Medicine St Maryrsquos
Hospital Central Manchester University Hospital NHS Foundation Trust
Manchester Academic Health Science Centre (MAHSC) Manchester M13 0JH
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable Word count 2904
Acknowledgement
The authors would like to thank colleagues Dr Greg Horne (Senior Clinical
Embryologist) Ann Hinchliffe (Clinical Biochemistry Department) and Helen
Shackleton (Information Operations Manager) for their help in obtaining
datasets for the study
169
Declaration of authorsrsquo roles
OR prepared the dataset conducted statistical analysis and prepared all
versions of the manuscript MK assisted in data extraction contributed in
discussion and the review of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed in discussion
and reviewed all versions of the manuscript
170
ABSTRACT
Objective
To estimate the effect of salpingectomy ovarian cystectomy and unilateral
salpingo-oopherectomy on ovarian reserve
Design
Single centre retrospective cross-sectional study
Setting
Women referred to secondary and tertiary level referral centre for management
of infertility
Participants
A total of 3179 patients were included in the study The AMH measurements
of 66 women were excluded due to haemolysed samples or delay in processing
the samples leaving 3113 women for analysis There were 138 women who
had unilateral or bilateral salpingectomy 36 women with history of unilateral
salpingo-oopherectomy 41 women with history of cystectomy for ovarian
cysts that other than endometrioma and 40 women had cystectomy for
endometrioma
Interventions
Serum AMH AFC and basal FSH measurements
Main outcome measure
Serum AMH basal serum FSH and basal AFC measurements
Results
The analysis did not find any significant differences in AMH (9 p=033)
AFC (-2 p=059) and FSH (-14 p=021) measurements between women
with a history of salpingectomy and those without history of surgery Women
with history of unilateral salpingo-oopherectomy were found to have
significantly lower AMH (-54 p=0001) and AFC (-28 p=034) and
increased FSH (14 p=006) The study did not find any significant
171
association between a previous history of ovarian cystectomy that was for
conditions other than endometrioma and AMH (7 p=062) AFC (13
p=018) or FSH (11 p=016) The analysis of the effect of ovarian
cystectomy for endometrioma showed that women with history of surgery had
around 66 lower AMH (p=0002) Surgery for endometrioma did not
significantly affect AFC (14 p=022) or FSH (10 p=028)
Conclusions
Salpingo-oopherectomy and ovarian cystectomy for endometrioma have a
significant detrimental impact on ovarian reserve Neither salpingectomy nor
ovarian cystectomy for cysts other than endometrioma has an appreciable
effect on ovarian reserve
Key Words
Salpingectomy Ovarian cystectomy Salpingo-oopherectomy ovarian reserve
AMH AFC FSH
172
INTRODUCTION
Human ovarian reserve is determined by the size of oocyte pool at birth
and decline in the oocyte numbers thereafter Both of these processes are
largely under the influence of genetic factors and to date no effective
interventions are available to improve physiological ovarian reserve (Shuh-
Huerta et al 2012) However various other environmental pathological and
iatrogenic factors appear to play a role in the determination of ovarian reserve
and consequently it may be influenced either directly or indirectly Evidently
the use of chemotherapeutic agents certain radio-therapeutic modalities and
surgical interventions that damage ovarian parenchyma can cause substantial
damage to ovarian reserve (Nielsen et al 2013 Somigliana et al 2012)
Estimation of the effect of each of these interventions is of paramount
importance in ascertainment of lesser ootoxic treatment modalities and safer
surgical methods
Age is the main determinant of the number of non-growing follicles
accounting for 84 of its variation and used as marker of ovarian reserve
(Hansen et al 2008) However biomarkers that allow direct assessment of the
dynamics of growing follicles AMH and AFC may provide more accurate
estimation of ovarian reserve Although these markers only reflect
folliculogenesis of already recruited growing follicles there appears to be a
good correlation between their measurements and histologically determined
total ovarian reserve (Hansen et al 2011) Thus the biomarkers can effectively
be utilized for estimation of the effect of above adverse factors on the
primordial oocyte pool
Surgical interventions that lead to disruption of the blood supply to
ovaries or involve direct damage to ovarian tissue may be expected to lead to a
reduction in the primordial follicle pool Indeed a number of studies have
reported an association between surgical interventions to ovaries and reduction
in ovarian reserve (Somigliana et al 2012) However given both underlying
disease and surgery may affect ovarian reserve disentanglement of the
individual effects of these factors may be challenging and requires robust
research methodology Here we present a study that intended to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease
173
METHODS
The effect of salpingectomy ovarian cystectomy and unilateral salpingo-
oopherectomy on ovarian reserve were studied using serum AMH AFC and
FSH measurements in a large cross sectional study
Population
All women between the ages of 20 to 45 who were referred to the
Womenrsquos Outpatient Department (WOP) and the Reproductive Medicine
Department (RMD) of Central Manchester University Hospitals NHS
Foundation Trust for management of infertility between 1 September 2008
and 16 November 2010 and had an AMH measurement using the DSL assay
(DSL Active MISAMH ELISA Diagnostic Systems Laboratories Webster
Texas) were included We excluded patients referred for fertility preservation
(eg prior to or after treatment for a malignant disorder) and those with a
diagnosis of polycystic ovaries (PCO) on transvaginal ultrasound scan which
was defined as volume of one or both ovaries more than 10ml Patients with
haemolysed AMH andor FSH samples were not included in the analysis of
these markers Non-smoking is an essential criteria for investigation prior to
assisted conception and therefore to our best knowledge our population
consisted of non-smokers
Measurement of AMH
Blood samples for AMH were taken without regard to the day of
womenrsquos menstrual cycle and serum samples were separated within two hours
of venipuncture in the Biochemistry laboratory of our hospital All samples
were processed strictly according to the manufacturerrsquos recommendations and
frozen at -20C until analysed in batches using the enzymatically amplified two-
site immunoassay (DSL Active MISAMH ELISA Diagnostic Systems
Laboratories Webster Texas) The working range of the assay was up to
100pmolL and a minimum detection limit was 063pmolL The intra-assay
coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and 29 (at
56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and 49 (at
56pmoll) In patients with repeated AMH measurements the first AMH of
the patients were selected
174
Measurement of FSH
Patients had measurement of basal FSH LH and oestradiol levels (E2)
during the early follicular phase (Day 2-5) of their menstrual cycle as a part of
their initial work up Blood samples were transported to the Biochemistry
Laboratory within two hours of venipuncture for sample processing and
analysis Specific immunoassay kits (Cobas Roche Diagnostics Mannheim
Germany) and an autoanalyser platform was used (Roche Modular Analytics
E170 Roche USA) for analysis of FSH The intra-assay CV was 60 and
inter-assay CV was 68 The FSH measurements in the samples with high E2
levels (gt250pmolL) were excluded from the analysis given these samples are
likely to have been taken outside of early follicular phase of menstrual cycle
In patients with repeated FSH measurements measurements conducted on the
same day as first AMH were selected If the patient did not have FSH
measurement on the day of AMH sampling the measurement with the closest
date to first AMH sample was selected
Measurement of AFC
Measurement of AFC is conducted in patients referred for assisted
conception during their initial work up Our department uses a stringent
protocol for the assessment of AFC and qualified radiographers who have
undergone specific training on measurement of AFC The methodology
consists of counting of all antral follicles measuring 2-6mm in longitudinal and
transverse cross sections of both ovaries using transvaginal ultrasound
scanning at early follicular phase (Day 0-5) of the menstrual cycle The AFC
measurement with the closest date to first AMH sample was selected
Data collection
Data was extracted from electronic clinical data management systems
and from information held in written hospital notes for each patient Data on
AMH and FSH measurements were obtained from the Biochemistry
Department and validated by checking the results documented in the hospital
case notes of randomly selected 50 patients against the results obtained from
electronic clinical data management system (Clinical Workstation) finding
100 concordance Information on AFC BMI the causes of infertility the
duration of infertility the history of reproductive pathology and reproductive
175
surgery were obtained from the hospital case notes The ethnicity of the
patients was established using a patient questionnaire and data were extracted
from the hospital database for the patient demographics (PAS)
Definitions and groups
First the datasets were merged using a unique patient identifier (hospital
number) Validation of the merger using additional patient identifiers (NHS
number name date of birth) revealed existence of duplicate hospital numbers
in patients transferred from secondary care infertility services of our hospital to
IVF Department We established that in our datasets combination of the
patientrsquos first name surname and date of birth in a continuous string variable
could be used as a unique identifier Hence we used this identifier to merge all
datasets achieving a robust merger of all independent datasets into a combined
final dataset Following creation of an anonymised a unique study number for
each patient all patient identifiers were dropped and the anonymised
combined dataset was used for the analysis
Body mass index (BMI) of patients was categorized using standard NHS
cut-off reference ranges Underweight (lt185) Normal (185-249)
Overweight (25-299) and Obese (30-40) (The Office for National Statistics
2011) Causes of infertility were established by searching the hospital notes
including the referral letters clinical notes and letters generated following clinic
consultations Patients with history of bilateral tubal block which was
confirmed by laparoscopic dye test and patients with history of bilateral
salpingectomy were categorized as having severe tubal factor infertility
Patients with unilateral tubal patency or unilateral salpingectomy were
categorized as having mild tubal factor infertility Severe male factor infertility
was defined as azoospermia or severe oligospermia (lt1mln sperm sample)
Patients with abnormal sperm count but do not meet above criteria were
classified as having mild male factor infertility
Patients with reproductive surgery were categorized as having history of
salpingectomy cystectomy for endometrioma cystectomy for ovarian cysts
other than endometrioma or unilateral salpingo-oopherectomy First
measurement of AMH AFC and FSH following surgery was selected for the
study
176
Statistical analysis
A multivariable regression model that included age ethnicity BMI
endometriosis presence of endometrioma the causes of infertility tubal and
ovarian surgery was fitted for each of the ovarian reserve markers AMH AFC
and FSH Difference between the groups were considered significant at
p005 Preliminary analysis of AMH AFC and FSH indicated that
logarithmically transformed values with a quadratic age term provided adequate
fits The precise age on the day measurement of each of the marker of ovarian
reserve (AMH AFC and FSH) was included in the model as a quadratic
function following centering to 30 years of age
Interactions between all explanatory variables were tested at a
significance level of 001 We observed significant interaction between BMI
and other covariates This may be due to biological complexity in the
relationship of BMI and other factors (eg ethnicity) in determination of
ovarian reserve However given data on BMI was not available in considerable
number of patients the observed interactions may be due to limitation of our
dataset Therefore in order to assist in interpretation of the results analyses
with and without BMI in the models were conducted
RESULTS
In total 3179 patients were included in the study The AMH
measurements of 66 women were excluded due to haemolysed samples or
delay in processing the samples leaving 3113 women for analysis 1934 of
patients had measurement of AFC and 2580 had FSH samples that met
inclusion criteria The mean age AMH AFC and FSH of patients were
328plusmn45 173plusmn148 139plusmn62 80plusmn75 respectively There were 138 women
who had unilateral or bilateral salpingectomy 36 women with history of
unilateral salpingo-oopherectomy 41 women with history of cystectomy for
ovarian cysts that other than endometrioma and 40 women had cystectomy for
endometrioma (Table 1) The results of regression analysis on the effect of
reproductive surgery on AMH AFC and FSH measurements are shown in
Table 2
The analysis did not find any significant differences in AMH (9
p=033) AFC (-2 p=059) and FSH (-14 p=021) measurements in
women with history of salpingectomy compared to women without history of
177
surgery and we did not observe marked change in the estimates in a smaller
subset where BMI was included in the model (Table 2)
Women with history of unilateral salpingo-oopherectomy were found
to have significantly lower AMH (-54 p=0001) and AFC (-28 p=034)
and increased FSH (14 p=006) measurements where effect on AMH
reached the level of statistical significance Similarly the analysis of the model
that included BMI showed significantly lower AMH and AFC and higher FSH
measurements in surgery group where both AMH and FSH analysis were
statistically significant (Table 2)
The study did not find a significant association between previous
history of ovarian cystectomy that was for disease other than endometrioma
and measurement of AMH (7 p=062) AFC (13 p=018) or FSH (11
p=016) which did not change noticeably following adding BMI in the model
(Table 2)
The analysis of the effect of ovarian cystectomy for endometrioma
showed that women with history of surgery had around 66 lower AMH
(p=0002) measurements The effect of surgery for endometrioma was not
significant in assessment of AFC (14 p=022) and FSH (10 p=028)
However in the model with BMI association of the surgery with both AMH (-
64 p=0005) and FSH (24 p=0015) were found to be significant (Table
2)
DISUCUSSION
Salpingectomy
The blood supply to human ovaries is maintained by the direct branches
of aorta ovarian arteries which form anastomoses with ovarian and tubal
branch of uterine arteries in mesovarium and mesosalpynx In salpingectomy
often tubal branches of uterine arteries are excised alongside mesosalpynx and
hence it is believed disruption to blood supply to ovaries may lead to a
reduction of ovarian reserve However in our study we did not observe an
appreciable association between salpingectomy and any of the biomarkers of
ovarian reserve suggesting this surgery does not appreciably affect ovarian
reserve These findings are supported by study that assessed the effect of tubal
178
dissection to AMH AFC FSH levels (n=49) using longitudinal data (Erkan et
al 2012) There were no differences between preoperative and 3 month
postoperative measurements with median AMH (15 vs 14 p=007) AFC
(8437 vs 7941 p=009) FSH (76 21 vs 7721 p=010) da Silva et al
assessed the effect of tubal ligation (n=52) in longer term postoperative period
(1 year) and reported that median AMH (143 IQR 063-262 vs and 130 IQR
053-285 p=023) and mean AFC ( 8 IQR 5-14 vs 11 IQR 7-15 p=012)
measurements did not change significantly Our results and on other published
evidence suggest that salpingectomy or tubal division does not have an
adverse effect to ovarian reserve
Unilateral salpingo-oopherectomy
Although salpingo-oopherectomy is rare in women of reproductive age
significant ovarian pathologies and acute diseases such as ovarian torsion may
necessitate unilateral salpingo-oopherectomy There is a plausible causative
relationship between this surgery and ovarian reserve although to our
knowledge there is no previous published evidence We found that women
with a history of unilateral salpingo-oopherectomy have significantly lower
AMH (-54) and higher FSH (13) measurements suggesting the surgery has
considerable negative impact to ovarian reserve Important clinical question in
