HUMAN SERUM TRANSFERRIN GLYCOSYLATION PATTERN

From the Division of Alcohol and Drug Dependence Research,

Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

HUMAN SERUM TRANSFERRIN

GLYCOSYLATION PATTERN

– POPULATION DIFFERENCES, ANALYTICAL METHODOLOGY AND APPLICATION AS

BIOMARKER FOR TESTING OF ALCOHOL ABUSE AND CDG

Jonas P Bergström

Stockholm 2007

All previously published papers were reproduced with permission from the publisher. Published by Karolinska Institutet. Printed by US-AB © Jonas P Bergström, 2007 ISBN 978-91-7357-432-7

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ABSTRACT Alcohol use and abuse is a major social and economic problem in many societies.

Carbohydrate-deficient transferrin (CDT) is a widely used and highly specific

biochemical alcohol marker of prolonged alcohol abuse. Increased knowledge about the

clinical characteristics of CDT may lead to better possibilities of employing CDT as a

biochemical alcohol marker of risky and heavy alcohol consumption and thus creating

better opportunities for prevention and intervention of alcohol dependence. The aim of

this thesis was to contribute to this knowledge by investigating the properties of CDT in

different populations and evaluating the sensitivity and specificity of different

analytical methodologies for analysis of CDT.

Serum samples (n=1387) from subjects originating from five different countries were

analyzed with a HPLC candidate reference method for CDT. In non-drinkers there were

minimal differences in the serum transferrin glycoform pattern with respect to different

ethnicity, gender, age and BMI. When evaluating disialotransferrin, the primary

glycoform in CDT, with respect to the same categories, no clinically significant

differences were detected. Furthermore the overall test accuracy for identification of

heavy drinkers (>210 g ethanol/week for men and >140 g ethanol/week for women)

showed no gender difference.

Serum samples (n=178) were analyzed from subjects with clinical or pharmacological

factors previously reported to cause false-positive CDT levels. Only ~5% showed a

relative disialotransferrin level exceeding the upper limit for the reference interval,

leading to a conclusion that earlier reports on reasons for false positive CDT values are

linked with the methodology used rather than with true physiological influences.

When compared with a HPLC candidate reference method, the Bio-Rad %CDT HPLC

test and the CEofix™ CDT assay proved to be appropriate for confirmatory and routine

%CDT testing, showing an overall good correlation and agreement.

The HPLC candidate reference method could readily be used for preliminary diagnosis

of CDG and for assignment of cases to either CDG-I or CDG-II.

LIST OF PUBLICATIONS I. Bergström JP, Helander A.

Influence of alcohol use, ethnicity, age, gender, BMI and smoking on the serum transferrin glycoform pattern: implications for the use of carbohydrate-deficient transferrin (CDT) as alcohol biomarker. Clin Chim Acta 2007;In press.

II. Bergström JP, Helander A. Clinical characteristics of carbohydrate-deficient transferrin (CDT) measured by HPLC: sensitivity, specificity, gender differences and relationship with other markers of prolonged alcohol abuse. Submitted 2007.

III. Bergström JP, Helander A. HPLC evaluation of clinical and pharmacological factors reported to cause false-positive carbohydrate-deficient transferrin (CDT) levels. Submitted 2007.

IV. Helander A, Bergström JP. Determination of carbohydrate-deficient transferrin in human serum using the Bio-Rad %CDT by HPLC test. Clin Chim Acta 2006;371:187-190.

V. Helander A, Wielders JPM, te Stroet R, Bergström JP. Comparison of HPLC and capillary electrophoresis for confirmatory testing of the alcohol misuse marker carbohydrate-deficient transferrin. Clin Chem 2005;51:1528-1531.

VI. Helander A, Bergström JP, Freeze H. Testing for congenital disorders of glycosylation by HPLC measurement of serum transferrin glycoforms. Clin Chem 2004;50:954-958.

The original articles (I, IV, V and VI) have been printed with permission from the publishers.

CONTENTS 1 Introduction .................................................................................................. 1

1.1 Biochemical alcohol markers............................................................. 1 1.1.1 Markers of acute alcohol consumption ................................. 1 1.1.2 Markers of chronic alcohol consumption.............................. 2

1.2 Carbohydrate-deficient transferrin (CDT)......................................... 3 1.2.1 Transferrin structure............................................................... 4 1.2.2 CDT pathomechanisms.......................................................... 5 1.2.3 CDT sensitivity ...................................................................... 6 1.2.4 CDT specificity ...................................................................... 6 1.2.5 Congenital disorders of glycosylation (CDG)....................... 7 1.2.6 Analysis of CDT .................................................................... 7

2 General aims of the thesis ............................................................................ 9 3 Materials and methods ............................................................................... 10

3.1 Study Populations............................................................................. 10 3.1.1 The WHO/ISBRA Collaborative Project (Paper I-II)......... 10 3.1.2 Clinical samples (Paper III-V)............................................. 12 3.1.3 CDG samples (Paper VI) ..................................................... 12

3.2 HPLC candidate reference method (Paper I-VI) ............................. 12 3.3 Other methods for CDT analysis ..................................................... 14

3.3.1 Bio-Rad %CDT by HPLC Reagent Kit (Paper IV) ............ 14 3.3.2 CEofix™, CE, (Paper V) ..................................................... 15 3.3.3 %CDT immunoassay (Paper V) .......................................... 15 3.3.4 CDTect™ (Paper II)............................................................. 15

3.4 Analysis of GGT and AST (Paper II) .............................................. 16 3.5 Statistics ............................................................................................ 16

4 Results ........................................................................................................ 17 4.1 Paper I ............................................................................................... 17 4.2 Paper II.............................................................................................. 20 4.3 Paper III ............................................................................................ 23 4.4 Paper IV............................................................................................ 24 4.5 Paper V ............................................................................................. 25 4.6 Paper VI ............................................................................................ 26

5 Discussion................................................................................................... 28 6 Conclusions ................................................................................................ 30 7 Acknowledgements .................................................................................... 31 8 References .................................................................................................. 33

LIST OF ABBREVIATIONS 5-HIAA 5-Hydroxyindole-3-Acetic Acid 5-HTOL 5-Hydroxytryptophol AED Antiepileptic Drug ALT Alanine Aminotransferase AST Aspartate Aminotransferase AUC Area Under the Curve BMI Body Mass Index CDG Congenital Disorders of Glycosylation CDT Carbohydrate-Deficient Transferrin CE Capillary Electrophoresis CRP C-Reactive Protein CV Coefficient of Variation CF Cystic Fibrosis DST Disialotransferrin EtG Ethyl Glucuronide EtS Ethyl Sulfate GC-MS Gas Chromatography-Mass Spectrometry GGT Gamma Glutamyltransferase HPLC High-Performance Liquid Chromatography FeNTA Ferric Nitrilotriacetic Acid ID Inner Diameter IFCC International Federation of Clinical Chemistry and Laboratory

Medicine IEF Isoelectric Focusing ISBRA International Society for Biomedical Research on Alcoholism LC-MS Liquid Chromatography-Mass Spectrometry LC-MS/MS Liquid Chromatography-Mass Spectrometry/Mass Spectrometry LOD Limit of Detection LOQ Limit of Quantification NIAAA National Institute on Alcohol Abuse and Alcoholism PEth Phosphatidylethanol RIA Radioimmunoassay ROC Receiver-Operating Characteristic U/L Units/Liter WHO World Health Organization

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1 INTRODUCTION

1.1 BIOCHEMICAL ALCOHOL MARKERS Alcoholic beverages, and the pleasures and problems they cause, have been known to

mankind since the beginning of recorded history. The use and abuse of alcohol is the

cause of immense costs in most of the western societies, with a rather big proportion of

its citizens being alcohol dependent or consuming harmful amounts of alcohol (1).

Therefore, screening for alcohol-related problems is an important task to detect alcohol

dependence or risky alcohol consumption behavior, and is usually accomplished by

structured interviews and/or laboratory tests. The most widely employed alcohol

questionnaires over the last years are the Alcohol Use Disorder Identification Test

(AUDIT) (2-4) and CAGE (5, 6), both of them relying on the patient’s self-reported

alcohol consumption and the associated problems. The self-reported amount of alcohol

consumed is however known to be frequently underreported, therefore leading to

possible under-diagnosis of alcohol related problems (7). Laboratory tests, or

biochemical markers of alcohol use and abuse, offers more objective methods to

monitor or detect excessive alcohol consumption.

1.1.1 Markers of acute alcohol consumption The biochemical markers of acute alcohol consumption are laboratory tests developed

in order to detect single intakes of alcohol, typically in the last 0-48 hours prior to

sampling, depending on the sensitivity of the test and the amount of alcohol consumed.

These markers provide a powerful tool within areas such as forensic medicine, criminal

applications, occupational medicine, and can also be used for confirmation of

abstinence in outpatient treatment (8-10).

1.1.1.1 Ethanol

After alcohol consumption, ethanol can be determined in breath or body fluids (11).

The major drawback with ethanol as a biochemical marker is that the human body

rapidly metabolizes even large amounts of ethanol in typically <12 hours, making the

time range for detection very small (12). Another drawback is that the ethanol

concentration does not provide information about hazardous drinking patterns and

eventual alcohol problems.

