DETERMINANTS OF ATHEROSCLEROSIS IN …...IV subclinical atherosclerosis 1.8.5.1 Rationale for the...

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DETERMINANTS OF ATHEROSCLEROSIS IN ELDERLY POST-MENOPAUSAL WOMEN: EFFECTS OF ENDOGENOUS ESTROGEN,

ESTROGEN-RELATED GENES AND ESTABLISHED CARDIOVASCULAR RISK

FACTORS

This thesis is presented for the degree of Master of Medical Science by Research of the

University of Western Australia by

Barry Hugh McKeown MBBS, FRACP

School of Medicine and Pharmacology University of Western Australia

2005

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CONTENTS

CONTENTS II

THESIS ABSTRACT IX

PERSONAL CONTRIBUTION TO THESIS XI

ACKNOWLEDGMENTS XII

ABSTRACTS ACCEPTED/PRESENTED XIV

ABBREVIATIONS XV

CHAPTER 1. BACKGROUND 1.1 Hypotheses and aims 1 1.1.1 Null Hypotheses 1

1.1.2 Aims 1

1.2 Background: Introduction 2 1.3 Atherosclerosis 4 1.3.1 Atherosclerosis: Introduction 4

1.3.2 Gender differences in atherosclerotic risk 6

1.3.2.1 Gender differences in risk factors for cardiovascular events 6

1.3.2.2 Gender differences in risk factors for carotid atherosclerosis 7

1.3.3 Relationship of Risk factors with atherosclerosis and 11

cardiovascular events in the elderly

1.4 Actions of estrogen 12 1.4.1 Postmenopausal estrogen biochemistry 12

1.4.2 The difference in action between oral exogenous estrogen and 13

non-oral estrogen

1.4.3 Molecular actions of estrogen 14

1.4.4 Estrogen, lipid effects 15

1.4.5 Estrogen, non-lipid effects 15

1.4.5.1 Estrogen, non-lipid effects: Introduction 15

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1.4.5.2 Estrogen and endothelial function 16

1.4.5.3 Estrogen, oxidation and metalloproteinases 16

1.4.5.4 Estrogen and thrombosis 16

1.4.5.5 Estrogen and CRP 17

1.4.5.6 Estrogen and diabetes 20

1.4.5.7 Estrogen and blood pressure 20 1.4.6 Actions of estrogen: summary 21

1.5 The relationship of estrogen with atherosclerosis 22

and cardiovascular disease 1.5.1 Relationship of estrogen with atherosclerosis: Introduction 22

1.5.2 Endogenous Estrogen and Atherosclerosis 23

1.5.2.1 Endogenous estrogen: evidence supporting 23

a beneficial (protective) effect

1.5.2.2 Endogenous estrogen: evidence supporting a null effect 24

1.5.2.3 Endogenous estrogen: summary 24

1.5.3 Exogenous Estrogen and Atherosclerosis 25

1.5.3.1 Exogenous estrogen: evidence for a beneficial effect 25

1.5.3.2 Exogenous estrogen: evidence against a beneficial effect 26

1.5.3.3 Exogenous estrogen; summary 28

1.6 Free estradiol index as a measure of 29

bioavailable estrogen 1.7 Candidate genes in postmenopausal atherosclerosis 29 1.7.1 Candidate genes in postmenopausal atherosclerosis: Introduction 29

1.7.2 Estrogen receptor alpha gene polymorphisms 30

1.7.3 Apolipoprotein E gene polymorphisms 31

1.8 Non-invasive tests of atherosclerosis 32 1.8.1 Non-invasive tests of atherosclerosis: Introduction 32

1.8.2 Ultrasound-based endothelial function studies 32

1.8.3 Electron-beam computed tomography 33

1.8.4 Magnetic resonance imaging 33

1.8.5 Carotid B-mode ultrasound for the assessment of 34

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subclinical atherosclerosis

1.8.5.1 Rationale for the use of carotid ultrasound

34

1.8.5.2 The difference between intimal-medial thickness 35

and plaque assessment

1.8.5.3 Predictive value of carotid ultrasound 37

CHAPTER 2. METHODS

2.1 Subjects 39 2.1.1 Subjects: Total study sample 39

2.1.2 Subjects: Estrogen receptor alpha subgroup 40

2.1.3 Subjects: High-sensitivity C-reactive protein sub-group 40 2.2 Risk factor assessment 42 2.3 Blood sampling 43

2.3.1 Biochemical tests 43

2.3.2 Genetic tests 44

2.4 B-mode carotid ultrasound examination 44

2.4.1 Image acquisition 44

2.4.2 Image capture 48

2.4.3 Image analysis 48

2.4.4 Data entry 49

2.4.5 Management of abnormal results 49

2.4.6 Carotid ultrasound reproducibility 49

2.4.7 Carotid ultrasound data analysis 49 2.5 Statistical Analysis-General Comments 50

CHAPTER 3. CHARACTERISTICS OF THE STUDY SUBJECTS 3.1 Characteristics of the study sample: Statistics 55 3.1.1 Statistics: Missing data 55

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3.2 Characteristics of study sample: Results 56 3.2.1 Characteristics of the total study sample 56

3.2.1.1 Characteristics of the total study sample: Missing data 58

3.2.1.2 Characteristics of the total study sample: Risk factor clustering 60

3.2.1.3 Characteristics of the total study sample: Sources 64

of bias and limitations of study sample

3.2.1.4 Characteristics of total study sample: Discussion 65

3.2.2 Characteristics of subjects with and without 66

free estradiol index measurement 3.2.3 Characteristics of the estrogen receptor-alpha (ER-α) sub-group 68

3.2.4 Characteristics of C-reactive protein sub-group 68

CHAPTER 4. ASSOCIATIONS OF FREE ESTRADIOL INDEX 4.1 Associations of free estradiol index: Background 71 4.2 Associations of free estradiol index: Statistics 71 4.3 Associations of free estradiol index: Results 71

4.4 Associations of free estradiol index: Discussion 75

CHAPTER 5. ASSOCIATION OF C-REACTIVE PROTEIN WITH FREE ESTRADIOL INDEX AND ESTABLISHED CARDIOVASCULAR RISK FACTORS 5.1 Associations of C-reactive protein: Background 77

5.2 Associations of C-reactive protein: Statistics 77 5.3 Associations of C-reactive protein: Results 78 5.3.1 Univariate associations of C-reactive protein 78

5.3.2 Multivariate associations of C-reactive protein 81

5.4 Associations of C-reactive protein: Discussion 81

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CHAPTER 6. DETERMINANTS OF CAROTID ATHEROSCLEROSIS 6.1 Determinants of carotid atherosclerosis: Background 84

6.2 Determinants of carotid atherosclerosis: Statistics 85 6.3 Determinants of carotid atherosclerosis: Results 87 6.3.1 Carotid intimal-medial thickness and focal plaque 87

6.3.2 Univariate relationships of established risk factors 89

with mean intimal-medial thickness

6.3.3 Free estradiol index and carotid intimal-medial thickness 90

6.3.4 Independent determinants of mean intimal-medial thickness 92

6.3.5 Univariate relationships of established risk factors with 92

focal plaque

6.3.6 Free estradiol index and carotid plaque 100

6.3.7 Independent determinants of focal plaque 101

6.4 Determinants of carotid atherosclerosis: Discussion 101

CHAPTER 7. APOLIPOPROTEIN E GENE POLYMORPHISM 7.1 Apolipoprotein E gene polymorphism: Background 106

7.2 Apolipoprotein E gene polymorphism: Statistics 106 7.3 Apolipoprotein E gene polymorphism: Results 107 7.3.1 Apolipoprotein E gene frequencies and association 107

with established cardiovascular risk factors

7.3.2 Apolipoprotein E genotype and carotid atherosclerosis 111

7.3.3 Apolipoprotein E genotype and FEI 112

7.4 Apolipoprotein E gene polymorphism: Discussion 116

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CHAPTER 8. ESTROGEN RECEPTOR ALPHA GENEOTYPE AND CAROTID ATHEROSCLEROSIS 8.1 Estrogen receptor alpha genotype and 118

carotid atherosclerosis: Background 8.2 Estrogen receptor alpha genotype and 118

carotid atherosclerosis: Statistics 8.3 Estrogen receptor alpha genotype and 120 carotid atherosclerosis: Results 8.3.1 PvuII polymorphism gene frequencies and 120

association with traditional risk factors

8.3.2 Thymidine-adenine (TA) repeat polymorphism 121

(6-group system) gene frequencies and association

with traditional risk factors

8.3.3 Thymidine-adenine (TA) repeat polymorphism 122

(3-group system) gene frequencies and association

with traditional risk factors

8.3.4 PvuII polymorphism and carotid atherosclerosis 124

8.3.5 TA repeat polymorphism (6-group system) and 125

carotid atherosclerosis

8.3.6 TA repeat polymorphism (3-group system) 127

and carotid atherosclerosis

8.3.7 PvuII polymorphism and free estradiol index 128

8.3.8 TA repeat polymorphism (6-group system) and

free estradiol index 132

8.3.9 TA repeat polymorphism (3-group system) and 134

free estradiol index

8.4 Estrogen receptor alpha gene polymorphisms: Discussion 137

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CHAPTER 9. GENERAL DISCUSSION 139

REFERENCES 144

APPENDIX 165

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THESIS ABSTRACT

Background & Aims- The determinants of atherosclerosis in elderly post-

menopausal women are poorly understood. We do not know if the traditional

coronary heart disease (CHD) risk factors remain important in this group. Despite

the growing body of data relating to exogenous estrogen, we know very little

about the relationship of endogenous estrogen with inflammation, CHD risk

factors and subclinical atherosclerosis in elderly women. Genes that may play a

role in post-menopausal cardiovascular disease (CVD)(ER-α and Apo E gene

polymorphisms) have not been examined in this population for their effect on

sub-clinical atherosclerosis and whether this effect is modified by the level of

endogenous estrogen. We have examined the effect of established

cardiovascular risk factors, endogenous estrogen and Apo E genotype on carotid

artery atherosclerosis in a large group of women over the age of 70 years. In

smaller sub-groups, we have examined the relationship between ER-α gene

polymorphisms and atherosclerosis and the relationship between endogenous

estrogen and CRP.

Methods- We studied 1149 ambulatory elderly women who were recruited from

the electoral role in Perth, Western Australia in 1998 and subsequently

underwent carotid ultrasound assessment in 2001 according to a standardised

protocol (for detection of focal plaque and measurement of intimal-medial

thickness). The subjects had a mean age of 75 years (range 70 to 82 years) at

baseline. We assessed the following variables in almost all subjects at baseline;

time from menopause, FEI (molar ratio of plasma estradiol to sex hormone

binding globulin (SHBG) x 1000), systolic and diastolic blood pressure, total

cholesterol, LDL and HDL cholesterol, triglycerides, body mass index, glycated

haemoglobin, homocysteine, apolipoprotein E (ApoE) genotype, history of

smoking, diabetes, cardiovascular disease and medication use. Four hundred

and thirty three women were analysed for estrogen receptor alpha (ERα)

genotype and 100 underwent measurement of high sensitivity C-reactive protein.

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Results- The mean carotid IMT was 0.77 ± 0.13 mm (mean ± SD), 49.5 % of

women had focal plaque. The independent determinants of IMT were age, pulse

pressure, smoking history and LDL-cholesterol in a model that only explained

4.3% of the variance of IMT. Those women with greater than the median level of

FEI (47.0) had greater IMT than those with lower levels, independent of other

factors (P=0.006). The independent determinants of focal plaque were pulse

pressure, glycated haemoglobin, LDL-cholesterol, history of smoking and

cardiovascular disease. There was a moderate positive correlation between FEI

and CRP (r=0.47, P<0.001). In multivariate modelling, FEI predicted CRP

independent of body mass index and other factors (p<0.001). While ApoE2 was

associated with lower LDL and total cholesterol and ApoE4 with higher levels,

ApoE genotype did not have a significant effect on carotid atherosclerosis.

Likewise, the ERα PvuII genotype had no direct effect on mean IMT or plaque

prevalence. However, the level of FEI modified the relationship between PvuII

genotype and carotid IMT. In the presence of higher levels of FEI, the presence

of a restriction site was associated with significantly greater IMT than when the

site was absent (0.80mm vs 0.75mm, p=0.02). There was no significant

relationship between PvuII genotype and IMT in those with lower levels of FEI.

The ER α thymidine-adenine (TA) repeat genotype appeared to influence plaque

prevalence; women with 15 or fewer TA repeats on both alleles (LL genotype)

were more likely to have focal plaque than other women (66.1% vs 50.1%,

p=0.02).

Conclusions- In elderly post-menopausal women, traditional risk factors were

independent predictors of IMT and plaque formation. Higher levels of

endogenous estrogen were associated with increased IMT independent of

traditional risk factors. Higher levels of endogenous estrogen may be pro-

inflammatory in elderly women. The apolipoprotein E genotype was not a

determinant of either IMT or plaque prevalence, Estrogen level modified the

relationship between the ERα PVUII polymorphism and IMT.

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PERSONAL CONTRIBUTION TO THIS THESIS

I conducted a comprehensive literature review to identify deficiencies in

our knowledge of the relationship between endogenous oestrogen, candidate

genotypes, inflammation and postmenopausal atherosclerosis. I attended a short

course in molecular biology; “Introduction to DNA Cloning, Sequencing and

Analysis” at the Western Australian Agricultural Biotechnology Centre - Murdoch

University. This improved my understanding of basic molecular biology and better

equipped me to research and statistically analyse the candidate genotypes

included in my Thesis.

I have supervised collection of the carotid ultrasound data and have

conducted short-term reproducibility studies (on 20 non-trial subjects) to ensure

adequate measurement precision. I repeated carotid imaging on 20 of the study

patients and analysed the images off-line using semi-automated edge-detection

software in order to familiarize myself with the acquisition and measurement of

carotid data. I extracted blood samples from our ultra cold freezer for all of the

lipid and CRP measurements. I designed a database for the carotid data, entered

and cleaned all of the carotid data.

I extracted and cleaned some of the other CAIFOS data including that

relating to blood pressure measurements, history of cardiovascular risk factors

and medication use and merged it with the carotid ultrasound database. I

conducted all of the statistical analysis with the use of skills acquired at the 2002

Biostatistics 1 course at the Department of Public Health, University of Western

Australia. I was supported by a biostatistician where required.

I presented preliminary results of this Thesis at an interstate Cardiology

Specialist meeting (2002 WASA Cardiology Specialist Meeting, Darwin, NT,

Australia). I was responsible for the authorship of abstracts presented at The

American College of Cardiology Annual Scientific Sessions 2003 and 2004 and

the Cardiac Society of Australia and New Zealand Annual Scientific Meeting

2003 and for the preparation of manuscripts for publication that are due to be

submitted.

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ACKNOWLEDGMENTS

This thesis would not have been possible without the assistance and

guidance of a number of my colleagues.

I would firstly like to acknowledge my supervisors, Associate Professor

Joseph Hung, Associate Professor Richard Prince and Clinical Professor Peter

Thompson. They have supported and guided me throughout the duration of my

Masters enrolment and have made themselves available and approachable.

They have been an invaluable source of advice and teaching given their vast

experience in clinical medicine and research. I am grateful for the opportunity that

they have given me to undertake this project.

I am thankful to the staff of the Heart Research Institute at the Sir Charles

Gairdner Hospital (SCGH) Campus for their support and friendship, Pamela

Bradshaw, Maxine Croot, Jo Crittendon, Nola Mammatt, Ros Stott, Trish Taaffe

and Dr Helen Hankey. Special thanks must go to Helen Coombs who performed

all of the carotid ultrasonography and Nicola Fillis who performed all of the off-

line ultrasound image analysis. Dr Brendan McQuillan is an experienced

researcher who provided insightful comments and guidance especially with

respect to the statistical aspects of this Thesis.

I am indebted to the staff of the Department of Endocrinology and

Diabetes, SCGH and the CAIFOS staff, in particular Amanda Devine, Ian Dick

and Rakhshanda Naheed. This group has performed the massive task of

recruitment and collection of baseline data on the CAIFOS subjects and have

facilitated and coordinated the return of subjects at 3 years for carotid

ultrasonography. The have allowed free access to all of their data have been

helpful in obtaining data relevant to this cardiovascular sub-study. They have

been enthusiastic and insightful collaborators without whom this study would not

have been possible.

Dr. John Beilby, Department of Clinical Biochemistry, PathCentre, Queen

Elizabeth II Medical Centre, has been a willing and enthusiastic collaborator in

this work. His expertise has been vital to the completion of this study. I am

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grateful to Richard Parsons for his assistance with the statistics components of

this thesis.

I am very grateful for the contribution made by the elderly women who

volunteered for the CAIFOS study and agreed to return for carotid

ultrasonography.

Finally, but very importantly, I owe an enormous debt of gratitude to my

wonderful wife Wendy who has been incredibly supportive and made many

sacrifices to allow me to complete this Thesis. Also, I am very thankful to my

three terrific children; Declan, Caitlin and Keely for being so understanding. The

support and love of my family has contributed heavily to making the production of

this Thesis an enormously rewarding and enriching experience.

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ABSTRACTS ACCEPTED/PRESENTED

1. “High Endogenous Estrogen Levels Are Predictive Of Increased Carotid Intimal -Medial Thickness In Elderly Postmenopausal Women”:

Barry H. McKeown, Richard L. Prince, Amanda Devine, John P. Beilby, Brendan

M. McQuillan, Joseph Hung, Peter L. Thompson, The Heart Research Institute of

Western Australia, Perth, Australia, The University of Western Australia, Perth,

Australia:

52nd Annual Scientific Session of the American College of Cardiology, Chicago,

Il, USA, March 2003.

2. “The Association Between Endogenous Estrogen And Carotid Intimal-Medial Thickness In Elderly Postmenopausal Women”

Barry H. McKeown, Richard L. Prince, Amanda Devine, John P. Beilby, Brendan



Australia

51st Annual Scientific Meeting of The Cardiac Society of Australia and New

Zealand, Adelaide, Australia, 10-13 August 2003.

3. “The Effect of Established Cardiovascular Risk Factors and Endogenous Estrogen on High Sensitivity C-reactive Protein in Elderly Women” Barry H. McKeown, Richard L. Prince, Amanda Devine, John P. Beilby, Brendan



Australia:

53rd Annual Scientific Session of The American College of Cardiology, New

Orleans, USA, March 2004.

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ABBREVIATIONS

ACE Angiotensin converting enzyme AMI Acute myocardial infarction ANOVA Analysis of variance ApoE Apolipoprotein E ARB Angiotensin II receptor blocker ARIC Atherosclerosis Risk in Communities study BMD Bone mineral density BMI Body mass index CAD Coronary artery disease CAIFOS Calcium Intake Fracture Outcome Study CCA Common carotid artery CEA Carotid endarterectomy CHD Coronary heart disease CHS Cardiovascular Health Study CRP High Sensitivity C-reactive protein CV Coefficient of variation DNA Deoxyribonucleic acid E1 Estrone E2 17β estradiol EBCT Electron beam-computed tomography EPAT Estrogen in the Prevention of Atherosclerosis Trial ERα Estrogen receptor alpha

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ERA Estrogen Replacement and Atherosclerosis Trial ERT Estrogen replacement therapy FEI Free estradiol index GLM Generalised linear model HDL High density lipoprotein HERS Heart and Estrogen/progestin Replacement Study HRT Hormone replacement therapy ICPC-Plus The International Classification of Primary Care – Plus IDL Intermediate density lipoprotein IL-6 interleukin-6 IMT Intimal-medial thickness LDL Low density lipoprotein Ln natural logarithm MEIA Microparticle enzyme immunoassay MI Myocardial infarction MRA Magnetic resonance angiography MRI Magnetic resonance imaging PAI-1 Plasminogen activator inhibitor – 1 PCR Polymerase chain reaction PE Pulmonary embolism PHOREA Postmenopausal Hormone Replacement Against

Atherosclerosis trial RFLP Restriction fragment length polymorphism

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RIA Radioimmunoassay RMS-CV Root mean square coefficient of variation RR Relative risk SD Standard deviation SHBG Sex hormone binding globulin TA Thymidine-adenine TNF Tumour necrosis factor VLDL Very low density lipoprotein WAVE Women’s Angiographic Vitamin and Estrogen Trial WELL-HART Women’s Estrogen and Lipid Lowering Heart and

Atherosclerosis Progression Trial WEST Women’s Estrogen for Stroke Trial WHI Women’s Health Initiative WISDOM Women's International Study of long Duration

Oestrogen after Menopause

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CHAPTER 1. BACKGROUND

1.1 Hypotheses and Aims 1.1.1 Null Hypotheses In a Population of Elderly Post-menopausal Women: 1. There is no relationship between endogenous estrogen, as measured by

FEI, and body mass index (BMI) or other CHD risk factors.

2. There is no relationship between CRP and FEI.

3. Traditional CHD risk factors have no effect on carotid IMT or plaque

prevalence.

4. FEI has no effect on carotid IMT or plaque prevalence.

5. ApoE genotype has no effect on carotid IMT or plaque prevalence.

6. Estrogen receptor alpha genotype has no effect on carotid IMT or plaque

prevalence.

1.1.2 Aims In a Sample of Elderly Post-menopausal Women:

1. To determine if FEI correlates with established risk factors.

2. To determine if FEI is predictive of CRP independent of BMI and other

CHD risk factors.

3. To determine if established CHD risk factors are important predictors of

carotid IMT and plaque.

4. To determine if FEI predicts IMT and plaque independent of established

risk factors.

5. To determine if IMT and plaque prevalence are influenced by ApoE

genotype.

6. To determine if IMT and plaque prevalence are influenced by ERα

genotype.

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1.2 Background: Introduction Cardiovascular disease (CVD) including ischaemic heart disease (IHD)

and cerebrovascular disease, is the leading cause of death in developed

countries such as Australia (Australian Bureau of Statistics 2001, see table

1.1). The most important underlying disease process is atherosclerosis which

in many cases is not symptomatic, but predisposes an individual to myocardial

infarction (MI) and stroke. At this subclinical stage atherosclerosis can be

assessed using ultrasound of the carotid arteries. Several major risk factors

have been identified that predispose an individual to atherosclerosis and CVD.

These include increased age, male gender, cigarette smoking, obesity,

diabetes mellitus, hypertension and plasma lipids1. Although gender

differences in the relative importance of traditional risk factors have been

recognised, no study has examined the determinants of subclinical

atherosclerosis in a large group of elderly post-menopausal women. It is not

known whether established risk factors will still be important in women over

the age of 70 years who are likely to have more robust genetic substrate and

who can be best viewed as a group of ‘survivors’. It is quite possible that the

relationship between established risk factors, genetic factors and

atherosclerosis will be quite different in this group compared to other women.

In women, symptoms of CVD are delayed by 10 to 15 years

compared to men2, this has traditionally been attributed to the protective effect

of estrogen prior to the menopause. However, there is conflicting data on the

role of menopausal estrogen withdrawal in the genesis of atherosclerosis in

older women, the increase in atherosclerosis and associated clinical events

after menopause may be purely age-related and not hormone-related3. In

recent randomised-controlled trials, oral exogenous estrogen has not

produced the expected vascular benefits and there has been evidence of a

small but measurable adverse effect4. However the effect of supra-

physiological doses given by mouth may be quite different to physiological

endogenous levels3. There is limited data on the association between

endogenous estrogen and traditional cardiovascular risk factors making it

difficult to predict the effect of endogenous estrogen on atherosclerosis. In

addition little is known about the relationship between post-menopausal

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endogenous estrogen and subclinical atherosclerosis. The role of estrogen

and in particular endogenous estrogen in the absence of exogenous

replacement needs further clarification in post-menopausal women.

Inflammation plays an important role in atherosclerosis and

cardiovascular events5. C-reactive protein is a widely utilised marker of

inflammation which itself is an independent predictor of cardiovascular

events6. There is a growing body of evidence suggesting that oral estrogen

therapy raises levels of CRP and may be pro-inflammatory7. Despite the

likelihood that oral exogenous estrogen acts quite differently to endogenous

estrogen, the association between post-menopausal endogenous estrogen

and CRP has not been examined.

The aetiology of atherosclerosis is multifactorial and likely due to an

interaction between many environmental and genetic influences. Estrogen

exerts its pleiotropic actions through estrogen receptors8, there is some

evidence that estrogen receptor alpha gene polymorphisms may play a role in

post-menopausal atherosclerosis but the data is limited9. The Apolipoprotein

E gene polymorphism influences lipid levels and is a predictor of

atherosclerosis and cardiovascular events10. Estrogen appears to up-regulate

Apo E gene expression via an estrogen-receptor alpha-mediated pathway11.

The effect of Apo E gene polymorphisms on postmenopausal atherosclerosis

has not been tested in a large group of women. It is possible that endogenous

estrogen level will affect the expression of these gene polymorphisms but this

has not yet been examined.

Clearly there are many deficits in our understanding of the

determinants of atherosclerosis in elderly postmenopausal women and in

particular the role of endogenous estrogen and estrogen-related genes. To

address these deficiencies I have examined the effect of established

cardiovascular risk factors, endogenous estrogen and Apolipoprotein E

genotype on carotid artery atherosclerosis in a large group of women over the

age of 70 years. In smaller sub-groups, I have examined the relationship

between estrogen receptor alpha gene polymorphisms and atherosclerosis

and the relationship between endogenous estrogen and CRP.

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Table 1.1 Australian leading underlying causes of death 2001

Cause of death and ICD code Number of Deaths 2001 (% of total)

All Causes 128 544 (100) Malignant neoplasm (cancer) (C00-C97)

36 750 (28.6)

Ischaemic heart diseases (I20-I25)

26 234 (20.4)

Cerebrovascular diseases (stroke) (I60-I69)

12 146 (9.4)

Chronic lower respiratory disease (including asthma, emphysema and bronchitis) (J40-J47)

5 916 (4.6)

Accidents (V01-X59) 4 840 (3.8) Diabetes mellitus (E10-E14) 3 078 (2.4) Influenza and pneumonia (J10-J18)

2 702 (2.1)

Diseases of arteries, arterioles and capillaries (including atherosclerosis and aortic aneurysm) (I70-I79)

2 625 (2.0)

Heart failure (I50) 2 612 (2.0) Intentional self harm (X60-X84) 2 454 (1.9) All other causes 29 187 (22.7) (Taken from the Australian Bureau of statistics Health Status Survey 2001)

1.3 Atherosclerosis 1.3.1 Atherosclerosis: Introduction Human arteries have three layers; the intima, media and adventitia.

The intima is the innermost layer, it is composed of connective tissue and with

increased age an increased number of smooth muscle cells. The media is the

muscular wall of the artery consisting of layers of smooth muscle cells

attached to one another. The adventitia is the outermost layer, it is a dense

collagenous structure which is highly vascular and provides much of the

nutrition for the vessel wall via the vasa vasorum.

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The term “atherosclerosis” is derived from the Greek words “athero”

(gruel) and “sclerosis” (hardening)12. This process occurs principally in the

intima of medium sized and large arteries such as the coronary, carotid,

vertebral, aortic and iliac arteries. The earliest lesion is the fatty streak which

can be found in children and young adults, it consists of lipid-laden

macrophages and smooth muscle cells and appears as an area of yellow

discolouration on the vessel wall. The advanced lesion is the fibrous plaque

that is composed of smooth muscle cells, lipid laden macrophages and T-

lymphocytes. This lesion often appears as a raised white area that may

encroach on the lumen of the vessel and is readily visualised using invasive

and non-invasive imaging modalities. However, atherosclerosis may not

always result in lumen narrowing (negative remodelling), there may be a

significant plaque burden without reduction in lumen diameter. The clinical

manifestations of these lesions are due to gradual occlusion of the artery

causing symptoms such as exertional angina or leg claudication, or erosion

and fissuring of the plaques with superimposed thrombosis resulting in an

acute event such as myocardial infarction or stroke13. Aneurysmal dilatation

may also occur in large vessels predisposing to vessel rupture.

Thickening of the wall of the artery is often seen with increasing

age, this is considered by some to represent part of the atherosclerotic

process however this finding may just represent a response to wall stress

associated with advancing years and elevated blood pressure12. As discussed

later the intimal-medial layer can be measured non-invasively using carotid

ultrasound, increased thickness is associated with an increased risk of

cardiovascular events.

Inflammation plays an important role in the genesis of

atherosclerosis, this role is summarized in a recent American Heart

Association Scientific Statement14. The atherosclerotic process may be

considered an inflammatory response to injury, the injurious factors being

cigarette smoking, hypertension, atherogenic lipoproteins and elevated blood

sugar. Injury is associated with secretion of leukocyte adhesion molecules

which facilitate the attachment of monocytes to endothelial cells, and

chemotactic factors, that promote monocyte’s migration into the subintimal

space. The subsequent transformation of monocytes into macrophages and

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the uptake of cholesterol lipoproteins are thought to initiate the fatty streak.

Further injury results in the continued accumulation of inflammatory cells in

the growing atherosclerotic lesion. The fibrous cap of the plaque is thinned

and made prone to rupture by the action of oxidised low-density lipoproteins

which cause apoptosis and loss of smooth muscle cells and

metalloproteinases which are activated by macrophages and break down the

collagen component of the fibrous cap. Rupture then exposes the

atheronecrotic core of the plaque to the blood resulting in thrombosis and the

clinical sequelae of myocardial infarction and stroke.

1.3.2 Gender Differences in Atherosclerotic Risk

1.3.2.1 Gender Differences in Risk factors for Cardiovascular

Events Although the risk profile is similar in men and women, there is some

gender variation in the relative importance of cardiovascular risk factors.

These differences are outlined in a recent review article by Roeters van

Lennep et al (see table 1.2)15. Smoking is associated with an early

menopause and reduced HDL levels and appears to be a stronger risk factor

for myocardial infarction in middle-aged women than men. Diabetes is a

strong risk factor for atherosclerotic vascular disease, compared to diabetic

men who have a doubling in risk, diabetic women may have a risk as high as

seven times that of their non-diabetic counterparts. Women have higher levels

of High Density Lipoprotein (HDL) which is a cardio-protective lipoprotein and

there is some evidence that low levels of HDL are more predictive of

cardiovascular disease in women than in men. While in men, low density

lipoprotein (LDL) cholesterol is the most important lipid marker, there is

evidence that in women lowered HDL and elevated triglycerides are more

important and independent cardiovascular risk factors. In addition there is

evidence from recent studies that the relative risks of cardiovascular events

associated with CRP are higher in middle-aged and elderly women than in

men.

The most obvious difference between men and women is their hormonal

status. Estrogen has beneficial lipid effects and pleiotropic non-lipid effects,

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many of which would suggest a positive cardiovascular effect. The influence

of estrogen persists until the menopause at which time ovarian production

ceases. The apparent protective effect of estrogen in pre-menopausal women

has been used to explain the lower risk of cardiovascular events in this group

compared to their male counterparts. As will be discussed later (see chapter

1.5), the effect of estrogen withdrawal after the menopause is uncertain.

Table 1.2: Cardiovascular risk factors for men and women

Risk Factor Men Women

Total Cholesterol +++ +++ LDL-cholesterol +++ +++ HDL-cholesterol ++ +++ Triglycerides + ++ Apo(a) ++ +(+) Smoking ++ ++(+) Diabetes ++ +++ Waist-hip ratio +++ +++ Hypertension ++ ++ Family History ++ ++(+) Homocysteine + + Inflammation (CRP) + ++ Hormones +++

Bolded risk factors may be more important in women than men.

(Adapted from Roeters van Lennep et al15)

1.3.2.2 Gender Differences in Risk factors for Carotid

Atherosclerosis The traditional cardiovascular risk factors (age, blood pressure, lipids,

smoking, diabetes mellitus and BMI) appear to be important in predicting

subclinical carotid atherosclerosis for both men and women albeit with some

differences between studies. Several studies have compared the risk factors

for carotid atherosclerosis in men and women: some of these are summarized

in table 1.3. In a case-control study of a subgroup from the Atherosclerosis

Risk in Communities (ARIC) Study, Heiss et al found associations between

increased IMT and the presence of plaque with unfavourable levels of systolic

blood pressure, total cholesterol, HDL-cholesterol, BMI and smoking in both

- 8 -

men and women.16 Work done in our department on a community-based

sample of men and women with a wide age range (Perth Carotid Ultrasound

Disease Assessment Study17) showed that age, systolic blood pressure,

smoking (pack-years), LDL-cholesterol and waist-to-hip ratio, were

independent predictors of increased carotid IMT in both men and women.

Diabetes and family history of premature vascular disease were predictors in

men but not in women. A long-term population-based longitudinal study of

3128 middle-aged men and women found that age, blood pressure, total

cholesterol and BMI were independent predictors of IMT in men and women18.

