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This is to certify that the
dissertation entitled

PHYSICAL ACTIVITY AND ANGIOTENSIN I-
CONVERTING ENZYME POLYMORPHISM EFFECT
ON CARDIOVASCULAR DISEASE RISK FACTORS IN
YOUNG ADULTS

presented by

Jeren‘ry Lynn Knous

has been accepted towards fulﬁllment
of the requirements for the

degree In Kinesiology

 

 

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PHYSICAL ACTIVITY AND ANGIOTENSIN l-CONVERTING ENZYME
POLYMORPHISM EFFECT ON CARDIOVASCULAR DISEASE RISK
FACTORS IN YOUNG ADULTS
BY

Jeremy Lynn Knous

A DISSERTATION
Submitted to
Michigan State University

in partial fulﬁllment of the requirement
for the degree of

DOCTOR OF PHILOSOPHY
Kinesiology

2010

 

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ABSTRACT
PHYSICAL ACTIVITY AND ANGIOTENSIN I-CONVERTING ENZYME
POLYMORPHISM EFFECT ON CARDIOVASCULAR DISEASE RISK
FACTORS IN YOUNG ADULTS
By

Jeremy Lynn Knous

Cardiovascular disease (CVD) is a multifactorial malady with several
modifiable risk factors including high blood pressure, unhealthy cholesterol
levels, smoking, obesity, and physical inactivity. While CVD primarily affects
older adults, younger individuals are not immune from the disease and/or its
risk factors. Young adulthood (20-30 years of age) may be a pivotal time in the
progression of CVD and its risk factors due to lifestyle choices related to food
and beverage consumption, smoking, aerobic fitness, and physical activity
(PA). Of interest was whether the well-documented decline in PA from youth to
adulthood may trigger a genetic predisposition to CVD, resulting in a negative
effect on health. For example, it is possible that a person’s PA behavior may
influence the expression of genetic components such as the angiotensin I-
converting enzyme (ACE) polymorphisms. The purpose of this study was to
determine how PA and the ACE polymorphism affect CVD risk factors in
young adults. METHODS: 191 college students enrolled in a healthy lifestyles
class completed: 1) an online survey assessing demographics and PA
participation, 2) a CVD risk factor assessment of blood pressure, blood

cholesterol, body mass index, percent fat and aerobic fitness and 3) collection

 

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of mouth buccal cells for genetic testing of the ACE polymorphism. RESULTS:
The majority of the sample was female (69%), Caucasian (84%), non-smoking
(85%), and of normal weight based on BMI (72%). Ninety percent reported
meeting PA guidelines. There was a gender effect for PA and percent fat, with
males being more active and having less body fatness. Physical activity
showed an inverse relationship with percent fat, but was not associated with
other risk factors. Those with the DD genotype of the ACE polymorphism had
the highest levels of PA and aerobic fitness. There was no association
between the ACE polymorphism and other modifiable CVD risk factors. In
addition, PA did not modify relationships between the ACE polymorphism and
any CVD risk factors. CONCLUSION: We Showed that PA may affect body
composition but not other modifiable CVD risk factors in healthy young adults.
While the ACE DD genotype in healthy young adults may associate with
higher PA and aerobic fitness, relationships did not exist with other modifiable
CVD risk factors. Potential reasons for our findings include the multifactorial
nature of CVD risk factors, the nature of the PA and ACE l/D polymorphism
relationship, the relative good health of the study participants, and/or the
inadequate statistical power with final sample size. Further studies Should
focus on the understanding of gene by environmental interaction, the nature of

the interaction, and timing or triggers of the interaction.

Copyright by
JEREMY LYNN KNOUS
2010

 

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ACKNOWLEDGEMENTS

I would ﬁrst like to thank my advisor Dr. Jim Pivarnik for his time,
patience, encouragement and guidance. Without his mentorship I would not be
where I am today. Further, I would like to thank the other members of my
committee Dr. John Gerlach, Dr. Mat Reeves and Dr. Chris Womack for their
expertise and time devoted to this project. Thank you to Dr, Karin Pfeiffer, Dr.
Lanay Mudd, Erin Kuffel and Linda Pivarnik for graciously reading through
various pieces of this dissertation. Your comments and support allowed me to
convert the dissertation from a rough draft to a completed project. To Dr. Josh
Ode, you are a great friend and colleague and I thank you for your unwavering
support. Finally, I would like to thank my family. Without their support,
nurturing, patience, guidance and love I would not be the person I am today. I

love you and thank you.

Acknowledgments of funding support
- This research was supported by a Student Award Program grant from
the Blue Cross and Blue Shield of Michigan Foundation
- This research was supported by the Plus One Active Research Grant
on Wellness Using the Internet from the American College of Sports

Medicine Foundation.

 

