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University of Groningen
Genetic variation, telomeres and heart failureHaver, Vincent Gerardus
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Genetic variation, Telomeres
and Heart Failure
Vincent Gerardus Haver
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2
Financial support for the publication of this thesis by the following
companies / institutes is gratefully acknowledged: Groningen University
Institute for Drug Exploration, Guerbet Nederland B.V., Servier
Nederland Farma B.V., University of Groningen, Van Buchem
Stichting, ZOLL Benelux B.V.
Haver, V.G.
Genetic variation, Telomeres and Heart Failure
Proefschrift Groningen
ISBN: 978-90-367-8284-5 (Printed version)
ISBN: 978-90-367-8282-1 (Electronic version)
©Copyright 2015 V.G. Haver
All rights reserved.
No parts of this publication may be reproduced, stored in a retrieval
system, or transmitted in any form or by any means, without permission
of the author
Lay-out: V.G. Haver
Printed by: Grafimedia, Groningen
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Genetic variation, Telomeres
and Heart Failure
Proefschrift
ter verkrijging van de graad van doctor aan de
Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken
en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op
maandag 26 oktober om 11.00 uur
door
Vincent Gerardus Haver
geboren op 17 augustus 1985
te Groningen
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Promotores:
Prof. dr. P. van der Harst
Prof. dr. W.H. van Gilst
Beoordelingscommissie:
Prof. dr. M. Walter
Prof. dr. P.E. Slagboom
Prof. dr. M.P. van den Berg
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Paranimfen:
R.N. Eppinga, M.Sc.
J.F. Feddema
Financial support by the Dutch Heart Foundation for the
publication of this thesis is gratefully acknowledged.
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Table of contents
Chapter 1 Introduction 9
Chapter 2 Aims of this thesis 15
Chapter 3
Telomere length and new onset heart failure: Data from
Prevention of Renal and Vascular End-stage Disease
(PREVEND)
Submitted
19
Chapter 4
Leukocyte telomere length and left ventricular function
after acute ST-elevation myocardial infarction: Data
from the Glycometabolic Intervention as adjunct to
Primary Coronary Intervention in ST Elevation
Myocardial Infarction (GIPS-III) trial
Clin Res Cardiol. 2015 (in press)
45
Chapter 5
Telomere Length and Outcomes in Ischemic Heart
Failure: Data from the COntrolled ROsuvastatin
multiNAtional Trial in Heart Failure (CORONA)
Eur J Heart Fail 2015;17(3):313-9
67
Chapter 6
The Impact of Coronary Artery Disease Risk Loci on
Ischemic Heart Failure Severity and Prognosis:
Association analysis in the COntrolled ROsuvastatin
multiNAtional trial in heart failure (CORONA)
BMC Med Genet 2014;15:140-7
89
Chapter 7 General discussion and future perspectives 125
Short summary 133
Samenvatting 135
Beknopte samenvatting 139
Bibliography 140
Dankwoord 143
Curriculum Vitae 149
References 150
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Chapter 1
Introduction
Genetic variation, Telomeres
and Heart Failure
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Society is aging. This phenomenon is the result of both successful preventive
measures1 and promoting healthy living, as well as improved medical care for
serious conditions and illnesses. Life expectancies in Western society are
higher than ever before. However, individuals with similar chronological age
(defined by date of birth) can differ greatly in biological age (defined by the
amount of physiological ‘damage’ an individual has encountered during life).2
Patients suffering from similar diseases and of the same chronological age, can
have different prognosis and life expectancies, based on their biological age.
Measurable (and potentially modifiable) biological parameters are desirable in
order to more accurately identify risk and predict disease outcomes, thereby
guiding the physician towards a more personalized prevention and treatment
regimen for each patient.
Cardiovascular disease continuum
In the Netherlands, cardiovascular disease (CVD) is one of the leading cause of
death and morbidity.4 The CVD continuum (Figure 1) conceptualizes a chain of
events occurring during life in the course of cardiovascular disease
progression.3 The continuum is initiated by a number of risk factors (e.g.
smoking, hypertension, dyslipidemia and obesity), which accumulate during
life, and evolves by means of a number of related and unrelated (patho-)
physiological processes towards mortality caused by end stage heart disease.
Since humans continue to have increased expected life spans with each
generation and survive more and more early manifestations of CVD, prediction
of the course of CVD is important for further improving our prevention and
treatment choices.
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Figure 1. Simplified version of the cardiovascular disease continuum.
Risk factors include dislipidemia, hypertension, diabetes, smoking and obesity
(among others). Adapted from Dzau V. J. et al.3
Coronary artery disease and ST-elevation Myocardial Infarction
One of the early disease entities in the CVD continuum is coronary artery
disease (CAD). In CAD, atherosclerotic plaques in the coronary arteries build
up, which reduces blood flow to distal parts of the myocardium, which causes
symptoms like angina and reduced physical capacity. Ultimately, this can lead
Risk factors
Coronary artery disease
Myocardial infarction
Heart failure
End-stage heart disease
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to acute coronary syndrome, of which ST-elevation myocardial infarction
(STEMI) is a sub-class. STEMIs are primarily caused by atherosclerotic plaque
rupture leading to (sub-)total blockage of coronary blood flow. Besides the
common risk factors for cardiovascular diseases which are described in the
CVD continuum, genetic variants (single nucleotide polymorphisms) have been
identified which are associated with an increased risk of CAD.5-7
Some of these
risk factors are simultaneously associated with the risk of lipid imbalances,
high blood pressure and/or diabetes, thereby increasing the risk of CAD and
STEMI. One of the chapters in this thesis focusses on the impact of CAD
variants in relation to the outcome of patients with heart failure (HF) due to
CAD.
Heart failure
With every heartbeat, a certain volume of blood is pumped into the aorta and
pulmonary artery (the so-called ‘stroke volume’) but due to structural
limitations, some blood remains in the right and left ventricle after systole. The
end diastolic volume minus the end systolic volume divided by the end
diastolic volume is called the ejection fraction (EF). HF is a clinical syndrome
in which the heart is unable to execute its function at a rate which fulfills the
bodily needs.8 Underlying abnormalities in cardiac structure and function are
the cause of HF, which is reflected by typical signs (e.g. elevated jugular
venous pressure, and pulmonary crackles) and symptoms (e.g. fatigue,
breathlessness, and ankle swelling). Two distinct types of HF are currently
being recognized, namely HF with reduced ejection fraction (HFrEF, left
ventricular EF (LVEF) ≤35% or ≤40%) and HF with preserved ejection fraction
(HFpEF, LVEF >50%). In practice, LVEF is determined by imaging
techniques, like echocardiography and magnetic resonance imaging (MRI).9,10
Causes of HF are numerous and include CAD, which accounts for
approximately two-thirds of HFrEF cases, viral infections, alcohol abuse and
chemotherapy.8
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HF symptoms are graded according to the New York Heart Association
(NYHA) functional classification systems. NYHA class I-IV represents no
symptoms attributable to heart disease, mild, moderate or severe symptoms,
respectively. Changes in NYHA class can occur rapidly and suddenly, for
which appropriate and immediate interventions are required, since acute
deterioration of HF symptoms is associated with increase the risk of
hospitalization and death.8 Regardless of the underlying cause leading to HF,
prognosis of patients HF patients is poor, with a median survival of ~5 years
after diagnosis.11
Many prognostic variables have been identified, for example
age, sex, aetiology, NYHA class, LVEF and co-morbidities (diabetes mellitus,
renal dysfunction, depression, pulmonary disease).
HF is a considerable burden for society. Approximately 1-2 percent of the
population in the western world will develop HF in the course of life (this
percentage rises with increasing age, with >10 percent at risk at the age of 85
and above), the yearly incidence approaches 5-10 per 1000 persons.12
This
underscores the need for increasing our basic knowledge on the mechanisms
involved as well as novel early diagnostic tools or sensitive parameters
predicting prognosis and outcome in HF patients as well as optimizing
treatment.
Telomere Biology
Telomeres are non-coding, hexameric nucleotide repetitions which are located
at both terminal ends of chromatids. The sequence in humans (Homo sapiens)
is (TTAGGG)n, but differs between species.13
Telomeres are important to
ensure genetic stability and prevent dimerization of chromosomes, which
impedes proper cell division. Inherent to their molecular structure, DNA
polymerase proteins are unable to duplicate the telomere as a whole, which is
known as the ‘end replication problem’.14
Therefore, telomere length shortens
with each event of mitotic cellular division. Except for embryogenic stem cells,
germline cells and malignant transformed cells, mature cells do not express
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telomerase,14
a protein which elongates telomeres, thereby causing the telomere
to shorten as the number of cell divisions accumulate. When a critical telomere
length is reached, cells stop dividing and enter a state of senescence or
apoptosis.15
In 1961, Leonard Hayflick described this phenomenon for the first
time and the maximum number of cell divisions a cell can undergo is known as
the “Hayflick limit”.16
Since cells, in general, undergo numerous divisions
during the course of human lifespan, telomeres in older people are shorter
compared to their younger peers, which has coined the hypothesis that
telomeres serve as a “bio-molecular clock”.13
This clock correlates not only
with the age of an individual, but could presumably also predict the length of
remaining life.
Telomere length is not solely correlated to the number of mitotic events.
Inflammatory mechanisms17
and oxidative stress18
exert negative effects on
telomere length. Risk factors involved in CVD (see above) are known to
accelerate telomere shortening and, consequently, a number of diseases in the
CVD continuum have been associated with decreased telomere lengths, for
example CAD,19
myocardial infarction20
and HF.21,22
Furthermore, our lab has
discovered that telomere length shortening correlates with disease severity and
worse outcomes in HF patients.23
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Chapter 2
Aims of this thesis
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Genetic mechanisms, including genetic variants and telomere biology, are
related to healthy ageing and the occurrence of cardiovascular disease.
Especially telomere length measured in leukocytes is currently being studied as
potential biomarkers of biological ageing and predictors of progression and in
the cardiovascular disease continuum.
The main goal of this thesis:
To describe the (possible) role of genetic variation and leukocyte telomere
length in the progression of disease through the cardiovascular disease
continuum, focusing on heart failure.
To further expand our knowledge on the role of telomere biology on the
development and progression of heart failure (HF) we studied telomere length
in three stages of the cardiovascular disease continuum. Chapter 3 aims to
describe the value of leukocyte telomere lengths in predicting new onset HF in
a cohort of healthy subjects, which have been followed up for nearly 13 years.
The aim of Chapter 4 is to determine the role of leukocyte telomere length in
patients with acute myocardial infarction and the future development of systolic
left ventricular dysfunction. The aim of Chapter 5 is to describe the value of
leukocyte telomere length in patients with established HF due to coronary
artery disease. Here we study clinical outcomes in HF patients. In Chapter 6,
we consider genetic variations as defined by single nucleotide polymorphisms
as a potential additional genetic mechanism to explain clinical outcomes in HF
patients. We aim to describe the potential impact of several genetic variants
with HF severity and clinical outcomes.
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Chapter 3
Telomere length
and new onset heart failure:
Data from Prevention of Renal and Vascular End-stage
Disease (PREVEND)
Vincent G. Haver
Frank P. Brouwers
Rudolf A. de Boer
Ron T. Gansevoort
Dirk J. van Veldhuisen
Wiek H. van Gilst
Pim van der Harst
Submitted
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Abstract
Telomeres protect against chromosomal instability and are considered a marker
for biological age. Accelerated telomere shortening has been associated with a
variety of cardiovascular diseases. In this cross-sectional experiment we tested
the hypothesis that shorter telomere length is associated with an increased risk
of new onset heart failure. Mean leukocyte telomere length was determined at
baseline in the Prevention of Renal and Vascular End-stage Disease
(PREVEND) study by a monochrome multiplex quantitative polymerase chain
reaction (PCR)-based assay. We were able to determine leukocyte telomere
length in 8053 subjects at baseline. 351 (4%) subjects developed heart failure
during a median follow-up of 12.6 years. We found that baseline telomere
length was significantly shorter in subjects developing heart failure compared
to subjects without new onset heart failure (P < 0.001). Compared to the
longest telomere length quartile, the shortest length quartile was associated
with increased risk of new onset heart failure (hazard ratio 2.26, 95%CI 1.42-
3.61, P = 0.001), mortality (hazard ratio 2.23, 95%CI 1.62-3.06, P < 0.001) and
the occurrence of cardiovascular events (hazard ratio 2.17, 95%CI 1.60-2.96, P
< 0.001). These associations did not remain statistically significant after
including chronological age (defined by date of birth) in the model. In the
PREVEND study cohort, healthy individuals who developed new onset heart
failure during follow-up are characterised by shorter leukocyte telomere lengths
compared to heart failure-free subjects. This observation appears mostly
dependent on chronological age.
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Introduction
Heart failure (HF) is a medical syndrome with high morbidity and mortality
rates and substantial socio-economic burden.24
Elderly are at increased risk to
develop HF and due to the increased life-expectancy in Western societies,
incidence and prevalence of HF is expected to increase in the next decades.
Therefore, identifying factors causing and predicting HF are of great interest
for risk stratification and the development of preventive and therapeutic
strategies. Two distinct types of HF are currently acknowledged, namely HF
with reduced ejection fraction (HFrEF), and HF with preserved ejection
fraction (HFpEF). For incident HFrEF, the most important predictors of
mortality are age, male gender, history of myocardial infarction, smoking, N-
terminal of the prohormone brain natriuretic peptide (NT-proBNP) and high
sensitive Troponin T. For HFpEF, age, female gender and history of atrial
fibrillation are associated with higher incidence.25
Telomeres are DNA-protein complexes constructed of tandem repeats of a
repeated DNA sequence ((TTAGGG)n in humans) and the shelterin complex
consisting of specific proteins, for example telomerase.26
Telomeres are located
at the terminal ends of chromosomes and protect against chromosomal
degradation, fusion and unwanted recombination.27
Due to the ‘end replication
problem’ during chromosomal duplication, telomere length shortening occurs
with every mitotic event.28,29
Environmental factors, especially oxidative stress,
can further accelerate telomere length shortening.18
When a critical telomere
length is reached, a cell becomes senescent, preventing pathogenic
deterioration. Telomere length is therefore considered as the molecular clock of
the natural aging process. Measuring telomere length in easily obtainable
leukocytes has been suggested as a proxy for telomere length in other cell types
and allows studying large number of subjects. Healthy lifestyle and longevity
are associated with longer telomeres.30
On the contrary, leukocyte telomere
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length has previously been correlated with a number of (cardiovascular)
diseases,27,28
for example coronary artery disease31
and HF.32
In humans
suffering from HF, telomere length in leukocytes is shorter compared to
presumably healthy age-matched controls. Furthermore, short telomeres are
linked to HF severity expressed in New York Heart Association functional
classification score and prognosis, which was shown in the prospective Co-
ordination study evaluating Outcomes of Advising and Counseling in Heart
Failure trial.32
We hypothesize that telomere length is shortened in subjects who will develop
HF later in life compared to non-HF controls. We analysed leukocyte telomere
length in the population-based Prevention of Renal and Vascular End-stage
Disease (PREVEND) study cohort and investigated the potential association
between leukocyte telomere length and new onset HF.
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Materials and Methods
Study population
We performed this observational study as part of The Prevention of Renal and
Vascular End-stage Disease (PREVEND) study, which was initiated in 1997 in
the city of Groningen, The Netherlands and enrolled 8.592 inhabitants. The
study details have been described previously.33
Once per three years,
participants visited an outpatient clinic and were extensively monitored by
anthropometric and blood pressure measurements, collection of two 24 hour
urine samples, electrocardiography recording, and fasting blood samples. All
participants of PREVEND provided written informed consent. The local
Medical Ethics of committee the University Medical Center Groningen has
approved the study, which was conducted in accordance with the guidelines of
the declaration of Helsinki.
Definitions
All PREVEND subjects were asked to complete a questionnaire regarding
demographic and smoking habits. Smoking was defined as current nicotine use
or quit smoking within the previous year. Systolic and diastolic blood pressures
were calculated as the mean of the last two measurements, using an automatic
Dinamap XP Model 9300 series device. Hypertension was defined as a systolic
blood pressure measurement above 140 mmHg and/or a diastolic blood
pressure above 90 mmHg and/or self-reported current blood pressure lowering
medication. Diabetes mellitus type II was defined as a fasting glucose
measurement of above 7.0 mmol/L (126 mg/dL) or a nonfasting glucose
measurement of above 11.1 mmol/L (200 mg/dL), or the use of anti-diabetic
drugs. Hypercholesterolemia was defined as a cholesterol measurement above
6.5 mmol/L (251 mg/dL), or above 5.0 mmol/L (193 mg/dL) if a history of MI
was present or when lipid-lowering medication was used. Previous history for
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cardiovascular disease was defined as participant-reported hospitalization for at
least three days as a result of MI or cerebrovascular disease. Urinary albumin
excretion was calculated as the average value from two consecutive 24h urine
collections. BMI was calculated as the ratio of weight to height squared
(kg/m2), and obesity was defined as a BMI >30kg/m
2. Body surface area was
calculated using the formula of Du Bois and Du Bois.34
The estimated
Glomerular Filtration Rate was calculated using the simplified Modification of
Diet in Renal Disease formula.35
Anti-hypertensive drugs were defined as
angiotension-converting enzyme inhibitors, angiotensin receptor blockers,
diuretics or calcium antagonists. Lipid-lowering drugs were defined as any kind
of statin. Glucose-lowering drugs were defined as oral anti-diabetic drugs.
Information on medication use was obtained from the InterAction DataBase, a
community-based pharmacy database, of the city of Groningen and was linked
to the PREVEND database.36
Prescription drugs were classified according to
the Anatomical Therapeutic Chemical System.
Additional assays
At baseline, EDTA plasma samples were drawn from all participants for
biomarker assessment. Aliquots of these samples were stored immediately after
collection at -80°C until analysis. NT-proBNP and highly sensitive C-reactive
protein were measured as described before.37,38
Highly sensitive troponin T was
measured using modular analytics serum work areas, with less than 10%
coefficient of variation at the 99th percentile of the reference range (Roche
Diagnostics). Urinary Albumin Concentration was determined by nepholometry
(BNII, Dade Behring Diagnostic, Marburg, Germany).
Cardiovascular events definition
Cardiovascular events were defined as suffering or dying from myocardial
infarction, ischemic heart disease, coronary artery bypass grafting,
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percutaneous transluminal coronary angioplasty, subarachnoidal haemorrhage,
intracerebral haemorrhage, other and unspecified intracranial haemorrhage,
occlusion and stenose of precerebral arteries, occlusion of cerebral arteries,
carotis desobstruction and/or aorta peripheral bypass surgery.
Telomere length measurement assay
Baseline white blood cell DNA was extracted from using a standard DNA
extraction kit (QIamp, Qiagen, Venlo, the Netherlands). Telomere length was
measured in triplicate using the monochrome multiplex quantitative
Polymerase Chain Reaction method, developed by R.M. Cawthon.39
The T/S
ratio is calculated by dividing the telomere (T) expression by the expression of
a reference gene (S). This T/S ratio measurement is reproducible and has a
reasonable correlation with the labour intensive Southern blot method39
and has
become the method of choice in large epidemiological studies. The intra-assay
coefficients of variation were 2.0% for T, 1.9% for S and 4.5% for the T/S
ratio. Standardized telomere length was calculated as (T.S.avg – mean
[T.S.avg]) divided by standard deviation [T.S.avg]. T/S ratios for quartile cut-
offs in our cohort were: 1st quartile ≤ -0.1847; 2
nd quartile: -0.18466 – 0.00288;
3rd
quartile 0.00293 – 0.20478; 4th
quartile ≥ 0.20479.
Heart failure definitions
Follow-up time for the present investigation was defined as the time between
the baseline visit date to the outpatient department and the date of new onset
HF up to January 1st, 2010. Subjects were censored at the date they moved to
an unknown destination or at the last date of the follow-up, whatever date came
first. Dates and causes of death for every diseased participant were obtained
from CBS Statistics Netherlands.40
and coded by the 10th revision of the
International Classification of Diseases. The details of identification and
classification of new onset HF in PREVEND has been described elsewhere.25
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HF was classified as HFrEF or HFpEF according to LVEF at diagnosis, with
cut-off values of ≤40% for HFrEF and ≥50% for HFpEF. Subjects in the grey
area, with a LVEF 41-49% (n = 8), were excluded from the analyses to prevent
blending and dilution of differential epidemiological profiles.
Statistical analysis
By design, subjects with an UAE >10 mg/L are overrepresented in the
PREVEND study. A design-based analysis was performed to overcome this
over-selection of subjects with elevated UAE. This statistical weighting method
allows conclusions to be generalized to the general population.41
Baseline
continuous data are reported as mean (standard deviation) for normally
distributed data. Because of skewed distribution, BMI, creatinine, highly
sensitive C-reactive protein, NT pro-BNP and triglycerides were transformed to
their natural logarithm and reported as median (inter-quartile range).
Furthermore, T/S ratios of the telomere length measurement assay were log-
transformed. The raw log-transformed T/S ratios were centered them around 0
(by subtracting the mean) and multiplied by 100, providing the final reported
dimensionless and arbitrary Relative Telomere Length Unit (RTLU), as
described before.42
To evaluate time to HF diagnosis for both risk groups, time
to cardiovascular event, and mortality, we performed Kaplan-Meier analyses
(log-rank) using cumulative incidence analysis, divided over quartiles of
telomere lengths, which were created in ascending order. We fitted Cox-
proportional hazards models to the data and adjusted our multivariate model for
age defined by date of birth. Schoenfeld residuals were calculated to assess
whether proportionality assumptions were satisfied. All statistical analyses
were done two-tailed and a P-value of <0.05 was used as nominal level of
statistical significance. The analyses were performed using StataIC (version 12
software for Windows).
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Results
Study population
Baseline leukocyte telomere length measurement was successful in 8.053
subjects of PREVEND (93.7% of the original PREVEND cohort). The median
follow-up time was 12.6 years (IQR 12.3 – 12.9). Subjects already diagnosed
with HF before collection of leukocytes for the telomere length measurements
were excluded from the analysis (n = 23). The baseline variables of participants
according to quartiles of telomere length are summarised in Table 1. Baseline
characteristics stratified by HFrEF and HFpEF are presented in Table S1.
Average age at inclusion was 49.2 ± 12.7 years and 49.2% were males.