Triglyceride levels were associated with an increase in IMT in women only,

while physical activity and smoking were predictors of IMT in men only.

Smoking was associated with increased risk of carotid plaque in men and

women.

Other studies have examined the associations between risk factors and

atherosclerosis in samples of either men or women. Lassila et al examined

the predictors of carotid IMT and the finding of at least one focal carotid

plaque in 200 postmenopausal women within 8 years of the menopause19.

After controlling for age and years since menopause; smoking history, LDL-

cholesterol and pulse pressure were independent predictors of focal plaque.

After controlling for age and years since menopause; smoking history, pulse

pressure and triglycerides were independently related to mean IMT. Salonen

and Salonen examined the predictors of IMT in a population-based sample of

1224 middle-aged Eastern Finnish men20. Age, pulse pressure, cigarette-

years of smoking, serum LDL cholesterol, history of ischaemic heart disease,

systolic blood and diabetes were most strongly associated with IMT. Pulse

pressure had a stronger relationship with IMT than systolic blood pressure,

diastolic blood pressure was not an important predictor of IMT.

Overall the established risk factors are important in both sexes. It is not

evident that any risk factors are more important in one sex that the other. In

both sexes, systolic blood pressure and pulse pressure appear to be more

influential than diastolic blood pressure. Age, blood pressure and LDL-

cholesterol are consistently associated with carotid atherosclerosis whereas

the relationship of diabetes, smoking and non-LDL lipids with carotid

atherosclerosis and in particular IMT is less consistent.

- 9 -

Table 1.3: Summary of Population-Based Studies of B-Mode Ultrasound Screening of Carotid Arteries

Study Protocol Findings

Edinburgh Artery Study (1992) Allan, 1997

ATL Ultramark 9, 10-MHz probe, 4 observers. Single measurement of far wall R+L CCA 2 cm proximal to bifurcation. IMT measured to nearest 0.1 mm. Maximum IMT used and dichotomized >1.05 mm in some analyses, quartiles of IMT in other analyses.

N=1156; age range 60–79 y. IMTcca F=0.79 mm, M=0.85 mm. Associations with IMTcca in men: fibrinogen, blood viscosity. No significant associations in women.

Vascular Aging (EVA) Study (1991) Bonithon-Kopp, 1996

Aloka SSD-650, 4 observers, 7.5-MHz probe. CCA, ICA, and bifurcation scanned for plaques. Far wall CCA using automated edge-detection algorithm. Mean of 2 R+L CCA measures used. Plaque extent and severity were graded.

N=1271; mean age 65 (59–71) y. Plaque prevalence: F=16.5%, M=25.6%. IMTcca F=0.65 mm, M=0.69 mm. Associations with IMTcca and plaque: age, SBP, cholesterol, diabetes.

Bruneck Study, Italy (1990) Bonora, 1997

ATL Ultramark 8, single observer. Multiple sites CCA and ICA. Plaque thickness summed into a score. Repeat at 5 years.

N=888; mean age 59 (40–79) y. Plaque prevalence: F=36%, M=48%. Associations with plaque: age, SBP, DBP, LDL cholesterol, U-shaped insulin.

Rotterdam Study, Netherlands (1990) Bots, 1993

ATL Ultramark IV, 7.5-MHz transducer, single observer. Mean far wall IMT (L+R/2) used. Beginning of distal CCA for 10 mm scanned. Plaques classified as present or absent.

N=1000+; mean age 69 y. IMTcca; F=0.76 mm, M=0.81 mm. Associations with IMTcca: age, SBP, BMI (men only), smoking (men only).

Suita Study, Japan (1989) Mannami, 1997

Toshiba SSA-250A, 7.5-MHz transducer, single observer. 30 mm proximal to bulb and 15 mm distal to flow divider scanned. IMT at 10 mm proximal to beginning of CCA bulb. Mean near+far wall IMT used. Plaque=IMT>1.1 mm, plaque thickness summed into a score.

N=1445; mean age 63 (50–79) y. Plaque prevalence (age 60–69): F=45%, M=57%. IMTcca: F=0.89 mm, M=0.92 mm. Associations with IMTcca and plaque: age, SBP, smoking (men only), cholesterol, glucose.

San Daniele Project, Italy (1989) Prati, 1992

Angioview 600, 7.5-MHz transducer, single observer. IMT far wall site not defined. 30 mm proximal to flow divider and 15 mm distal examined for plaque mineralization or protrusion into lumen.

N=1348 aged 18–99, N=569 aged 50–79 y. Plaque prevalence: age 50–79: F=26%, M=35%. No mean IMT reported (treated as categorical variable). Associations with IMTcca and plaque: age, SBP, smoking, alcohol, HDL.

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Cardiovascular Health Study (1988) O'Leary, 1996

Toshiba SSA-270A, 6.7-MHz probe. Mean of maximum near and far wall or R+L CCA and ICA. Multiple machines and observers.

N=5176; age range 65+ y. IMTcca: F=0.96 mm, M=1.04 mm. IMTica: F=1.35 mm, M=1.57 mm. Associations with IMTcca: age, SBP, smoking, cholesterol, diabetes.

Atherosclerosis Risk in Communities (ARIC) Study, USA (1987) Heiss, 1991 Li, 1994

Device and probe not stated in primary publications. Multiple observers, near and far wall at CCA, ICA, bifurcation at multiple sites. IMT treated as a dichotomized variable using maximum IMT>1.6 mm.

N=772 (1991), 12 841 (1997); mean age 57 (45–64) y. Plaque prevalence: 34%. Disease-free IMTcca: F=0.60 mm, M=0.66 mm. Associations with IMTcca and plaque: age, SBP, DBP, BMI, smoking, cholesterol, income, education.

Koupio Ischaemic Heart Disease Risk Factor Study, Finland (1987) Salonen, 1991

ATL Ultramark IV, 10-MHz probe. Single observer. x3 of far wall R+L CCA, bifurcation, mean IMT recorded, plaque included if not mineralized.

N=1224; age range 42–60 y, men only. Mean IMT not reported, range IMTmax 0.48–4.09 mm. Associations with IMTmax and plaque: age, SBP, smoking, LDL, diabetes, history of IHD, serum copper, education, income, manual occupation.

MONICA Project, Augsburg, Germany (1984) Gostomzyk, 1988

Biosound, 8-MHz probe. CCA, ICA, ECA examined. No other details. Only detected plaques.

N=1338; age range 25–65 y. Plaque prevalence: 23.8%. Associations with plaque in men: age, cholesterol, diabetes, history of IHD. No association with SBP or smoking. In women, no associations found.

Seven Countries Study, Finland (1989) Salonen, 1994

ATL Ultramark V, 7.5-MHz probe. Protocol as in Koupio Study. Mean maximum IMT in L+R CCA measured from 3 readings.

N=182; age range 70–89 y. Plaque prevalence: 93%. Mean IMTcca: 1.5 mm. Associations with plaque: smoking and cholesterol.

R indicates right; L, left; CCA, common carotid artery; ICA, internal carotid

artery; ECA, external carotid artery; F, female; M, male; DBP, diastolic blood

pressure; BMI, body mass index; IHD, ischemic heart disease; and IMTmax,

maximum IMT measured from any site. (Adapted from Ebrahim S. et al21)

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1.3.3 Relationship of Risk Factors with Atherosclerosis and Cardiovascular events in the Elderly

Few studies have examined the relationship between cardiovascular

risk factors and atherosclerosis in elderly populations. The available data

suggests that the predisposing atherosclerotic risk factors (hypertension,

diabetes, cigarette smoking and dyslipidaemia) are similar in the elderly and

the young. However, there appears to be a diminished relative importance of

established cardiovascular risk factors in the prediction of cardiovascular

events with increasing age22. A similar association has been reported for

aging and carotid atherosclerosis. Fabris et al examined the relationship

between carotid artery atherosclerosis and cardiovascular risk factors at

different ages in a group of 231 men and 226 women (aged 18 to 97 years)

sampled from the general population23. They found that cigarette smoking and

cholesterol levels were not as strongly associated with carotid atherosclerosis

in the elderly compared to the young.

Alencar et al compared the importance of risk factors in 152 men and

364 women aged over 60 years. In men the independent predictors of

atherosclerotic complications were diabetes mellitus, LDL-cholesterol, HDL-

cholesterol and hypertension. Among women the independent risk factors

were elevated triglycerides and hypertension. This suggests that in older

individuals, the established risk factors persist but with differences in

importance between women and men.

Women represent an increasing proportion of the population with

increasing age, such that the importance of risk factors in elderly women

deserves special attention. No study has examined the relationship between

established risk factors and atherosclerosis in a large group of women over

the age of 70 years.

There is strong evidence that the prevalence of plaque and the

thickness of the intima-media layer increase with age. Fabris et al found that

the prevalence of atherosclerosis, number of plaques, and severity of carotid

stenosis increased with age across an age range of 18 to 97 years23.

Ebraham et al examined the relationship between age and carotid

atherosclerosis in a random community sample of 425 men and 375 women

- 12 -

(aged 56 to 77 years)21. They found that age was an independent predictor of

greater IMT, also the prevalence of focal plaque increased significantly with

increasing age, affecting 49% men and 39% of women aged <60 years and

65% and 75% of men and women, respectively, aged >70 years.

1.4 Actions of Estrogen 1.4.1 Postmenopausal Estrogen Biochemistry Current knowledge on the production and action of estrogen is

summarized in a recent review article by Gruber et al8. In premenopausal

women 17β estradiol (E2) is the dominant estrogen. Production occurs in the

theca and granulosa cells of the ovaries as a result of testosterone

aromatization. In the peri-menopausal period levels of ovarian estrogen

production fall due to depletion of ovarian follicles and there is a large

variation in serum estradiol levels. In postmenopausal women estrone (E1)

becomes the dominant estrogen but is less biologically potent than estradiol,

serum estradiol levels are usually less than 73 pmol/L (see table 1.4). Most of

the estradiol is formed by extra-gonadal conversion of androgenic steroids (by

aromatisation) in adipose tissue, liver and kidney.

From a longitudinal prospective study of 160 women with

spontaneous menopause, a marked decrease in estrogen levels occurred

during the 6 month period around the menopause, most marked for E1. Over

the next 3 years, E1 and E2 showed a moderate parallel decline and from 3

years onwards the levels were relatively constant for the next 5 years24. Little

is known about the factors that regulate estrogen production in

postmenopausal women. There is some evidence that estradiol production in

extra-gonadal tissues increases with increasing age and body mass index

(BMI)8. In one study of healthy postmenopausal women of mean age 58

years, neither age nor time since the menopause was a significant predictor of

sex hormone levels25, however obesity was a major determinant, with estrone

levels 40% higher in obese women. The effect of obesity is most likely related

to the aromatization of androgenic steroids in adipose tissue to produce

estrogen, such that the more adipose tissue, the higher the level of estrogen.

- 13 -

Both estrone and estradiol levels declined with increasing alcohol

consumption, and estrone levels were lower in more active women.

Table 1.4 Production Rates and Serum Levels of Estrogens

17β ESTRADIOL

ESTRONE PHASE

SERUM LEVEL (ρmol/l)

DAILY PRODUCTION

(µg)

SERUM LEVEL (ρmol/l)

DAILY PRODUCTION

(µg)

Premenstrual 146-183 50-70 55-146 30-60

Postmenopausal <73 5-25 55-294 30-80

(Modified from Gruber et al8)

1.4.2 The Difference in Action Between Oral Exogenous

Estrogen and Non-Oral Estrogen It is essential to know that almost all of the studies that examined the

mechanism of estrogen used oral preparations, and may or may not be

relevant to the action of endogenous estrogen. It seems likely that non-oral

(transdermal) estrogen preparations are more physiologic and more likely to

reflect the actions of endogenous estrogen.

The differences in effects of oral preparations and non-oral

preparations are well summarised as follows in a review article by Rossouw et

al3. Oral estrogens are absorbed in the gastrointestinal tract, enter the portal

vein and undergo extensive first-pass hepatic metabolism. In order to achieve

adequate blood levels the dose of oral estrogen needs to be ten times that of

non-oral estrogen. This high dose significantly influences the hepatic

metabolism of a large number of proteins, including lipid apoproteins,

coagulation proteins and probably CRP. The substantial effects on lipids,

coagulation and other proteins described for oral estrogens appear to be

greatly attenuated, absent or in the opposite direction with non-oral estrogen

preparations (transdermal estrogen replacement), however the data is much

- 14 -

more limited. Non-oral estrogens have at most modest effects on lowering

LDL-cholesterol, have no effect or lower triglycerides, have no effect on HDL-

cholesterol and have modest or no effect on coagulation proteins. When

referring to the actions of estrogen in the following sub-chapters it must be

remembered that most of the data refers to the effects of oral estrogen. It is

possible that much of these data may not be applicable to non-oral estrogen

and more specifically endogenous estrogen. We need to discover more about

the actions of endogenous estrogen and the relationship between

endogenous estrogen and established cardiovascular risk factors.

1.4.3 Molecular Actions of Estrogen

Estrogen exerts its biological effects via estrogen receptors (α and β)

that are found in the liver, vascular smooth muscle cells, endothelial cells,

myocardium and cardiac fibroblasts as well as many other cell types

throughout the body8. Estradiol has a greater affinity for ERα than ERβ. It

appears from studies in genetically altered mice that both receptors are

required to mediate estrogen’s protective vascular effect3. The indirect effects

of estrogen on lipids, coagulation, fibrinolysis, antioxidant effects and effects

on vasoactive compound production are produced via ER mediated effects on

the hepatic expression of the various relevant genes26. As described above,

the effect of oral exogenous estrogen may be quite different to endogenous

estrogen.

The cellular location of estrogen and other steroid-hormone receptors

is not clear, they are probably in an equilibrium distribution between the

cytoplasm and nucleus8. Free estrogen diffuses into the cell and binds with its

receptor, the estrogen-estrogen receptor complex then diffuses into the

nucleus and binds to DNA sequences called estrogen-response elements. It is

through this interaction that estrogen regulates gene transcription27.

Estrogens can also regulate the transcription of genes that do not possess

estrogen-response elements by binding to and modulating the action of other

transcription factors28. Transcription regulation by the ER is also modulated by

nuclear-receptor co-activators and co-repressors. Transcriptional activity is

greatly increased by the 160-kD steroid-receptor coactivator protein29 and the

p300-cyclic AMP response-element-binding proteins30.

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1.4.4 Estrogen, Lipid Effects

The association between raised cholesterol and cardiovascular disease

is well established1, as are the cardiovascular benefits of interventions that

reduce cholesterol levels31,32. Estrogen upregulates LDL receptors, increases

production of apolipoprotein A-1 and influences hepatic lipase activity15. In the

presence of low endogenous estrogen levels, LDL receptor activity is reduced

leading to the elevated LDL concentration seen in post-menopausal women.

In clinical studies, menopause has been associated with an increase in LDL-

cholesterol and total cholesterol but little effect on HDL-cholesterol. A study of

357 postmenopausal women not on HRT showed that these individuals had

higher total and LDL-cholesterol levels than 372 premenopausal women of

similar age, other risk factors including HDL, body mass index, triglycerides

and blood pressure did not differ by menopausal status33. There has been

little published on the association between endogenous estrogen and

cholesterol levels. One study found that total cholesterol was negatively

correlated with the concentration of estrone, a weak postmenopausal

estrogen34.

Most of the data on the actions of estrogen comes from studies of oral

estrogen replacement and suggests a beneficial effect. Oral estrogen

replacement reduces LDL-cholesterol, total cholesterol, apolipoprotein B and

lipoprotein (a) concentrations while increasing HDL, apolipoprotein A1 and A2

(cardioprotective), the only potentially detrimental effect is an increase in

triglycerides35,36. The effect of transdermal estrogen therapy is less consistent

with some studies showing no effect of transdermal estrogen on any of the

lipid parameters35.

1.4.5 Estrogen, Non-lipid Effects

1.4.5.1 Estrogen, Non-lipid Effects: Introduction Until recently it had been thought that the effects of estrogen on lipid

concentrations explained its vascular effects, but it is now recognised that

other factors are likely to be important. Animal studies show that estradiol

inhibits aortic intimal hyperplasia following ovariectomy independent of the

- 16 -

hormone’s effect on lipid metabolism37. Other actions include effects on

endothelial function, coagulation, inflammation and oxidation.

1.4.5.2 Estrogen and endothelial function The endothelium is responsible for maintaining vascular homeostasis.

Factors that cause endothelial dysfunction may promote vasoconstriction,

thrombosis and inflammation and result in an increased risk of

atherosclerosis38. Both animal and human data suggest that estrogen

improves endothelial function. In animals estrogen has beneficial effects on

vascular reactivity and endothelial function39, partially by increasing basal

endothelial nitric oxide release40. In postmenopausal women estrogen

replacement produced improvement in markers of endothelial function, with

increased nitric oxide breakdown products, reduced endothelin levels and

increased flow-mediated endothelium-dependent vasodilation of the brachial

artery41.

1.4.5.3 Estrogen, oxidation and metalloproteinases

LDL-cholesterol oxidation is an important step in the development of

atherosclerosis. Estrogen may have antioxidant properties. Exogenous

estrogen can reduce oxidation of LDL-C perhaps through effects on the local

production and break-down of superoxide42.

An acute effect of HRT is to increase local concentrations of matrix

metalloproteinases which may result in weakening and rupture of the thin

fibrous cap in vulnerable atherosclerotic plaques precipitating an acute

coronary syndrome or stroke.43

1.4.5.4 Estrogen and thrombosis

Thrombosis plays a pivotal role in vessel occlusion that leads to acute

coronary syndromes. Oral estrogen replacement therapy in humans has been

associated with increased fibrinolysis and potentially reduced thrombosis by

reducing plasminogen activator inhibitor (PAI-1) antigen, PAI-1 activity, tissue-

type plasminogen activator concentrations, fibrinogen levels and increasing D-

dimer levels35,,44. Conversely, oral estrogen has been demonstrated to

promote coagulation in the venous system45 and there is some recent

- 17 -

evidence for an arterial pro-thrombotic effect dependent on genotype.

Prothrombin is a coagulation factor that promotes thrombosis. Its levels are

increased by the prothrombin G20210A mutation. Cross-sectional and case-

controlled studies suggest that the combination of the prothrombin G20210A

mutation and the use of HRT has a synergistic effect to increase the risk of

atherothrombotic disease.46,47 . In the case-control study by Psaty et al, HRT

had a different effect on outcomes depending on prothrombin genotype47. In

the presence of the prothrombin mutation, HRT use was associated with 10.9

times increased odds of MI (2.2 to 55.2;p=0.002). In the absence of the

mutation, there was no increase in odds for MI (odds ratio 0.9, 0.3 to 7.7)47.

This may help to explain some of the individual variation in cardiovascular

outcomes in response to HRT.

1.4.5.5 Estrogen and C- Reactive Protein It is well established that inflammation plays an important role in the

atherosclerotic process. Estrogen (estradiol) has some anti-inflammatory

effects; it inhibits leukocyte adhesion and transendothelial migration in

rabbits,48 which are important steps in the genesis of atherosclerosis. More

recently, however there has been growing concern regarding possible pro-

inflammatory effects via increased levels of CRP.

C-Reactive Protein is an inflammatory protein which is principally

produced by the liver under the influence of a cytokine called Interleukin 6 (IL-

6), however IL-6 independent production also occurs. Increased CRP is

consistently associated with an increased risk of major cardiovascular events

in population-based studies, it is not clear whether its role is causative or just

a marker of disease. A meta-analysis of prospective population-based studies

with women and elderly persons represented, compared subjects in the lower

tertile of CRP with those in the upper tertile6. Compared to the lower tertile,

those in the upper tertile had a relative odds of 2.0 (95% CI, 1.6-2.5) for major

coronary events. This association persists after adjustment for established risk

factors and studies show a dose-response relationship between level of CRP

and risk of CHD. In considering CRP as a marker of cardiovascular disease

one must be mindful that other conditions can raise CRP including systemic

inflammatory processes, active infection or trauma, it is recommended

- 18 -

therefore that a CRP level greater than 10mg/L be discarded and repeated to

allow time for these processes to subside14.

C-reactive protein has been examined for its association with

cardiovascular risk factors. In a population-based cross-sectional study

involving 388 men, raised high-sensitivity CRP was associated with increasing

age, smoking, body mass index, raised total cholesterol, LDL-cholesterol and

triglyceride and negatively associated with HDL-cholesterol49. In another

study, plasma CRP was positively correlated with smoking, body mass index,

systolic blood pressure, fasting blood glucose and triglycerides and inversely

associated with HDL-cholesterol50. A study of healthy middle-aged women

examined the relationship between CRP and measures of obesity51. CRP was

strongly associated with BMI and waist circumference, BMI explained 29.7%

of the variance of CRP. Moderate alcohol consumption is associated with

lower cardiovascular mortality and has also been associated with lower CRP

concentrations independent of alcohol-related effects on lipids52. Overall,

measures of obesity are the strongest predictors of CRP but associations of

CRP with blood pressure and lipids have also been found. The relationship

between CRP and obesity is possibly due to co-association with prevalent

cardiovascular disease14. In addition, interleukin 6 (IL 6) and tumour necrosis

factor (TNF) which drive hepatic synthesis of CRP are both produced in

adipose tissue53,54.

The relationship of CRP with measures of atherosclerosis is not as

consistent or as strong as that with cardiovascular events. The offspring

cohort of the Framingham Heart Study (3173 subjects) had a CRP

measurement and then underwent carotid ultrasonography 4 years later55.

CRP was associated with internal carotid IMT and carotid stenosis in women

but not men. After adjustment for traditional cardiovascular risk factors,

women in the fourth CRP quartile had a higher mean internal carotid IMT than

those in the lowest quartile (p<0.001), in addition they also had greater odds

of carotid stenosis (OR 2.97, CI:1.72 to 5.25). The Rotterdam Study

investigated the association between CRP and carotid atherosclerosis in 1317

subjects56. Increasing CRP was associated with increasing carotid IMT and

compared to the lowest tertile, the odds ratio for moderate to severe carotid

plaques associated with levels of CRP in the highest tertile was 2.0 (95%CI

- 19 -

1.3 to 3.0). The NHBLI family heart study, which included 948 women, found a

weak positive association between CRP and carotid intima-media thickness in

both sexes that did not persist when adjusted for other risk factors57. A

population-based study of 186 healthy middle-aged women found that an

association between CRP and carotid IMT was only present in ever-

smokers51. The lack of a strong consistent association between CRP and

measures of atherosclerosis has prompted speculation that CRP may reflect

characteristics other than just the atherosclerotic burden, but rather the

activity of inflammatory cells within an atherosclerotic plaque or the degree of

plaque destabilization, ulceration or thrombosis14.

Oral HRT consistently produces an increase in CRP levels. In one

study plasma CRP was 3- fold higher in women receiving unopposed oral

estrogen and twice as high in women receiving combined oral HRT compared

to untreated women58. The mechanism for the increase in CRP is not clear as

oral HRT has not been shown to increase levels of inflammatory cytokines

that stimulate hepatic production of CRP (interleukin 6 or interleukin 1)59. As

stated previously, much of the data on the actions of estrogen comes from

studies using exogenous and usually oral estrogen therapy. It has been

postulated that oral estrogen may directly stimulate CRP synthesis by the liver

as part of a “first-pass” effect rather than acting through increased IL-6

production3. Studies that have compared oral verses transdermal delivery of

estrogen show that transdermal delivery which is likely to be more physiologic,

is not associated with any increase in CRP despite the production of similar

plasma hormone levels60. Sites et al investigated menopause-related

differences in inflammatory markers61. They found that IL6 and CRP did not

differ by menopausal status suggesting that like transdermal replacement,

endogenous estrogen may have little effect on plasma CRP. A study by

Ricoux et al in 1994 measured CRP in 30 premenopausal women undergoing

ovarian stimulation for in-vitro-fertilisation, no correlation was found between

the concentrations of estradiol and CRP for the 30 women62. There have not

been any studies that have related postmenopausal endogenous estrogen

levels in the absence of HRT to CRP levels.

In a case controlled study both IL6 and CRP were independent

predictors of CHD but when comparing individuals with comparable baseline

- 20 -

levels of either CRP or IL6, those taking or not taking HRT had similar odds

for CHD. This suggests that the use or not of HRT is less important than

levels of CRP or IL6 in the prediction of cardiovascular events59.

1.4.5.6 Estrogen and Diabetes

The relationship between estrogen replacement therapy and diabetes

was summarized recently in a review by Wilson63. He reports that over the last

few years investigators have continued to report neutral or favourable effects

of estrogen and HRT on glucose homeostasis. These effects include lower

levels of fasting glucose and insulin after oral estrogen replacement. There is

some evidence for a protective effect of estrogen against development of type

2 diabetes mellitus, this has been shown consistently with oral estrogens and

inconsistently with transdermal estrogens. The mechanism of the beneficial

effect on glucose homeostasis is not clear but is currently under investigation.

In animals, estradiol produces little change in glucose and insulin levels but

estrone decreases glucose-6-phosphatase activity and normalizes blood

glucose levels.

1.4.5.7 Estrogen and Blood Pressure

Estrogen has several potentially anti-hypertensive effects. It can exert

rapid non-genomic membrane effects via ER activation of nitric oxide

synthase and possibly opening of calcium- activated potassium ion channels,

resulting in rapid vascular smooth muscle relaxation26. Estrogen also causes

reduced smooth muscle cell proliferation64. Transdermal estradiol has been

shown to increase serum levels of prostaglandin E2 which causes

vasodilatation65, estrogen decreases sympathetic nervous system activity66. In

rats, estrogen depletion has been shown to raise arterial pressure in middle-

aged females by a decreasing hypothalamic norepinephrine, arterial pressure

was subsequently reduced by oral estrogen treatment67.

Despite significant mechanistic data suggesting that estrogen should

be antihypertensive, studies of endogenous and oral and transdermal

exogenous estrogen show no effect or a mild beneficial effect on blood

pressure. Cagnacci et al showed that 2 months of low-dose transdermal

estrogen replacement reduced nocturnal but not day-time blood pressure in

- 21 -

18 normotensive healthy postmenopausal women68. Enstrom et al found no

difference in blood pressure or heart rate between 32 post-menopausal

women taking oral hormone replacement and 32 women not taking estogen69.

Shelley et al found no association between endogenous estrogen (estradiol)

level and diastolic blood pressure in 363 women aged 45 to 56 years70.

Cacciatore et al randomly assigned 73 women to start hormone replacement

therapy with either orally (n = 38) or transdermally (n = 35) administered

medication. Ambulatory blood pressure was recorded for a 24-hour period

before the start of hormone replacement therapy and again 2 and 6 months

later. They found that both formulations caused small but significant falls in

the daytime ambulatory blood pressure of normotensive postmenopausal

women at 2 months of treatment. This fall persisted as long as 6 months of

treatment in the oral treatment group but not in the transdermal treatment

group71.

1.4.6 Actions of Estrogen: Summary

In an individual with a vulnerable atherosclerotic plaque, the potentially

prothrombotic, plaque destabilizing and pro-inflammatory effects of oral

estrogen could result in plaque rupture and superimposed thrombosis causing

an acute vascular event such as myocardial infarction or stroke. However

there is also a body of evidence supporting anti-thrombotic and anti-

inflammatory actions that should protect against an acute event. In addition,

there is a large amount of evidence for positive effects of estrogen on lipids,

endothelial function and LDL-oxidation which should result in reduced

atherosclerosis and fewer cardiovascular events. Given the more limited

recent evidence for a detrimental effect, from a mechanistic standpoint one

would predict that estrogen should have a beneficial effect on subclinical

atherosclerosis and cardiovascular events. However, as mentioned previously

much of the available data reflects the action of oral exogenous estrogen that

may be quite different to the action of endogenous estrogen, for example all of

the evidence for a detrimental effect on CRP comes form studies using oral

estrogen replacement therapy, whereas there is little data on effect of

endogenous estrogen on CRP . Even though mechanistically the overall effect

should be beneficial, estrogen appears to have some conflicting actions. It is

- 22 -

therefore important to move beyond mechanistics and examine the effect of

estrogen on atherosclerosis.

1.5 The Relationship of Estrogen with Atherosclerosis and Cardiovascular Disease 1.5.1 The Relationship of Estrogen with Atherosclerosis: Introduction As already discussed the weight of mechanistic evidence suggests that

estrogen should have a favourable effect on atherosclerosis and

cardiovascular disease. Until recently these data were strongly supported by

observational studies that reported up to 80% reduced incidence of

cardiovascular disease in users of HRT89. It has become clear however, with

the recent availability of data from large randomised trials72,73, that hormone

replacement therapy is not protective against CVD but rather may be

associated with an early increased risk of CHD and stroke. This highlights the

importance of randomised studies that are not subject to the same potential

biases as observational studies. In addition, the results of the large HRT trials

highlight the importance of assessing hard outcomes rather than surrogate

risk factors. The relationship of endogenous estrogen with cardiovascular

disease is uncertain. The notion that the increased incidence of CVD after

menopause is related to withdrawal of the beneficial effects of endogenous

estrogen may not be true. In fact this phenomenon may just be age-related

and not hormone related3 . One could postulate that the effect of endogenous

estrogen after menopause might best be examined by relating estrogen levels

to CVD events and measures of atherosclerosis however the data in this area

is limited.

Atherosclerosis represents the substrate for cardiovascular events and

may precede overt CVD by a prolonged period and therefore be detected

much earlier. There are various measures of atherosclerosis such as carotid

IMT which represents an atherosclerosis surrogate, is easily measured and is

strongly predictive of future CVD events74. Examination of the relationship of

estrogen with carotid IMT and other atherosclerotic measures can provide

information on the effects of estrogen on the underlying substrate for CVD.

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In this chapter I will summarize the evidence relating estrogen (both

exogenous and endogenous) to both measures of atherosclerosis (including

carotid IMT) and cardiovascular events.

1.5.2 Endogenous Estrogen and Atherosclerosis

1.5.2.1 Endogenous Estrogen: Evidence Supporting a Beneficial

(Protective) Effect Estrogen levels fall after the menopause and production occurs as a

result of peripheral conversion from adrenal androstenedione in adipose

tissue, liver and kidney rather than in the ovaries75. This estrogen withdrawal

has traditionally been thought to increase the risk of atherosclerosis and its

clinical manifestations.

There is some evidence that menopause is associated with increased

subclinical atherosclerosis. A study of 294 premenopausal and 319

postmenopausal women showed that postmenopausal women had 3.4 times

greater risk of atherosclerosis in the abdominal aorta than premenopausal

women76. A cross-sectional population based study measured the carotid

IMT of 2588 postmenopausal women and found that women with late

menopause had significantly less atherosclerosis than those with early

menopause77. In women, symptoms of coronary heart disease are delayed by

10 to 15 years in comparison with men, possibly due to the protective effect of

endogenous estrogen.78 A study of 2873 women from the original

Framingham cohort showed that in each age group studied, rates of coronary

heart disease and cardiovascular disease were higher in postmenopausal

than premenopausal women79. In a population-based cohort study, early

menopause, reflecting reduced endogenous estrogen exposure was

associated with increased cardiovascular mortality80. In a large observational

study, bilateral oophorectomy was associated with an increased rate of CHD

in univariate but not multivariate analysis. This risk however did seem to be

eliminated by estrogen replacement81. A problem with interpreting this result is

that, as in other observational studies there are likely to be significant biases,

the most important of which being healthy-user selection bias such that

- 24 -

women who take HRT are also likely to have other healthy health-related

behaviours which might reduce their risk of cardiovascular disease.

1.5.2.2 Endogenous Estrogen: Evidence Supporting a Null Effect Endogenous postmenopausal estrogen levels have not correlated with

carotid IMT or coronary atherosclerosis in case-controlled studies75,82,

although the data is limited. A 651 patient, population based prospective study

failed to find any association between postmenopausal endogenous estrogen

levels and cardiovascular risk factors or cardiovascular mortality this study

however did not examine other cardiovascular endpoints34.

There is little evidence for any change in the year-on-year rate of

increase in CHD around the menopause and some authors have suggested

that this may be just an age effect and unrelated to any change in hormonal

mileau3. A study by Lawlor et al examined age related trends in coronary

heart disease and breast cancer in Britain and Japan83. They also found no

upward inflection in age specific mortality from CHD around the age of the

menopause, however there was a deceleration in mortality from breast cancer

which is an estrogen-dependant malignancy. In this study the reduced sex

difference in CHD death after the menopause appeared to be due to

deceleration in death rates in men rather than acceleration in death rates in

women.

The Nurses’ Health Study (NHS) was a prospective study of 121,700

U.S. women 30 to 55 years old who were followed from 1976 to 1982.

Information on menopausal status and the type of menopause was collected.