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TABLE OF CONTENTS

List of Tables ........................................................................................................ X
List of Figures .................................................................................................... XIII
Chapter 1 — Introduction ........................................................................................ 1
Chapter 2 - Literature Review ............................................................................. 11
L1 Brief Cardiovascular Disease Overview ..................................................... 11
L2 Angiotensin I-Converting Enzyme (ACE) ................................................... 12
L2.1 ACE role in the Renin-angiotensin system (RAS) and Kinin-Kallikrein
system ......................................................................................................... 1 3
L22 ACE ID Polymorphism ......................................................................... 15
L2.3 ACE ID Polymorphism Association with Hard CVD Endpoints ............ 17
L2.4 ACE Summary ..................................................................................... 19
L3 ACE ID Polymorphism and Blood Pressure/Hypertension ......................... 19
L3.1 Evidence for an association between ACE ID Polymorphism and
BP/Hypertension .......................................................................................... 20
L32 Evidence against an association between ACE ID Polymorphism and
BP/Hypertension .......................................................................................... 23
L3.3 ACE ID Polymorphism and BP/Hypertension Summary ...................... 25
L4 ACE ID Polymorphism and Lipids .............................................................. 26
L4.1 Evidence for an association between ACE ID Polymorphism and
Lipids ........................................................................................................... 29
L42 Evidence against an association between ACE ID Polymorphism and
Lipids ........................................................................................................... 31
L43 ACE ID Polymorphism and Lipids Summary ....................................... 32
L5 ACE ID Polymorphism and OvenNeight/Obesity ........................................ 33
L5.1 Evidence for an association between ACE ID Polymorphism and
Overweight/Obesity ..................................................................................... 34
L52 Evidence against an association between ACE ID Polymorphism and
OvenNeight/Obesity ..................................................................................... 35
L5.3 ACE ID Polymorphism and Overweight/Obesity Summary .................. 36
L6 ACE ID Polymorphism and Physical Activity/Aerobic Fitness .................... 37
L6.1 Evidence for an association between ACE ID Polymorphism and
Physical Activity/Aerobic Fitness ................................................................. 39
L62 Evidence against an association between ACE ID Polymorphism and
Physical Activity/Aerobic Fitness ................................................................. 43
L63 ACE ID Polymorphism and Physical Activity/Aerobic Fitness
Summary. .................................................................................................... 44

VI

L7 ACE ID Polymorphism and Cardiovascular Disease Risk Factor

Literature Review Summary ............................................................................ 45
Chapter 3 - Methodology .................................................................................... 46
M1 Research Design ....................................................................................... 46
M2 Subject Recruitment .................................................................................. 46
M3 Eligibility Criteria (Inclusion/Exclusion) ...................................................... 47
M4 Survey Data Collection .............................................................................. 47
M5 Demographics and Physical Activity Data ................................................. 48
M6 Cardiovascular Disease Risk Factors and Fitness .................................... 49
M7 Genetic Sampling ...................................................................................... 50
M8 Genetic Testing ......................................................................................... 51
M9 Statistical Analysis ..................................................................................... 53
M9.1 Statistical Analysis - Data Management .............................................. 53
M92 Statistical Analysis - Data Linkage ...................................................... 53
M93 Statistical Analysis - Data Cleaning .................................................... 54
M9.4 Statistical Analysis - Variable Deﬁnitions ............................................ 54
M95 Statistical Analysis — Sample Size Calculation ................................... 56
M96 Statistical Analysis .............................................................................. 57
Chapter 4 - Results ............................................................................................. 61
R1 Descriptive Data (Study Aim #1) ................................................................ 61
R2 Association between Physical Activity and Cardiovascular Disease Risk
Factors (Study Aim #2) .................................................................................... 63
R21 Association between Physical Activity and Systolic BP (Study Aim
#2)... ............................................................................................................ 63
R22 Association between Physical Activity and Diastolic BP (Study Aim
#2) ................................................................................................................ 64
R23 Association between Physical Activity and TC (Study Aim #2) ........... 64
R24 Association between Physical Activity and HDL Levels (Study Aim
#2) ............................................................................................................... 65
R25 Association between Physical Activity and Body Composition (Study
Aim #2) ........................................................................................................ 65
R26 Association between Physical Activity and BMI (Study Aim #2) .......... 66
R27 Summary of the association between Physical Activity and
Cardiovascular Disease Risk Factors (Study Aim #2) ................................. 67
R3 Association between ACE ID Polymorphism and Cardiovascular Disease
Risk Factors (Study Aim #3) ............................................................................ 67
R31 Association between ACE ID Polymorphism and Systolic BP (Study
Aim #3) ........................................................................................................ 69

VII

 

 

 

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R32 Association between ACE ID Polymorphism and Diastolic BP (Study
Aim #3) ........................................................................................................ 70
R33 Association between ACE I/D Polymorphism and TC (Study Aim #3). 71
R34 Association between ACE I/D Polymorphism and HDL (Study Aim

#3). .. ............................................................................................................ 71
R35 Association between ACE ID Polymorphism and Body Composition
(Study Aim #3) ............................................................................................. 72

R36 Association between ACE ID Polymorphism and BMI (Study Aim #3) 73
R37 Association between ACE ID Polymorphism and PAIeVOzmax

(Study Aim #3) ............................................................................................. 74
R38 Summary of the association between ACE ID Polymorphism and
Cardiovascular Disease Risk Factors (Study Aim #3) ................................. 75

R4 Determination of the interaction effect between ACE ID Polymorphism

and Physical Activity on Cardiovascular Disease Risk Factors (Study aim

#4) ................................................................................................................... 76
R41 Association between ACE ID Polymorphism and Physical Activity on
Systolic BP (Study aim #4) .......................................................................... 76
R42 Association between ACE ID Polymorphism and Physical Activity on
Diastolic BP (Study aim #4) ......................................................................... 77
R43 Association between ACE I/D Polymorphism and Physical Activity on
TC (Study Aim #4) ....................................................................................... 77
R44 Association between ACE ID Polymorphism and Physical Activity on
HDL Levels (Study Aim #4) .......................................................................... 78
R45 Association between ACE ID Polymorphism and Physical Activity on
Body Composition (Study Aim #4) ............................................................... 78
R46 Association between ACE I/D Polymorphism and Physical Activity on
BMI (Study Aim #4) ...................................................................................... 79
R47 Summary of the association between ACE ID Polymorphism and
Physical Activity on Cardiovascular Disease Risk Factors (Study Aim #2) .. 80

Chapter 4 - Discussion ....................................................................................... 81
01 Descriptive Data (Study Aim #1) ................................................................ 81
02 Association between Physical Activity and Cardiovascular Disease Risk
Factors (Study Aim #2) .................................................................................... 83

02.1 Association between Physical Activity and BP (Study Aim #2) ........... 83
02.2 Association between Physical Activity and Blood Lipid