this clinical scenario is ldquoDo these patients have comparable reproductive
lifespan or experience accelerated loss of oocytes resulting premature loss of
fertilityrdquo as this would allow appropriate pre-operative counseling of patients
regarding long term effect of the surgery to fertility and age at menopause
Considering our data had relatively small number of patients with a history of
salpingo-oopherectomy we were not able to obtain reliable estimates on age-
related decline of ovarian reserve in this study We suggest that studies with
larger number of patients preferably using longitudinal data should address
this research question
Ovarian cystectomy
In women with a history of ovarian cystectomy for ovarian cysts other
than those due to endometrioma we did not observe any significant
association between the surgery and markers of ovarian reserve However
women that had ovarian cystectomy for endometrioma appear to have
179
significantly lower AMH (-66) measurements compared to those without
history of surgery
During the last few years a number of studies have assessed the effect of
cystectomy on AMH levels in patients with endometrioma (Chang et al 2010
Erkan et al 2010 Lee et al 2011) The studies have been summarised by a
recent systematic review which concluded that cystectomy results in damage
to ovarian reserve (Somigliana et al 2012) Further studies evaluated the
mechanism of damage and these suggest that coagulation for purpose of
hemostasis as well as stripping of the cyst wall may cause direct damage to
ovarian reserve Sonmezer et al compared the effect of diathermy coagulation
(n=15) for hemostasis compared to use of hemostatic matrix (n=13) in a
randomized controlled trial and reported that use of diathermy coagulation is
associated with significantly lower AMH measurements (164 plusmn 093 vs 272 plusmn
149 ngmL) in the first postoperative month
Similarly stripping of the cyst wall also appears to have detrimental
effect of ovarian reserve due to inadvertent removal of ovarian tissue (Donnez
et al 1996) Using histological data Roman et al demonstrated that normal
ovarian tissue was removed in 97 specimens of surgically removed
endometriomata (Roman et al 2010) Furthermore it appears that ovarian
cortex containing endometrioma appears to have significantly reduced density
compared to normal ovarian cortex and therefore loss of oocyte containing
normal ovarian cortex may be unavoidable in cystectomy for endometrioma
(Sanchez et al 2014) Matsuzaki et al conducted histological assessment of
cystectomy specimens and found that normal ovarian tissue adjacent to cyst
wall was found in 58 (71121) of patients with endometrioma whereas
normal ovarian tissue was excised in 54 (356) following cystectomy for
other benign cyst (Matsuzaki et al 2008) Similarly in our study women with a
history of cystectomy for endometrioma had significantly lower AMH
measurements whilst those had cystectomy for other benign cysts do not
appear to have lower AMH measurements In view of our findings and other
published research evidence it seems clear that cystectomy for endometrioma
results in significant reduction in ovarian reserve and women undergoing
surgery should be counseled regarding the adverse effect of surgery
180
Strengths and Limitations
The published studies have used longitudinal data comparing biomarkers
before and after cystectomy and provide reliable estimates on the effect of the
intervention on ovarian reserve However data on the effect of salpingectomy
and unilateral salpingoophorectomy is lacking In addition to reevaluation of
the effect of cystectomy this is study has assessed the impact of salpingectomy
and unilateral salpingoophorectomy on the markers of ovarian reserve In
contrast to published studies this study employed analysis of cross sectional
data Given a robust adjustment for all relevant factors has been conducted
our analysis of the cross sectional data should provide reliable estimates of the
effects of various intervention on the markers of ovarian reserve Furthermore
the effect of surgery on all the main biomarkers of ovarian reserve has been
assessed which improves our understanding of the clinical value of each test in
the assessment of patients with history of tubal or ovarian surgery In addition
the analyses adjusted for other relevant factors that may affect ovarian reserve
In patients with history of cystectomy for endometrioma we estimated
independent effects of pathology and surgery providing important data for
preoperative counseling It is important to note that the study evaluated The
effect of surgery using retrospective data which has limitations due variation in
recording of surgical history and missing data In addition given BMI results
for around one third of patients were not available we were not able to fully
explore the effect of BMI However data on the analyses with and without
BMI in the model have been provided to evaluate the effect of this factor The
study employed the data obtained using first generation DSL AMH assay
which is no longer in use However the paper describes the effects of the
interventions in percentage terms and therefore the results are interpretable in
any AMH assay measurement
Important to note although the effects are significant in population level
there is considerable variation between individuals which is evident from the
fact there is overlap between median and interquartile ranges of the groups
(Figure 1) This indicates that clinicians should exercise caution in predicting
the effect of surgery to ovarian reserve of individual patients Nevertheless
given I used a robust methodology for data extraction and conducted careful
analysis I think that the study provides fairly reliable estimates on the effect of
surgery to ovarian reserve
181
CONCLUSION
This multivariable regression analysis of retrospectively collected cross-
sectional data suggests that neither salpingectomy nor ovarian cystectomy for
cysts other than endometrioma has an appreciable effect on ovarian reserve
determined by AMH AFC and FSH In contrast salpingoophorectomy and
ovarian cystectomy for endometrioma have a significant detrimental impact to
ovarian reserve On the basis of findings of this study and other published
studies women undergoing reproductive should be counseled with regards to
the effect of the surgery on their ovarian reserve
182
References
Biacchiardi CP Piane LD Camanni M Deltetto F Delpiano EM Marchino GL et al Laparoscopic stripping of endometriomas negatively affects ovarian follicular reserve even if performed by experienced surgeons Reprod Biomed Online 201123740ndash6 Chang HJ Han SH Lee JR Jee BC Lee BI Suh CS et al Impact of laparoscopic cystectomy on ovarian reserve serial changes of serum anti-Mullerian hormone levels Fertil Steril 201094343ndash9 Dogan E Ulukus EC Okyay E Ertugrul C Saygili U Koyuncuoglu M Retrospective analysis of follicle loss after laparoscopic excision of endometrioma compared with benign nonendometriotic ovarian cysts Int J Gynaecol Obstet 2011114124ndash7 Ercan CM Sakinci M Duru NK Alanbay I Karasahin KE Baser I (2010) Antimullerian hormone levels after laparoscopic endometrioma stripping surgery Gynecol Endocrinol 201026468ndash72 Ercan CM Duru NK Karasahin KE Coksuer H Dede M Baser I (2011) Ultrasonographic evaluation and anti-mullerian hormone levels after laparoscopic stripping of unilateral endometriomas Eur J Obstet Gynecol Reprod Biol 2011158280ndash4 Hansen KR Hodnett GM Knowlton N Craig LB Correlation of ovarian reserve tests with histologically determined primordial follicle number Fertil Steril 201195170ndash175 Hachisuga T Kawarabayashi T Histopathological analysis of laparoscopically treated ovarian endometriotic cysts with special reference to loss of follicles Hum Reprod 200217432ndash5 Hirokawa W Iwase A Goto M Takikawa S Nagatomo Y Nakahara T et al The post-operative decline in serum anti-Mullerian hormone correlates with the bilaterality and severity of endometriosis Hum Reprod 201126904ndash10 Hwu YM Wu FS Li SH Sun FJ Lin MH Lee RK The impact of endometrioma and laparoscopic cystectomy on serum anti-Mullerian hormone levels Reprod Biol Endocrinol 2011980 Iwase A Hirokawa W Goto M Takikawa S Nagatomo Y Nakahara T et al Serum anti-Mullerian hormone level is a useful marker for evaluating the impact of laparoscopic cystectomy on ovarian reserve Fertil Steril 201094 2846ndash9 Kitajima M Khan KN Hiraki K Inoue T Fujishita A Masuzaki H Changes in serum anti-Mullerian hormone levels may predict damage to residual normal ovarian tissue after laparoscopic surgery for women with ovarian endometrioma Fertil Steril 2011952589ndash91e1 Kitajima M Defr_ere S Dolmans MM Colette S Squifflet J van
183
Langendonckt A et al Endometriomas as a possible cause of reduced ovarian reserve in women with endometriosis Fertil Steril 201196685ndash91 Lee DY Young Kim N Jae Kim M Yoon BK Choi D Effects of laparoscopic surgery on serum anti-Meuroullerian hormone levels in reproductive-aged women with endometrioma Gynecol Endocrinol 201127733ndash6 Matsouzaki S Houlle C Darcha S Pouly JL Mage G Canis M Analysis of risk factors for the removal of normal ovarian tissue during laparoscopic cystectomy for ovarian endometriosis Hum Reprod 2009 241402ndash1406 Muzii L Bianchi A Croc_e C Manci N Panici PB Laparoscopic excision of ovarian cysts is the stripping technique a tissue-sparing procedure Fertil Steril 200277609ndash14 Office for National Statistics (ONS) Social Trends 41 Health 2011 Roman H Tarta O Pura I Opris I Bourdel N Marpeau L et al Direct proportional relationship between endometrioma size and ovarian parenchyma inadvertently removed during cystectomy and its implication on the management of enlarged endometriomas Hum Reprod 201025 1428ndash32 Romualdi D Franco Zannoni G Lanzone A Selvaggi L Tagliaferri V Gaetano Vellone V et al Follicular loss in endoscopic surgery for ovarian endometriosis quantitative and qualitative observations Fertil Steril 201196374ndash8
13 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012 273085-3091
14 Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG Pemberton PW Reply reproducibility of AMH Hum Reprod 2012273641-3642 Sanchez A P Viganograve P Somigliana E Panina-Bordignon P Vercellini and Candiani M The distinguishing cellular and molecular features of the endometriotic ovarian cyst from pathophysiology to the potential endometrioma-mediated damage to the ovary Hum Reprod Update (MarchApril 2014)
Shi J Leng J Cui Q Lang J Follicle loss after laparoscopic treatment of ovarian endometriotic cysts Int J Gynaecol Obstet 2011115277ndash81 Tsolakidis D Pados G Vavilis D Athanatos D Tsalikis T Giannakou A et al The impact on ovarian reserve after laparoscopic ovarian cystectomy versus three-stage management in patients with endometriomas a prospective randomized study Fertil Steril 20109471ndash7 Vicino M Scioscia M Resta L Marzullo A Ceci O Selvaggi LE Fibrotic tissue in the endometrioma capsule surgical and physiopathologic considerations from histologic findings Fertil Steril 200991(4 Suppl)1326ndash8
184
Figure 1 Box plots of AMH by various groups Upper panel shows the raw data and the lower panel the AMH measurement (in pmolL) adjusted for age ethnicity BMI causes of infertility endometriosis endometrioma and surgery Groups (left to right) 1) Endometrioma without history of cystectomy (endoma-no surg) 2) Cystectomy for endometrioma (endoma+surg) 3) Endometriosis without endometrioma (endsisonly) 4) Without endometriosis or any surgery (No end+no surg) 5) Oopherectomy (oe) 6) Cystectomy for cyst other than those for endometrioma (other cyst) 7) Salpingectomy (se)
185
Table1 Distribution of patients
BMI excluded
BMI Included
Age AMH AFC FSH AMH AFC
FSH
Mean (SD) N Mean n Mean (SD) N Mean (SD) n n N
Non-surgery 328plusmn45 2880 175plusmn150 18100 139plusmn63 23770 79plusmn72 1976 15830 17880
Oophorectomy 324plusmn50 36 106plusmn84 2 115plusmn77 34 118plusmn230 25 2 23
Salpingectomy 331plusmn42 138 154plusmn119 91 13plusmn43 122 82plusmn 123 121 84 27
Cystectomy Other 336plusmn42 41 168plusmn132 18 148plusmn50 29 122plusmn249 27 15 20
Cystectomy Endometrioma
327plusmn51 40 119plusmn140 17 137plusmn41 37 89plusmn56 23 10 22
186
Table 2 Multivariable regression analysis Adjusted for age ethnicity causes of infertility endometriosis (without endometrioma) endometrioma and reproductive surgery
BMI(+)
BMI(-)
N
Coeff
95 CI
P
N
Coeff
95 CI
P
Oophorectomy
AMH 2128 -0779 -1135 -0422 00005 3049 -0540 -0868 -0213 0001
AFC 1697 -0278 -0848 0292 0340 1946 -0280 -0857 0298 0342
FSH 1929 0266 0110 0422 0001 2546 0139 -0006 0284 0060
Salpingectomy
AMH 2128 0067 -0118 0252 0476 2128 0094 -0097 0285 0333
AFC 1697 -0027 -0128 0075 0605 1697 -0027 -0126 0072 0595
FSH 1929 -0085 -0167 -0004 0041 1929 -0056 -0143 0032 0210
Cystectomy Other
AMH 2128 0102 -0230 0433 0548 2128 0075 -0226 0376 0626
AFC 1697 0102 -0107 0311 0339 1697 0130 -0064 0323 0189
FSH 1929 0134 -0028 0297 0106 1929 0110 -0044 0265 0161
Cystectomy Endometrioma
AMH 2128 -0647 -1100 -0194 0005 2128 -0667 -1081 -0252 0002
AFC 1697 0115 -0172 0402 0433 1697 0144 -0089 0376 0225
FSH 1929 0243 0047 0439 0015 1929 0103 -0084 0290 0281
187
ASSESSMENT OF DETERMINANTS OF OOCYTE
NUMBER USING RETROSPECTIVE DATA ON
IVF CYCLES AND EXPLORATIVE STUDY OF
THE POTENTIAL FOR OPTIMIZATION OF AMH-
TAILORED STRATIFICATION OF CONTROLLED
OVARIAN HYPERSTIMULATION
Oybek Rustamov
Cheryl Fitzgerald Stephen A Roberts
6
188
Title
Assessment of determinants of oocyte number using large retrospective
data on IVF cycles and explorative study of the potential for
optimization of AMH-tailored stratification of controlled ovarian
stimulation
Authors
Oybek Rustamova Cheryl Fitzgeralda Stephen A Robertsc
Institutions
a Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester
Academic Health Science Centre (MAHSC) Manchester M13 0JH UK
b Centre for Biostatistics Institute of Population Health Manchester
Academic Health Science Centre (MAHSC) University of Manchester
Manchester M13 9PL UK
Word count 7520
Grants or fellowships No funding was sought for this study
Disclosure summary There were no potential conflicts of interest
Clinical Trial registration number Not applicable
Acknowledgement
Authors would like to thank Dr Monica Krishnan (Foundation Trainee
Manchester Royal Infirmary) for her assistance in data extraction We would
also like to thank colleagues Dr Greg Horne (Senior Clinical Embryologist)
Ann Hinchliffe (Clinical Biochemistry Department) and Helen Shackleton
(Information Operations Manager) for their help in obtaining datasets for the
study
189
Declaration of authorsrsquo roles
OR prepared the study protocol prepared the dataset conducted statistical
analysis and prepared all versions of the manuscript SR and CF oversaw and
supervised preparation of dataset statistical analysis contributed to the
discussion and reviewed all versions of the manuscript
190
ABSTRACT
Objectives
1) To determine the effect of age AMH AFC causes of infertility and
treatment interventions on oocyte yield
2) To explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Design
Retrospective cross sectional study using multivariable regression analysis
First the effect of a set of plausible factors that may affect the outcomes have
been established including assessment of the effect of age AMH AFC causes
of infertility attempt of IVFICSI cycle COH protocol changes
gonadotrophin preparations operator for oocyte recovery pituitary
desensitisation regime and initial daily dose of gonadotrophins Then the
regression models that examined the effect of gonadotrophin dose and regime
categories on total and mature oocyte numbers have been developed
Setting
Tertiary referral centre for management of infertility St Maryrsquos Hospital
Central Manchester University Hospitals NHS Foundation Trust
Participants
Women without ultrasound features of polycystic ovaries who underwent
IVFICSI cycle using pituitary desensitisation with GnRH long agonist or
GnRH antagonist regimes and had previous measurement of AMH with the
DSL assay In total of 1847 IVF or ICSI cycles of 1428 patients met the
inclusion criteria for the study AMH measurements of all cycles and AFC
measurements for 1671 cycles (n=1289 patients) were available In the analysis
of total oocytes 1653 cycles were included and the analysis of metaphase II
oocytes comprised of 1101 ICSI cycles
Interventions
None (observational study)
191
Main outcome measures
Total oocyte number Metaphase II oocyte number
Results
After adjustment for all the above factors age remained a negative predictor of
oocyte yield whereas we observed a gradual and significant increase in oocyte
number with increasing AMH and AFC values suggesting all these markers
display an independent association with oocyte yield
Compared to 1st IVF cycles those with 2nd (8 p=001) and particularly 3rd
attempt (24 p=0001) had considerably higher total oocytes The effect of
attempt on mature oocyte yield was not significant (p=045) Similarly there