Subjects in the lower quartiles were younger than in the higher quartile of
telomere length and the percentage of males was larger in the shorter telomere
quartiles (Table 1). In participants with shortest telomeres there was a higher
incidence of diabetes, smokers, hypertension, hypercholesterolemia and obesity
(Table 1). Shorter leukocyte telomeres were associated with deteriorated renal
function (as indicated by estimated glomerular filtration rate and creatinine),
attenuated lipid profile and increased NT-proBNP levels. At last, subjects with
shorter telomeres were more intensively treated with pharmacological agents.
New onset HF
Leukocyte telomere length was successfully measured at baseline in 351
PREVEND participants who developed HF during 12.6 years of follow-up. Of
these patients, 224 (63.8%) suffered from HFrEF and 119 (33.9%) from
HFpEF. Leukocyte telomere length at baseline was shorter in patients
experiencing new onset HF compared to non-HF subjects (relative leukocyte
telomere length of -0.056 ± 0.264 and 0.023 ± 0.291, P < 0.001, respectively).
The Kaplan-Meier curves of new onset HF incidence with telomere lengths
divided into quartiles are shown in Figure 1.
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Table 1. Baseline characteristics of subjects, divided over quartiles of
telomere length.
Characteristic 1st quartile
(n = 2014)
2nd quartile
(n = 2013)
Telomere length < -0.18 -0.18 - 0.00
HF n (%) 119 93
HFrEF n (%) 77 (64.7) 59 (63.4)
HFpEF n (%) 42 (35.3) 34 (36.6)
Demography
Age (years) 53.0 (12.8) 49.8 (12.6)
Male (%) 53.5 51.7
Systolic BP (mmHg) 132.7 (21.1) 129.2 (20.4)
Diastolic BP (mmHg) 75.4 (9.8) 74.2 (9.7)
Heart rate (bpm) 69.9 (10.3) 69.4 (10.0)
BMI (kg/m2) 26.3 (23.7-28.9) 25.6 (23.2-28.5)
Waist hip ratio 0.90 (0.09) 0.88 (0.10)
Medical history (%)
Diabetes 6.15 3.62
Smoking 41.18 38.79
Hypertension 39.96 33.13
Hypercholesterolemia 32.39 28.56
Obesity 18.87 16.15
Laboratory values
eGFR (ml/min/1.73m2) 78.7 (14.9) 80.4 (14.9)
Creatinine (umol/L) 84 (75-93) 83 (74-93)
hs-CRP (mg/L) 1.64 (0.7-3.6) 1.29 (0.56-3.1)
Glucose (mmol/L) 5.1 (1.3) 4.9 (1.1)
Cholesterol (mmol/L) 5.8 (1.1) 5.7 (1.1)
LDL (mmol/L) 3.83 (1.04) 3.71 (1.02)
HDL (mmol/L) 1.27 (0.39) 1.31 (0.40)
Triglycerides (mmol/L) 1.25 (0.9-1.8) 1.22 (0.9-1.7)
NT pro-BNP (ng/L) 41.3 (18.2-82.5) 36.7 (16.3-71.7)
Medication at baseline (%)
Anti-hypertensive drugs 17.8 15.84
Anti-diabetic drugs 2.24 1.27
Lipid-lowering drugs 5.95 4.84
Telomere lengths are divided over quartiles. BP: blood pressure; BMI: body
mass index; eGFR: estimate glomerular filtration rate; HF: heart failure;
HFrEF: heart failure with reduced ejection fraction; HFpEF: heart failure with
preserved ejection fraction; hs-CRP: highly sensitive C-reactive protein; LDL:
low-density lipoprotein; HDL: high-density lipoprotein; NT pro-BNP: N-
terminal pro-B-type natriuretic peptide. Normally distributed data are expressed
as mean (standard deviation), non-Gaussian data as median (interquartile
range).
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Table 1 (continued). Baseline characteristics of subjects, divided over
quartiles of telomere length.
3rd quartile
(n = 2013)
4th quartile
(n = 2013) P for trend
Total
(n = 8053)
0.00 - 0.20 > 0.20 0.020
75 56 <0.001 351
55 (73.3) 33 (58.9) 224 (65.3)
20 (26.7) 23 (41.1) 119 (34.7)
48.3 (12.3) 45.8 (11.9) <0.001 49.2 (12.7)
48.3 45.6 <0.001 49.8
128.4 (19.9) 125.8 (18.7) <0.001 129.0 (20.2)
73.7 (9.7) 72.7 (9.6) <0.001 74.0 (9.7)
68.8 (10.2) 68.9 (10.1) <0.001 69.2 (10.2)
25.4 (23.0-28.3) 25.1 (22.7-27.7) <0.001 25.6 (23.1-28.4)
0.88 (0.09) 0.86 (0.09) <0.001 0.88 (0.09)
3.68 2.41 <0.001 3.96
35.98 35.91 <0.001 37.97
29.64 24.53 <0.001 31.8
25.47 20.75 <0.001 26.79
15.65 12.36 <0.001 15.76
81.1 (14.1) 82.1 (14.1) <0.001 80.6 (14.5)
82 (73-91) 81 (73-90) <0.001 82 (74-92)
1.2 (0.5-2.7) 1.05 (0.5-2.5) <0.001 1.28 (0.6-3.0)
4.9 (1.2) 4.7 (1.0) <0.001 4.9 (1.2)
5.6 (1.1) 5.5 (1.2) <0.001 5.6 (1.1)
3.64 (1.05) 3.54 (1.05) <0.001 3.68 (1.05)
1.34 (0.40) 1.37 (0.40) <0.001 1.32 (0.40)
1.14 (0.8-1.7) 1.07 (0.8-1.6) <0.001 1.16 (0.9-1.7)
38.3 (16.6-72.8) 34.6 (15.7-68.1) <0.001 37.6 (16.7-73.5)
11.89 10.13 0.01 13.93
1.24 0.93 0.12 1.42
3.04 2.72 0.93 4.14
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Leukocyte telomere length was associated with the incidence of new onset HF
(Table 2). Subjects in the two lowest quartiles of telomere length had
significantly higher incidence of new onset HF compared to the quartile with
longest telomeres (HR 2.3, 95% CI 1.4-3.6, P = 0.001 and HR 1.9, 95% CI 1.2-
3.4, P = 0.007, respectively). When chronological age (defined by the data of
birth) was introduced in the model the risk of shorter telomere length did not
remain a significant independent predictor of new onset HF (HR 1.2, 95% CI
0.8-1.9, P = 0.41).
We tested whether leukocyte telomere lengths were associated with the two
distinct forms of HF. Kaplan-Meier cumulative incidence curves for HFrEF
and HFpEF are shown in Figure 2. Both HFrEF and HFpEF were associated
with shorter leukocyte telomere lengths when tested univariately. However,
when chronological age was introduced into the model the telomere length
association did not remain significant (Table 3).
Prognostic value of baseline telomere lengths on the incidence
of cardiovascular events and mortality
In total, there were 788 cardiovascular events and 615 subjects who were
diseased during follow-up. The cumulative incidence of cardiovascular events
and all-cause mortality are represented in Figure 3. Subjects in the shortest
quartile of telomere length had a significantly higher incidence of
cardiovascular events when compared to the quartile with longest telomeres
(HR 2.2, 95% CI 1.6-3.0, P = <0.001), see Table 4. The shortest telomere
length quartile was associated with increased mortality during follow-up (HR
2.2, 95% CI 1.6-3.1, P = 0.001), see Table 4. However, these associations were
not independent of chronological age.
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Figure 1. Cumulative incidence of new onset HF.
Cumulative incidence curves of new onset heart failure during follow-up in
PREVEND, divided over quartiles of leukocyte telomere length in PREVEND.
Quartile 1 represents the shortest telomere lengths. P value represents P for
trend between Quartile 1 and 4.
Table 2. Cox regression analyses of telomere length in quartiles and new
onset HF in PREVEND.
New onset HF
Model 1 Model 2
Telomere
quartiles HR 95% CI P HR 95% CI P
1 2.26 1.42 - 3.61 0.001 1.21 0.77 - 1.92 0.408
2 1.95 1.20 - 3.14 0.007 1.30 0.81 - 2.09 0.285
3 1.54 0.94 - 2.53 0.086 1.30 0.80 - 2.13 0.291
4 1
1
Telomere quartiles: 1 = RTLU < -0.18; 2 = RTLU 0.18 – 0.00; 3 = RTLU 0.00
– 0.20; 4 = RTLU > 0.20. Hazards ratios are calculated for quartiles with
shorter telomeres with the longest quartile as reference. Model 1: unadjusted;
Model 2: adjusted for age. HF: heart failure; HFrEF: heart failure with reduced
ejection fraction; HFpEF: heart failure with preserved ejection fraction; RTLU:
relative telomere length in arbitrary units.
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32
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Page 34
33
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Page 35
34
Table 3. Cox regression analyses of telomere length in quartiles and
HFrEF and HFpEF in PREVEND.
HFrEF
Model 1 Model 2
Telomere
quartiles HR 95% CI P HR 95% CI P
1 2.20 1.20 - 4.03 0.010 1.23 0.68 - 2.22 0.503
2 2.06 1.10 - 3.83 0.023 1.41 0.76 - 2.60 0.275
3 2.05 1.10 - 3.81 0.023 1.74 0.94 - 3.22 0.078
4 1
1
Telomere length quartiles: 1 = RTLU < -0.18; 2 = RTLU 0.18 – 0.00; 3 =
RTLU 0.00 – 0.20; 4 = RTLU > 0.20. Hazards ratios are calculated for quartiles
with shorter telomeres with the longest quartile as reference. Model 1:
unadjusted; Model 2: adjusted for age. HF: heart failure; HFrEF: heart failure
with reduced ejection fraction; HFpEF: heart failure with preserved ejection
fraction; RTLU: relative telomere length in arbitrary units.
Table 4. Cox regression analyses of telomere length in quartiles and
cardiovascular events and mortality
Cardiovascular events
Model 1 Model 2
Telomere
quartiles HR 95% CI P HR 95% CI P
1 2.17 1.60 - 2.96 <0.001 1.33 0.98 - 1.81 0.07
2 1.74 1.26 - 2.40 0.001 1.30 0.94 - 1.80 0.11
3 1.20 0.86 - 1.68 0.281 1.04 0.75 - 1.45 0.80
4 1
1
Telomere length quartiles: 1 = RTLU < -0.18; 2 = RTLU 0.18 – 0.00; 3 =
RTLU 0.00 – 0.20; 4 = RTLU > 0.20. Hazards ratios are calculated for quartiles
with shorter telomeres with the longest quartile as reference. Model 1:
unadjusted; Model 2: adjusted for age. HFrEF: heart failure with reduced
ejection fraction; HFpEF: heart failure with preserved ejection fraction; RTLU:
relative telomere length in arbitrary units.
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Table 3 (continued). Cox regression analyses of telomere length in quartiles
and HFrEF and HFpEF in PREVEND.
HFpEF
Model 1 Model 2
Telomere
quartiles HR 95% CI P HR 95% CI P
1 2.71 1.27 - 5.78 0.010 1.36 0.64 - 2.87 0.428
2 2.06 0.94 - 4.52 0.070 1.31 0.59 - 2.88 0.504
3 0.86 0.35 - 2.13 0.744 0.72 0.29 - 1.78 0.478
4 1
1
Table 4 (continued). Cox regression analyses of telomere length in quartiles
and cardiovascular events and mortality
Mortality
Model 1 Model 2
Telomere
quartiles HR 95% CI P HR 95% CI P
1 2.23 1.62 - 3.06 <0.001 1.20 0.87 - 1.65 0.27
2 2.02 1.45 - 2.79 <0.001 1.35 0.97 - 1.88 0.07
3 1.53 1.09 - 2.15 0.014 1.28 0.91 - 1.79 0.15
4 1
1
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Discussion
We studied leukocyte telomere length, as a marker of biological age, in a large
community-based cohort and observed that a shorter leukocyte telomere length
is associated with new onset HF. However, leukocyte telomere length was not a
stronger predictor than chronological age as defined by date of birth.
This is the first study investigating the association of leukocyte telomere length
in a large cohort of healthy subjects with long-term follow-up in which new
onset HF was adjudicated, including the HFrEF and HFpEF subtypes.25
Indeed,
on the association of telomere length and incidence of cardiovascular
pathologies has been reported before. One nested case-control study by
Brouilette et al. investigated a cohort of hypercholesterolemic subjects, who
were subsequently treated with pravastatin or placebo analysed telomere length
and risk of developing coronary artery disease, the main cause of HFrEF.
Telomere length had a predicting value for the risk of developing coronary
artery disease, and statin treatment played a protective role. Another study by
Farzeneh-Far et al. investigated the role of telomere length attenuation in a
cohort of individuals with stable coronary artery disease, and found evidence
for an association between leukocyte telomere length shortening and mortality,
even after adjustment for covariates. Additionally, they found an inverse
association between telomere length and hospitalization for HF.43
A third study
including 150 middle-aged males who were admitted for acute coronary
syndrome and subsequently followed up for more than 600 days, identified
telomere length as a predictor of advantageous prognosis, as patients with the
longest telomeres were less likely to measure up to the combined endpoint of
death, recurrent ischemia, need for revascularization and HF, defined by a
combined event-free survival endpoint.44
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Some previous studies have investigated the role of telomere biology in HF
patients. In 2007, we reported a substudy of the Metoprolol CR/XL
Randomized Intervention Trail in Congestive Heart Failure trial, in which
leukocyte telomere length was measured in 803 participants, of whom 620 were
HF patients. We observed shorter telomere length in HF patients compared to
control subjects, also after adjustment for age and gender. Furthermore,
telomere length was shorter with higher New York Heart Association
functional class and also more atherosclerotic manifestations.32
In a genetic
sub-study of the Co-ordination study evaluating Outcomes of Advising and
Counseling in Heart Failure trial, we reported a significant association between
shorter leukocyte telomere length and HF outcomes. This result was
independent of chronological age, age of onset of HF and gender.23
The
difference in timing of the telomere measurement could have accounted for this
aberrance, since baseline non-HF leukocyte telomere length has been under less
somatic pressure compared to telomeres of actual HF patients.
Telomere length has been hypothesized as a marker (“biomolecular clock”) for
chronological aging.29,45
However, recently this view has been challenged by a
system biologists. Boonekamp et al. took into account the observation that the
association of telomere length and mortality diminishes with age, and tested
whether somatic stress (the capacity of the body to absorb damage during life),
provides a better framework upon which telomere length could be interpreted.
Using computer simulation models and meta-analysis of 16 studies including a
total of 10.157 individuals, the hypothesis that telomere length is a measure of
somatic stress gave a better fit than the perception of telomere length as a
marker for biological age.2 In our analyses, new onset HF patients were
characterized by shorter leukocyte telomere length, however we did not find
this correlation when adjustment for chronological age was applied. Since we
measured telomere length at different time-intervals before the diagnosis new
onset HF (telomere length was measured at baseline in PREVEND), the power
of this telomere measurement could have been attenuated, thereby reducing
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38
power in our analyses. Earlier experiments, reporting significantly lower
telomere lengths in HF patients even after adjustment for age,23
could be due to
the fact that these telomere measurements were performed in chronic HF
patients, whose telomeres have endured prolonged somatic stress during the
course of chronic disease. Furthermore, single telomere length measurements
could be insufficient to represent the prolonged process towards HF, despite
both telomere length and HF are modulated by similar pathophysiological
factors, like inflammation and oxidative stress levels.27,29
We acknowledge that our data does not yet encourage the use of leukocyte
telomere length as a biomarker for new onset HF in the general population.
However, recent studies suggest telomere length is genetically determined46,47
and longer in females48
but also influenced by life style factors, including
smoking and physical activity.49,50
Since a healthy life style normally protects
against HF, telomere length could serve as a proxy to identify individuals
whose HF risk is low. Further investigations are warranted to elucidate this
intriguing hypothesis.
Whether telomere length attrition is a causal factor in the development of HF
remains to be elucidated. Although our data is valuable in being prospective,
based upon a large study cohort with extensive follow-up, a pivotal role for
leukocyte telomere biology in the development of new onset HF is not
supported by our data. However, we did not measure telomere length in
myocardial cells and cannot exclude an important and causal role for telomere
biology in the development of HF. It has been well established that telomeres
are causally involved in senescence, which is an important factor for organ
function.29
The human adult heart is composed of a mixture of post-mitotic
(senescent) cells and non-senescent cells which originate from an active stem
cell pool.22
Under pathological conditions, cardiac cells appear to age
prematurely and telomeres length is impaired compared to healthy cells. Since
accelerated telomere length shortening is associated with HF severity,32
this
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could represent a highly senescent state of the myocardium, thereby impairing
cardiac function or making it more vulnerable for external or environmental
challenges. In addition, recent genetic data on telomere length suggest a causal
role for telomere length biology in heart disease.47
We measured leukocyte telomere lengths in a substantial population-based
cohort with unique data on new onset HF, including stratification for HFrEF
and HFpEF. However, some limitations of our study are notable. We measured
telomere length in leukocytes rather than cardiomyocytes using a PCR-based
method, in which relative telomere length is determined instead of actual
telomere length which can be achieved using Southern blot techniques.
However, this PCR-based method is widely used and the only feasible and
cost-effective method to determine thousands of samples in large
epidemiological studies.27
Another limitation is the cross-sectional nature of the
data, which does not take into account changes in telomere length over time nor
allows conclusions on causation.
Conclusion
Healthy individuals who developed new onset HF during follow-up in the
PREVEND study cohort are characterised by shorter leukocyte telomeres,
albeit not independent of date of birth.
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Table S1. Baseline characteristics of the PREVEND cohort and new onset HF
patients, as well as stratification by HFrEF and HFpEF
Total HF p value
Demography n = 7702 n = 351
Age 49.6 (12.5) 62.3 (9.54) <0.001
Male % 49.1 63.8 <0.001
Systolic BP 128.2 (19.7) 146.3 (25.6) <0.001
Diastolic BP 73.7 (9.7) 79.8 (9.72) <0.001
Heart rate 69.2 (10.1) 70.1 (11.5) 0.12
BMI 25.5 (23.1-28.3) 27.9 (25.4-30.7) <0.001
Waist hip ratio 0.88 (0.09) 0.94 (0.09) <0.001
Medical history (%)
Diabetes 3.6 11.9 <0.001
Smoking 38.1 36.1 0.46
Hypertension 30.0 70.6 <0.001
Hypercholesterolemia 25.9 47.2 <0.001
Obesity 15.1 30.6 <0.001
Laboratory values
Relative Telomere
length 0.02 (0.29) -0.06 (0.26) <0.001
eGFR (ml/min/1.73m²) 80.8 (14.4) 75.3 (16.1) <0.001
Creatinine (umol/L) 82.0 (73-92) 87 (76-100) <0.001
hs-CRP (mg/L) 1.3 (0.6-2.9) 2.5 (1.2-4.8) <0.001
Glucose (mmol/L) 4.7 (4.3-5.1) 5.1 (4.6-45.7) <0.001
Cholesterol (mmol/L) 5.5 (4.8-6.3) 5.9 (5.3-6.7) <0.001
LDL (mmol/L) 3.7 (1.1) 4.0 (1.0) <0.001
HDL (mmol/L) 1.3 (1.0-1.6) 1.2 (1.0-1.4) <0.001
Triglycerides (mmol/L) 1.2 (0.8-1.7) 1.4 (1.0-1.9) <0.001
NT pro-BNP (ng/L) 36.3 (16.1-69.9) 101.2 (41.2-276.3) <0.001
Medication at baseline (%)
Anti-hypertensive
drugs 12.6 43.1 <0.001
Anti-diabetic drugs 1.3 3.8 <0.001
Lipid-lowering drugs 3.7 14.5 <0.001
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Table S1 (continued). Baseline characteristics of the PREVEND cohort and
new onset HF patients, as well as stratification by HFrEF and HFpEF
HFrEF HFpEF p value
n = 224 n = 119
61.9 (10.1) 63.0 (8.6) 0.313
72.8 47.9 <0.001
144.7 (21.0) 148.9 (25.2) 0.101
80.3 (10.0) 78.8 (9.0) 0.184
70.1 (11.5) 69.9 (11.6) 0.891
27.7 (25.3-30.4) 28.2 (25.9-30.9) 0.118
0.96 (0.10) 0.92 (0.10) 0.001
11.0 12.7 0.642
40.5 28.6 0.029
67.4 75.4 0.124
48.4 42.2 0.284
29.3 32.2 0.576
-0.06 (0.26)
-0.06 (0.27) 0.840
75.0 (14.7) 75.8 (18.8) 0.652
90.0 (79-102) 81.0 (71-97) 0.001
2.5 (1.2-4.8) 2.1 (0.9-4.4) 0.202
5.0 (4.6-5.7) 5.1 (4.7-5.8) 0.386
5.9 (5.3-6.7) 5.9 (5.3-6.6) 0.955
4.0 (1.0) 4.0 (1.0) 0.876
1.1 (1.0-1.4) 1.2 (1.0-1.5) 0.061
1.4 (1.0-2.0) 1.4 (1.0-1.8) 0.568
117.4 (44.2-351.6) 80.2 (36.4-158.7) 0.013
41.2 48.7 0.255
3.6
4.3
0.426
14.9 14.5 0.423
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Acknowledgements
We thank dr. L.S.M. Wong, dr. J. Huzen, drs. G.F. Benus, and M.M. Dokter for
their contribution in the telomere length measurements. We are grateful to all
participants of the PREVEND study.
Supporting information
Table S1. Baseline characteristics of the PREVEND cohort and new onset
HF patients, as well as stratification by HFrEF and HFpEF. BP: blood
pressure; BMI: body mass index; eGFR: estimate glomerular filtration rate; HF:
heart failure; HFrEF: heart failure with reduced ejection fraction; HFpEF: heart
failure with preserved ejection fraction; hs-CRP: highly sensitive C-reactive
protein; LDL: low-density lipoprotein; HDL: high-density lipoprotein; NT pro-
BNP: N-terminal pro-B-type natriuretic peptide. Normally distributed data are
expressed as mean (standard deviation) and compared with the use of Student’s
t-test, non-Gaussian data as median (interquartile range) and compared using
the Kruskall–Wallis test. Binary variables were compared using chi-squared
tests.
Table legend: BP: blood pressure; BMI: body mass index; eGFR: estimate
glomerular filtration rate; HF: heart failure; HFrEF: heart failure with reduced
ejection fraction; HFpEF: heart failure with preserved ejection fraction; hs-
CRP: highly sensitive C-reactive protein; LDL: low-density lipoprotein; HDL:
high-density lipoprotein; NT pro-BNP: N-terminal pro-B-type natriuretic
peptide. Normally distributed data are expressed as mean (standard deviation)
and compared with the use of Student’s t-test, non-Gaussian data as median
(interquartile range) and compared using the Kruskall–Wallis test. Binary
variables were compared using chi-squared tests.