After controlling for age and cigarette smoking, women who had a natural

menopause and who had never taken replacement estrogen had no increase

in the risk of CHD, as compared with premenopausal women (adjusted rate

ratio, 1.2; 95 percent confidence limits, 0.8 and 1.8)81.

1.5.2.3 Endogenous Estrogen: Summary

The role of menopausal estrogen withdrawal in potentiating

atherosclerosis and its complications is unclear given conflicting observational

data, it may be that the increased incidence of CVD after menopause is an

- 25 -

age-related rather than an estrogen-related effect. Post-menopausal

endogenous estrogen levels have not consistently correlated with measures

of atherosclerosis or cardiovascular events or cardiovascular risk factors but

the data are limited. There are no data on the association between

endogenous estrogen and atherosclerosis in elderly post-menopausal women. 1.5.3 Exogenous Estrogen and Atherosclerosis 1.5.3.1 Exogenous Estrogen: Evidence for a Beneficial Effect

Two cross-sectional studies of healthy postmenopausal women found

that HRT was associated with reduced carotid IMT84,85. An autopsy study of

56 women found that the coronary arteries from estrogen treated

postmenopausal women had lower calcium content, and plaque area than

untreated menopausal women86. The Estrogen in the Prevention of

Atherosclerosis Trial (EPAT) was a randomized, double blind study of 222

healthy postmenopausal women designed to test whether unopposed oral

estrogen therapy would slow progression of carotid intimal-medial thickening

compared to placebo87. After 2 years there was a significant reduction in IMT

progression in women taking unopposed estrogen replacement.

Observational studies have consistently shown that CHD risk is 35% to

50% lower in postmenopausal women who take oral estrogen replacement88.

The association has been especially strong for secondary prevention, with

hormone users having 35% to 80% fewer events than non-users89. However

these early studies were subject to possible selection bias as healthy women

are more likely to take HRT and its use may be associated with a more

favourable cardiovascular risk profile, health related behaviours and

socioeconomic and demographic factors3. In addition, such studies are biased

in favour of successful long-term users of HRT who will be over-represented,

users who have suffered a fatal adverse event will be completely absent and

the experience of new users will be diluted by the large numbers of successful

long-term users90.

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1.5.3.2 Exogenous Estrogen: Evidence against a Beneficial Effect Two cross-sectional studies of healthy postmenopausal women found

that HRT was not associated with reduced carotid IMT91,92. This may be

explained by the tendency of HRT to promote thickening of layers with the

highest connective tissue component (externa and media) and delay

thickening of the atheromatous intimal layer92. In a prospective randomised

trial of 309 patients (Estrogen Replacement and Atherosclerosis trial-ERA)

with angiographically proven coronary disease, HRT did not affect the

progression of coronary atherosclerosis despite changes in LDL and HDL that

would be predictive of a significant benefit93. The Women’s Angiographic

Vitamin and Estrogen (WAVE) trial was a secondary prevention trial in which

women with established CHD were randomised to HRT or placebo. The

outcome measure was the annualised mean change in coronary artery

diameter. There was a trend for worse progression in those taking HRT

compared to placebo (0.047 mm/y vs 0.024mm/y, p=0.17)94. Once again in

the Women’s Estrogen and Lipid Lowering Heart and Atherosclerosis

Progression Trial (WELL-HART) there was no improvement in coronary artery

lesion progression in those taking HRT compared to placebo. This is in

contrast to the EPAT study, also by Hodis et al, which as already mentioned

did show a better outcome for those on HRT. The difference may be because

the women in WELL-HART were 18 years post menopause compared to 13

years in EPAT consistent with the theory that vessels may respond differently,

especially prior to the development of advanced atherosclerosis, closer to the

menopause. The Postmenopausal Hormone Replacement against

Atherosclerosis (PHOREA) trial was a randomized, placebo controlled study

of 321 healthy postmenopausal women who had thickened carotid IMT

(>1mm) that compared combined HRT to placebo95. There was no difference

in IMT progression after 1 year of follow-up.

The first large randomized controlled clinical trial of the effects of

exogenous estrogen replacement on cardiovascular clinical outcomes was the

Heart and Estrogen/progestin Replacement Study (HERS), published in

199872. This study showed that combination HRT did not reduce the overall

rate of CHD events in patients with established disease. There appeared to

- 27 -

be an early increase in CHD events after starting combined HRT. An open-

label follow-up study of the HERS participants achieved an average follow-up

of 6.8 years and showed no cardiovascular benefit for those taking combined

HRT96. The Women’s Estrogen for Stroke Trial (WEST), which randomized

661 patients with recent stroke to unopposed estrogen or placebo, found no

difference in the incidence of death or recurrent stroke after 3 years follow-up.

In this trial there was an increased risk of stroke for the ERT group in the first

6 months of therapy97.

The results of HERS do not provide us with any information about the

effect of estrogen replacement in a healthier postmenopausal population. This

question was addressed by the large 27000 patient Women’s Health Initiative

trial of hormone replacement which examined various outcomes in

asymptomatic postmenopausal women. The 16000 patient arm of this trial

that studied the effects of combined HRT was prematurely terminated in 2002

at 5 years because of an excess risk of invasive breast cancer and an excess

global risk that included cardiovascular endpoints73. The relative risk (RR) for

CHD was 1.29 (95%CI:1.02,1.63) and for stroke 1.41 (95%CI:1.0,1.59) for

those taking combined estrogen and progestin compared to placebo. Once

again there was an early cardiovascular hazard within the first year of use.

The unopposed estrogen arm of WHI randomized 10 739 women with a prior

hysterectomy to 0.625 mg of estrogen or placebo98. It was stopped in

February 2004 (after 6.8 years average follow-up) when an excess of stroke

was noted associated with no evidence of benefit on cardiovascular

outcomes. There was a significant excess of 12 cases of stroke with estrogen

(44 cases in those on estrogen alone vs 32 in those on placebo) and an

excess of six cases of venous thrombosis (21 cases vs 15), including a trend

to more pulmonary embolism (PE) in treated women. There was no significant

difference in the risk of CHD, although, similar to other trials of estrogen, there

was a slight excess with estrogen early after treatment began that subsided

over time. This recently terminated part of the trial was important as it had

been postulated that progestins may have attenuated some of the beneficial

lipid and non-lipid effects of estrogen, therefore unopposed estrogen could

conceivably have had a positive effect in healthy postmenopausal women.

The WISDOM trial of 3,400 women in Australasia and Britain was also

- 28 -

investigating the effects of HRT in a healthier postmenopausal population but

was terminated prematurely in light of the results of WHI.

There is animal evidence that estrogen is more effective in preventing

atherosclerosis than slowing progression99, and that the ability of estrogen to

prevent the accumulation of cholesterol in the vessel wall requires an intact

endothelium100, this may partially explain the lack of benefit of HRT in

secondary prevention trials and in primary prevention trials in which HRT was

started many years post-menopause.

These findings from large randomized trials highlight the difficulties with

interpreting data from observational studies and the importance of assessing

hard outcomes rather than surrogate risk factors. There is now substantial

evidence for a deleterious or at best neutral effect of combined or estrogen-

only HRT on cardiovascular outcomes in both the primary and secondary

prevention settings.

1.5.3.3 Exogenous Estrogen: Summary

Cross-sectional studies that have investigated the association between

exogenous estrogen therapy and measures of atherosclerosis have yielded

conflicting results. Randomised studies have generally suggested no benefit.

The one study that suggested a beneficial effect on progression of carotid

atherosclerosis (EPAT) included a relatively young group of women,

suggesting that estrogen therapy may be beneficial if commenced before the

development of established atherosclerosis.

The negative results of recent randomized-controlled trials that have

investigated the relationship between HRT and cardiovascular events have

contradicted the large body of data from earlier observational studies that

suggested a positive role for estrogen therapy. These findings represent an

example of the necessity of large randomized controlled trials for directing

patient management. Clearly there is no present role for combination hormone

replacement therapy or unopposed estrogen therapy in the prevention of

cardiovascular disease in post-menopausal women. Although these findings

suggest a null effect of exogenous estrogen on atherosclerosis and a

potentially detrimental effect on cardiovascular events, it is not clear that one

can use these results to predict the relationship between endogenous

- 29 -

estrogen and subclinical atherosclerosis. It is quite possible that combined

HRT may have a different effect on atherosclerosis and cardiovascular

outcomes than endogenous estrogen. In addition cardiovascular events and

measures of subclinical atherosclerosis may be assessing different aspects of

the effect of estrogen on the cardiovascular system such that their relationship

with estrogen, whether exogenous or exogenous may be quite different.

1.6 Free Estradiol Index as a Measure of Bioavailable Estrogen

In the serum estradiol binds with high affinity to sex hormone binding

globulin (SHBG) and with less affinity to albumin, about 2 to 3% is free

estradiol8. The free estrogen is biologically active and the portion bound to

albumin can be rendered active through rapid dissociation. Therefore the pool

of free and albumin-bound estradiol is often referred to as the “bioavailable” or

“non-SHBG-bound” estradiol. The SHBG level is significant because a

decreased SHBG level in the presence of a normal or slightly elevated total

estradiol level results in more bioavailable estradiol, with higher peripheral

estradiol activity. Therefore, for adequate assessment of estrogen status, it is

better to use a measure that incorporates changes in SHBG levels and

therefore better reflects the level of bioavailable estradiol. Free estradiol index

(FEI), the molar ratio of estradiol to SHBG multiplied by 1000, is such a

measure. Free estradiol index has not been previously related to measures of

atherosclerosis or cardiovascular outcomes in any population.

1.7 Candidate Genes in Postmenopausal Atherosclerosis 1.7.1 Candidate Genes in Postmenopausal Atherosclerosis : Introduction

The causes of atherosclerosis and CHD are multifactorial, both

environmental and genetic factors are important in determining an individual’s

risk. It is likely that several genes will contribute to a person’s genetic risk.

Through mechanisms already discussed, estrogen appears to modulate the

expression of many genes including genes involved in lipid metabolism. One

possible explanation for the poor performance of estrogen replacement in

- 30 -

randomised trials and the lack of correlation between endogenous estrogen

level and cardiovascular disease is a differential effect depending on the

patient’s genotype. It is therefore important to consider gene polymorphisms

and the interaction between genes and environmental factors and estrogen

when examining the relationship between endogenous estrogen and

atherosclerosis.

1.7.2 Estrogen Receptor Alpha Gene Polymorphisms As mentioned previously, estrogen exerts its actions via estrogen

receptors α and β8. A restriction fragment length polymorphism (RFLP) in the

estrogen receptor-α intron 1 may be important in atherosclerosis and the

genesis of acute coronary events. A study by Lehtimaki et al investigated

whether the PvuII polymorphism was associated with autopsy-verified

coronary atherosclerosis and thrombosis9. Coronary arteries were taken from

300 Finish male autopsy cases aged 33 to 69 included in the Helsinki Sudden

Death Study. The mean area of complicated lesions of the three major

coronary arteries and the presence of coronary thrombosis were significantly

associated with ERα genotype. After adjustment for age and BMI, those with

the P/p (heterozygotes) and P/P (wild type homozygotes) genotypes had

areas of complicated lesions 2 to 5-fold larger than those with the p/p

(homozygous for the PvuII restriction site) genotype. This finding persisted

after adjustment for diabetes and hypertension (p=0.007). There is no

information regarding the mechanism by which the PvuII polymorphism may

affect the arterial wall, however these results suggest that the presence of a

restriction site may alter the tissue’s expression of estrogen to produce

beneficial vascular effects. One study found that PvuII genotype was

significantly associated with BMI (ANOVA, P=0.04)101. Individuals of the PP

and Pp genotypes had respectively 11.4% and 4.8% higher BMI than those

with pp genotype. In another study no association was found between PvuII

genotype and lipid levels102. There is no data relating this ER polymorphism to

other cardiovascular risk factors, carotid atherosclerosis or cardiovascular

events.

- 31 -

A second polymorphism, the thymine-adenine dinucleotide (TA) repeat

in the promoter region of the gene is significantly related to bone mineral

density and risk of osteoporotic fracture, those with fewer repeats being at

increased risk103,104. One study investigated this polymorphism in 98

postmenopausal women with familial hypercholesterolaemia in relation to

CAD. The frequency of alleles with more than 17 TA repeats was found to be

significantly higher in postmenopausal women with CAD than in those without

CAD (P=0.04)105. There is no information regarding the mechanism by which

the TA repeat polymorphism may affect either bone or the arterial wall.

1.7.3 Apolipoprotein E Gene Polymorphisms Apolipoprotein E is a small protein that is synthesized in the liver and is

found in various lipoproteins including intermediate density lipoprotein (IDL),

very low density lipoprotein (VLDL), HDL and chylomicrons. It is a ligand that

mediates the uptake of these compounds by the LDL-receptor and LDL-

receptor-related protein. Three major alleles of the ApoE gene encode E2, E3

and E4 isoforms that have frequencies of about 0.12, 0.75 and 0.13 in the

general population106. The isoforms bind to the LDL-receptor with different

affinities. Subjects with the E2 alleles have higher apolipoprotein E levels and

lower total and LDL-cholesterol levels compared to those with E3 alleles.

Subjects with E4 alleles have the lowest apolipoprotein E levels and highest

total and LDL-cholesterol levels.107,108. Studies have confirmed these

observations in postmenopausal women109,110. HDL-cholesterol and

triglyceride levels are not consistently affected by ApoE genotype, however

some studies have demonstrated lower HDL and higher triglyceride levels in

those with E4 alleles111,112. In a community-based sample of men (n = 1034)

and women (n = 916) aged 40 to 77 years, apolipoprotein E alleles were not

associated with hypertension, obesity, smoking, or diabetes, but the E 4 allele

frequency was reduced in women after 60 years of age10.

The Apo E4 genotype has also been demonstrated to be a strong

independent risk factor for cardiovascular disease in men and women

independent of its effect on cholesterol levels and other risk factors10,113.Our

studies in a randomly selected community population have also shown that

- 32 -

the E4 allele is associated with plaque formation (unpublished data). The

other data relating apoE genotype to carotid atherosclerosis is limited and

inconsistent with some studies demonstrating a protective effect of E2/3

genotype and others a detrimental effect compared to other ApoE

genotypes114,115.

A study by Garry et al compared lipid levels in 66 postmenopausal

women receiving HRT and 174 postmenopausal women not receiving HRT,

controlling for apoE genotype. Only in patients with the apoE4 genotype was

mean cholesterol significantly lower for those on HRT compared with those

not on HRT116. This suggests that HRT has a differential effect on serum lipids

in postmenopausal women depending on ApoE genotype. As mentioned

previously, estrogen has beneficial lipid effects, one of these is to increase

apolipoprotein E protein levels11. In animal studies, this effect has been

explained by estrogen’s ability to up-regulate Apo E gene expression via an

estrogen-receptor alpha-mediated pathway. The effect of Apo E

polymorphisms on atherosclerosis and the possible interaction with estrogen

in the prediction of atherosclerosis has not previously been studied in elderly

postmenopausal women.

1.8 Non-Invasive Tests of Atherosclerosis

1.8.1 Non-invasive tests of atherosclerosis: Introduction Individuals with atherosclerosis may remain asymptomatic for many

years before developing manifest disease. There are a variety of imaging

modalities that can assess disease at this subclinical stage. These include

carotid artery ultrasound, ultrasound-based endothelial function studies,

electron beam-computed tomography (EBCT) and magnetic resonance

imaging (MRI).

1.8.2 Ultrasound-Based Endothelial Function Studies

The endothelium plays an important role in the prevention of

atherosclerosis; through the production of nitric oxide it has anti-inflammatory

and anti-proliferative effects, inhibits platelet adhesion and causes

endothelium-dependent vasodilatation38. Endothelial dysfunction resulting

- 33 -

from vascular injury is almost certainly involved in the genesis of

atherosclerosis. Endothelial function is most often assessed by delivering a

stimulus to an endothelium dependent increase in blood flow in the forearm

such as ischaemia-induced hyperaemia. High-resolution ultrasound then

measures the diameter of the brachial artery before and after the stimulus, the

change in diameter then provides information about endothelial function38.

Patients with coronary risk factors and those with a history of ischaemic heart

disease have impaired vasodilator responses117. However there is limited

information about how endothelial function translates to clinical outcomes, and

the technique is skill and labour intensive.

1.8.3 Electron Beam-Computed Tomography

Electron Beam-Computed Tomography detects coronary artery calcium

which is present in advanced atherosclerotic lesions. The amount of calcium

is quantified to produce a coronary calcium score. High scores are associated

with an increased risk of cardiac events and add to the ability of traditional risk

factors to predict coronary artery disease117. The limitations of EBCT are that

it is expensive, it only detects advanced disease and soft, non-calcified

plaques are not detected.

1.8.4 Magnetic Resonance Imaging Magnetic Resonance Imaging appears to be a useful tool for non-

invasive evaluation of the vessel wall and assessment of plaque size and

composition. Autopsy studies show that MRI of the carotid, aortic and

coronary arteries correlates well with pathology117. Magnetic resonance

angiography (MRA) can detect coronary artery stenoses and the latest

machines can to a large extent overcome the problems of small vessel size

and cardiorespiratory motion. A recent clinical study showed that MRA has a

100% negative predictive value for coronary artery left main or three vessel

disease but an overall accuracy of only 72% for coronary artery stenoses118.

The limitations of MRI are that it is expensive and inconvenient.

- 34 -

1.8.5 Carotid B-mode Ultrasound for the Assessment of Subclinical Atherosclerosis

1.8.5.1 Rationale for the Use of Carotid Ultrasound The arterial structural changes of atherosclerosis are often present long

before the development of occlusive plaque and clinical complications. The

‘gold standard’ for the assessment of coronary atherosclerosis is coronary

angiography, however this modality can only detect the late changes of

atherosclerosis resulting in plaque formation and luminal encroachment.

Carotid ultrasound provides a safe, non-invasive, portable and relatively

inexpensive means of direct examination of the arterial wall and detection of

the early (wall thickening) and late (focal plaque) changes of atherosclerosis.

The carotid artery is chosen because it is difficult to image the coronary

arteries with non-invasive techniques and because this vessel lies at a

shallow tissue depth, allowing for high-resolution ultrasound imaging of the

arterial wall119. Examination of the far wall of the common carotid proximal to

the carotid bulb is used in preference to other points because this

measurement is best correlated with coronary artery changes and is the most

reliable and reproducible measurement for predicting coronary disease120.

Hulthe et al demonstrated that both carotid bulb IMT and plaque size

correlated with average coronary artery diameter stenosis, whereas

ultrasound findings in the common carotid or femoral arteries did not correlate

with quantitative coronary angiographic measurements.121 Carotid ultrasound

is able to accurately assess the thickness of the arterial wall. Wong et al

evaluated the correlation between histological and echocardiographic

measurements of intima-media thickness122; for combined intima-media

thickness, the differences between histology and imaging were insignificant,

averaging only 4% for the carotid artery.

Carotid ultrasound assessment of IMT has proven to be highly

reproducible. Selzer et al examined the reproducibility of IMT assessment

using semi-automated edge-detection software123. Replicate scans obtained 1

week apart of eight subjects by three sonographers showed an inter-

sonographer variability of only 5.4%. Comparisons of manual and automated

or computerised assessments of IMT show that a computerised contour

- 35 -

detection technique is more reproducible and accurate than calliper or manual

assessment. Figure 1.1 shows a comparison of computerised quantitative IMT

assessment (QIMT; IMTHeartScan, Ultrascan Health Technologies, Salt Lake

City, UT) and manual calliper assessment. There is a very strong correlation

between inter-observer measurements for the automated method and only a

moderate correlation for the manual method (r= 0.95 vs 0.63, p=<0.01)120.

Figure 1.1: Comparison of computerised quantitative IMT assessment

and manual calliper assessment. There is a stronger linear correlation

between observers using computerised assessment

(Barth et al120 ).

____________________________________________________________________

1.8.5.2 The Difference between Intimal-medial Thickness and Plaque

Assessment

Carotid ultrasound is unable to easily discriminate between the layers

of the carotid artery wall, such that a combined intimal and medial thickness is

measured. In normal arteries IMT is 97.5 % media and 2.5% intima and in

- 36 -

diseased arteries 20% intima and 80% media124. Intimal and medial

thickening is a diffuse process, it is not clear whether it represents an early

stage of atherosclerosis or a response to increased stress on the artery wall.

The intimal component consists of an increase in smooth muscle cells and

connective tissue, which may be associated with medial hypertrophy but does

not necessarily progress to advanced atherosclerosis.

The fibrous plaque is a focal or multifocal process and represents the

advanced lesion of atherosclerosis, that can cause luminal obstruction or

become complicated and result in an acute coronary or cerebrovascular

event.

Use of carotid IMT as a surrogate for atherosclerotic vascular disease

and in particular coronary artery disease has been criticized because

atherosclerosis is primarily an intimal process and intimal dimensions

contribute so little to the IMT measurement125, however as outlined below IMT

does predict cardiovascular outcomes.

Several studies have shown a consistent and graded association

between carotid IMT and the presence or development of carotid

plaque126,127,128. Despite considerable overlap as measures of atherosclerosis,

there is some evidence that focal plaque and IMT represent somewhat

different pathological processes. Intimal-medial thickness correlates better

with left ventricular mass than with CAD, and has not correlated consistently

with coronary calcium scores, which reflect advanced coronary

atherosclerosis.129 However focal carotid plaque has been highly correlated

(OR 4.94, 95% CI, 1.08 TO 23) with extensive coronary calcium. Ebraham et

al examined the different risk factor profiles for carotid plaque and IMT in 425

men and 375 women from the British Regional Heart Study21. They found that

common carotid IMT and plaque were correlated with each other but showed

differing patterns of association with risk factors and prevalent disease.

Intimal-medial thickness was strongly associated with risk factors for stroke

(age, SBP, but not with social or lifestyle factors) and with prevalent stroke,

whereas focal plaque was more directly associated with ischemic heart

disease risk factors (smoking, social class, fibrinogen) and prevalent ischemic

heart disease.

- 37 -

Despite these findings, IMT assessment is a well validated

measurement which appears to reflect early atherosclerosis and

atherosclerotic burden, while focal plaque suggests more advanced disease.

1.8.5.3 Predictive Value of Carotid Ultrasound

Carotid IMT has been used extensively internationally over the last 13

years in numerous studies as a measure of subclinical atherosclerosis.

Several studies have demonstrated cross-sectional associations between

carotid IMT and all of the established cardiovascular risk factors130,131, and

there is evidence that treatment of risk factors such as lowering cholesterol

can reduce the progression of intimal-medial thickening132. Carotid IMT has

been associated with prevalent cardiovascular disease in cross-sectional

studies133. In addition, several studies have found that carotid IMT is a

predictor of the presence of coronary atherosclerosis74,134. Salonen and

Salonen were the first to perform a large prospective study to investigate the

ability of carotid ultrasound abnormalities to predict coronary events. They

examined the association between the extracranial carotid morphology of

1288 eastern Finnish men and the risk of acute coronary events. Intimal-

medial thickening was associated with a 2.17-fold (95% confidence interval,

0.70-6.74; p = NS) and small carotid plaques with a 4.15-fold (95% confidence

interval, 1.51-11.47; p<0.01) increased risk of myocardial infarction compared

to men with no carotid abnormalities at baseline74. The same authors

performed a study in which ultrasonographic assessment of 1,257 men was

compared with diagnostic information obtained from a prospective registry for

acute myocardial infarction (AMI). The presence of any atherosclerotic

findings was associated with a 3.0-fold risk of AMI. For each 0.1 mm of

common carotid IMT, AMI risk increased by 11% (p < 0.001)135.

The presence of focal plaque has been previously assessed in other

studies and found to be negatively associated with cardiorespiratory fitness136,

and positively associated with the established cardiovascular risk factors137. A

2322 patient prospective cohort study found that carotid and femoral arterial

morphology predicted cardiovascular events in asymptomatic individuals138.

The morphological classification incorporated intima/media appearance, the

presence of increased IMT and the presence of focal plaque, those with the

- 38 -

highest class had stenotic focal plaque (>50% lumen diameter). Of these

latter patients 44% had normal IMT (<1mm) and yet this group had the

highest event rate. Assessment of plaque and the degree of luminal

encroachment therefore appears to add to the predictive value of carotid IMT.

The strongest evidence relating IMT to the incidence of cardiovascular

events comes from two large prospective clinical studies. The multicenter

ARIC (Atherosclerosis Risk in Communities) Study enrolled 7289 women and

5552 men aged 45 to 64 years who did not have clinical disease at

baseline139. The relation of carotid IMT to CHD incidence was studied over 4

to 7 years. The hazard ratio for coronary heart disease comparing mean IMT

>1mm to mean IMT <1mm was 5.07 for women (95%CI 3.08 to 8.36) and

1.85 for men (95%CI 1.28 to 2.69). The Cardiovascular Health Study (CHS)

enrolled 5858 individuals ≥ 65 years, who were free of clinical CVD at

baseline140. The relation of carotid IMT to new MI or stroke was studied. The

relative risk for MI or stroke for the quintile with the greatest thickness

compared with the least thickness was 3.87 (95%CI 2.72 to 5.51). The

American Heart Association now regards carotid ultrasound measurements of

IMT as a good surrogate for atherosclerosis141.

- 39 -

CHAPTER 2. METHODS

2.1 Subjects 2.1.1 Subjects: Total Study Sample

This study sample was defined by the 1149 women who had an

assessment of focal plaque 3 years after baseline. Initially a cohort of 1500

women was recruited between February and December 1998 for a five-year

study of calcium supplementation (Calcium Intake Fracture Outcome Study -

CAIFOS). To achieve this letters were sent to 24800 of the 33600 women

over the age of 70 years who were listed in the Electoral Role in Perth,

Western Australia. Of these 4284 (17.3%) sent a reply of interest and were

contacted by phone. Of these 2739 were excluded because on further

discussion they were not interested in the study (54%), they were prescribed

bone active agents including hormone replacement therapy (35%), they were

unlikely to survive a 5 year long study (7%) or they were participants in

another study or were reluctant to take a placebo tablet (4%). Regarding the

HRT exclusion criterion, women were excluded if they had been using HRT in

the 3 months immediately prior to trial commencement. No information was

collected regarding more remote use of exogenous hormones. Of the

remaining 1545 women the first 1500 were recruited for the trial, representing

6% of the available population.

At 3 years 1154 (77%) of the women agreed to have carotid ultrasound

assessment for IMT and focal plaque, 5 of these women gave a history of

carotid endarterectomy (CEA) and were excluded from further analysis. The

remaining 1149 women were included in carotid plaque analysis. Nineteen

subjects could not have adequate IMT assessment (IMT measurement at all

three sites on at least one side), leaving 1130 subjects to be included in IMT

analysis. A summary study plan is shown in figure 2.1. Written, informed

consent was obtained for all study participants. Ethics approval was obtained

from The Human Research Ethics Committee of the University of Western

Australia.

- 40 -

2.1.2 Subjects: Estrogen Receptor Alpha Sub-group One thousand three hundred and ninety women remained in the

CAIFOS study at 1 year, 504 of these women had phlebotomy for the

assessment of estrogen receptor genotype. This was a subgroup chosen at

random at baseline principally for the assessment of bone mineral density

(BMD) and therefore the numbers were restricted by the resources available

to perform regular DEXA BMD scanning. Of the 1149 women who then had

plaque assessment at 3 years, 433 (37.7%) had assessment of ER α

genotype.

2.1.3 Subjects: High Sensitivity C-Reactive Protein Sub-group

C-reactive protein was measured on baseline blood samples in 100

(8.7%) of the 1149 women who had plaque assessment. To achieve this, 25

women were selected at random from each quartile of FEI. This method of

selection was used principally to investigate whether any relationship between

FEI and carotid atherosclerosis might be explained by an association between

estrogen and CRP. Inflammatory processes such as connective tissue

disorders, infection and acute trauma will cause a significant rise in CRP

independent of the effect of estrogen or atherosclerosis, it is considered likely

that these processes are active when the CRP level is greater than 10 mg/L.

Therefore subjects with CRP > 10 mg/L were excluded from analyses.

- 41 -

Not Interested1479

Bone Active Agents959

Other301

Excluded2739

Previous CEA5

Excluded

Inadequate Carotid IMT19

Excluded FromIMT Analysis

Adequate Carotid IMT1130

Included in IMTAnalysis

No Previous CEA1149

Included inPlaque Analysis

Agreed to have Carotid Studiesat Three Years

1154

Did Not Agree/Deceased346

Randomizedat Baseline

1500

Not excluded1545

Number replied4284

Number did not Reply20516

Women From Electoral RoleOver Age 70 in

Perth Western Australia24800

Figure 2.1: Flow diagram representing the CAIFOS Study Plan. Exclusion

criteria are outlined. From 1500 randomized to the CAIFOS study, 1149

underwent carotid ultrasound, defining the current study sample.

______________________________________________________________

- 42 -

2.2 Risk Factor Assessment At baseline a self-reported list of medications and previous medical

history (medical history and medication data sheet, Appendix A) was

obtained, the women were encouraged to verify this information with their

General Practitioner, these data were then coded using a well validated

General Practice based system; The International Classification of Primary

Care – Plus (ICPC-Plus )142. The methodology of ICPC-Plus allows

aggregation of different terms for similar pathologic entities as defined by ICD-

10. This source was used to obtain data on previous history of diabetes,

hyperlipidaemia and cardiovascular disease (ischaemic heart disease,

peripheral vascular disease or stroke) and data on baseline use of

medications. Medication data were grouped into the following four important

drug classes: beta-blockers, angiotensin-converting enzyme inhibitors (ACEIs)

and angiotensin II receptor blockers (ARBs), anti-platelet agents and HMG-Co

A reductase inhibitors (statins). Smoking history was obtained as a

component of the “Patient Questionnaire” (see appendix B). The use of at

least one cigarette per day for at least 3 months was considered a significant

smoking history. Pack-years of smoking was then calculated as the product of

years of smoking and number of packs of cigarettes consumed per day.

Women were weighed and measured for height in light clothes and

without shoes, weight was assessed using digital scales and height was

assessed using a stadiometer. Body mass index was calculated as follows;

weight(Kg)/(height(metres))2. Women were classified as obese if their BMI

was 30 or more. Time of menopause was taken as the date of the last known

menstrual period (obtained from the Patient Questionnaire). Blood pressure

was measured on the right arm with a mercury column manometer using an

adult cuff after the patient had been seated and resting for at least 5 minutes,

the average of 3 such measurements was obtained. Women were classified

as hypertensive if their BP was greater than or equal to 140/90 mmHg,

consistent with JNC-VI guidelines143

- 43 -

2.3 Blood Sampling 2.3.1 Biochemical Tests

Serum SHBG was measured at baseline using an

immunochemiluminometric assay (Imulite, Los Angeles, USA), the inter and

intra assay coefficient of variation (CV) were 7.1% and 6.8% respectively at

24nmol/L. Serum estradiol was measured at baseline using a

radioimmunoassay (RIA, Orion Diagnostica, Espoo, Finland) with an analytical

sensitivity of 5pmol/L. We found the inter-assay CV to be 6.6% at a mean of

101 pmol/L and 7.2% at a mean of 48 pmol/L. The intra-assay CV was 5.1%

at a mean of 103 pmol/L and 7.5% at a mean of 49 pmol/L. Free estradiol

index (FEI) was calculated as the molar ratio of estradiol to SHBG multiplied

by 1000.

Glycated haemoglobin was measured using Ion-exchange HPLC using

the Variant II (Bio-Rad), the CV was 2.00% at 5.4 and 1.44% at 13.7.

Homocysteine was measured with FPIA using the AxSYM (Abbott), minimum

reportable level: 1.0 micromol/L, CV: 4.3% at 6.2 micromol/L and 3.9% at 17.6

micromol/L. Red cell folate was measured at baseline with the Microparticle

Enzyme Immunoassay (MEIA) using the AxSYM (Abbott), minimum

reportable level: 1 microgram/L (serum folate),CV: 6.8% at 8.8 microgram/L

and 5.4% at 16.7 microgram/L.

The test for cholesterol was Enzymatic (Cholesterol oxidase /

Peroxidase) using the Hitachi 917 (Roche diagnostics), minimum reportable

level: 0.5 mmol/L, CV: 1.0% at 5.9 mmol/L. The test for triglyceride was

Enzymatic (Lipase / Glycerol Kinase / Peroxidase) using the Hitachi 917

(Roche diagnostics), minimum reportable level: 0.2 mmol/L, CV: 1.54% at 2.0

mmol/L. The test for HDL cholesterol was Enzymatic (Cyclodextrin Sulphate /

PEG modified enzymes) using the Hitachi 917 (Roche diagnostics), minimum

reportable level: 0.3 mmol/L, CV: 0.6% at 0.85 mmol/L and 1.2% at 1.47

mmol/L. LDL-cholesterol was calculated using Friedewald’s method144.