was significant between-operator variability in total oocyte number when
oocyte recovery practitioners were compared (p=00005) However the effect
of oocyte recovery practitioner on mature oocyte yield did not reach statistical
significance (p=0058) Comparison of the effect of gonadotrophin type
showed that rFSH was associated with higher total oocyte yield compared to
that of HMG (p=0008) although the numbers of mature oocytes were not
significantly different between the groups (p=026)
After adjustment for all above factors compared to a reference group (Agonist
with 75-150 IU hMGrFSH) none of the regime and dose categories provided
higher total oocyte yield and Antagonist with 75-150 IU hMGrFSH (-36
p=00005) provided significantly less total oocyte With regards to the mature
oocyte yield Antagonist with 187-250 IU rFSHhMG (43 p=005) and
Antagonist 375 IU rFSHhMG (47 p=002) were associated with
significantly higher oocyte number compared to that of above reference group
This implies that compared to long Agonist down regulation Antagonist
regime is associated with higher mature oocyte yield
Following adjustment for all above variables we did not observe significant
increase in oocyte number with increasing gonadotrophin dose categories
192
Conclusions
Given there was no expected increase in oocyte number with increasing
gonadotrophin dose categories we believe there may not be significant direct
dose-response effect Consequently strict protocols for tailoring the initial
dose of gonadotrophins may not necessarily improve ovarian performance in
IVF treatment It is important to note our COS protocols instructed the use
of cycle monitoring with ultrasound follicle tracking and oestradiol levels and
corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
Key Words
Ovarian reserve AMH AFC IVF Controlled ovarian stimulation AMH-
tailored ovarian stimulation Individualisation of ovarian stimulation
193
INTRODUCTION
According to the HFEA around 12 of IVF cycles in the UK are
cancelled due to poor or excessive ovarian response in the UK which
highlights the importance of the provision of optimal ovarian stimulation in
improving the outcomes (Kurinczuk et al 2010) Traditionally patientrsquos age and
basal FSH measurements were used for the assessment of ovarian reserve with
subsequent tailoring of the initial dose of gonadotrophins and regime for
pituitary desensitisation for controlled ovarian stimulation in IVF Studies on
the prognostic value of markers of ovarian reserve show that AMH and AFC
are the best predictors of ovarian response in cycles of IVF (Broer et al 2011)
Furthermore unlike most other markers AMH has potential discriminatory
power due to significantly higher between-patient (CV 94) variability
compared to its within-patient (CV 28) variation (Rustamov et al 2011)
which allows stratification of patients into various degrees of (eg low normal
high) ovarian reserve Consequently development of optimal ovarian
stimulation protocol for each band of ovarian reserve using AMH may be
feasible
Controlled ovarian stimulation (COS) based on tailoring the pituitary
desensitisation and initial dose of gonadotrophins to AMH measurements is
known under various names individualisation of ovarian stimulation AMH-
tailored stratification of COS personalization of IVF are the most commonly
used This strategy is believed to be effective and has been widely
recommended (Nelson et al 2013 Dewailly et al 2014 La Marca et al 2014)
Although AMH based assessment of ovarian reserve with pituitary down
regulation in patients with extremes of ovarian reserve may improve the
outcomes of ovarian response compared to conventional ovarian stimulation
protocols (Nelson et al 2009 Yates et al 2011) there is no robust data on
AMH-tailored individualisation of ovarian stimulation To establish
individualisation of ovarian stimulation the studies should ideally assess
various pituitary desensitisation regimes and initial doses of gonadotrophins in
patients across the full range of ovarian reserve For instance in AMH-tailored
individualisation of pituitary desensitisation regime studies should evaluate the
effect of both GnRH Agonist and GnRH Antagonist regimes for the groups
for each band of AMH levels (eg low normal high) necessitating 6
comparison groups (Figure 1) In individualisation of the initial dose of
194
gonadotrophins the groups of each band of AMH should be treated with the
range of doses of gonadotrophins (eg low moderate high dose) which
requires 9 treatment groups (Figure 2) Consequently to evaluate the
individualisation of both the stimulation regime and the initial dose of
gonadotrophin across the full range of AMH measurements in a single study
ideally 18 comparison groups are needed Indeed the study should have a large
enough sample to adjust for the confounders and obtain sufficient power for
the estimates of each treatment group In addition assessment of ovarian
reserve should be based on reliable AMH measurements with minimal sample-
to-sample variation which appears to be an issue at present (Rustamov et al
2013) Finally evidence on AMH-tailored individualisation of ovarian
stimulation should ideally be based on randomized controlled trials given in
this context AMH is being used as a therapeutic intervention At present there
is no single RCT that assessed AMH-tailored individualisation of ovarian
stimulation and most quoted research evidence appear to have been based on
two retrospective studies (Nelson et al 2009 Yates et al 2011) Both studies
display a number of methodological issues including small sample size and
centre-dependent or time-dependent selection of cohorts Therefore the role
of confounding factors on the obtained estimates of these studies is unclear
The first study on AMH-tailored individualisation ovarian stimulation
compared outcomes of the cohorts who had IVF cycles in two different IVF
centers (Nelson et al 2009) In this case control study the patients in the 1st
centre (n=370) had minimal tailoring of dose of gonadotrophins and were
offered mainly GnRH agonist regime for pituitary desensitisation except
patients with very low AMH (lt10pmolL) who had GnRH antagonist regime
In patients undergoing treatment in the 2nd centre (n=168) the daily dose of
the gonadotrophins was tailored on the basis of AMH levels and GnRH
antagonist based protocol employed for women with low (1-5 pmolL) and
high (gt15 pmolL) AMH levels whereas patients with normal (5-15 pmolL)
AMH levels had standard long GnRH agonist regimen In addition the
patients with very low AMH (lt10 pmolL) had modified natural cycle IVF
treatment in 2nd centre The study reported that the group that had significant
tailoring of both mode and degree of stimulation to AMH levels (2nd centre)
had higher pregnancy rate and less cycle cancellation However given the
methodological weaknesses the findings of the study ought to be interpreted
with caution First the study compared the outcomes of small number of
195
patients who had treatment in two different centers suggesting that differences
in the outcomes may be due to variation in the characteristics of patient
populations andor performance of two different centers Moreover both
cohorts had some degree of tailoring of pituitary desensitisation regimens as
well as the daily dose of gonadotrophins to AMH levels suggesting estimation
of the effect of AMH tailoring to the outcome of treatment may not be
reliable
A subsequent study attempted to address the above issues by assessing a
somewhat larger number of IVF cycles from the same fertility centre (Yates et
al 2011) The study compared IVF outcomes of the cohorts that underwent
ovarian stimulation using chronological age and serum FSH (n=346) with
women that had AMH-tailored (n=423) treatment cycles (Yates et al 2011)
The study found that the group that had AMH-tailored ovarian stimulation
had significantly higher pregnancy rate less cycle cancellation due to poor or
excessive ovarian response and had significantly lower treatment costs
However this study also has appreciable weaknesses given that it was based
on retrospective data that compared outcomes of treatment cycles that took
place over two year period During this period apart from introduction of
AMH-tailored stimulation protocols other new interventions were introduced
particularly in the steps involved in embryo culture Although the outcomes of
the ovarian response to stimulation could have mainly been due to
performance of the stimulation protocols downstream outcomes such as
clinical pregnancy rate may be associated with the introduction of new
interventions in embryo culture techniques Nevertheless the study
demonstrated that tailoring of ovarian stimulation protocol to AMH levels
could reduce the incidence of cycle cancellation OHSS and the cost of
treatment supporting the need for more robust studies on the use of AMH in
the individualisation of ovarian stimulation in IVF
It appears despite a lack of good quality evidence that AMH-tailored
individualisation has been widely advocated and has been introduced in clinical
practice in a number of fertility units In the absence of good quality evidence
we decided to obtain more reliable estimates on the feasibility of AMH-tailored
ovarian stimulation using more robust methodology Availability of the data on
a large cohort of patients with AMH measurements who subsequently
underwent IVF treatment cycles in a single centre may allow us to obtain more
reliable estimates on the effectiveness of AMH-tailored COS Furthermore due
196
to changes on COS protocol combination of various regime and initial dose of
gonadotrophin were used for patients in each band of ovarian reserve This
may facilitate development of predictive models for both regime and dose for
the whole range of AMH measurements In addition as a part of the study we
decided to establish the role of patient and treatment related factors in
determination of ovarian response in cycle of IVF I believe that
understanding the effect of various factors on ovarian performance in COS
will improve the methodology of the study and can be used as a guide for
identification of confounders in future studies The first step in such an
analysis is to develop a statistical model to describe the relationship between
ovarian response and patient and treatment factors This can then be utilized
to explore the effects of treatment on outcome and potentially to allow optimal
treatments to be identified for given patient characteristics and ovarian reserve
METHODS
Objective
The objectives of the study were 1) to determine the effect of age AMH
AFC causes of infertility and treatment interventions on oocyte yield and 2) to
explore potential for optimization of AMH-tailored individualisation of
ovarian stimulation
Population
Women of 21-43 years of age undergoing ovarian stimulation for
IVFICSI treatment using their own eggs at the Reproductive Medicine
Department of St Maryrsquos Hospital Manchester from 1st October 2008 to 8th
August 2012 were included Patients with previous AMH measurements using
DSL assay were included and patients that had AMH measurement with only
Gen II assay were excluded given the observed issues with this assay
(Rustamov et al 2012) The patients with ultrasound features of PCO previous
history of salpingectomy ovarian cystectomy andor unilateral
salpingoophorectomy have been excluded from the analysis Similarly cycles
with ovarian stimulation other than GnRH agonist long down regulation or
Short GnRH antagonist cycles were not included in the study
197
Dataset
The dataset for the study was prepared using a protocol for the data
extraction management linking and validation which is described in Chapter
4 In short first the data contained in clinical data management systems were
obtained on patient demography AMH measurements and IVF treatment
cycles Then data not available in electronic format were collected from the
patient case notes which includes causes of infertility previous history of
reproductive surgery AFC and folliculogram for monitoring of ovarian
stimulation Each dataset was downloaded in original Excel format into Stata
12 Data Management and Statistics Software (StataCorp LP Texas USA) and
analysis datasets were prepared in Stata format All IVF cycles commenced
during the study period were identified and the combined study dataset was
created by linking all datasets in cycle level using the anonymised patient
identifiers and the dates of interventions All steps of data handling have been
recorded using Stata Do files to ensure reproducibility and provide a record of
the data management process
Categorization of diagnosis
Patients with history of unilateral tubal occlusion or unilateral
salpingectomy were categorized as mild tubal factor infertility and patients with
blocked tubes bilaterally or with history of bilateral salpingectomy were
allocated to severe tubal disease Severe male factor infertility was defined if
the partner had azoospermia surgical sperm extraction or severe oligospermia
which necessitated Multiple Ejaculation Resuspension and Centrifugation test
(MERC) for assisted conception Mild male factor was defined as abnormal
sperm count that do not above meet criteria for severe male infertility
Diagnosis of endometriosis was based on a previous history of endometriosis
confirmed using Laparoscopy Diagnosis of endometrioma was established
using transvaginal ultrasound scan prior to IVF treatment In couples without a
definite cause for infertility following investigation the diagnosis was
categorized as unexplained Women with features of polycystic ovaries on
transvaginal ultrasound were categorized as PCO and excluded from analyses
198
Measurement of AMH and AFC
AMH measurements were performed by the in-house laboratory Clinical
Assay Laboratory of Central Manchester NHS Foundation Trust and the
procedure for sample handling and analysis was based on the manufacturerrsquos
recommendations Venous blood samples were taken without regard to the day
of womenrsquos menstrual cycle and serum samples were separated within two
hours of venipuncture Samples were frozen at -20C until analysed in batches
using the enzymatically amplified two-site immunoassay (DSL Active
MISAMH ELISA Diagnostic Systems Laboratories Webster Texas) The
intra-assay coefficient of variation (CV) (n=16) was 39 (at 10pmoll) and
29 (at 56pmoll) The inter-assay CV (n=60) was 47 (at 10pmoll) and
49 (at 56pmoll) Haemolysed samples were not included in the study In
patients with repeated AMH the measurement closest to their IVF treatment
cycle was selected The working range of the assay was up to 100pmolL and a
minimum detection limit was 063pmolLThe results with minimum detection
limit were coded as 50 of the minimum detection limit (031 pmolL) and
the test results that are higher than the assay ranges were coded as 150 of the
maximum range (150 pmolL)
In our department the measurement of AFC is conducted as part of
initial clinical investigation before first consultation with clinicians and prior to
IVF cycle Qualified radiographers performed the assessment of AFC during
early follicular phase (Day 0-5) of menstrual cycle The methodology of
measurement of AFC consisted of the counting of all antral follicles measuring
2-6mm in longitudinal and transverse cross sections of both ovaries using
transvaginal ultrasound scan The AFC closest to the IVF cycle was selected
for the analysis
Description of COS Protocols
On the basis of their AMH measurement patients were stratified into
the treatment bands for ovarian stimulation using COS protocols During the
study two different COS protocols were used in our centre and in addition
three minor modifications were made in the 2nd protocol Time periods AMH
bands down regulation regimes initial dose of gonadotrophins and adjustment
of daily dose of gonadotrophins of the protocols are described in Table 1
Similarly the management of excessive ovarian response was tailored to
199
pretreatment AMH measurements although mainly based on the results of
oestradiol and scan monitoring the cycle stimulation (Table 2) Assessment of
transvaginal ultrasound guided follicle tracking and serum oestradiol levels in
specific days of the stimulation were used for monitoring of COS (Table 2)
The criteria for the cycle cancellation for poor ovarian response were same
across all protocols fewer than 3 follicles gt15mm in size on Day 10 of ovarian
stimulation
In patients undergoing their first IVF cycle AMH measurement
obtained at the initial assessment was used for determination of which band of
COS the patient would be allocated In the patients with repeated IVF cycles
AMH measurements were obtained prior to each IVF cycle unless a last
measurement performed within 12 months of period was available During the
study period two different assay methods for measurement of AMH was used
in our centre DSL Assay (1 October 2008- 16 November 2010) and Gen II