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1.1.1.2 EtG and EtS

Ethyl glucuronide (EtG) and ethyl sulfate (EtS) are water-soluble, stable, conjugated

direct metabolites of ethanol (13, 14). EtG is formed from a minor part of the ingested

ethanol (<0.1%) via reaction with uridine-5-diphospho-β-glucuronic acid (15). EtS is

also formed from a minor part of the ingested ethanol (<0.1%) after reaction between

ethanol and sulfate by sulfotransferase (16). Both EtG and EtS are excreted in the urine

and, sharing approximately the same elimination profiles, are washed out from the

body at a much slower rate than ethanol itself, and can often be detected days after

alcohol consumption (17, 18). Therefore EtG and EtS offer an extended window for

assessment of single ethanol intake. Urinary EtG and EtS can today be determined with

a range of analytical methodology (14, 19-21).

1.1.1.3 5-HTOL/5-HIAA

During normal conditions, the major part of all serotonin is metabolized to 5-

hydroxyindole-3-acetic acid (5-HIAA) with only a smaller part (<1%) forming into 5-

hydroxytryptophol (5-HTOL). However, alcohol consumption results in a shift towards

an increased formation of 5-HTOL, and consequently an elevated ratio of 5-HTOL/5-

HIAA can be detected in urine for several hours after all ethanol is cleared from the

body. This makes it possible to detect a single alcohol intake for a much longer period

of time (22-24). 5-HTOL and 5-HIAA have traditionally been analyzed by gas

chromatography-mass spectrometry (GC-MS) and high-performance liquid

chromatography (HPLC), respectively (25, 26), but lately there has been suggestions to

replace 5-HTOL as target analyte and instead analyze 5-HTOL glucuronide (GTOL)

together with 5-HIAA with a highly sensitive and specific LC-MS/MS method (27).

1.1.2 Markers of chronic alcohol consumption Biochemical markers of chronic alcohol consumption are laboratory tests that can be

used for identification of a chronic, sustained consumption that has been going on for

typically over a week (28). These markers are currently in use in areas such as traffic

medicine, occupational medicine and in various clinical settings as indicators of a

chronic alcohol misuse (29). The marker in focus in this thesis, CDT, is presented in

detail in section 1.2.

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1.1.2.1 GGT

Gamma-glutamyltransferase is an intracellular liver enzyme that in response to acute

hepatocellular damage, e.g. following prolonged alcohol abuse, can leak into the blood.

The reported alcohol consumption needed for an elevated serum level of GGT have

shown great variations. Still, it is today maybe the most used laboratory marker for

chronic alcohol consumption (30, 31). The specificity of GGT for chronic alcohol

abuse is very poor. Some of the reported factors that cause an elevated GGT level are

smoking, male gender, obesity, age, non-alcoholic liver diseases, medication and

diabetes (28, 32, 33). The half-life of GGT is between 2-3 weeks (34).

1.1.2.2 AST and ALT

Elevated serum concentrations of the liver enzymes aspartate aminotransferase (AST)

and alanine aminotransferase (ALT) are indicators of non-specific liver dysfunction,

and concentrations are frequently heightened also in alcoholic patients (35, 36). These

liver enzymes leaks into the blood following hepatocellular damage in the same way as

GGT. The sensitivity and specificity for identification of alcohol misuse are in most

cases reported low or moderate for both AST and ALT (37). An elevated AST level can

also arise from non-hepatic sites (e.g., heart and muscles), and from conditions such as

non-alcoholic liver disease, myocardial infarction and skeletal muscle trauma (38). The

half-life of AST and ALT is reported to be 2-3 weeks (37).

1.1.2.3 PEth

Phosphatidylethanol (PEth) is an abnormal phospholipid formed exclusively by the

action of phospholipase D in the presence of ethanol and is considered a promising new

biochemical marker of chronic alcohol misuse (39, 40). PEth has a half-life of ~4 days,

making it possible to detect PEth in blood of chronic alcohol users up to 3 weeks after

alcohol withdrawal. The amount of PEth in blood is reported to be highly correlated

with the past alcohol intake in alcohol abusers (41). The sensitivities and specificities

that are reported so far are high, but this biomarker is in need of further evaluation

within different clinical settings. The current methods for analysis of PEth include

HPLC with evaporative light scattering detection (42, 43) and LC-MS (44).

1.2 CARBOHYDRATE-DEFICIENT TRANSFERRIN (CDT) Transferrin is a glycoprotein mainly synthesized in the hepatocytes(45). It is the major

Fe3+-transport protein in the body, with a normal serum concentration range of 1.9-3.3

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g/L (46). Under prolonged heavy alcohol consumption, the microheterogeneity of the

glycoform pattern of transferrin changes towards a higher proportion of so called

carbohydrate-deficient transferrin (CDT), making it useful as a laboratory marker of

sustained alcohol abuse (47, 48). This discovery was first reported in 1976 by Stibler

and Kjellin, after studies on the transferrin glycoform pattern in cerebrospinal fluid

from alcoholics (49).

1.2.1 Transferrin structure Transferrin consists of three different sub-structural domains: a single polypeptide

chain with 679 amino acids, two Fe3+ ion-binding sites, one within the N-terminal

domain and one within the C-terminal domain, and two N-linked complex

oligosaccharide chains (Figure 1) (45). There are many different polypeptide chain

variants reported (~40) (50). The most common polypeptide chain is the homozygous

transferrin C variant, with the subtype transferrin C1 being the most frequent occurring

in Caucasians (>95%). Heterozygous transferrin BC, CD and other variants seldom

show a prevalence over 1% of the population. Another microheterogeneity is the

varying iron load of transferrin in blood. With a normal iron saturation of transferrin in

blood (~30%) there are transferrin molecules with none, one or two Fe3+ ions present

simultaneously. The microheterogeneity between the oligosaccharide chains is

complex: the chains can be biantennary, triantennary or even tetraantennary, each

antennary usually terminating in a sialic acid residue, the total number of residues

traditionally giving name to the glycoform (51).

N-Acetylglucosamine

Mannose

Galactose

Sialic Acid

Fe3+ Fe3+N C

N-Acetylglucosamine

Mannose

Galactose

Sialic Acid

Fe3+ Fe3+N C

Figure 1. Detailed structure of the main transferrin glycoform tetrasialotransferrin that normally represents ~80% of the total serum transferrin in healthy adults. The line between N and C represents the single polypeptide chain.

5

For example, the most common transferrin glycoform in healthy adults,

tetrasialotransferrin (~80%; Figure 1), consists of two disialylated N-linked glycans,

e.g. a total of four terminal sialic acid residues, hence its structural name. Other

normally occurring transferrin glycoforms in healthy adults are disialotransferrin

(<2%), trisialotransferrin (~4%), pentasialotransferrin (~14%) and hexasialotransferrin

(~1%) (52-54).

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Trisialotransferrin ~4% Pentasialotransferrin ~14% Hexasialotransferrin ~1%

Asialotransferrin

( alcohol)

Monosialotransferrin

( with high trisialotransferrin)Disialotransferrin ~1%

( alcohol)

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Fe3+ Fe3+N C

Trisialotransferrin ~4% Pentasialotransferrin ~14% Hexasialotransferrin ~1%

Asialotransferrin

( alcohol)

Monosialotransferrin

( with high trisialotransferrin)Disialotransferrin ~1%

( alcohol)

Figure 2. Detailed structure of the most common minor transferrin glycoforms and their relative occurrence in serum from healthy adults. Prolonged alcohol abuse elevates the relative level of disialotransferrin. In subjects with high levels of disialotransferrin, sometimes asialotransferrin can be detected. Monosialotransferrin can often be seen in individuals with a genetically high trisialotransferrin level.

Under prolonged alcohol abuse, the glycoform disialotransferrin increases relatively to

the total transferrin, and in subjects with high levels of disialotransferrin, sometimes

asialotransferrin can be detected (Figure 2) (55-57). Monosialotransferrin can

sometimes be detected in subjects with genetically very high levels of trisialotransferrin

(57).

1.2.2 CDT pathomechanisms The detailed pathomechanisms behind the increase of CDT during prolonged alcohol

consumption are not fully evaluated yet. There is however some evidence that ethanol

or its metabolite acetaldehyde interferes in the transferrin N-glycan chain synthesis in

the Golgi apparatus. The activities of the glycoprotein glycosyltransferases

galactosyltransferase and N-acetylglucosaminyltransferase were lower in serum from

alcoholics than in subjects from a healthy control group (58). In alcohol fed rats,

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ethanol lowers the levels of sialyltransferase mRNA, and consequently lowers the

sialyltransferase activity (59). In another study on alcohol fed rats, an increase of

sialidase activity in liver plasma was seen (60). Accordingly with these studies, ethanol

or maybe more likely acetaldehyde seems to hamper glycoprotein glycosyltransferase

activity in general, which in combination with an increased sialidase activity give rise

to increased levels of CDT .