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Chapter 4
Leukocyte telomere length and left
ventricular function after acute ST-
elevation myocardial infarction
Data from the Glycometabolic Intervention as adjunct to
Primary Coronary Intervention in ST Elevation Myocardial
Infarction (GIPS-III) trial
Vincent G. Haver
Minke H.T. Hartman
Irene Mateo Leach
Erik Lipsic
Chris P. Lexis
Dirk J. van Veldhuisen
Wiek H. van Gilst
Iwan C. van der Horst
Pim van der Harst
for the GIPS-III Investigators
Clin Res Cardiol. 2015 [in press]
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47
Abstract
Background
Patients with coronary artery disease or heart failure are characterized by
shorter leukocyte telomere lengths (LTL) compared to healthy people. We
studied whether LTL is associated with left ventricular ejection fraction
(LVEF) after ST-elevation myocardial infarction (STEMI).
Methods and results
LTL was determined using the monochrome multiplex quantitative PCR
method in 353 patients participating in the Glycometabolic Intervention as
Adjunct to Primary Percutaneous Coronary Intervention in STEMI (GIPS) III
trial. LVEF was assessed by magnetic resonance imaging at 4 months follow-
up. The mean age of patients was 58.9±11.6 years, 75% was male. In age and
gender adjusted models, LTL at baseline was significantly associated with age
(beta±standard error; -0.33±0.01; P < 0.01), gender (0.15±0.03; P < 0.01),
'Thrombolysis In Myocardial Infarction' (TIMI) flow pre-Percutaneous
Coronary Intervention (PCI) (0.05±0.03; P < 0.01), TIMI flow post-PCI
(0.03±0.04; P < 0.01), myocardial blush grade (-0.05±0.07; P < 0.01), serum
glucose levels (-0.11±0.01; P = 0.03), and total leukocyte count (-0.11±0.01; P
= 0.04). LVEF was well preserved (54.1±8.4%) and was not associated with
baseline LTL (P = 0.95). Baseline LTL was associated with n-terminal pro-
brain natriuretic peptide at 4 months (-0.14±0.01; P = 0.02), albeit not
independent from age and gender.
Conclusion
Our study does not support a role for LTL as a causal factor related to left
ventricular ejection fraction after STEMI.
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Introduction
ST-segment elevation myocardial infarction (STEMI) is a serious medical
condition with a high incidence in Western societies.51
Timely reperfusion of
the culprit artery by primary percutaneous coronary intervention (PCI) is the
cornerstone of treatment to reduce mortality and the risk of left ventricular
(LV) dysfunction. Nevertheless, up to 30% of patients develop systolic LV
dysfunction after STEMI,52
which is an important predictor for clinical
outcome.9 However, even when considering factors as ischemic time and
culprit lesion characteristics, a large variety in susceptibility to develop LV
dysfunction among individuals with STEMI remains to be explained.
Increasing our knowledge on these factors might provide novel avenues for risk
stratification and future development of therapy.
Telomere length might be one of the driving factors associated with
the development of LV dysfunction after STEMI. In humans, telomeres are
repetitive hexameric sequences (TTAGGG)n located at the terminal end of
chromosomes, which protect genes from degradation during cell division due to
the ‘end replication problem’.53,54
With each mitotic cell division, a terminal
part of the telomere is lost since DNA polymerases fails to completely replicate
the strand which begins at the 3’ chromosomal end.55
Aging is consequently
associated with gradual loss of telomere length. If a critical telomere length is
reached, cellular senescence or apoptosis is induced.15
Cellular senescence and
apoptosis are associated with left ventricular dysfunction.56
Patients with
cardiovascular diseases, like coronary artery disease,57
myocardial infarction,58
and heart failure59
are characterized by shorter telomeres compared to healthy
controls.54
Telomere length has also been associated with LVEF in
octogenarians in a non-STEMI setting,60
nevertheless PCI treatment for STEMI
has been proven safe and effective in this age group.61,62
In addition, genetic
variants implicated in LTL have also been associated with LVEF suggesting a
potential causal relationship.63
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We present a sub-study of the Glycometabolic Intervention as Adjunct
to Primary Coronary Intervention in STEMI (GIPS-III) trial, in which we
measured leukocyte telomere length to investigate whether baseline leukocyte
telomere length is associated with LVEF 4 months after STEMI.
Methods
Study population
The design and primary outcomes of the GIPS-III trial have been published
previously.64,65
In brief, the GIPS-III was a double-blinded, placebo-controlled
trial including 380 non-diabetic STEMI patients undergoing PCI and who were
subsequently randomly assigned to metformin (N=191) or placebo (N=189)
treatment, twice daily for a period of 4 months. Major exclusion criteria
included: (1) known diabetes; (2) previous myocardial infarction; (3) the need
for coronary artery bypass surgery (CABG); (4) severe renal dysfunction; and
(5) standard contraindications for magnetic resonance imaging (MRI). The
primary outcome was LVEF 4 months after STEMI. After 4 months, LVEF of
metformin and placebo treated patients was similar.65
All investigators of the
GIPS-III trial can be found in the Appendix. The trial is registered with
clinicaltrials.gov identifier: NCT01217307.
Study outcomes
Primary study outcome was LVEF determined 4 months after STEMI using a
3.0 Tesla whole-body MRI (Achieva; Philips) using a phased array cardiac
receiver coil. Secondary outcomes were among others MRI measured
parameters (left ventricular end diastolic volume (LVEDV); left ventricular end
systolic volume (LVESV); left ventricular end diastolic mass (LVEDM)) and
levels of n-terminal pro-brain natriuretic peptide (NT-proBNP).
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Telomere length measurements
Blood for DNA isolation was collected from the patients at arrival at the
catheterization laboratory (baseline) was used for telomere length
determination. White blood cell DNA was extracted by LGC Genomics.
Telomere length was measured in quadruplicate on 4 different plates with each
replicate in the same well position on the Polymerase Chain Reaction (PCR)
plate by the monochrome multiplex quantitative PCR method, originally
developed by Cawthon.66
The telomere primers were: TelC: 5’-TGT TAG GTA
TCC CTA TCC CTA TCC CTA TCC CTA TCC CTA ACA-3’ (final
concentration 900 nM); TelG: 5’-ACA CTA AGG TTT GGG TTT GGG TTT
GGG TTT GGG TTA GTG T-3’ (900 nM); the albumin primers were: AlbDgc:
5’-GCC CGG CCC GCC GCG CCC GTC CCG CCG GAA AAG CAT GGT
CGC CTG TT-3’ (300 nM); AlbUgc: 5’-CGG CGG CGG GCG GCG CGG
GCT GGG CGG AAA TGC TGC ACA GAA TCC TTG-3’ (300 nM). The
final concentrations of the reagentia per 10 µl reaction were: 1X Titanium® Taq
DNA Polymerase (Clontech Laboratories, Inc.); 1X Titanium® Taq PCR Buffer
(Clontech Laboratories, Inc.); 0.2mM of each dNTP (Promega); 0.75X SYBR®
Green I nucleic acid gel stain (Sigma-Aldrich); 1M Betaine (Sigma); and 1mM
DL-Dithiothreitol (Sigma). DNA of a human leukemia cell line (1301) with
extreme long telomeres was used as a positive control.67
The thermal cycling
profile was as follows: Stage 1: 15 min at 95°C; Stage 2: 2 cycles of 15 s at
94°C, 15 s at 49°C; Stage 3: 32 cycles of 15 s at 94°C, 10 s at 60°C, 15 s at
72°C with signal acquisition, 10 s at 85°C, and 15 s at 89°C with signal
acquisition. The T/S ratio was calculated by dividing the telomere (T) signal by
the signal of a reference gene (albumin, S). This T/S ratio, hereafter called
Leukocyte Telomere Length (LTL), is a relative measurement of leukocyte
telomere content in a sample, which serves as a proxy for actual leukocyte
telomere lengths.66
The median intra-assay coefficients of variation were 9.4%
for T, 10.1% for S and 3.4% for the T/S ratio. Samples were excluded from
further analyses if the coefficient of variation for the T/S ratio was >0.1 after
deletion of one of the four replicate measurements.
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Statistical analysis
Continuous variables are reported as mean (standard deviation) for normally
distributed data. Since LTL and NT-proBNP were non-normally distributed,
log transformation was performed to obtain a near normal distribution. Outliers
were defined as >2SD from the median of LTL. For continuous and
dichotomous data, we performed linear regression analyses using LTL as
dependent variable and baseline characteristics and outcome parameters as
independent variables, categorical data were tested using expanded interaction
linear regression analyses. All analyses were first performed univariately and
then adjusted for age and gender. Graphical representation of interaction
analyses were performed using the “margins” command in STATA. Statistical
tests were performed two-tailed and a P-value of <0.05 was used as nominal
level of statistical significance. The analyses were performed using StataMP
version 13.1 (StataCorp).
Results
Study population
Genomic DNA was successfully extracted from 362 (95.5%) patients of the
GIPS-III cohort. LTL was successfully determined in 356 (98.3%) of the DNA
samples (3 samples exhibited insufficient DNA quality, 3 samples were
excluded due to coefficient of variation > 0.1 after repeated measurement).
ANOVA test revealed no significant difference between LTL of both treatment
groups (P = 0.15). Another 3 samples were regarded as outliers based on >2SD
deviation of the mean LTL, leaving 353 (97.5%) samples for the current
analyses. MRI data at 4 months after STEMI was available for 253 (71.6%)
patients of patients whose LTL was determined. Baseline characteristics of the
study cohort are represented in Table 1.
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Table 1. Baseline characteristics
Variable Level Value
N 353
Age (Years), mean (SD) 58.9 (11.6)
Gender Male 265 (75.1%)
Female 88 (24.9%)
Body Mass Index (kg/m2), mean (SD) 26.9 (3.7)
Ethnicity Caucasi
an 339 (96.0%)
Asian 10 (2.8%)
Black 4 (1.1%)
Hypertension No 250 (70.8%)
Yes 103 (29.2%)
Hypercholesterolemia No 132 (37.4%)
Yes 221 (62.6%)
Active smoker No 160 (45.3%)
Yes 193 (54.7%)
Cerebrovascular accident No 350 (99.2%)
Yes 3 (0.8%)
Previous PTCA No 349 (98.9%)
Yes 4 (1.1%)
Systolic blood pressure (mmHg), mean (SD) 134.1 (23.5)
Diastolic blood pressure (mmHg), mean (SD) 84.0 (14.4)
Heart rate (bpm), mean (SD) 75.4 (16.0)
Total ischemic time (min), median (IQR) 161 (109, 251)
Single vessel disease No 111 (31.4%)
Yes 242 (68.6%)
Culprit vessel LAD 135 (38.2%)
LCX 60 (17.0%)
RCA 158 (44.8%)
TIMI flow grade
(pre-interventional) 0 195 (55.2%)
1 26 (7.4%)
2 60 (17.0%)
3 72 (20.4%)
2 33 (9.3%)
TIMI flow grade
(post-interventional) 3 320 (90.7%)
Myocardial blush grade 0 9 (2.6%)
1 27 (7.7%)
2 70 (20.0%)
3 244 (69.7%)
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Table 1 (continued). Baseline characteristics
Variable Level Value
TIMI flow grade
(post-interventional) 3 320 (90.7%)
Myocardial blush grade 0 9 (2.6%)
1 27 (7.7%)
2 70 (20.0%)
3 244 (69.7%)
CK total (U/L),
median (IQR) 129 (83, 208)
CK-MB (U/L),
median (IQR) 16 (13, 24)
AUC CK total (U*hr/L),
median (IQR)
1.0x108
(4.0x107, 2.3x10
8)
AUC CK-MB (U*hr/L),
median (IQR)
9.8x106
(4.2x106, 2.0x10
7)
Creatinine (umol/L),
median (IQR) 72 (62, 82)
NT-proBNP (ng/L),
median (IQR) 80 (38, 179)
Total leukocyte count
(10^9/L),
median (IQR)
11 (8.8, 13.6)
Glucose (mmol/L),
median (IQR) 8.2 (7, 9.5)
HBA1c (%),
median (IQR) 5.8 (5.6, 6)
Abbreviations: AUC: area under the curve; BP: blood pressure; BMI: body
mass index; eGFR: estimate glomerular filtration rate; HF: heart failure;
HFrEF: heart failure with reduced ejection fraction; HFpEF: heart failure with
preserved ejection fraction; hs-CRP: highly sensitive C-reactive protein; IQR:
inter-quartile range; LAD: Left anterior descending coronary artery; LCX: Left
circumflex coronary artery; NT pro-BNP: N-terminal pro-B-type natriuretic
peptide; RCA: Right coronary artery; SD: standard deviation; TIMI:
Thrombolysis in Myocardial Infarction. Body mass index was calculated by
dividing weight (in kilograms) by squared height (in meters). Normally
distributed data are expressed as mean (standard deviation), non-Gaussian data
as median (interquartile range).
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Patients were aged on average 58.9±11.6 years old and 75.1% was male.
Systolic blood pressure was 134.0±23.5 mmHg and diastolic blood pressure
was 84.0±14.4 mmHg. The majority (68.6%) of the patients presented with
single vessel disease. The culprit vessel was predominantly the right coronary
artery (RCA). At baseline, NT-proBNP levels were 80 U/L (IQR 38-179), and
CK-MB levels were 16 U/L (IQR 13-24).
Associations between baseline patient characteristics and LTL
LTL was negatively associated with age (Figure 1). Univariate linear regression
analyses revealed a significant association between baseline LTL with age,
gender, active smoking behavior, single vessel disease, serum creatinine and
glucose levels (Table 2). Although univariately, active smokers seem to have
longer LTL than non-smokers, this could be explained by the large age
difference between smokers and non-smokers (54.4±10.5 for smokers versus
64.3±10.6 years for non-smokers). After including age and gender in the
model, only serum glucose levels remained significantly associated with LTL.
Univariately, 'Thrombolysis In Myocardial Infarction' (TIMI) flow (both pre-
and post-PCI), myocardial blush grade and total leukocyte count were not
associated with baseline LTL; however, after adjustment for age and gender,
the association became significant. We tested for an effect of age underlying
these association but could not identify a significant interaction effect
(interaction coefficient myocardial blush grade = 3.2*10-4
; 95% confidence
interval (CI) = -5.5*10-3
– 6.1*10-3
; P = 0.91; interaction coefficient TIMI flow
pre-PCI = <0.01; 95% CI = -0.01 – 0.01; P = 0.97; interaction coefficient TIMI
flow post-PCI = <0.01; 95% CI = -0.02 – 0.00; P = 0.15; interaction coefficient
total leukocyte count = -0.14; 95% CI = -0.29 – 0.01; P = 0.08).
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Figure 1. Scatter plot showing association between LTL and age, with
superimposed 95% confidence interval and regression line.
LTL: leukocyte telomere length. Individual data points are shown as well as the
superimposed regression line including the 95% confidence interval.
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Figure 2. Scatter plot graph showing no association between LTL and LVEF at
4 months with superimposed 95% confidence interval and regression line.
LVEF: Left Ventricular Ejection Fraction; LTL: Leukocyte telomere length.
Individual data points are shown as well as the superimposed regression line
including the 95% confidence interval.
Figure 3. Interaction between baseline LTL and levels of NT-proBNP at 4
months after Metformin or Placebo treatment.
LTL: Leukocyte telomere length. Linear prediction represents the predicted
NT-proBNP for both metformin as well as placebo treated patients. Regression
line and 95% confidence intervals are shown.
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Table 2. Association of baseline characteristics with LTL
Univariate model Multivariate model
Std.
Beta SE
P
value
Std.
Beta SE
P
value
Age (Years) -0.31 0.00 <0.01 -0.33 <0.01 <0.01
Gender 0.11 0.03 0.03 0.15 0.03 <0.01
Body Mass Index
(kg/m2)
<0.01 <0.01 0.99 -0.04 <0.01 0.42
Ethnicity
Caucasian 0.18 0.18
Asian 0.01 0.07 0.02 0.07
Black 0.10 0.11 0.07 0.11
Hypertension -0.04 0.03 0.48 0.02 0.03 0.73
Hypercholesterolemia 0.03 0.02 0.58 -0.02 0.02 0.74
Active smoker (y/n) 0.16 0.02 <0.01 0.02 0.03 0.72
Cerebrovascular
accident -0.05 0.13 0.33 -0.02 0.12 0.68
Previous PTCA -0.02 0.11 0.71 <0.01 0.11 0.99
Systolic blood pressure
(mmHg) -0.02 <0.01 0.77 -0.02 <0.01 0.71
Diastolic blood
pressure (mmHg) 0.04 <0.01 0.44 <0.01 <0.01 0.99
Heart rate (bpm) -0.03 <0.01 0.59 -0.05 <0.01 0.34
Total ischemic time
(min) 0.06 <0.01 0.26 0.07 <0.01 0.16
Single vessel disease 0.14 0.03 0.01 0.10 0.02 0.05
Culprit vessel
LAD 0.51 0.73
CX 0.07 0.03 0.06 0.03
RCA 0.03 0.03 0.02 0.02
TIMI flow (pre-PCI)
0 0.10 <0.01
1 -0.06 0.05 -0.05 0.04
2 -0.10 0.03 -0.05 0.03
3 0.05 0.03 0.05 0.03
TIMI flow (post-PCI)
2 0.20 <0.01
3 0.07 0.04 0.03 0.04
Myocardial blush
grade
0 0.82 <0.01
1 0.02 0.09 -0.05 0.08
2 0.09 0.08 -0.04 0.08
3 0.11 0.08 -0.05 0.07
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Table 2 (continued). Association of baseline characteristics with LTL
Univariate model Multivariate model
Std.
Beta SE P value
Std.
Beta SE P value
CK total (U/L) <0.01 <0.01 0.97 0.02 <0.01 0.71
CK-MB (U/L) -0.01 <0.01 0.92 0.01 <0.01 0.81
AUC CK
(U*hr/L) 0.03 <0.01 0.58 0.05 <0.01 0.33
AUC CK-MB
(U*hr/L) -0.02 <0.01 0.78 0.02 <0.01 0.69
Creatinine
(umol/L) -0.13 <0.01 0.02 -0.04 <0.01 0.52
NT-proBNP
(ng/L) -0.04 <0.01 0.49 -0.02 <0.01 0.64
Total leukocyte
count (109/L)
-0.02 <0.01 0.78 -0.11 <0.01 0.04
Glucose (mmol/L) -0.15 <0.01 <0.01 -0.11 <0.01 0.03
HBA1c (%) -0.05 0.01 0.39 -0.02 0.01 0.64
Linear regression analyses of baseline characteristics with LTL are presented
for dichotomous and continuous variables, categorical variables were tested by
interaction expanded linear regression analyses. Standardized (Std.) beta,
standard error (SE) and P values are shown. Multivariate tests were adjusted
for age and gender (except for age and gender, which were only adjusted for
age (gender) or gender (age). Abbreviations: AUC: area under the curve; CK:
creatine kinase; CK-MB: creatine kinase myocardial band; HBA1c: glycated
haemoglobin; LAD: Left anterior descending coronary artery; LCX: Left
circumflex coronary artery; NT-proBNP: n-terminal pro-brain natriuretic
peptide; PTCA: percutaneous transluminal coronary angioplasty; RCA: Right
coronary artery; SE: standard error; TIMI: Thrombolysis In Myocardial
Infarction. Body mass index was calculated by dividing weight (in kilograms)
by squared height (in meters).
Table 3. STEMI outcomes at 4 months after STEMI
Outcome Values
LVEF (%) 54.1 (8.4)
LVEDV (mL) 193.4 (45.1)
LVESV (mL) 90.8 (35.5)
LVEDM (g) 100.9 (23.1)
Infarct size (g) 9.3 (9.0)
NT-proBNP (ng/L) 264 (119-631)
LVEF: left ventricular ejection fraction; LVEDV: left ventricular end diastolic
volume; LVESV: left ventricular end systolic volume; LVEDM: left ventricular
end diastolic mass; NT-proBNP: n-terminal pro-brain natriuretic peptide.
Values are presented as mean (SD), except for NT-proBNP, which is presented
as median (IQR).
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Cardiac MRI at 4 months after STEMI and associations with
baseline LTL
Mean LVEF, as determined by MRI, was well preserved at 4 months after
STEMI (54.1±8.4%). LVEF, left ventricular end diastolic volume (LVEDV),
left ventricular end systolic volume (LVESV), left ventricular end diastolic
mass (LVEDM) and infarct size are represented in Table 3. LTL measurement
at baseline was not associated with LVEF at 4 months (Figure 2), neither with
the other parameters of cardiac remodeling (Table 3).
Treatment effect of metformin on LTL
We have explored the possible interaction of metformin with LTL on LVEF at
4 months. Interaction analyses revealed no significant interaction of treatment
with the association of baseline LTL and LVEF at 4 months (interaction
coefficient = 4.0; 95% CI = -5.5-13.5; P = 0.41, Table 4)). However, we found
evidence for effect modulation by metformin treatment on the association of
LTL with NT-proBNP at 4 months (interaction coefficient = -1.3; 95% CI = -
2.5 - -0.1; P = 0.04). NT-proBNP levels were similar for patients with different
levels of LTL after placebo treatment but in patients treated with metformin,
longer LTL was associated with lower NT-proBNP levels (Figure 3).
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Table 4. Associations of baseline LTL measurement with STEMI outcomes 4
months after STEMI
Univariate analyses
Std. Beta SE P value
LVEF (%) 0.00 <0.01 0.95
LVEDV (ml) 0.09 <0.01 0.17
LVESV (ml) 0.06 <0.01 0.37
LVEDM (g) 0.08 <0.01 0.20
Infarct size (g) 0.02 <0.01 0.77
Log NT-proBNP (ng/l) -0.14 0.01 0.02
LVEF: left ventricular ejection fraction; LVEDV: left ventricular end diastolic
volume; LVESV: left ventricular end systolic volume; LVEDM: left ventricular
end diastolic mass; NT-proBNP: n-terminal pro-brain natriuretic peptide.
Univariate and age + gender adjusted analyses are presented. P for interaction
represents P value of interaction between outcome parameter, LTL and
metformin treatment. Standardized (Std.) beta, standard error (SE) and P values
are shown. Multivariate tests were adjusted for age and gender. P for
interaction represents the statistical test for outcome modification by metformin
or placebo treatment.