Assay (17 November 2010- 8 August 2012) Correspondingly during the study
period two different COS Protocols were used 1st Protocol (1 October 2008-
31 December 2010) and 2nd Protocol (1 January 2011-8 August 2012)
Consequently allocation into the ovarian reserve bands of the patients of 1st
protocol were based on DSL assay samples whereas the stratification of
patients of 2nd protocol was based either on DSL assay or Gen II assay
samples Specifically the patients with recent DSL measurements (lt12 months
old) who had IVF treatment during the period of 2nd Protocol had
stratification on the basis of their DSL measurements In these patients in
order to obtain equivalent Gen II value the DSL result was multiplied by 14
in accordance with the manufacturerrsquos recommendation at the time In the
patients without previous or recent (lt12 months old) DSL measurements
stratification into ovarian reserve bands was achieved using their most recent
Gen II measurements Therefore DSL measurements presented in this study
may or may not have been used for formulation of the treatment strategies for
individual patients In fact in this study DSL measurements have been
included in order to understand the role of AMH in determination of ovarian
response in IVF cycles rather than an evaluation of AMH-tailored COS
protocols In addition to introduction of 2nd protocol further modifications
were made to the protocol and therefore 2nd protocol comprised of 4 different
versions (Table 1-2) These changes in the protocols allowed us to compare the
effect of the various modifications to COS protocols on oocyte yield
200
Pituitary desensitisation regimes
Selection of pituitary desensitisation regime was based on the patientrsquos
AMH according to the COH protocol at the time of commencement of IVF
cycle (Table 1) Long agonist regime involved daily subcutaneous injection of
250g or 500 g of the GnRH agonist Buseralin acetate (Supercur Sanofi
Aventis Ltd Surrey UK) from the mid-luteal phase (Day 21) of preceding
menstrual cycle which continued throughout ovarian stimulation Women
treated with Antagonist regime had daily subcutaneous administration of
GnRH antagonist Ganirelex (Orgalutran Organon Laboratories Ltd
Cambridge UK) from Day 4 post-stimulation until the day of HCGGnRH
agonist trigger Ovarian stimulation was achieved by injection of daily dose of
hMG Menopuir (Ferring Pharmaceuticals UK) or rFSH Gonal F (Merck
Serono) as per AMH-tailored protocols (Table 1) Oocyte maturation was
triggered using 5000 international units of HCG (Pregnyl Organon
Laboratories Ltd Cambridge UK) and the criteria for timing of HCG
injection was consistent across all protocols one (or more) leading follicle
measuring gt18mm and two (or more) follicle gt17mm
Oocyte collection
Oocyte collection was conducted 34-36 hours following injection of
HCG for follicle maturation An Ultrasound Guided Oocyte Recovery (USOR)
was conducted by experienced clinicians under sedation The names of
practitioners were anonymised and the practitioner with the largest number of
oocyte recovery was categorized as a reference group Practitioners with a
small number (lt10) of oocyte collection were pooled (group J) If the cycle
was cancelled before oocyte recovery it was categorized under the practitioner
who was on-call for oocyte recovery session on the day of cycle cancellation
In cycles with pre-USOR cancellation for excessive ovarian response
total oocyte number was coded as 27 and Metaphase II oocyte number was
coded as 19 This was based on mean oocyte number in the patients who had
post-USOR cancellation for excessive ovarian response or OHSS
Quantitative assessment of total oocytes were conducted immediately
post-USOR by an embryologist In patients undergoing ICSI the assessment
of the quality of oocytes were conducted 4-6 hours post-USOR and the
201
oocytes assessed as in Metaphase II stage (MII) of maturation were categorized
as mature oocytes
Statistical analysis
The total number of collected oocytes in all cycles and the number of
mature oocytes in the subset of ICSI cycles were used as outcome measures
for the study Oocyte was selected as the primary outcome measure for
assessment of ovarian performance as this provides an objective measure
which is largely determined by effectiveness of ovarian stimulation regimens
In contrast downstream measures such as clinical pregnancy and live birth are
influenced by factors related to management gametes and embryos
Statistical analysis was conducted using multivariable regression models
and the process of model building included following steps 1) Analyses of
distribution of the groups and variables 2) Univariate analysis to establish the
factors that likely to affect total oocyte number 3) Evaluation of
representation of continuous variables 4) Analysis of interaction between
explanatory variables 5) Sensitivity analysis
First the distribution of patients the ovarian reserve markers
interventions and the outcomes were explored using cross tabulation
histograms Box Whisker and scatter plots Then in order to establish the
factors that likely to affect the oocyte number univariate analyses of Age
AMH AFC PCO status attempt of IVFICSI ethnicity BMI protocol
regime USOR practitioner and initial dose of gonadotrophins were conducted
Following this all these explanatory variables were run as part of initial
multivariable regression model Adjustment for confounders related to the
modifications of the protocols and unknown time-dependent changes
conducted by inclusion of the COS protocol categories in the regression
model
Evaluation of representation of oocyte number Age AMH AFC initial
dose of gonadotrophins were conducted by establishing best fit on the basis of
Akaike and Bayesian Information Criteria In addition interpretability of the
data and clinical applicability of the results (eg cut off ranges) were used as a
guide for selection of optimal representation Given the oocyte number was
not normally distributed it was represented in logarithmic scale (log(oocyte
number+5) To establish best representation for AMH AFC and initial dose
202
the models in following scales were run for each variable Linear quadratic
cubic 4th order polynomial linear (log) quadratic (log) cubic (log) 4th order
polynomial (log) cut-off ranges according to distribution Age adjustment in
quadratic scale following centering it to 30 years of age was found to provide
the most parsimonious representation AMH was found to be best represented
using following cut-off ranges 0-3 4-5 6-8 9-10 11-12 13-15 16-18 19-22
23-28 and 29-200 The best representation for AFC was found to be cut-off
ranges of 0-7 8-910-1112-14 15-19 20-24 and 25-100 Initial dose of
gonadotrophins were categorized as following 75-150IU 187-250IU 300IU
375IU 450IU
Subsequently interactions between explanatory variables were tested at
significance level of plt001 which revealed there were significant interaction
between PCO status and other covariables Given these interactions were
found to be complex and not easily computable we decided to restrict the
regression analysis to the non-PCO group We observed significant interaction
between regime and initial dose and therefore these variables were fitted with
interaction term in the model Finally sensitivity analyses of final regression
models were conducted Significance of the results was interpreted using p
value (lt005) effect size and clinical significance For assessment of feasibility
of individualization of stimulation regime and initial dose visual representation
of data was achieved using plots for observed and fitted values (Figure 1-4)
RESULTS
Description of data
A total of 1847 IVF or ICSI cycles of 1428 patients met inclusion criteria for
the study AMH measurements of all cycles and AFC measurements for 1671
cycles (n=1289 patients) were available In the analysis of total oocytes 1653
cycles were included and the analysis of MII oocytes comprised of 1101 ICSI
cycles
Mean AMH was found to be 178 (125) mean AFC was 142 56
mean number of total oocytes was 101 64 and mean number of mature
oocytes was 74 53 The distribution of the cycles according to patient
characteristics and interventions is shown in Tables 3
203
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
Table 4a and 4b show that there was a significant negative association of
oocyte yield with age and oocyte number following adjustment for AMH
AFC causes of infertility attempt of IVFICSI cycle USOR practitioner COS
protocol pituitary desensitisation regime type of gonadotrophin preparation
and initial daily dose of gonadotrophins (Table 4a) With each increase of age
by 1 year we observed approximately a 3 reduction in total oocyte
(p=00005) and a 2 decrease in mature oocyte number (p=0006) which was
independent of age and other covariables
In the analysis of AMH there was significant gradual increase in total
oocyte as well as mature oocyte number with increasing AMH following
adjustment for all covariables (Figure 1 and 2) Compared to an AMH range of
0-3 pmolL there was increase of 25 in the range of 4-5 pmolL (p=007)
36 in 6-8 pmolL (p=0008) 60 in 9-10 pmolL (p=00005) 65 in 11-12
pmolL (p=00005) 77 in 13-15 pmolL (p=00005) 83 in 16-18 pmolL
(p=00005) 80 in 19-22 pmolL (p=00005) 95 in 23-28 pmolL
(p=00005) and 112 in the range of 29-150 pmolL (p=00005) in total
oocyte number (Table 4a) Similar but less marked increase in MII oocyte
number was observed with increasing AMH
The data on AFC also showed that there was gradual increase in total
oocyte number with increasing AFC following adjustment of all covariables
(Table 4a) Compared to an AFC of 0-7 there was increase of 14 in the
range of 10-11 (p=003) 22 in AFC of 12-14 (p=0001) 26 in AFC of 15-
19 (p=00005) 34 in AFC of 20-24 (p=00005) and 40 in AFC of gt25
(p=0005) However there was no increase in total oocyte number in AFC
range of 8-9 compared to that of 0-7 AFC-related Increase in MII oocytes was
less marked compared to that of total oocytes (Table 4a)
Causes of infertility
We did not observe any significant associations between the causes of
infertility and number of retrieved oocytes However women diagnosed with
unexplained infertility appear to have marginally higher (10 p=002) total
number of oocytes compared to women whose causes of infertility were
204
known Diagnosis of severe tubal (-37 p=019) and severe male (-37
p=035) factor infertility was found to be associated with lower number of MII
oocytes compared to other causes of infertility However neither of these
parameters reached statistical significance Similarly there was no significant
association between oocyte number and diagnosis of endometriosis with or
without endometriomata compared to women that were not diagnosed with
the disease (Table 4a)
Attempt
Analysis of total number of oocytes showed that women who had their
2nd attempt of IVFICSI cycle had slightly higher (85 p=001) and those
that had their 3rd or 4th attempt of treatment had significantly higher total
oocyte yield (24 p=0001) compared to women undergoing their 1st attempt
of IVFICSI cycle (Table 4a) Similarly overall effect of attempt on total
oocyte yield was significant (p=0001)
However we did not observe any association between the attempt and
MII oocyte number in the analysis of the subset of ICSI cycles (p=045)
USOR practitioner COS protocol and gonadotrophin preparation
There was a significant association (p=00005) between total oocyte yield
with USOR practitioner (Table 4b) However the association of USOR
practitioner with MII oocyte number did not reach statistical significance
(p=0058)
We observed significant association between the COS protocols in the
analysis of total number of oocytes 1st version of 2nd Protocol (-18
p=00005) 2nd amp 3rd versions of 2nd Protocol (-14 p=005) and 4th version of
2nd Protocol (-24 p=0009) provided significantly lower number of total
oocytes compared to 1st Protocol However the effect of the COS Protocol
changes to MII oocyte number was not significant (p=024)
Compared to hMG ovarian stimulation using rFSH provided 13
higher total oocytes (p=0008) In the analysis of Metaphase II oocytes there
was no significant difference in oocyte yield between hMG and rFSH (026)
205
Regime and Initial dose of gonadotrophins
The regression analyses of the regimes for pituitary desensitisation and
initial dose categories were conducted in comparison to the reference group
(Agonist with 75-150IU hMGrFSH) IVFICSI cycles where Antagonist
with 75-100IU of hMGrFSH (-36 p=00005) was used provided
significantly lower total oocyte yield whereas cycles with Agonist and 300IU
hMGrFSH (15 p=005) provided marginally higher total oocyte number
In the analysis of MII oocytes cycles using Antagonist with 187-250IU
of hMGrFSH (43 p=005) Agonist with 300IU of hMGrFSH (25
p=016) and Antagonist with 375IU hMGrFSH (47 p=002) yielded higher
number of oocytes Use of Agonist with 375IU hMGrFSH (-18 p=05) and
Agonist with 450IU of hMGrFSH (-28 p=02) was associated with lower
mature oocyte number although the analysis did not reach statistical
significance
AMH-tailored individualization of COS
The overall effect of initial gonadotrophin dose to total oocyte yield
was found to be significant (plt0001) However other than the lowest dose
category with Antagonist regime the analysis did not show any consistent
dose-response effect on total oocyte number with increasing gonadotrophin
dose (Table 4b Figure 3a Figure 3b Figure 4a and Figure 4b)
In the analysis of MII compared to reference group of 75-150 IU of
initial daily gonadotrophins we observed increased oocyte yield in the
categories of 187-250 IU (43 p=005) and 375 IU (47 p=002) of
gonadotrophins However both of these groups had Antagonist regime for
pituitary desensitisation compared to that of Agonist in the reference group
and therefore the observed effect may be related to the regime of COS rather
than daily dose of gonadotrophins
206
DISCUSSION
In this study we explored the effect of age AMH AFC causes of
infertility attempt of IVF ICSI treatment and interventions of COS on
ovarian performance using a retrospective data on large cohort of IVF ICSI
cycles of non-PCO patients To our knowledge this is largest study to have
conducted a detailed analysis of the effect of AMH and AFC on ovarian
performance in IVFICSI cycles The study utilized a dataset that was
prepared using a robust protocol for data extraction and handling Similarly
the statistical analysis was based on a systematic exploration of the effect of all
relevant factors followed by adjustment for all relevant factors and finally
careful analysis
With regards to the outcome measures the quantitative response of
ovaries were measured using total collected oocytes in IVFICSI cycles and
the MII oocyte number in the subset of ICSI cycles were used as a
measurement of quantitative response of ovaries to COS Arguably oocyte
number is the best outcome measure for determination of ovarian response to
COS given it is mainly determined by patientrsquos true ovarian reserve the quality
of assessment of ovarian reserve and treatment strategies for ovarian
stimulation In contrast downstream outcomes such as clinical pregnancy and
live birth are subject to additional clinical and interventional factors which may
not always be possible to adjust for using retrospective data Indeed large
observational studies suggest that achieving optimal ovarian response is one of
the most important determinants of success of IVFICSI cycles and
recommend to use oocyte number as a surrogate marker for live birth (Sunkara
et al 2011) It appears around 10-15 total oocytes or 3-4 mature oocytes
provide optimal chance for a one live birth in IVFICSI cycles (Sunkara et al
2011 Stoop et al 2012) Therefore oocyte number appears to be most useful
marker for assessment of ovarian response to COS as well as in prediction of
live birth in cycles of IVFICSI
207
Effect of patient and treatment related factors on oocyte yield
Age AMH AFC
After adjusting for AMH AFC the patient characteristics and above
mentioned treatment interventions age remained as an independent predictor
of ovarian response to COS Our data showed approximately 3 (p=00005)
decrease in total oocyte and 2 (p=0006) reduction in mature oocyte number
with increase of age by factor of 1 year (Figure 3b and Figure 4b)
Interestingly the effect of AMH was also found to predict oocyte yield
independently of age with an effect actually more pronounced compared to
that of age After adjusting for age and all other factors there was gradual
increase in total oocyte number with increasing AMH which were both
clinically (25-110) and statistically (p=007-p=00005) significant (Table 4a)
We observed a largely similar effect of AMH in the analysis of mature
oocytes It is important to note that due to the issues with Gen II AMH assay
(Rustamov et al 2012) in this study we included only measurements obtained