Concentrations (Study Aim #2) .................................................................... 86
02.3 Association between Physical Activity and BMI and Body

Composition (Study Aim #2) ........................................................................ 88

VIII

 

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03 Association between ACE I0 Polymorphism and Cardiovascular Disease

Risk Factors (Study Aim #3) ............................................................................ 89
03.1 Association between ACE I0 Polymorphism and BP (Study Aim #3).. 94
03.2 Association between ACE l0 Polymorphism and Blood Lipid

Concentrations (Study Aim #3) .................................................................... 96
03.3 Association between ACE I0 Polymorphism and BMI and Body
Composition (Study Aim #3) ........................................................................ 98

04 Determination of the interaction effect between ACE I0 Polymorphism
and Physical Activity on Cardiovascular Disease Risk Factors (Study aim

#4) ................................................................................................................... 99

05 Conclusion ............................................................................................... 104
Appendix 1 - Consent Form .............................................................................. 129
Appendix 2 - Questionnaire .............................................................................. 132
Appendix 3 - Equations .................................................................................... 139
Appendix 4 - Health Assessment data Sheet ...................... . ............................ 140
Appendix 5 - Description of Variables ............................................................... 142
References ....................................................................................................... 144

IX

 

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List of Tables

Table 1A: Descriptive categorical data for study participants based on
gender .............................................................................................................. 1 01

Table 1B: Descriptive continuous data for study participants based on
gender. ............................................................................................................ 1 02

Table 1C: Descriptive characteristics of participants by Physical Activity
quartiles. .......................................................................................................... 1 03

Table 2.1 A: Descriptive categorical data for Systolic Blood Pressure (mmHg) 104
Table 2.1 B: Descriptive continuous data for Systolic Blood Pressure (mmHg) 105

Table 210: Summary of multiple regression model for Systolic Blood Pressure
(mmHg) ............................................................................................................ 105

Table 2.2A: Descriptive categorical data for Diastolic Blood Pressure (mmHg) 106
Table 2.23: Descriptive continuous data for Diastolic Blood Pressure (mmHg) 107

Table 2.20: Summary of multiple regression model for Diastolic Blood

Pressure (mmHg) ............................................................................................. 107
Table 2.3A: Descriptive categorical data for Total Cholesterol (mg/dL) ............ 108
Table 2.33: Descriptive continuous data for Total Cholesterol (mg/dL) ............ 109

Table 230: Summary of multiple regression model for Total Cholesterol
(mg/d L) ............................................................................................................ 109

Table 2.4A: Descriptive categorical data for High Density Lipoprotein levels
(mg/dL) ............................................................................................................. 110

Table 2.43: Descriptive continuous data for High Density Lipoprotein levels
(mg/dL) ............................................................................................................. 111

Table 2. 4C: Summary of multiple regression model for High Density
Lipoprotein levels (mg/dL) ................................................................................ 111

 

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Table 2.5A: Descriptive categorical data for Percent Fat (%) ........................... 112

Table 2.53: Descriptive continuous data for Percent Fat (%) ........................... 113
Table 250: Summary of multiple regression model for Percent Fat (%) .......... 113
Table 2.6A: Descriptive categorical data for Body Mass Index (kg/m2) ............ 114
Table 2.63: Descriptive continuous Body Mass Index (kg/m2) ......................... 115

Table 260: Summary of multiple regression model for Body Mass Index
(kg/m2) .............................................................................................................. 115

Table 3.0A: Descriptive categorical data based on the ACE I0 Polymorphism 117
Table 3.03: Descriptive continuous data based on the ACE l0 Polymorphism 118

Table 3.00: Descriptive continuous data by gender and the ACE ID
Polymorphism ................................................................................................... 1 18

Table 3.1A: Final multiple regression model for ACE I0 Polymorphism and
Systolic Blood Pressure (mmHg) ...................................................................... 119

Table 3.2A: Final multiple regression model for ACE ID Polymorphism and
Diastolic Blood Pressure (mmHg) .................................................................... 119

Table 3.3A: Final multiple regression model for ACE ID Polymorphism and
Total Cholesterol (mg/d L) ................................................................................. 1 1 9

Table 3.4A: Final multiple regression model for ACE l0 Polymorphism and
High Density Lipoprotein Levels (mg/dL) .......................................................... 120

Table 3.5A: Final multiple regression model for ACE I0 Polymorphism and
Percent Fat (%) ................................................................................................ 120

Table 3.6A: Final multiple regression model for ACE l0 Polymorphism and
Body Mass Index (kg/m2) ................................................................................. 120

Table 3.7A: Final multiple regression model for ACE ID Polymorphism and
Physical Activity (kcaIs/kglweek) ...................................................................... 1 21

XI

 

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Table 3.73: Final multiple regression model for ACE ID Polymorphism and
Estimated VOzmax (ml/kg/min) ......................................................................... 121

Table 4.1A: Final multiple regression model for ACE ID Polymorphism and
Physical Activity on Systolic Blood Pressure (mmHg) ...................................... 122

Table 4.2A: Final multiple regression model for ACE I0 Polymorphism and
Physical Activity on Diastolic Blood Pressure (mmHg) ..................................... 122

Table 4.3A: Final multiple regression model for ACE ID Polymorphism and
Physical Activity on Total Cholesterol (mg/dL) ................................................. 123

Table 4.4A: Final multiple regression model for ACE ID Polymorphism and
Physical Activity on High Density Lipoprotein Levels (mg/dL) .......................... 123

Table 4.5A: Final multiple regression model for ACE ID Polymorphism and
Physical Activity on Percent Fat (%) ................................................................. 124

Table 4.6A: Final multiple regression model for ACE I0 Polymorphism and
Physical Activity on Body Mass Index (kg/m2) .................................................. 124