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Table 4. (continued) Associations of baseline LTL measurement with STEMI
outcomes 4 months after STEMI
Multivariate analyses Treatment interaction
Std. Beta SE P value P value
-0.01 <0.01 0.88 0.41
0.04 <0.01 0.55 0.37
0.03 <0.01 0.63 0.31
0.12 <0.01 0.07 0.18
0.03 <0.01 0.59 0.30
-0.07 0.01 0.25 0.04
Discussion
LTL has been proposed as a marker of biological age and has been suggested to
play an important role in cellular senescence or apoptosis.15
Previously,
associations have been reported between LTL with coronary artery disease,57
heart failure,68
and LVEF.60
We hypothesized that LTL is associated with
cardiac remodeling after STEMI as can be reflected by LVEF at 4 months. The
main finding of the present study is that we could not find support for this
hypothesis.
In our study we did observe the well-established association of LTL
with baseline characteristics such as the inverse association with age,68,69
and
gender (females having longer LTL70
). The direction of smokers was opposite
as frequently reported (active smokers in GIPS-III were found to have longer
LTL),67,71
but this was completely explained by the large age difference
between non-smokers and smokers. These associations suggest that our main
finding is unlikely due to measurement error of LTL. A possible explanation
for the absence of an association between LTL and LVEF in the GIPS-III trial
might be the relatively well preserved LVEF after STEMI. Considering the
mean LVEF of approximately 54% after STEMI, the variation of the primary
endpoint might have been too small to establish an association with LTL.
However, even in the absence of STEMI and the resulting cardiac remodeling,
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one could speculate on an association between LVEF and LTL. In a cohort of
octogenarians (N=64; average age 85.2 years old) without evidence of previous
myocardial infarction, LTL was strongly and independently associated with
LVEF as determined by echocardiography.60
In this cohort, approximately 12%
of the observed variability in LVEF could be explained by LTL alone. In
addition, an association between LTL and LVEF has been reported in subjects
with hypertension (N=1,106; average age 57.9 years old). A 1.5 fold larger
LTL was associated with 0.6% increase in absolute LVEF.63
On the other hand,
there are also several studies reporting a lack of an association between LTL
and LVEF in other settings. In a cohort with established heart failure patients
(N=610; average age 66.2 years old), we did not observe an association with
LVEF.59
In another cohort of patients with idiopathic cardiomyopathies
(N=223; average age 51.1 years old) LTL was also not associated with LVEF
as determined.72
Also in subjects derived from the general population the
absence of an association between LTL and LVEF has been reported. In the
Malmö Preventive Project, a cross-sectional observational study including
1,588 subjects (average age 67.7 years old), an association with LTL with
LVEF was lacking.73
In an additional population based cohort of Chinese Han
people (N=139; average age 60.3 years old) there was also no association with
LTL and LVEF.74
Our data contributes to the previous studies by investigating
a specific population (STEMI) in which the role of LTL might be relevant.
However, our data demonstrates that even in the setting of STEMI and the
subsequent remodeling process of the heart, LTL does not seems to be
associated and therefore is unlikely to be involved. Biomarkers for predicting
outcomes in coronary heart disease outcomes have been reported,75
but the
present study does not support the use of LTL as a biomarker in the setting of
STEMI. The well preserved LVEF 4 months after STEMI, which is the result
of the high level of acute care in our STEMI network,76
could have nullified the
potential role of LTL in STEMI outcome prediction.
The major limitation of our study that needs to be considered is that
the cells we investigated are leukocytes. Therefore, we cannot exclude an
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important role of telomere length in other cell types, e.g. cardiomyocytes or
endothelial cells.77
For practical (and ethical) reasons it is not feasible to study
cardiomyocytes of STEMI patients. Another limitation is that our analyses are
based on a single LTL measurement. Therefore, we cannot exclude that LTL
measurements in the stable setting or cross-sectionally at time of LVEF
determination are associated with LVEF. This remains to be determined.
Finally, telomere length is only one of the parameters of telomere biology
related to apoptosis and senescence. Telomere biology is more complex than
telomere length alone. It also involves many regulatory and stabilizing protein
complexes (sheltering) interacting with the telomere DNA sequence to protect
the DNA.54,78
The exclusion of telomere length as a factor associated with
LVEF does not exclude a role of telomere biology per se. The strengths of our
study include that we have executed the current study within the framework of
a clinical trial using the golden standard to determine LVEF.
In conclusion, LTL measured in the setting of STEMI is not associated
with cardiac remodeling or LVEF as determined by MRI after 4 months. Our
study does not lend support for a role of LTL as a causal factor in LV
remodeling or for the use as a biomarker to predict clinical outcome in patients
with STEMI.
Acknowledgements
We thank J. Takens and M.M. Dokter for their excellent technical assistance
during LTL measurements. The GIPS-III trial was supported by grant
95103007 from ZonMw, the Netherlands Organization for Health Research and
Development, The Hague, the Netherlands. The present analyses were
supported by grant 95103007 from ZonMw and the Innovational Research
Incentives Scheme (NWO VENI, Grant Number 916.76.170 to PvdH) of the
Netherlands Organization for Health Research and Development, The Hague,
the Netherlands.
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Conflict of interest
The authors declare that they have no conflict of interest.
Ethical standards
The GIPS-III study has been approved by the local ethics committee
(Groningen, the Netherlands) and national regulatory authorities and has
therefore been performed in accordance with the ethical standards laid down in
the 1964 Declaration of Helsinki and its later amendments. All patients gave
their informed consent prior to their inclusion in the study. One patient
retracted his informed consent during the study, leaving 379 patients eligible
for the current analysis.
Appendix
Members of the GIPS-III Investigator group are as follows: Publication/Writing
Committee: I.C.C. van der Horst (chair), C.P.H. Lexis, D.J. van Veldhuisen, E.
Lipsic, P. van der Harst, H.L. Hillege, J.G.P. Tijssen; Steering Committee:
I.C.C. van der Horst (chair), D.J. van Veldhuisen, E. Lipsic, P. van der Harst,
R.A. de Boer, A.N.A. van der Horst-Schrivers, B.H.R. Wolffenbuttel;
Adjudication Committee: F. van den Berg, V.M. Roolvink, A.P. van Beek;
Data Safety Monitoring Board: J.G.P Tijssen (chair), R.J. de Winter, A.J.
Risselada, R.M. de Jong, R.K. Gonera; Investigators: all in the Netherlands:
University Medical Center Groningen, Groningen – I.C.C. van Horst, C.P.H.
Lexis, E. Lipsic, P. van der Harst, D.J. van Veldhuisen, A.F.M. van den
Heuvel, W.G. Wieringa, H.W. van der Werf, Y. Tan, G.P. Pundziute, R.A.J.
Schurer, (B.J.G.L. de Smet), A.N.A. van der Horst-Schrivers, B.H.R.
Wolffenbuttel, W. Nieuwland, P. van der Meer, R.A. Tio, J. Coster, (A.A.
Voors, J.P. van Melle,Y.M. Hummel) B.H.W. Molmans, University of
Groningen, Groningen – G.J. ter Horst, R. Renken, A.J. Sibeijn-Kuiper; VU
University Medical Center, Amsterdam – A.C. van Rossum, R. Nijveldt;
Academic Medical Center, Amsterdam – J.G.P Tijssen.
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Chapter 5
Telomere Length and Outcomes in
Ischemic Heart Failure
Data from the COntrolled ROsuvastatin multiNAtional Trial
in Heart Failure (CORONA)
Vincent G. Haver
Irene Mateo Leach
John Kjekshus
Jayne C. Fox
Hans Wedel
John Wikstrand
Rudolf A. de Boer
Wiek H. van Gilst
John J. V. McMurray
Dirk J. van Veldhuisen
Pim van der Harst
European Journal of Heart Failure. 2015;17(3):313-9
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Abstract
Aims
Leukocyte telomere length is considered a marker of biological ageing and has
been suggested to be shorter in patients with coronary artery disease and heart
failure compared to healthy controls. The aim of this study was to determine
whether telomere length is associated with clinical outcomes in patients with
ischemic heart failure and whether this association is superior to chronological
age as defined by date of birth.
Methods and Results
We measured leukocyte telomere length in 3,275 patients with chronic
ischemic systolic heart failure in patients participating in the COntrolled
ROsuvastatin multiNAtional Trial in Heart Failure (CORONA) study. The
primary composite endpoint was cardiovascular death, non-fatal myocardial
infarction, and non-fatal stroke, which occurred in 575 patients during follow-
up. We observed a significant association of leukocyte telomere lengths with
the primary endpoint (hazard ratio 1.10; 95% confidence interval 1.01-1.20; P
= 0.03). However, this observation was not superior to age as defined by date
of birth. The neutral effect of rosuvastatin treatment on clinical outcomes was
not modified by baseline telomere length.
Conclusion
Biological age as defined by leukocyte telomere length was associated with
clinical outcomes in patients with ischemic heart failure but this association did
not add prognostic information above age as defined by date of birth.
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Introduction
Telomeres are repetitive nucleotide sequences located at the extreme ends of
chromosomes, which act as protective caps preventing damage to gene coding
information of the DNA. In humans, telomeres are composed of numerous
repeats of the TTAGGG nucleotide sequence. The number of repeats declines
with each cell division, because of the so-called “end replication problem”.14
In
addition to mitosis, environmental pathogenic processes can accelerate
telomere attrition or cause telomeric instability. For example, smoking, obesity,
oxidative stress and inflammation have been shown accelerate telomere
attrition.79,80
Eventually, if a critical short length of the telomere is reached, the
cell loses its capability to divide, and enters a state of cellular senescence.
Telomere attrition has been considered a modifiable biomarker for biological
ageing and has been studied in ageing related diseases and conditions,
including heart failure (HF).16,81
HF is a devastating age-related condition with a high incidence in the
elderly. Due to the high morbidity and mortality associated with HF, the socio-
economic burden for society is substantial.82
HF is characterized by increased
senescence and shorter telomere length in cardiomyocytes.83
Data from animal
studies have suggested a causal role for telomere length in cardiomyocyte
senescence and development of HF.84
Clinical studies have predominantly
evaluated telomere length of the more easily obtainable circulating leukocytes.
Leukocyte telomere length was previously shown to be 40% shorter in patients
with chronic HF as compared to age- and gender matched controls.21
Telomere
length of leukocytes predicted the occurrence of death or hospitalization for HF
in a previous small study.85
We undertook the current study to validate these
earlier findings in a large and independent cohort of systolic HF patients. We
tested whether leukocyte telomere length is associated with fatal and non-fatal
clinical outcomes, as well as overall survival of HF patients participating in the
COntrolled ROsuvastatin multiNAtional Trial in Heart Failure (CORONA)
trial.
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Methods
This is a retrospective sub-study of the CORONA trial, a randomized, double-
blinded, placebo-controlled trial which examined the effects of rosuvastatin in
patients with systolic HF of ischemic origin. Rosuvastatin did not have a
beneficial effect on the primary composite outcome (a composite endpoint of
cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke.86
Study population
In- and exclusion criteria of the CORONA trial have been reported in detail
previously.86
In brief, patients were 60 years of age or older, suffering from HF
of ischemic aetiology, and a left ventricular ejection fraction (LVEF) of ≤ 40%
with New York Heart Association (NYHA) class III-IV symptoms or ≤ 35% if
NYHA class II-IV symptoms. Patients had to be clinically stable and on
optimal treatment for at least 2 weeks before inclusion. Exclusion criteria
included: recent myocardial infarction (MI) (<6 months), unstable angina or
stroke within the past 3 months, percutaneous coronary intervention (PCI) or
coronary-artery bypass grafting (CABG), the implantation of a cardioverter
defibrillator (ICD) or biventricular pacemaker within the past 3 months, heart
transplantation, clinically significant/uncorrected primary valvular heart
disease, hypertrophic cardiomyopathy, or systemic disease (e.g., amyloidosis).
The CORONA trial included 5,011 patients86
and DNA was obtained from
3,340 of them. We excluded 20 non-Caucasian subjects (8 black, 7 Asian, 5
‘other’), leaving 3,320 subjects. Telomere length could be reliably quantified in
3,275 patients. The ethics committee at each of the participating hospitals
approved the CORONA trial, and all patients provided written informed
consent. This study conforms to the principles outlined in the “Declaration of
Helsinki”.87
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Telomere length measurement assay
Mean telomere length was determined by a monochrome multiplex quantitative
polymerase chain reaction (PCR)-based assay as described before88
using a
384-well Bio-Rad CFX384 platform with C1000 thermal cycler (Bio-Rad
Laboratories, Denmark). For each DNA sample, the relative average telomere
lengths were determined by calculating the relative ratio of telomere repeat
copy number (T) to the signal from a single-copy gene copy number (albumin
gene; S). All samples were compared to the same reference DNA sample.
Telomere length was analysed for all samples in triplicate on separate plates.
Samples with a coefficient of variation above 10% were excluded from further
analyses. The median (IQR) coefficients of variation were 5% (3-7%) for the T
as well as for the S assay. Determination of T and S quantities was performed
in a blinded set-up without knowledge of clinical data.
Statistical analysis
Leukocyte telomere length was natural log-transformed because of the skewed
distribution. The primary endpoint of this sub-study was defined as the primary
endpoint of the CORONA trial: composite endpoint of cardiovascular death,
non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes
were: time to death of any cause, time to first coronary event or time to death
from cardiovascular causes. Tertiary outcomes were: the number of
hospitalization for cardiovascular causes, unstable angina, or worsening HF.
Standard linear regression models were used to test for correlation of
continuous baseline patient characteristics with log (telomere length) adjusted
for age and gender. Correlations of binary baseline characteristics were tested
using logistic linear regression. Kaplan-Meier curves were plotted for the
quartiles of telomere length and tested for significance using the log-rank test.
For the Cox proportional hazards regression models, log-transformed telomere
length as a continuous variable were fitted as follows: Model 1 = unadjusted;
Model 2 = adjusted for age and gender; Model 3 = adjusted for age, gender, and
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rosuvastatin/placebo treatment; Model 4 = adjusted for age, gender, and
baseline variables with P value < 0.05. Hazard ratios, 95% confidence intervals
(CI) and P-values are reported for each model. To study effect modification of
telomere length on the efficacy of rosuvastatin treatment we included an
interaction term of telomere length × treatment in the model with telomere
length and treatment and analysed the primary and secondary outcome
variables. AstraZeneca Pharmaceuticals (JCF, Alderley Park, Gadbrook Park,
Northwich, UK) performed all analysis without knowledge of clinical data,
using R statistical package version 2.10.1. A two-sided P value of <0.05 was
considered statistically significant.
Results
Study population
The baseline variables of the 3,275 patients are summarised in Table 1. The
mean age of the participants was 72.2 ± 6.8 years, 2,498 (76%) were male and
2,005 (61%) were suffering New York Heart Association (NYHA) class III
symptoms. The mean relative average log-transformed leukocyte telomere
length in the study cohort was 0.58 (SD 0.3). T/S ratios for quartile cut-offs
were ≤ 1.48 for the 1st quartile, > 1.48 to ≤ 1.81 for the 2nd quartile, > 1.81 to
≤ 2.17 for the 3rd quartile, > 2.17 for the 4th quartile.
Correlation of telomere length with baseline characteristics
Leukocyte telomere length decreased with increasing age (β = -0.004; 95% CI -
0.005 to -0.002; P < 0.001) and female patients had on average longer
telomeres (β = 0.076; 95% CI 0.052 to 0.100; P < 0.001). Baseline factors
associated with shorter telomere length were smoking (β -0.752; 95% CI -1.145
to -0.362; P = 0.0002), a history of myocardial infarction (β -0.048; 95% CI
0.011 to -0.069; P < 0.001) and atrial fibrillation (β -0.029; 95% CI -0.054 to -
0.005; P = 0.019). Other baseline variables, like LVEF, body mass index
(BMI), systolic blood pressure, and heart rate were not associated with
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telomere length. Serum creatinine was significantly increased in patients with
short telomeres. Other laboratory parameters like total cholesterol, low-density
lipoprotein (LDL), N-terminal fragment B-type natriuretic peptide (NT-
proBNP) and C-reactive protein (CRP) were not associated with leukocyte
telomere length.
Figure 1 Time to composite endpoint (first event of cardiovascular (CV) death,
non-fatal myocardial infarction (MI) and non-fatal stroke) in days by Kaplan–
Meier estimates for the four quartiles of telomere lengths.
Quartile 1 represents the quartile with shortest telomeres. The represented P
value is calculated between quartile 1 and 4 using log-rank test.
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Table 1 Baseline patient characteristics. Telomere data is divided in quartiles
for statistical purposesa
1st Quartile 2nd Quartile 3rd Quartile n = 819 n = 819 n = 818
Age (years) 73.0 ± 7.0 72.4 ± 6.7 72.2 ± 6.8
Female (%) 137 (17) 199 (24) 203 (25)
NYHA class II (%) 303 (37) 307 (38) 300 (37)
NYHA class III (%) 507 (62) 505 (62) 511 (63)
NYHA class IV (%) 9 (1) 7 (1) 7 (1)
LVEF (%) 31 ± 1 31 ± 1 32 ± 1
BMI (kg/m2) 27 ± 4 28 ± 5 28 ± 5
Systolic BP (mmHg) 130 ± 16 130 ± 16 131 ± 16
Diastolic BP (mmHg) 77 ± 9 76 ± 9 78 ± 9
Heart rate (beats/min) 71 ± 11 71 ± 11 71 ± 11
Smoking status
Ex-smoker (%) 381 (47) 352 (43) 355 (43)
Habitual smoker (%) 94 (12) 66 (8) 53 (7)
Non-smoker (%) 325 (40) 384 (47) 394 (48)
Occasional smoker (%) 17 (2) 17 (2) 16 (2)
Medical history
Myocardial infarction (%) 529 (65) 507 (62) 489 (60)
Angina Pectoris (%) 617 (75) 610 (75) 619 (76)
PCI, PTCA, or CABG (%) 215 (2) 204 (3) 193 (2)
Hypertension (%) 519 (64) 539 (66) 542 (66)
Diabetes Mellitus (%) 234 (29) 244 (30) 222 (27)
Atrial fibrillation (%) 595 (78) 565 (74) 574 (75)
Stroke (%) 101 (12) 96 (12) 90 (11)
Pacemaker implanted (%) 86 (11) 93 (11) 84 (10)
ICD implanted (%) 24 (3) 19 (2) 20 (2)
Laboratory measurements
Total cholesterol (mmol/L) 5.4 ± 1.1 5.4 ± 1.0 5.5 ± 1.1
HDL (mmol/L) 1.2 ± 0.3 1.2 ± 0.3 1.2 ± 0.3
LDL (mmol/L) 3.6 ± 1.0 3.6 ± 0.9 3.7 ± 0.9
ApoB : ApoA-I ratio 0.9 ± 0.3 0.9 ± 0.2 0.9 ± 0.2
Triglycerides (mmol/L) 1.7 ± 1.3 1.7 ± 1.3 1.7 ± 1.3
Serum creatinine (µmol/L) 115.8 ± 27.5 114.3 ± 27.7 110.6 ± 25.2
NT-proBNPb (pmol/L) 141 (58–237) 161 (68–263) 137 (59–234)
hsCRP (mg/L) 3.4 (1.6–6.5) 3.4 (1.6–6.9) 3.2 (1.5–6.7)
Pharmacological treatment
ACE inhibitor (%) 668 (82) 654 (80) 674 (82)
ACE inhibitor or ARB (%) 746 (91) 760 (93) 755 (92)
MR antagonist (%) 310 (38) 347 (42) 298 (36)
Beta-blocker (%) 617 (75) 637 (78) 635 (78)
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Table 1 (continued) Baseline patient characteristics. Telomere data is divided
in quartiles for statistical purposesa
4th Quartile Total P value n = 819 n = 3275
71.32 ± 6.9 72.2 ± 6.8 < 0.001
238 (29) 777 (24) < 0.001
326 (40) 1,236 (38) 0.27+
482 (59) 2,005 (61)
11 (1) 34 (1)
31 ± 1 31 ± 1 0.43
27 ± 4 28 ± 5 0.15
130 ± 16 131 ± 16 0.24
77 ± 9 77 ± 9 0.74
71 ± 11 71 (11) 0.68
353 (43) 1,441 (44)
58 (7) 271 (8)
397 (49) 1,500 (46)
11 (1) 61 (2)
431 (53) 1,956 (60) < 0.001
582 (71) 2,428 (74) 0.43
196 (2) 808 (2) 0.76
545 (67) 2,145 (66) 0.35
225 (28) 925 (28) 0.15
560 (73) 2,294 (75) 0.02
90 (11) 377 (12) 0.66
78 (10) 341 (10) 0.91
11 (1) 74 (2) 0.05
5.4 ± 1.1 5.4 ± 1.1 0.61
1.3 ± 0.4 1.2 ± 0.3 0.34
3.6 ± 1.0 3.6 ± 0.9 0.86
0.9 ± 0.3 0.9 ± 0.3 0.46
1.6 ± 1.2 1.7 ± 1.3 0.07
110.2 ± 24.8 112.7 ± 26.4 0.01
163 (60–234) 150 (62–312) 0.19
3.0 (1.3–6.4) 3.3 (1.5–6.9) 0.94
663 (81) 2,659 (81) 0.83
762 (93) 3,023 (92) 0.29
316 (39) 1,271 (39) 0.79
620 (76) 2,509 (77) 0.73
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ACE, angiotensin-converting-enzyme; Apo-A, Apolipoprotein A-I; Apo-B,
Apolipoprotein B; ARB, angiotensin receptor blockers ; BMI, body mass
index; BP, blood pressure; CABG, coronary artery bypass surgery; HDL, High-
density lipoprotein; hsCRP, high-sensitivity C-reactive protein; LDL, Low-
density lipoprotein; LVEF, left ventricular ejection fraction; MR,
mineralocorticoid receptor; NT-proBNP, N-terminal pro-B-type natriuretic
peptide; NYHA, New York Heart Association; PCI, percutaneous coronary
intervention; PTCA, percutaneous transluminal coronary angioplasty. a
Variables are represented as mean values ± standard deviation except for
hsCRP and NT-proBNP, which are represented median values (inter-quartile
range). b
Measurements were performed in 2,394 patients. P values have been
calculated using linear regression test with baseline variable as dependent
variable and log(telomere length) as independent variable, adjusted for age +
gender. + denotes comparing NYHA II against III + IV classes.
Effect of telomere length on HF patient prognosis and survival
The median follow-up for this study was 1,040 days (inter-quartile range 874 –
1,159). The primary endpoint had occurred in 575 (17.6%) patients. Kaplan-
Meier curves of time to the primary composite endpoint (cardiovascular death,
non-fatal myocardial infarction, and non-fatal stroke) are presented in Figure 1.