with the DSL assay Consequently presented cut-off ranges may not be
applicable with current assay methods We suggest that future studies should
revisit the optimality of the cut-off ranges once a reliable assay method has
been established
Similarly after adjusting for all factors the effect of AFC on total
oocytes remained significant (14-40 plt003) However the effect of AFC
appears to be less marked compared to AMH It is important to note that the
AFC assessment in this study is based on the measurement of 2-6mm antral
follicles using two-dimensional transvaginal ultrasound scan The cut-off
ranges may not be applicable in centers where AFC measurement is obtained
using different criteria
Our analysis suggests that age AMH and AFC are independent
determinants of total and MII oocyte number in IVFICSI cycles and can be
used as predictors of ovarian performance irrespective of patient and treatment
characteristics However assessment of oocyte number is the quantitative
response of ovaries to COS and may not necessarily reflect qualitative
outcome
208
Causes Endometriosis Endometrioma
The causes of infertility do not appear to make a significant contribution
in determining total oocyte number after controlling for age AMH AFC the
attempt and treatment interventions Although in the analysis of MII oocytes
we observed reduced oocyte yield in women with severe tubal (-37) and
severe male (-37) infertility this was not statistically significant The analysis
of MII oocytes only included the subset of ICSI cycles consisting of women
with male factor infertility Therefore the effect of severe male factor infertility
may have been more marked in this model
We did not observe a significant difference in total or MII oocyte
number in women with a history of endometriosis with or without
endometriomata Current understanding of the effect of endometriosis in the
outcomes of IVF treatment suggests that the disease has detrimental effect on
IVF outcomes (Barnhart et al 2007 Barnhart et al 2002) However some argue
that no association is observed if the analysis conducted using proper
adjustment for all relevant confounders (Surrey 2013) Our data suggests that
after adjustment for all relevant factors there is no measurable association with
endometriosis (with or without endometriomata) and oocyte number Some
suggest that using ultra-long down regulation using depot GnRH analogue up
tp 3-6 months prior to ovarian stimulation improves ovarian performance in
patients with endometriomata Our dataset did not have information on
pituitary desensitisation prior IVF treatment cycles and we are therefore unable
to assess the effect of this intervention
Attempt
Our study found that 2nd and 3rd cycles were associated with 8
(p=001) and 24 (p=0001) higher total oocytes compared to that of 1st IVF
cycle However the effect of the attempt on MII oocytes was not significant
In our centre only patients with a previously unsuccessful IVF treatment are
offered subsequent cycles and therefore compared to the patients with
repeated attempts the group with first cycle may be expected to have better
oocyte yield However when controlled for all relevant confounders including
adjustment of treatment interventions 1st IVF cycle does not appear to provide
better oocyte yield In keeping with our findings a recent study demonstrated
independence of attempts of IVF cycles in terms of outcomes (Roberts SA and
209
Stylianou C 2012) Increased total oocyte yield with progressed attempts is
likely to be due to the adjustment of COS on the basis of information on the
ovarian response in previous cycles It is important to note that in this study
we assessed oocyte yield as the outcome measure and this may not necessarily
translate into live birth which is desired outcome for the couples Therefore
availability of data on the attempt-dependency of live birth in IVF cycles is
important and we suggest future studies should explore it
USOR practitioner
To our knowledge this is the first study that explored the effect of an
oocyte recovery practitioner on oocyte yield adjusting for all relevant
confounders We observed a considerable operator-dependent effect on total
oocyte yield which may be due to a variation of patients across the days of the
week (p=00005) The practitioners were allocated to the sessions of oocyte
recovery using a specific rota template according to the day of the week Given
in our centre we do not conduct oocyte recovery at weekends there may be
day-dependent variation in selection of patients For instance the patients who
are likely to have maturation of leading follicles during the weekend may have
been scheduled slightly earlier Similarly the patients with confirmed
maturation of leading follicles whose oocyte recovery would have fallen on
weekends may have been scheduled after the weekend allowing maturation of
additional follicles Therefore practitioners conducting the sessions of oocyte
recovery in extremes of weekdays may not necessarily have similar patients
compared to that of other days which may have introduced some bias in
estimating the outcomes of individual practitioners Nevertheless given the
statistical analysis adjusted for age ovarian reserve and treatment interventions
we think there is considerable true between-operator variability on total oocyte
number We suggest that future studies should assess it further by including
adjustment for follicle number and size on the day of HCG
Interestingly overall effect of the operator did not reach statistical
significance in the analysis of MII oocytes in ICSI subset (p=0058) This may
suggest irrespective of total oocyte yield aspiration of only follicles of larger
than a certain size provides oocytes with potential for fertilization
210
COS Protocol
Controlled ovarian hyperstimulation in IVF is conducted using a pre-
defined protocol which contains the policy on selection of regime for pituitary
desensitisation the initial daily dose of gonadotrophins the monitoring of
ovarian response the adjustment of daily dose of gonadotrophins the policy
for cancellation due to poor or excessive ovarian response and criteria for
HCG trigger for final maturation of oocytes Determination of the optimal
treatment regime and the initial dose of gonadotrophins for each patient is
frequently achieved by stratification of patients into various bands of ovarian
reserve on the basis of the assessment of ovarian reserve The assessment of
ovarian reserve prior to IVF cycle is performed using biomarkers which usually
consist of one or combination of following Age AMH AFC and FSH In our
centre stratification of patients into the bands of ovarian reserve was
determined on the basis of the patientrsquos AMH measurements For instance the
patients deemed to have lower ovarian reserve were allocated to the treatment
band with higher daily dose of gonadotrophins and vice versa (Table 1)
The study found that the 2nd protocol was associated with 14-24 lower
total oocyte yield compared to the 1stprotocol The differences in the
interventions between the protocols are described in Table 1 and Table2
Compared to the 1st protocol the 2nd protocol had a) some patients allocated
to COS bands using Gen II assay measurements which later was found to
provide inaccurate measurements b) more AMH cut-off bands for COS
bands c) strict monitoring of ovarian response and corresponding adjustment
of daily dose of gonadotrophins and d) strict criteria for cycle cancellation for
excessive response Therefore our data suggests that the COS protocols with
broader AMH cut-off bands with less strict criteria for adjustment of daily
gonadotrophins may provide higher oocyte yield However given it is
retrospective analysis the limitation of the study should be recognized and we
recommend more robust prospective studies on optimization of AMH tailored
protocols should be conducted
Gonadotrophin type
The study showed that rFSH was associated with higher total oocyte
number (13 p=0008) Interestingly analysis of MII oocyte showed a larger
confidence interval and did not reach statistical significance suggesting the
211
effect of rFSH was not a strong determinant of mature oocytes Perhaps
observation of higher total oocytes in rFSH cycles compared to that of HMG
and yet comparable mature oocyte number in the study suggest that regardless
of total oocyte yield only follicles with a potential for maturation will achieve a
stage of metaphase II
Ovarian stimulation in cycles for IVF is largely achieved by two different
analogues of follicle stimulating hormone human menopausal gonadotrophin
(hMG) and recombinant follicle stimulating hormone r(FSH) Although
purified hMG contains more luteinising hormone compared to rFSH which is
believed to assist endometrial maturation and improve odds of implantation in
cycles of IVF Furthermore the LH component of hMG is believed to assist in
maturation of oocyte with subsequent improvement in live birth On the other
hand historically rFSH was believed to have less batch-to-batch variation
compared to that of HMG which allows administration of more precise daily
dose of gonadotrophins To date a number of studies have been published
comparing these two forms of gonadotrophin preparations which provide
conflicting findings However systematic review that compared of the effect of
these types of gonadotrophins on live birth rate suggests that there is no
significant difference on live birth rate (van Wely et al 2011) This supports our
findings on that irrespective of total oocyte yield clinically useful mature
oocyte number is comparable between the groups
Regime and dose of gonadotrophins
The study found that compared to the reference group (Agonist 75-
150IU) none of the combination of the regime and gonadotrophin dose
provided a higher total oocyte yield Women that were in Antagonist regime
group with an initial daily dose of 75-150 IU gonadotrophins produced
approximately 36 fewer total oocytes (p=00005) However comparison of
MII oocytes of these groups did not reach statistical significance and the effect
size was much smaller (-19 p=023) This and reference groups represent the
patients with high ovarian reserve who had milder ovarian stimulation because
of risk of excessive ovarian response and OHSS Lower total oocyte yield and
comparable mature oocyte number in the Antagonist regime may explain why
this regime is reported to be associated with reduction in the risk of OHSS and
212
yet comparable live birth in patients with high ovarian reserve (Yates et al
2012)
In the analysis of MII oocytes Antagonist with 187-250 IU of
gonadotrophin and Antagonist with 375 IU of gonadotrophin provided around
43 (p=005) and 47 (p=002) more oocytes compared to that of the
reference group (Agonist 75-150 IU) Interestingly total oocytes of these
groups were comparable to that of reference group suggesting that using
Antagonist protocol may be associated with improvement in oocyte
maturation compared to Long Agonist regime Perhaps in addition to the
effect of exogenous HCG endogenous LH may play role in oocyte maturation
in IVFICSI cycles and shorter desensitisation of pituitary using Antagonist
regime may allow secretion of LH during COS in lower quantities
AMH-tailored individualisation of COS
Given that we did not observe a significant dose-dependent effect on
oocyte number we were not able to develop AMH or AFC tailored
individualisation protocols for COS Although the initial dose of
gonadotrophin is believed to be one of the main determinants of oocyte yield
our study suggests that the association between these variables is weak
Consequently strict protocols for tailoring the initial dose of
gonadotrophins may not necessarily improve ovarian performance in IVF
treatment It is important to note that our COS protocols recommended close
monitoring of ovarian response and corresponding dose adjustment starting
from 3rd day of COS which may have masked the effect of initial dose
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not have significant impact on
oocyte yield Nevertheless further time series regression analysis with full
parameters of cycle monitoring and the dose adjustments in the model should
be conducted in order to ascertain the role of AMH in tailoring the dose of
gonadotrophins in cycles of IVF
213
Strengths of the study
Here we presented the largest study on assessment of the role of patient
and treatment related factors on oocyte yield and exploration of optimization
of AMH-tailored COS using a validated dataset Statistical analysis included
systematic assessment of the effect possible confounders on measured
outcome including of age AMH AFC causes of infertility attempt of IVF
treatment USOR practitioner type of gonadotrophin pituitary desensitisation
regime and initial dose of gonadotrophins On the basis of above analysis a
robust multivariable regression models for assessment of the effect all above
factors on total and mature oocyte number have been developed
Prior to conducting this study previous projects explored the
performance of AMH assay methods The studies found that Gen II assay may
yield highly non-reproducible measurements compared to that of DSL assay
(Rustamov et al 2012a) Therefore in this study only DSL AMH assay
measurements were included Furthermore previous projects (Chapter 5 and 6)
explored the effect of various patient related factors on AMH AFC and FSH
measurements and found that some of the factors had measurable impact on
ovarian reserve These findings were used in establishing which patient related
factors ought to be explored in the building of regression models for this
study However the DSL assay is no longer available and most clinics are
mainly using Gen II AMH assay in formulation of COS in IVF Given its
observed instability AMH-tailoring based on Gen II samples may lead to
erroneous allocation of patients to the band that is significantly inconsistent
with patientrsquos ovarian reserve Subsequently this may result in the extremes of
ovarian response to COS including severe OHSS and cycle cancellation
Weaknesses of the study
The main weakness of the study is that the analysis is based on
retrospectively collected data The methodology included an extensive
exploration for possible confounders and adjustment for the ones that were
found to be significant However there are may be unmeasured factors that
that might have affected the estimates In addition the study included only
patients that did not have PCO appearance on ultrasound scan The analysis in
all patients showed that interaction of PCO status with other covariables was
complex which could introduce bias in estimation of the effects of other
214
factors Therefore analyses of the groups with and without PCO were run
separately Subsequently results of non-PCO group was presented in the thesis
given it had the largest number of cycles Compared to non-PCO analysis we
did not observe significant difference in the results of PCO model
The study assessed ovarian response using oocyte yield only Other
outcomes of ovarian response such as duration of ovarian stimulation total
dose of gonadotrophins cycle cancellation due to poor or excessive ovarian
response and OHSS have not been analysed Therefore it is important to
interpret the findings of this study in the context of ovarian response
determined by oocyte yield Specifically the study should not be used to
interpret cycle cancellation for excessive ovarian response As described in the
methodology of the study the oocyte number in the cycles with cancellation of
oocyte recovery due to excessive response were recoded with comparable
values with cycles that were cancelled following oocyte recovery for OHSS
Given the main desired outcome of IVF treatment is live birth the
overall success of a treatment cycle should reflect this outcome measure This
study does not assess the effect of above factors to overall success of IVF
treatment However the study provides a robust data on research methodology
in assessment of IVF outcomes which can assist in the assessment of other
outcome measures in future studies
SUMMARY
After adjustment for all the above factors age remained a negative
predictor of oocyte yield whereas we observed a gradual and significant
increase in oocyte number with increasing AMH and AFC values suggesting
all these markers display an independent association with oocyte yield IVF
attempt oocyte recovery practitioner type of gonadotrophin were found to
have significant effect on total oocyte yield However the effect of these
factors on mature oocyte number did not reach statistical significance Whilst
total oocyte number was comparable between pituitary desensitisation regimes
GnRH antagonist cycles were found to provide significantly higher mature
oocytes compared to that of long GnRH agonist regime
In terms of the effect of initial dose on oocyte yield following
adjustment for all above variables we did not observe significant increase in
215