XII

 

 

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Figure 3.1: Frequency of the ACE I0 Polymorphism in a sample of 149 young
adults ................................................................................................................ 1 16

XIII

 

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CHAPTER 1
INTRODUCTION

Cardiovascular disease (CVD) is the number one killer in the United
States, accounting for greater than 831,000 deaths or approximately 34% of
all mortalities in 2005‘”136 It is estimated that more than one in every three
adults has at least one form of CVD, that may manifest as myocardial
infarction, stroke, heart failure, etc.109 CVD is a multifactorial disease with
several modifiable risk factors including high blood pressure (BP), elevated
total cholesterol (TC), elevated low density Iipoprotein (LDL) cholesterol
concentration, low high density Iipoprotein (HDL) cholesterol concentration,
smoking, obesity, and physical inactivity.9 The prevalence of these risk factors
is staggering in adults. It is estimated that one in three adults has high blood
pressure, while an additional 28% of Americans are diagnosed as pre-
hypertensive.109 In 2006, the prevalence of individuals 20 years of age or
greater having borderline (2200 mg/dl) or high (2240 mg/dl) total cholesterol
was 47% and 16%, respectively.109 Further, 16% of adults over 20 years of
age had low HDL cholesterol (<40 mg/dl)109 and 21% of Americans greater
than 18 years of age are current smokers.109 According to body mass index
(BMI) criteria, 66% of US adults are considered overweight (BMI = 25.0-29.9
kg/mz) with approximately 32% being classified as obese (BMI 2 30 kg/mz).109
In addition, only 32.5% percent of Americans meet American College of Sports

Medicine (ACSM) recommended guidelines for physical activity (PA),109

 

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deﬁned as 230 minutes of moderate intensity activity performed on ﬁve or
more days per week or 220 minutes of vigorous PA performed on three or
more days of the week.86 These modiﬁable risk factors are often accompanied
by other morbidities such as type II diabetes mellitus, stress, alcohol
consumption or non-modiﬁable risk factors such as age, race and gender.
Thus, it is not surprising that the incidence and mortality of CVD is high in
American adults.

While CVO is considered a disease of older age, younger individuals
are not immune from the disease and/or its risk factors. Although CVD risk
factors in children and adolescents do not have the same effect on morbidity
and mortality as in older adults,62 the presence of these risk factors in early life
leads to earlier and/or more severe development of CVD later in life. Children,
even infants, can have high 3P that could potentially contribute to early

I“ The prevalence of high BP in US children has been

development of CVD.
reported to be 1-3%, but this statistic appears to be increasing.84 The National
Health and Nutrition Examination Survey (NHANES) I" reported that
approximately one fourth of children have borderline high (170-199 mgldL) TC,
with another 11% being in the elevated (>200 mgldL) TC category.”204 This
ﬁnding is alarming since high plasma lipid values tend to track into adulthood.
To further illustrate the importance of controlling cholesterol levels in children,
the American Academy of Pediatrics issued a position stand stating that

phannacologic intervention be considered for children with LDL cholesterol

concentration 2190 mgldL.57 Further, the prevalence of overweight and

 

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obesity in US children is rising and has become one of the most common
pediatric disorders.155 According to the 2003-2004 NHANES data, 17% of
children and adolescents are overweight, as deﬁned by having an age- and
sex—speciﬁc BMI greater than or equal to the 95th percentile.160 Further, since
the early 1970’s, the prevalence of adolescents classiﬁed as ovenrveight has
increased. This documented evidence is alarming given that children are
demonstrating the same co-morbidities as their adult counterparts, only
decades earlier.60 Among high school adolescents, only 44% of males and
28% of females meet the recommended level of leisure time PA.”160

CVD risk factors act independently or cluster synergistically to cause
disease.148 Clustering is the phenomenon of risk factors occurring together
more frequently than expected by chance.148 For example, overweight and
obesity have been shown to cluster with other CVD risk factors such as high
BP and unfavorable lipid proﬁles. These risk factors have also been found to
cluster in children.74 From the Bogalusa Heart study data, Berenson et al.31
reported that as the number of CVD risk factors increased, so did the severity
of atherosclerosis in youth. Speciﬁcally, 60% of overweight children 5-10 years
of age had one additional CVD risk factor while more than 20% of them had
two or more CVD risk factors.74 Further, it has been shown that overweight
children and adolescents are approximately 2-3 times more likely to have high
TC, seven times more likely to have elevated triglycerides, and approximately
three times more likely to have high 3P.”175 This demonstrates the

detrimental effects of pediatric obesity and risk factor clustering.

 

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Research shows that clustered CVD risk factors track strongly from
adolescence to young adulthood,26 signifying a need for increased research in
this area. The Cardiovascular Risk in Young Finns study reported that 25% of
the participants initially identiﬁed as at risk for CVD during adolescents (i.e.,
having three risk factors - high TC, low HDL cholesterol, and high diastolic BP)
remained in the highest categories as young adults.148 In sUpport of these
ﬁndings, the Danish youth study showed that participants with clustered risk
factors at baseline had a higher probability (odd ratio 6.0) of clustering during
the follow-up.26 Thus, not only do risk factors cluster in youth and
adolescence, they tend to track into adulthood. Individual risk factors during
childhood can predispose an individual to an increased risk of developing
CVD, but when clustered together, risk factors can accelerate the onset and/or
severity of the disease. Those experiencing risk factors during childhood and
adolescents are at the highest risk as an adult.156 Therefore, reducing the
prevalence of modiﬁable CVD risk factors in children, adolescents and young
adults could lead to delay and reduce severity of CVD in the future.

