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Genetic variation, telomeres and heart failureHaver, Vincent Gerardus

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Genetic variation, Telomeres

and Heart Failure

Vincent Gerardus Haver

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

3

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

4

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

5

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.

6

7

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

8

9

Chapter 1

Introduction

Genetic variation, Telomeres

and Heart Failure

10

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.

11

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

12

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

13

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

14

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

15

Chapter 2

Aims of this thesis

16

17

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.

18

19

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

20

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.

21

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

22

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.

23

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

24

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,

25

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

26

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).

27

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.

28

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).

29

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

30

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.

31

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.

32

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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.

35

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

36

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

37

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

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

39

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.

40

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

41

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

42

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.

43

44

45

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]

46

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.

48

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

49

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).

50

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.

51

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.

52

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%)

53

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).

54

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).

55

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.

56

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.

57

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

58

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).

59

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).

60

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.

61

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,

62

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

63

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.

64

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.

65

66

67

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

68

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.

69

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.

70

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

71

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

72

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

73

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.

74

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)

75

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

76

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.

77

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.

78

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

79

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

80

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

81

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

82

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.

83

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.

84

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.

85

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.

86

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.

87

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).

88

89

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

90

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.

91

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).

92

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

93

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

94

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,

95

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.

96

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)

97

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.

98

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.

99

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

100

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).

101

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.

102

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.

103

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

104

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.

105

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

106

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.

107

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

108

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.

109

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.

110

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

111

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

112

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.

113

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

114

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.

115

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

116

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.

117

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

118

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

119

120

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

121

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

122

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

123

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

124

125

Chapter 7

General discussion

and future perspectives

126

127

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

128

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

129

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.

130

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

131

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.

132

133

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.

134

135

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

136

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

137

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.

138

139

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.

140

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WH, de Boer RA, van Veldhuisen DJ, van der Harst P. The impact of coronary

artery disease risk loci on ischemic heart failure severity and prognosis:

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Veldhuisen DJ, van Gilst WH, van der Horst IC, van der Harst P. 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]

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Submitted.

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143

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.

144

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

145

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!

146

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!

147

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!

148

149

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.

150

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