1.2.3 CDT sensitivity The sensitivity of CDT as a marker of chronic alcohol abuse is reported with great

variation depending on factors such as study population, mean daily alcohol intake and

drinking patterns (47, 61-63). When reviewed, a sensitivity of 30-50% for women and

50-70% for men seemed to be of average (48). The amounts of alcohol reported to

increase the CDT value over the reference interval for social drinkers have been

debated, but it seems that at least 50-80 grams of ethanol per day for 2 weeks or longer

is necessary to produce elevated CDT concentrations (64-66). Factors that have been

reported to affect the sensitivity of CDT include age, gender, drinking patterns, body

mass, hypertension and smoking, to name a few (67-70). It is however unclear how

much the often aged and less specific analysis methodology (e.g., CDTect™) used in

many reports on this issue have influenced the reliability of the results (71-74). The

half-life of CDT is reported to be 1.5-2 weeks, and following alcohol abstinence a

normalization of the CDT level occurs within approximately 4 weeks (47).

1.2.4 CDT specificity The single biggest advantage of using CDT as a marker of chronic alcohol abuse is that

of all laboratory tests available CDT is the most specific (48). That said, there have

over the years been numerous reports published on clinical conditions or other factors

that could produce false-positive (i.e., non alcohol related) CDT results (70). These

reports identify, among other things, genetic transferrin variants, congenital disorders of

glycosylation (CDG), different liver diseases, iron deficiency, haemochromatosis,

hypertension, cystic fibrosis (CF), various medication and sepsis to be factors that

elevate CDT values (75-83). However, these findings are often based on very small

subject groups or with a methodology for CDT analysis that is now out of date (e.g.,

CDTect), making it hard to estimate the true impact of the different clinical factors

investigated on CDT levels.

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1.2.5 Congenital disorders of glycosylation (CDG) Congenital disorders of glycosylation (CDG) are a group of rare hereditary diseases

characterized by defects in the synthesis of the glycan moieties of glycoproteins or

other glycoconjugates caused by mutations in the genes coding for enzymes involved in

the glycoprotein synthesis (84, 85). The two main types of protein glycosylation are N-

glycosylation and O-glycosylation, where the latter will not be further discussed here.

In general, N-glycosylation consists of an assembly pathway (in cytosol and

endoplasmatic reticulum) followed by a processing pathway (in endoplasmatic

reticulum and Golgi). The two main types of N-glycosylation CDG are defects in

glycoprotein synthesis during the assembly pathway (CDG-I) or during the processing

pathway (CDG-II). These two main groups are further sub-categorized, so that each

defective gene correspond to one specific CDG (e.g. CDG-Ia, CDG-Ib, CDG-IIa etc.),

and today there are ~10 CDG-I and ~5 CDG-II categorized (86). N-Glycosylation

defects affects primarily the nervous system with the clinical expression including

psychomotor, growth and mental retardation, and the effect varies between being

extremely severe to very mild. Since the clinical symptoms often are unspecific, CDG

are probably under-diagnosed. The main laboratory tool used to identify and categorize

N-glycosylation CDG has traditionally been isoelectric focusing (IEF) of the transferrin

glycoform pattern (87), but mass spectrometry techniques for identification of CDG are

also available (88).

1.2.6 Analysis of CDT CDT was originally defined as the three transferrin glycoforms with a pI > 5.7 after

IEF, i.e. asialo-, monosialo- and disialotransferrin (47). IEF was the first reference

method for serum transferrin glycoform analysis, and is still used today as a reference

method for identification of genetic transferrin variants and CDG, much because of its

high selectivity. The major drawback with IEF is difficulties with quantitative CDT

analysis (89).

Jeppsson et al. developed the first HPLC method for identification and quantification of

transferrin glycoforms in 1993 (100), and it has later been followed by commercial

HPLC kits (101). Some of the general advantages with HPLC methods for CDT

analysis is the ability to identify genetic transferrin variants, the specificity (i.e., only

disialotransferrin is measured) and that HPLC is a well known methodology in many

8

clinical laboratories. Later Helander et al. developed an improved HPLC method that is

now a suggested candidate reference method for CDT analysis (102).

Over the years there have been several capillary electrophoresis (CE) and capillary

zone electrophoresis methods available for analysis of CDT, with gradually increasing

sensitivity and selectivity, and lately some methods have been commercially available

for routine use (90-92).

The immunoassay CDTect RIA (Pharmacia & Upjohn, Uppsala, Sweden) was the first

commercial test kit for CDT analysis and was launched 1993 (93, 94). This method,

and later its follower the %CDT immunoassay (Axis-Shield ASA, Oslo, Norway) have

both been available in a number of different variants for different applications (details

on methodology in section 3.3.3 and 3.3.4) (95-98). Notably, both are measuring the

sum of asialo-, monosialo-, disialo- and sometimes a fraction of trisialotransferrin

making them unspecific and not capable of identifying genetic transferrin variants that

are known to produce false-high or false-low CDT values (99).

Recently the first direct immunoassay for CDT, N Latex CDT (Dade Behring,

Marburg, Germany), became available. Measurement with N Latex CDT is based on a

monoclonal antibody that recognizes transferrin glycoforms that lack one or both of the

complete N-glycans (i.e., asialo-, monosialo- and disialotransferrin) (103). The

difference compared with other methods that measures the sum of asialo-, monosialo-

and disialotransferrin is that the monoclonal antibody used in N Latex CDT does not

discriminate between different genetic transferrin variants, and thus genetic variants do

not interfere with measurements, making it a more specific method.

During the last years, reports on analysis of transferrin with mass spectrometry have

begun to be published, providing valuable information on the structural

microheterogeneity of the transferrin glycans (104-106).

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2 GENERAL AIMS OF THE THESIS

• To evaluate any baseline differences in the transferrin glycoform pattern in

relation to ethnicity, age, gender, body mass index and smoking in a large study

population from five different countries.

• Within the same study population evaluate the sensitivity and specificity of

serum disialotransferrin as a biochemical marker for chronic alcohol abuse in

relation to ethnicity, gender, age and body mass index, and to compare the

performance of disialotransferrin with the traditional alcohol biomarkers GGT

and AST.

• Evaluate clinical and pharmacological factors previously reported to cause

false-positive CDT levels.

• Evaluate two potential confirmatory and routine methods for CDT testing, the

Bio-Rad %CDT by HPLC test and the CEofix™ CDT assay.

• To study the usefulness of a HPLC candidate reference method for the detection

and preliminary diagnosis of CDG.

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3 MATERIALS AND METHODS

3.1 STUDY POPULATIONS The serum samples analyzed in Paper I and II were all from subjects participating in the

WHO/ISBRA Study on State and Trait Markers of Alcohol Use and Dependence, an

international multicenter study (107). The serum samples analyzed in Paper III, IV and

V were anonymous leftover volumes from the Karolinska University Hospital or (in

Paper V) also from Meander Medical Center, Amersfoort, The Netherlands. In Paper

VI, the serum samples from subjects with different CDG types were from The

Burnham Institute, La Jolla, California, USA, all other serum samples were de-

identified leftover volumes from the Karolinska University Hospital.

3.1.1 The WHO/ISBRA Collaborative Project (Paper I-II)

The WHO/ISBRA Study on State and Trait Markers of Alcohol Use and Dependence

was established in 1988. The aim with the study was to assess and compare markers of

recent alcohol use and also of the trait of alcohol dependence in a multicenter trial

(107). The study group consisted of representatives from the World Health

Organization (WHO), the International Society for Biomedical Research on

Alcoholism (ISBRA) and the National Institute on Alcohol Abuse and Alcoholism

(NIAAA). The samples used within this study were collected from both the community

and from alcohol treatment services in 5 countries: Australia (Sydney), Brazil (São

Paolo), Canada (Montreal), Finland (Helsinki) and Japan (Sapporo). A total of 1863

subjects aged over 18 were recruited, 67% of the subjects men and 33% women. In

Australia and Finland, only men were recruited. All participants were extensively

characterized for socio-demographic, health and lifestyle factors, and thoroughly

interviewed about their drinking habits by trained researched staff using the structured

WHO/ISBRA Interview Schedule (108). Based on this information, each subject was

classified as either “non-drinker” = totally abstinent, or one who drinks alcohol on no

more 6 special occasions per year (e.g., birthdays), and no more than 15 g ethanol on

each occasion; “light/moderate drinker” = drinks at least once per month but <210 g

ethanol/week for men and < 140 g ethanol/week for women, but no past treatment for

alcohol-related problems; “heavy drinker” = drinks > 210 g ethanol/week for men and

> 140 g ethanol/week for women, but no past treatment for alcohol-related problems; or

“under alcohol treatment” = a person currently receiving treatment for alcohol

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Table 1. Demographic data on the country of origin, gender, age, ethnicity and drinking status of the WHO/ISBRA collaborative project study population used in Paper I and II.