As has been discussed previously, CVD risk factors often originate
during childhood and their prevalence intensiﬁes until the disease
manifestation in adulthood. Young adulthood (20-30 years of age) is a pivotal
time in the progression of CVD and its risk factors. However, there is a paucity
of research on this “generational change” with respect to cardiovascular
health. Studies on college students can provide data that is currently lacking

to bridge the “generational change" gap from adolescence to adulthood.

 

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According to The US Department of Education’s Digest of Education
Statistics 2006, a record 17.5 million students were enrolled in colleges or
universities and 28.4% of all young adults between ages 25-29 have
completed a bachelor’s degree or higher.6 In addition, the 2003 US Census
Bureau report showed that nearly 53% of the population has completed some
college.178 The young adult population represents an important key in how
CVD risk factors track and/or intensify into adulthood, as young adults are
exposed to signiﬁcant changes associated with moving away from home and
living with others going through similar experiences. This “generational
change” consists of transforming from parental dependence to virtual
independence and all the accompanying consequencesm Examples of this
independence include paying bills, managing money, living independently and
choosing lifestyle behaviors.17o Choices related to sleep cycles, food and
beverages consumption, smoking and PA behavior could all affect the health
of young adults. Thus, environmental exposures for a college student may act
independently or interact with other factors, both environmental and genetic, to
inﬂuence health. While new lifestyle inﬂuences may not manifest themselves
immediately as disease, it is possible that lifestyle decisions made during this
“generational change”, like smoking, alcohol use, unhealthy eating or lack of
PA, could drastically affect the development of chronic ailments such as CVD.

A decline in PA from adolescence to adulthood has been documented.
”6'1“ According to Youth Risk Behavior Surveillance System (Y RBSS) data,

28% and 44% of adolescent females and males, respectively, are meeting

 

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current PA recommendations.7'65 A Finnish longitudinal study of 12 to 27 year
olds showed a decrease (20-55% gender dependent) in PA with age.187
Physical activity participation decreases from youth to adulthood.‘“5'"5""187

The decline in PA during the “generational change” may potentially
have a negative effect on cardiovascular health. It is suspected that
environmental factors such as diet, body composition, physical inactivity,
smoking, alcohol consumption, etc., account for the majority of chronic health
conditions; accounting for at least 60% of CVD.36 Physical inactivity has been
shown to increase the incidence of CVD both independently and in
combination with other risk factors.71

Environmental factors are thought to inﬂuence genes. An environmental
inﬂuence may alter gene expression, resulting in a phenotype that surpasses a
threshold of biological signiﬁcance, invoking the appearance of pathologic
states.36 Further, responses to environmental factors are often variable among
individuals due to genetic differences. In many cases, a genetic proﬁle results
in disease development only after being triggered by the presence of
necessary environmental factors.3‘3'159 Thus, an individual’s genotype
combined with environmental factors leads to individualized susceptibilities to
health problems, such as CVD.

Genetic differences may be due to the functional role of polymorphisms,
or variations in the DNA sequence. A CVO related polymorphism of interest is
the angiotensin-l converting enzyme (ACE) insertion (I)/deIetion (0)

polymorphism. The ACE I/D polymorphism has been associated with the

 

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development of CVD and associated with risk factors of high 3P, high
cholesterol and obesity. Speciﬁcally, the DD genotype is shown to be
detrimental to cardiovascular health.“7'56'195

Since genetic predisposition to a particular disease can be affected by
the interaction of triggering environmental factors, it is possible that a person’s
PA behavior may inﬂuence the expression of the ACE I/D polymorphisms,
potentially altering the phenotype. The interaction between environmental
factors, such as PA, and genetic factors, like the ACE I/D polymorphism, could
facilitate the early phenotypic expression of CVD or its risk factors.

To date, most ACE l/D polymorphism and CVO related studies have
assessed older adults (> 50 years of age) suffering from myocardial
infarctions, strokes or diagnosed CVD. Cambien et al.42 reported that the DD
genotype was more frequent in participants who suffered a myocardial
infarction than those who had not. Furthermore, a meta-analysis by Sayed-
Tabatabaei et al.168 reported a significant positive association between the 0
allele and vessel wall thickness in the common carotid artery, a measure of
atherosclerosis. Thus, studies indicate that older CVD patients with the ACE
I/D polymorphism are associated with the expression of a diseased
phenotype.

Although it is known that atherosclerosis begins in childhood with most
individuals having some level of arterial plaque buildup by age ﬁfteen,180
research on the ACE I/D polymorphism and CVD risk factors in young adults is

scarce. While the plausible association between ACE l/D polymorphism and

 

CVC

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CVD risk factors has been studied preliminarily 42'76'167'171 there are few
deﬁnitive results.

It is important to evaluate CVD risk factors associated with genetic and
environmental factors in young adults (college aged) during the “generational
change,” as this is a critical time for developing lasting health behaviors
affecting CVD progression and/or severity. An understanding of the interaction
between genetic (ACE I/D polymorphism) and environmental factors, such as
PA, on the development of CVD risk factors in a young adult can assist in
reducing the development and/or severity of diseased phenotypes and the
future burden of CVD. Increased knowledge of CVD risk factors associated
with the ACE I/D polymorphism and PA independently and their interaction
(ACE-PA) will assist future researchers and clinicians to better understand the
protective effect of PA, determine the change in risk factor proﬁles in response
to PA, and facilitate effective intervention strategies.

CVD risk factors occur through a complex association between genetic
factors, environmental factors, and their interactions. A major environmental
effect on these risk factors is PA. With the decline of PA from youth to
adulthood, the college years may represent a “generational change” that could
signiﬁcantly affect the development of CVD. Genetic determinants of CVD
factors, such as the ACE I/D polymorphism, may be affected by physical
inactivity. Therefore determining the contribution of PA, ACE I/D
polymorphism, and their interaction during the “generational change” could

provide information to curb the development and burden of CVD.