The hs-CRP assay was performed on a Hitachi 917 analyser using the Roche

hs-CRP assay. It uses a particle enhanced immunoassay system with an

assay range from 0.1 to 20 mg/L. For values > 1mg/L the CV of the assay is

<5%.

- 44 -

2.3.2 Genetic Tests Genomic deoxyribonucleic acid (DNA) was extracted and purified from

EDTA whole blood samples. The region of intron 1 in the estrogen receptor-α

gene containing the PvuII polymorphism (T to C point mutation) was amplified

with primers described elsewhere145. The polymerase chain reaction (PCR)

product was digested with PvuII restriction endonuclease (Promega, USA)

and electrophoresed in 1.5% agarose gel. TET labelled primers were used to

amplify the region upstream of the ER containing the TA microsatellite. The

PCR product was electrophoresed in pre-heated (55°C) 6% polyacrylamide

gel. Samples were visualised on the Hitachi FMBIO (Tokyo, Japan) using a

585nm filter. PCR product lengths of 160 to 196 bases were obtained,

equating to 10 to 28 TA repeats.

A 227 base pair region of the ApoE gene that spans polymorphic sites

at codons 112 and 158 results in a number of cutting sites for the CFo1

restriction endonuclease146.This region was amplified by PCR using

previously described primers. Restriction digests were electrophoresed on

20% acrylamide gels, resulting in DNA fragments unique for each isotype and

coded ApoE2, ApoE3 and ApoE4, as previously described elsewhere147.

2.4 B-Mode Carotid Ultrasound Examination 2.4.1 Image Acquisition

The same sonographer performed all of the carotid imaging for

assessment of carotid IMT and the presence of focal plaque.

Images were acquired using an 8.0 mHz linear-array transducer fitted to an

Acuson Sequoia 512 ultrasound machine. Images were recorded onto TDK

Super-VHS XP180 cassettes using an in-built Sony Super-VHS video-

cassette recorder. A standard image acquisition protocol was followed as

detailed by Salonen and Salonen148.

- 45 -

Figure 2.2: Photograph of the carotid ultrasound technique. The subject is

reclined, the head is tilted approximately 30 degrees away from the

transducer. The transducer is placed to obtain a longitudinal view of the

common carotid artery.

Figure 2.3: Photograph of the distal common carotid artery showing the flow

divider which is the bifurcation of this vessel to form the internal and external

carotid arteries.

______________________________________________________________

- 46 -

The subject lies flat with his/her head turned approximately 30 degrees

away from the side to be measured (see figure 2.2). He/she is connected to a

three-lead ECG, the output of which is continuously displayed on the Sequoia

monitor.

The ultrasound transducer is placed on the right side of the neck to

obtain a transverse image of the common carotid artery (CCA), once the

artery is located the transducer is rotated 90 degrees to obtain a longitudinal

view. The distal 2 cm of the CCA is targeted for measurements of IMT. The

distal end of this vessel is defined by the origin of the carotid bulb, where the

artery dilates and the walls are no longer parallel. The carotid bulb ends at the

flow divider which is the point of bifurcation of the common carotid to become

the internal and external carotid arteries (see figure 2.3). The transducer is moved rostrally and caudally to find a straight

segment of distal CCA that is positioned parallel across the screen and that

has clearly identifiable far wall interfaces throughout its length. To achieve this

subtle adjustments of transducer pressure, angle and rotation and adjustment

of gain settings are often required. The far wall is used for IMT assessment.

The intimal-medial thickness is the distance between the first leading edge

(first bright line; lumen-intima interface) and the second leading edge (second

bright line; media-adventitia interface) (see figure 2.4). Images of the CCA are taken from 3 different angles (anterolateral,

lateral and posterolateral) to account for the possibility of asymmetrical wall

thickening. If focal plaque is present in the distal CCA then an area as close

as possible to this site is chosen. Intima-medial thickness can vary with the

cardiac cycle, the images are therefore ECG-gated and end-diastolic images

are always used (gated on the ECG R-wave). In some cases a straight

segment of suitable CCA with measurable IMT interfaces cannot be found

usually because of vessel tortuosity, heavy plaque burden or poor ultrasound

images or a combination of these factors. In this situation it is not possible to

record an IMT measurement in one or more of the desired views or on one or

both sides of the neck.

- 47 -

Figure 2.4: Photograph of the IMT Interfaces of the distal common carotid

artery. Leading edge 1 is the interface between the lumen and intima, leading

edge 2 is the interface bewteen the media and adventitia.

Figure 2.5: Photograph of the distal common carotid artery showing a focal

plaque which is a region of focal thickening ≥ 1mm.

______________________________________________________________

- 48 -

The selected images need to be enlarged prior to recording. To do this

the resolution function is used which involves placing a “box” around the

relevant arterial segment. Images of each of the three sites are recorded on

super VHS tape for subsequent off-line analysis. Once IMT images have been recorded, the entire carotid tree (CCA,

Carotid bulb, internal and external carotid) is examined for the presence of

focal plaque. This is defined as a clearly identified area of focal thickening (≥

1mm) of the intimal-medial layer149 (see figure 2.5). After assessment of IMT and focal plaque on the right, this entire process is

repeated on the left side.

2.4.2 Image Capture

The videotaped images are digitised using a commercial frame-grabber

package (Creative Labs, Milpitas California) with 8-byte 256 grey scale and a

486/66 MHz desktop computer (Acer). Images suitable for analysis are frozen

on-screen, the technician then chooses images from anterolateral, lateral and

posterolateral projections which are saved to bitmap (.bmp) files for

subsequent measurement.

2.4.3 Image analysis

A semi-automated edge-detection software program (developed by Dr

B Bailey, Royal Prince Alfred Hospital, Sydney) is used for image analysis.

This program automatically identifies intimal and medial points from the areas

of interest of the far wall of the distal common carotid artery. The distance

between the characteristic echoes from the lumen-intima (leading edge 1) and

media-adventitia (leading edge 2) interfaces is the intimal-medial thickness.

Files saved from the frame grabber program (containing digitised images) are

opened within the IMT program for analysis. The area for analysis is selected

along an approximately l cm segment of the distal common carotid artery. The

software will eliminate incorrect points, but manual elimination of other points

is sometimes required to ensure that the program has correctly identified the 2

interfaces. Once the technician is satisfied that the edge-detection points are

accurate the mean, minimum, maximum and standard deviation (SD) of the

measurements are recorded on data entry sheets.

- 49 -

2.4.4 Data Entry

For each case, IMT data (mean, maximum, minimum and SD of

measurements for each of the 3 views on both sides) and plaque data

(presence or absence of focal plaque) were entered on a data entry sheet. All

of these data were then entered into a Microsoft Access database together

with identification data (name, CAIFOS ID, date of study).

2.4.5 Management of Abnormal Results Women who had occlusive carotid disease (≥ 50% lumen

encroachment by plaque) were notified of this result and their general

practitioner was contacted for further management.

2.4.6 Carotid Ultrasound Reproducibility

For all subjects a single sonographer obtained carotid images and a

separate individual measured IMT using semi-automated edge-detection

software. A short-term precision study was undertaken using the same

combination of operators (see figure 2.6). Twenty non-trial subjects were

selected and repeat IMT measurements made between 0 and 31 days apart

(mean 10.3 days). The coefficient of variation for the repeat measures was

5.98% (calculated using the root-mean –square method150 (RMS-CV)).

2.4.7 Carotid Ultrasound Data Analysis

The average of the 6 mean measurements (3 from each side) was

used as the measurement of carotid IMT in subsequent analyses and is

referred to as “mean IMT” in subsequent chapters. In those individuals who

only had measurements from one side then the average of 3 mean

measurements was used.

- 50 -

Number = 20

RMS-CV = 5.98%

Measurement 1

1.0.9.8.7.6.5.4

Mea

sure

men

t 2

1.0

.9

.8

.7

.6

.5

.4 Rsq = 0.8703

Figure 2.6: Scatter diagram with a fit line reflecting the correlation between

repeat carotid IMT measures. Measurements were performed on 20 non-

study subjects an average of 10 days apart.

______________________________________________________________ 2.5 Statistical Analysis-General Comments

Normality for continuous variables was assessed through visual

inspection of the histogram, stem and leaf plots and through measurement of

skewness and kurtosis. Variables that had a skewed distribution were

transformed to their natural logarithm for comparison of means (student’s t-

test), analysis of variance (ANOVA) and for entry into a generalised linear

model (GLM). Means presented for these variables (IMT, estradiol, SHBG,

FEI, glycated haemoglobin, homocysteine and CRP) are geometric means.

Comparison of means between two groups was conducted using the

student’s t-test for independent samples. Comparison of means between

more than two categorical variable groupings was conducted using analysis of

variance (ANOVA). If the ANOVA yielded a significant p-value, the observed

- 51 -

significance rate was adjusted for multiple comparisons using the Bonferroni

correction.

Comparison of proportions was performed using the Pearson Chi-

square test. For those comparisons in which the expected number in one or

more cells (of a Chi square table) was less than 5, the Fisher’s exact test was

used (eg: proportion of women using vaginal estrogen in those with and

without FEI measurement).

Bivariate correlations were performed using Spearman’s Rho Rank

given that some of the continuous variables were not normally distributed.

To examine for independent determinants of a continuous outcome

variable (eg: mean IMT, CRP), variables that had a significant univariate

relationship with the dependent variable and other variables thought to be

biologically important were entered into a GLM. Homogeneity of variance

across groups of categorical variables was confirmed using Levene’s test,

observed vs predicted residual plots for the dependent variable were also

inspected. A backward model-building strategy was used to eliminate non-

significant variables from the model using a p-value of 0.05 as the threshold

for exit from the model. Many of the explanatory variables had a small amount

of missing data, at each step of backward elimination from the GLM all of the

available cases with a complete dataset contributed to the analysis, such that

the numbers of cases increased as the modelling progressed.

To examine for independent determinants of a dichotomous outcome

variable (eg: presence of focal plaque), variables that had a significant

univariate relationship with the dependent variable and other variables

thought to be biologically important were entered into a logistic regression

model. A backward model-building strategy was used to eliminate non-

significant variables from the model using a p-value of 0.05 as the threshold

for exit from the model.

Power calculations were made for the relationship between genotypes

and carotid atherosclerosis. Power calculations for presence of

carotid plaque suggest that we would be able to detect an OR between

affected and not-affected of 2.0 with good power for alleles with >10%

frequency under a dominant model (see tables 2.1 through 2.3). Our study

- 52 -

also has acceptable power to detect common (frequency>30%) variants

acting on risk of carotid plaque given a recessive effect.

Table 2.1: Power to Detect OR≥2.0 in ApoE Group

Table 2.2: Power to Detect OR≥2.0 in Pvu II Group

Allele frequency a Exposure (Dominant / Recessive SNP) b

Plaque 225 affected / 208 NA

10% 19% / 1% 95.3% / Low 20% 36% / 4% 98.7% / Low 30% 51% / 9% 98.5% / 79.6% 40% 64% / 16% 96.2% / 93.0% 50% 75% / 36% 90.5% / 98.7% a Allele frequency in controls. b Exposure (=prevalence) in ‘non-affected’ assuming a diallelic locus with a dominant or recessive allele at Hardy Weinberg equilibrium.

Table 2.3: Power to Detect OR≥2.0 in TA Repeat Group

Allele frequency a Exposure (Dominant / Recessive SNP) b


10% 19% / 1% 99.9% / Low 20% 36% / 4% 100% / 86.4% 30% 51% / 9% 100% / 99.1% 40% 64% / 16% 99.9% / 99.9% 50% 75% / 36% 99.9% / 100% a Allele frequency in controls. b Exposure (=prevalence) in ‘non-affected’ assuming a diallelic locus with a dominant or recessive allele at Hardy Weinberg equilibrium.

Allele frequency a Exposure (Dominant / Recessive SNP)b


10% 19% / 1% 94.8% / Low 20% 36% / 4% 98.4% / Low 30% 51% / 9% 98.3% / 78.6% 40% 64% / 16% 96.2% / 92.4% 50% 75% / 36% 89.7% / 98.4% a Allele frequency in controls. b Exposure (=prevalence) in ‘non-affected’ assuming a diallelic locus with a dominant or recessive allele at Hardy Weinberg equilibrium.

- 53 -

Power calculations for carotid IMT suggest that we would be able to

detect relatively modest differences of 0.3 SD between those bearing the

phenotype-associated allele and those who do not with good power for alleles

with >10% frequency under a dominant model (see tables 2.4 through 2.6).

Our study also has acceptable power to detect common (frequency>30%)

variants acting on carotid IMT in a recessive fashion.

Table 2.4: Power to Detect a Difference of 0.3SD between Genotypes in ApoE Group Allele freq. a

Exposure (Dom / Rec SNP) b

IMT (n=1108) c

10% 19% / 1% 97.5% / Low 20% 36% / 4% 99.8% / Low 30% 51% / 9% 99.9% / 81.5% 40% 64% / 16% 99.8% / 95.4% 50% 75% / 36% 99.1% / 99.1% a Allele frequency in entire cohort. b Exposure (=prevalence) in cohort assuming a diallelic locus with a dominant or recessive allele at Hardy Weinberg equilibrium. c Power to detect a difference of 0.3SD between subjects not possessing a copy of the phenotype-associated allele and subjects possessing at least one copy (dominant) or two copies (recessive) of the phenotype-associated allele.

Table 2.5: Power to Detect a Difference of 0.3SD between

Genotypes in PvuII Group

Allele freq. a


IMT (n=433) c

10% 19% / 1% 68.3% / Low 20% 36% / 4% 85.0% / Low 30% 51% / 9% 87.7% / Low 40% 64% / 16% 84.9% / 62.9% 50% 75% / 36% 76.8% / 77.1% a Allele frequency in entire cohort. b Exposure (=prevalence) in cohort assuming a diallelic locus with a dominant or recessive allele at Hardy Weinberg equilibrium. c Power to detect a difference of 0.3SD between subjects not possessing a copy of the phenotype-associated allele and subjects possessing at least one copy (dominant) or two copies (recessive) of the phenotype-associated allele.

- 54 -

Table 2.6: Power to Detect a Difference of 0.3SD Between Genotypes in TA Repeat Group

Allele freq. a


IMT (n=418) c

10% 19% / 1% 67.2% / Low 20% 36% / 4% 83.8% / Low 30% 51% / 9% 86.5% / Low 40% 64% / 16% 83.6% / 61.3% 50% 75% / 36% 75.4% / 75.7% a Allele frequency in entire cohort. b Exposure (=prevalence) in cohort assuming a diallelic locus with a dominant or recessive allele at Hardy Weinberg equilibrium. c Power to detect a difference of 0.3SD between subjects not possessing a copy of the phenotype-associated allele and subjects possessing at least one copy (dominant) or two copies (recessive) of the phenotype-associated allele.

Statistical significance for all analyses was taken as a two-sided p

value <0.05. Analyses were performed using SPSS for Windows version 11

(SPSS Inc, Chicago).

- 55 -

CHAPTER 3. CHARACTERISTICS OF THE STUDY SUBJECTS 3.1 Characteristics of the Study Sample: Statistics

Mean ± SD was calculated for all important continuous variables for the

total study sample (plaque group) and the group with adequate IMT

assessment. The number and percentage of women within the unfavourable

dichotomous variable groupings was also presented in a tabulated form.

These calculations were then repeated for those with and without FEI

measurement, ERα genotyping and CRP measurement, the measured and

unmeasured groups were then compared using the student’s t-test for

independent samples (for continuous variables) and the Chi-square test (for

dichotomous variables) in order to examine for sub-group selection bias.

Comparison of categorical variables for purposes of examining risk

factor clustering was performed using the Chi-Square test. Means of

continuous variables in different categorical variables groupings were

compared using the student’s t-test for independent samples (eg: comparison

of mean pulse pressure in those with and without a history of cardiovascular

disease). Continuous variables were correlated with each other using the

Spearman’s Rho Rank.

3.1.1: Statistics: Missing Data

Missing data has been handled using the SPSS default settings which

results in either pair-wise or list-wise exclusion of cases depending on which

statistical test is used. For analysis of descriptives (ie mean, median, standard

deviation etc), the number of non-missing values were used. For frequencies,

missing values were excluded and percentages were based on the number of

non-missing values. Correlations were computed based on the number of

pairs with non-missing data (pair-wise deletion of missing data). For logistic

regression if any of the variables were missing, the entire case was excluded

from the analysis (list-wise deletion of missing data). For the GLM, only cases

with a complete dataset were analysed at each step but because variables

with missing data were removed at some steps the number of cases in the

analysis increased between the start of modelling to the production of the best

- 56 -

multivariate model. Missing data was analysed using the SPSS missing value

analysis (MVA), to examine whether list-wise exclusion of missing values

affected means and standard deviations and whether data was missing at

random.

3.2 Characteristics of Study Sample: Results

The baseline characteristics of the CAIFOS – Cardiovascular Sub-

study subjects are shown in table 3.1. The characteristics of the overall group

(1149 women) and the group with carotid IMT assessment (1130 women)

were virtually identical. The mean (geometric mean) and median FEI in the

plaque group were 46.8 and 47.0, the corresponding values in the IMT group

were 46.5 and 46.9. For the purposes of presenting the characteristics of the

total study sample the total group of 1149 women will be used, the

characteristics of the IMT group will not be presented separately. The number

of women who had estrogen receptor α (433women) and CRP (92 women)

analyses were significantly fewer and therefore their characteristics will be

presented separately. Given the importance of FEI measurement and

inclusion in subsequent analyses, the characteristics of those with and without

FEI measurement have also been presented separately. 3.2.1 Characteristics of Total Study Sample

The present study sample were predominantly Caucasian with 68.1%

recording Australia as their country of birth, 17.2% were born in The United

Kingdom and the remaining 14.7% were born in a wide range of other

countries. The mean age was 75.2 years (± SD 2.7, range 70.2 to 82.2) and

subjects were on average 27.1 years (± 6.5, range 12.7 to 53.2) from the

- 57 -

Table 3.1: Baseline Characteristics of The Study Subjects

Group With Plaque Assessment

(total n=1149)

Group With IMT Assessment

(total n=1130)

Variable

Number with valid

data

Mean (SD) or n(%)

Number with valid

data

Mean (SD) or n(%)

Age, y 1149 75.2 (2.7) 1130 75.2 (2.7) Time From Menopause, y 1140 27.1 (6.5) 1123 27.1 (6.5) Sex Hormone Status Estradiol, ρmol/L 1135 23.5 (16.1) 1117 23.4 (16.1) SHBG, ηmol/L 1040 50.4 (24.5) 1025 50.5 (24.5) FEI 1038 46.8 (53.8) 1025 46.5 (53.6) Use of Vaginal Estrogen, n(%)

1149 18 (1.6) 1130 18 (1.6)

Blood Pressure Systolic BP, mmHg 1115 137.4 (18.1) 1097 137.4 (18.1) Diastolic BP, mmHg 1115 73.1 (11.0) 1097 73.2 (11.0) Pulse Pressure, mmHg 1115 64.3 (15.2) 1097 64.3 (15.2) Hypertension, n(%) 1115 382 (34.3) 1097 378 (34.5) Plasma Lipids Total Cholesterol, mmol/L 1067 5.9(1.1) 1051 5.9(1.1) LDL-C, mmol/L 1059 3.7(1.0) 1044 3.7(1.0) HDL-C, mmol/L 1067 1.4(0.4) 1051 1.5(0.4) Triglycerides, mmol/L 1067 1.6(0.7) 1051 1.6(0.7) Hypercholesterolaemia (>5.5 mmol/L), n(%)

1067 656(61.5) 1051 645(61.4)

Low HDL (<1.0 mmol/L), n(%)

1067 106(9.9) 1051 103(9.8)

History Hyperlipidaemia, n(%)

1149 212 (18.5) 1130 209 (18.5)

Cigarette Smoking Smoking Exposure, py 1144 7.3 (17.3) 1125 7.1 (17.1) Ever Smoked, n(%) 1144 404 (35.3) 1125 391 (34.8) Body Habitus Body Mass Index, kg/m2 1146 27.1 (4.5) 1127 27.0 (4.5) Obese (BMI>30 kg/m2) 1146 253 (22.1) 1127 246 (21.8) Glycaemia Glycated Haemoglobin 1072 5.2 (0.7) 1054 5.2 (0.7) Diabetes Mellitus n(%) 58 (5.0) 1130 55(4.9) Vascular Disease IHD, PVD or Stroke, n(%) 1149 141 (12.3) 1130 137 (12.1) Therapy ACEI or ARB, n(%) 1149 219 (19.1) 1130 216 (19.1) Statin, n(%) 1149 206 (17.9) 1130 203 (18.0) Anti-platelet Agent, n(%) 1149 311 (27.1) 1130 306 (27.1) Beta-Blocker, n(%) 1149 186 (16.2) 1130 183 (16.2) Other Alcohol Consumption, g/d 1141 6.1 (8.8) 1122 6.1 (8.8) Homocysteine 1005 11.4 (4.7) 989 11.4 (4.7)

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menopause. With respect to other conventional risk factors, 34.3 % had

measured hypertension, 22.1 % were obese, 35.3 % had a history of

smoking, 4.3% were current smokers, 61.5% had measured

hypercholesterolaemia, 18.5 % had self-reported hyperlipidaemia and 5.0%

had self-reported diabetes at baseline. At study commencement 12.3 % gave

a history of cardiovascular disease (IHD, stroke or PVD) and eighteen women

(1.6%) were using vaginal estrogen preparations.

3.2.1.1 Characteristics of the Total Study Sample: Missing Data

There was a small amount of missing data for many of the explanatory

variables and for carotid IMT. For IMT, 19 women (1.7%) could not be

adequately assessed and for FEI, 111 women (9.7%), did not have both

SHBG and estradiol measured so that FEI could be calculated. The following

variables also had a small amount of missing data; history of smoking (5

missing, 0.4%) blood pressure (34 missing, 3.0%), homocysteine (144

missing, 12.5%), glycated haemoglobin (77 missing, 6.7%), BMI (3 missing,

0.3%) and cholesterol (82 missing, 7.1%). As a result of this patchy missing

data the overall numbers in multivariate models was reduced, for example 952

out of a possible 1130 women (84.2%) contributed to the best generalised

linear model for carotid IMT. The means and standard deviations of the

explanatory variables which have some missing data are shown in table 3.2.

There is little difference in the mean values and standard deviations for

inclusion of all cases compared to when list-wise exclusion of cases is used

(see table 3.3 and 3.4). The pattern of missing variables appears to be

random (see table3.5), the maximum number of cases with 2 missing

variables was only 39 (3.4%) and the maximum number of cases with 3

missing variables was only 6 (0.5%).

- 59 -

Table 3.2 Statistics for Variables with Missing Data: Univariate Statistics

Missing

N Mean Std. Deviation Count %

FEI 1038 46.80 53.82 111 9.7 Pulse pressure 1115 64.28 15.24 34 3.0 Homocysteine 1005 11.40 4.73 144 12.5 HbA1c 1072 5.22 0.68 77 6.7 BMI 1146 27.10 4.49 3 0.3 Cholesterol 1067 5.90 1.09 82 7.1 Smoking 1144 5 0.4 Table 3.3 Statistics for Variables with Missing Data: Summary of Estimated Means

FEI Pulse Pressure

Homocysteine HbA1c BMI Cholesterol

List-wise

47.29

63.51 11.42 5.24 27.23 5.91

All Values

46.80

64.28 11.40 5.22 27.10 5.90

Table 3.4 Statistics for Variables with Missing Data: Summary

of Estimated Standard Deviations

FEI Pulse pressure

Homocysteine HbA1c BMI Cholesterol

List-wise 55.10 14.99 4.70 0.71 4.35 1.06 All Values

53.82 15.24 4.73 0.68 4.49 1.09

- 60 -

Table 3.5 Statistics for Variables with Missing Data: Tabulated Patterns Missing Patternsa Complete

if ...b

Number of Cases

BMI Smoking PP HbA1c CHOL FOI Homocysteine

807 807 37 X 844 9 X X 868 15 X 822 1 X X 880 57 X 864 3 X X 971 104 X 911 22 X X 970 1 X X X 1031 4 X X X 998 1 X X X 899 2 X X 873 27 X 834 39 X X 930 2 X X X 949 1 X X 850 2 X X 940 6 X X X 1045 1 X X X X 1065 1 X X X 933 2 X 809 3 X 810 1 X X 915 1 X X 848

Patterns with less than 0.05% cases are not displayed.

a: Variables are sorted on missing patterns.

b: Number of complete cases if variables missing in that pattern (marked with X) are

not used.

3.2.1.2 Characteristics of the Total Study Sample: Risk Factor Clustering

The correlations for continuous risk variables are shown in table 3.6.

Increased age was associated with increased pulse pressure, increased

homocysteine and as one would expect, a greater time since menopause.

Those greater than the median age at baseline (74.9 years) were more likely

to have a history of cardiovascular disease (16.0% vs 8.5%, Chi-square 15.0,

- 61 -

p<0.001) and to use anti-platelet agents (30.8% vs 23.3%, Chi-square 8.2,

p=0.004).

Increased systolic blood pressure was associated with increased body

mass index and increased glycated haemoglobin (table 3.6). Hypertensive

(BP>140/90) women were more likely to use ACE inhibitor or ARB

medications (27.5% vs 14.9%, Chi-square 25.8, p<0.0001) and beta-blockers

(19.9% vs 14.6%, Chi-square 5.1, p= 0.02). Obese women were more likely to

have low HDL-cholesterol (15.0% vs 8.4%, Chi-square 8.9, p= 0.003).

Increased BMI was associated with reduced HDL-cholesterol, increased

triglycerides, blood pressure, homocysteine and glycated haemoglobin (table

3.6).

Women with greater than the median glycated haemoglobin were more

likely to be hypertensive (36.1% vs 30.1%, Chi-square 4.3, p=0.04), have low

HDL-cholesterol (10.6% vs 6.9%, Chi-square 4.1, p=0.04). to have smoked

cigarettes (38.7% vs 32.1%, Chi-square 5.0, p=0.02), to be obese (26.3% vs

16.2%, Chi-square 16.1, p<0.001) to have self-reported hyperlipidaemia (22%

vs 13.7%, Chi-square 12.4, p<0.001) and to have a baseline history of

cardiovascular disease (14.1% vs 8.6%, Chi-square 7.9, p=0.005). These

women were more likely to be taking ACEI or ARB medications (22.0% vs

15.3%, Chi-square 7.7, p=0.005) and statins (21.3% vs 13.7%, Chi-square

10.6, p=0.001).

Women with a baseline history of self-reported diabetes had a higher

glycated haemoglobin level (6.6 vs 5.2, p<0.001) than those without diabetes.

These women were more likely to be obese (37.9% vs 21.2%, Chi-square 8.9,

p=0.003) and have a history of hyperlipidaemia (36.2% vs 17.5%, Chi-square

12.8, p<0.001). They were also more likely to take ACEIs or ARBs (44.8% vs

17.7%, Chi-square 26.3, p<0.0001) and statins (32.8% vs 17.1%, Chi-square

9.1, p=0.003).

Women with a history of hyperlipidaemia were more likely to have a

history of cardiovascular disease (24.5% vs 9.5%, Chi-square 36.3,

p<0.0001). They were also more likely to use ACEI or ARB medications

(28.8% vs 16.9%, Chi-square 16.0, p<0.001), statins (95.8% vs 0.3%, Chi-

square 1070, p<0.001) and anti-platelet agents (50% vs 21.9%, Chi-square

69.3, p<0.001).

- 62 -

Table 3.6: Bivariate Correlations for Continuous Risk Variables (Spearman’s Rho Rank) Age Time

from meno

SBP DBP PP Homo Hb A1c

Age Corr Coeff

1.000 .481 .025 -.092 .077 .094 .015

p-value . <.001** .398 .002** .010* .003** .621 Number 1149 1140 1115 1115 1115 1005 1072 Time from meno

Corr Coeff

.481 1.000 .012 -.057 .033 .051 -.017

p-value <.001** . .692 .057 .268 .106 .582 Number 1140 1140 1107 1107 1107 998 1063 SBP Corr

Coeff .025 .012 1.000 .494 .776 .024 .062

p-value .398 .692 . <.001** <.001** .460 .043* Number 1115 1107 1115 1115 1115 976 1052 DBP Corr

Coeff -.092 -.057 .494 1.000 -.099 -.013 .040

p-value .002** .057 <.001** . .001** .692 .192 Number 1115 1107 1115 1115 1115 976 1052 PP Corr

Coeff .077 .033 .776 -.099 1.000 .025 .037

p-value .010* .268 <.001** .001** . .428 .228 Number 1115 1107 1115 1115 1115 976 1052 Homo Corr

Coeff .094 .051 .024 -.013 .025 1.000 -.026

p-value .003** .106 .460 .692 .428 . .417 Number 1005 998 976 976 976 1005 955 HbA1c Corr

Coeff .015 -.017 .062 .040 .037 -.026 1.000

p-value .621 .582 .043* .192 .228 .417 . Number 1072 1063 1052 1052 1052 955 1072 Alcohol-g/d

Corr Coeff

-.015 .021 -.064 .005 -.080 -.115 -.046

p-value .606 .489 .033* .880 .008** <.001** .137 Number 1141 1132 1110 1110 1110 998 1065 Pack-Yrs Corr

Coeff -.028 .058 -.041 -.034 -.031 .006 .049

p-value .343 .052 .171 .261 .308 .839 .109 Number 1141 1135 1107 1107 1107 999 1065 BMI Corr

Coeff -.053 -.053 .113 .104 .050 .136 .172

p-value .074 .074 <.001** .001** .097 <.001** <.001** Number 1146 1137 1112 1112 1112 1002 1069 Total Chol

Corr Coeff

-.019 -.029 .004 .092 -.051 .014 -.040

p-value .542 .349 .896 .003** .101 .673 .208 Number 1067 1060 1038 1038 1038 932 993 LDL Corr

Coeff -.028 -.056 -.024 .082 -.070 .025 -.047

p-value .358 .069 .436 .009** .025* .438 .139 Number 1059 1052 1030 1030 1030 927 986 HDL Corr

Coeff -.005 .049 -.006 .014 -.021 -.088 -.126

p-value .875 .111 .835 .653 .503 .007** <.001** Number 1067 1060 1038 1038 1038 932 993 Tg Corr

Coeff .040 -.003 .051 .009 .047 .076 .153

p-value .194 .924 .101 .776 .128 .020* <.001** Number 1067 1060 1038 1038 1038 932 993

- 63 -

Table 3.6: Bivariate Correlations for Risk Variables Alcohol-g/d Pack-

Yrs BMI Total Chol LDL HDL TG

Age Corr Coeff -.015 -.028 -.053 -.019 -.028 -.005 .040

p-value .606 .343 .074 .542 .358 .875 .194 Number 1141 1141 1146 1067 1059 1067 1067 Time from meno

Corr Coeff .021 .058 -.053 -.029 -.056 .049 -.003

p-value .489 .052 .074 .349 .069 .111 .924 Number 1132 1135 1137 1060 1052 1060 1060 SBP Corr Coeff -.064 -.041 .113 .004 -.024 -.006 .051

p-value .033 .171 <.001** .896 .436 .835 .101

Number 1110 1107 1112 1038 1030 1038 1038 DBP Corr Coeff .005 -.034 .104 .092 .082 .014 .009

p-value .880 .261 .001** .003** .009** .653 .776 Number 1110 1107 1112 1038 1030 1038 1038 PP Corr Coeff -.080 -.031 .050 -.051 -.070 -.021 .047

p-value .008** .308 .097 .101 .025* .503 .128 Number 1110 1107 1112 1038 1030 1038 1038 Homo Corr Coeff -.115 .006 .136 .014 .025 -.088 .076

p-value <.001** .839 <.001** .673 .438 .007** .020* Number 998 999 1002 932 927 932 932 HbA1c Corr Coeff -.046 .049 .172 -.040 -.047 -.126 .153

p-value .137 .109 <.001** .208 .139 <.001** <.001** Number 1065 1065 1069 993 986 993 993 Alcohol-g/d

Corr Coeff 1.000 .207 -.079 .047 .006 .187 -.071

p-value . <.001** .008** .130 .851 <.001** .020* Number 1141 1133 1138 1059 1051 1059 1059 Pack-Yrs Corr Coeff .207 1.000 -.017 .002 -.021 .054 .011

p-value <.001** . .576 .950 .497 .078 .722

Number 1133 1141 1138 1059 1051 1059 1059 BMI Corr Coeff -.079 -.017 1.000 -.022 .021 -.277 .257

p-value .008** .576 . .471 .499 <.001** <.001** Number 1138 1138 1146 1065 1057 1065 1065 Total Chol Corr Coeff .047 .002 -.022 1.000 .936 .112 .289

p-value .130 .950 .471 . <.001** <.001** <.001** Number 1059 1059 1065 1067 1059 1067 1067 LDL Corr Coeff .006 -.021 .021 .936 1.000 -.068 .210

p-value .851 .497 .499 <.001** . .026* <.001**

Number 1051 1051 1057 1059 1059 1059 1059 HDL Corr Coeff .187 .054 -.277 .112 -.068 1.000 -.497

p-value <.001** .078 <.001** <.001** .026* . <.001** Number 1059 1059 1065 1067 1059 1067 1067 Tg Corr Coeff -.071 .011 .257 .289 .210 -.497 1.000

p-value .020* .722 <.001** <.001** <.001** <.001** . Number 1059 1059 1065 1067 1059 1067 1067

* Significant at the 0.05 level. ** Significant at the 0.01 level.