Log-rank testing of quartile 1 (shortest telomeres) versus the quartile 4 (longest
telomeres) showed a significant correlation between leukocyte telomere length
and the primary endpoint (P (log-rank) = 0.01), see Table 2.
Table 2 Comparison of quartiles of telomere length using log-rank test
Endpoint Q1 vs Q2 Q1 vs Q3 Q1 vs Q4 All
Time to composite endpoint 0.16 0.21 0.01 0.11
Time to death from any cause 0.51 0.04 0.28 0.23
Time to coronary event 0.32 0.98 0.04 0.13
Time to cardiovascular death 0.52 0.07 0.11 0.23
The composite endpoint is composed of cardiovascular death, non-fatal
myocardial infarction, and non-fatal stroke. P values (log-rank) are represented.
Kaplan-Meier curves of secondary endpoints (time to death from any cause,
coronary event or cardiovascular death) are shown in Supplementary Figures 1-
3.
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The incidence of the time to first coronary endpoint was significantly higher for
patients in the quartile with shortest telomere lengths compared to the longest
telomere lengths quartile (P (log-rank) = 0.04; Table 2). Cox regression
analyses with telomere length as continuous variable are represented in Table 3.
Leukocyte telomere length was univariately associated with the primary
endpoint (HR 1.10; 95% CI 1.01-1.20; P = 0.03) and a trend was shown for
association with coronary events and cardiovascular death (HR 1.08; 95% CI
0.99-1.18; P = 0.08 and HR 1.09; 95% CI 0.99-1.21; P = 0.08, respectively).
When adding chronological age and gender to the regression model, the
associations of telomere length were no longer significant. Baseline telomere
length was also not associated with future hospitalisations due to cardiovascular
causes (P = 0.79) or worsening HF symptoms (P = 0.79), see Supplementary
Table 1.
Effect modification by rosuvastatin treatment
We studied whether rosuvastatin treatment might be more effective in patients
with shorter telomere length by analysing the interaction term of telomere
length × treatment in the Cox proportional hazard model similar to those
described above. The interaction terms for the primary endpoint, death,
coronary events or cardiovascular death were all non-significant (see
Supplementary Table 2), suggesting the absence of effect modification of study
treatment by baseline telomere length.
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Table 3 Cox regression of time to various endpoints with telomere length as
continuous variable
Endpoint Model Hazard Ratio 95% CI P value
Lower Upper
Composite endpoint 1 1.10 1.01 1.20 0.03
2 1.07 0.98 1.17 0.12
3 1.07 0.98 1.17 0.12
4 1.06 0.97 1.15 0.23
Death from any cause 1 1.05 0.97 1.15 0.24
2 1.01 0.92 1.10 0.84
3 1.01 0.92 1.10 0.84
4 0.99 0.91 1.08 0.83
Coronary endpoint 1 1.08 0.99 1.18 0.08
2 1.07 0.98 1.17 0.12
3 1.07 0.98 1.17 0.11
4 1.05 0.96 1.15 0.30
Cardiovascular death 1 1.09 0.99 1.21 0.08
2 1.06 0.96 1.17 0.28
3 1.06 0.96 1.17 0.29
4 1.04 0.94 1.15 0.50
The composite endpoint is composed of cardiovascular death, non-fatal
myocardial infarction, and non-fatal stroke. Model 1 = no adjustment; Model 2
= adjusted for age and gender; Model 3 = adjusted for age, gender, and
rosuvastatin/placebo treatment; Model 4 = adjusted for age, gender, and
baseline variables with P value < 0.05. Hazard ratio is presented per SD
decrease of telomere length.
Discussion
In the present study, we studied the association of leukocyte telomere length
and clinical outcomes in HF patients participating in the CORONA trial. Our
main finding is that the primary composite endpoint of cardiovascular death,
non-fatal myocardial infarction and non-fatal stroke, is univariately associated
with shorter leukocyte telomeres in HF patients. However, leukocyte telomere
length as a marker of biological ageing was not a superior predictor than
chronological age (as defined by date of birth). In addition, telomere lengths
were not stronger associated with secondary endpoints (time to death from any
cause, coronary event and cardiovascular death) than age and gender were. We
also tested whether rosuvastatin treatment had a more beneficial effect on HF
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outcomes in subjects with shorter telomere length compared to those with
longer telomeres but did not observe evidence of effect modification.
Previously, data from the sub-study of the Co-ordinating study evaluating
Outcomes of Advising and Counselling in Heart Failure (COACH), included a
smaller cohort of 890 chronic HF patients and found suggestive evidence for an
association between time to death or hospitalization for HF with telomere
length.85
The current study was designed to validate and extend these findings
but instead suggests that telomere length is not a superior predictor of outcomes
compared to chronological age. There are important differences that might
contribute to the observed discrepancy between the COACH and the CORONA
studies. For example, the definition of the primary endpoint and duration of
follow-up were different. Our earlier findings were mainly driven by
hospitalisations while the current study included more fatal events. In addition,
the patients participating in the COACH study were not limited to systolic heart
failure due to ischemic aetiology, and also included a significant number of
subjects with preserved ejection fraction and of HF of non-ischemic aetiology
(43%). Finally, the COACH trial included acute HF patients as well, while
CORONA only included patients with stable clinical condition over the two
weeks before randomisation and this might have introduced an selection bias.
We cannot exclude a potential effect of older age at entry and potential effects
on telomere length shortening caused by differences of external factors, such as
oxidative stress or inflammation, in patients participating in the CORONA trial
compared to earlier studies.
Although baseline telomere length measurements do not appear to be a
valuable marker to predict clinical outcomes in ischemic HF patients, there is
evidence that environmental factors might have an impact on leukocyte
telomere length.80,89
The question remains whether repeated intra-individual
measurements can reflect the effect of interventions such us discontinuation of
smoking80
, increased physical activity89
or pharmacotherapy or might allow
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disease monitoring. A previous study suggested that telomere length could
identify patients who would benefit most from statin treatment in primary
prevention setting.90
We addressed this question in relation to statin treatment
in HF patients but we did not find any evidence that rosuvastatin treatment
might be more beneficial in HF subjects with shorter telomere length. It also
has been suggest that statin treatment might prevent telomere attrition.91
In
addition to cholesterol lowering effects, statins have been reported to elicit
pleiotropic (cholesterol-independent) effects that might influence telomere
length or telomere biology. For example, statin treatment reduces oxidative
stress, a factor known to cause telomere attrition.17
Statins also have been
shown to increase the expression of telomere repeat factor 2 (TRF2), which
stabilizes telomeres and thereby reduces telomere length decline.92
Unfortunately, we did not collect DNA to measure telomere length after
rosuvastatin treatment was initiated and we were unable to confirm these
previous observations. In addition to environmental factors, telomere length is
determined by genetic factors.93
Interestingly, these genetic factors, have also
been linked to an increased risk of coronary artery disease suggesting a causal
link.93
At baseline, HF patients in the current study were on average 72 years. In the
age group 70 and older, there is conflicting evidence on whether telomere
length predicts mortality. In the Cardiovascular Health Study (N = 1,136
subjects, mean age of 74 years old)94
and in the Danish twins study (N = 548
twins, mean age 79 years old)95
telomere length was associated with outcome.
Contradictory, in the Swedish cohort of the Osteoporotic Fractures in Men
Study (MrOS, N = 2,744, mean age 76 years),96
telomeres had no predictive
value in this age group. However, in cardiovascular disease patients younger
than 70 years, shorter telomere length has been more consistently associated
with poor outcome. 97
Patients in the CORONA study with short telomeres were also characterised by
significantly higher serum creatinine measurements suggesting decreased renal
function. In two earlier studies (MERIT-HF98
and COACH99
) including HF
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patients, we observed an similar association between telomere length and
serum creatinine. However, in non-HF100
patients and in the general
population80
this association seems to be less convincing. A possible
explanation might be that the kidney in patients with shorter telomeres is less
resistance to the challenges provoked by HF and become dysfunctional or even
apoptotic.25
The strengths of the current study are that we have studied telomere biology in
the largest HF cohort with DNA available to date. We have studied hard
endpoints during a relatively long follow-up period. However, there are also
some limitations to take into account. First of all, due to practical reasons, we
have measured telomere length in leukocytes instead of cardiomyocytes.
Although there are data suggesting there is considerable correlation among
different cell types101
the most relevant cells might not be the leukocytes. It
could be speculated that telomere length of cardiac (stem)cells are more
important for outcome in HF. Differentiated cardiomyocytes do not divide and
shortening in non-replicating cells should be differentiated from that present in
dividing cells. The cardiac stem cells are a source of replenishing
cardiomyocytes102
and loss of telomeric DNA with each division has been
reported in human cardiac stem cells.103
Therefore, we cannot exclude an
important role of telomere length, or telomere biology in general, in
determining cardiac repair and consequently clinical outcome. By extension,
telomere length in cardiac stem cells can be a superior to chronological aging
as a biomarker of chronic heart failure. Secondly, we measured telomere length
at one time point, which does not allow us to study temporal changes. Thirdly,
while telomere biology is a complex and sophisticated system, additional
measurements of level and activity of regulatory proteins like telomerase could
have given more insights in the underlying mechanisms leading to aberrant
telomere attrition.104
Fourthly, we did not include a healthy reference
population. The CORONA trial was an international trial including HF patients.
Within that framework no healthy control subjects were included and
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consequently we are unable to directly compare telomere lengths of our
patients with healthy control subjects.
In conclusion, leukocyte telomere length was associated with clinical outcomes
in systolic ischemic HF patients, although the effects were statistically
dependent on chronological age. Leukocyte telomere length was not associated
with different effects of statin treatment in HF.
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Acknowledgements
We are indebted to all the participants of the CORONA cohort and the
CORONA study group (see Supplement) for their important contributions. We
like to thank Martin M. Dokter for assisting with the telomere measurements.
Funding
This work was supported by AstraZeneca Pharmaceuticals (JCF, Alderley Park,
Gadbrook Park, Northwich, UK) and the Innovational Research Incentives
Scheme program of the Netherlands Organization for Scientific Research
(NWO VENI, grant 916.76.170 to P. van der Harst).
Conflict of Interest
None declared.
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Supplementary Table 1 Number of hospitalised patients in CORONA, divided
over leukocyte telomere quartiles
Q1 Q2 Q3 Q4 P value
Cardiovascular cause 0.79
Number of patients with
hospitalizations 351 337 364 341
Total number of hospitalizations 786 725 748 732
For worsening heart failure 0.79
Number of patients with
hospitalizations 195 190 183 190
Total number of hospitalizations 393 382 327 370
Q: quartile.
Supplementary Table 2 Interaction model analyses for telomere length x
rosuvastatin treatment
Endpoint Model Hazard Ratio 95% CI P value
Lower Upper
Composite endpoint 1 1.39 0.80 2.41 0.24
2 1.36 0.77 2.38 0.29
Death from any cause 1 0.98 0.55 1.74 0.95
2 0.95 0.53 1.72 0.87
Coronary endpoint 1 1.00 0.57 1.73 0.99
2 1.09 0.61 1.94 0.77
Cardiovascular death 1 1.01 0.53 1.90 0.98
2 0.95 0.49 1.84 0.88
Model 1 = adjusted for age and gender, telomere length × rosuvastatin / placebo
treatment, and rosuvastatin / placebo treatment; Model 2 = adjusted for age,
gender, telomere length × rosuvastatin / placebo treatment, rosuvastatin /
placebo treatment and baseline variables with p-value < 0.05.
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Supplementary Figure 1 Time to death from any cause in days by Kaplan–
Meier estimates for the four quartiles of telomere lengths.
Quartile 1 represents the quartile with shortest telomeres. The represented P
value is calculated between quartile 1 and 3 using log-rank test.
Supplementary Figure 2 Time to first coronary event in days by Kaplan–
Meier estimates for the four quartiles of telomere lengths.
Quartile 1 represents the quartile with shortest telomeres. The represented P
value is calculated between quartile 1 and 4 using log-rank test.
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Supplementary Figure 3 Time to death from any cardiovascular cause in days
by Kaplan–Meier estimates for the four quartiles of telomere lengths.
Quartile 1 represents the quartile with shortest telomeres. The represented P
value is calculated between quartile 1 and 4 using log-rank test.
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Appendix 1: CORONA Study Group
The members of the CORONA Study Group are as follows: Executive
Committee: Peter Dunselman, Amphia Hospital, Breda, Netherlands; Åke
Hjalmarson, Wallenberg Laboratory for Cardiovascular Research, Sahlgrenska
University, Hospital, Gothenborg, Sweden (Chairman of the Executive
Committee); John Kjekshus, Dept. of Cardiology, Rikshospitalet University
Hospital, Oslo, Norway; Magnus Lindberg (AstraZeneca biostatistician); John
JV McMurray, BHF Glasgow Cardiovascular Research Centre, University of
Glasgow, UK; Finn Waagstein, Wallenberg Laboratory for Cardiovascular
Research, Sahlgrenska University, Hospital, Gothenborg, Sweden; Hans
Wedel, Nordic School of Public Health, Gothenborg, Sweden (Independent
biostatistician); John Wikstrand, Wallenberg Laboratory for Cardiovascular
Research, Sahlgrenska University, Hospital, Gothenborg; also consultant
AstraZeneca, Mölndal, Sweden. Writing Committee: Peter Dunselman, Åke
Hjalmarson, John Kjekshus, John McMurray (Chairman of the Writing
Committee), Finn Waagstein, Hans Wedel and John Wikstrand. Steering
Committee: Chairman John Kjekshus; Co-chair Peter Dunselman and Åke
Hjalmarson. Members of the Steering Committee are the Executive Committee
members and the National Co-ordinating Investigators).86
The Steering
Committee also includes one AstraZeneca monitor from each of the 21
participating countries (non-voting; for names see reference).86
Investigators:
see reference.86
Data and Safety Monitoring Board: Henry Dargie, Scottish
Advanced Heart Failure Service, Glasgow Royal Infirmary, Glasgow, Scotland
(Chairman); David DeMets, Dept. of Biostatistics and Medical Informatics,
School of Medicine and Public Health, University of Wisconsin, Madison, WI,
USA (DSMB biostatistician); Rory Collins, Clinical Trial Service Unit,
University of Oxford, Oxford, UK; Jan Feyzi, Dept. of Biostatistics and
Medical Informatics, School of Medicine and Public Health, University of
Wisconsin, Madison, WI, USA (SDAC biostatistician); Barry Massie, Veterans
Affairs Medical Center and University of California San Francisco, San
Francisco. Independent Endpoint Committee: Bengt-Olov Fredlund, Dept. of
Emergency and Cardiovascular Medicine, Sahlgrenska University Hospital
Östra, Gothenborg University, Gothenborg, Sweden; Mikael Holmberg, Dept.
of Cardiology, Sahlgrenska University Hospital, Gothenborg University,
Gothenborg, Sweden; Katarina Saldeen, Dept. of Cardiology, Sahlgrenska
University Hospital, Gothenborg University, Gothenborg, Sweden; Ola
Samuelsson (Secretary), Dept. of Nephrology, Sahlgrenska University
Hospital, Gothenborg University, Gothenborg, Sweden and Karl Swedberg,
Dept. of Emergency and Cardiovascular Medicine, Sahlgrenska University
Hospital Östra, Gothenborg University, Gothenborg, Sweden (Chairman).
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Chapter 6
The Impact of Coronary Artery Disease
Risk Loci on Ischemic Heart Failure
Severity and Prognosis
Association analysis in the COntrolled ROsuvastatin
multiNAtional trial in heart failure (CORONA)
Vincent G. Haver
Niek Verweij
John Kjekshus
Jayne C. Fox
Hans Wedel
John Wikstrand
Wiek H. van Gilst
Rudolf A. de Boer
Dirk J. van Veldhuisen
Pim van der Harst
BMC Medical Genetics 2014;21(15):140-7
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Abstract
Background
Recent genome-wide association studies have identified multiple loci that are
associated with an increased risk of developing coronary artery disease (CAD).
The impact of these loci on the disease severity and prognosis of ischemic heart
failure due to CAD is currently unknown.
Methods
We undertook association analysis of 7 single nucleotide polymorphism
(rs599839, rs17465637, rs2972147, rs6922269, rs1333049, rs501120, and
rs17228212) at 7 well established CAD risk loci (1p13.3, 1q41, 2q36.3, 6q25.1,
9p21.3, 10q11.21, and 15q22.33, respectively) in 3,320 subjects diagnosed with
systolic heart failure of ischemic aetiology and participating in the COntrolled
ROsuvastatin multiNAtional Trial in Heart Failure (CORONA) trial. The
primary outcome was the composite of time to first event of cardiovascular
death, non-fatal myocardial infarction and non-fatal stroke, secondary
outcomes included mortality and hospitalization due to worsening heart failure.
Results
None of the 7 loci were significantly associated with the primary composite
endpoint of the CORONA trial (death from cardiovascular cases, nonfatal
myocardial infarction, and nonfatal stroke). However, the 1p13.3 locus
(rs599839) showed evidence for association with all-cause mortality (after
adjustment for covariates; HR 0.74, 95%CI [0.61 to 0.90]; P=0.0025) and we
confirmed the 1p13.3 locus (rs599839) to be associated with lipid parameters
(total cholesterol (P=1.1x10-4
), low-density lipoprotein levels (P=3.5×10-7
) and
apolipoprotein B (P=2.2×10-10
)).
Conclusion
Genetic variants strongly associated with CAD risk are not associated with the
severity and outcome of ischemic heart failure. The observed association of the
1p13.3 locus with all-cause mortality requires confirmation in further studies.
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Keywords: Coronary Artery Disease – Heart Failure – Genetics – Healthy
Ageing – SNP
Financial support
This study was supported by AstraZeneca, Mölndal, Sweden. Dr. van
Veldhuisen is an established investigator of the Netherlands Heart Foundation,
Den Haag, Netherlands (Grant 2006T037). N. Verweij is supported by the
Netherlands Heart Foundation (grant NHS2010B280).
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Background
Heart Failure (HF) is a highly prevalent condition affecting more than 15
million patients in Europe alone and has a poor prognosis.105
In several familial
forms of HF, disease-causing mutations have been identified including several
mutations in genes coding for structural components of the sarcomere.106,107
For
example, mutations in the genes encoding cardiac β-myosin heavy chain
(MYH7) or cardiac myosin-binding protein C (MYBPC3) are known to cause
hypertrophic and dilated cardiomyopathies.108
However, these Mendelian
diseases only account for a small minority of all HF cases and only explain a
minor proportion of the population attributable risk for HF. The vast majority
of HF is a consequence of more complex genetic and environmental factors and
the interactions among them. Coronary artery disease (CAD) is considered the
major cause of HF. For the development of CAD, a number of genetic risk loci
with common variants have recently been identified.5-7
The earliest findings
derived from genome wide association studies were reported by the Wellcome
Trust Case Control Consortium (WTCCC) and the German MI Family GWA
studies, as well as the Coronary Artery Disease consortium, which together
have identified 7 common variants that were robustly associated with CAD.5,6
Whether these variants, with strong prior evidence to be associated
with increased CAD risk, are also relevant for ischemic HF progression as
reflected by HF severity and prognosis remains to be determined. In the present
study, we have evaluated these 7 CAD risk loci in ischemic HF patients
participating in the COntrolled ROsuvastatin multiNAtional study in heart
failure (CORONA) and tested the hypothesis that the SNPs associated with
CAD are also associated with ischemic HF disease severity and outcome.
Methods
The current study is a genetic sub-study of the CORONA trial. The CORONA
trial aimed to determine the effect of rosuvastatin treatment on clinical outcome
in patients with systolic heart failure from ischemic aetiology. The CORONA
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study group is represented in the supplement. The CORONA trial has been
published in detail, the main result was that rosuvastatin did not reduce the
primary composite outcome of death from cardiovascular causes, nonfatal
myocardial infarction, or nonfatal stroke.109,110
Study Population
In- and exclusion criteria of the CORONA trial have been reported in detail
previously.109,110
In brief, patients were 60 years of age or older, HF of ischemic
aetiology, and a left ventricular ejection fraction (LVEF) of ≤ 40% with NYHA
class III/IV symptoms or LVEF ≤ 35% with NYHA class II symptoms. Patients
had to be clinically stable and on optimal treatment for at least 2 weeks before
inclusion. Exclusion criteria included the following; recent myocardial
infarction (MI) (<6 months); unstable angina or stroke within the past 3
months; percutaneous coronary intervention (PCI) or coronary-artery bypass
grafting (CABG); the implantation of a cardioverter defibrillator (ICD) or
biventricular pacemaker within the past 3 months; heart transplantation;
clinically significant/uncorrected primary valvular heart disease; hypertrophic
cardiomyopathy; or systemic disease (e.g., amyloidosis). The CORONA trial
included 5,011 patients,110
and DNA was obtained from 3,340 of them. We
excluded 20 non-Caucasian subjects (8 black, 7 Asian, 5 ‘other’), leaving 3,320
subjects for the current genetic sub-study of CORONA. The ethical committee
at each of the participating hospitals approved this trial (see Supplemental
Methods), and patients provided written informed consent.
Definition of Phenotypes and outcome
The severity of HF was assessed by LVEF and serum N-terminal pro B-type
Natriuretic Peptide (NT-proBNP) levels. As one of the loci has also been
identified in a GWAS for lipid traits, we specifically studied the association of
this locus with available lipid traits. The primary outcome of the CORONA
trial was the composite of death from cardiovascular cases, nonfatal myocardial
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infarction, and nonfatal stroke, analysed according to the time to the first event.
Secondary outcomes were death from any cause, any coronary event (defined
as sudden death, fatal or nonfatal myocardial infarction, the performance of
PCI or CABG, ventricular defibrillation by an ICD, resuscitation after cardiac
arrest, or hospitalization for unstable angina), death from cardiovascular causes,
sudden death, death from worsening heart failure, combination of mortality or
worsening heart failure and athero-thrombotic endpoint.
SNP selection and genotyping
We studied 7 loci (1p13.3, 1q41, 2q36.3, 6q25.1, 9p21.3, 10q11.21, 15q22.33)
which have been linked to CAD risk by previous GWAS.5,6
We genotyped the
7 lead SNPs within these loci; rs599839, rs17465637, rs2972147, rs6922269,
rs1333049, rs501120, and rs17228212 (Supplementary Table 1). Genotyping
was carried out with TaqMan (Applied Biosystems) using standard protocols
and was performed at the laboratory of AstraZeneca Pharmaceuticals (JCF),
Alderley Park, UK without knowledge of clinical data.