 

 

 

Ann:
Spec
Moor

index

 

 

Speci
Mood

inyou

Aims:
Speciﬁc Aim 1: To measure and describe physical activity, blood pressure,
blood lipid (total cholesterol and HDL cholesterol) concentration, body mass
index, and body fatness in young adults.

0 The presentation of the distribution of these study participant

characteristics will be descriptive and not hypothesis driven.

Speciﬁc Aim 2: To determine the association between physical activity and
blood pressure, blood lipid concentration, body mass index and body fatness
in young adults.
. Physical activity will be inversely associated with blood pressure, total
cholesterol, body mass index, percent body fatness and directly

associated with HDL cholesterol.

Speciﬁc Aim 3: To determine the association between ACE l/D polymorphism
and blood pressure, blood lipid concentrations, body mass ‘index, and body
fatness in young adults.
. The 00 genotype will be associated with increased blood pressure,
total cholesterol, body mass index, percent body and inversely

associated with HDL cholesterol.

Speciﬁc Aim 4: To explore whether physical activity modiﬁes the effect of the
ACE l/D polymorphism on blood pressure, blood lipid concentrations, body
mass index and body fatness in a college students.
0 Increased physical activity will suppress the relationships between the
DD genotype and blood pressure, lipid concentration, body mass index,

and body fatness.

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CHAPTER 2

LITERATURE REVIEW

L1 Brief Cardiovascular Disease Overview

It is well recognized that cardiovascular disease (CVD) is the number
one killer in the United States.136"6° Risk factors for CVD included high blood
pressure (BP), high total cholesterol (TC), low high density lipoprotein
cholesterol (HDL), obesity, smoking and physical inactivity. Cardiovascular
disease and associated risk factors are present in younger individuals
including children, adolescents, or young adults; without the same effect on
morbidity and mortality as in older adults.62 Risk factors and disease
progression is multifactorial, with both environmental and genetic factors
having signiﬁcant roles. A primary environmental factor associated with CVD
and risk factors is physical activity (PA) or aerobic ﬁtness. The beneﬁcial
effects of PA or exercise on CVD risk factors may slow the progression and
onset of CVD risk factors and disease. Physiologic responses to
environmental factors, like PA, are often variable among individuals possibly
due to genetic differences. In many cases, a genetic proﬁle results in disease
development only after being triggered by the presence of necessary
environmental factors.”159 Thus, an individual’s genotype combined with
environmental factors leads to individualized susceptibilities to health

problems, such as CVD.

ll

 

DNA

COTIV

polyr.

 

with ,
relati'

increc

L2A

 

Genetic differences may be due to polymorphisms, or variations in the
DNA sequence. A CVD related polymorphism of interest is the angiotensin-l
converting enzyme (ACE) insertion (I)/deletion (D) polymorphism. The ACE l/D
polymorphism has been associated with the development of CVD and linked
with risk factors of high 3P, high cholesterol and obesity. Understanding the
relationship between the ACE I/D polymorphism, PA, and CVD is important for

increased understanding of CVD progression.

L2 Angiotensin I-converting enzyme (ACE)

Angiotensin I-converting enzyme (ACE) is a major part of the renin-
angiotensin system (RAS), a highly complicated hormonal l autocrine/
paracrine system that controls the cardiovascular system, kidneys and adrenal
glands. 10° This system regulates BP and ﬂuid/electrolyte homeostasis. 66'10"
ACE is responsible for the chief reaction in RAS, converting angiotensin I, an
inactive decapeptide, into the active octapeptide angiotensin II. “'56
Angiotensin II functions as a potent vasoconstrictor and has been linked to
additional physiologic functions (to be discussed in subsequent sections). An
additional function of ACE is to inactivate bradykinins and other peptides that
are potent vasodilators.58

ACE is an acidic glycoprotein, composed of a metallopeptidase with an
active zinc center,”69 that functions as a dipeptidyl carboxypeptidase to

catalyze the hydrolysis of carboxy-terminal dipeptides from substrates.66 ACE

is located in a variety of tissues including, but not limited to, vascular, heart,

12

 

 

L2.1 A

Kallikl

 

 

functio
and SIC
oirculat
SYmpat
the Ina:
Convert
This C0
Ihe func

;

brain, kidney, adipose as well as macrophages and smooth muscle cells of
human atherosclerotic plaques.”87 ACE can be found in solution as part of
blood plasma, lymph, amniotic and cerebrospinal ﬂuids,21'69 though it is
thought that circulating ACE originates from endothelial cells.56

Results from ACE inhibitor studies have demonstrated that disrupting
the RAS system can lead to reversals of cardiac ﬁbrosis and vascular
remodeling along with reductions in cardiac myocyte apoptosis.153 Reductions
in ACE levels have led to reduction in free radical generation and oxidative

stress, both of which lead to atherosclerosis.87

L2.1 ACE role in the Renin-angiotensin system (RAS) and Kinin-
Kallikrein system

RAS plays a role in the homeostasis of the cardiovascular system by
functioning as a long-term regulator of BP and blood volume.169 Renin is made
and stored in the juxtoglomerular cells of the kidney and is released into the
circulation under conditions of increases sodium, volume loss, and/or
sympathetic activation.5‘5'169 Circulating renin converts angiotensinogen into,
the inactive decapeptide, angiotensin l.“3'56'169 The key RAS reaction is the
conversion of the angiotensin I into the active angiotensin II via ACE.“3'55'169
This conversion, that occurs in most tissues of the body,2 is what generates
the functional octapeptide, angiotensin II.