Australia Brazil Canada Finland Japan Total

Serum samples, n 239 385 432 200 131 1387

Gender, n (%)

Male 239 (100%) 211 (54.8%) 231 (53.5%) 200 (100%) 65 (49.6%) 946 (68.2%)

Female 0 174 (45.2%) 201 (46.5%) 0 66 (50.4%) 441 (31.8%)

Age, years

Mean ± SD 37.0 ± 13.1 35.5 ± 11.7 36.8 ± 12.0 39.2 ± 11.4 37.5 ± 11.3 36.9 ± 12.0

Median 36 33 36 38 35 35

Range 18-65 18-60 18-60 18-60 21-59 18-65

Age <31 (%) 87 (36.4%) 161 (41.8%) 156 (36.1%) 57 (28.5%) 45 (34.4%) 506 (36.5%)

Age 31-40 51 (21.3%) 95 (24.7%) 95 (22.0%) 53 (26.5%) 34 (26.0%) 328 (23.6%)

Age 41-50 51 (21.3%) 73 (19.0%) 110 (25.5%) 52 (26.0%) 30 (22.9%) 316 (22.8%)

Age >50 50 (20.9%) 56 (14.5%) 71 (16.4%) 38 (19.0%) 22 (16.8%) 237 (17.1%)

Ethnicity, n (%)

White 206 (86.2%) 268 (69.6%) 382 (88.4%) 198 (99.0%) 1 (0.8%) 1055 (76.0%)

Asian/Indian 24 (10.0%) 12 (3.1%) 23 (5.3%) 0 129 (98.5%) 188 (13.6%)

Black 5 (2.1%) 58 (15.1%) 9 (2.1%) 0 0 72 (5.2%)

Pacific 1 (0.4%) 0 2 (0.5%) 0 0 3 (0.2%)

American Indian 0 1 (0.3%) 2 (0.5%) 0 1 (0.8%) 4 (0.3%)

Other/Unknown 3 (1.3%) 46 (11.9%) 14 (3.2%) 2 (1.0%) 0 65 (4.7%)

Drinking status, n (%)

Non-drinker 65 (27.2%) 90 (23.4%) 132 (30.6%) 61 (30.5%) 12 (9.2%) 360 (25.9%)

Light drinker 97 (40.6%) 180 (46.8%) 189 (43.8%) 117 (58.5%) 106 (80.9%) 689 (49.7%)

Heavy drinker 77 (32.2%) 115 (29.9%) 111 (25.7%) 22 (11.0%) 13 (9.9%) 338 (24.4%)

dependence. However, in Paper I and II, the samples from the category with subjects

undergoing alcohol treatment were excluded from determination of CDT with the

HPLC candidate reference method, since these samples did not meet the criteria of the

aims of the studies. In the remaining three drinking categories some serum samples

were excluded because of too small sample volumes. In the end, serum samples from a

total of 1387 subjects were analyzed with the HPLC candidate reference method for

CDT (102, 109). Detailed information about the study population concerning gender,

age, ethnicity and drinking status is shown in Table 1. Based on the HPLC transferrin

pattern, 1362 (98%) subjects were indicated to be of transferrin C phenotype (a further

sub-classification into C1, C2 and C3 was not performed). The other 25 samples were

of different genetic variants and were excluded from the calculations, due to

overlapping peaks causing unreliable quantification of individual glycoforms (57).

12

3.1.2 Clinical samples (Paper III-V) The serum samples in these studies were all anonymous leftover volumes from

different departments at the Karolinska University Hospital, with the exception of 37

serum samples in Paper V that were collected at the Meander Medical Center,

Amersfoort, The Netherlands. In Paper III serum samples were collected from subjects

with various clinical conditions or undergoing medication with drugs previously

reported to cause non-alcohol related elevations of CDT. The samples were from

subjects with end-stage liver disease (n=50), diabetes mellitus type 2 (n=46), an

elevated C-reactive protein (CRP) level (n=15), medication with enzyme or non-

enzyme inducing antiepileptic drugs (AED; n=43), and cystic fibrosis (CF; n=24). In

Paper IV the serum samples used were two human serum pools (containing ~1.3% and

~2.6% disialotransferrin, respectively, by HPLC), 150 surplus clinical sera selected

from routine samples pool in order to cover the entire measurement range from

low/normal to highly elevated disialotransferrin values (0.7-22% by HPLC), as well as

18 serum samples with genetic transferrin variants. In Paper V, 42 anonymous surplus

sera were collected from the routine samples pool to cover the measuring range from

low/normal to highly elevated %CDT levels (1.3-24.2% by %CDT immunoassay), as

well as some genetic transferrin variants. In addition, 37 serum samples for this study

were collected at the Meander Medical Center, Amersfoort, The Netherlands, as

mentioned above.

3.1.3 CDG samples (Paper VI) In this study, serum samples from 9 patients with biochemically and/or genetically

confirmed CDG type I (a, b and g subtypes) and 4 with undefined CDG type IIx defects

were obtained from The Burnham Institute, La Jolla, California, USA. Serum samples

used for comparison from 42 children and adolescents (0-3 weeks, n=13; 1-9 months,

n=11; 1-18 years, n=18), 132 adult social drinkers and 74 chronic alcohol misusers

were all randomly selected leftover volumes from routine samples at the Karolinska

University Hospital (110).

3.2 HPLC CANDIDATE REFERENCE METHOD (PAPER I-VI) The HPLC candidate reference method is an improved HPLC method for measurement

of CDT in serum based on anion-exchange chromatographic separation of the different

transferrin glycoforms followed by photometric detection (Figure 3) (102). The method

13

Time (min) 5 10 15 20 25

0.2

0.4

0.6

0.8

Abs

orba

ncy

(mA

U)

1.0

0.0

d

Time (min) 5 10 15 20 25

0.2

0.4

0.6

0.8

Dis

ialo

trans

ferr

in

Tris

ialo

trans

ferr

in

Tetra

sial

otra

nsfe

rrin

Pen

tasi

alot

rans

ferr

in

Hex

asia

lotra

nsfe

rrin

Abs

o rba

ncy

(mA

U)

1.0

0.0

a

Time (min) 5 10 15 20 25

0.2

0.4

0.6

0.8

Abs

orb a

ncy

(mA

U)

1.0

0.0

Dis

ialo

trans

ferr

in

Tris

ialo

trans

ferri

n

Tetra

sial

otra

nsfe

rrin

Pen

tasi

alot

rans

ferri

n

Hex

asia

lotra

nsfe

rrin

Asi

alot

rans

ferr

in

b

Time (min) 5 10 15 20 25

0.2

0.4

0.6

0.8

Abs

orba

ncy

(mA

U)

1.0

0.0

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferri

n

Tetra

sial

otra

nsfe

rrin

Pen

tasi

alot

rans

ferr

in

Hex

asia

lotra

nsfe

rrin

Mon

osia

lotra

nsfe

rrin

c

Time (min) 5 10 15 20 25

0.2

0.4

0.6

0.8

Abs

orba

ncy

(mA

U)

1.0

0.0

d

Time (min) 5 10 15 20 25

0.2

0.4

0.6

0.8

Dis

ialo

trans

ferr

in

Tris

ialo

trans

ferr

in

Tetra

sial

otra

nsfe

rrin

Pen

tasi

alot

rans

ferr

in

Hex

asia

lotra

nsfe

rrin

Abs

o rba

ncy

(mA

U)

1.0

0.0

a

Time (min) 5 10 15 20 25

0.2

0.4

0.6

0.8

Abs

orb a

ncy

(mA

U)

1.0

0.0

Dis

ialo

trans

ferr

in

Tris

ialo

trans

ferri

n

Tetra

sial

otra

nsfe

rrin

Pen

tasi

alot

rans

ferri

n

Hex

asia

lotra

nsfe

rrin

Asi

alot

rans

ferr

in

b

Time (min) 5 10 15 20 25

0.2

0.4

0.6

0.8

Abs

orba

ncy

(mA

U)

1.0

0.0

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferri

n

Tetra

sial

otra

nsfe

rrin

Pen

tasi

alot

rans

ferr

in

Hex

asia

lotra

nsfe

rrin

Mon

osia

lotra

nsfe

rrin

c

Figure 3. Shown in (a) is a control serum sample from a light drinker with the predominant transferrin C homozygote variant; (b), transferrin C serum from a heavy drinker with increased disialotransferrin (3.2% of total AUC for transferrin) and detectable (~0.4%) asialotransferrin; (c), transferrin C serum from a person with high trisialotransferrin (8.4%) and a measurable (~0.5%) monosialotransferrin; (d), transferrin BC heterozygote, a genetic transferrin variant. Figure edited from (102).

is based on earlier HPLC methodology developed by Jeppson et al. (100). The primary

target molecule for this method is disialotransferrin, as recommended by the IFCC

working group on CDT standardization (111). Transferrin was first iron-saturated by

mixing 10-100 µL (depending on the available volume) of serum at a volume ratio of

5:1 with FeNTA (final concentration, 1.7 mmol/L), a well-known transferrin iron donor

(112). Lipoproteins were precipitated by mixing the iron-saturated sample 6:1 (by

volume) with dextran sulfate and CaCl2 (1.4 mg/L and 70 mmol/L, respectively). The

samples were mixed gently and left in the cold (~5 ºC) 30-60 minutes, and then

centrifuged at 3500g for 10 minutes. The clear supernatant was diluted fivefold with

water and then transferred to glass HPLC vials. If available, the injection volume was

200 µL. The HPLC system consisted of an Agilent 1100 Series Liquid Chromatograph,

equipped with a quarternary pump and degasser, thermostated autosampler (4 ºC) and

column compartment (25 ºC), a G1365B multiple wavelength detector and

ChemStation software. The transferrin glycoforms were separated by use of a

SOURCE® 15Q PE 4.6/100 anion-exchange chromatography column (Amersham

Biosciences, Uppsala, Sweden) with a linear salt gradient elution at a flow rate of 1

14

mL/min. Quantification of the different transferrin glycoforms relied on the selective

absorbance of the iron-transferrin complex at 470 nm. Baseline integration was used for

all transferrin peaks, typically from monosialotransferrin (if visible) or

disialotransferrin to hexasialotransferrin, with asialotransferrin (if visible) integrated

separately. With this method, the relative amount of any single glycoform (e.g.,

disialotransferrin) or combination of glycoforms to total transferrin (all quantified

glycoforms) are measured in terms of the relative area under the curve (%AUC). The

typical LOD and LOQ for the transferrin glycoforms were ~0.05% and 0.10%,

respectively, of total serum transferrin in the normal transferrin concentration range

(reference interval 1.9-3.3 g/L) (46). The intra- and inter-assay imprecision (CV)

determined for serum samples containing normal and elevated disialotransferrin (range

1.0-5.6%) are below 5% (102).