Statistical Analysis
Normality of the data was determined by visual inspection. HsCRP and NT-
proBNP were non-normally distributed and therefore log-transformed.
Genotypes were coded additively as 0, 1 or 2 in terms of the number of minor
alleles. The baseline variables were analysed in linear model with only
genotype as predictor and if significant, the following covariates were added to
the adjusted model: age, sex, ejection fraction, NYHA class, systolic blood
pressure, heart rate, body mass index, history of myocardial infarction, angina
pectoris, diabetes mellitus, hypertension, stroke, intermittent claudication,
aortic aneurysm, percutaneous coronary intervention, coronary artery bypass
graft surgery, atrial fibrillation, implanted pacemaker, implanted cardiac
defibrillator, smoking status, serum creatinine, alanine aminotransferase,
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creatine kinase, thyroid-stimulating hormone, triglycerides, hsCRP and NT-
proBNP, as explained in detail by Wedel et al.111
HF outcome determinants
were analysed using Cox regression of outcome versus number of minor
alleles. Analyses were conducted unadjusted and after adjusted for the above
mentioned co-variates. We considered a P-value <0.0071 for the primary
endpoint statistically significant (Bonferroni adjustment of <0.05 for 7
independent loci) and as suggestive for all secondary endpoint analyses.
Results
The baseline characteristics of the study population are presented in Table 1.
The study population consisted of 3,320 HF patients with mean LVEF of 31%
and a median NT-proBNP level of 151 pmol/L. There was a high prevalence of
hypertension, diabetes mellitus, and chronic kidney disease in our population.
Subjects were well treated for HF, with 87% using diuretics, 77% using beta-
blockers, 92% using ACE inhibitors or AT1-receptor antagonist, and 91% were
treated with an antiplatelet agent or anticoagulant. The mean follow-up time
was 2.73 years, accumulating 9,062 patient-years of follow-up. At baseline,
1,986 patients (60%) had a history of MI, and 823 (25%) a history of CABG or
PCI. Genotyping was successful in excess of 98% for all SNPs but one and the
distributions of genotypes were consistent with the Hardy-Weinberg
equilibrium as calculated using the chi-square test for deviation. Minor allele
frequencies were similar in the current analyses compared to the discovery
GWAS.5,6
Details on genotyping can be found in Supplementary Table 1.
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Table 1. Baseline characteristics of subjects in genetic sub-study of CORONA
n = 3,320
Age (years) 72.3 ± 6.9
Males n (%) 2530 (76.2)
Left ventricular ejection fraction (%±SD) 31 ± 6.3
NYHA class n (%)
II 1251 (37.7)
III 2035 (61.3)
IV 34 (1.0)
History of n (%)
Angina Pectoris 2463 (74.2)
Aortic Aneurysm 84 (2.5)
Aortic Aneurysm Surgery Performed 47 (1.4)
Atrial Fibrillation/Flutter 1318 (39.7)
Diabetes Mellitus 933 (28.1)
Hypertension 2173 (65.5)
Implantable cardioverter-defibrillator 79 (2.4)
Implanted pacemaker 349 (10.5)
Intermitted claudication 392 (11.8)
Myocardial infarction 1986 (59.8)
Coronary Artery Bypass Surgery 537 (16.2)
Percutaneous Coronary Intervention 358 (10.8)
CABG or PCI 823 (24.8)
Stroke 386 (11.6)
Smoking status n (%)
Non Smoker 1521 (45.8)
(Ex-)smoker 1797 (54.1)
Heart Failure Medication at baseline n (%)
Loop diuretic 2421 (72.9)
Thiazide diuretic 776 (23.4)
Loop or Thiazide 2879 (86.7)
Beta-Blocker 2542 (76.6)
ACE inhibitor 2696 (81.2)
AT1-receptor blocker 428 (12.9)
ACE inhibitor or AT1-receptor blocker 3063 (92.3)
Aldosterone antagonist 1284 (38.7)
Digitalis 1072 (32.3)
Anti-platelet or Anti-coagulant 3020 (91.0)
Blood pressure (mmHg)
Systolic 130.5 ± 16.1
Diastolic 77.0 ± 8.6
Heart rate (beats/min) 71.2 ± 10.9
BMI (kg/m2) 27.5 ± 4.4
Serum creatinine (umol/L) 112.8 ± 26.5
eGFR (ml/min/1.73m/m2BSA) 58.5 ± 14.0
hs-CRP (mg/L) 3.3 (0.02-230)
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Table 1 (continued). Baseline characteristics of subjects in genetic sub-study
of CORONA
NT-proBNP (pmol/L) 151 (1-3868)
Lipids
Total cholesterol (mmol/L) 5.41 ± 1.07
LDL-cholesterol (mmol/L) 3.60 ± 0.94
HDL-cholesterol (mmol/L) 1.19 (0.47-3.55)
Apo-A1 (g/L) 1.51 ± 0.27
Apo-B (g/L) 1.28 ± 0.30
Apo-B / Apo-A (mean) 0.87 ± 0.24
Triglycerides (mmol/L) 1.68 (0.41-14.43)
Abbreviations: NYHA = New York Heart Association; CABG = Coronary
Artery Bypass Graft; PCI = Percutaneous Coronary Intervention; ACE =
Acetylcholinesterase; AT1 = Angiotensin-1; BMI = body mass index; eGFR =
estimated Glomerular Filtration Rate; hs-CRP = high sensitive C-reactive
protein; NT-proBNP = N-terminal pro B-type natriuretic peptide, LDL = low-
density lipoprotein; HDL = high-density lipoprotein; Apo = apolipoprotein.
Variables are expressed as mean (SD) when normally distributed and as median
(min-max) when non-normally distributed.
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Table 2. CAD loci (P < 0.05) and heart failure disease markers in genetic sub-
study of CORONA
Locus SNP Model n Beta
estimate
LVEF 9p21.3 rs1333049 Unadj. 3,300 0.0044
Adj.a
2,212 0.0038
10q11.21 rs501120 Unadj. 3,300 0.0054
Adj.a 2,216 0.0022
15q22.33 rs17228212 Unadj. 3,303 -0.005
Adj.a 2,218 -0.0031
log NT-proBNP
(pmol/L) 10q11.21 rs501120 Unadj. 2,412 -0.11
Adj.a 2,216 -0.064
BMI (kg/m2) 1q41 rs17465637 Unadj. 3,298 0.35
Adj.a 2,220 0.3
Serum creatinine
(umol/L) 10q11.21 rs501120 Unadj. 3,300 -2.01
Adj.a 2,216 -0.43
Abbreviations: SNP = single nucleotide polymorphism; LVEF = left ventricular
ejection fraction; BMI = body mass index; NT-proBNP = N-terminal pro B-
type natriuretic peptide. aAdjusted analyses were adjusted for age, sex, ejection
fraction, NYHA class, systolic blood pressure, heart rate, body mass index,
history of myocardial infarction, angina pectoris, diabetes mellitus,
hypertension, stroke, intermittent claudication, aortic aneurysm, percutaneous
coronary intervention, coronary artery bypass graft surgery, atrial fibrillation,
implanted pacemaker, implanted cardiac defibrillator, smoking status, serum
creatinine, alanine aminotransferase, creatine kinase, thyroid-stimulating
hormone, triglycerides, hsCRP and NT-proBNP.111
As some covariates were
also baseline variables or strongly associated to a baseline variable, covariates
were excluded from analyses (see Supplementary Table 2). Results of all
regression analyses for all SNPs are in Supplementary Table 3.
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Table 2 (continued). CAD loci (P < 0.05) and heart failure disease markers in
genetic sub-study of CORONA
95%CI
P value
Excluded from adjusted
analysis
0.0015 - 0.0074 3.6×10-3
LVEF
0.0005 - 0.0071 2.3×10-2
0.0012 - 0.0097 1.2×10-2
-0.0025 - 0.0068 0.36
-0.0084 - -0.0016 4.0×10-3
-0.0069 - 0.0008 0.12
-0.20 - -0.01 2.7×10-2
log NT-proBNP (pmol/L)
-0.14 - 0.02 0.12
0.11 - 0.60 4.8×10-3
BMI (kg/m2)
0.03 - 0.58 2.9×10-2
-3.80 - -0.21 2.8×10-2
Serum creatinine (umol/L)
-2.25 - 1.39 0.65
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CAD loci and HF disease severity; LVEF and NT-proBNP
LVEF and NT-proBNP were taken as indicators of HF disease severity and
their association with the 7 genetic loci was determined. Although some of the
unadjusted association P-values were smaller than 0.05 (Table 2), we
considered none of these loci associated with LVEF or NT-proBNP considering
the multiple testing burden of these secondary endpoints.
Prognostic value of CAD loci for cardiovascular events and
disease progression in HF
Next, we tested the association between the CAD-associated loci with HF
disease outcome. None of the 7 loci predicted the occurrence of the primary
endpoint (composite endpoint of cardiovascular mortality, non-fatal myocardial
infarction or non-fatal stroke, analysed as time to first event) or death caused by
cardiovascular events. When the individual components of the primary
endpoint were considered, we observed that the 1p13.3 (rs599839) locus,
showed a borderline association with all-cause mortality (HR 0.86, 95%CI
[0.74-1.00], P=0.499) which became somewhat stronger after adjustment for
covariates (HR 0.74, [0.61-0.90], P=0.0025). Using ordered Jonckheere-
Terpstra test, the 1p13.3 locus also showed association with the total number of
hospitalizations due to cardiovascular causes (P=0.0093) and the 10q11.21
locus showed an association with the number of hospitalizations due to
worsening HF. All associations with P<0.05 are presented in Table 3 (all
associations are presented in Supplementary Table 4 and 5).
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Associations of CAD loci with lipid profile in HF
The 7 loci were evaluated for association with the available serum lipid profile
parameters. After adjustments, the 1p13.3 locus (rs599839) was associated with
total cholesterol (P=1.1×10-4
), low-density-lipoprotein (LDL) cholesterol
(P=3.5×10-7
), serum Apolipoprotein-B (Apo-B) levels (P=5.1×10-8
) and the
Apo-B/Apo-A1 ratio (P=8.0×10-9
). Associations with lipid parameters with P-
values smaller than 0.05 are presented in Table 4 (all associations are presented
in Supplementary Table 6.
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Table 3. CAD loci (P < 0.05) and prognosis of ischemic heart failure in genetic
sub-study of CORONA
Locus SNP Model
All-cause mortality 1p13.3 rs599839 Unadj.
Adj.a
Mortality or WHF
hospitalization
10q11.2
1 rs501120 Unadj.
Adj.a
Number of hospitalizations
due to cardiovascular cause 1p13.3 rs599839
Ordered
Jonkeheere-
Terpstra test
Number of hospitalizations
due to WHF
10q11.2
1 rs501120
Ordered
Jonkeheere-
Terpstra test
Abbreviations: SNP = single nucleotide polymorphism; WHF = worsening
heart failure. aAdjusted analyses were adjusted for age, sex, ejection fraction,
NYHA class, systolic blood pressure, heart rate, body mass index, history of
myocardial infarction, angina pectoris, diabetes mellitus, hypertension, stroke,
intermittent claudication, aortic aneurysm, percutaneous coronary intervention,
coronary artery bypass graft surgery, atrial fibrillation, implanted pacemaker,
implanted cardiac defibrillator, smoking status, serum creatinine, alanine
aminotransferase, creatine kinase, thyroid-stimulating hormone, triglycerides,
hsCRP and NT-proBNP.111
* directions were concordant with previous
observations.6 Regression data of all SNPs are presented in Supplementary
Table 4.
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Table 3 (continued). CAD loci (P < 0.05) and prognosis of ischemic heart
failure in genetic sub-study of CORONA
n (total) n (events) Hazard
ratio
95%CI
P value
3,300 527 0.86* 0.74-1.00 4.99×10-2
2,218 341 0.74* 0.61-0.90 2.5×10-3
3,300 1046 0.85* 0.75-0.97 1.2×10-2
2,216 670 0.82* 0.70-0.96 1.5×10-2
9.3×10-3
3.2x10
-2
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Table 4. CAD loci (P < 0.05) and lipid characteristics in genetic sub-study of
CORONA
Locus SNP Model n
Total cholestorol (mmol/L) 1p13.3 rs599839 Unadj. 3,285
Adj.a 2,219
10q11.21 rs501120 Unadj. 3,285
Adj.a 2,217
LDL cholesterol (mmol/L) 1p13.3 rs599839 Unadj. 3,285
Adj.a 2,219
10q11.21 rs501120 Unadj. 3,285
Adj.a 2,217
Triglycerides (mmol/L) 1p13.3 rs599839 Unadj. 3,285
Adj.a 2,219
9p21.3 rs1333049 Unadj. 3,285
Adj.a 2,213
Apo-B (g/L) 1p13.3 rs599839 Unadj. 3,264
Adj.a 2,218
10q11.21 rs501120 Unadj. 3,267
Adj.a 2,216
Apo-B / Apo-A1 ratio 1p13.3 rs599839 Unadj. 3,264
Adj.a 2,218
Abbreviations: SNP = single nucleotide polymorphism; LDL = low-density-
lipoprotein; HDL = high-density-lipoprotein; Apo-B = apolipoprotein-B; Apo-
A1 = apolipoprotein-A1. aAdjusted analyses were adjusted for age, sex, ejection
fraction, NYHA class, systolic blood pressure, heart rate, body mass index,
history of myocardial infarction, angina pectoris, diabetes mellitus,
hypertension, stroke, intermittent claudication, aortic aneurysm, percutaneous
coronary intervention, coronary artery bypass graft surgery, atrial fibrillation,
implanted pacemaker, implanted cardiac defibrillator, smoking status, serum
creatinine, alanine aminotransferase, creatine kinase, thyroid-stimulating
hormone, triglycerides, hsCRP and NT-proBNP.111
As some covariates were
also baseline variables or strongly associated to a baseline variable, covariates
were excluded from analysis (see Supplementary Table 2). Data for all SNPs
are presented in Supplementary Table 6.
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Table 4 (continued). CAD loci (P < 0.05) and lipid characteristics in genetic
sub-study of CORONA
Beta estimate 95%CI P value
-0.14 -0.20 - -0.08 1.2×10-5
-0.14 -0.21 - -0.07 1.1×10-4
-0.07 -0.15 - -0.001 4.7×10-2
-0.05 -0.13 - 0.03 0.24
-0.16 -0.22 - -0.11 1.8×10-9
-0.17 -0.23 - -0.1 3.5×10-7
-0.07 -0.13 - -0.004 3.6×10-2
-0.05 -0.12 - 0.02 0.19
0.08 0.007 - 0.15 3.2×10-2
0.07 -0.005 - 0.15 6.6×10-2
-0.08 -0.14 - -0.02 7.0×10-3
-0.06 -0.13 - 0.001 5.4×10-2
-0.06 -0.07 - -0.04 2.2×10-10
-0.06 -0.08 - -0.04 5.1×10-8
-0.02 -0.04 - -0.002 2.8×10-2
-0.02 -0.05 - 0.002 7.5×10-2
-0.05 -0.06 - -0.03 3.3×10-11
-0.05 -0.07 - -0.03 8.0×10-9
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Discussion
HF is a common condition in which cardiac function is affected, leading to a
variety of symptoms like dyspnoea, fatigue, and fluid retention. The most
frequent cause of HF is CAD. In the past few years, several genetic variants
have been associated with CAD resulting in new insight in the pathophysiology
of CAD.5 We tested the hypothesis that the presence of variants associated with
prevalent CAD is also causally linked to increased severity of HF and worse
prognosis. Our findings lend little support to this hypothesis. Since this was a
candidate analyses, the observed associations are suggestive and require further
replication.
The loci we evaluated included the 9p21 locus, which is considered the most
robust common genetic risk factor for CAD with the rs1333049 variant
showing the strongest signal for association with CAD in WTCCC and German
studies.5 This locus harbours a large non-coding transcript of unknown
function, designated the name CDKN2B antisense RNA (CDKN2BAS or
ANRIL). This locus is related to atherosclerotic disease burden in different
vascular beds112
and deletion of ANRIL in human aortic smooth muscle cells
leads to a increase in proliferative capacity in culture.113
Furthermore, the rate
of proliferation of vascular smooth muscle cells is attenuated by the 9p21
genotype and the CAD risk allele (C allele) increases vascular smooth muscle
cell proliferation, thereby likely playing an important role in the development
of atherosclerosis.114
Despite these findings, the presence of this genetic
variant, and presumably increased atherosclerotic burden, did not translate into
increased HF severity or worse outcome in patients with ischemic HF in the
present study. A possible explanation is that the effect of this locus acting
through increased atherosclerotic disease burden is confounded by events
defined by the severity of heart failure. In addition, the vulnerability to develop
HF given a similar atherosclerotic disease burden might also differ among
patients and could be a consequence of complex gene-environment interactions.
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The presence or absence of concomitant conditions and diseases including
diabetes, renal dysfunction, or pulmonary disease may also modify the
phenotype. Furthermore, patients in our study were well treated with beta-
blockers, angiotensin-converting-enzyme inhibitors, and aldosterone
antagonists, which might further have decreased the differences between
patients due to genetic risk factors. Also related to optimisation of
pharmacotherapy, Wedel et al. calculated specifically for the CORONA trial,
that the proportionate contribution of myocardial infarction to total mortality,
related to the atherosclerotic disease burden, was relatively small. The Wald
statistic of myocardial infarction for total mortality was 3.9, while for example
log NT-proBNP showed a Wald statistic for total mortality of 167.111
This
suggests that the pathological role of genetic risk variants acting through CAD
might also be relatively small, and could therefore have remained undetected in
our study. Nevertheless, the current sample size of systematically collected
ischemic HF patients with DNA available to perform genotyping studies is the
largest available in the world to date and it will be difficult, if not impossible,
to extend the sample size within reasonable timeframes. The results of this
study concurs with the lack of effect on CAD with rosuvastatin.111
Considering prior knowledge, we also studied the association of the genotyped
genetic variants with lipid parameters.115-117
We were able to confirm previous
reported associations between the 1p13.3 locus (rs599839) and lipids. We also
provided novel data on this locus and its association with apolipoproteins. The
1p13.3 locus was most strongly associated with Apo-B levels (stronger than the
known LDL association), suggesting Apo-B is a candidate effector molecule.
The genomic region at 1p13.3 encodes 4 genes: proline/serine-rich coiled
protein 1 (PSRC1), cadherin EGF LAG sevenpass G-type receptor 2 (CELSR2),
myosin-binding protein H-like (MYBHL), and sortilin 1 (SORT1). Recent
studies have revealed a role for SORT1 in Apo-B secretion and LDL
catabolism: overexpression of SORT1 resulted in increased serum LDL and
loss-of-function mutations were associated with protection against
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hypercholesterolemia and atherosclerosis.118,119
In our secondary analyses we
observed an association with all-cause mortality and the number of
hospitalizations due to cardiovascular causes for the 1p13.3 locus. As these
were secondary analyses, which would not survive strict adjustment for
multiple testing, the interpretation of these findings should be cautious as they
require further confirmation. In addition, the explanation and possible relation
to lower cholesterol and apolipoprotein levels cannot be deducted from the
current study.
Among the strengths of the present study are the size and quality of the study
cohort, which is the largest heart failure cohorts with DNA available to date.
Patient characteristics and outcomes have been collected and documented
systematically within the framework of a clinical trial. However, there are some
limitations which we need to address. At first, we have limited the variants
tested to the N.J. Samani paper published in 20075 and the variants reported by
the Coronary Artery Disease Consortium in 2009.6 Recent GWA studies have
identified additional variants involved in CAD development. The recent
publication by the CARDIoGRAMplusC4D consortium reported 46 genetic
variants associated with CAD risk.120
We cannot exclude that there might be
variants among these 46 that are related to heart failure outcomes and this
remains an objective for further study. We performed a post-hoc power
calculation to consider the possibility that our study might have lacked
power.121
Assuming an effect size of the risk variant comparable to the CAD
discovery GWAS (genotype relative risk Aa = 1.3; genotype relative risk AA =
1.6),5,6
we calculated the power to detect a significant effect for a variant
(prevalence cases = 0.175; number of cases = 581; control:case ratio = 4.7).
The power of our analyses ranged from 0.90 to 0.91 depending on the
frequency of the risk allele (ranging from 0.25 to 0.55). Future studies with
larger sample sizes, containing all CAD loci, are warranted to further evaluate
the influence of CAD genetic loci in heart failure.
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Conclusions
Genetic variants associated with CAD and atherosclerotic disease burden are
not associated with the severity and prognosis of patients with ischemic HF in
the CORONA trial. Therefore, the observed secondary association of the
1p13.3 locus with all-cause mortality requires confirmation in further studies.
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Competing interests
The authors have no competing interests to declare, financial or otherwise.
Acknowledgements
We acknowledge M. J. McLoughlin for the genotyping. Ethically approved
collection and banking of biomaterials and genotyping was funded by
AstraZeneca. The present analyses were supported by grant 95103007 from
ZonMw and the Innovational Research Incentives Scheme (NWO VENI, Grant
Number 916.76.170 to PvdH) of the Netherlands Organization for Health
Research and Development, The Hague, the Netherlands.
Supplement
Supplementary Table 1. Genotyping details in the genetic sub-study of
CORONA
Locus SNP Minor
allele
Major
allele n MAF n AA n AB
n
BB
P-
HWE
1p13.3 rs599839 G A 3,300 0.23 1,953 1,169 178 0.85
1q41 rs17465637 A C 3,305 0.26 1,840 1,246 219 0.68
2q36 rs2972147 T C 3,299 0.37 1,317 1,521 461 0.53
6q25.1 rs6922269 A G 3,297 0.26 1,779 1,303 215 0.26
9p21.3 rs1333049 C G 3,321 0.50 870 1,589 841 0.03
10q11.21 rs501120 C T 3,300 0.15 2,425 794 81 0.11
15q22.33 rs17228212 C T 3,303 0.27 1,795 1,258 250 0.15
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Supplementary Table 2. Excluded variables in adjusted analyses
Variables in adjusted analyses Excluded from the
adjusted analysis of
Age/10
Diabetes Mellitus
LVEF*100 LVEF
Sex
Hypertension SBP
Myocardial Infarction
NYHA class
Total Cholesterol Cholesterol, LDL, HDL, TG, apo-A1,
Apo-B & Apo-B/Apo-A1
Angina pectoris
Aortic Aneurysm
Atrial fibrillation
BMI BMI
CABG
Heart rate/10 HR
Implanted cardioverter defibrillator
Implanted pacemaker
Intermittent claudication
PCI
SBP/10 SBP
Smoking
Stroke
ALAT
CK
TSH
Apo-A1 Cholesterol, LDL, HDL, TG, Apo-
A1, Apo-B and Apo-B/Apo-A1
Apo-B Cholesterol, LDL, HDL, TG, Apo-
A1, Apo-B and Apo-B/Apo-A1
Creatinine/10 Creatinine
Triglycerides Cholesterol, LDL, HDL, TG, Apo-
A1, Apo-B and Apo-B/Apo-A1
NT-proBNP NT-proBNP
hs-CRP hs-CRP
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Supplementary Table 3. CAD loci and heart failure disease markers in genetic
sub-study of CORONA
Variable Locus SNP Model
LVEF 1p13.3 rs599839 Unadj.