Angiotensin II functions to control BP directly by binding to receptors,

predominately in the heart, smooth muscle, kidneys, and adrenal glands,

13

 

 

modt.

prollle

Io vas

 

 

 

Angiot
Pmduc
endotf
DOlent
DIOdLlc

breakd
Group I
hYDOte
and ex1

angIOte

which leads to peripheral vasoconstriction and sodium re-absorption in the
kidneys. Angiotensin II increases BP indirectly through the stimulating adrenal
cortex and increases aldosterone production.58'7°'100'102 Additionally,
angiotensin II augments the formation of endothelin, a peptide released by the
lining of blood vessels, which has powerful vasoconstriction properties and
has an established position in the development of hypertension as well as
other cardiac conditions.58

Angiotensin II also plays a role in vascular smooth muscle cell
modulation by directly inducing vascular endothelial growth factor.”70 This cell
proliferation is mediated by the angiotensin II receptor type I56 as a response
to vascular inﬂammation and leads to the development of atherosclerosis.
Angiotensin II interacts with pro-inﬂammatory cytokines to boost free radical
production by activating oxidase.5"'100 Free radical generation can damage
endothelial cells or stimulate vascular endothelial dysfunction. Nitric oxide, a
potent vasodilator that also inhibits platelet aggregation, is decreased by the
production of free radicals.58 Thus, the beneﬁcial effects of nitric oxide are
suppressed by angiotensin II.100

ACE also functions as a type II kininase in the Kinin-Kallikrein system to
breakdown kinins, which function as powerful vasodilators.124 Kinins are a
group of polypeptides that inﬂuence smooth muscle contraction, induce
hypotension and increase blood ﬂow. Kinins lead to the release of nitric oxide
and exert anti-proliferation effects while opposing vasoconstrictor effects of

angiotensin ".173

I4

 

hna
ischt
aggr
ﬁbnn
mock
Kinin
Inady
when

pbcet

 

WI
endot
mmug
asan
Impmw
Canﬁo

other k

L22 At

(A

arm Ofc
Span 21

inSenior

The Kinin-Kallikrein system disrupts the coagulation/ﬁbrinolysis process
(making of/breaking down of blood clots) in a way that can lead to acute
ischemic coronary events. Kinins, when bound to platelets, inhibit platelet
aggregation thus blocking the effects of thrombin which functions to convert
ﬁbrinogen into ﬁbrin.121 Further, kinins inhibit the coagulation process by
blocking the attachment of ﬁbrinogens, precursors to ﬁbrin, to platelets.182
Kinins counter the coagulation processes by increasing ﬁbrinolysis via
bradykinin increasing the release of tissue plasminogen activator,40 which
when active, functions to create enzymes that breaks down clots into smaller
pieces.

Within the Kinin-Kallikrein system, ACE functions at the surface of
endothelial cells,124 degrading 95% exogenous bradykinin after a single pass
through the pulmonary system}4 Bradykinin is a potent vasodilator that acts
as an antagonist to proliferation, can increase coronary blood ﬂow, and
improves myocardial metabolism. ACE inhibitor studies suggest that some

cardioprotective effects are elicited by the increase in bradykinin as well as

other kinins.

L2.2 ACE ID polymorphism

Serum ACE levels are inﬂuenced by the ACE gene, located on the long
arm of chromosome 17 at band 23 (17q23) and is divided into 26 exons that
span 21 kilobases. This area contains a polymorphism characterized by either

insertion (I) or deletion (0) of a 287 base pair fragment in intron 16.197 Due to

15

 

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the l/D polymorphism being located in an intron, it is thought to be a neutral
marker. However, the l/0 polymorphism is in strong linkage disequilibrium
(statistical association between alleles at separate but neighboring loci) with
one, or possibly more, unknown variants.147

Conventional belief is that the 0 (deletion) allele of the ACE
polymorphism is associated with CVO through increasing serum ACE levels,
which would increase angiotensin II and decrease bradykinin activity thus
enhancing the ACE double effect.147 Rigat et al.158 were the ﬁrst to show that
the ACE l/D polymorphism was associated with the amount of ACE enzyme
available, with the 0 allele linked to higher enzyme levels. The presence of the
0 allele is associated with elevated vascular smooth muscle tone along with
vasoconstriction.147 Homozygotes for the 0 allele have been shown to have
higher levels of plasminogen activator inhibitor-1 (inhibitor of ﬁbrinolysis), von
Willebrand factor (marker of coagulation), and thromobomodulin (marker of
coagulation),98 thus associating the 0 allele with endothelial dysfunction.
Further, the 0 allele is associated with increased CVD, including coronary
artery disease and myocardial infarction.56

The ACE I/D polymorphism is not only associated with CVD but has
been associated with PA and aerobic ﬁtness. The Iallele has been associated
with improved endurance performance by enhancing the mechanical efﬁciency
of trained muscle.‘3’°'202 Further, the [allele has been associated with
increased type 1 skeletal muscle ﬁbers,210 potentially explaining the

association between the [allele and increased endurance performance. The

16

ACE I/D polymorphism has been associated with PA. A study on mild
hypertensive individuals showed that a sedentary lifestyle was more common
among those with the DD genotype and followed a linear trend of increased
PA from DD-->I0—»ll."’°5 This result in no way implies that there is causal
relationship between PA and the ACE polymorphism; it merely provides a
potential explanation as to the association of the ACE polymorphism with CVD

and PA.