oocyte number with increasing gonadotrophin dose categories Therefore
strict protocols for tailoring the initial dose of gonadotrophins may not
necessarily improve ovarian performance in IVF treatment However further
time series regression analysis with full parameters of cycle monitoring and the
dose adjustments in the model should be conducted in order to ascertain the
role of AMH in tailoring the dose of gonadotrophins in cycles of IVF
This study demonstrates complexity of the factors that determine
ovarian response in IVF cycles Therefore assessment of AMH-tailored
individualisation of ovarian stimulation should be based on a robust
methodology preferably using a large randomized controlled trial
Furthermore measurement of AMH ought to be based on a reliable assay
method which is currently not available In the meantime the limitations of
available evidence on AMH-tailored individualisation of ovarian stimulation
should be taken into account in the management of patients
216
References
Broer SL et al AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 17(1) p 46-54 Barnhart K Dunsmoor-Su R Coutifaris C Effect of endometriosis on in vitro fertilization Fertil Steril 2002771148ndash55 Dechaud H Dechanet C Brunet C et al Endometriosis and in vitro fertilization a review Gynecol Endocrinol 200925717ndash21 Dewailly D Andersen CY Balen A Broekmans F Dilaver N Fanchin R Griesinger G Kelsey TW La Marca A Lambalk C Mason H Nelson SM Visser JA Wallace WH Anderson RA The physiology and clinical utility of anti-Mullerian hormone in women Hum Reprod Update 2014 Kurinczuk JJ et al Fertility Treatment in 2006 A Statistical Analysis HFEA 2010 La Marca A and Sunkara S K Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers from theory to practice Human Reproduction Update Vol20 No1 pp 124ndash140 2014
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421 Nelson SM et al Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 2009 24(4) p 867-75 Nelson SM Biomarkers of ovarian response current and future applications Fertil Steril 201399963ndash969
Roberts SA Stylianou C The non-independence of treatment outcomes from repeat IVF cycles estimates and consequences Hum Reprod 2012 Feb27(2)436-43
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum
217
Reprod 2012a273085-3091
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071 Stoop D Ermini B Polyzos NP Haentjens P De Vos M Verheyen G and Devroey P Reproductive potential of a metaphase II oocyte retrieved after ovarian stimulation an analysis of 23 354 ICSI cycles Human Reproduction 2012 Vol27 No7 pp 2030ndash2035 2012 Sunkara SK Rittenberg V Raine-Fenning N Bhattacharya S Zamora J Coomarasamy A Association between the number of eggs and live birth in IVF treatment an analysis of 400 135 treatment cycles Hum Reprod 2011 261768ndash1774 Sunkara SK Coomarasamy A Faris R Braude P Khalaf Y Effectiveness of the GnRH agonist long GnRH agonist short and GnRH antagonist regimens in poor responders undergoing IVF treatment a three arm randomised controlled trial (ESHRE) 2013London UK SurreyES Endometriosis and Assisted Reproductive Technologies Maximizing Outcomes Semin Reprod Med 201331154ndash163 van Wely M1 Kwan I Burt AL Thomas J Vail A Van der Veen F Al-Inany HG Recombinant versus urinary gonadotrophin for ovarian stimulation in assisted reproductive technology cycles Cochrane Database Syst Rev 2011 Feb 16(2)CD005354
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
218
Figure 1 Study groups for assessment of Individualisation of pituitary desensitisation regime
Individualisation of pituitary desensitisation regimens can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high ovarian reserve
Individualisation of COS Regime
Low AMH
(eg DSL assay
22-157 pmolL)
GnRH
Antagonist
GnRH
Agonist
Normal AMH
(eg DSL assay
158-288pmolL)
GnRH
Antagonist
GnRH
Agonist
High AMH
(eg DSL assay
gt288 pmolL)
GnRH
Antagonist
GnRH
Agonist
219
Fiure 2 Study groups for assessment of individualisation of initial gonadotrophin dose
Individualisation of daily dose of gonadotrophins can be studied comparing the effectiveness of AMH-tailoring in women of low medium and high
ovarian reserve
Individualisation
Gonadotrophin
Dose
Low AMH
(eg DSL assay 22-157 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
Normal AMH
(eg DSL assay158-288pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-300 IU)
Low Dose
(eg HMG 75-150 IU)
High AMH
(eg DSL assay gt288 pmolL)
High Dose
(eg HMG 375-450 IU)
Moderate Dose
(eg HMG 225-375 IU)
Low Dose
(eg HMG 75-150 IU)
220
Table 1 AMH-tailored stratification protocols for regime starting dose of hMGrFSH and adjusting daily dose of gonadotrophins (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (V1) (01 Jan 2011-30 Apr 2011)
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Initial dose (Day 1-3) 1) lt22 AMH (DSL) Exclude 2) 22-156 AMH (DSL) Antagonist 300 hMG 3) 157-285 AMH (DSL) Long Agonist 200 rFSH225 hMG 4) gt286 AMH (DSL) Antagonist 150 hMG
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 375 hMG 3) 11-21 AMH (Gen II) Long Agonist 300 hMG 4) 22-30 AMH (Gen II) Long Agonist 225 hMG 5) 31-39 AMH (Gen II) Long Agonist 150 hMG 6) 40-67 AMH (Gen II) without PCO Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCO Long Agonist 125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) lt3 AMH (Gen II) Co-Flare 450 hMG 2) 3-10 AMH (Gen II) Antagonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Long Agonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Long Agonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Long Agonist 1125 rFSH 8) gt67 AMH (Gen II) Long Agonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 450 hMG 2) 3-10 AMH (Gen II) Long Agonist 300 hMG 3) 11-21 AMH (Gen II) Long Agonist 225 hMG 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 rFSH 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 rFSH 8) gt67 AMH (Gen II) Antagonist 1125 rFSH
Initial dose (Day 1-3) 1) 2-3 AMH (Gen II) Antagonist 300 rFSH 2) 3-10 AMH (Gen II) Long Agonist 225 rFSH 3) 11-21 AMH (Gen II) Long Agonist 1875 rFSH 4) 22-39 AMH (Gen II) without PCOS Long Agonist 150 hMG 5) 22-39 AMH (Gen II) with PCOS Antagonist 150 hMG 6) 40-67 AMH (Gen II) without PCOS Antagonist 150 hMG 7) 40-67 AMH (Gen II) with PCOS Antagonist 1125 hMG 8) gt67 AMH (Gen II) Antagonist 1125 hMG
Dose adjustment No or minimum change on daily dose of gonadotrophin
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
Dose adjustment Step up or down using Oestradiol levels (Day 3amp6) and Ultrasound follicle tracking (Day 8)
221
Table 2 AMH-tailored stratification protocols for management of suspected excessive response (St Maryrsquos Hospital)
Protocol 1 (01 Sep 2008-31 Dec 2010)
Protocol 2 (v1) (01 Jan 2011-30 Apr 2011)
amp
Protocol 2 (v2) (01 May 2011-31 Jul 2011)
Protocol 2 (v3) (01 Aug 2011-30 Nov 2011)
Protocol 2 (v4) (01 Dec 2011-08 Aug 2012)
Coasting for excessive response on day 8
Oestradiol gt20000 pgml 30-40 follicles larger than 10mm or Oestradiol gt18000 pgml
30-40 follicles larger than 12mm
No coasting
Coasting for excessive response once follicle maturation meets criteria
Oestradiol gt20000 pgml
30-40 follicles larger than 10mm
25-40 follicles larger than 10mm
25-30 follicles larger than 15mm
Cancellation for excessive response
Day 8 or thereafter Oestradiol lgt20000 pgml and symptoms of OHSS after gt3 days of coasting
Day 8 or thereafter More than 40 follicles larger than 10mm
Day 10 or thereafter More than 40 follicles larger than 15mm
Day 8 or thereafter Cancel only if symptoms of OHSS
222
Table 3 Distribution of patient characteristics and interventions
In total 1847 cycles included in the study
n
Causes
Unexplained 591 32
Mild tubal 325 176
Severe tubal 37 2
Mild male 589 3189
Severe male 18 097
Endometriosis 91 493
Endometrioma 47 28
Attempt
1 1346 7287
2 406 2198
3 91 493
4 4 022
USOR practitioner
A 570 317
B 412 2291
C 147 818
D 15 083
E 153 851
F 86 478
G 118 656
H 136 756
I 141 784
J 20 111
Protocol
1 1265 6849
2 (v1) 399 216
2 (v2ampv3) 79 428
2 (v4) 104 563
FSH preparation
HMG 1594 87
rFSH 237 13
Regime
Long Agonist 820 444
Antagonist 1027 556
Initial dose
75-150IU 298 1617
187-250IU 483 2621
300IU 914 4959
375IU 60 326
450IU 88 477
223
Table 4a Results of multivariable regression analysis for total and MII oocytes
Total oocytes (n=1653) Metaphase II oocytes (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
Age -0031 -004 -002 00005 -0021 -004 -001 0006
age2 -0002 000 000 0047 -0002 -001 000 0206
AMH categories (Ref0-3 pmolL) 00005 00005
4-5 pmolL 0254 -003 054 0078 -0073 -054 040 0761
6-8 pmolL 0368 010 064 0008 0250 -019 069 0267
9-10 pmolL 0605 034 087 00005 0474 004 091 0034
11-12 pmolL 0651 039 091 00005 0305 -016 077 0198
13-15 pmolL 0779 051 104 00005 0372 -008 083 0109
16-18 pmolL 0836 057 111 00005 0655 018 113 0007
19-22 pmolL 0803 051 109 00005 0381 -013 089 0142
23-28 pmolL 0954 067 123 00005 0832 034 132 0001
29-200 pmolL 1126 084 141 00005 0872 035 139 0001
AFC categories (Ref 0-7) 00005 0008
8-9 -0039 -018 010 0589 0001 -024 024 0992
10-11 0145 001 028 0037 0185 -005 042 0119
12-14 0223 009 036 0001 0254 002 049 0031
15-19 0263 013 040 00005 0113 -013 036 0362
20-24 0344 017 052 00005 0456 013 078 0006
25-100 0405 021 060 00005 0455 009 082 0015
Causes of infertility
Unexplained 0103 002 019 0021 0090 -010 028 0354
Mild tubal -0012 -010 008 0797 -0098 -029 009 0307
Severe tubal -0066 -030 017 0579 -0371 -093 019 0194
Mild male 0014 -007 009 0729 0135 -002 029 009
Severe male -0074 -055 040 0758 -0377 -117 042 0351
Endometriosis -0108 -026 005 0169 -0139 -041 013 0314
Endometrioma -0016 -018 015 0843 0043 -035 044 083
Attempt (Ref 1st) 0001 045
2nd 0085 002 015 0016 0080 -006 022 0274
3rd4th attempt 0243 010 039 0001 0116 -014 037 0367
224
Table 4b Results of multivariable regression analysis for total and MII oocytes Continuation of Table 4a)
Total oocyte (n=1653) Metaphase II oocyte (ICSI)(n=1101)
Coef 95 CI P Coef 95 CI P
USOR Practitioner (Ref A) 00005 0058
B -0009 -009 007 0823 -0129 -031 005 0153
C 0104 -003 024 0129 0111 -012 034 0348
D -0260 -059 007 0125 -0287 -108 051 0478
E -0297 -044 -016 0 -0246 -048 -001 0043
F -0173 -032 -003 0017 -0367 -072 -001 0043
G -0213 -039 -003 002 -0311 -061 -001 0044
H -0007 -012 011 0909 0022 -020 025 0849
I -0149 -025 -004 0005 -0082 -030 014 0462
J -0549 -095 -015 0007 -0408 -095 014 0143
Protocol (Ref 1st) 00003 024
2nd (v1) -0186 -027 -010 0 -0066 -024 010 0449
2nd (v2ampv3) -0140 -028 000 0056 0175 -007 042 0156
2nd (v4) -0244 -043 -006 0009 0002 -031 031 0989
Gonadotrophin (Ref HMG)
rFSH 0137 004 024 0008 0119 -009 033 0262
Dose amp Regime (RefAgonist 75-150IU) 00005 00052
Antagonist 75-150IU -0364 -053 -020 0 -0199 -051 011 0203
Agonist 187-250IU 0104 -003 024 0139 0028 -031 036 0869
Antagonist 187-250IU 0124 -006 030 0176 0436 -002 089 0059
Agonist 300IU 0151 -001 031 0059 0258 -011 062 0165
Antagonist 300IU 0003 -016 017 0968 0143 -022 050 0433
Agonist 375IU 0072 -023 037 0639 -0185 -086 049 0591
Antagonist 375IU 0124 -011 035 0291 0478 005 090 0028
Agonist 450IU -0129 -041 015 037 -0285 -080 023 0278
Antagonist 450IU -0207 -048 006 0134 0046 -041 051 0843
Intercept 1342 102 166 0 0993 043 155 0001
225
Figure 3a Total oocytes
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Tota
l E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fit0
Non-PCO
226
Figure 3b Total oocytes
Plots show the raw data as dots Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 stimulation with HMG USOR practitioner-A none of the specific causes of infertility
25 30 35 40
12
510
20
Age
To
tal E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
To
tal E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
To
tal E
gg
s
AFC
fit0
Non-PCO
227
Figure 4a Metaphase II oocytes (ICSI subset)
Plots show raw data as boxplots and fitted lines are shown with shaded area at plusmn1SEM
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
LDR
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
12
51
02
0
Prescribed Initial Dose
Matu
re I
CS
I E
ggs
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
Antagonist
75-150IU 187-250IU 300IU 375IU 450IU
12
51
02
0
fitm0
Non-PCO
228
Figure 4b Metaphase II oocytes (ICSI subset)
Plots show raw data as dot Fitted lines are shown with shaded area at plusmn1SEM and are for a reasonable prognosis patient with following
characteristics Age 31 AMH in the 23-29 pmolL group AFC in the 12-15 group first attempt of IVFICSI Protocol-2nd version 4 simulation with HMG USOR practitioner-A None of the specific causes of infertility
25 30 35 40
12
510
20
Age
Ma
ture
IC
SI E
gg
s
Age
2 5 10 20 50 100
12
510
20
AMH
Ma
ture
IC
SI E
gg
s
AMH
10 20 30 40 50
12
510
20
AFC
Ma
ture
IC
SI E
gg
s
AFC
fitm0
Non-PCO
229
GENERAL SUMMARY
7
230
GENERAL SUMMARY
Anti-Muumlllerian hormone a dimeric glycoprotein secreted from granulosa cells
of growing ovarian follicles appears to play a central role in the regulation of
oocyte recruitment and folliculogenesis (Durlinger et al 2002)
Serum anti-Muumlllerian hormone concentration has been found to be one of
the best predictors of ovarian performance in IVF treatment (van Rooij et al
2002 Broer et al 2011) Therefore an evaluation of the role of AMH in assisted
conception has been of great interest and consequently a considerable body of
research work has been performed during last two decades Most published
studies with varying methodological quality have suggested that AMH is one
of the most reliable predictors of ovarian performance in IVF treatment cycles
Consequently many fertility centers have introduced measurement of AMH for
the assessment of ovarian reserve and as a tool for formulation of treatment
strategies for controlled ovarian hyperstimulation in assisted conception
However the studies described in this thesis suggest that some assumptions on
the clinical value of AMH particularly reliability of AMH assay methods and
the role of AMH-tailored individualisation of daily dose of gonadotrophins in
IVF were not based on robust data
For the purpose of this thesis I conducted a comprehensive review of the
published literature on the biology of ovarian reserve the role of AMH in
female reproduction the assay methods and clinical application of AMH in
assisted conception (Chapter 1) I established that a) published work on
sampling variability of AMH measurements and comparability of various assay
methods provide conflicting results b) data on the effect of ethnicity BMI
reproductive pathology and surgery is scarce and c) good quality data on
individualisation of AMH-tailored controlled ovarian hyperstimulation in IVF
is lacking Consequently I decided to conduct a series of studies that directed
towards an improvement of the scientific evidence in these areas of research
Our previous work on within-patient variability of the first generation DSL
assay samples showed that AMH measurements may exhibit considerable (CV
28) sample-to-sample variability (Rustamov et al 2011) In view of this it was
decided to evaluate the validity of newly introduced Gen II assay (Chapter
21) In order to achieve adequately powered results all available AMH
samples of women of 20-46 years of age who had investigation for infertility at
231
secondary and tertiary care divisions of St Maryrsquos Hospital during the study
period were selected for the study According to the manufacturerrsquos
recommendation haemolysed AMH samples may provide erroneous results
and therefore women with haemolysed samples were excluded from the
analysis Inclusion of all women during the study period was also important in
reducing the risk of selection bias particularly in this study which compared
historical and current AMH assay Given the referral criteria of patients did not
change throughout the study period I could confidently report that observed
comparison between DSL and Gen II samples were the reflection of true
differences of the assay methods It is important to note that validity and
performance of a new test should ideally be compared to a reliable ldquogold
standardrdquo test However to date there appears to be no gold standard test in
measurement of AMH and hence an evaluation of the performance of assay
methods can be chllanging Given the lack of a gold standard I decided to
assess the quality of the new test in comparison to what was considered the
most reliable test available at that time accepting that such a comparison may
have limitations Previously two AMH assays (DSL and IOT) were in use and
there is no research evidence on the superiority of one assay over other
Therefore in this study the new Gen II assay was compared to the DSL assay
method which was previously available in our clinic
Once I prepared a robust and validated dataset the quality of Gen II assay
was evaluated by taking following steps of investigation First within-patient
between-sample variability of AMH measurements of Gen II assay samples
were obtained and compared to that of DSL assay samples Then the validity