3.3 OTHER METHODS FOR CDT ANALYSIS Apart from the HPLC candidate reference method described above, which served as the

principal analysis method during the work with this thesis, other methods for analysis

of CDT were also used; Bio-Rad %CDT by HPLC Reagent Kit (Paper IV) (101), the

CEofix™ CDT Assay for CE analysis (Paper V) (113), the %CDT immunoassay

(Paper V) (114) and the CDTect™ radioimmunoassay (RIA) (94) method (Paper II).

3.3.1 Bio-Rad %CDT by HPLC Reagent Kit (Paper IV) Bio-Rad %CDT by HPLC Reagent Kit (Bio-Rad, Munich, Germany) is a new

commercial application for HPLC that allows separation of asialo-, disialo-, trisialo,

tetrasialo- and pentasialotransferrin in serum within ~6 min (total analysis time for 1

sample is ~10 min) (101). The method measures individual glycoforms in proportion to

total transferrin using baseline integration. The samples were prepared following the

manufacturers instructions for the reagent kit. The transferrin glycoforms were

separated on a gradient HPLC system with anion-exchange cartridge (guard, 5*4.6 mm

ID; analytical, 30*4 mm ID), followed by specific measurement of the iron-transferrin

complex at 460 nm. With this HPLC method monosialo- and disialotransferrin are co-

eluating, thus CDT is defined as the sum of asialo-, monosialo- and disialotransferrin,

and according to the manufacturer’s instructions the upper 95% confidence limit (mean

+ 2 SD) for this kit is 1.7%.

15

3.3.2 CEofix™, CE, (Paper V) The CEofix™ CDT Assay (Analis, Namur, Belgium) is a commercial CE method for

separation of individual transferrin glycoforms and for determination of CDT (92, 115,

116). The serum samples were first iron saturated. The fused capillary had an ID of 50

µm with a total length of 50 cm and had 40 cm to the detector. Before separation, the

capillary is dynamically coated by rinsing the capillary with an initiator buffer

containing a polycation and with the separation buffer containing a polyanion, creating

a dynamic double coating. Electrophoresis was carried out with an overall separation

time of ~6 min with ultraviolet detection at 214 nm on a Beckman Coulter P/ACE

5000, according to the manufacturer’s instructions. The relative amounts of single

transferrin glycoforms were calculated from peak areas by valley-to-valley integration.

All analysis with CEofix™ was performed at the Department of Clinical Chemistry,

Meander Medical Center, Amersfoort, The Netherlands (117).

3.3.3 %CDT immunoassay (Paper V) The %CDT immunoassay (Axis-Shield ASA, Oslo, Norway) is a heterogeneous

immunoassay with column separation followed by turbidimetric measurement that

measures CDT, i.e. the sum of asialo-, monosialo-, disialo-, and a portion (~50%) of

trisialotransferrin as the relative amount to the total transferrin (114) . The serum

transferrin in the sample was saturated with Fe3+ and applied to an ion-exchange

column where the different glycoforms are separated due to differences in charge. The

CDT glycoforms (as defined above) were eluted and determined by turbidimetric

measurement after formation of an immune complex with anti-transferrin antibodies.

The total transferrin content in the sample was determined separately using the same

anti-transferrin antibodies, and the %CDT concentration could then be calculated as the

ratio between CDT and total transferrin. According to the manufacturer the cut-off is

2.6%.

3.3.4 CDTect™ (Paper II) The CDTect RIA method (Pharmacia & Upjohn, Uppsala, Sweden) is based on column

separation followed by a double antibody RIA procedure measuring CDT (94), i.e. the

sum of asialo-, monosialo, part of disialo-, and traces of trisialotransferrin (71). The

serum transferrin in the sample was iron saturated with ferric citrate solution, and

elution buffer was added. An aliquot of the sample-buffer mixture was applied to the

anion-exchange microcolumns, and the CDT glycoforms (as defined above) were after

16

separation obtained in the column effluxes. Finally, the CDT from the eluate was

quantified with a double antibody RIA procedure and expressed as an absolute value in

units/liter (U/L). According to the manufacturer the cut-off value is 20 U/L for men and

26 U/L for women. Analysis with CDTect™ of the serum samples in this study was

already performed at an earlier point at the Alcohol Laboratory, Karolinska University

Hospital, Stockholm, Sweden, when the work with Paper II began.

3.4 ANALYSIS OF GGT AND AST (PAPER II) Gamma glutamyltransferase (GGT) and Aspartate Aminotransferase (AST) were

assayed by reflectance spectrophotometry using a Vitros 250 Analyser (Ortho Clinical

Diagnostics, Rochester, NY). Analysis of plasma samples for GGT and AST was

already performed at an earlier point at the laboratories of ALKO and KTL, Helsinki,

Finland, when the work with Paper II began (118).

3.5 STATISTICS

Statistical calculations were performed using the Student-Newman-Keuls test for

pairwise comparisons, a T-test (parametric) when the examined groups showed a

Gaussian distribution or Wilcoxon (non-parametric) if not. For statistical analysis of

correlations, Pearson´s correlation coefficient (parametric) or Spearman´s coefficient

of rank correlation (nonparametric) were used. All statistical analysis was performed

with MedCalc statistical software.

17

4 RESULTS

4.1 PAPER I The relative amounts of serum transferrin glycoforms in non-drinkers (n=358),

light/moderate drinkers (n=677) and heavy drinkers (n=327) within the WHO/ISBRA

Study on State and Trait Markers of Alcohol Use and Dependence are found in Table 2.

The major differences in the transferrin glycoform pattern between the three drinking

categories were the higher levels of disialo- (P<0.0001) and trisialotransferrin

(P<0.001) in light/moderate drinkers and heavy drinkers compared with non-drinkers.

Also, there were no detectable amounts of asialotransferrin in non-drinkers, but in 2.2%

of the light/moderate drinkers and in 18.3% of the heavy drinkers.

Table 2. Distribution of serum transferrin glycoforms in different drinking categories.

Non-drinkers Light/Moderate drinkers Heavy drinkers

Serum samples, n 358 677 327

Transferrin glycoform

Unknown peak 0.63 ± 0.65 (n = 36) 0.39 ± 0.32 (n = 39) 0.65 ± 0.47 (n = 13)

0.47, 0.11-3.26* 0.22, 0.10-1.16 0.46, 0.11-1.53

Asialotransferrin (n = 0) 0.40 ± 0.23 (n = 15) 0.53 ± 0.48 (n = 60)

0.30, 0.16-0.90 0.39, 0.10-2.43

Monosialotransferrin 0.17 ± 0.06 (n = 200) 0.18 ± 0.09 (n = 323) 0.22 ± 0.12 (n = 197)

0.16, 0.10-0-58 0.16, 0.10-0.69 0.19, 0.10-0.90

Disialotransferrin 1.14 ± 0.19 1.34 ± 0.55 2.25 ± 1.57

1.14, 0.54-1.87 1.22, 0.50-5.87 1.72, 0.86-10.6

Trisialotransferrin 4.02 ± 1.10 4.38 ± 1.35 4.89 ± 1.46

3.89, 1.78-10.0 4.16, 1.45-10.4 4.64, 2.05-12.7

Tetrasialotransferrin 79.9 ± 1.75 79.7 ± 2.04 78.1 ± 3.23

80.1, 74.0-85.5 80.0, 68.3-84.7 78.7, 61.1-84.4

Pentasialotransferrin 14.0 ± 1.48 13.8 ± 1.64 13.8 ± 1.99

13.9, 9.52-19.4 13.7, 9.27-26.7 13.7, 9.37-22.2

Hexasialotransferrin 0.73 ± 0.36 (n = 348) 0.64 ± 0.35 (n = 646) 0.75 ± 0.45 (n = 311)

0.67, 0.11-2.72 0.59, 0.10-3.15 0.68, 0.11-2.80

*Mean ± SD, median, range.

18

When comparing the correlations between the serum transferrin glycoforms for all

samples, the strongest positive correlation (r=0.80) was obtained between disialo- and

asialotransferrin, whereas disialo- and asialotansferrin were negatively associated with

tetrasialotransferrin. These associations were dependent on the alcohol consumption

level. The level of trisialotransferrin was positively correlated with

monosialotransferrin but this association was indicated to be unrelated to the alcohol

consumption level.