Adj.
1q41 rs17465637 Unadj.
Adj.
2q36 rs2972147 Unadj.
Adj.
6q25.1 rs6922269 Unadj.
Adj.
9p21.3 rs1333049 Unadj.
Adj.
10q11.21 rs501120 Unadj.
Adj.
15q22.33 rs17228212 Unadj.
Adj.
log NT-proBNP (pmol/L) 1p13.3 rs599839 Unadj.
Adj.
1q41 rs17465637 Unadj.
Adj.
2q36 rs2972147 Unadj.
Adj.
6q25.1 rs6922269 Unadj.
Adj.
9p21.3 rs1333049 Unadj.
Adj.
10q11.21 rs501120 Unadj.
Adj.
15q22.33 rs17228212 Unadj.
Adj.
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Supplementary Table 3 (continued). CAD loci and heart failure disease
markers in genetic sub-study of CORONA
n Beta estimate Lower CI Upper CI P value
3300 0.0002 -0.0034 0.0038 0.918
2218 0.0012 -0.0029 0.0053 0.576
3305 0.0003 -0.0032 0.0038 0.859
2220 -0.0002 -0.0041 0.0037 0.919
3299 0.0013 -0.0018 0.0044 0.42
2216 0.001 -0.0025 0.0044 0.586
3297 -0.0025 -0.006 0.001 0.157
2214 -0.0031 -0.0069 0.0008 0.118
3300 0.0044 0.0015 0.0074 0.0036
2212 0.0038 0.0005 0.0071 0.023
3300 0.0054 0.0012 0.0097 0.012
2216 0.0022 -0.0025 0.0068 0.362
3303 -0.005 -0.0084 -0.0016 0.004
2218 -0.0031 -0.0069 0.0008 0.115
2413 0.0018 -0.079 0.083 0.965
2218 -0.006 -0.076 0.064 0.866
2416 -0.004 -0.082 0.074 0.92
2220 0.016 -0.05 0.082 0.634
2407 -0.011 -0.081 0.059 0.76
2216 0.0062 -0.053 0.065 0.838
2407 -0.0041 -0.082 0.074 0.917
2214 -0.03 -0.096 0.035 0.365
2407 -0.017 -0.084 0.05 0.615
2212 0.0007 -0.056 0.057 0.98
2412 -0.11 -0.2 -0.012 0.027
2216 -0.064 -0.14 0.016 0.116
2410 0.029 -0.048 0.11 0.459
2218 -0.0068 -0.072 0.059 0.839
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Supplementary Table 3 (continued). CAD loci and heart failure disease
markers in genetic sub-study of CORONA
Variable Locus SNP Model
BMI (kg/m2) 1p13.3 rs599839 Unadj.
Adj.
1q41 rs17465637 Unadj.
Adj.
2q36 rs2972147 Unadj.
Adj.
6q25.1 rs6922269 Unadj.
Adj.
9p21.3 rs1333049 Unadj.
Adj.
10q11.21 rs501120 Unadj.
Adj.
15q22.33 rs17228212 Unadj.
Adj.
Serum creatinine (umol/L) 1p13.3 rs599839 Unadj.
Adj.
1q41 rs17465637 Unadj.
Adj.
2q36 rs2972147 Unadj.
Adj.
6q25.1 rs6922269 Unadj.
Adj.
9p21.3 rs1333049 Unadj.
Adj.
10q11.21 rs501120 Unadj.
Adj.
15q22.33 rs17228212 Unadj.
Adj.
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Supplementary Table 3 (continued). CAD loci and heart failure disease
markers in genetic sub-study of CORONA
n Beta estimate Lower CI Upper CI P value
3293 0.044 -0.21 0.3 0.736
2218 -0.073 -0.36 0.22 0.621
3298 0.35 0.11 0.6 0.0048
2220 0.3 0.03 0.58 0.029
3292 0.081 -0.14 0.3 0.473
2216 0.23 -0.014 0.48 0.065
3290 0.0047 -0.24 0.25 0.97
2214 -0.11 -0.38 0.17 0.444
3294 0.025 -0.19 0.24 0.817
2212 0.042 -0.19 0.28 0.728
3293 -0.11 -0.41 0.18 0.453
2216 -0.17 -0.5 0.16 0.325
3296 -0.15 -0.39 0.093 0.231
2218 -0.022 -0.29 0.25 0.872
3300 1.78 0.27 3.3 0.021
2218 0.83 -0.76 2.43 0.306
3305 -0.26 -1.72 1.2 0.729
2220 0.081 -1.42 1.58 0.916
3299 0.28 -1.04 1.6 0.676
2216 0.47 -0.87 1.82 0.49
3297 0.27 -1.2 1.74 0.714
2214 0.76 -0.74 2.26 0.319
3300 -0.49 -1.75 0.76 0.44
2212 -0.32 -1.62 0.97 0.625
3300 -2.01 -3.8 -0.21 0.028
2216 -0.43 -2.25 1.39 0.645
3303 1.14 -0.29 2.57 0.119
2218 -0.091 -1.59 1.4 0.905
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Supplementary Table 4. CAD loci and prognosis of ischemic heart failure in
genetic sub-study of CORONA
Variable Locus SNP
All-cause mortality 1p13.3 rs599839 Unadj.
Adj.
1q41 rs17465637 Unadj.
Adj.
2q36 rs2972147 Unadj.
Adj.
6q25.1 rs6922269 Unadj.
Adj.
9p21.3 rs1333049 Unadj.
Adj.
10q11.21 rs501120 Unadj.
Adj.
15q22.33 rs17228212 Unadj.
Adj.
Mortality or WHF hospitalization 1p13.3 rs599839 Unadj.
Adj.
1q41 rs17465637 Unadj.
Adj.
2q36 rs2972147 Unadj.
Adj.
6q25.1 rs6922269 Unadj.
Adj.
9p21.3 rs1333049 Unadj.
Adj.
10q11.21 rs501120 Unadj.
Adj.
15q22.33 rs17228212 Unadj.
Adj.
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Supplementary Table 4 (continued). CAD loci and prognosis of ischemic
heart failure in genetic sub-study of CORONA
Total n Events n Hazard ratio Lower CI Upper CI P value
3300 527 0.862 0.744 1 0.0499
2218 341 0.739 0.608 0.899 0.0025
3305 530 0.934 0.813 1.074 0.3372
2220 341 0.912 0.762 1.091 0.3124
3299 528 1.022 0.903 1.157 0.7327
2216 340 1.063 0.911 1.241 0.4344
3297 524 1.017 0.886 1.167 0.814
2214 338 1.132 0.955 1.341 0.1523
3300 527 1.046 0.929 1.178 0.4596
2212 339 1.04 0.896 1.207 0.6099
3300 524 0.91 0.763 1.085 0.2939
2216 339 0.845 0.674 1.059 0.1431
3303 528 1.098 0.962 1.253 0.1668
2218 340 1.042 0.879 1.234 0.6383
3300 1049 0.985 0.89 1.089 0.7635
2218 673 0.94 0.826 1.069 0.3475
3305 1051 0.96 0.87 1.06 0.4211
2220 672 0.949 0.836 1.077 0.4199
3299 1052 0.94 0.86 1.027 0.1696
2216 674 0.939 0.84 1.05 0.2715
3297 1048 1.042 0.946 1.148 0.4002
2214 672 1.087 0.964 1.227 0.1739
3300 1050 0.993 0.913 1.08 0.8647
2212 672 1.009 0.908 1.122 0.8626
3300 1046 0.851 0.75 0.965 0.0122
2216 670 0.82 0.699 0.962 0.0151
3303 1052 1.063 0.967 1.17 0.2065
2218 674 1.05 0.929 1.188 0.4313
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Supplementary Table 5. CAD loci and hospitalizations of ischemic heart
failure in genetic sub-study of CORONA
Number of hospitalizations due to cardiovascular cause
Locus SNP Ordered P value
1p13.3 rs599839 0.064 0.0093
1q41 rs17465637 0.356 0.489
2q36 rs2972147 0.165 0.37
6q25.1 rs6922269 0.258 0.194
9p21.3 rs1333049 0.34 0.723
10q11.21 rs501120 0.322 0.417
15q22.33 rs17228212 0.024 0.268
Number of hospitalizations due to WHF
Locus SNP Ordered P value
1p13.3 rs599839 0.336 0.341
1q41 rs17465637 0.904 0.975
2q36 rs2972147 0.056 0.058
6q25.1 rs6922269 0.202 0.248
9p21.3 rs1333049 0.226 0.885
10q11.21 rs501120 0.532 0.032
15q22.33 rs17228212 0.121 0.588
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Supplementary Table 6. CAD loci and lipid characteristics in genetic sub-
study of CORONA
Variable Locus SNP Model n
Total Cholesterol (mmol/L) 1p13.3 rs599839 Unadj. 3285
Adj. 2219
1q41 rs17465637 Unadj. 3290
Adj. 2221
2q36 rs2972147 Unadj. 3285
Adj. 2217
6q25.1 rs6922269 Unadj. 3282
Adj. 2215
9p21.3 rs1333049 Unadj. 3285
Adj. 2213
10q11.21 rs501120 Unadj. 3285
Adj. 2217
15q22.33 rs17228212 Unadj. 3289
Adj. 2219
LDL (mmol/L) 1p13.3 rs599839 Unadj. 3285
Adj. 2219
1q41 rs17465637 Unadj. 3290
Adj. 2221
2q36 rs2972147 Unadj. 3285
Adj. 2217
6q25.1 rs6922269 Unadj. 3282
Adj. 2215
9p21.3 rs1333049 Unadj. 3285
Adj. 2213
10q11.21 rs501120 Unadj. 3285
Adj. 2217
15q22.33 rs17228212 Unadj. 3289
Adj. 2219
Triglycerides (mmol/L) 1p13.3 rs599839 Unadj. 3285
Adj. 2219
1q41 rs17465637 Unadj. 3290
Adj. 2221
2q36 rs2972147 Unadj. 3285
Adj. 2217
6q25.1 rs6922269 Unadj. 3282
Adj. 2215
9p21.3 rs1333049 Unadj. 3285
Adj. 2213
10q11.21 rs501120 Unadj. 3285
Adj. 2217
15q22.33 rs17228212 Unadj. 3289
Adj. 2219
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Supplementary Table 6 (continued). CAD loci and lipid characteristics in
genetic sub-study of CORONA
Beta estimate Lower CI Upper CI P value
-0.14 -0.2 -0.075 1.2x10-5
-0.14 -0.21 -0.07 1.1x10-4
0.0089 -0.05 0.068 0.768
0.036 -0.032 0.1 0.301
0.0009 -0.052 0.054 0.974
0.022 -0.039 0.084 0.478
0.024 -0.035 0.083 0.427
-0.015 -0.083 0.053 0.662
-0.045 -0.095 0.0061 0.084
-0.044 -0.1 0.015 0.145
-0.073 -0.15 -0.001 0.047
-0.05 -0.13 0.033 0.235
0.028 -0.03 0.086 0.345
0.055 -0.013 0.12 0.111
-0.16 -0.22 -0.11 1.8x10-9
-0.17 -0.23 -0.1 3.5x10-7
-0.0008 -0.053 0.051 0.976
0.021 -0.04 0.083 0.498
0.024 -0.023 0.071 0.318
0.034 -0.021 0.089 0.231
0.023 -0.029 0.075 0.384
-0.0059 -0.067 0.055 0.85
-0.028 -0.073 0.016 0.212
-0.034 -0.086 0.019 0.211
-0.068 -0.13 -0.0044 0.036
-0.05 -0.12 0.024 0.188
0.017 -0.033 0.068 0.503
0.033 -0.028 0.094 0.286
0.079 0.0066 0.15 0.032
0.074 -0.0049 0.15 0.066
0.018 -0.052 0.088 0.619
0.062 -0.014 0.14 0.112
-0.12 -0.19 -0.061 1.1x10-4
-0.074 -0.14 -0.0063 0.032
-0.0056 -0.076 0.065 0.875
-0.0089 -0.084 0.067 0.816
-0.083 -0.14 -0.023 0.007
-0.064 -0.13 0.0011 0.054
-0.049 -0.14 0.036 0.259
-0.069 -0.16 0.023 0.143
0.017 -0.052 0.085 0.631
-0.021 -0.096 0.055 0.593
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Supplementary Table 6 (continued). CAD loci and lipid characteristics in
genetic sub-study of CORONA
Variable Locus SNP Model n
Apo B (g/L) 1p13.3 rs599839 Unadj. 3264
Adj. 2218
1q41 rs17465637 Unadj. 3270
Adj. 2220
2q36 rs2972147 Unadj. 3265
Adj. 2216
6q25.1 rs6922269 Unadj. 3262
Adj. 2214
9p21.3 rs1333049 Unadj. 3265
Adj. 2212
10q11.21 rs501120 Unadj. 3267
Adj. 2216
15q22.33 rs17228212 Unadj. 3269
Adj. 2218
Apo B/Apo A1 ratio 1p13.3 rs599839 Unadj. 3264
Adj. 2218
1q41 rs17465637 Unadj. 3270
Adj. 2220
2q36 rs2972147 Unadj. 3265
Adj. 2216
6q25.1 rs6922269 Unadj. 3262
Adj. 2214
9p21.3 rs1333049 Unadj. 3265
Adj. 2212
10q11.21 rs501120 Unadj. 3267
Adj. 2216
15q22.33 rs17228212 Unadj. 3269
Adj. 2218
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Supplementary Table 6 (continued). CAD loci and lipid characteristics in
genetic sub-study of CORONA
Beta estimate Lower CI Upper CI P value
-0.055 -0.073 -0.038 2.2x10-10
-0.056 -0.076 -0.036 5.1x10-8
0.0036 -0.013 0.02 0.671
0.01 -0.0094 0.029 0.311
-0.0078 -0.023 0.0072 0.308
-0.0016 -0.019 0.016 0.86
0.0063 -0.01 0.023 0.462
-0.005 -0.024 0.014 0.613
-0.013 -0.027 0.0017 0.085
-0.015 -0.032 0.0014 0.072
-0.023 -0.043 -0.0024 0.028
-0.021 -0.045 0.0021 0.075
0.0037 -0.013 0.02 0.658
0.0062 -0.013 0.026 0.527
-0.047 -0.061 -0.033 3.3x10-11
-0.049 -0.066 -0.032 8.0x10-9
0.0032 -0.01 0.017 0.648
0.0058 -0.01 0.022 0.48
-0.011 -0.023 0.0016 0.089
-0.0065 -0.021 0.0079 0.376
-0.0007 -0.014 0.013 0.923
-0.0082 -0.024 0.0077 0.313
-0.011 -0.023 0.0003 0.056
-0.017 -0.03 -0.0028 0.018
-0.015 -0.031 0.0018 0.08
-0.015 -0.034 0.0047 0.139
0.0027 -0.011 0.016 0.686
-0.005 -0.021 0.011 0.539
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Chapter 7
General discussion
and future perspectives
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Ever since the eighties of the last century, the role of telomere biology in the
cardiovascular disease (CVD) continuum is being studied. In this thesis, we
have focussed on increasing our understanding of two types of genetic markers;
leukocyte telomere length (LTL) and single nucleotide polymorphisms (SNPs).
We have explored these genetic markers in the population based Prevention of
Renal and Vascular End-stage Disease (PREVEND) cohort, a cohort with
patients with acute ST-elevation myocardial infarction (STEMI) and in a large
cohort of chronic ischemic heart failure (HF) patients. The main focus was to
increase our knowledge on the association of these markers with HF
development and progression.
In Chapter 1 we investigated the association between baseline LTL and the
incidence of new onset HF during 12 years of follow-up in the large
community based PREVEND cohort. The main conclusion of this project was
that healthy individuals who develop new onset HF during follow-up are
characterized by shorter LTL at baseline, albeit not independent of age as
defined by date of birth. One possible explanation for the lack of an
independent association might be that LTL at baseline of healthy individuals
might not yet have been severely influenced with stressors causing telomeric
attrition, like inflammatory and oxidative damage. Presumably, the time span
and degree of stress on leukocytes to develop measurable differences in LTL is
might be too short to be of value as a predictor for HF in the long term. In
addition, single LTL measurements could be insufficient to determine inter-
individual LTL differences, since LTL differs greatly between individuals and
also changes over time.80
Therefore, analysis of repeated measurements of LTL
could provide additional insights in the telomere biology of healthy individuals
at risk of developing HF.
Previous studies suggested important associations between left ventricular
ejection fraction (LVEF) and LTL.60
LVEF is considered an important
determinant of the development of HF signs and symptoms. Since STEMI is
one of the major causes for the development of reduced LVEF and HF, we
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have measured LTL in the Glycometabolic Intervention as adjunct to Primary
Coronary Intervention in STEMI trial (GIPS-III). In Chapter 2, we studied the
potential association between baseline LTL and LVEF 4 months after STEMI.
There was no correlation between LTL at determined at presentation with
STEMI and LVEF after 4 months. One of the limitations to acknowledge is that
STEMI outcome on LVEF was very limited (average LVEF 53% after STEMI)
as the result of the well implemented STEMI care. Future objective remains to
study the long term consequences of STEMI in the GIPS-III trial. Next, we
considered whether LTL might have relevant predictive value in a stable setting
of stable chronic HF due to coronary artery disease. Therefore, in Chapter 3
we studied LTL in chronic ischemic HF patients within the framework of the
COntrolled ROsuvastatin multiNAtional Trial in Heart Failure (CORONA)
trial. We studied whether LTL was associated with the composite endpoint
consisting of cardiovascular death, non-fatal myocardial infarction, and non-
fatal stroke during a median follow-up time of three years. The overall
conclusion this project was that LTL was indeed associated with clinical
outcomes in systolic ischemic HF patients. However, also in this setting, this
association was not stronger than age when defined by date of birth. In
addition, we tested whether baseline LTL might identify subjects who would
benefit from statin treatment in the CORONA trial. We did not observe an
effect modification of LTL on the efficacy of statin treatment in ischemic HF
patients.
In the previous three Chapters, we show that LTL is associated with new onset
HF and HF outcomes, albeit not stronger than chronological age. In contrast to
chronological age, which is not modifiable, LTL has been shown to be affected
by multiple factors and interventions. Factors considered ‘healthy’, including
high density lipoprotein (HDL)80
and physical exercise,30
have been shown to
be related with longer telomeres, whereas ‘unhealthy’ factors, for example
smoking, high glucose levels and high waist-hip ratio, are associated with
shorter telomeres and increased telomere attrition rate.80
This suggests that
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lifestyle and possibly pharmacological interventions might have a measurable
effect on LTL. Therefore, LTL could presumably serve as a marker of the
effectiveness of lifestyle or treatment interventions for an individual patient.
Furthermore, LTL has more recently been hypothesized as a marker of
frailty.122
One general limitation in our experiments is that telomere lengths have been
measured, as average, in circulating leukocytes. Circulating leukocytes are
easily obtainable in contrast to other, possibly more relevant cells composing
the heart, like cardiomyocytes, fibroblasts or endothelial cells. Circulating
leukocytes are under dissimilar somatic pressure compared to myocardial cells.
Therefore, despite previous experiments suggesting similar telomere attritions
among cells,101
LTL does not necessarily represent the telomere length or
attrition rate of cardiomyocytes or other cardiac cells. In our experiments, LTL
was not a superior predictor of new onset HF and HF outcomes compared to
chronological age. However, this does not imply that telomere biology itself is
not involved in new onset HF or HF outcomes.
In addition to LTL, we also considered several SNPs as genetic markers
possibly associated with outcomes in HF. In Chapter 4, we tested the
hypothesis that several SNPs, previously associated with risk of developing
coronary artery disease, were associated with outcomes in ischemic HF. In the
CORONA trial, the studied SNPs were not significantly associated with the
composite primary endpoint (time to first event of cardiovascular death, non-
fatal myocardial infarction and non-fatal stroke. However, 1 SNP in the 1p13.3
locus (rs599839) showed some evidence for association with all-cause
mortality. This SNP was also associated with lipid parameters. To further
enhance our understanding of the pathobiological role of these SNPs for HF
outcomes, we do encourage testing the additional, recently identified SNPs
associated with coronary artery disease as well. Furthermore, other HF
phenotype related SNPs (e.g. blood pressure123
and heart rate related124
) could
also be taken into account when predicting HF prognosis and outcomes.
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Conclusions
The present thesis suggests that LTL is associated with the new onset and
outcomes of HF patients, albeit not stronger than age defined by date of birth.
However, there seems to be no correlation between LTL in the acute setting of
a STEMI and LVEF 4 months later. Telomere biology of systemically active
leukocytes is therefore not likely to play a pivotal role in the progression of HF.
In addition, genetic variants associated with coronary artery disease are not
necessarily equally important for outcome of patients with ischemic HF.
Future perspectives
Given the trend in health care research and development towards a more
personalized approach of tailoring treatment for individual patients, the role of
biomarkers is likely to become of more importance. In addition to plasma,
serum and image biomarkers the potential role of (epi-) genetic variants and
LTL will continue to be subject of investigation. In this thesis, single LTL
measurements did not provide additional information of disease progression
compared to knowledge of chronological age. However, one intriguing idea is
to further study the potential role of repeated measurements of LTL to
determine the LTL attrition rate. Attrition rate of LTL should be tested as a
biomarker to evaluate treatment or other interventions (e.g. lifestyle) and might
differentiate between ‘responders’ versus ‘non-responders’. The ‘non-
responders’ might require more rigorous or additional treatments to improve
healthy ageing. Changes of LTL might provide an additional, novel dimension,
to currently ongoing and future pharmacogenetic trials. Changes in LTL over
time could presumably serve as a measurement of good health and fitness and
motivate patients to adapt healthy lifestyles. Furthermore, on population level,
LTL could be used to monitor the effect of preventive measures undertaken by
the government or possibly to quantify its effects. One of the most important
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questions in telomere biology, whether the reported associations of LTL with
different CVD entities are causal or just a secondary result of ongoing somatic
pressure, remains to be answered. Causal relationships or individual
consequences are not deducible from the presented experiments. Recently,
genome wide association studies have identified genetic variants near (among
others) the TERT and TERC gene, causing differential LTL, which were
associated with the incidence of CAD.93
This indeed suggests a causal
relationship between telomere biology (although not necessarily LTL) and the
risk of CAD. We encourage in vitro as well as in vivo experiments (for example
using the TERC-/- knock-out mice model125
) to further explore causality in the
field of telomere research.