- 64 -

3.2.1.3 Characteristics of the Total Study Sample: Sources of Bias

and Limitations of Study Sample The present study has certain limitations, as is the case with all cross-

sectional study designs, there is a possibility of selection bias. The final

CAIFOS cohort of 1500 women represents only a small percentage (6%) of

the total number of women approached, raising concerns that it may not be

representative of the population of ambulatory women over the age of 70

years in Perth Western Australia. It is possible that women who responded to

the initial contact and then showed further interest in the study may be a more

health-conscious group and potentially have more favourable health-related

behaviours than the majority of women who did not reply or show further

interest once contacted by phone.

We excluded women who were unlikely to survive a 5-year study,

which will likely have excluded women with end-stage or advanced illness

including end-stage CVD in favour of a healthier group of women. This

however, cannot be viewed as a source of bias, as our intention was to

produce a study sample representative of relatively healthy ambulatory elderly

women. Women taking bone active agents including HRT were also excluded

from participation in the study. It is possible that this will have introduced bias

opposite in direction to those factors already stated. As mentioned previously,

women taking HRT may have a more favourable cardiovascular risk profile

and health-related behaviours. The exclusion of these women may have

selected a group with a worse cardiovascular profile and less healthy health

behaviours.

The data relating to previous medical history and medication use relies

on the ability of an elderly individual to recollect the past thus potentially

degrading the quality of these data. In addition, as with all observational

studies, it is possible that un-measured variables may confound the results.

We attempted in subsequent analyses to minimize this possibility by including

all variables that might have a biologically plausible relationship to the

dependant variable in multivariate modelling.

- 65 -

3.2.1.4 Characteristics of the Total Study Sample: Discussion

This is a study sample of elderly females reflecting the inclusion

criterion that women be greater than 70 years at entry. There is however a

very narrow age range of only 12 years, one would therefore expect difficulty

in demonstrating significant associations with increasing age. Despite this

increasing age does correlate with increasing pulse pressure and

homocysteine. Other studies have also demonstrated an increase in pulse

pressure with increasing age151. As mentioned previously, age is one of the

major risk factors for cardiovascular disease, it is therefore not surprising that

older individuals more frequently gave a history of cardiovascular disease at

baseline and were more frequently on anti-platelet agents, possibly for

secondary prevention of cardiovascular disease.

The strength of the association between higher levels of glycated

haemoglobin and unfavourable levels of other risk factors is a little

unexpected given that we are not dealing with a diabetic or pre-diabetic

population but rather a relatively healthy group of women with an average

glycated haemoglobin of only 5.2. There appeared to be clustering of

increased BMI with increased glycaemia, hypertension, reduced HDL and

elevated triglycerides. This combination of risk factors is characteristic of the

metabolic syndrome, which is related to a sedentary lifestyle and poor diet,

the major feature is insulin resistance and it is a major predictor of

cardiovascular events and the development of overt diabetes mellitus152.

We found a 5% prevalence of self-reported diabetes. This is likely to

under-represent the true prevalence of this condition in our population, results

from the Ausdiab study suggest a prevalence of 6.6% in Australian women

between 65 and 74 years of age and 8.8% in women 75 years or older153.

This difference in prevalence may be related to the method of data collection,

we relied on a self-reported history of diabetes rather than a biochemical

diagnosis.

- 66 -

3.2.2 Characteristics of Subjects with and Without Free Estradiol Index Measurement

A significant number of women did not have a FEI measurement (111,

9.7%) therefore the characteristics of those women with and without FEI

measurement are presented separately (see table3.7). Those women with FEI

assessment were on average 0.5 years older than those without FEI

assessment, had a higher diastolic pressure by 3.9mmHg and a lower pulse

pressure by 5.3mmHg, they had 11.8% lower prevalence of hypertension.

They had higher total and LDL-cholesterol (0.8mmol/L and 0.6mmol/L higher

respectively), 15.6% greater prevalence of hypercholesterolaemia but 8.2%

lower prevalence of low HDL. Their glycated haemoglobin was higher by 0.2

units. There were no significant differences in triglyceride concentration, BMI

or the prevalence of obesity. Although there was no significant difference in

mean IMT or history of cardiovascular disease, those women without FEI

assessment had 19.9% lower prevalence of focal plaque. The differences in risk factors between those with and without FEI

measurement appear balanced, this finding would make it difficult to establish

whether one group was higher risk for atherosclerosis and cardiovascular

disease than another. However the 20% lower prevalence of plaque in those

without FEI suggests that this group may have less advanced atherosclerosis

than those women who had an FEI measurement. It would seem unlikely that

this difference reflects a selection bias as there is no clear reason why those

women who did not have either estradiol or SHBG or both measured should

have a reduced prevalence of plaque. It is likely that this difference occurred

by chance. The prevalence of plaque in those with FEI assessment is very

similar to the prevalence for the overall study sample, the same holds true for

all of risk factor prevalences and for mean IMT. Therefore it is likely that

analyses performed in the FEI group will be representative of the overall study

sample.

- 67 -

Table 3.7: Characteristics of subjects with and without Free Estradiol Index Measurement

Group with FEI (total n=1038)

Variable

Group With Plaque

Assessment (n=1149)

Mean(SD) or n(%)

Nb Mean(SD) or n(%)

Group Without FEI

(total n=111)

Mean(SD) or n(%)

p-valuea

Mean IMT (mm) 0.77(0.13) 1024 0.77(0.13) 0.76(0.12) 0.30 Presence of focal plaque n(%)

569(49.5) 1038 53.4(51.4) 35(31.5) <0.001**

Age, y 75.2 (2.7) 1038 75.2(2.7) 74.7(2.5) 0.04* Time From Menopause, y

27.1 (6.5) 1032 27.2(6.5) 25.9(6.1) 0.05

Sex Hormone Status

Use of Vaginal Estrogen, n(%)

18 (1.6) 1038 17(1.6) 1(0.9) 1.0 (fisher’s exact)

Blood Pressure Systolic BP, mmHg 137.4 (18.1) 1008 137.3(18.1) 138.7(18.0) 00.44

Diastolic BP, mmHg 73.1 (11.0) 1008 73.5(10.7) 69.6(12.9) 0.001**

Pulse Pressure, mmHg

64.3 (15.2) 1008 63.8(15.1) 69.1(15.6) 0.001**

Hypertension, n(%) 382 (34.3) 1008 334(33.1) 48(44.9) 0.02*

Plasma Lipids Total Cholesterol, mmol/L

5.9(1.1) 1005 5.9(1.1) 5.1(1.4) <0.001**

LDL-C, mmol/L 3.7(1.0) 997 3.7(1.0) 3.1(1.0) <0.001** HDL-C, mmol/L 1.4(0.4) 1005 1.4(0.4) 1.4(0.5) 0.56 Triglycerides, mmol/L 1.6(0.7) 1005 1.5(0.7) 1.4(0.8) 0.07

Hypercholesterolaemia (>5.5 mmol/L), n(%)

656(61.5) 1005 627(62.4) 29(46.8) 0.01*


106(9.9) 1005 95(9.5) 11(17.7) 0.03*


212 (18.5) 1038 194(18.7) 18(16.2) 0.52

Cigarette Smoking

Smoking Exposure, py 7.3 (17.3) 1033 7.2(17.1) 8.4(19.0) 0.47

Ever Smoked, n(%) 404 (35.3) 1033 362(35.0) 42(37.8) 0.56

Body Habitus Body Mass Index, kg/m2

27.1 (±4.5) 1036 27.1(4.4) 27.2(5.1) 0.88

Obese (BMI>30 kg/m2) 253 (22.1) 1036 227(21.9) 26(23.6) 0.68

Glycaemia Glycated Haemoglobin 5.2 (0.7) 962 5.2(0.7) 5.0(0.6) 0.02*

Diabetes Mellitus n(%) 58 (5.0) 1038 54(5.2) 4(3.6) 0.46

Vascular Disease

IHD, PVD or Stroke, n(%)

141 (12.3) 1038 129(12.4) 12(10.8) 0.62

Other Alcohol Consumption, g/d

6.1 (8.8) 1032 6.1(8.9) 5.2(7.5) 0.28

Homocysteine 11.4 (4.7) 905 11.4(4.8) 11.2(3.4) 0.43 a: p-value represents comparison of groups with and without FEI measurement, student’s t-test for independent

samples used for continuous variables, Chi-square test used for comparing proportions (unless otherwise specified).

b: N represents the number of subjects with both FEI and explanatory variable data.

* Significant at the 0.05 level. ** Significant at the 0.01 level.

- 68 -

3.2.3 Characteristics of Estrogen Receptor Alpha Sub-group As detailed previously, 433 women were analysed for ERα genotype.

The characteristics of this group are displayed in table 3.8. Women who had

ERα genotyping were less likely to have low HDL than other women, however

the mean HDL in the two groups was very similar. There were no other

significant differences between the groups including no difference in mean

IMT, history of cardiovascular disease or prevalence of focal plaque. These

findings suggest that the group with ERα genotyping is likely to be

representative of the total study sample.

3.2.4 Characteristics of C-Reactive Protein Sub-group As detailed previously, 100 women underwent assessment of CRP and

92 were included in the analysis. The characteristics of this group are shown

in table 3.9. Women who had CRP assessment were on average 0.5 years

younger but 1.4 years further from the menopause than other women,

however these differences were not significant. They had a higher prevalence

of hypercholesterolaemia although their mean total cholesterol was not

significantly different from women without CRP assessment. The mean LDL-

Cholesterol was 0.2 mmol/L higher than women without CRP assessment.

There were trends toward lower BMI, pulse pressure and mean HDL and

lower prevalences of obesity and baseline cardiovascular disease in the CRP

subgroup. Although there was no difference in prevalence of focal plaque,

women with CRP measurement had 0.03 mm thinner intimal-medial layer.

This finding may suggest that the CRP group has a lower atherosclerotic

burden than the remainder of the study sample.

- 69 -

Table 3.8: Characteristics Subjects with and without Estrogen Receptor Alpha Genotyping

Group With ERα (n=433)

Variable

Group With Plaque

Assessment (n=1149)

Mean(SD) or

n(%)

Nb

Mean(SD)

or n(%)

Group Without

ERα (n=716)

Mean(SD)

or n(%)

p-valuea

Mean IMT (mm) 0.77(0.13) 429 0.78(0.12) 0.76(0.12) 0.11 Presence of focal plaque n(%)

569(49.5) 433 225(52.0) 344(48.0) 0.20

Age, y 75.2 (±2.7) 433 75.1(2.6) 75.2(2.7) 0.41 Time From Menopause, y 27.1 (±6.5) 431 26.9(6.4) 27.2(6.6) 0.38 FEI 46.8 (±53.8) 389 45.9(53.2) 47.3(54.2) 0.53 Blood Pressure Systolic BP, mmHg 137.4 (±18.1) 424 138.0(19.3) 137.0(17.3) 0.42 Diastolic BP, mmHg 73.1 (±11.0) 424 73.3(11.0) 73.0(11.0) 0.62 Pulse Pressure, mmHg 64.3 (±15.2) 424 64.6(15.2) 64.1(15.3) 0.55 Hypertension, n(%) 382 (34.3) 424 150(35.4) 232(33.6) 0.54 Plasma Lipids Total Cholesterol, mmol/L 5.9(±1.1) 370 5.9(0.9) 5.8(1.1) 0.16 LDL-C, mmol/L 3.7(±1.0) 369 3.8(0.9) 3.7(1.1) 0.09 HDL-C, mmol/L 1.4(±0.4) 370 1.4(0.3) 1.4(0.4) 0.74 Triglycerides, mmol/L 1.6(±0.7) 370 1.5(0.7) 1.6(0.7) 0.45 Hypercholesterolaemia (>5.5 mmol/L), n(%)

656(61.5) 370 242(65.4) 414(59.4) 0.06


106(9.9) 370 23(6.2) 83(11.9) 0.003**


212 (18.5) 433 76(17.6) 136(19.0) 0.54

Cigarette Smoking Smoking Exposure, py 7.3 (±17.3) 431 7.7(17.9) 7.1(16.9) 0.56 Ever Smoked, n(%) 404 (35.3) 432 164(38.0) 240(33.7) 0.14 Current Smoker, n(%) 49(4.3) 432 15(3.5) 34(4.8) 0.29 Body Habitus Body Mass Index, kg/m2 27.1 (±4.5) 431 27.2(4.3) 27.0(4.6) 0.64 Obese (BMI>30 kg/m2) 253 (22.1) 431 101(23.4) 152(21.3) 0.39 Glycaemia Glycated Haemoglobin 5.2 (±0.7) 417 5.2(0.6) 5.3(0.7) 0.96 Diabetes Mellitus n(%) 58 (5.0) 433 17(3.9) 41(5.7) 0.18 Vascular Disease IHD, PVD or Stroke, n(%) 141 (12.3) 433 45(10.4) 96(13.4) 0.13 Other Alcohol Consumption, g/d 6.1 (±8.8) 433 6.2(8.5) 6.0(8.9) 0.64 Homocysteine 11.4 (±4.7) 403 11.4(4.7) 11.4 (4.7) 0.82

a: p-value represents comparison of groups with and without ERα measurement,

student’s t-test for independent samples used for continuous variables, Chi-square

test used for comparing proportions (unless otherwise specified).

b: N represents the number of subjects with both ERα and explanatory variable data.

** Significant at the 0.01 level.

- 70 -

Table 3.9: Characteristics of Subjects with and without C-Reactive Protein Analysis

Group included in CRP analysis (total n=92)

Variable

Group With Plaque

Assessment (total n=1149)

Mean(SD) or

n(%)

Nb

Mean(SD) or n(%)

Group not included in

CRP analysis (total n=1057)

Mean(SD) or

n(%)

p-valuea

Mean IMT (mm) 0.77(0.13) 92 0.74(0.12) 0.77(0.13) 0.02* Presence of focal plaque n(%)

569(49.5) 92 52(52.0) 517(49.3) 0.60

Age, y 75.2 (±2.7) 92 75.6(2.8) 75.1(2.6) 0.10 Time From Menopause, y

27.1 (±6.5) 92 28.4(6.5) 27.0(6.5) 0.05

FEI 46.8 (±53.8) 92 45.6(40.3) 46.9(54.9) 0.36 Blood Pressure Systolic BP, mmHg 137.4 (±18.1) 87 135.3(16.6) 137.6(18.2) 0.26 Diastolic BP, mmHg 73.1 (±11.0) 87 74.0(11.0) 73.0(11.0) 0.43 Pulse Pressure, mmHg 64.3 (±15.2) 87 61.3(13.3) 64.5(15.4) 0.06 Hypertension, n(%) 382 (34.3) 87 24(27.6) 358(34.8) 0.17 Plasma Lipids Total Cholesterol, mmol/L

5.9(±1.1) 92 6.0(1.0) 5.9(1.1) 0.33

LDL-C, mmol/L 3.7(±1.0) 92 3.9(0.9) 3.7(1.0) 0.03* HDL-C, mmol/L 1.4(±0.4) 92 1.37(0.3) 1.45(0.4) 0.05 Triglycerides, mmol/L 1.6(±0.7) 92 1.6(0.7) 1.6(0.7) 0.92 Hypercholesterolaemia (>5.5 mmol/L), n(%)

656(61.5) 92 67(72.8) 589(60.4) 0.02*


106(9.9) 92 9(9.8) 97(9.9) 0.96


212 (18.5) 92 20(21.7) 192(18.2) 0.40

Cigarette Smoking Smoking Exposure, py 7.3 (±17.3) 92 5.9(14.3) 7.4(17.6) 0.42 Ever Smoked, n(%) 404 (35.3) 92 28(30.4) 376(35.7) 0.31 Body Habitus Body Mass Index, kg/m2 27.1 (±4.5) 92 26.3(4.4) 27.2(4.5) 0.07 Obese (BMI>30 kg/m2) 253 (22.1) 92 13(14.1) 240(22.8) 0.06 Glycaemia Glycated Haemoglobin 5.2 (±0.7) 83 5.1(0.7) 5.2(0.7) 0.63 Diabetes Mellitus n(%) 58 (5.0) 92 7.0(7.6) 51(4.8) 0.24 Vascular Disease IHD, PVD or Stroke, n(%)

141 (12.3) 92 6.0(6.5) 135(12.8) 0.08

Other Alcohol Consumption, g/d

6.1 (±8.8) 91 6.1(9.1) 6.0(8.8) 0.96

Homocysteine 11.4 (±4.7) 74 11.4(3.7) 11.4(4.8) 0.81 a: p-value represents comparison of groups with and without CRP measurement,

student’s t-test for independent samples used for continuous variables, Chi-square

test used for comparing proportions (unless otherwise specified).

b: N represents the number of subjects with both CRP and explanatory variable data.

* Significant at the 0.05 level.

- 71 -

CHAPTER 4. ASSOCIATIONS OF FREE ESTRADIOL INDEX 4.1 Associations of Free Estradiol Index: Background

Measures of obesity are consistently the strongest determinants of

estrogen levels in post-menopausal women25,8. There is single-study data

suggesting that age and alcohol consumption also affect estrogen levels25.

However we know very little about the determinants of estrogen level in

women over the age of 70 years or the relationship between endogenous

estrogen and CHD risk factors in postmenopausal women. In order to

examine the relationship between endogenous estrogen and atherosclerosis it

is important to understand the determinants of estrogen and its relationship

with risk factors for atherosclerosis. This chapter will examine these

relationships in the majority of the CAIFOS cardiovascular sub-study subjects.

4.2 Associations of Free Estradiol Index: Statistics

Free estradiol index was treated as a continuous variable, given its

skewed distribution a geometric mean is presented and 95% confidence

interval calculated. Correlations between FEI and other continuous variables

(eg: BMI, HDL-cholesterol)) were performed using the Spearman rho Rank

test. To examine the possibility that variance overlap between several

variables and FEI could be explained by their overlap with BMI, partial

correlations between these variables and FEI were calculated while correcting

for BMI. Those variables that were not normally distributed (including FEI)

were transformed to their natural logarithm for calculation of partial

correlations.

The mean level of FEI in each dichotomous cardiovascular risk variable

grouping (eg: those with and without baseline hypertension) was compared

using the student’s t-test for independent samples.

4.3 Associations of Free Estradiol Index: Results

The mean FEI was 46.8 (95% CI: 44.6 to 49.1) with a very wide range

of values (3.1 to 485.0). The frequency distribution for FEI is shown in figure

4.1, as is the case for plasma SHBG and estradiol, it is positively skewed.

- 72 -

FEI

480.0440.0

400.0360.0

320.0280.0

240.0200.0

160.0120.0

80.040.0

0.0

Freq

uenc

y

300

200

100

0

Figure 4.1: Histogram showing the positively skewed frequency distribution of

FEI.

______________________________________________________________

Correlations of FEI with other continuous variables are shown in table 4.1.

There is a moderate positive correlation between FEI and body mass index,

weak positive correlations with glycated haemoglobin, systolic blood pressure

and triglyceride level and very weak positive correlations with DBP, pulse

pressure and homocysteine. There is a weak negative correlation with HDL-

Cholesterol, there are very weak negative correlations with age and years

since menopause suggesting a tendency for estrogen levels to fall as women

get older and further from menopause. There was no association between FEI

and smoking or alcohol consumption.

- 73 -

Table 4.1: Correlation of Free Estradiol Index with Continuous CHD Risk Variables

Variable

Correlation Coefficient (Spearman Rho rank)

P Value

Age, y -0.08 0.01 Time From Menopause, y -0.07 0.03 Blood Pressure Systolic BP, mmHg 0.12 <0.001 Diastolic BP, mmHg 0.07 0.03 Pulse Pressure, mmHg 0.09 0.005 Plasma Lipids Total Cholesterol, mmol/L 0.004 0.906 LDL-C, mmol/L 0.005 0.871 HDL-C, mmol/L -0.257 <0.001 Triglycerides, mmol/L 0.325 <0.001 Cigarette Smoking Smoking Exposure, py 0.04 0.15 Body Habitus Body Mass Index, kg/m2 0.48 <0.001 Glycaemia Glycated Haemoglobin 0.24 <0.001 Other Alcohol Consumption, g/d -0.008 0.81 Homocysteine 0.08 0.02

Body mass index correlates with many of these variables as follows;

SBP (r=0.11, p<0.001), DBP (r=0.10, p<0.001), HDL (r=-0.28, p<0.001),

triglycerides (r=0.26, p<0.001), glycated haemoglobin (r=0.17, p<0.001) and

homocysteine (r=0.14, p<0.0001). It may therefore be expected that some of

the variance overlap between these variables and FEI may be explained by

their overlap with BMI. To investigate this possibility, partial correlations were

calculated to determine the correlation between these variables and FEI

controlling for BMI. The partial correlation coefficients were as follows; SBP;

r= 0.07, p=0.03, DBP; r= -0.01, p=0.83, HDL; -0.15, p<0.0001, triglycerides

r=0.27, p<0.0001, glycated haemoglobin; 0.16, p<0.0001, homocysteine; r=

0.14, p=0.24. These results show that there is no longer a significant

correlation between FEI and DBP or homocysteine when corrected for BMI. In

addition the magnitude of the correlations between FEI and SBP, HDL,

- 74 -

triglycerides and glycated haemoglobin are all reduced but remain significant

when corrected for BMI.

The association between FEI and cardiovascular risk variables is

shown in table 4.2. The mean estrogen level in obese women was almost

double that in non-obese women. Women with a baseline history of diabetes

also had significantly higher mean FEI, this relationship remained significant

even after adjustment for baseline obesity (p=0.009). Estrogen levels were not

significantly different in women with hypertension, a history of smoking, self-

reported hyperlipidaemia, measured hypercholesterolaemia or baseline

cardiovascular disease compared to women without these risk factors.

Table 4.2: The Association between Free Estradiol Index and Dichotomous Cardiovascular Risk Factors

Risk Variable

Mean FEI P Value (t-test for means of

independent samples)

Hypertension No 45.3 0.09 Yes 50.9 Ever smoked No 45.3 0.38 Yes 49.5 Obesity No 40.2 <0.001 Yes 79.4 Hypercholesterolaemia No 46.8 0.20 Yes 46.2 Self reported hyperlipidaemia

No 46.2 0.94

Yes 49.8 History of diabetes mellitus No 45.9 0.001 Yes 66.9 Baseline CVD No 46.1 0.37 Yes 52.3 As mentioned previously, 17 women were using vaginal estrogen preparations

at baseline. The mean FEI for women using vaginal estrogen was 46.7 and for

those not using vaginal estrogen 53.2. Although those using vaginal estrogen

had higher plasma estrogen levels the difference was not statistically

significant (p=0.58).

- 75 -

4.4 Associations of Free Estradiol Index: Discussion There was a moderately strong association between endogenous

estrogen and adiposity, but only weak associations with other cardiovascular

risk factors. The relationship between FEI and adiposity (obese women had

almost double the level of FEI as their non-obese counterparts) is not

surprising given the moderate correlation between BMI and FEI and is

consistent with the notion that adipose tissue functions as a factory for

postmenopausal estrogen production from androgenic steroids25,8. Even after

adjusting for BMI, FEI still correlated with SBP, HDL-cholesterol, triglycerides

and glycated haemoglobin. However these findings may not represent a true

relationship between estrogen and these factors independent of adiposity.

This is because BMI may not represent a perfect measure of adiposity, it is

possible that both FEI and BMI are functioning as markers of obesity.

There was evidence of an extremely weak association between FEI

and SBP that was of borderline significance and may represent a chance

finding. There is no mechanistic support for a detrimental effect of estrogen on

blood pressure, the weight of evidence from previous studies suggests a

neutral or beneficial effect of exogenous estrogen on blood pressure68,69,70,71.

We found that FEI was negatively associated with HDL and positively

associated with triglycerides. This is a surprising finding given that no

consistent association has been found between non-oral estrogens and these

parameters in other studies3. This is at least partially explained by the

relationship between FEI and adiposity, as the strength of these associations

diminished after controlling for BMI. There is very little data relating post-

menopausal endogenous estrogen level to lipid level and no data relating FEI

to lipid level. We found no association between FEI and LDL which is

consistent with the lack of a consistent association between transdermal

estrogen and LDL in previous studies3.

The finding that those women with a baseline history of diabetes had

higher levels of endogenous estrogen, even after adjustment for BMI was

unexpected. This finding has not been previously demonstrated and is not

currently supported by a plausible biological mechanism. We found that

- 76 -

vaginal estrogen was associated with higher levels of FEI, however this

difference was not statistically significant.

- 77 -

CHAPTER 5. ASSOCIATION OF C-REACTIVE PROTEIN WITH FREE ESTRADIOL INDEX AND ESTABLISHED

CARDIOVASCULAR RISK FACTORS 5.1 Associations of C-Reactive Protein: Background

There is now a large body of evidence linking oral HRT to elevated

CRP in post-menopausal women21,58,60. This association may be important in

the context of CHD and atherosclerosis given substantial evidence that CRP

is an independent predictor of CHD events6. There is very little data relating

endogenous estrogen to markers of inflammation, It is possible that

endogenous estrogen may also be pro-inflammatory after menopause which

may have an impact on the relationship of endogenous estrogen with

atherosclerosis and CHD events.

Obesity is consistently the most important determinant of CRP49,51,

while this inflammatory marker has also been associated with all of the

traditional CHD risk factors in other populations, these relationships have not

been tested in elderly women.

We have examined the association of CRP with FEI, BMI and

established risk factors in a sub-group who were randomly selected across

the four quartiles of FEI levels.

5.2 Associations of C-Reactive Protein: Statistics

High sensitivity CRP was treated as a continuous variable, given its

skewed distribution a geometric mean is presented and 95% confidence

interval calculated. Correlations between CRP and other continuous variables

(including FEI) were sought using the Spearman Rho Rank. Free estradiol

index was also divided into quartiles and the mean levels of CRP in each

quartile compared using ANOVA. The result was then adjusted for multiple

comparisons using the Bonferroni correction. Significant correlations with CRP

were further examined for confounding by BMI, this was achieved by entering

the explanatory variable (eg: age, HDL-cholesterol) and BMI into a GLM.

Variables that were not normally distributed were transformed to their natural

logarithm for performance of the t-test, ANOVA and entry into a GLM.

- 78 -

The mean level of CRP in each dichotomous cardiovascular risk

variable grouping (eg: those with and without baseline hypertension) was

compared using the student’s t-test for independent samples.

To examine for independent determinants of CRP (transformed to its

natural logarithm), the following variables were entered into a GLM; age, time

from menopause, use of vaginal estrogen, FEI, systolic blood pressure, pulse

pressure, homocysteine, glycated haemoglobin, alcohol consumption,

smoking history, BMI, medication use (anti-platelet agents, beta-blockers,

statins, ACEIs and ARBs), history of CVD, self-reported diabetes, LDL, HDL,

triglycerides. The best multivariate model for CRP was produced using a

backward model-building strategy, non-significant variables were removed

from the model. Interactions between important explanatory variables were

then sought within the GLM (eg: FEI and BMI).

5.3 Associations of C-Reactive Protein: Results 5.3.1 Univariate Associations of C-Reactive Protein

One hundred women had CRP measurements (25 chosen at random

from each quartile of FEI), as mentioned previously 8 women with CRP >10

mg/dl were excluded, leaving 92 included in analyses. The mean CRP was

2.3 mg/L (95% CI: 2.0 to 2.7 mg/L) with a wide range of values (0.2 to 9.0).

The frequency distribution for CRP is shown in figure 5.1, as was the case for

the sex hormones, it is positively skewed.

Correlations of CRP with other continuous variables are shown in table

5.1. There was a moderate positive correlation between CRP and BMI, there

was a moderate but highly significant positive univariate correlation between

CRP and FEI in this group. The mean lnCRP (natural logarithm of CRP) for

each quartile of FEI (quartiles based on FEI values for the total study sample)

is represented in figure 5.2. The ANOVA was highly significant (F=10.7,

p<0.001). After adjustment for multiple comparisons (Bonferroni correction),

the following quartile comparisons were statistically significant; 1 versus 3

(p<0.001), 1 versus 4 (p<0.001), and 2 versus 4 (p=0.02). There was a weak

positive correlation between age and CRP, this relationship however did not

persist after adjustment for BMI in a multivariate model (p=0.15) .There was a

- 79 -

High Sensitivity CRP (mg/L)

9.008.50

8.007.50

7.006.50

6.005.50

5.004.50

4.003.50

3.002.50

2.001.50

1.00.50

0.00

Freq

uenc

y

20

10

0

Figure 5.1: Histogram showing the positively skewed frequency distribution of

CRP.

ANOVA, p<0.001

Quartiles of FEI

4321

Mea

n ln

CR

P

1.4

1.2

1.0

.8

.6

.4

.2

Figure 5.2: Mean lnCRP (natural logarithm of CRP) by quartile of FEI. This

graph demonstrates a gradual increase in lnCRP across the full range of FEI.

Only the 92 subjects with CRP ≤10mg/L were included.

______________________________________________________________

- 80 -

weak negative correlation between HDL-cholesterol and CRP, this

relationship however did not persist after adjustment for BMI in a multivariate

model (p=0.09), there was a trend for reduced HDL with increasing BMI (r= -

0.19, p=0.07).

The association between CRP and categorical cardiovascular risk

variables is shown in table 5.2. Obesity had a significant association with

CRP, obese women had a significantly greater CRP than their non-obese

counterparts. Those taking anti-platelet agents had a lower CRP than those

not taking anti-platelet agents. There was a significantly greater proportion of

women taking anti-platelet agents who had a history of cardiovascular disease

compared to those not using these agents (3.1% vs 14.3%, p=0.046).

Table 5.1: Correlates of C-Reactive Protein

Variable


P Value

Age, y 0.21 0.045 Time From Menopause, y -0.16 0.12 FEI 0.47 <0.001 Blood Pressure Systolic BP, mmHg -0.03 0.77 Diastolic BP, mmHg -0.02 0.84 Pulse Pressure, mmHg 0.032 0.77 Plasma Lipids Total Cholesterol, mmol/L 0.16 0.13 LDL-C, mmol/L 0.17 0.11 HDL-C, mmol/L -0.22 0.04 Triglycerides, mmol/L 0.18 0.08 Cigarette Smoking Smoking Exposure, py -0.06 0.65 Body Habitus Body Mass Index, kg/m2 0.44 <0.001 Glycaemia Glycated Haemoglobin 0.17 0.12 Other Alcohol Consumption, g/d 0.02 0.86 Homocysteine -0.09 0.45

- 81 -

Table 5.2: Association between CRP and Dichotomous Cardiovascular Risk Variables

Risk Variable

Number Mean CRP (mg/dl)

p- value

Hypertension No 63 2.29 0.60 Yes 24 2.09 Ever smoked No 64 2.31 0.99 Yes 28 2.31 Current Smoker No 88 2.32 0.88 Yes 4 2.19 Obesity No 79 2.11 0.003 Yes 13 4.00 Hypercholesterolaemia (measured)

No 25 2.01 0.27

Yes 67 2.43 Self reported hyperlipidaemia No 72 2.32 0.92 Yes 20 2.28 History of diabetes mellitus No 85 2.29 0.61 Yes 7 2.65 Baseline CVD No 86 2.33 0.63 Yes 6 2.01 Anti-platelet agents No 64 2.67 0.003 Yes 28 1.66

5.3.2 Multivariate Associations of C-Reactive Protein The best multivariate model for lnCRP contained 3 variables; lnFEI (B=

0.36, p<0.001), BMI (B= 0.05, p=0.001) and the use of anti-platelet agents

(B= 0.37, p=0.005); together these explained 38.1% of the variance in CRP.

FEI predicted CRP independent of BMI, there was no interaction between FEI

and BMI in the prediction of CRP (p-value for interaction 0.98).