When the relative amounts of serum transferrin glycoforms in non-drinkers from the

five different countries in the WHO/ISBRA project were compared, it could be seen

that the general differences were very small (Table 3). The only statistically significant

differences (P<0.05) observed were the higher mean levels of trisialotransferrin in the

Finnish and Japanese subjects compared with Australians, Brazilians and Canadians.

When the relative amounts of serum transferrin glycoforms for the non-drinkers of

different ethnic origin were compared, the general differences between the transferrin

glycoform levels were also found to be very small. In this case, the only statistically

significant difference observed was a lower trisialotransferrin level in Asian/Indians

compared with whites (P<0.05).

Compared with female non-drinkers (n=117), male non-drinkers (n=241) showed small

but statistically significant higher levels of tetrasialotransferrin (mean 79.5% vs. 80.2%

respectively) and lower levels of pentasilaotransferrin (mean 14.4% vs. 13.9%). Most

important for CDT testing was that there were no gender associated statistically

significant difference between the relative amounts of disialotransferrin (mean 1.16%

vs. 1.13%). When all non-drinkers were divided into four age groups (<31, 31-40, 41-

50, and >50 years; n=66-128/group) the only statistically significant difference between

the levels of transferrin glycoforms were the slightly lower tetrasialotransferrin level in

those aged 41-50 years compared with those <31 years. When all non-drinkers were

subdivided based on their BMI (<20, 20-24.9, 25-30 or >30). Those with a BMI >30

(obese individuals) showed a significantly (P<0.05) higher mean value for

disialotransferrin (mean 1.22%) compared with the other subgroups (range 1.11-

1.12%). Other small but statistically significant differences were the higher levels of

monosialotransferrin (mean 0.20% vs. 0.14-0.17%), trisialotransferrin (4.53% vs. 3.39-

4.07%) and lower levels of hexasialotransferrin (0.57% vs. 0.74-0.77%) for those with

a BMI <20 compared with the other BMI subgroups.

19

Tabl

e 3.

Dis

tribu

tion

of s

erum

tran

sfer

rin g

lyco

form

s in

non

-drin

kers

from

diff

eren

t stu

dy s

ites.

20

Significantly (P<0.05) higher disialotransferrin levels were found in smokers compared

with non-smokers for all 3 drinking categories. In non-drinkers, the disialotransferrin

mean value was 1.13% compared with 1.20% for smokers, but between the other

drinking categories the differences were higher. However, in the light/moderate and

heavy drinkers, the higher disialotransferrin levels in smokers could largely be

explained by a higher alcohol consumption level compared with non-smokers.

4.2 PAPER II The relative levels of different serum transferrin glycoforms in samples from the

WHO/ISBRA Study on State and Trait Markers of Alcohol Use and Dependence were

determined. There was a statistically significant difference between the relative

amounts of disialotransferrin (%DST) in men and women in both light/moderate

drinkers (mean 1.40% vs. 1.23%) and heavy drinkers (2.40% vs. 1.75%). When the

whole sample instead was divided into four age groups (<31, 31-40, 41-50 and >50

years), the only significant (P<0.05) difference in %DST was that heavy drinkers aged

41-50 years showed higher levels (mean 2.80%) compared with those aged <31 years

(2.08%) and >50 years (2.02%). Notably, the self reported average alcohol

consumption level during the last month was similar for all age subgroups (range for

means 74-95 g/day). When the whole sample was grouped based on body mass index

(BMI <20, 20-25, >25-30, >30), the only statistically significant (P<0.05) difference

was that heavy drinkers with a normal BMI of 20-25 showed higher %DST values

(mean 2.66%) compared with the other groups (range for means 1.53-2.05%), and

again this was with rather similar alcohol consumption levels for all BMI subgroups

(range for means 60-95 g/day).

Correlations of %DST, CDTect, GGT and AST with self-reported mean daily alcohol

consumption in the month prior to blood sampling are given in Table 4. For all drinkers

combined, and for light/moderate drinkers of both genders, and also for male heavy

drinkers, the strongest correlation with the self-reported alcohol intake was found for

%DST. In female heavy drinkers GGT was the strongest correlate with mean alcohol

intake.

21

Table 4. Correlations between self-reported mean daily alcohol consumption and serum levels of %disialotransferrin (%DST), CDT by CDTect, gamma-glutamyltransferase (GGT) and aspartate aminotransferase (AST).

Self-reported mean daily alcohol consumption

Light/Moderate drinkers Heavy drinkers All drinkers

Men Women Men Women

%DST

0.47***

(n = 436)

0.27***

(n = 241)

0.30***

(n = 250)

0.24*

(n = 77)

0.59***

(n = 1004)

CDTect

0.32***

(n = 444)

0.18**

(n = 245)

0.22***

(n = 259)

0.22

(n = 79)

0.31***

(n = 1027)

GGT

0.16*

(n = 442)

0.06

(n = 243)

0.13*

(n = 258)

0.31***

(n = 78)

0.34***

(n = 1021)

AST

0.14**

(n = 442)

0.00

(n = 243)

0.20*

(n = 258)

0.26*

(n = 78)

0.25***

(n = 1021)

*** P<0.0001, **P<0.01, *P<0.05

Figure 4. Receiver-operating characteristic (ROC) analysis was used to distinguish female and male heavy drinkers from the combination of non-drinkers and light/moderate drinkers by measurement of serum %disialotransferrin. ROC analysis was also used analysing the CDTect and GGT values for non-drinkers and light/moderate drinkers compared with heavy drinkers.

0 20 40 60 80 100

80

60

40

20

0

Sens

itivi

ty (%

)

%DST Men

%DST Women

GGT

CDTect

100-Specificity (%)

22

For evaluation of the overall test accuracy of %DST as an alcohol biomarker and for

comparision with CDTect, GGT and AST, receiver-operating characteristics (ROC)

analysis was performed (Figure 4). The area under the ROC curves (AUC) for the

combination of the non-drinkers and light/moderate drinkers in comparison with heavy

drinkers was not significantly different between men (AUC 0.83) and women (0.82).

The AUC for %DST was significantly (P<0.001) higher than for CDTect (0.68) and

GGT (0.69). The sensitivities and specificities of serum %DST for “heavy drinking”,

according to the WHO/ISBRA Interview Schedule classification, at different threshold

limits are shown in Table 5 for all subjects combined and for men and women

separately. At any potential cut-off, women showed lower sensitivity but higher

specificity compared with men.

Table 5. Comparisons of sensitivities and specificities of serum %disialotransferrin (%DST) for “heavy drinking” at different cut-off limits for all subjects combined and for men and women separately.

Non-drinkers + Light/Moderate drinkers vs. Heavy drinkers

All subjects Men Women

%DST cut-off Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity

1.5 66.1 87.0 68.8 84.5 57.1 91.6

1.6 59.6 90.7 63.2 88.5 48.1 95.0

1.7 51.1 92.8 55.6 90.7 35.1 96.6

1.8 44.6 94.4 48.8 92.9 31.2 97.5

1.9 38.2 95.2 42.8 93.6 23.4 98.0

2.0 36.1 95.9 40.8 94.4 20.8 98.9

23

4.3 PAPER III The relative disialotransferrin level as determined by HPLC in samples collected from

subjects with various clinical conditions or undergoing medication with drugs

previously reported to cause non-alcohol related elevations of CDT (end-stage liver

disease, diabetes mellitus type 2, enzyme or non-enzyme inducing AEDs, CRP > 100

mg/L and CF) are given in Table 6. None of the groups showed a statistically

significant higher %disialotransferrin level in comparison with the average level for the

whole sample.

Table 6. Relative amount of disialotransferrin to total transferrin in samples originating from subjects with clinical conditions or under medications previously reported to cause false-positive CDT levels.

Clinical condition or medication Disialotransferrin

(% of total transferrin)

End-stage liver diseases (n = 50)

Primary biliary cirrhosis (n=17)

Haemochromatosis (n=8)

Hepatitis C (n=10)

Others (n=15)

1.42, 3.22, 1.27, 0.90 - 5.07*

1.22, 1.72, 1.20, 1.01 - 1.72

2.03, 5.07, 1.53, 1.14 - 5.07

1.35, 2.16, 1.15, 1.07 - 2.16

1.40, 2.60, 1.31, 0.90 - 2.60 Diabetes Type 2 (n = 46) 1.18, 1.64, 1.17, 0.78 - 1.81

Antiepileptic drugs

Enzyme inducing (n = 31)

(carbamazepine, phenytoin)

1.41, 4.91, 1.22, 0.81 - 5.50

Non-enzyme inducing (n=12)

(valproate, lamotrigine)

1.22, 1.48, 1.18, 1.06 - 1.48

CRP > 100 mg/L (n=15) 1.15, 1.50, 1.16, 0.62 - 1.50

Cystic fibrosis (n=24) 1.21, 1.73, 1.20, 0.90 - 1.76

*Mean, 97.5th percentile, median and range

Of the 178 samples, only 9 (5%) had a %disialotransferrin level at or above 1.8% and

were thus considered “positive”. Of the positive samples, 6 originated from patients

with end-stage liver disease, 1 from a patient with type 2 diabetes and 2 from patients

taking enzyme-inducing AED. The samples collected from subjects with end-stage

liver diseases were further categorized into the four subgroups primary biliary cirrhosis

(n = 17), haemochromatosis (n = 8), hepatitis C (n = 10), and “others” (including

autoimmune hepatitis, toxic liver damage, non-alcoholic steatohepatitis and primary

24

sclerosing cholangitis; n = 14) (Table 6). The highest frequency of positive results was

found in subjects with haemochromatosis with 3 of 8 (38%) samples showing

%disialotransferrin levels ≥ 1.8%.