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Short summary
Improving our understanding of underlying factors and mechanisms leading to
cardiovascular diseases (CVD) is of utmost importance in order to successfully
predict, prevent and treat CVD. Telomeres, which are located at the terminal
ends of chromosomes, protect against loss of genetic code during cellular
replication. Telomeres shorten in association with cellular replication and,
therefore, people of older age are characterized by shorter telomeres. On top of
that, patients suffering from CVD are characterized by shorter telomeres
compared to healthy age-matched controls. In this thesis, the potential role of
telomere length and genetic variants in predicting the onset and course of
CVDs has been examined. We examined whether leukocyte telomere lengths
predicts new onset heart failure (HF) in a large population based cohort,
however, the observed associations were not stronger than age itself. We also
determined leukocyte telomere lengths of ST-elevated Myocardial Infarction
(STEMI) patients and evaluated whether telomere length could predict STEMI
outcomes. Left ventricular function 4 months after STEMI appeared unrelated
to baseline telomere length. In patients suffering from ischemic HF leukocyte
telomere length predicted outcomes but also not more strongly than age itself.
In order to further examine the role of genetic background in HF outcomes, we
examined whether risk loci for coronary artery disease (CAD) were predictive
for HF outcomes and they were not. Based on our findings, there is no clear
role for examining leukocyte telomere length to accurately predict clinical
outcomes over and beyond age.
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Samenvatting
Hartfalen is een zeer ernstige aandoening met een hoge mate van morbiditeit en
mortaliteit. Gezien deze aandoening sterk geassocieerd is met leeftijd en de
almaar stijgende levensverwachting in onze samenleving, zal het aantal mensen
dat lijdt aan deze aandoening toenemen. De druk op de gezondheidszorg zal
hierdoor stijgen en de vraag naar werkzame behandelstrategieën wordt steeds
belangrijker. In dit proefschrift is gekeken naar de rol van bepaalde genetische
varianten en telomeerlengte als voorspeller van de prognose in verschillende
groepen mensen.
Telomeren zijn repetitieve nucleotidensequenties ((TTAGGG)n in mensen) van
enkele duizenden basenparen lang die zich bevinden aan de uiteinden van
chromosomen. Telomeren beschermen deze dragers van genetische informatie
tegen degradatie, fusie en ongewenste recombinatie. Elke keer dat een cel deelt,
wordt de lengte van de telomeer verkort doordat het uiteinde van het
chromosoom niet volledig kan worden gerepliceerd. Jonge cellen hebben
daardoor langere telomeren dan verouderde (‘senescente’) cellen, die bij een
kritische telomeerlengte niet meer zullen delen. Naast de relatie tussen
veroudering en kortere telomeren, worden patiënten die lijden aan bepaalde
aandoeningen (bijvoorbeeld chronisch hartfalen), gekarakteriseerd door
verkorte telomeren. Sinds telomeerverkorting wordt versneld onder invloed van
oxidatieve stress en ontstekingsprocessen, welke beide in verhoogde mate
vóórkomen bij patiënten met hartfalen, denkt men dat de verkorte telomeren
een gevolg zijn van deze processen. In dit proefschrift zijn verschillende studies
gedaan naar de rol van telomeerlengte, gemeten in leukocyten, in het
voorspellen van toekomstige gebeurtenissen.
In de eerste studie hebben wij in een cohort met meer dan 8000 personen
gekeken naar de waarde van telomeerlengte in leukocyten voor het voorspellen
van het ontstaan van hartfalen, gedurende een periode van 12 jaar. Hoewel
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toekomstig hartfalenpatiënten aan het begin van de studie gekarakteriseerd
werden door kortere telomeren waren deze patiënten ook eerder geboren
(ouder) en voegde kennis over de telomeer lengte niets toe aan de voorspelling
wanneer leeftijd al bekend was. Leeftijd was de belangrijkste factor die
telomeer lengte bepaalde en waarschijnlijk was de somatische stress waaraan
de patiënten blootgesteld zijn nog onvoldoende om een meetbaar additioneel
van telomeerlengte te kunnen aantonen.
De tweede studie werd uitgevoerd in een groep van ongeveer 350 patiënten met
een acute hartinfarct. Telomeerlengte in leukocyten werd bepaald uit bloed
afgenomen tijdens het acute hartinfarct en gekeken is of de uitkomst van het
infarct gerelateerd was aan de telomeerlengte. Er kon geen verband aangetoond
worden tussen de linker ventrikelfunctie 4 maanden na het infarct en de
gemeten telomeerlengte. Wel werd een interactie gevonden tussen lage n-
terminaal pro-brein natriuretisch peptide (NT-proBNP) waarden,
telomeerlengte en metforminebehandeling, waarbij een lage NT-proBNP
waarde geassocieerd was met langere telomeren in de met metformine
behandelde groep maar niet in de met placebo behandelde groep. Tevens was
een verslechterde nierfunctie (op basis van creatinine bepaling) geassocieerd
met verkorte telomeren, maar dit verband was leeftijd- en geslachtsafhankelijk.
Op basis van de resultaten zien wij geen meerwaarde van telomeerlengte
meting om hartinfarctuitkomsten te voorspellen.
In het derde hoofdstuk is de voorspellende waarde van telomeerlengte bepaald
in de ‘COntrolled ROsuvastatin multiNAtional’ (CORONA) trial, bestaande uit
meer dan 3000 chronisch hartfalenpatiënten, die gedurende ongeveer 3 jaar
vervolgd werden. Op het moment van hartfalen diagnostisering zijn witte
bloedcellen afgenomen en hiervan is de telomeerlengte bepaald. Er werd
getoetst of telomeerlengte geassocieerd was met het primaire eindpunt, een
compositie van dood door cardiovasculaire aandoeningen, niet-fataal
myocardinfarct en niet-fataal herseninfarct. Hoewel patiënten met kortere
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telomeren dit eindpunt eerder bereikten, bleek dit verband afhankelijk van de
chronologische leeftijd van de patiënt. Tevens werd een trend gevonden voor
mortaliteit op basis van een cardiovasculaire oorzaak, echter was ook dit
verband niet sterker dan leeftijd. Er werd geen effect modulatie door
behandeling met een statine gevonden. Al met al bieden deze onderzoeken geen
aanleiding tot het meten van telomeerlengte bij hartfalenpatiënten om de
prognose te voorspellen en verwachten wij in bredere zin geen oorzakelijke rol
van telomeerlengte in leukocyten betreffende het beloop van ziekte bij
hartfalenpatiënten.
In het laatste hoofdstuk hebben wij gekeken naar de voorspellende rol van
genetische varianten (‘single nucleotide polymorphisms’), die geassocieerd zijn
met het ontstaan van coronairlijden (de belangrijkste oorzaak van hartfalen), bij
hartfalenpatiënten in het CORONA cohort. Na een follow-up periode van
ongeveer 3 jaar, was geen van de zeven getoetste loci, significant geassocieerd
met het hierboven genoemde primaire eindpunt Enkele loci bleken wel
geassocieerd met secundaire eindpunten, zoals mortaliteit door alle oorzaken en
het aantal hospitalisaties met cardiovasculaire oorzaak. Tevens waren enkele
loci geassocieerd met het lipideprofiel van hartfalenpatiënten. Ondanks dat
deze zeven loci geen significante rol lijken te spelen bij de progressie van
hartfalen, leent de hypothese van deze associatie studie zich voor een
uitgebreidere analyse inclusief alle 46 ontdekte risicovarianten voor
coronairlijden.
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Beknopte samenvatting
Het begrijpen van onderliggende factoren en mechanismen die leiden tot
cardiovasculaire aandoeningen is van groot belang om voorspellingen te doen
over het ontstaan en beloop van deze ziekten. Telomeren, welke zich bevinden
aan het einde van chromosomen, beschermen tegen verlies van genetische code
tijdens celdeling. Telomeren verkorten in samenhang met celdeling en oudere
personen worden gekarakteriseerd door verkorte telomeren. Tevens zijn
telomeren van patiënten die leiden aan cardiovasculaire aandoeningen verkort
ten opzichte van leeftijdsgenoten zonder cardiovasculaire aandoeningen. In
deze thesis is onderzocht of leukocyten telomeerlengte het ontstaan van
hartfalen kan voorspellen. Dit bleek het geval, echter was de voorspellende
waarde niet sterker dan leeftijd. Daarnaast is onderzocht of leukocyten
telomeerlengte bij hartinfarctpatiënten de uitkomst van het infarct op lange
termijn kan voorspellen. De pompfunctie van het hart vier maanden na het
infarct bleek niet gerelateerd aan de gemeten telomeerlengte. In patiënten met
hartfalen op basis van coronairlijden was telomeerlengte voorspellend voor het
beloop van de ziekte, echter de voorspellende waarde was niet sterker dan de
leeftijd van de patiënt. Om de rol van genetische achtergrond van hartfalen
uitkomsten te onderzoeken, is geanalyseerd of risicovarianten voor
coronairlijden gerelateerd zijn aan hartfalen uitkomsten. Hiervoor vonden we
geen bewijs. Gebaseerd op deze onderzoeken, hebben telomeerlengte en
risicovarianten van coronairlijden geen meerwaarde bovenop leeftijd, om het
ontstaan en de uitkomst van cardiovasculaire aandoeningen te voorspellen.
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Frontini M, van den Akker E, Bertone P, Bielczyk-Maczyńska E, Farrow S,
Fehrmann RS, Gray A, de Haas M, Haver VG, Jordan G, Karjalainen J,
Kerstens HH, Kiddle G, Lloyd-Jones H, Needs M, Poole J, Soussan AA,
Rendon A, Rieneck K, Sambrook JG, Schepers H, Silljé HH, Sipos B, Swinkels
D, Tamuri AU, Verweij N, Watkins NA, Westra HJ, Stemple D, Franke L,
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Ouwehand WH, Albers CA. SMIM1 underlies the Vel blood group and
influences red blood cell traits. Nat Genet. 2013;45(5):542-5.
3. Haver VG, Verweij N, Kjekshus J, Fox JC, Wedel H, Wikstrand J, van Gilst
WH, de Boer RA, van Veldhuisen DJ, van der Harst P. The impact of coronary
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5. Haver VG, Hartman MH, Mateo Leach I, Lipsic E, Lexis CP, van
Veldhuisen DJ, van Gilst WH, van der Horst IC, van der Harst P. Leukocyte
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myocardial infarction: data from the glycometabolic intervention as adjunct to
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Dankwoord
Een promotie afronden is een teamprestatie. Naast een gemotiveerde
promovendus, zijn begeleiders onmisbaar, die de promovendus een kans
gunnen om gedurende een aantal jaar te werken aan een onderzoeksproject.
Daarnaast is een sterk netwerk van collegae en vrienden vereist om de
promovendus te ondersteunen. In dit dankwoord volgt een opsomming van
personen die onmisbaar zijn geweest bij het afronden van mijn promotie.
Hoewel deze lijst nooit álle personen die hebben bijgedragen aan de promotie
kan bevatten, heb ik mijn best gedaan om niemand te vergeten.
Allereerst wil ik mijn begeleider Pim bedanken. Al vanaf de dag dat ik je leerde
kennen was ik onder de indruk van je ‘drive’ en intelligentie, waarmee je al
zoveel projecten tot een goed einde hebt weten te brengen. Elk overleg zorgde
voor nieuwe inzichten en jouw bijdragen aan de manuscripten waren onmisbaar
voor mij om deze promotie succesvol af te ronden. Ik ben je erg dankbaar voor
het vertrouwen dat je me gegeven hebt, ook in de tijden dat ik even de draad
kwijt was. Op dit moment ben je de jongste professor binnen de cardiologie in
Nederland en ik ben ervan overtuigd dat je in de toekomst nog vele succesvolle
promoties zult begeleiden.
Op de tweede plaats wil ik graag mijn promotor Wiek bedanken. Hoewel we
elkaar misschien niet vaak (genoeg) gezien hebben de afgelopen jaren, was de
hulp die ik van je kreeg altijd zeer waardevol. De ‘helikopterview’ waarmee je
projecten en promoties overziet, zorgt ervoor dat de in mijn ogen grote
obstakels worden verkleind tot werkbare situaties en makkelijker oplosbaar
blijken dan ik van tevoren had gedacht. Recentelijk liepen we elkaar tegen het
lijf op de afdeling en naar aanleiding van de goedkeuring van de Thesis door de
leescommissie kwam je tot de volgende conclusie: “Alles komt altijd goed”.
Die overtuiging zal me in de toekomst zeker bijblijven en helpen om verder te
komen in de rest van mijn leven.
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Ten derde wil ik graag Irene bedanken. Hoewel het meeste werk dat je verricht
‘achter de schermen’ plaatsvindt, ben je een onmisbare schakel in de
begeleiding van de PhD’s binnen onze groep en ik ben je bijzonder dankbaar
voor het houden van het overzicht en het bewaken van de vooruitgang
gedurende mijn promotie. Hoewel je jezelf niet graag ziet als ‘politieagente’,
was soms een spreekwoordelijke 'schop onder mijn kont' van jouw kant soms
nodig om mij weer aan het werk te krijgen. Vooral ten tijde van het bepalen van
de telomeerlengte van de GIPS-III populatie was je hulp onmisbaar. Ook
Martin en Janny waren nauw betrokken bij dat project en waarschijnlijk stond
het project zonder de hulp van jullie nog in de kinderschoenen stond. De vele
overlegmomenten en ‘testruns’ hebben uiteindelijk geleid tot een mooie
publicatie, waarvoor ik jullie zeer dankbaar ben!
Prof. dr. Eline Slagboom en Prof. dr. Maarten van den Berg, hartelijk bedankt
voor het beoordelen van mijn proefschrift. Prof. Dr. Michael Walter, ich danke
Ihnen vielmals für Ihre Beurteilung meiner Dissertation. Alle deelnemers aan
de studies welke zijn beschreven in dit proefschrift, hartelijk bedankt voor jullie
inzet. Zonder jullie had dit proefschrift simpelweg niet kunnen bestaan.
Jouke en Ruben, mijn paranimfen, bedankt voor alle hulp en steun die jullie
hebben geboden tijdens de voorbereidingen van deze bijzondere dag.
Vele andere supervisors en collegae hebben mij bijgestaan om mijn promotie af
te ronden. Riemer en René, bedankt dat jullie mij hebben begeleid tijdens het
schrijven van mijn eerste publicatie en met het schrijven van de MD/PhD
aanvraag. Vele co-auteurs hebben een onmisbare inbreng gehad tijdens de
voorbereiding van de manuscripten, waarvoor hartelijk bedankt! Hein, JJ en de
andere collega’s van de Hematologie onderzoeksafdeling, bedankt voor de tijd
en moeite die jullie hebben gestoken in mijn begeleiding tijdens de
‘LOC388588’ experimenten. Frank, bedankt voor de hulp met de statistiek en
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het meeschrijven van het PREVEND manuscript. Jardi en Liza, jullie voorwerk
in het telomeren vakgebied is van onschatbare waarde geweest voor wat
uiteindelijk mijn Thesis is geworden. Mijn kamergenoten op de Experimentele
Cardiologie afdeling (eerst Michael en Reinout, daarna Laura en Martijn en
uiteindelijk Ruben, Mohsin, Minke, Niek, Ruben en Yanick), bedankt voor al
het lief en leed dat we gedeeld hebben samen. De koffiepauzes waren
onmisbaar om het lief en leed van onze werkzaamheden (en de overige zaken
des levens) te doorstaan. Alle andere collega’s van de afdeling (Alexander,
Anne-Margreet, Arnold, Atze, Beatrijs, Carla, Daan, Danielle, Diederik, Edgar,
Harmen, Hasan, Herman, Hilde, Jan-Renier, Janny, Jasper, Linda, Lysanne,
Mariusz, Martin, Mathilde, Megan, Michiel, Nicolas, Niels, Peter, Renée,
Rogier, Rudolf, Silke, Wardit, Weijie, Wouter M, Wouter te R en anderen),
bedankt voor de goede samenwerking en discussies. De mooie momenten ten
tijde van de labuitjes zal ik niet licht vergeten. Also, Carolien, Hendrik, Marco
(and his girlfriend Cecilia), Marta, Niccolò, and Pallavi), my dear collegues at
the Hematology research department, thanks for the joyful moments in the
lentilab! I really appreciated our trip to Zwolle! Kor, zingende dokter, bedankt
voor de nuttige discussies die we zo nu en dan voeren.
Natuurlijk wil ik ook graag mijn familie bedanken. Pap en mam, bedankt voor
de onvoorwaardelijke steun die ik heb gekregen tijdens het doorlopen van mijn
opleidingen en promotie. Zowel op mentaal maar ook op financieel vlak zijn
jullie onmisbaar geweest, zonder jullie had ik niet gestaan waar ik nu ben en
waren de afgelopen 11 jaar waarschijnlijk minder succesvol geweest. De
waarde van een warm thuis wordt vaak onderschat en ik ben dan ook erg blij
dat ik bij voor en tegenspoed terecht kan in Wageningen voor een goed
gesprek, al dan niet onder het genot van een goed glas wijn. Ook Wouter en
Bart, de beste broers die ik me kan voorstellen, bedankt voor jullie steun en
interesse tijdens mijn promotie. Ik hoop dat onze onderlinge band zo sterk blijft
als hij nu is. Daarbij zijn natuurlijk ook jullie vriendinnen, Aleid en Myrthe,
onmisbaar. Ik wens jullie dan ook allen alle geluk van de wereld samen!
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Dan wordt het nu tijd om mijn vrienden te bedanken. Piet, hoewel we elkaar de
laatste tijd minder zien dan vroeger het geval was, zijn onze chillsessies me
altijd heel dierbaar. Met je intelligentie en je eigen ervaring als promovendus
ben je altijd erg behulpzaam geweest, waar ik veel aan heb gehad. Succes met
de verdediging van je eigen proefschrift in december! Aad, ondanks dat je niet
heel lang in Groningen gewoond hebt, hebben we elkaar ongeacht onze
bezigheden regelmatig opgezocht. Avonden met jou zijn nooit voorspelbaar
qua afloop en altijd bijzonder vermakelijk. Hopelijk drinken we snel een biertje
in Genua om te proosten op het leven! Rudel, ook jij bent inmiddels alweer een
tijdje vertrokken uit het mooie Groningen, maar gelukkig zijn we elkaar niet uit
het oog verloren. Je bijzondere humor en dito opmerkingen kunnen de
gemoederen soms flink op scherp zetten. Iwe, bij jou in het voormalige St.
Antonius Ziekenhuis voel ik me altijd gelijk thuis en dat komt niet alleen maar
omdat je in een oud ziekenhuis woont! Jouw aanwezigheid staat garant voor
hilarische grappen. Mooie herinneringen koester ik aan onze zeilavonturen,
laten we proberen dit in de toekomst ook te blijven doen! Joppe, laten we
vooral nog vaak genieten van fijne bands en live optredens. Thijs, onze
vriendschap stamt nog uit de tijd dat we nog maar net in Groningen woonden.
Laten we binnenkort weer eens een kilo friet frituren, for old times’ sake. Lars,
bedankt voor de kopjes thee tijdens mijn studiewerkzaamheden. De Steef,
bedankt voor de mooie avonturen in Manchester en succes met de verdediging
van je eigen promotie. Heren van de FC (Bernd, Eelko, Erwin, Frodo, Jelmer,
Jord, Karl, Kippy, Mark, Rudolf, Wander), bedankt voor de mooie tijden en
discussies langs de velden! Vince, ondanks dat je nu een andere uitdaging hebt
gevonden en we elkaar niet vaak meer zien, koester ik onze technosessies van
vroeger nog altijd. Je hebt een opmerkelijke gave voor het ontdekken van
heerlijke platen en ik hoop hier in de toekomst ook nog van te kunnen geniet´n!
Samen met Wietse, Jasper, Sander, Mark en anderen stonden de avonden garant
voor bijzondere discussies en mooie gekkigheid!
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En last, but not least, Tara, bedankt voor de mooie, fijne tijden samen! Ik hoop
dat we nog lang bij elkaar blijven en van elkaars liefde kunnen genieten!
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Curriculum vitae
Vincent Gerardus Haver werd op 17 augustus 1985 geboren in Groningen en
heeft daar gewoond tot hij eind 1987 verhuisde naar Wijk bij Duurstede, alwaar
hij naar de rooms-katholieke basisschool De Hoeksteen ging. Vervolgens is hij
in 1993 verhuisd naar Wageningen, alwaar hij de naar de protestants-
christelijke basisschool de Johan Frisoschool ging. In 2003 heeft hij op
scholengemeenschap Pantarijn te Wageningen zijn Gymnasium diploma
behaald met als profiel Natuur en Gezondheid en met Economie 1 als bijvak.
Vincent ging in 2003 Biologie studeren aan de Rijksuniversiteit Groningen. Na
meerdere malen te zijn uitgeloot, werd hij in 2008 via het zij-instroom traject
toegelaten tot de studie Geneeskunde. Na het zij-instroomjaar heeft hij eerst
nog een half jaar een wetenschappelijke onderzoeksstage gedaan in het kader
van de masteropleiding Medische Biologie, onder begeleiding van Dr. Tio en
Prof. Dr. Slart. Hiermee sloot hij deze masteropleiding af in 2010 (cum laude).
Vervolgens is hij aan de masteropleiding Geneeskunde begonnen en liep hij co-
schappen in het Universitair Medisch Centrum in Groningen, het Nij
Smellinghe ziekenhuis in Drachten en het Medisch Centrum Leeuwarden.
Tegelijkertijd begon hij aan een promotietraject bij de afdeling Experimentele
Cardiologie van het Universitair Medisch Centrum Groningen onder
begeleiding van Prof. Dr. Van Gilst en Prof. Dr. Van der Harst. Resultaten
werden gepresenteerd op het jaarlijkse internationale congres van de European
Society of Cardiology. Op 26 oktober 2015, zal Vincent zijn proefschrift
getiteld “Genetic variation, Telomeres and Heart Failure” verdedigen.
Vincent is vanaf 1 juni 2015 werkzaam als arts-assistent op de Intensive Care
afdeling van het Martiniziekenhuis te Groningen.
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