5.4 Associations of C-Reactive Protein: Discussion

We have demonstrated a significant relationship between CRP and FEI

that persisted after adjustment for BMI, this has not been previously

demonstrated in any population. The limited available (indirect) data suggests

that endogenous estrogen should have little effect on CRP60,61,62, however no

previous study has investigated the relationship between endogenous

estrogen levels and CRP in elderly women. It may well be that endogenous

estrogen, like oral estrogen therapy has pro-inflammatory effects in this

population. This notion is further supported by the observation that FEI was a

- 82 -

highly significant independent determinant of CRP in multivariate modelling

that included both established and more novel CHD risk factors. The

mechanism for the increase in CRP is not clear as estrogen has not been

shown to increase levels of inflammatory cytokines that stimulate hepatic

production of CRP (interleukin 6 or interleukin 1)59. It has been postulated that

oral estrogen may directly stimulate CRP synthesis by the liver as part of a

“first-pass” effect rather than acting through increased IL-6 production3. While

it is tempting to conclude that endogenous estrogen after the menopause is

pro-inflammatory, one must be cautious in reaching this conclusion. It is

possible that the relationship between FEI and adiposity explains the

relationship of FEI with CRP. This is because BMI does not necessarily

equate to “fatness” or adiposity but is rather just one measure of it. It is

therefore conceivable that FEI and BMI are just different markers of the

number of fat cells in these post-menopausal women.

In previous studies, measures of obesity have demonstrated the

strongest associations with CRP51. This is likely explained by the observation

that adipose tissue is a major source of interleukin-6 (IL-6) and tumour

necrosis factor (TNF) which are drivers of hepatic synthesis of CRP53,54. It is

therefore no surprise that BMI and obesity were the strongest predictors of

CRP in our study.

We found that there was a weak linear relationship between age and

CRP despite the narrow age-range in this sample (71 yo to 81yo). This

suggests that there is a continued rise in CRP even after the age of 70 years.

When adjusted for BMI, this relationship was no longer significant suggesting

that an age- related increase in adiposity explained much of the age-related

increase in CRP. The data show decreasing CRP concentrations with

increasing HDL-cholesterol which has been demonstrated previously in

younger populations154,50,155. This relationship was no longer significant when

adjusted for BMI, suggesting that the negative association between BMI and

HDL confounded the relationship between CRP and HDL.

The use of anti-platelet agents (aspirin, clopidogrel) was associated

with lower CRP independent of other risk factors. This association has not

previously been demonstrated in post-menopausal women. There is evidence

that both clopidogrel and aspirin are more efficacious in patients with elevated

- 83 -

CRP156,157,158, suggesting that these agents act at least partially through anti-

inflammatory mechanisms. Despite this finding, studies that have examined

the short and long-term effects of anti-platelet agents on CRP concentrations

in different populations have yielded inconsistent results, some suggesting no

effect159,160,161,162,163. It is conceivable that given the advanced age of our

group, a possible age-related higher mean CRP concentration would make a

significant therapy-related (anti-platelet agent related) change in levels more

likely. Against this notion is that use of statins, that predictably cause a

reduction in CRP in other populations164,165,166, was not associated with lower

CRP in our subjects. In addition, some studies have failed to demonstrate an

aspirin-related reduction in CRP in the setting of acute inflammatory states

such as unstable angina167, during which the CRP is abnormally elevated to

levels above those in our study. There was a significantly greater proportion

of women on anti-platelet agents that had a self-reported history of CVD

compared to those not taking these agents, despite this finding, anti-platelet

medications were still associated with lower CRP levels. This may be because

a history of CVD did not significantly influence CRP levels (possibly due to

small numbers with CVD in this group (6)). While the relationship between

anti-platelet agents and CRP may be a chance finding, it is also possible that

this class of drugs has unique anti-inflammatory actions in elderly post-

menopausal women.

- 84 -

CHAPTER 6. DETERMINANTS OF CAROTID ATHEROSCLEROSIS

6.1 Determinants of Carotid Atherosclerosis: Background

Results of previous studies that have examined the determinants of

carotid atherosclerosis suggest that the established risk factors are important

in both sexes15. Systolic blood pressure and pulse pressure appear to be

more influential than diastolic blood pressure19,20. Age, blood pressure and

LDL-cholesterol are consistently associated with carotid atherosclerosis

whereas the relationship of diabetes, smoking and non-LDL lipids with carotid

atherosclerosis and in particular IMT is less consistent (see table 1.3). It

appears that the established risk factors continue to play a role but are

relatively less important in men and women with advancing years23, however

the data is somewhat limited. The relationship between established risk

factors and carotid atherosclerosis is well established for a range of

populations and in both sexes, however there is little data in elderly

postmenopausal women.

If one considers the summation of data concerning the actions of

estrogen on lipids and non-lipid factors one would predict a favourable effect

on atherosclerosis. However these data largely relate to the actions of oral

HRT which is likely to be different to endogenous estrogen3. The role of

estrogen after menopause is not clear and the available evidence is quite

conflicting. It is possible that the increase in CHD events after menopause and

increased prevalence of atherosclerosis is purely age-related and not due to

estrogen withdrawal3. In recent large clinical trials HRT has not produced the

expected vascular benefits72,73,96,97,98. There is very little known about the

association between endogenous estrogen level and atherosclerosis. Given

these observations it is clear that we need to know more about the

relationship between endogenous estrogen and atherosclerosis after

menopause.

In this chapter I have examined the relationship of established risk

factors and bioavailable endogenous estrogen level with carotid IMT and

- 85 -

plaque prevalence in the majority of the CAIFOS cardiovascular sub-study

subjects.

6.2 Determinants of Carotid Atherosclerosis: Statistics

Only women with an adequate assessment of carotid IMT were

included in IMT analyses. Assessment was considered adequate when at

least one full side (3 measurements from either the left or right side) was

measured. A total of 49 women did not have the full set of 6 measurements, of

these 19 women had inadequate assessments and were therefore excluded

from IMT analyses, the remaining 30 were included in IMT analyses.

Assessment of plaque was possible in all women. Carotid artery-mean IMT was treated as a continuous outcome

variable, carotid plaque was treated as a binary outcome variable (present or

not). Given its mildly skewed distribution, mean IMT is presented as a

geometric mean and was transformed to its natural logarithm for t-tests,

ANOVA and entry into generalised linear models.

The proportion of women with focal plaque in each quartile of mean

IMT was examined using the Chi-square test for linear trend. The ability of

IMT (entered as a continuous variable) to determine the presence of focal

plaque independent of cardiovascular factors was examined in a logistic

regression model. The following factors were entered together with IMT; age,

time from menopause, use of vaginal estrogen, FEI, systolic blood pressure,

pulse pressure, homocysteine, glycated haemoglobin, alcohol consumption,


statins, ACEIs and ARBs), history of CVD, self-reported diabetes, LDL, HDL

and triglycerides.

Mean IMT was correlated with continuous risk variables using

Spearman’s Rho Rank. Continuous variables were also categorized into

quartiles and then plotted against IMT to examine for non-linear threshold

effects. Where threshold effects were demonstrated (eg at median FEI) the

explanatory variable was re-categorized into a dichotomous variable and a

comparison of IMT means between the groups was made using the t-test for

independent samples. The mean IMT in each dichotomous cardiovascular risk

variable grouping (eg: those with and without baseline hypertension) was also

- 86 -

compared using the student’s t-test for independent samples. Women in

quartiles 3 and 4 of FEI had greater IMT than those in quartiles 1 and 2, given

this finding, the mean IMT was compared for those with greater than or equal

to, and less than the median level of FEI using the student’s t-test for

independent samples.

To examine for confounding by obesity in the relationship between FEI

and IMT, BMI and FEI (together with age and years from menopause) were

entered into a GLM as independent variables, IMT was the dependent

variable. All other variables that had a univariate association with both FEI

and IMT were tested in the same manner individually to determine whether

they confounded the relationship between FEI and IMT.

The best multivariate model for mean IMT (transformed to its natural

logarithm) was generated by entering the following variables into a GLM; age,

time from menopause, use of vaginal estrogen, FEI (dichotomised at the

median), systolic blood pressure, pulse pressure, homocysteine, glycated

haemoglobin, alcohol consumption, smoking history, BMI, medication use

(anti-platelet agents, beta-blockers, statins, ACEIs and ARBs), history of CVD,

self-reported diabetes, LDL, HDL and triglycerides. A backward model-

building strategy was used. Interactions between important explanatory

variables were then sought within the GLM.

The percentage of women with focal plaque in each categorical

explanatory variable grouping and in each quartile for continuous explanatory

variables was determined. I then examined for threshold effects for continuous

variables and when these were found (eg at the fourth quartile for FEI), the

variable was re-categorized into a dichotomous variable for analysis of carotid

plaque. The Chi - squared test was used to compare proportions of women

with plaque in the binary variable groupings (eg: proportion of ever-smokers

with plaque vs never-smokers with plaque). Given the observation of a greater

percentage of women with plaque in quartile 4 compared to the other

quartiles, the proportion of women with plaque in this group was compared to

the rest of the study sample using the Chi square test. To adjust for the effect

of obesity, BMI and FEI (together with age and time from menopause) were

entered together into a logistic regression model for focal plaque and an odds

ratio for the fourth quartile of FEI was generated independent of BMI.

- 87 -

The presence of a linear trend in plaque prevalence across the

quartiles of continuous variables (eg: pulse pressure) was assessed using the

Chi square test for trend. These variables were also entered individually as

continuous variables into a logistic regression model to calculate an odds ratio

for the presence of plaque for each unit increase in the variable (eg: the odds

of plaque for each 1mmHg increase in pulse pressure). Similarly the

relationships between categorical risk variables and focal plaque were

examined by entering these variables individually into a logistic regression

model to generate odds ratios for the presence of plaque. All variables that

had a univariate relationship with both FEI and plaque were entered

individually into a logistic regression model to determine whether they

confounded the relationship between FEI and plaque.

The best multivariate model for focal plaque was generated by entering

the following variables into a logistic regression model and executing a

backward model-building strategy; age, time from menopause, use of vaginal

estrogen, FEI (entered as a dichotomous variable), systolic blood pressure,

pulse pressure, homocysteine, glycated haemoglobin, alcohol consumption,


statins, ACEIs and ARBs), history of CVD, self-reported diabetes, LDL, HDL

and triglycerides. Interactions between important explanatory variables were

then sought.

6.3 Determinants of carotid atherosclerosis: Results 6.3.1 Carotid Intimal Medial Thickness and Focal Plaque

The mean carotid IMT was 0.77 mm (95%CI: 0.76 to 0.78), the

frequency distribution of IMT was mildly positively skewed as demonstrated in

figure 6.1. The prevalence of focal plaque was 49.5 %, the prevalence

increased from the lowest (36.5%) to the highest quartile (60.1%) of IMT (test

for linear trend, p<0.001)(figure6.2). After adjustment for age, blood pressure,

cholesterol, BMI, glycated haemoglobin, FEI, history of smoking and

- 88 -

Mean IMT (mm)

2.061.94

1.811.69

1.561.44

1.311.19

1.06.94.81.69.56.44

Freq

uenc

y

300

200

100

0

Figure 6.1: Histogram showing the mildly positively skewed frequency

distribution of mean IMT in 1130 study subjects.

χ2 test for trend, p< 0.001

Quartiles of Mean IMT

4321

% F

ocal

Pla

que

64

62

60

58

56

54

52

50

48

46

44

42

40

38

3634

Figure 6.2: Percentage with focal plaque by quartile of mean IMT.

There is a gradual increase in plaque prevalence across the quartiles of mean

IMT.

______________________________________________________________

- 89 -

history of CVD, mean IMT was independently predictive of the presence of

focal plaque (p<0.001).

6.3.2 Univariate Relationships of Established Risk Factors with Mean Intimal Medial Thickness

The relationships between mean IMT and continuous risk variables can

be found in table 6.1. The following variables had statistically significant,

correlations with IMT; age, pulse pressure, SBP, glycated haemoglobin, LDL-

cholesterol, triglycerides, smoking (pack years) and body mass index. HDL-

cholesterol had a weak negative correlation with mean IMT.

Table 6.1: Univariate Correlation between Intimal Medial Thickness and Continuous Risk Variables

Variable


p-value

Age, y 0.15 <0.001

Time From Menopause, y 0.05 0.08 Sex Hormone Status Estradiol, ρmol/L 0.06 0.06 SHBG, ηmol/L -0.04 0.23 FEI 0.06 0.06 Blood Pressure Systolic BP, mmHg 0.14 <0.001 Diastolic BP, mmHg 0.00 0.97 Pulse Pressure, mmHg 0.16 <0.001 Plasma Lipids Total Cholesterol, mmol/L 0.04 0.08 LDL-C, mmol/L 0.07 0.03 HDL-C, mmol/L -0.08 0.008 Triglycerides, mmol/L 0.07 0.02 Cigarette Smoking Smoking Exposure, py 0.09 0.003 Body Habitus Body Mass Index, kg/m2 0.08 0.01 Glycaemia Glycated Haemoglobin 0.06 0.04 Other Alcohol Consumption, g/d -0.03 0.35 Homocysteine -.01 0.76

- 90 -

The association between categorical cardiovascular risk variables and mean

IMT is shown in table 6.2. Women with a history of previous cigarette smoking

had greater mean IMT than non-smokers, those with hypertension (previously

defined by measurement) had greater IMT than non-hypertensives. There was

no difference in mean IMT between those with or without

hypercholesterolaemia, obesity, self reported hyperlipidaemia, diabetes or

history of CVD.

Table 6.2: The Association between Dichotomous Cardiovascular Risk Variables and Mean Intimal Medial Thickness

Risk Variable

Mean IMT (mm)

p-value (t-test)

Hypertension No 0.76 0.001 Yes 0.79 Ever smoked No 0.76 0.01 Yes 0.78 Obesity No 0.77 0.64 Yes 0.77 Hypercholesterolaemia No 0.76 0.14 Yes 0.77 Self reported hyperlipidaemia No 0.77 0.94 Yes 0.77 History of diabetes mellitus No 0.77 0.83 Yes 0.77 Baseline CVD No 0.77 0.96 Yes 0.77 6.3.3 FEI and Carotid Intimal Medial Thickness

There was a borderline significant rank correlation between FEI and

mean IMT (r = 0.06, p = 0.06). A threshold effect was demonstrated in

univariate analysis; women with greater than the median FEI (47.0) had

significantly greater carotid IMT than those with lower estrogen levels

(0.78mm vs 0.76mm, p= 0.007). The association between mean IMT and

quartiles of FEI is shown in figure 6.3.

The predictive value of having greater than median level of FEI

persisted after adjustment for age, years from menopause and BMI (GLM,

- 91 -

p=0.009). The magnitude of the effect was unchanged after adjustment for

these factors; women with greater than the median FEI (47.0) had significantly

greater carotid IMT than those with lower estrogen levels (0.78mm vs

0.76mm).

Figure 6.3: Mean IMT by Quartile of FEI. There appeared to be a

threshold effect whereby those with FEI above the median (47.0) had greater

IMT than those with lower levels (0.78 vs 0.76mm, p=0.007).

______________________________________________________________

Other variables that had a univariate association with FEI and IMT

were also tested individually to determine whether they confounded the

relationship between FEI and IMT. The p-value remained significant for FEI

when each of these variables was added into a GLM for the prediction of IMT;

systolic blood pressure (p-value for greater than median FEI; 0.01), HDL-

Cholesterol (p-value for greater than median FEI; 0.006), triglycerides (p-value

for greater than median FEI; 0.006) and glycated haemoglobin (p-value for

greater than median FEI; 0.017).

0.725

0.735

0.745

0.755

0.765

0.775

0.785

0.795

0.805

Mean IMT (mm)

1 2 3 4 Quartiles of FEI

- 92 -

6.3.4 Independent Determinants of Mean Intimal Medial Thickness

The best multivariate model for IMT is displayed in table 6.3. Overall

the model was weak, explaining only 4% of the variance of mean carotid IMT.

The magnitude of the effect of having been a smoker vs non-smoker and

having a FEI level greater than or equal to the median vs less than the median

were similar. The adjusted (for other factors) IMT in those with less than the

median FEI was 0.76 mm, the adjusted IMT in those with greater than or

equal to the median FEI was 0.78 mm.

There was no significant interaction between FEI and established risk

factors (including BMI) when these interaction terms were added to the best

multivariate model for carotid IMT.

Table 6.3: The Best Multivariate Model for Mean Intimal Medial Thickness Dependent Variable: ln mean IMT (R2=4.3%)

Parameter B Std. Error

t p-value 95% Confidence Interval

Lower Bound

Upper Bound

FEI below vs above median

-0.026 0.009 -2.769 0.006 -0.044 -0.0076

Never smoker vs ever smoker

-0.027 0.010 -2.742 0.006 -0.046 -0.0077

Age 0.0063 0.002 3.567 <0.001 0.00283 0.0098 Pulse Pressure

0.0012 .000 3.971 <0.001 0.00062 0.0018

LDL 0.011 .005 2.271 0.023 0.0015 0.020 6.3.5 Univariate Relationships of Established Risk Factors with Focal Plaque

Figures 6.4 to 6.15 represent the percentages of women with focal

plaque by quartile of the continuous explanatory variables. Age, BMI,

triglycerides, pack years of smoking and DBP were not predictive of focal

plaque. For the following variables there was an increasing percentage of

women with plaque across quartiles 1 to 4; glycated Hb (χ2 test for trend,

- 93 -

p<0.001), pulse pressure (χ2 test for trend, p<0.001), LDL-cholesterol (χ2 test

for trend, p=0.02) and SBP (χ2 test for trend, p=0.005). The relationship

between HDL-cholesterol and plaque was less consistent with a greater

prevalence of plaque in quartile 2 versus 1, but then a decline in plaque

prevalence in quartiles 2 through 4 (χ2 test for trend, p=0.03). There was a

trend for increased plaque prevalence across the range of total cholesterol

values (χ2 test for trend, p=0.08).

When HDL-cholesterol was treated as a continuous variable in a

logistic regression model, the odds of focal plaque per 1mmol/L increase in

HDL-cholesterol was 0.72 (p=0.04). The odds of focal plaque for every unit

increase in glycated haemoglobin was 1.77 (p<0.001), pulse pressure was

1.02 (p<0.001), LDL-cholesterol was 1.16 (p=0.02) and systolic blood

pressure was 1.01 (p=0.002).

Table 6.4 describes the relationship between categorical risk variables

and focal plaque. Baseline measured hypertension and a baseline history of

smoking, hyperlipidaemia, diabetes mellitus and cardiovascular disease were

all associated with a greater odds of having focal plaque. However baseline

obesity and measured hypercholesterolaemia were not predictive of focal

plaque.

Table 6.4: Univariate Association between Risk Factors and Focal Plaque

Risk Factor

Odds Ratio

95% CI P Value

Hypertension

1.45 1.13 to 1.86 0.003

Ever smoked

1.60 1.26 to 2.05 <0.001

Obesity

1.05 0.79 to 1.39 0.73

Hypercholesterolemia

1.23 0.989 to 1.62 0.06

History of hyperlipidaemia

1.60 1.18 to 2.16 0.002

History of Diabetes Mellitus

2.17 1.24 to 3.8 0.007

History of cardiovascular disease

2.02 1.40 to 2.91 <0.001

- 94 -

χ2 test for trend, p=0.005

Quartiles of SBP

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.4: Percentage with Focal Plaque by Quartile of SBP. This graph

shows a step-up in plaque prevalence across the quartiles of SBP.


Quartiles of DBP

4321

% F

ocal

Paq

ue

60.0

58.0

56.0

54.0

52.0

50.0

48.0

46.0

44.0

42.0

40.0

38.0

Figure 6.5: Percentage with focal plaque by quartile of DBP. There is no

relationship between DBP and plaque prevalence.

- 95 -

χ2 test for trend, p<0.001

Quartiles of Pulse Pressure

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.6: Percentage with focal plaque by quartile of pulse pressure. This

graph shows a step-up in plaque prevalence across the quartiles of pulse

pressure.


Quartiles of Homocysteine

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.7 Percentage with focal plaque by quartile of homocysteine. There

was no significant relationship between homocysteine level and plaque

prevalence.

- 96 -


Quartiles of Glycated Hb

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.8: Percentage with focal plaque by quartile of glycated haemoglobin.

There was a step-up in plaque prevalence from the lowest to the highest

quartile of glycated haemoglobin.


Quartiles of BMI

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.9: Percentage with focal plaque by quartile of BMI. There was no

significant relationship between level of BMI and plaque prevalence.

______________________________________________________________

- 97 -


Quartiles of Total Cholesterol

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.10: Percentage with Focal Plaque by Quartile of Total Cholesterol.

There was a non-significant trend for increased plaque prevalence from the

lowest to the highest quartile of total cholesterol.


Quartiles of LDL

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.11: Percentage with focal plaque by quartile of LDL-cholesterol.

There was a step-up in plaque prevalence from the lowest to the highest

quartile of LDL-cholesterol.

- 98 -


Quartiles of HDL

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.12: Percentage with Focal Plaque by Quartile of HDL-cholesterol.

Despite the increase in plaque prevalence from quartile 1 to quartile 2, overall

there was a significant reduction in plaque prevalence with increasing HDL

level.


Quartiles of Triglyceride

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.13: Percentage with focal plaque by quartile of triglycerides. There

was a non-significant trend for increased plaque prevalence from the lowest

to the highest quartile of triglycerides.

- 99 -


Quartiles of Age

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.14: Percentage with Focal Plaque by Quartile of Age. There was a

non-significant trend for increased plaque prevalence from the lowest to the

highest quartile of age.


Quartiles of Smoking Exposure (Pack Years)

4321

% F

ocal

Pla

que

62

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.15: Percentage with focal plaque by quartile of smoking exposure

(pack years).

- 100 -

Comparison of plaque prevalence in the 4th versus other quartiles

of FEI, p= 0.03

Quartiles of FEI

4321

% F

ocal

Pla

que

60

58

56

54

52

50

48

46

44

42

40

38

Figure 6.16: Percentage with focal plaque by quartile of FEI. Those in the 4th

quartile of FEI had significantly greater plaque prevalence than those with

lower levels.

______________________________________________________________

6.3.6 FEI and Carotid Plaque

The relationship between quartiles of FEI and frequency of focal plaque

is displayed in figure 6.16. There is a threshold effect at the 4th quartile of FEI,

57.0% of women in the fourth quartile of FEI (>79.5) had focal plaque

compared to 49.6% of women in quartiles 1 to 3 (χ2 4.48, p = 0.03). After

adjusting for age, years from menopause and baseline obesity, women in the

4th quartile of FEI were still more likely to have focal plaque 3 years later (OR

1.41, 95% CI: 1.04 to 1.92, p = 0.03).

The following variables had univariate associations with both focal

plaque and FEI and were added individually to a logistic regression model to

- 101 -

determine whether they confounded the relationship between FEI and plaque;

glycated haemoglobin (p-value for upper quartile of FEI; 0.21), pulse pressure

(p-value for upper quartile of FEI; p=0.056), systolic blood pressure (p-value

for upper quartile of FEI; p=0.056) and history of diabetes (p-value for upper

quartile of FEI; p=0.062). Each of these variables confounded the relationship

between FEI and focal plaque with glycated haemoglobin having the greatest

influence.

6.3.7 Independent Determinants of Focal Plaque

The best multivariate model for the prediction of focal plaque is

displayed in table 6.5. The odds ratio for focal plaque was 1.61 for ever

smokers (versus never-smokers) and 2.21 in those with a history of CVD

(versus no history of CVD). The odds ratio for focal plaque was 1.01 for every

1mmHg increase in pulse pressure, 1.23 for every 1mmol/L increase in LDL-

Cholesterol and 1.81 for every 1 unit increase in glycated haemoglobin. When

entered into a logistic regression model with other important risk variables,

FEI was not an independent predictor of focal plaque.

Table 6.5: The Best Multivariate Model for Focal Plaque Explanatory Variable

Odds Ratio

p-value 95% Confidence Interval for OR

Lower Upper Pulse Pressure 1.014 0.002 1.005 1.023 Glycated Haemoglobin

1.813 <0.001 1.427 2.303

Ever Smoker 1.607 0.001 1.219 2.118 LDL 1.228 0.003 1.071 1.407 History of Cardiovascular Disease

2.214 <0.001 1.428 3.434

6.4 Determinants of Carotid Atherosclerosis: Discussion

We found that standard risk factors predict IMT in this sample of elderly

women, however they explain only a small percentage of the variance of IMT

(4.3%). This may be in part due to our study design, in which one - off

measures were related to IMT measured 2 to 3 years later. It is possible that

- 102 -

in some individuals these baseline measures were not representative of the

levels of risk factors in the years prior to baseline or in the time between

baseline and carotid examination due to biological variation, therapeutic

interventions or lifestyle changes, therefore weakening the overall correlation

between risk factors and IMT. We adjusted for the impact of medical therapies

by including major cardiovascular drug groups as variables in multivariate

modelling. Age is a consistent and powerful predictor of IMT21,23, the

multivariate model may have been greatly weakened due to the very narrow

age range of 12 years in our study, therefore limiting the impact of age on

IMT. Another possibility is that in elderly women conventional risk factors are

relatively weak determinants of IMT. There is evidence in men and women for

a reduction in the relative importance of traditional risk factors with increasing

age with respect to atherosclerosis and CHD events, this may represent a

“survivor” effect22,23.

There was a strong relationship between IMT and plaque suggesting a

significant overlap as measures of atherosclerosis in this elderly female

population. This finding is consistent with those studies sited earlier which

also demonstrated a graded association between carotid plaque and

IMT126,127,128. This finding is further supported by the finding that the risk

factor profiles for the two measures were similar. Pulse pressure, smoking

history and LDL-cholesterol were independent determinants of both IMT and

plaque, additionally age was an independent determinant of IMT while

glycated haemoglobin was an independent determinant of plaque prevalence.

Established Risk factors appear to be better determinants of plaque

prevalence than IMT in this population, for example smoking history alone is

associated with 60% greater odds of focal plaque. The difference in the

magnitude of their association with established risk factors, the observation

that a history of CVD independently predicted plaque prevalence but not IMT

and the slightly different risk factor profile suggests that while there is

significant overlap, there are some differences in these tests as measures of

atherosclerosis.

Blood pressure correlated best with IMT and was also an independent

determinant of plaque prevalence. This is consistent with other studies which

show a consistent independent relationship between blood pressure and

- 103 -

carotid atherosclerosis (see table 1.3). Pulse pressure and systolic blood

pressure were more influential than diastolic blood pressure consistent with

evidence for a particular role of systolic hypertension in elderly individuals168.

Pulse pressure persisted in multivariate modelling for both plaque and IMT,

while SBP did not. There is growing evidence that pulse pressure is an

independent predictor of coronary mortality and cardiovascular disease in

elderly females, and a better predictor than systolic or diastolic pressure

alone. An Italian study of 3282 elderly subjects showed that compared to the

first tertile, the third tertile of pulse pressure was associated with a 2.9 times

relative risk of coronary mortality in elderly women, whereas diastolic and

systolic pressure had no effect on mortality169.

The finding of a progressive step-up in plaque prevalence across

quartiles 1-4 of glycated haemoglobin in this group suggests that increases in

glycated haemoglobin even within the “normoglycaemic” range may result in a

greater risk of atherosclerosis. This finding is further strengthened by the

finding that glycated haemoglobin (treated as a continuous variable) persisted

as an important independent determinant of plaque prevalence in multivariate

modelling and had a univariate association with IMT. While it is well

established that there is a graded relationship of both cholesterol and blood

pressure with cardiovascular disease, even within the “normal range” of these

factors, no such relationship has been demonstrated for glycated

haemoglobin. One must remember that we did not examine a diabetic or pre-

diabetic population, only 5% of the women gave a history of diabetes at

baseline and the median glycated haemoglobin was only 5.2%.

In this study there was evidence for a threshold effect on mean carotid

IMT at the median level of FEI, independent of other factors. A similar

relationship was found between FEI and plaque prevalence but at the upper

quartile of FEI, that did not persist in multivariate modelling. This suggests that

higher levels of endogenous estrogen in elderly post-menopausal women may

promote subclinical atherosclerosis. The deleterious effect of estrogen in our

study contrasts with the limited indirect data that suggests no effect of estrone

and estradiol levels on measures of atherosclerosis34,75,82. One possible

explanation for the divergence of results is the use of FEI in our study rather

that estradiol or estrone, measuring bioavailable estrogen may more

- 104 -

accurately reflect the relationship between estrogen and IMT. There is no

documented biological mechanism by which FEI should have a threshold

effect on IMT, raising the possibility that this finding may have occurred by

chance. The relationship however was highly statistically significant with a

magnitude similar to the effect of smoking history and persisted in multivariate

modelling. The levels of estrogen in our study were very low (mean estradiol

23.5 pmol/L) consistent with the normal range of post-menopausal levels

reported elsewhere (<73 pmol/L8), this is very low compared to normal trough

levels of between 146 pmol/L and 183 pmol/L in premenopausal women. It is

possible that a threshold level of estrogen corresponding to the median FEI

(47.0) in our study is required to manifest the detrimental effects of estrogen in

elderly post-menopausal women. One then has to question why endogenous

estrogen should be cardioprotective in pre-menopausal women and yet

promote atherosclerosis in post-menopausal women. It may be that factors

other than estrogen are responsible for the sex difference in CHD incidence

and that estrogen may not be cardioprotective in pre-menopausal women. For

example, there is evidence for an androgen-induced decline in HDL-

cholesterol in males after puberty3, that could also be responsible for the sex

difference in CHD events. In addition the reduction in the sex difference in

mortality from CHD with advancing years appears to be due to a deceleration

in death rates in men rather than an acceleration in female death rates related

to estrogen withdrawal83.

There is emerging evidence for deleterious actions of estrogen

including pro-thrombotic and pro-inflammatory effects. Oral estrogen has been

demonstrated to promote coagulation in the venous system45and there is

some recent evidence for an arterial pro-thrombotic effect dependent on

genotype46,47. Oral estrogen therapy results in increased levels of high

sensitivity CRP7 and given the mounting evidence that there is a dose-

response relationship between level of CRP and risk of CHD events170,171, this

may explain the cardiovascular hazard of HRT in recent trials. C-reactive

protein has a less consistent relationship with measures of atherosclerosis,

however some studies have demonstrated pro-atherosclerotic effects55,56, an

estrogen-related rise in CRP may therefore promote atherosclerosis. These

findings tend to lend mechanistic support to the results of the present study,

- 105 -

however as mentioned previously, the effects of oral and non-oral estrogens

may be very different such that one must be cautious in making this

conclusion.

In our study of elderly, ambulatory post-menopausal women, the

combined influence of established risk factors and bio-available estrogen on

carotid IMT was weak. Established Risk factors appeared to be stronger

determinants of focal carotid plaque than carotid IMT. Endogenous estrogen

above a threshold level appeared to promote carotid atherosclerosis

independent of age, BMI and established risk factors.

- 106 -

CHAPTER 7. APOLIPOPROTEIN E GENE POLYMORPHISM

7.1 Apolipoprotein E Gene Polymorphism: Background Apolipoprotein E genotype is established as an independent

determinant of CHD events10,113 and has predictable effects on lipid levels (E4

unfavourable and E2 favourable107,108,109,110) however its effect on

atherosclerosis is less certain114,115. This is possibly because the effect of

genes on atherosclerosis may be quite different to their effect on

cardiovascular outcomes such as unstable angina or myocardial infarction. In

addition to underlying atherosclerosis, acute events require a combination of

additional processes such as the development of lipid-laden plaques with thin

fibrous caps, acute inflammation, plaque rupture and platelet activation

resulting in vessel occlusion.

There is some evidence that estrogen may influence ApoE gene

expression resulting in a more favourable lipid profile116. It is possible

therefore that the natural decline in estrogen levels with menopause may

modify the relationship of ApoE with lipids and atherosclerosis and that these

relationships will be modified by the level of postmenopausal estrogen.

We have examined the relationship of ApoE genotype with carotid IMT

and plaque in the majority of the study sample. In addition we have

investigated whether this relationship is modified by the level of FEI.

7.2 Apolipoprotein E Gene Polymorphism: Statistics

A χ2 test using a contingency table of observed vs expected genotype

frequencies was used to test for deviation from Hardy-Weinberg

equilibrium172. As performed elsewhere in a younger group of post-

menopausal women, the ApoE polymorphisms were re-grouped to

demonstrate the relative effects of the E2,E3 and E4 alleles on cholesterol

and carotid atherosclerosis110.

The mean levels of important continuous cardiovascular risk variables

(eg: age, pulse pressure, lipids) and FEI were calculated for each of the ApoE

genotypes. The means for the ApoE genotypes were compared using

ANOVA, and if a significant p-value was found inter-group comparisons were

- 107 -

made with the Bonferroni correction for multiple comparisons. The proportions

of women with FEI greater than the median and who were ever-smokers were

compared between ApoE groupings using the Chi-Square test.