4.4 PAPER IV The Bio-Rad %CDT by HPLC test allowed for reproducible separation of the asialo-,

disialo-, trisialo-, tetrasialo-, and pentasialotransferrin glycoforms within ~6 min and

the total analysis time for one sample was ~10 min. Genetic transferrin variants and

glycoform types known to cause falsely high or low results with the ion-exchange

minicolumn–immunoassay combination for %CDT, such as the transferrin CD and

BC heterozygotes, produced similar unique peak patterns, and were readily identified,

by either HPLC method. The disialotransferrin values obtained for the 150 clinical

sera by the Bio-Rad %CDT by HPLC test (mean 3.61%, range 0.6–21.4%) and the

HPLC candidate reference method (mean 3.87%, range 0.7–22.4%) were highly

correlated (r2=0.998, p<0.0001), and no outliers were noted (Figure 5). The HPLC

candidate reference method produced slightly higher disialotransferrin values (+0.20–

0.25% on average) over the entire measuring range.

Figure 5. Correlation between relative disialotransferrin values obtained using the Bio-Rad %CDT by HPLC test and the HPLC candidate reference method. The dashed line is x=y.

0 5 10 15 20 25

25

20

15

10

5

0

Rel

ativ

e di

sial

otra

nsfe

rrin

valu

es

by th

e Bi

o-R

ad %

CD

T te

st (%

)

Relative disialotransferrin values by the HPLC candidate reference method (%)

25

4.5 PAPER V The HPLC and CE methods allowed for reproducible separation and quantification of

single transferrin glycoforms with similar peak patterns. Rare genetic transferrin

variants and glycoform types, including transferrin B homozygotes and BC and CD

heterozygotes, another variant tentatively identified as “C2C3”, and serum samples

containing high relative amounts of monosialo- and trisialotransferrin, all identified

as actual or potential causes of falsely high or low %CDT results with the

minicolumn immunoassays, were readily identified by both methods. The relative

amounts of disialotransferrin to total transferrin obtained by HPLC and CE were

highly correlated (r2=0.972; P<0.0001; Figure 6). However, the HPLC method

constantly yielded higher results, the values being 1.36% higher, on average (range,

0.15%–4.20%; P<0.0001), than the CE values.

25

Figure 6. Passing and bablok regression line (solid line) with 95% confidence interval (dashed lines) for relative disialotransferrin values (in percentage to total transferrin) obtained by HPLC and CE.

0 5 10 15 20 25 Disialotransferrin by HPLC (%)

20

15 10

5

0

Dis

ialo

trans

ferri

n by

CE

(%)

26

4.6 PAPER VI Sera from CDG-I patients showed increased relative amounts of disialo- and

asialotransferrin and concomitant reductions in tetrasialotransferrin (Figure 7).

Patients with the CDG-Ia subtype had the highest amounts of disialo- (range, 19–

42%) and asialotransferrin (3.4–26%), whereas the patients with type Ib and Ig CDG

had less asialotransferrin (<3%). This compares with disialotransferrin concentrations

<2% and undetectable asialotransferrin in the controls. In heavy drinkers, disialo- and

asialotransferrin concentrations were 0.8–16% and 0–4.0% of total transferrin,

respectively. After the CDG-Ib patient was treated with low-dose mannose, the

abnormal transferrin pattern improved, as indicated by marked reductions in disialo-

(from 18% to 6%) and asialotransferrin (from 2.6% to 0.7%).

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

o-tra

nsfe

rrin

Pen

tasi

alot

rans

ferri

n

Hex

asia

lotra

nsfe

rrin

Asi

alot

rans

ferr

in

b

Mon

osia

lotra

nsfe

rrin

CDG-Ia

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

otra

nsfe

rrin

Pent

asia

lotra

nsfe

rrin

Hex

asia

lotra

nsfe

rrin

Abs

orba

nce

(mA

U)

1.5

0.0

a

Asi

alot

rans

ferr

in

Mon

osia

lotra

nsfe

rrin

Control Alcohol

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

cCDG-Ib

Asi

alot

rans

ferri

n

Dis

ialo

trans

ferri

n

Tetra

sial

otra

nsfe

rrin

Before mannoseAfter mannose

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

o-tra

nsfe

rrin

Pen

tasi

alot

rans

ferri

n

Hex

asia

lotra

nsfe

rrin

Asi

alot

rans

ferr

in

d

Mon

osia

lotra

nsfe

rrin

CDG-II

BA

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

o-tra

nsfe

rrin

Pen

tasi

alot

rans

ferri

n

Hex

asia

lotra

nsfe

rrin

Asi

alot

rans

ferr

in

b

Mon

osia

lotra

nsfe

rrin

CDG-Ia

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

o-tra

nsfe

rrin

Pen

tasi

alot

rans

ferri

n

Hex

asia

lotra

nsfe

rrin

Asi

alot

rans

ferr

in

b

Mon

osia

lotra

nsfe

rrin

CDG-Ia

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

otra

nsfe

rrin

Pent

asia

lotra

nsfe

rrin

Hex

asia

lotra

nsfe

rrin

Abs

orba

nce

(mA

U)

1.5

0.0

a

Asi

alot

rans

ferr

in

Mon

osia

lotra

nsfe

rrin

Control Alcohol

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

otra

nsfe

rrin

Pent

asia

lotra

nsfe

rrin

Hex

asia

lotra

nsfe

rrin

Abs

orba

nce

(mA

U)

1.5

0.0

a

Asi

alot

rans

ferr

in

Mon

osia

lotra

nsfe

rrin

Control AlcoholControl Alcohol

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

cCDG-Ib

Asi

alot

rans

ferri

n

Dis

ialo

trans

ferri

n

Tetra

sial

otra

nsfe

rrin

Before mannoseAfter mannose

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

cCDG-Ib

Asi

alot

rans

ferri

n

Dis

ialo

trans

ferri

n

Tetra

sial

otra

nsfe

rrin

Before mannoseAfter mannoseBefore mannoseAfter mannose

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

o-tra

nsfe

rrin

Pen

tasi

alot

rans

ferri

n

Hex

asia

lotra

nsfe

rrin

Asi

alot

rans

ferr

in

d

Mon

osia

lotra

nsfe

rrin

CDG-II

BA

Time (min) 5 10 15 20 25

0.3

0.6

0.9

1.2

Abs

orba

nce

(mA

U)

1.5

0.0

Dis

ialo

trans

ferri

n

Tris

ialo

trans

ferr

in

Tetra

sial

o-tra

nsfe

rrin

Pen

tasi

alot

rans

ferri

n

Hex

asia

lotra

nsfe

rrin

Asi

alot

rans

ferr

in

d

Mon

osia

lotra

nsfe

rrin

CDG-II

BA

Figure 7. Shown in (a) are HPLC chromatograms for a control serum from a light drinker, showing the predominant transferrin C homozygous variant (solid line), and for serum from a heavy drinker with increased asialo- and disialotransferrin (dashed line). (b), serum from a CDG-Ia patient showing markedly increased asialo- and disialotransferrin and reduced tetrasialotransferrin. (c), serum from a CDG-Ib patient showing increased asialo- and disialotransferrin (solid line) and another sample from the same patient after mannose therapy (dashed line). (d), serum from a CDG-II patient with uncharacterized defects (type IIx) showing increased mono- and trisialotransferrin and reduced tetrasialotransferrin, as well as two unknown peaks (A and B).

27

Sera from patients with undefined CDG-IIx defects had the type II pattern with

typical increases in trisialo- (range, 7.1–32%) and monosialotransferrin (5.7–15%),

indicating the presence of truncated glycans. This compares with trisialotransferrin

values typically <8% in controls and heavy drinkers, and monosialotransferrin was

measurable only in individuals with high trisialotransferrin. In all four CDG-II sera,

two unidentified peaks were also observed (Figure 7). In the control group, children

up to 3 weeks of age, 6 of 13 (46%) showed a disialotransferrin concentration below

the range in older children, adolescents, and adults. Trisialotransferrin concentrations

were below the adult range in 9 of 11 (82%) children 1–9 months of age and 7 of 18

(39%) of those 1–18 years of age, and penta- + hexasialotransferrin concentrations

were above the adult range in ~50% of the children in both age groups. Otherwise,

there were no marked age-related differences in the relative amounts of transferrin

glycoforms, and no gender differences were observed in children and adolescents.

Our results demonstrate that measurement of serum transferrin glycoforms by HPLC

can be used for preliminary diagnosis of CDG and for assignment of cases to either

type I or type II.