of the manufacturer recommended between-assay conversion factor was
evaluated by comparing the Gen II assay sample measurements to that of DSL
assay method using both cross-sectional and longitudinal datasets The stability
of the Gen II assay samples was assessed by examining a) stability of the
samples in room temperature b) the linearity of dilution of the samples c)
comparing the standard assay preparation method to that of an equivalent
method and d) stability of samples during storage in frozen condition
Worryingly the study found that the Gen II AMH assay which was
reported to be more reliable than previous assays gave significantly higher
sampling variability (CV 59) compared to that of DSL samples (CV 28)
This significant variation in between repeated measurements of Gen II samples
indicated that there might be a profound fault in the assay method The
232
comparison of the assay methods using a large cohort of clinical samples
suggested that Gen II assay provided 40 lower measurements compared to
that of DSL contradicting the manufacturerrsquos reported 40 higher
measurements (Kumar et al 2011) These discrepancies in the sampling
variability and assay-method comparability suggested that Gen II assay samples
may lack stability which had not been observed previously
When different assays are available for a particular analyte it is critical that
the comparability of results is established and reliable conversion factors or
calibration curves are determined The study demonstrated that the difference
between the previously recommended (Kumar et al 2011 Wallace et al 2011)
conversion factor and the conversion formula obtained in this study was as
high as 60-80 All three studies followed the manufacturersrsquo
recommendations as supplied in the kit insert In terms of the study design
and analysis previous studies assessed the within-sample difference between
the two assays considered this involved the thawing of samples splitting into
two different aliquots and analysis of each aliquot with a different assay In
contrast I conducted between-sample comparison of historical DSL
measurements to that of Gen II using cross sectional and longitudinal
population based analyses The laboratory based within-sample conversion
formula should be reproducible in population based between-sample
comparison particularly in longitudinal analysis Observed discrepancies in the
conversion factors again suggested that AMH samples may suffer from pre-
analytical instability
Thus in collaboration with the scientific team of the Clinical Assay
Laboratory of our hospital we investigated the stability of Gen II assay
samples The studies on sample storage and preparation confirmed the Gen II
assay samples exhibited considerable instability under the storage and
processing conditions recommended by the manufacturer It was suggested
that Gen II samples remain stable when stored in unfrozen conditions up to 7
days and many IVF clinics adopted the practice of shipping unfrozen AMH
samples to centralized laboratories for processing and analysis (Kumar et al
2010 Nelson and La Marca 2011) This study demonstrated that storage of
unfrozen samples can affect obtained results considerably Evaluation of the
stability of samples (n=48) at room temperature found that in the majority of
samples AMH levels in serum increased progressively during 7 days of storage
with an overall increase as high as 58 Contrary to the manufacturerrsquos report
233
even storage of samples in frozen condition (-20 ordmC) does not ensure the
stability of the samples Storage at -20ordmC for 5 days increased AMH levels by
23 compared to fresh samples Linearity is one of the cornerstones of assay
validation and it is essential that a proportional response is obtained on
dilution of sample In contrary the study showed that Gen II samples exhibit
considerable increase with the dilution Pre dilution of serum prior to assay
gave AMH levels up to twice that found in the corresponding neat sample
Similarly pre-mixing of serum with assay buffer prior to addition to the
microtitre plate gave overall 72 higher readings compared to sequential
addition These experiments confirmed that Gen II assay methodology was
completely flawed and routine clinical samples were likely to provide highly
erroneous results which could lead to adverse clinical consequences in
patients
To evaluate the robustness of our data I validated the study on the
variability of Gen II samples using external data (Chapter 22) Assessment of
samples obtained from different patient population and different assay-
laboratory found that within-patient between-sample variability of Gen II
AMH measurements were similar to that of my study (CV 62) This
confirmed that Gen II assay sampling variability was independent of
population or laboratory and specific to the assay-method
Findings of this series of studies suggested that the use of Gen II
measurements might have considerable clinical implications particularly when
used as a marker for triaging patient to ovarian stimulation regimens in cycles
of IVF In order to obtain equivalent clinical cut-off ranges for Gen II
samples previously used DSL assay based guidance ranges were recommended
to be increased by 40 However my study found that Gen II assay may
actually provide 20-40 lower measurements compared to that of DSL which
might led to allocation of patients to inappropriate treatment regimens Given
that using the above conversion formula may underestimate ovarian reserve by
60-80 the patients may inadvertently be given significantly higher dose of
gonadotrophins than appropriate in the individual IVF treatment cycles This
can increase the patientrsquos risk of excessive ovarian response resulting in
cancellation of IVF cycles andor severe ovarian hyperstimulation syndrome
(OHSS) In addition significant variation of Gen II assay sample
measurements (CV 59) may also lead to inconsistency in allocation of
patients to appropriate cut off ranges Indeed this was demonstrated by a
234
recent study which found that 7 out of 12 patients moved from one cut-off
range to another when Gen II assay was used for AMH measurements
(Hadlow et al 2013) Therefore we suggested that Gen II assay samples should
not be used in allocating patients to ovarian stimulation regimens
Immediate steps were taken to report these findings to the manufacturer
scientists clinicians and the quality assessment agencies The findings of the
study were presented at the annual meetings of European Society of Human
Reproduction and Embryology as well as British Fertility Society The study
was also published in Human Reproduction which generated an important debate
on the validity of Gen II assay measurements Further independent studies by
other research groups and re-evaluation of the assay by the manufacturer have
confirmed our results (Han et al 2013) This led to recognition of the issues of
the Gen II assay by the manufacturer and consequent modification of the assay
method (King 2012) Subsequent evaluation of Gen II assay by the Medicines
and Healthcare Products Regulatory Agency (MHRA) and the National
External Quality Assessment Service (NEQAS) have confirmed the above
findings As a result the Human Fertility and Embryology Authority have
circulated a field safety notice with the regards to the pitfalls of the AMH Gen
II assay We informed National Institute for Health and Care Excellence
(NICE) of the problems of AMH measurements and urged it to review its
current recommendation on the use of AMH in the investigation and
treatment of infertility With regards to the impact of this work it is important
to note that AMH is widely used in fertility clinics around the world and Gen
II assay is the only commercially available kit for the measurement of AMH in
most countries Consequently this study has made a direct significant impact
in the improving safety and effectiveness of fertility investigation and
treatment around the world However further studies are required to
determine the cause of the instability In addition the validity of the modified
protocol for Gen II assay and other new AMH assays need to be evaluated In
the meantime caution should be exercised in the interpretation of Gen II
AMH measurements
Studies above established that invalid commercial AMH assay was
introduced for clinical use without full and independent validation Regretfully
the issues with the assay were not identified early enough to prevent
widespread use of this faulty test in clinical management of patients around the
world In order to avoid above failures and improve reliability of future AMH
235
assays I recommend following steps should be taken 1) International
standards for the evaluation of validity of existing and future AMH assays
should be developed 2) Independent research groups should evaluate validity
of AMH assays before introduction of the test for clinical application 3)
Validity and performance of already introduced AMH assays ought to be
evaluated by independent research groups periodically to ensure timely
detection of the deterioration in the quality of the test
In view of the observed issues with AMH measurements we conducted
a critical appraisal of the published research on the previous and current assay
methods that reported AMH measurement variability assay method
comparison and sample stability (Chapter 3) Following a systematic search
for all published studies on the evaluation of performance of historic and
current AMH assays ten sample stability studies 17 intrainter-cycle variability
studies and 14 assay method comparability studies were identified Previously
most studies reported that variability of AMH in serum was very small and
suggested a random single measurement provides an accurate assessment of
circulating AMH in serum Therefore using a random AMH measurement for
assessment of ovarian reserve has become a routine practice It appears that
both in reporting particularly in its interpretation the term ldquoAMH variabilityrdquo
was used too broadly and had a various meanings Reviewing all published
studies that used term ldquoAMH variabilityrdquo I identified that the term was used in
interpretation of four distinct outcomes for measurement of variability of
AMH in serum 1) circadian 2) within the menstrual cycle 3) between
menstrual cycles and 4) between repeated samples without consideration of the
day of menstrual cycle In order to delineate the reported variability of AMH
for each outcome I divided the variability studies into four separate groups
and reviewed each study within its appropriate group The review found that
most studies were based on small sample sizes and did not report the
methodology for sample processing and analysis fully The studies also appear
to refer to their outcomes as biological variability of AMH without taking into
account the variability arising due to errors in its measurement More
importantly the review demonstrated that there is clinically significant
variability between AMH measurements in repeated samples which was
reported to be markedly higher with currently used Gen II assay compared to
that of historic DSL and IOT assays
236
Appraisal of assay method comparability found that despite using the
standard manufacturer protocols for the sample analysis the studies have
generated strikingly different between-assay conversion factors The studies
comparing first generation AMH assays (DSL vs IOT) reported conversion
factors ranging from five-fold higher with the IOT assay compared to both
assays giving equivalent AMH concentrations Similarly studies comparing first
and second-generation assays (DSL vs Gen II or IOT vs Gen II) derived
conflicting conclusions The apparent disparity in results of the assay
comparison studies implies that AMH reference ranges and guidance ranges
for IVF treatment which have been established using one assay cannot be
reliably used with another assay method without full and independent
validation Similarly caution is required when comparing the outcomes of
research studies using different AMH assay methods Correspondingly the
review of studies on sample stability revealed conflicting reports on the
stability of AMH under normal storage and processing conditions which was
reported to be a more significant issue with the Gen II assay Similarly there
was considerable discrepancy in the reported results on the linearity of dilution
of AMH samples particularly in Gen II studies In view of above findings we
concluded that AMH in serum may exhibit pre-analytical instability which may
vary with assay method Therefore robust international standards for the
development and validation of AMH assays are required
Although AMH assays have been in clinical use for more than a decade
this appears to be first published review that examined the studies on the
performance of AMH assay methods Indeed a number of review articles
comparing clinical performance of AMH test to other markers of ovarian
reserve have been published (Broer et al 2009 Broer et al 2011b La Marca et
al 2009) Reviewing observational studies the articles concluded that AMH
measurement was one of the most robust methods of assessment of ovarian
reserve However there appears to be no review article that specifically
evaluated the validity of the AMH assay methods suggesting AMH assay
methods were assumed to be reliable despite the lack of robust data on the
validity of assay methods
Reassuringly the report of instability of the Gen II assay samples has
generated significant research interest directed towards understanding the
causes of the issue As a result several hypotheses have been proposed and are
undergoing testing by various research groups For instance in the work
237
described here it was proposed that AMH molecule may undergo proteolytic
changes under certain storage and processing conditions exposing additional
antibody binding sites (Rustamov et al 2012a) The manufacturer of the assay
suggested that the sample instability is due to the presence of complement
interference (King 2012) More recent studies have reported the presence of
another form of AMH molecule pro-AMH in the serum may be the source of
erroneous measurements (Pankhurst et al 2014) Furthermore this study
demonstrated that Gen II assay detects both AMH and pro-AMH suggesting
that the mechanism of sample instability may be more complex than previously
thought It is indeed important to continue the quest to determine the cause of
the sample instability in order to develop reliable method for measurement of
AMH in future In the meantime clinicians should exercise caution when using
AMH measurements in the formulation of treatment strategies for individual
patients
Using a robust protocol for extraction of data and preparation of
datasets I have built a large validated research database (Chapter 4) Utilizing
the clinical electronic data management systems and case notes of patients I
have prepared a validated dataset that will enable study of ovarian reserve in a
wide context including a) assessment of ovarian reserve b) evaluation of the
performance of the biomarkers c) study individualization of ovarian
stimulation in IVF d) association of biomarkers of ovarian reserve with
outcomes of IVF (eg oocytes embryos live birth) The database has been
used to address research questions posed in chapter 5 and chapter 6 of this
thesis In addition it can be utilized for future studies on assessment of ovarian
reserve and IVF treatment interventions
Both formation and decline of ovarian reserve appears to be largely
determined by genetic factors although at present data on genetic markers are
scarce (Shuh-Huerta et al 2012) Therefore availability of data on clinically
measurable determinants of ovarian reserve is important Consequently I
explored the role of ethnicity BMI endometriosis causes of infertility and
reproductive surgery to ovarian reserve using AMH AFC and FSH
measurements of a large cohort of infertile patients (Chapter 51)
Multivariable regression analysis of data on the non-PCO cohort showed the
association between ethnicity and the markers of ovarian reserve is weak In
contrast I observed a clinically significant association between BMI and
ovarian reserve obese women were found to have higher AMH and lower
238
FSH measurements compared to those of non-obese With regard to the role
of the causes of infertility I did not observe a significant association between
the markers of ovarian reserve and subsets diagnosed with unexplained or
tubal factor infertility In contrast those diagnosed with male factor infertility
had significantly higher AMH and lower FSH measurements which increased
with the severity of the disease In conclusion the study demonstrated that
some of the above factors have a significant impact on above biomarkers of
ovarian reserve and therefore I suggest future studies on ovarian reserve
should include adjustment for the effects these factors