The mean IMT was compared between ApoE genotype groupings

using ANOVA. The Chi-square test was used to compare proportions of

women with focal plaque between ApoE groupings. The study sample was

then separated at the median level of FEI. The same tests were then

performed in those with less than the median FEI and in those with greater

than or equal to the median level of FEI to examine whether the relationship of

ApoE with carotid atherosclerosis was modified by FEI level. In order to

further examine whether the level of FEI modified the relationship between

ApoE genotype and either lipids or carotid atherosclerosis, the relevant

interaction terms were placed into GLM or logistic regression models, FEI was

entered as both a continuous and categorical model (dichotomized at the

median level for the total study sample).

It should be noted that for most analyses the total number of cases

contributing to ApoE group (E2+, E3 and E4+) analyses will be fewer than the

number contributing to the overall ApoE genotype analyses because the Apo

E 2/4 genotype grouping does not contribute to 3 groups.

7.3 Apolipoprotein E Gene Polymorphism: Results 7.3.1 Apolipoprotein E Gene Frequencies and Association with Established Cardiovascular Risk Factors

The allele frequencies were as follows; E2: 9.1%, E3: 78.7%, E4:

12.2% (see table 7.1). None of the women was homozygous for E2 and only

21 women (1.9%) were homozygous for E4, the most common genotype was

E3/3 (61.5%). The distribution of genotypes for ApoE was not consistent with

Hardy-Weinberg equilibrium (χ2 =12.9, p~0.02), there were fewer E2

homozygotes and more E4 homozygotes than expected under Hardy-

Weinberg assumptions.

- 108 -

Table 7.1: Apolipoprotein E Genotype Frequencies Apo E Genotype Frequency Percent Cumulative Percent

2/2 0 0.0 0.0 2/3 177 16.0 16.0 2/4 24 2.2 18.1 3/3 681 61.5 79.6 3/4 205 18.5 98.1 4/4 21 1.9 100.0

Total 1108 100.0

The relationship between ApoE genotype and risk variables is shown in

table 7.2. Total cholesterol, LDL-cholesterol and HDL-cholesterol were

significantly related to ApoE genotype. While the association of ApoE

genotype with total and LDL-cholesterol was highly significant (p<0.001), the

association with HDL-cholesterol was more borderline (p=0.02), given the

presence of 5 ApoE sub-groups and therefore multiple between group

comparisons, there is a 23% possibility that the comparison of HDL means

between any 2 sub-groups would yield a p-value <0.05 purely by chance. The

observed significance rate for total cholesterol, LDL-cholesterol and HDL-

cholesterol was subsequently adjusted for multiple inter-group comparisons

as shown in table 7.3. The differences in mean HDL-cholesterol between

ApoE groups was no longer significant at the 0.05 level, there were however

highly significant differences in mean total cholesterol and LDL-cholesterol

between ApoE groupings.

Women who were homozygous for E4 had significantly higher total

cholesterol levels than other women (6.6 mmol/L vs 5.8 mmol/L, p=0.005),

women with at least one E2 allele had significantly lower total cholesterol

levels than other women (5.6 mmol/L vs 5.9 mmol/L, p<0.001). There was a

relationship between ApoE and LDL-Cholesterol which was similar to that for

total cholesterol. Mean LDL-Cholesterol was higher in those homozygous for

E4 than in other women (4.4 mmol/L vs 3.7 mmol/L, p=0.001) and lower in

those with at least one E2 allele than in other women (3.3 mmol/L vs 3.8

mmol/L, p<0.001).

The ApoE polymorphisms were re-grouped to demonstrate the relative

effects of the E2, E3 and E4 alleles on total and LDL-cholesterol (see table

- 109 -

7.4). Those with E2/2 and E2/3 were grouped together as group E2+ (n=

162), E3/4 and E4/4 were grouped together as group E4+ (n= 211) and E3/3

formed the third group as group E3 (n=627). With respect to total cholesterol,

the E2+ group had lower total cholesterol than the E3 group by 0.32 mmol/L

and lower than the E4+ group by 0.45 mmol/L. While the E4+ group had

higher total cholesterol than the E3 group by 0.13mmol/L, this difference did

not reach statistical significance (using Bonferroni correction). These results

suggest that E4 is associated with the highest total cholesterol, E2 with the

lowest total cholesterol and E3 with intermediate levels. With respect to LDL-

cholesterol, the findings were very similar but greater in magnitude; the E2+

group had lower LDL-cholesterol than the E3 group by 0.44 mmol/L and lower

than the E4+ group by 0.59 mmol/L. While the E4+ group had higher LDL-

cholesterol than the E3 group by 0.15mmol/L, this difference did not reach

statistical significance. These results suggest that E4 is associated with the

highest LDL-cholesterol, E2 with the lowest LDL-cholesterol and E3 with

intermediate levels.

There was no significant relationship between ApoE genotype and

triglyceride levels. Age, blood pressure, BMI, homocysteine and glycated

haemoglobin levels and smoking history did not differ between the ApoE

genotypes (see table 7.2).

- 110 -

Table 7.2: Risk Variables and Apolipoprotein E Genotype ApoE Genotype Risk Variable

2/3 2/4 3/3 3/4 4/4 Statistic ANOVA (p-value)

Age,y 75.1 (2.6)

74.2 (2.3)

75.0 (2.6)

74.5 (2.8)

75.2 (2.6)

0.25

Pulse Pressure, mmHg 62.8 (14.5)

66.1 (15.8)

63.5 (14.6)

73.3 (17.0)

65.3 (16.0)

0.56

Glycated Haemoglobin 5.2 (0.6)

5.3 (0.5)

5.2 (0.7)

5.3 (0.4)

5.4 (0.8)

0.59

Total Cholesterol, mmol/L 5.549 (0.98)

5.745 (0.89)

5.887 (1.06)

5.962 (1.20)

6.558 (1.49)

<0.001

LDL-Cholesterol, mmol/L 3.30 (0.91)

3.43 (0.82)

3.74 (0.96)

3.83 (1.09)

4.45 (1.43)

<0.001

HDL-Cholesterol, mmol/L 1.52 (0.38)

1.63 (0.40)

1.44 (0.37)

1.43 (0.38)

1.39 (0.26)

0.02

Triglyceride, mmol/L 1.58 (0.71)

1.50 (0.53)

1.55 (0.65)

1.53 (0.70)

1.57 (0.45)

0.88

BMI 27.5 (4.7)

27.4 (4.8)

26.9 (4.4)

27.2 (4.2)

26.4 (3.5)

0.49

Homocysteine 12.1 (4.4)

14.2 (6.6)

12.0 (5.0)

11.9 (4.0)

12.0 (3.7)

0.29

Chi-Square (p-value)

Greater than median FEI n(%) 77 (47.5)

12 (52.2)

311 (50.8)

92 (48.9)

9 (45.0)

0.93

History of Smoking n(%) 58 (32.8)

8 (33.3)

241 (35.5)

80 (39.4)

3 (14.3)

0.19

Values are expressed as mean (standard deviation) unless otherwise specified.

Table 7.3: Relationship between Apolipoprotein E Genotype and Cholesterol Adjusted for Multiple Comparisons (Bonferroni)

95% Confidence Interval

Dependent Variable

(I) ApoE genotype

(J) ApoE genotype

Mean Difference (I-

J)

p-value

Lower Bound

Upper Bound

Total Cholesterol

2/3 3/3 -0.323 0.007 -0.590 -0.057

3/4 -0.396 0.006 -0.719 -0.073 4/4 -0.989 0.002 -1.726 -0.252 LDL-cholesterol

2/3 3/3 -0.438 <0.001 -0.683 -0.194

3/4 -0.530 <0.001 -0.826 -0.234 4/4 -1.148 <0.001 -1.820 -0.475 4/4 2/4 1.023 0.01 0.154 1.892 3/3 0.709 0.02 0.063 1.355 HDL-cholesterol

NS

Only statistically significant comparisons are presented. Values are in mmol/L.

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Table 7.4: Relative Effects of the E2, E3 and E4 Alleles on Total and LDL-cholesterol Adjusted for Multiple Comparisons

(Bonferroni) 95% Confidence

Interval Dependent Variable

(I) ApoE group

(J) ApoE group

Mean Difference

(I-J)

p-value

Lower Bound

Upper Bound

Total cholesterol

2/3 and 2/2

3/3 -0.323 0.002 -0.552 -0.095

3/4 and 4/4

-0.449 <0.001 -0.721 -0.178

3/3 2/3 and 2/2

0.323 0.002 0.095 0.552

3/4 and 4/4

-0.126 0.43 -0.333 0.081

3/4 and 4/4

2/3 and 2/2

0.449 <0.001 0.178 0.721

3/3 0.126 0.43 -0.081 0.333 LDL-cholesterol

2/3 and 2/2

3/3 -0.438 <0.001 -0.648 -.228

3/4 and 4/4

-0.585 <0.001 -0.834 -0.337

3/3 2/3 and 2/2

0.438 <0.001 .228 .648

3/4 and 4/4

-0.147 0.19 -.337 .042

3/4 and 4/4

2/3 and 2/2

0.585 <0.001 .337 .834

3/3 0.147 0.19 -.042 .337 Values are expressed in mmol/L.

7.3.2 Apolipoprotein E Genotype and Carotid Atherosclerosis

The relationship of Apo E genotype with IMT and focal plaque is shown in

tables 7.5 and 7.6. Although E4 homozygotes had the greatest IMT the

numbers were small and this finding was not statistically different from other

groups. Overall ApoE genotype was not predictive of carotid IMT or plaque.

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Table 7.5: Apolipoprotein E genotype and Focal Plaque Presence of Plaque

Total N=1084a

No Plaque

n(%)

Plaque Present

n(%)

p-value (χ2 test)

ApoE Genotype 2/3 90(50.8) 87(49.2) 0.26 2/4 16(66.7) 8(33.3) 3/3 354(52.0) 327(48.0) 3/4 94(45.9) 111(54.1) 4/4 9(42.9) 12(57.1) ApoE Group E2+ 90(50.8) 87(49.2) 0.25 E3 354(52.0) 327(48.0) E4+ 103(45.6) 123(54.4) a: Total number of women with ApoE genotype and plaque data.

Table 7.6: Apolipoprotein E Genotype and Intimal Medial

Thickness

n(%) (total

N=1089a)

Mean IMT Mean (SD)

(mm)

p-value (ANOVA)

ApoE genotype

2/2 0(0) N/A

2/3 176(16.2) 0.77 (0.14) 0.62 2/4 24(2.2) 0.74(0.08)

3/3 667(61.2) 0.77(0.12) 3/4 202(18.5) 0.77(0.13) 4/4 20(1.8) 0.80(0.11) ApoE Group E2+ 176(16.5) 0.77(0.14) 0.95

E3 667(62.6) 0.77(0.12) E4+ 222(20.8) 0.77(0.13)

a: Total number of women with both IMT and ApoE genotype data.

7.3.3 Apolipoprotein E Genotype and Free Estradiol Index

The relationship between ApoE genotype and FEI is shown in table

7.7. There was no significant association between Apo E genotype and FEI

when FEI was treated as a continuous or dichotomous variable (see tables

7.2 and 7.7). In order to determine whether FEI level modified the relationship

between ApoE genotype and either IMT or plaque, these relationships were

examined separately in those with less than and in those with greater than or

- 113 -

equal to the median level of FEI (see tables 7.8 through 7.11). In addition,

FEI and ApoE genotype were entered as interaction terms into multivariate

models to examine for interaction between these genotypes and FEI level in

the prediction of lipid levels or carotid atherosclerosis (see table 7.12). The

relationship of ApoE genotype with either lipids or carotid atherosclerosis was

not influenced by the level of endogenous estrogen.

Table 7.7: Apolipoprotein E Genotype and FEI

n (%) (total N=1005a)

FEI Mean

p-value (ANOVA)

ApoE genotype 2/2 0(0) N/A 2/3 162(16.1) 45.4 .30 2/4 23(2.3) 41.7

3/3 612(60.9) 47.6 3/4 188(18.7) 44.3 4/4 20(2.0) 43.9 ApoE Group E2+ 162(16.1) 45.4 .18

E3 612(60.9) 47.6 E4+ 208(20.7) 44.2

a: Total N represents those women with both ApoE genotype and FEI data. Table 7.8: Apolipoprotein E Genotype and Focal Plaque in Women with Less than the Median level of FEI

Presence of Plaque (total N=504a)

No Plaque n(%)

Plaque Present

n(%)

p-value (χ2 test)

ApoE Genotype 2/3 49(57.6) 36(42.4) 0.62 2/4 6(54.5) 5(45.5) 3/3 148(49.2) 153(50.8) 3/4 45(46.9) 51(53.1) 4/4 5(45.5) 6(54.5) ApoE Group E2+ 49(57.6) 36(42.4) 0.23 E3 148(49.2) 153(50.8) E4+ 50(46.7) 57(53.3) a: Total number of women with valid plaque, ApoE genotype and FEI data

who had a FEI level less than the median.

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Table 7.9: ApoE genotype and IMT in women with less than the median level of FEI

n(%) (total N=498 a)

Mean IMT mean (SD) (mm)

p-valueANOVA

ApoE genotype

2/2 0 n/a 0.97

2/3 84(16.9) 0.77(0.18) 2/4 11(2.2) 0.76(0.09)

3/3 297(59.6) 0.76(0.12) 3/4 96(19.3) 0.75(0.10) 4/4 10(2.0) 0.77(0.12) (Total N=487) ApoE Group E2+ 84(17.2) 0.77(0.18) 0.88

E3 297(61.0) 0.76(0.12) E4+ 106(21.8) 0.75(0.10)

a: Total number of women with valid IMT, ApoE genotype and FEI data who

had a FEI level less than the median level of FEI.

Table 7.10: ApoE genotype and focal plaque in women with greater than or equal to the median level of FEI

Presence of Plaque (total N=501a)

No Plaque n(%)

Plaque Present

n(%)

p-value (χ2 test)

ApoE Genotype 2/3 31(40.3) 46(40.3) 0.09 2/4 9(75.0) 3(25.0) 3/3 157(50.5) 154(49.5) 3/4 39(42.4) 53(48.1) 4/4 3(33.3) 6(66.7) ApoE Group E2+ 31(40.3) 46(59.7) 0.13 E3 157(50.5) 154(49.5) E4+ 42(41.6) 59(58.4) a: Total number of women with valid plaque, ApoE genotype and FEI data

who had a FEI level greater than or equal to the median.

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Table 7.11: ApoE genotype and IMT in women with greater than or equal to the median level of FEI

n(%) (total N=492 a)

Mean IMT mean (SD)

(mm)

p-value (ANOVA)

ApoE genotype

2/2 0 n/a 0.19

2/3 77(15.7) 0.77(0.10) 2/4 12(2.4) 0.71(0.15)

3/3 304(61.8) 0.78(0.12) 3/4 90(18.3) 0.78(0.16) 4/4 9(1.8) 0.83(0.10) (Total N=480) ApoE Group E2+ 77(16.0) 0.77(0.10) 0.88

E3 304(63.3) 0.78(0.12) E4+ 99(20.6) 0.78(0.15)

a: Total number of women with valid IMT, ApoE genotype and FEI data who

had a FEI level greater than or equal to the median. Table 7.12: Interaction between ApoE Genotype and FEI in Relationships with Lipids and Carotid Atherosclerosis

Interaction p-valuea for Dependent Variables Interaction Terms Total

Chol

LDL

HDL

Tg

Plaqueb

IMT

ApoE and FEI (continuousc)

.48 .66 .39 .43 .34 .34

ApoE and FEI (dichotomousd)

.16 .09 .08 .25 .25 .49

a: p-value is for the interaction term when placed into a model for the listed

dependent variables.

b: Model was a GLM for all dependent variables except Plaque which was a

logistic regression model.

c: continuous refers to treatment of FEI as a continuous variable.

d: dichotomous refers to treatment of FEI as a dichotomous variable, divided

at the median level of FEI.

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7.4 Apolipoprotein E Gene Polymorphism: Discussion It is not clear why ApoE genotype deviated from Hardy-Weinberg

equilibrium. The study sample is large with 1108 women tested and non-

random mating within this group and inbreeding would seem unlikely. The

apparent excess of E4 homozygotes and absence of E2 homozygotes may

suggest selection for E4 homozygosity within this population however this also

would seem unlikely as E4 homozygosity is associated with raised lipids and

an increased rate of cardiovascular events and E2 homozygosity is

associated with the opposite beneficial effects that should promote

longevity10,113. In fact a previous study demonstrated that Apo E4 genotype

frequency decreased in women over 60 years10 presumably due to premature

vascular death in those with ApoE4. Compared to another cross-sectional

study of Western Australian subjects, the Perth Carotid Ultrasound Disease

Assessment Study (CUDAS17), the genotype frequencies are similar (see

table 7.13). CUDAS examined the relationship between the ApoE genotype

and carotid atherosclerosis in a community based sample of 1111 men and

women aged between 27 and 77 years. In this group the frequency of the

ApoE 4/4 genotype was identical (1.9%) and ApoE 2/2 very similar (0.4% vs

0.0%) to our study. It is most likely that chance alone eliminated the ApoE 2/2

genotype from our study sample given the low prevalence of this genotype in

the more general population and that the ApoE4 genotype frequency in

CAIFOS is indeed similar to the more general population.

Table 7.13: Comparison of Genotype Frequencies between CUDAS and CAIFOS. Apo E Genotype

CUDAS (%) CAIFOS (%)

2/2 0.4 0.0 2/3 13.2 16.0 2/4 1.9 2.2 3/3 58.8 61.5 3/4 23.8 18.5 4/4 1.9 1.9

Total 100 100.0

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Apolipoprotein E genotype was predictive of lipid levels but not carotid

atherosclerosis, FEI did not modify the relationship of ApoE genotype with

IMT and plaque prevalence.

The relationship between ApoE genotype and lipid levels is consistent

with previous findings in other populations107,108,109110. Subjects with E4 had

the highest total and LDL-cholesterol levels, those with E2 had the lowest

levels and those with E3 had intermediate levels. The demonstrated effect of

E2 is likely to be a conservative estimate of the true effect given the absence

of an E2/2 genotype in our sample, which represents maximum E2 dose. Endogenous estrogen level did not influence the effect of Apo E on

either cardiovascular risk factors or carotid atherosclerosis. As mentioned

previously, exogenous estrogen can up-regulate Apo E gene expression and

therefore increase plasma concentration of Apolipoprotein E protein11

potentially resulting in a more favourable lipid profile. However the effect of

ApoE genotype on lipids in this study was not affected by endogenous

estrogen levels. This may be related to different effects of oral exogenous

estrogen versus endogenous estrogen or may be a dose effect such that the

higher plasma estrogen levels achieved with oral estrogen may be required to

significantly effect ApoE gene expression.

The lack of any significant association between ApoE genotype and

carotid IMT or plaque is consistent with previous studies that have yielded

conflicting results114,115. However our analysis is limited by the low numbers of

subjects in some of the ApoE genotypes, for example genotypes 2/2 (n=0)

and 4/4 (n=21), this limits our ability to demonstrate effects on carotid

atherosclerosis. One must also remember that we have enrolled a selected

“survivor” population of relatively healthy elderly women, this may result in a

reduced genetic effect compare to a younger population.

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CHAPTER 8. ESTROGEN RECEPTOR ALPHA GENOTYPE AND CAROTID ATHEROSCLEROSIS

8.1 Estrogen Receptor Alpha Genotype and Carotid Atherosclerosis: Background

Two estrogen receptor polymorphisms (PVUII polymorphism in intron 1

of the ER alpha gene and the thymidine-adenine (TA) dinucleotide repeat in

the promoter region of the gene) have been investigated for their effect on

bone mineral density and osteoporotic fracture173,104 ,174, however little is

known about their effect on cardiovascular disease. The limited available data

(2 studies) suggests that the presence of a PVU II restriction site may be

associated with a reduced prevalence of complicated atherosclerotic lesions9

and that fewer TA repeats may be associated with a reduced prevalence of

CHD105. However the effect of these gene polymorphisms on carotid

atherosclerosis is unknown. Given the fact that estrogen exerts its actions

through estrogen receptors (which are found in many tissues throughout the

body including vascular tissue) it is plausible that the level of endogenous

estrogen will modify the effects of these polymorphisms. These questions

have not been previously addressed in postmenopausal women.

We have examined the relationship of both ERα polymorphisms with

carotid IMT and plaque in a sub-group (433 subjects) of the CAIFOS

cardiovascular sub-study sample. We have also examined whether the level

of endogenous bioavailable postmenopausal estrogen affects the relationship

between these polymorphisms and carotid atherosclerosis.

8.2 Estrogen Receptor Alpha Genotype and Carotid Atherosclerosis: Statistics

A χ2 test using a contingency table of observed vs expected genotype

frequencies was used to test for deviation from Hardy-Weinberg equilibrium172.

Examination of the ERα TA repeat polymorphism was done using three

different methods (models) of grouping PVUII genotypes; the co-dominant

model (wild type, heterozygote, homozygote), dominant model (PvuII

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restriction site present, PvuII restriction site absent) and the recessive model

(homozygous for PVUII site, not homozygous for PVUII site).

Examination of the ERα TA repeat polymorphism was done using a 3

and a 6 group system both of which have been described elsewhere175,176.

The groups of the 3 group system are as follows; both alleles less than or

equal to 20 repeats (designated “0,0”), one allele less than or equal to 20

repeats and one allele greater than 20 repeats (“0,1”) and two alleles greater

than 20 repeats (“1,1”). The groups of the 6 group system are as follows; LL,

LM, LH, MM, MH, HH, where H is ≥ 20 repeats, M is >15 but <20 repeats and

L is ≤ 15 repeats, ie LL genotype refers to the occurrence of ≤ 15 repeats on

both alleles, ML refers to the occurrence of >15 but <20 repeats on one allele

and ≤ 15 repeats in the other allele, and so on. Twenty repeats is a PCR

fragment of 180 base-pairs, 15 TA repeats corresponds to a PCR product of

170 BP's. These cut-off points are arbitrary and assume a trend in effect from

fewer to greater number of repeats on the phenotype of interest. These cut-

offs are necessary as there would not be enough statistical power to observe

an effect on phenotype if every possible combination of repeats was

examined individually.

Each ERα polymorphism was examined for its relationship with

continuous CHD risk factors (age, blood pressure, BMI, lipid levels,

homocysteine and glycated haemoglobin) by comparing the mean value of

each risk factor between groupings using ANOVA. The relationships with

categorical variables (FEI dichotomized at the median level and smoking

history) were examined by comparison of proportions using the Chi-square

test.

Each ERα polymorphism was examined for its relationship with carotid

IMT and plaque. The mean IMT was compared between ERα groupings using

ANOVA. The proportions of women with focal plaque were compared

between ERα groupings using the Chi-square test. These procedures were

then repeated after splitting the study sample by the median level of FEI to

determine whether FEI level modified the relationship between ERα genotype

and carotid atherosclerosis. To further examine for any interaction, FEI

(entered as a continuous variable) was placed together with ERα genotype as

- 120 -

interaction terms into generalised linear models (for IMT) and logistic

regression models (for focal plaque).

8.3 Estrogen Receptor Alpha Genotype and Carotid Atherosclerosis: Results 8.3.1 PvuII Polymorphism gene Frequencies and Association with Traditional Risk Factors Table 8.1 shows the frequencies of the different PvuII genotypes. The

allele frequencies were as follows; P (wild type): 46.7% and p (restriction site

present): 53.3%. The distribution of PvuII genotypes was consistent with

Hardy-Weinberg equilibrium (χ2 = 1.45, p~0.3).

Table 8.1: PvuII Polymorphism Frequencies Genotype Frequency Percent

Cumulative

Percent Wild type (P/P*) 88 20.3 20.3 Heterozygote (P/p) 228 52.7 73.0 Homozygote (p/p) 117 27.0 100.0 Total 433 100.0 *Capital letters signify the absence of and lower-case letters the presence of

the PvuII restriction site.

Age, blood pressure, lipid levels, BMI, homocysteine and glycated

haemoglobin levels and smoking history do not differ between the PvuII


- 121 -

Table 8.2: PvuII Genotype and Cardiovascular Risk Variables Risk Variable Wild Type

(P/P) mean (SD)

or n(%)

Heterozygote (P/p)

mean (SD) or n(%)

Homozygote (p/p)

mean (SD) or n(%)

ANOVA (p-value)

Age, y 75.4(2.5) 75.0(2.6) 75.0(2.5) 0.49 Pulse Pressure, mmHg

64.8(14.8) 62.9(15.1) 65.1(15.4) 0.61

Glycated Haemoglobin

5.2(0.5) 5.2(0.7) 5.2(0.5) 0.96

Total Cholesterol , mmol/L

5.93(0.99) 5.90(0.86) 5.95(0.94) 0.80

LDL-Cholesterol, mmol/L

3.78(0.95) 3.74(0.82) 3.79(0.84) 0.81

HDL-Cholesterol, mmol/L

1.49(0.33) 1.46(0.34) 1.42(0.32) 0.26

Triglyceride, mmol/L

1.44(0.63) 1.54(0.66) 1.61(0.62) 0.07

BMI , kg/m2 27.5(4.3) 27.0(4.3) 27.2(4.2) 0.59 Chi-

Square (p-value)

Greater than median FEI n(%)

45(57.0) 94(46.5) 60(55.6) 0.16

History of Smoking n(%)

31(35.2) 86(37.9) 47(40.2) 0.77

8.3.2 Thymidine-adenine Repeat Polymorphism (6-Group

System) Gene Frequencies and Association with Traditional Risk Factors

Four hundred and eighteen women were examined for TA-repeat

genotype. Table 8.3 shows the frequencies of the different TA-repeat

genotypes. The allele frequencies were as follows; L (≤ 15 TA repeats):

39.7%, M (>15 but <20 repeats): 14.7%, H (≥ 20 repeats): 45.6%. The most

common genotype was LH, present in 39.2% of women. The distribution of

TA-repeat genotypes was consistent with Hardy-Weinberg equilibrium (χ2 =

2.14, p~0.7).

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Table 8.3: Six-Group TA Repeat Polymorphism Frequencies

Frequency Percent Cumulative Percent

Genotype LL 59 14.1 14.1 LM 50 12.0 26.1 LH 164 39.2 65.3 MM 9 2.2 67.5 MH 54 12.9 80.4 HH 82 19.6 100.0 Total 418 100.0

Age, blood pressure, BMI, homocysteine, lipids, FEI, glycated

haemoglobin levels and smoking history do not differ between the TA repeat


8.3.3 Thymadine-adenine Repeat Polymorphism (3-Group System) gene Frequencies and Association with Traditional Risk Factors

Table 8.5 shows the frequencies of the three TA repeat genotypes. The

allele frequencies were as follows; 0 (≤ 20 TA repeats): 59.0%, 1 (> 20 TA

repeats): 41.0%. Heterozygotes were the most common group (50.7%). The

distribution of TA repeat genotypes was consistent with Hardy-Weinberg

equilibrium (χ2 = 1.00, p~0.4).

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Table 8.4: Six-Group TA Repeat Polymorphism and Cardiovascular Risk Variables

TA Repeat Genotype LL

mean (SD) N=59

LM mean (SD) N=50

LH mean (SD)

N=164

MM mean (SD) N= 9

MH mean (SD)

N= 54

HH mean (SD) N=82

p-value ANOVA

Age,y 75.1 (2.6)

74.2 (2.3)

75.0 (2.6)

74.5 (2.8)

75.2 (2.6)

75.4 (2.5)

0.25

Pulse Pressure, mmHg

62.8 (14.5)

66.1 (15.8)

63.5 (14.6)

73.3 (17.0)

65.3 (16.0)

64.3 (15.4)

0.56


5.2 (0.6)

5.3 (0.5)

5.2 (0.7)

5.3 (0.4)

5.4 (0.8)

5.2 (0.5)

0.59

Total Cholesterol, mmol/L

5.97 (0.87)

5.88 (0.98)

5.83 (0.81)

5.80 (1.13)

6.08 (1.01)

5.96 (0.99)

0.63


3.80 (0.79)

3.67 (0.88)

3.69 (0.77)

3.69 (1.14)

3.91 (0.98)

3.80 (0.93)

0.68


1.47 (0.37)

1.43 (0.32)

1.47 (0.35)

1.44 (0.35)

1.38 (0.29)

1.52 (0.33)

0.42

Triglyceride, mmol/L

1.53 (0.48)

1.68 (0.78)

1.48 (0.58)

1.46 (0.25)

1.71 (0.84)

1.41 (0.58)

0.14

BMI, kg/m2 26.5 (4.3)

27.6 (4.1)

27.5 (4.2)

27.3 (4.6)

26.8 (4.7)

27.0 (4.1)

0.63

n(%)

n(%)

n(%)

n(%)

n(%)

n(%)

Chi-Square (Fisher’s exact

test) Greater than median FEI n(%)

28 (50.0)

25 (52.1)

72 (49.7)

3 (50.0)

25 (53.2)

39 (52.7)

1.00


28 (47.5)

18 (36.0)

63 (38.4)

3 (33.3)

14 (25.9)

31 (37.8)

0.33

Table 8.5: Three-Group TA Repeat Polymorphism Frequencies

Frequency Percent Cumulative Percent Genotype 0,0 141 33.7 33.7

1,0 212 50.7 84.4 1,1 65 15.6 100.0 Total 418 100.0

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Age, blood pressure, lipid levels, BMI, homocysteine and glycated

haemoglobin levels and smoking history did not differ between the TA repeat


Table 8.6: TA Repeat (3-Group System) and Cardiovascular Risk Factors Risk factor 0,0

Mean(SD)

1,0 Mean(SD)

1,1 Mean(SD)

p-value (ANOVA)

Age,y 74.8(2.6) 75.1(2.6) 75.4(2.5) .40 Pulse Pressure, mmHg

64.4(15.7) 64.7(15.3) 62.3(13.4) .47


5.2(0.5) 5.3(0.7) 5.2(0.5) .79

Total Cholesterol, mmol/L

5.88(0.89) 5.93(0.89) 5.93(1.02) .96


3.70(0.81) 3.79(0.84) 3.76(0.97) .71


1.46(0.35) 1.45(0.34) 1.51(0.31) .34

Triglycerides, mmol/L 1.57(0.61) 1.53(0.65) 1.44(0.63) .20 BMI, kg/m2 27.0 (4.3) 27.3(4.2) 27.0(4.3) 0.79 p-value

(Chi-Square)

Greater than median FEI n(%)

63(50.4) 98(51) 31(52.5) .96


56(39.7) 75(35.4) 26(40.0) .64

8.3.4 PvuII Polymorphism and carotid atherosclerosis

Tables 8.7 and 8.8 show the relationship of PvuII genotype with focal

plaque and IMT. There was a non-significant trend for an association

between the presence of a PvuII restriction site and increased IMT (p=0.11)

and for an association between PvuII homozygosity and a greater likelihood of

focal plaque (p=0.12). Overall however, PvuII genotype was not significantly

predictive of carotid IMT or plaque.

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8.3.5 Thymidine-adenine Repeat Polymorphism (6-Group System) and Carotid Atherosclerosis Tables 8.9 and 8.10 show the relationship of the 6 group TA repeat

system with focal plaque and IMT. There was no association between the

number of TA repeats and IMT, however those women with 15 or fewer

repeats on both alleles (LL genotype) were more likely to have focal plaque

than women in other groups (66.1% vs 50.1%, χ2 = 5.2; p=0.02).

Table 8.7: PvuII Genotype and Focal Plaque

Presence of plaque (Total n= 433 a)

No Plaque

n(%)

Plaque Present

n(%)

p-value

(χ2 test)

PvuII Genotype

Wild type (P/P) 42(47.7) 46(52.3) p=0.25

Heterozygote (P/p)

117(51.3) 111(48.7)

Homozygote (p/p)

49(41.9) 68(58.1)

Presence of PvuII Site

Not Present (P/P) 42(47.7) 46(52.3) p=0.95

Present (P/p or p/p)

166(48.1) 179(51.9)

Homozygous for PvuII Site

Not Homozygous (P/P or P/p)

159(50.3) 157(49.7) p=0.12

Homozygous (p/p)

49(41.9) 68(58.1)

a: Total number of women with valid plaque and PvuII genotype data.