28

5 DISCUSSION The original definition of CDT as the sum of asialo-, monosialo- and disialotransferrin

was closely related to the analytical methods that were available for CDT analysis at

the time, methods that only could measure those added fractions (47). Later scientific

work with more specific methods for transferrin glycoform analysis, further confirmed

in this thesis, has proven that the only transferrin glycoforms clearly related to chronic

heavy drinking in a dose-dependent manner are asialo- and disialotransferrin (57, 109).

With that background, disialotransferrin was recommended to be the primary target

molecule for CDT analysis by an IFCC working group on CDT standardization (111).

Over the years there have been numerous reports on non-alcohol related reasons for

differences in the transferrin glycoform pattern (4, 48, 70). This thesis shows that there

on the contrary are minimal differences in the serum transferrin glycoform pattern

between non-drinkers from different countries and of different ethnicity, gender, age

and BMI. When it comes to disialotransferrin, the primary glycoform in CDT, some

small differences could be seen between groups with different ethnic origin, BMI, age

and gender. Although sometimes reaching statistical significance, these differences in

relative amounts of disialotransferrin in non-drinkers were never big enough to even

hypothetically cause any systematical misinterpretations with respect to CDT testing,

i.e. clinical false positive samples. A question that has been discussed in many reports

on CDT is the potential difference in CDT sensitivity between men and women. Based

on the drinking limits for men and women for each drinking category within the

WHO/ISBRA project, the ROC curve analysis did not reveal any statistically

significant gender difference in the detection of heavy drinkers. That said, male

light/moderate and heavy drinkers in general showed slightly higher relative mean

levels of disialotransferin compared with women, and showed higher correlations

between the different biomarkers of alcohol analyzed. It should be remembered that

most of the early reports on CDT were performed with CDTect, a method using

different reference values for men and women. It seems highly plausible that many of

the reports on non-alcohol related differences in the transferrin glycoform pattern due

to different physiological or clinical factors instead were linked to the analysis

methodology used. In general, disialotransferrin performed much better as a laboratory

biomarker for prolonged alcohol abuse than AST and GGT, much because of its higher

29

specificity, but disialotansferrin also showed superior sensitivity in most subgroups

examined compared with the other laboratory markers.

When samples from subjects belonging to different groups with previously reported

clinical risk factors for non-alcohol induced elevated CDT were analyzed, only ~5%

had a relative disialotransferrin level over the upper limit for the reference interval of

the HPLC method used. Notably, all samples within the study came from subjects with

an unclear drinking history, meaning that heavy drinking could not be excluded. As

already mentioned above, there are reasons to believe that most of the reports on non-

alcohol related differences in the transferrin glycoform pattern are linked to the non-

specific methodology used for CDT analysis, rather than representing true

physiological or pharmacological influences. An interesting exception was the relative

disialotransferrin levels in samples from subjects with haemochromatosis, where 3 of 8

were elevated. This clinical condition needs further investigation.

Asialotransferrin is a potential laboratory marker of prolonged alcohol abuse, its main

advantage being the very high specificity, theoretically 100%, or close. When

compared with disialotransferrin in Paper II and IV in this thesis, asialotransferrin in

general showed a much lower sensitivity for identification of heavy drinking. Perhaps

with development of new and much more sensitive analytical methods for identification

of very low concentrations of asialotransferrin in serum, this might be a promising

candidate marker for identification of chronic heavy drinking.

Immunoassays that do not identify different genetic transferrin variants, and thus are

likely to produce false positive and false negative results, should always be used

together with a reference method that do (119). The HPLC and CE methods evaluated

in this thesis all showed high correlation and allowed for reproducible separation and

quantification of single transferrin glycoforms with similar peak patterns. Different

genetic transferrin variants were readily identified with all evaluated methods.

Paper VI shows that CDG testing could be combined with CDT testing by HPLC.

This could enable testing for the preliminary diagnosis of CDG to be carried out in

many more clinical laboratories. This could counteract the underdiagnosis of CDG

that probably exists due to a combination of unspecific clinical symptoms and a lack

of reference methods for CDG testing in most clinical laboratories.

30

6 CONCLUSIONS CDT, defined as the relative amount of disialotransferrin to total transferrin

(%disialotransferrin), is a biomarker of chronic alcohol abuse with very high specificity

and medium sensitivity.

With respect to CDT testing, the results in this thesis indicate that adjustment of

reference intervals for %disialotransferrin in relation to ethnicity, age, gender, BMI and

smoking is not required.

In a study population with 1387 subjects from five different countries, the relative

concentration of serum disialotransferrin in general performed better than GGT and

AST, two traditional biomarkers of alcohol abuse.

It is most likely that reports on clinical and pharmacological factors reported to cause

false-positive CDT levels performed with methods for CDT analysis that don’t have the

ability to measure single glycorforms separately or don’t identify genetic transferrin

variants needs re-evaluation. Many of these findings are probably linked to the

methodologies used for CDT analysis, rather than representing true physiological or

pharmacological influences on the transferrin glycosylation pattern.

Asialotransferrin might in the future be a potential biomarker of prolonged alcohol

abuse, its main advantage being a superior specificity. But with the analytical

methodology available today the sensitivity in general is too low compared with the

relative level of disialotransferrin.

The Bio-Rad %CDT HPLC test and the CEofix™ CDT assay are appropriate for

confirmatory and routine %CDT testing. They showed an overall good correlation and

agreement with the corresponding results of a HPLC candidate reference method.

The HPLC candidate reference method could readily be used for preliminary diagnosis

of CDG and for assignment of cases to either CDG-I or CDG-II.

31

7 ACKNOWLEDGEMENTS This work was carried out at the Karolinska Institutet, Stockholm, Sweden. Many persons have contributed to and supported this work in different ways. Very often have I been inspired and impressed by all your knowledge and skills. I would like to express my sincere gratitude to all of you and especially to: Anders Helander, my supervisor, for excellent guidance, invaluable support and encouragement and for always being full of ideas and ready for discussion. Hudson Freeze, Jos Wielders and Riekie te Stroet, my co-authors, for great scientific knowledge and fruitful co-operation. Helen Dahl, for great collaboration, support, and for looking after me ever since my first day at the Alcohol Laboratory. Kristian Björnstad, for support, friendship and endless movie discussions. Good luck with finishing you Ph.D. studies! So many drugs, so little time... Naama Kenan, for finally having someone to discuss all my memories from my time in Israel with, and for being a great friend. Toda! Yufang Zheng, for always putting me in a good mood. Take good care of the baby! Thomas Gustafsson, Eva Lindmark and the rest of the CDT laboratory at the Karolinska University Hospital. You’ve always been kind and helpful to me, and I truly appreciate your assistance over the years. Jan-Olov Jeppsson, Anna Arnetorp and the rest of the CDT laboratory at the Malmö University Hospital. For great co-operation, enthusiasm and professionalism. Wayne Jones, my co-supervisor during my master’s thesis work. For all good advices on science and writing. Olof Beck, for being a scientific role model in every way and for kindly sharing your knowledge about ducks. Bim Linderholm, Andreas Almlén, Marie Hegerstrand-Björkman and Guido Stichtenoth at the Surphactant Laboratory for friendship, encouragement and many laughs. Past Alcohol Laboratory and Surphactant Laboratory employees for good company, interest and support. Stefan Borg and Hans Bergman, for financial support and for allowing me to launch my Ph.D. project to begin with. Eva Severinsson and Camilla Ahlqvist, from the Stockholm Graduate School of Biomedical Research for giving me the opportunity to start exploring the wonderful world of biomedical sciences. Mats Fjellner, for via MSN Messenger, live and direct from various parts of Spain, keeping me company during thousands of hours in the lab. And for over the last years keeping me company during ~1500 kilometres of trekking in Spain, Nepal and Catalunya. Malin Roiha, for many discussions on philosophy, vegan food, music and Catalan beer brands, and for reminding me to visit Barcelona as often as possible.

32

Katja Kozian, for always making me smile and simply just being a fantastic person. Mattias Bergman and Amanda Derwinger, friends, neighbours and baby-sitters. With more neighbours like you, I would never leave my neighbourhood. Gunnar Ljungqvist, for tons of encouragement, being a great company during numerous cultural activities, and for teaching me a lot about wine, not only in theory but also in practice. Erik Södersten, fellow Ph.D. student and long time friend. For fantastic discussions and adventures over the years. Erik Andersson, fellow Ph.D. student, for excellent lunch company and for being not only the tallest guy I know, but also the friendliest. Fredrik Lundberg, for always being worse off than I am, and for great company at the Camp Nou during el Clásico. Robert Lindberg, for great friendship and spiritual guidance. Joakim Bergström and Malin Norman, for always being interested and supportive, with many questions and great curiosity. With persons like you around, living is easy. Göran and Gun-Inger Bergström, my parents, for always being there. Dante Wahlund, who arrived three days before I was supposed to hold my half-time seminar. Thank you for being the best child one could ever possibly imagine and for always encouraging me to wake up early. Katarina Wahlund, for endless support and encouragement during all those years, but above all, for being exactly who you are. Puss!

“Ett stort tack till er alla!” / J B

33

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HUMAN SERUM TRANSFERRIN GLYCOSYLATION PATTERN

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