The study showed that in the absence of endometrioma endometriosis
was not found to have a strong association with markers of ovarian reserve
compared to those without the disease Interestingly women with an
endometrioma had significantly higher AMH measurements than those
without endometriosis This is the first study that has reported increased
AMH in serum in the presence of endometrioma Interestingly recent studies
have demonstrated that AMH and its receptor are expressed in tissue samples
obtained from ovarian endometriosis (Wang et al 2009 Carelli et al 2014) It
appears that AMH inhibits growth of both epithelial and stromal cells
(Signorille et al 2014) I believe these intriguing findings warrant further
research on the role of AMH in the pathophysiology of endometriosis With
regards to assessment of ovarian reserve AMH may not reflect ovarian reserve
in the presence of endometrioma and therefore caution should be exercised
With respect to reproductive surgery I conducted a study to estimate the
effect of tubal and ovarian surgery on ovarian reserve independent of
underlying disease (Chapter 52) Multivariable regression analysis of the
cross-sectional data showed that salpingo-ophorectomy and ovarian
cystectomy for endometrioma have a significant detrimental impact on ovarian
reserve as estimated by AMH AFC and FSH In contrast neither
salpingectomy nor ovarian cystectomy for cysts other than endometrioma was
found to have appreciable effects on the markers of ovarian reserve I suggest
that women undergoing surgery should be counseled regarding the potential
impact of surgical interventions to their fertility However there was
appreciable overlap between the interquartile ranges of the comparison groups
This suggests that although the effects are significant at a population level
there is considerable variation between individuals Therefore clinicians should
239
exercise caution in predicting the effect of surgery on ovarian reserve of
individual patients
Published studies on the prognostic value of AMH in assisted
conception suggested there is a strong correlation between AMH and extremes
of ovarian response in cycles of IVF (Nelson et al 2007 Nardo et al 2007)
Later case control studies showed that tailoring the daily dose of
gonadotrophins to individual patientrsquos AMH levels and pituitary
desensitisation with GnRH antagonist in patients with the extremes of ovarian
reserve improved the outcomes of IVF treatment (Nelson et al 2009 Yates et
al 2012) However these studies displayed a number of methodological issues
largely due to retrospective analysis small sample size and centre-dependent or
time-dependent selection of cohorts Therefore the role of confounding
factors on the obtained estimates of these studies is unclear Ideally clinical
application of these treatment interventions should be based on research
evidence based on large randomized controlled trials In the absence of
controlled trials I decided to obtain best available estimates on the role of
AMH in individualisation of controlled ovarian stimulation using a robust
methodology in my large cohort of treatment cycles (Chapter 6) Oocyte yield
was used as the outcome measure given it is mainly determined by the
effectiveness of treatment strategies for ovarian stimulation which is the
question the study has addressed In contrast downstream outcomes such as
clinical pregnancy and live birth are subject to additional clinical and
interventional factors The study developed multivariable regression models of
total oocyte yield in all included IVF ICSI cycles (n=1653) and Metaphase II
oocytes of the ICSI subset (n=1101) to measure ovarian response to COH In
view of the significant interaction of PCO status with other variables I
restricted the analysis to non-PCO patients First in order to identify the
confounders I established the effect of a set of plausible factors that may affect
the outcomes including assessment of the effect of age AMH AFC causes of
infertility attempt of IVFICSI cycle COH protocol changes gonadotrophin
preparations operator for oocyte recovery pituitary desensitisation regime and
initial daily dose of gonadotrophins Then I developed the regression models
that examined the effect of gonadotrophin dose and regime categories on total
and mature oocyte numbers
240
The study found that after adjustment for all the above factors age
remained a negative predictor of oocyte yield whereas I observed a gradual
and significant increase in oocyte number with increasing AMH and AFC
values suggesting all these markers display an independent association with
oocyte yield Interestingly after adjustment for all above variables in non-PCO
patients I did not observe the expected increase in oocyte number with
increasing gonadotrophin dose categories beyond the very lowest doses This
suggests that there may not be a significant direct dose-response effect and
consequently strict protocols for tailoring the initial dose of gonadotrophins
may not necessarily optimize ovarian performance in IVF treatment It is
important to note our COH protocols utilized extensive cycle monitoring
using ultrasound follicle tracking and measurement of serum oestradiol levels
with corresponding adjustment of daily dose of gonadotrophins during ovarian
stimulation which may undermine the effect of initial dose of gonadotrophins
However further analysis with adjustment for the total gonadotrophin dose
and dose adjustment during the stimulation did not demonstrate a significant
impact on oocyte yield Nevertheless further longitudinal regression analysis
including full time course parameters of cycle monitoring and the dose
adjustments in the model should be conducted in order to ascertain the role of
AMH in tailoring the dose of gonadotrophins in cycles of IVF Moreover the
role of AMH on downstream outcomes of IVF cycles particularly on live
birth should be examined in this dataset Now equipped with a better
understanding of the research methodology and a robust database I am
planning to visit these research questions in future work
Although clinical biomarkers have improved the assessment of ovarian
reserve there remains a significant limitation in their performance in terms of
accurate estimation of ovarian reserve Given that ovarian reserve is believed
to be largely determined genetically recent large Genome-Wide Association
Studies (GWASs) have focused on the identification of genetic markers of
ovarian aging A meta-analysis of these 22 studies identified four genes with
nonsynonymous SNPs as being significantly associated with an age at
menopause (Stolk et al 2012 He et al 2012) However these SNPs were found
to account for only 25-41 of association of the age at menopause
Furthermore studies in mice and humans have identified more than 400 genes
that are involved in ovarian development and function (Wood et al 2013)
Given this genetic heterogeneity it is unlikely that a single genetic determinant
241
of ovarian reserve will be identified In addition epigenetic noncoding RNAs
and gene regulatory regions may play an important role in determination of
ovarian reserve which is yet to be fully explored (Bernstein et al 2012) Indeed
further large scale studies for ascertainment of genetic markers of ovarian
reserve are needed However current biomarkers including AMH appear to
remain as the most useful tests for the assessment of ovarian reserve in the
foreseeable future and further efforts to improve the performance of these
tests are therefore important
In summary some of the assumptions on performance of AMH
measurements particularly Gen II assay appear to have been based on weak
research evidence Similarly there are significant methodological limitations in
the published studies on AMH-tailored individualisation of controlled ovarian
hyperstimulation in IVF I believe the studies described in this thesis have
revealed instability of Gen II assay samples and raised awareness of the pitfalls
of AMH measurements These studies have also demonstrated the effect of
clinically measurable factors on ovarian reserve and provided data on the effect
of AMH other patient characteristics and treatment interventions on oocyte
yield in cycles of IVF Furthermore a robust database and statistical models
have been developed which can be used in future studies on ovarian reserve
and IVF treatment interventions I believe the work presented here has
provided a better understanding of the performance of AMH as an
investigative tool and its role in management of infertile women and provided
resource for future work in this area
242
References Bernstein BE Birney E Dunham I Green ED Gunter C Snyder M ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome Nature 2012 489(7414)57ndash74 [PubMed 22955616] Broer SL Mol BWJ Hendricks D Broekmans FJM The role of antimullerian hormone in prediction of outcome after IVF comparison with the antral follicle count Fertil Steril 200991705-14
Broer SL Doacutelleman M Opmeer BC Fauser BC Mol BW Broekmans FJ AMH and AFC as predictors of excessive response in controlled ovarian hyperstimulation a meta-analysis Hum Reprod Update 2011 Jan-Feb 17(1)46-54 Epub 2010 Jul 28 Carrarelli P Rocha A Belmonte Zupi E Abrao M Arcuri F Piomboni P and Petraglia F Increased expression of antimullerian hormone and its receptor in endometriosis Fertil Steril_ 20141011353ndash8
Durlinger AL Gruijters MJ Kramer P Karels B Kumar TR Matzuk MM Rose UM de Jong FH Uilenbroek JT Grootegoed JA and Themmen AP Anti-Mullerian hormone attenuates the effects of FSH on follicle development in the mouse ovary Endocrinology 20011424891ndash4899 Hadlow N Longhurst K McClements A Natalwala J Brown SJ Matson PL Variation in antimuumlllerian hormone concentration during the menstrual cycle may change the clinical classification of the ovarian response Fertil Steril 2013 May 99(6)1791-7 Han X Ledger W Pre-mixing samples with assay buffer is an essential pre-requisite for reproducible Antimullerian Hormone (AMH) measurement using the Beckman Coulter Gen II assay (Gen II) Human ReproductionJun2013 Vol 28 Issue suppl_1 He C Murabito JM Genome-wide association studies of age at menarche and age at natural menopause Mol Cell Endocrinol 2012
King D URGENT FIELD SAFETY NOTICE- FSN 20434 AMH Gen II ELISA (REF A79765) Beckman Coulter United Kingdom Limited November 27 2012
Kumar A Kalra B Patel A McDavid L Roudebush WE Development of a second generation anti-Muumlllerian hormone (AMH) ELISA J Immunol Methods 201036251-59
La Marca A et al Anti-Mullerian hormone (AMH) what do we still need to know Hum Reprod 2009 24(9) p 2264-75
Nardo LG Gelbaya TA Wilkinson H Roberts SA Yates A Pemberton P and Laing I Circulating basal anti-Muumlllerian hormone levels as predictor of ovarian
243
response in women undergoing ovarian stimulation for in vitro fertilization Fertil Steril 2009 921586-1593
Nelson SM Yates RW and Fleming R Serum anti-Muumlllerian hormone and FSH prediction of live birth and extremes of response in stimulated cycles--implications for individualization of therapy Hum Reprod 2007222414-2421
Nelson SM Yates RW Lyall H Jamieson M Traynor I Gaudoin M Mitchell P Ambrose P Fleming R Anti-Mullerian hormone-based approach to controlled ovarian stimulation for assisted conception Hum Reprod 200924867-875
Nelson SM La Marca A The journey from the old to the new AMH assay how to avoid getting lost in the values Reprod Biomed Online 201123411-420
Pankhurst M Chong Y H and McLennan ISEnzyme-linked immunosorbent assay measurements of antimeuroullerian hormone (AMH) in human blood are a composite of the uncleaved and bioactive cleaved forms of AMH Fertility and Sterility2014
Rustamov O Pemberton PW Roberts SA Smith A Yates AP Patchava SD and Nardo LG The reproducibility of serum anti-Muumlllerian hormone in subfertile women within and between patient variability Fertil Steril 2011951185-1187
Rustamov O Smith A Roberts SA Yates AP Fitzgerald C Krishnan M Nardo LG and Pemberton PW Anti-Mullerian hormone poor assay reproducibility in a large cohort of subjects suggests sample instability Hum Reprod 2012a273085-3091 Schuh-Huerta SM Johnson NA Rosen MP Sternfeld B Cedars MI Reijo Pera RA Genetic variants and environmental factors associated with hormonal markers of ovarian reserve in Caucasian and African American women Hum Reprod (2012a) 27594ndash608 Signorile P Petraglia F Baldi A Anti-mullerian hormone is expressed by endometriosis tissues and induces cell cycle arrest and apoptosis in endometriosis cells Journal of Experimental amp Clinical Cancer Research 2014 3346 Stolk L Perry JR Chasman DI et al Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways Nat Genet 2012 44(3)260ndash268
Yates AP Rustamov O Roberts SA Lim HY Pemberton PW Smith A Nardo LG Anti-Mullerian hormone-tailored stimulation protocols improve outcomes whilst reducing adverse effects and costs of IVF Hum Reprod 2011262353-2362
van Rooij IA Broekmans FJ te Velde ER Fauser BC Bancsi LF de Jong FH
244
and Themmen AP Serum anti-Mullerian hormone levels a novel measure of ovarian reserve Hum Reprod 2002173065-3071
Wang J Dicken C Lustbader JW and Tortoriello DV Evidence for a Mullerian-inhibiting substance autocrineparacrine system in adult human endometrium Fertility and Sterility_ Vol 91 No 4 April 2009 Wood M and Rajkovic A Genomic Markers of Ovarian Reserve Semin Reprod Med 2013 31(6) 399ndash415
245
Authors and affiliations
Stephen A Roberts PhD
Centre for Biostatistics Institute of Population Health Manchester Academic
Health Science Centre (MAHSC) University of Manchester Manchester M13
9PL United Kingdom
Cheryl Fitzgerald MD
Department of Reproductive Medicine St Maryrsquos Hospital Central
Manchester University Hospital NHS Foundation Trust Manchester M13 0JH
United Kingdom
Philip W Pemberton MSc
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Alexander Smith PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Luciano G Nardo MD
Reproductive Medicine and Gynaecology Unit GyneHealth
Manchester M3 4DN United Kingdom
Allen P Yates PhD
Department of Clinical Biochemistry Central Manchester University Hospitals
NHS Foundation Trust Manchester M13 9WL United Kingdom
Monica Krishnan MBChB
Manchester Royal Infirmary Central Manchester University Hospitals NHS
Foundation Trust Manchester M13 9WL United Kingdom
246
Acknowledgments
First and foremost I would like to thank my supervisors Dr Stephen A
Roberts and Dr Cheryl Fitzgerald I am indebted to you for introducing me
into the world of science showing its wonders and guiding me through its
terrains Without your 247 advise and support none of these projects would
have been possible Thank you
I would also like to thank other members of our team Dr Philip W
Pemberton Dr Luciano G Nardo Dr Alexander Smith Dr Allen P Yates and
Monica Krishnan It has been exciting and fun to be a part of the Manchester
AMH Group
I am grateful for the support and friendship of all secretaries nurses
embryologists and consultants of IVF Department at St Maryrsquos Hospital I
would like to express my special thanks to Professor Daniel Brison for his
advice on the projects and providing a great opportunity for research I would
like to express my gratitude to Dr Greg Horne Senior Embryologist for his
patience in taking me through tons of IVF data It was a privilege to be part of
this team
Indeed without support of my wife Zilola Navruzova I could not have
completed my MD programme Thank you for being there for me through
thick and thin of life You are love of my life Your optimism can make
anything possible Your sense of humor and kindness brightened my long
research hours after on-call shifts Only because of your enthusiasm we could
juggle work research and family And thanks for pretending that AMH is
interesting
My children Firuza Sitora and Timur You are most great kids Always stay
cool and funny like this Sorry for not taking you to holiday during my never-
ending research during last year Hope I havenrsquot put you off doing research in
future You get lots of conference holidays after research
247
I canrsquot thank enough my mother Karomat Rajobova and father Dr Sohib
Rustamov Your love kindness and wisdom have always been inspiration and a
guide in my life I always strive to follow your example albeit impossible to
achieve
My brother Ulugbek Rustamov thank your selfless support As always you
have been my guide and strength during these three years My friends Odil
Nizomov Dr Rohit Arora Tarek Sharif and Sabiha Sharif I am grateful for
your friendship and support during my MD Programme
248
I would like to dedicate this thesis to my mother father my wife and
children
Shu Doctorlik Dissertaciysini
Onam (Karomat Rajabova)
Dadam (Dr Sohib Rustamov)
Turmush Urtogim (Zilola Navruzova)
Farzandlarim (Firuza Sohibova Sitora Sohibova
Timur Rustamov) ga bagishlayman
Sizlar mani kuzimni nuri sizlar
Yaratgandan sizlarga mustahkam sogliq va quvonch tilayman
_______________________
Oybek
31 March 2014 Manchester United Kingdom