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Table 8.8: PvuII Genotype and IMT

n(%) (total n=428a)

Mean IMT Mean (SD)

(mm)

p-value

PvuII Genotype

Wild type (P/P) 86(20.1) 0.76(0.12) p=0.27 (ANOVA)

Heterozygote (P/p)

226(52.8) 0.78(0.12)

Homozygote (p/p) 116(27.1) .78(0.11) Presence of PvuII Site

Not Present (P/P) 86(20.1) .76(0.12) p=0.11 (student’s t-

test) Present (P/p or

p/p) 342(79.9) .78(0.12)



312(72.9) .78(0.12) p=0.87 (student’s t-

test) Homozygous

(p/p) 116(27.1) .78(0.11)

a: Total number of women with valid PvuII genotype and IMT data. Table 8.9: TA Repeat Polymorphism (6-Group System) and focal plaque

Presence of Plaque (Total N=418a)

TA repeat Genotype

No Plaque

n(%)

Plaque Present n(%)

p-value (χ2 test)

LL 20(33.9) 39(66.1) 0.13 LM 25(50) 25(50) LH 78(47.6) 86(52.4) MM 7(77.8) 2(22.2) MH 28(51.9) 26(48.1) HH 41(50) 41(50) a: total number of women with both TA repeat genotype and plaque data.

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Table 8.10: TA Repeat Polymorphism (6-Group System) and mean IMT

TA repeat genotype n(%) (total n=413a)

Mean IMT Mean (SD)

(mm)

p-value (ANOVA)

LL 58(14.0) 0.78(0.99) 0.36 LM 50(12.1) 0.76(0.11) LH 163(39.5) 0.79(0.13)

MM 9(2.2) 0.75(0.14) MH 53(12.8) 0.77(0.10) HH 80(19.4) 0.76(0.11) a: Total number of women with both IMT and TA repeat data. 8.3.6 Thymidine-adenine Repeat Polymorphism (3-Group System) and Carotid Atherosclerosis Tables 8.11 and 8.12 show the relationship of the 3 group TA repeat

system with focal plaque and IMT. When a cut-off value of 20 TA repeats is

used, there is not any relationship between the number of TA repeats and

mean IMT or plaque prevalence. Table 8.11: TA Repeat Polymorphism (3-Group System) and focal plaque


TA repeat Genotype

No Plaque n(%)

Plaque Present n(%)

p-value (χ2 test)

0,0 71(50.4) 70(49.6) 0.51 1,0 95(44.8) 117(55.2) 1,1 33(50.8) 32(49.2) a: Total number of women with both TA repeat genotype and plaque data.

Table 8.12: TA Repeat Polymorphism (3-Group System) and mean IMT

TA repeat genotype n(%) (total N=413a)

Mean IMT Mean (SD)

(mm)

p-value (ANOVA)

0,0 140(33.9) 0.77(0.11) 0.40 1,0 210(50.8) 0.78(0.13) 1,1 63(15.3) 0.76(0.12)

a: Total number of women with both IMT and ApoE genotype data.

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8.3.7 PvuII Polymorphism and Free Estradiol Index

Table 8.13 shows the relationship between PvuII genotype and FEI

levels. The mean endogenous estrogen level did not differ significantly

between PvuII groupings.

There was a significant interaction between FEI and PvuII genotype

(using the dominant model) in the prediction of carotid IMT when these

variables were entered into a GLM, either when FEI was entered as a

continuous variable (p-value for interaction term= 0.02) or as a dichotomous

variable (p=0.03). This interaction remained significant when adjusted for

smoking history, pulse pressure, LDL-Cholesterol and age (p=0.03). In the

presence of a PvuII restriction site, FEI greater than or equal to the median

was associated with greater IMT whereas in the absence of this site a higher

FEI was associated with reduced IMT (figure 8.1). There was no significant

interaction between FEI (treated as a continuous variable) and PVUII

genotype in the prediction of focal plaque when entered into a logistic

regression model when using the co-dominant (p-value for interaction= 0.63),

dominant (p-value for interaction=0.44), or recessive (p-value for interaction=

0.42) models.

Tables 8.14 through 8.17 show the relationship of PvuII genotype with

carotid IMT and plaque in those with FEI levels above and below the median

level for the total study sample (47.0). There was no significant relationship in

women with less than the median level of FEI. In those with higher levels of

FEI, the presence of a PVUII restriction site was associated with greater IMT

compared to the absence of this site (0.80mm vs 0.75mm, p=0.02).

Conversely the prevalence of focal plaque was lower in those with a PvuII

restriction site (54.5% vs 62.2%), however this difference did not reach

statistical significance (p=0.36). In those with a FEI level less than the median,

the presence of this site was associated with reduced IMT (0.77mm vs

0.78mm), however this did not reach statistical significance (p=0.50).

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GLM interaction, p=0.03

0.72

0.73

0.74

0.75

0.76

0.77

0.78

0.79

0.8

0.81

Mean IMT (mm)

PvuII site absent PvuII site present

< median FEI= median FEI

Figure 8.1: Interaction between FEI level and PvuII genotype in the prediction

of IMT. In those with FEI ≥ median, the presence of a PvuII site was

associated with greater IMT, in those with FEI < median, this site was

associated with lower IMT.

______________________________________________________________

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Table 8.13: PvuII Genotype and FEI levels n(%)

(total N=389 a) Mean FEI

p-value

PvuII Genotype

Wild type (P/P) 79(20.3) 52.3 p=0.10 (ANOVA)

Heterozygote (P/p)

202(51.9) 42.5

Homozygote (p/p) 108(27.8) 48.2 Presence of PvuII Site

Not Present (P/P) 79(20.4) 52.3 p=0.10 (student’s t-

test) Present (P/p or

p/p) 310(79.6) 44.4



281(72.2) 45.1 p=0.45 (student’s t-

test) Homozygous

(p/p) 108(27.8) 48.2

a: Total number of women with both PvuII genotype and FEI data. Table 8.14: PvuII Genotype and Focal Plaque in Women with Less than the Median FEI

Presence of plaque (Total N=190 a)

No Plaque

n(%)

Plaque Present

n(%)

p-value

(χ2 test)

PvuII Genotype Wild type (P/P) 18(52.9) 16(47.1) 0.36

Heterozygote (P/p) 55(50.9) 53(49.1) Homozygote (p/p) 19(39.6) 29(60.4) Presence of PvuII Site

Not Present (P/P) 18(52.9) 16(47.1) 0.56

Present (P/p or p/p) 74(47.4) 82(52.6) Homozygous for PvuII Site


73(51.4) 69(48.6) 0.16

Homozygous (p/p) 19(39.6) 29(60.4) a: Total number of women with valid PvuII genotype and plaque data in women with

less than the median FEI.

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Table 8.15: PvuII Genotype and IMT in Women with Less Than the Median FEI n(%)

(total n=189a) Mean IMT Mean (SD)

(mm)

p-value (ANOVA)

PvuII Genotype

Wild type (P/P) 34(18) 0.77(0.10) 0.35

Heterozygote (P/p) 107(56.6) 0.77(0.11) Homozygote (p/p) 48(25.4) 0.75(0.10) Presence of PvuII Site

Not Present (P/P) 34(18) 0.78(0.10) 0.50

Present (P/p or p/p) 155(82) 0.77(0.11) Homozygous for PvuII Site


141(74.6) 0.77(0.11) 0.15

Homozygous (p/p) 48(25.4) 0.75(0.10) a: Total number of women with valid PvuII genotype and IMT data in women with

less than the median FEI.

Table 8.16: PvuII Genotype and Focal Plaque In Women with Greater Than or Equal to the Median FEI

Presence of plaque (Total n=199 a)

No Plaque

n(%)

Plaque Present

n(%)

p-value

(χ2 test)

PvuII Genotype Wild type (P/P) 17(37.8) 28(62.2) 0.24

Heterozygote (P/p) 47(50.0) 47(50.0) Homozygote (p/p) 23(38.3) 37(61.7) Presence of PvuII Site

Not Present (P/P) 17(37.8) 28(62.2) 0.36


70(45.5) 84(54.5)



64(46.0) 75(54.0) 0.31

Homozygous(p/p) 23(38.3) 37(61.7) a: Total number of women with valid PvuII genotype and plaque data in women with

greater than or equal to the median FEI.

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Table 8.17: PvuII Genotype and IMT in Women with Greater Than or Equal to the Median FEI n(%)

(total n=196 a)Mean IMT Mean (SD)

(mm)

p-value

PvuII Genotype

Wild type (P/P) 44(22.4) 0.76(0.14) 0.07

Heterozygote (P/p)

93(18.2) 0.80(0.13)

Homozygote (p/p)

59(30.1) 0.80(0.12)

Presence of PvuII Site

Not Present (P/P)

44(22.4) 0.75(0.14) 0.02


152(77.6) 0.80(0.13)



137(69.9) 0.78(0.14) 0.25

Homozygous (p/p)

59(30.1) 0.80(0.12)

a: Total number of women with valid PvuII genotype and IMT data in women

with greater than or equal to the median FEI.

8.3.8 Thymidine-adenine Repeat Polymorphism (6-Group System) and FEI Table 8.18 shows the relationship between TA-repeat genotype (6-

Group System) and FEI. There was no association between the number of TA

repeats and the level of endogenous estrogen (ANOVA, p=0.56).

Tables 8.18 through 8.22 show the relationship of TA repeat genotype (6-

group system) with carotid IMT and plaque in those with FEI levels above and

below the median level for the total study sample (47.0). The level of FEI did

not affect the relationship between TA repeat genotype and carotid

atherosclerosis.

When FEI was entered as a continuous variable, there was no

evidence of a significant interaction between TA repeat genotype (6-group

system) and FEI in the prediction of IMT (GLM, p-value for interaction: 0.15)

or Plaque (logistic regression, p-value for interaction: 0.86).

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Table 8.18 TA Repeat Polymorphism (6-Group System) and FEI TA repeat genotype n(%)

(total n=376 a) Mean FEI

p-value

(ANOVA)

LL 56(14.9) 48.3 0.56 LM 48(12.8) 44.3 LH 145(38.6) 44.2

MM 6(1.6) 31.4 MH 47(12.5) 53.6 HH 74(19.7) 46.0 a: Total number of women with TA repeat genotype and FEI data. Table 8.19: TA Repeat Polymorphism (6-Group System) and Focal Plaque in Women with Less Than the Median FEI


TA repeat Genotype

No Plaque n(%)

Plaque Present n(%)

p-value (Fisher’s exact

test) LL 7(25.0) 21(75.0) 0.11 LM 14(60.9) 9(39.1) LH 35(47.9) 38(52.1) MM 2(66.7) 1(33.3) MH 11(50.0) 11(50.0) HH 19(54.3) 16(45.7) a: Total number of women with TA repeat genotype and plaque data in

women with less than the median level of FEI.

Table 8.20: TA Repeat Polymorphism (6-Group System) and Mean IMT in Women with Less Than the Median FEI TA repeat genotype n(%)


(mm)

p-value (ANOVA)

LL 28(15.3) 0.77(0.10) 0.08 LM 23(12.6) 0.72(0.10) LH 72(39.3) 0.78(0.12)

MM 3(1.6) 0.79(0.07) MH 22(12.0) 0.73(0.10) HH 35(19.1) 0.77(0.08) a: Total number of women with both IMT and TA repeat data in women with

less than the median level of FEI.

- 134 -

Table 8.21: TA Repeat Polymorphism (6-Group System) and Focal Plaque in Women with Greater Than or Equal to the

Median FEI Presence of Plaque

(Total N=192a) TA repeat Genotype

No Plaque

n(%)

Plaque Present n(%)

p-value (Fisher’s exact

test) LL 11(39.3) 17(60.7) 0.48 LM 9(36.0) 16(64.0) LH 32(44.4) 40(55.6) MM 3(100.0) 0(0.0) MH 12(48.0) 13(52.0) HH 16(41.0) 23(59.0) a: Total number of women with TA repeat genotype and plaque data in

women with greater than or equal to the median level of FEI.

Table 8.22: TA Repeat Polymorphism (6-Group System) and Mean IMT in Women with Greater Than or Equal to the Median FEI TA repeat genotype n(%)


(mm)

p-value (ANOVA)

LL 27(14.3) 0.78(0.10) 0.25 LM 25(13.2) 0.80(0.11) LH 72(38.1) 0.80(0.15)

MM 3(1.6) 0.72(0.18) MH 24(12.7) 0.79(0.08) HH 38(20.1) 0.75(0.14) a: Total number of women with TA repeat genotype and IMT data in women

with greater than or equal to the median level of FEI.

8.3.9 Thymidine-adenine Repeat Polymorphism (3-Group System) and FEI Table 8.23 shows the relationship between the TA repeat genotype (3-

Group System) and FEI (ANOVA, p=0.85). There was no significant difference

in FEI levels between TA repeat (3-group system) genotypes.

Tables 8.24 through 8.27 show the relationship of TA repeat genotype (3-

group system) with carotid IMT and plaque in those with FEI levels above and

- 135 -

below the median level for the total study sample (47.0). The level of FEI did

not affect the relationship between TA repeat genotype and carotid

atherosclerosis.

When FEI was entered as a continuous variable, there was no

evidence of a significant interaction between TA repeat genotype (3-group

system) and FEI in the prediction of IMT (GLM, p-value for interaction: 0.14)

or plaque (logistic regression, p-value for interaction: 0.26).

Table 8.23 TA Repeat Polymorphism (3-Group System) and

FEI TA repeat genotype n(%)

(total n=376 a) Mean FEI p-value

(ANOVA)

0,0 125(33.2) 44.5 0.85 1,0 192(51.1) 46.9 1,1 59(15.7) 46.3

a: Total number of women with TA repeat genotype and FEI data. Table 8.24: TA Repeat Polymorphism (3-Group System) and Focal Plaque in Women with less than the Median FEI


TA repeat Genotype

No Plaque n(%)

Plaque Present n(%)

p-value (χ2 test)

0,0 31(50.0) 31(50.0) 0.22 1,0 40(42.6) 54(57.4) 1,1 17(60.7) 11(39.3) a: Total number of women with TA repeat genotype and plaque data in

women with less than the median level of FEI.

- 136 -

Table 8.25: TA Repeat Polymorphism (3-Group System) and Mean IMT in Women with less than the Median FEI TA repeat genotype n(%)

(total N=183a) Mean IMT Mean (SD)

(mm)

p-value (ANOVA)

0,0 62(33.9) 0.75(0.10) 0.41 1,0 93(50.8) 0.77(0.11) 1,1 28(15.3) 0.76(0.08)

a: Total number of women with TA repeat genotype and IMT data in women

with less than the median level of FEI.

Table 8.26: TA Repeat Polymorphism (3-Group System) and

Focal Plaque in Women with Greater than or Equal to the Median FEI


TA repeat Genotype

No Plaque n(%)

Plaque Present n(%)

p-value (χ2 test)

0,0 28(44.4) 35(55.6) 0.86 1,0 43(43.9) 55(56.1) 1,1 12(38.7) 19(61.3) a: Total number of women with TA repeat genotype and plaque data in

women with greater than or equal to the median level of FEI.

Table 8.27: TA Repeat Polymorphism (3-Group System) and Mean IMT in Women with Greater than or equal to the Median FEI TA repeat genotype n(%)

(total N=189a) Mean IMT Mean (SD)

(mm)

p-value (ANOVA)

0,0 62(32.8) 0.79(0.11) 0.24 1,0 97(51.3) 0.79(0.14) 1,1 30(15.9) 0.76(0.14)

a: Total number of women with TA repeat genotype and IMT data in women

with greater than or equal to the median level of FEI.

- 137 -

8.4 Estrogen Receptor Alpha Gene Polymorphisms - Discussion

There was no significant relationship between PvuII or TA repeat

genotype and the measured cardiovascular risk factors. There are a number

of possibilities to explain this observation; estrogen may have little effect on

these factors in elderly women, these polymorphisms may not significantly

modulate the effect of endogenous estrogen on these risk factors, the effects

may have been confounded by other relationships or the study may be

underpowered to detect these associations.

The PvuII polymorphism had no direct effect on IMT or the prevalence

of focal plaque. Likewise the TA repeat genotype, when divided at 20 TA

repeats (3-group system), had no effect on carotid atherosclerosis. However,

when divided into 6 groupings, those with 15 or fewer repeats on both alleles

(LL genotype) were more likely to have focal plaque than women in other

groups (66.1% vs 50.1%). This finding contrasts with the findings of the only

other study that has investigated the relationship between this polymorphism

and cardiovascular disease. The study by Lu et al105, showed that in

postmenopausal women with CHD, the frequency of alleles with more than 17

TA repeats (rather than a fewer number of repeats) was found to be

significantly higher than in women without CHD, suggesting that a greater

number of repeats may have detrimental effects. While these results appear

contradictory, one must be careful in reaching this conclusion, as previously

mentioned the cut-off points (ie 15 vs 17 vs 20 repeats etc) are arbitrary and

assume a trend in effect from fewer to greater number of repeats on the

phenotype of interest, such a trend may not actually exist.

While there was no direct effect of the PVUII genotype on carotid

atherosclerosis, there was evidence that the level of endogenous estrogen

modified this relationship. In women with greater than the median level of FEI

for the total study sample (47.0), the presence of a PVUII restriction site was

associated with greater IMT compared to the absence of this site. This

suggests that estrogen level may influence the expression of this

polymorphism in postmenopausal women. However one must be mindful that

numerous statistical tests were performed in this chapter, such that

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statistically significant findings may represent chance findings. Estrogen level

did not modify the relationship between the TA repeat polymorphism and

atherosclerosis. This examination was however hampered by the small

numbers of subjects with some of the TA repeat 6-group system genotypes

and especially the MM genotype. For example, when divided by the median

level of FEI, there were only 3 women with the MM genotype who had less

then the median FEI and had IMT analysis.

- 139 -

CHAPTER 9. GENERAL DISCUSSION We have examined the determinants of atherosclerosis in a large group

of elderly postmenopausal women who were an average of 75 years old at

baseline and 78 years at the time of carotid examination. The results of this

study are not applicable to the wider female population, but rather relate to

ambulatory women over the age of 70 years. We have selected a group of

“survivors” in whom the relationships between estrogen, cardiovascular risk

factors, genetics and atherosclerosis may be quite different to other

populations.

The risk factors for focal carotid plaque and carotid intimal-medial

thickness were similar. Pulse pressure, smoking history and LDL cholesterol

were independent determinants of both measures, additionally age was an

independent determinant of IMT while glycated haemoglobin was an

independent determinant of plaque prevalence. These findings suggest that

established factors still play a role in elderly women. Established risk factors

appeared to be more predictive of plaque than IMT. The reason for this is not

clear, while the study by Ebraham et al demonstrated that IMT was more

strongly associated with risk factors for stroke and focal plaque more

associated with risk factors for IHD21, this pattern of association was not

apparent in our study.

No previous study has examined the determinants of atherosclerosis in

such elderly women making it difficult to make comparisons with other studies.

The Vascular Aging Study examined the determinants of focal plaque and

common carotid IMT in 1271 women and men of mean age 65 years recruited

from the electoral roles of the city of Nantes, France126. The prevalence of

focal plaque in women was 16.5%, compared to 49.5% in our study, which

likely reflects the 13 year age difference of the two studies. Also we included

external carotid plaque in our assessment whereas they only examined the

common and internal carotid arteries which would tend to reduce the plaque

prevalence. The mean IMT was 0.65 mm in their women, compared to 0.77

mm in our study, once again likely reflecting the different ages of the

populations but also they measured the mid CCA rather than the thicker distal

- 140 -

CCA. In the ARIC study139 which measured distal CCA IMT, the mean left

CCA IMT in 65 year old women was 0.73mm, much closer to the mean IMT in

our study. The determinants of atherosclerosis in the Vascular Aging Study

were similar to our study; age, blood pressure, history of smoking and BMI

were related to both IMT and focal plaque, additionally diabetes was

predictive of IMT while cholesterol was predictive of plaque prevalence.

In our study, there appeared to be a threshold effect of bioavailable

endogenous estrogen on carotid atherosclerosis, the threshold level of FEI

was higher for plaque than IMT. The presence of focal plaque represents

more advanced atherosclerosis, it is possible that a higher level of

endogenous estrogen is required to produce atherosclerotic plaque as

opposed to intimal-medial thickening, although one has to be careful when

suggesting causality from cross-sectional data. Free estradiol index remained

an independent determinant of IMT in multivariate modelling and after

adjusting for BMI suggesting that the observed effect is independent of

established risk factors and the degree of adiposity. It is however difficult to

separate the effects of adiposity and FEI on atherosclerosis given that FEI

and BMI were strongly associated, for example obese women had double the

level of FEI compared to non-obese women. It is possible that FEI represents

another marker of adiposity and that its associations with IMT and plaque

prevalence are manifestations of the effects of obesity rather than estrogen.

These results conflict with the limited previous data that suggests no

association between post-menopausal estrogen levels and atherosclerosis.

Golden et al examined the relationship between estrone levels and the odds

of having increased IMT (>95th centile) in 182 postmenopausal women75.

They found no association between the odds of atherosclerosis and quartiles

of estrone. Cauley et al related estrone levels to coronary artery stenoses in

87 postmenopausal women (age 50 to 81 years) admitted for diagnostic

catheterization82. They found no association between estrone levels and the

risk of CAD or the number of diseased vessels. The major limitations of both

of these studies are that they are small and they measured estrone which is a

biologically weak estrogen, despite being the quantitatively dominant estrogen

after menopause. It is therefore quite possible that an effect of estrogen on

atherosclerosis could have been detected had a measure of bioavailable and

- 141 -

bioactive estrogen, such as FEI, been used. In a longitudinal study, Barrett-

Connor et al examined the relationship between hormone levels and

cardiovascular death in 651 women, mean age 68 years34. They found that

neither estrone nor bioavailable estradiol levels were associated with the risk

of death from cardiovascular disease or ischaemic heart disease over 19

years follow-up. They did not however examine the relationship of estrogen

with non-fatal heart disease or with measures of atherosclerosis such as

carotid ultrasound. It is quite possible that estrogen could have significant

effects on carotid IMT and plaque prevalence that would not be reflected in

cardiovascular mortality.

We found that endogenous estrogen levels increased with increasing

BMI. This has been previously described and is most likely due to the

observation that postmenopausal estrogen is produced in adipose tissue from

androgenic steroids8,25. After adjustment for BMI, FEI still correlated with SBP,

HDL-cholesterol, triglycerides and glycated haemoglobin. It is likely that given

the absence of recognised mechanisms for these associations, they are due

to confounding by the degree of adiposity. This is because BMI does not

necessarily equate with adiposity but rather is just one measure of it, these

relationships may be due to FEI also acting as a marker of adiposity.

There was an increase in high sensitivity C-reactive protein with

increasing FEI, independent of BMI. This is a novel finding, as no previous

study has examined the relationship between endogenous estrogen and CRP

in postmenopausal women. It may be that endogenous estrogen, like

exogenous hormone replacement, has pro-inflammatory effects after

menopause. Raised CRP has been associated with increased atherosclerosis

in some studies55,56, it is therefore possible that the relationship between FEI

and carotid atherosclerosis in our study is in-part explained by higher levels of

CRP in those above the threshold level of estrogen.

Apolipoprotein E genotype had no direct effect on carotid

atherosclerosis despite predictable effects on lipid levels. This finding is

consistent with other studies that demonstrated variable effects on measures

of atherosclerosis116117. It is not clear why the effects on lipids do not translate

to measurable effects on carotid atherosclerosis given that lipid levels were

- 142 -

associated with both carotid IMT and focal plaque. It may be that other factors

confounded these relationships.

Women with 15 or fewer ERα TA repeats were 16% more likely to have

focal plaque than other women. This is a novel finding as this genotype has

not been previously examined for its effect on measures of atherosclerosis. It

may be that this group of women are at increased risk for the development of

atherosclerotic plaque. While there was no direct effect of PvuII genotype on

carotid atherosclerosis, there was evidence that the level of endogenous

estrogen modified this relationship. In women with greater than or equal to the

median level of FEI (47.0), the presence of a PvuII restriction site was

associated with greater IMT compared to the absence of this site. This

suggests that there is an interaction between endogenous estrogen level and

PvuII genotype in their relationships with carotid atherosclerosis.

This study has certain limitations, as is the case with all cross-sectional

study designs, there is a possibility of selection bias. In addition the data

relating to previous medical history and medication use relies on the ability of

elderly women to remember the past, potentially degrading the accuracy of

these data. In an attempt to improve the quality of these data we verified this

information with the subjects’ general practitioner. We have related a variety

of risk factors and estrogen level to atherosclerosis measured 3 years later

but did not measure atherosclerosis at the time of risk factor assessment and

therefore assess progression of disease. This makes it more difficult to relate

differences in IMT and plaque prevalence to differences in the measured risk

factors and FEI at baseline. These women have had a lifetime of accumulated

risk factors that will likely have fluctuated in response to therapy or

environmental factors making it difficult to summarize their impact by a one-off

cross-sectional measurement after the age of 70 years. This will have likely

contributed to the weak predictive power of the multivariate model for carotid

IMT. Likewise, we have measured FEI at one point approximately 28 years

after menopause which may not reflect the impact of endogenous estrogen in

the preceding years. However there is some evidence that estrogen levels

remain fairly constant from 3 years post menopause to at least 8 years post

menopause24. It is also possible that some of the women may have used

HRT for varying periods prior to the three months leading up to study

- 143 -

enrolment, such that endogenous estrogen alone may not totally reflect the

postmenopausal estrogen status of these women and the impact of hormones

on carotid atherosclerosis. We do know however that none of the women

used exogenous estrogen for at least 39 months prior to carotid ultrasound

examination.

This study has added significantly to our understanding of the

determinants of atherosclerosis in elderly women. It is novel in relating

bioavailable endogenous estrogen to atherosclerosis and to C-reactive

protein. It is the first study to examine the impact of estrogen receptor alpha

TA repeat and PvuII polymorphisms on atherosclerosis. We have shown that

established risk factors are influential in elderly women. It appears that higher

levels of endogenous estrogen may promote carotid atherosclerosis, but it is

not certain that this effect is independent of the degree of adiposity. We must

however recognise the limitations of our cross-sectional study design. These

results should be viewed as hypothesis generating; more studies are required

in this area.

- 144 -

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165

APPENDIX A

“Medication and Medical History Data Sheet”

166

APPENDIX B

“Patient Questionnaire”

L:\lab_rlp\caifos\demographics questionnaire-2.doc 07/04/04

1

PATIENT QUESTIONNAIRE

All personal details contained within this questionnaire will be treated in the strictestconfidence. All the questions asked are designed to aid our research.

Thank you for your time and efforts with completing this questionnaire.

You may find some of the details required are hard to recall – if so, please answer these to thebest of your ability by making estimates that are as accurate as possible.

INSTRUCTIONS:1. Please complete the green and pink sections of the Patient Questionnaire. Please

tick the appropriate answer where required.2. Ask your GP to check and assist with the completion of the pink sheets.3. Please bring the completed questionnaire along with you to your appointment at Sir

Charles Gairdner Hospital.

NAME: (Mrs/Miss/Ms) ______________________________________________(First name) (Second name) (Family name)

Date of Birth

1.What is your country of birth?

(a) What language do you usually speak at home?

(b) Do you need an interpreter? (please tick one) Yes No

If yes, bring a friend or relative with you to the appointment.

2. Which one of the following best describes your marital status? (please tick one) Married Divorced Never married Separated Widowed

day month year


2

3. Which one of the following best describes your normal place of residence?(please tick one)

House/Flat/Unit/Villa Granny flat/Self-care unit/Retirement village Boarding house/Rooming house Hostel/Hostel type Caravan

4. Do you (or your Husband or partner):(please tick one)

Own your own home or have a mortgage? Pay rent?

5. Which one of the following best describes your living arrangements?(please tick one)

Live alone Lives with Husband/partner ONLY Lives with relative(s) Has resident housekeeper Other __________________________

6. Which one of the following best describes your main source of income?(please tick one)

Government pension or benefit Superannuation (including annuities, interest and dividends) Wage or salary from an employer Private business or rental property(ies) Other __________________________


3

7. Have you ever undertaken paid employment for more than one year?(please tick one)

Yes No

If yes, which one of the following best describes your main paid occupation during yourworking life? (please tick one)

Professional

Teaching/Nursing

Clerical

Domestic Duties

Factory/Agriculture

None of the above

8. At what age did you achieve your highest level of education? yrs

9. How many children have you had?(Do not count miscarriages)

10. How many of your children were breast-fed?(3 or more times a day for the first month of their life.)For each pregnancy tick “Yes” or “No”.

(a) First child Yes (b) Second child Yes No No

(c) Third child Yes (d) Fourth child Yes No No

(e) More than four children Yes No

11. How old were you when you had your last menstrual period? yrs


4

12. Have you had a hysterectomy? (please tick one) Yes No

(a) If yes, at what age was this? yrs

(b) If you had a hysterectomy:

How many ovaries did you have removed? (please tick one) None One Two Don’t Know

Did you have hot flushes? (please tick one) Yes No

If yes, how old were you when they started? yrs

13. (a) How old was your mother when she died? yrs(if still alive leave blank)

(b) How old was your father when he died? yrs(if still alive leave blank)

14. Do you participate in any sports recreation or regular physical exercise? (please tick one) Yes No

15. Please list any sports recreation or regular physical activity, including walking, that youundertook in the last three months:

(a) Activity _____________________Hours per week _____________________

(b) Activity _____________________Hours per week _____________________

(c) Activity _____________________Hours per week _____________________

(d) Activity _____________________Hours per week _____________________

16. Do you use a walking aid? (please tick one)


5

Yes No

If yes, what aid(s) do you use inside the house? _____________________If yes, what aid(s) do you use outside the house? _____________________

17. How many times have you fallen in the last 3 months?(If no falls write “0”)

18. Are you afraid of falling? (please tick one) Yes No

19. Do you limit any household activities because you are frightened you may fall?(please tick one)

Yes No

20. Do you limit any outdoor activities because you are frightened you may fall?(please tick one)

Yes No

21. Please tick the category that best describes the number of times you experience pain in eachof the following parts of your body:

Never Less than oncea month

Once a week toonce a month

Once a day toonce a week

Once a dayor more

Hip jointsKnee jointsFeet jointsLowerBackUpper Back


6

22. How often do you have numb feet? (please tick one) Never Less than once a month Once a week to once a month Once a day to once a week Once a day or more

23. Has your back become more curved as you have become older? (please tick one) Yes No

If yes, are you concerned about the change in shape of your back? (please tick one) Yes No

24. How often do you get dizzy or have giddy spells? (please tick one) Never Less than once a month Once a week to once a month Once a day to once a week Once a day or more

25. Does your eyesight (without glasses) prevent you from reading the newspaper?(please tick one)

Yes No

26. Does your eyesight (without glasses) prevent you from watching television?(please tick one)

Yes No

27. Do you suffer from deafness? (please tick one) Yes No


7

28. Have you used any of the following community support services in the last three months?(please tick one)

Yes No

If yes, please tick appropriate service and fill in times per month and hours per visit:

Times per month Hours per visit Silver chain-nursing ____________ ____________

Silver chain-home help ____________ ____________

Care aid ____________ ____________

Home help-Private ____________ ____________

Assistance from husband ____________ ____________

Assistance from relative/friend____________ ____________

Meals on Wheels ____________ ____________

Day Care ____________ ____________

Other ____________ ____________

29. How do you get to the shops? (please tick one) Taxi

Public Transport

Car – either drive yourself or as a passenger

Walk

Don’t go

Other – please specify


8

30. Have you ever broken any bones at all?This includes bones in your hands and feet (please tick one)

Yes No

If yes, please answer for each broken bone you have had:

(a) FIRST FRACTUREWhich bone was broken? _____________________How old were you when it happened?_____________________How did it happen? _____________________Did X-ray confirm it? Yes

No

(b) SECOND FRACTUREWhich bone was broken? _____________________How old were you when it happened?_____________________How did it happen? _____________________Did X-ray confirm it? Yes

No

(c) THIRD FRACTUREWhich bone was broken? _____________________How old were you when it happened?_____________________How did it happen? _____________________Did X-ray confirm it? Yes

No

(d) FOURTH FRACTUREWhich bone was broken? _____________________How old were you when it happened?_____________________How did it happen? _____________________Did X-ray confirm it? Yes

No

31. Have you ever taken female hormones (HRT)? (please tick one) Yes No

(a) If yes, what year did you start?(b) What year did you stop?


9

32. Have you ever taken steroid tablets (eg. Cortisone) for more than 3 months?(please tick one)

Yes No

(a) If yes, what year did you start?

(b) What year did you stop?(Leave blank if still taking steroid tablets)

33. Have you ever smoked at least one cigarette per day for as long as three months?(please tick one)

Yes No

(a) If yes, what year did you start?

(b) What year did you stop?(Leave blank if currently smoking)

(c) On average, how many cigarettes do/did you smoke per day?

THANK YOU FOR COMPLETING THIS QUESTIONNAIRE

PLEASE REMEMBER TO BRING THE COMPLETED QUESTIONNIARE ALONGWITH YOU TO YOUR APPOINTMENT AT SIR CHARLES GAIRDNER HOSPITAL

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