L2.3 ACE ID Polymorphism Association with Hard CVD Endpoints

The ACE l/D polymorphism has been associated with hard CVD
endpoints, like myocardial infarction (MI), stroke or mortality, as well as CVD
risk factors (to be discussed later). Volzke et al.200 assessed the hard
endpoints of mortality, cardiovascular mortality, and recurrent coronary artery
bypass graft in 249 patients who underwent an initial coronary artery bypass
graft. The 00 genotype was an independent predictor of total mortality after
coronary artery bypass graft, accounting for approximately 10% of the
variance. Further, the DD genotype accounted for approximately 18% of the
total variance in mortality from cardiovascular events or recurrent coronary
artery bypass graft.200 Cambien et aI42 examined the association between the
ACE l0 polymorphism in a group of 610 patients with who suffered a
myocardial infarction and compared them to 733 health controls. They found
that the DD genotype was more frequent in those who suffered a myocardial

infarction as compared to health controls, with the association being stronger

l7

 

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(OR: 3.2) in low risk cases.42 Margaglione et al.114 assessed the relationship of
the ACE genotype with in 210 participants with and without a history of stroke.
The 00 genotype was more common in participant who suffered stroke than
those who were stroke free.114 In 2267 male Caucasian participants,

Gardemann et al.76

examined coronary artery disease, deﬁned as a minimum
of 50% stenosis of the coronary vessels, angina pectoris and acute myocardial
infarction, deﬁned using World Health organization criteria, to assess their
relationship with the ACE ID polymorphism. They reported an association
between the 0 allele and coronary artery disease in younger participant (< 61
years of age) and non-fatal myocardial infarction in older adults (>61 years of
age).76 Both of these associations were stronger in participants without other
CVD risk factors.76 Similar, Sekuri et al.171 reported that participants with the
DD genotype had higher risk (OR: 2.82) of developing premature (younger
than 55 years of age for men) coronary heart disease (deﬁned as greater than
50% stenosis of a major coronary artery).171 Finally, in a meta-analysis

I.17 compared the DD genotype to the I allele and found

AgerhoIm-Larsen eta
odds ratio for myocardial infarction, ischemic heart disease and ischemic
cerebrovascular disease to be 1.21, 1.16, and 1.18, respectively. These

results demonstrate that the DD genotype of the ACE I0 polymorphism is

associated with increased cardiovascular disease endpoints.

l8

L2.4 ACE summary

The activation of the RAS, through the conversion of angiotensin I to
angiotensin II via ACE, results in systemic vasopressor response and blocks
the hypotensive effects of Kinin-Kallikrein system by ACE degrading
bradykinin.34'43'5‘5'70'169 ACE activity also triggers free radical formation and
release while decreasing nitric oxide activity.“ Through these physiologic
responses, ACE may have a role in CVO development.

The ACE l/D polymorphism is associated with the amount of ACE
enzyme produced while also determining the metabolism of
bradykinin.‘3"‘147'15‘3 The ACE I/D polymorphism is associated with many forms
of CVD as well as PA and aerobic ﬁtness.56"3°'205 Due to the formation of and
degradation of vasoactive substances, future studies are need to tease out the
effect, if any, of the ACE I/D polymorphism and its role with PA/ﬁtness on CVD

risk factors.

L3 ACE ID Polymorphism and Blood Pressure/Hypertension

The RAS regulates blood pressure (BP) through increases in blood
volume and constriction of the vessels.70 Homeostatic control of the RAS is
mediated primarily through the production of angiotensin II. Angiotensin It
increases 3P via sodium re-absorption, by binding to speciﬁc receptors or by
increasing aldosterone secretion, and through peripheral
vasoconstriction?“00'102 The production of angiotensin II is controlled by ACE,

which converts angiotensin I to angiotensin II. The concentration of

19

 

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angiotensin II is dependent on the amount of angiotensin I and, more
importantly, circulating ACE levels.43 The greater the presence of ACE, the
greater the production of angiotensin II. Plasma ACE levels have high inter-
Individual variability, with as much as a ﬁve-fold difference.22"‘3'56 The ACE l/D
polymorphism has been shown to be associated with differences in ACE

191

levels, accounting for 47% of the total variance.158 Speciﬁcally, the 0 allele

appears to be associated with higher ACE concentrations than the I
allele.15’3'191

Genetic variability in ACE l/D polymorphism could partially explain the
differences in resting BP and could contribute to the pathogenesis of
hypertension. The 0 allele leads to more ACE enzyme which in turn produces
more angiotensin II and an increase BP. In the Nancy study of 87 families (2
parents < 60 years of age and at least 1 child of > 10 years of age), similarities
in plasma ACE levels were explained more by genetic factors than by the

environment.“ Genetics accounted for 29% of the ACE variance in parents

and up to 75% in offspring."1

L3.1 Evidence for an association between ACE ID Polymorphism and
BPIHypertension

The ACE I/D polymorphism has been found to be associated with
higher resting BP. The Rochester Family Heart Study performed linkage
analyses among a cross-sectional sample of 1488 participants, to evaluate the

contribution of the ACE gene region in systolic, diastolic and mean arterial

20

 

 

me

 

gen
esﬁ
chﬁe
(has
aocc
and
sgni

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haWn

 

havnl
wnht
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genol
95%C
Up Stl
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highe

pressures.73 Participants were siblings with a mean age 15 years who were 3rd
generation members of the Rochester Family Heart study. Investigators
established that the ACE gene region contributed signiﬁcantly to interindividual
differences in resting 3P, accounting for variances of 14% and 17% of
diastolic BP and mean arterial pressure.73 Among males, the ACE gene region
accounted for 38%, 38% and 54% of the variance in systolic BP, diastolic BP
and mean arterial pressure, respectively.73 Results for females did not show
signiﬁcant differences in BP outcomes based on ACE genotype. Data
collected on more than 3000 men and women participating in Framingham
Heart Study found the odds of being hypertensive statistically greater in men
having the DD genotype (OR=1.59, 95%CI=1.13—2.23) as compared to those
having the II genotype.138 Further, increases in diastolic BP were associated
with the DD genotype.138 Similarly, in a case-control study of 244 Croatian
adults who were matched on age, sex, and place of residence, the DD
genotype was more prevalent in hypertensive participants (OR=2.50,
95%CI=1.19—5.25) than in normotensive' participants.” During a 6 year follow
up study to the familial occurrence of hypertension, investigators assessed BP
in a cohort 3500 Finnish children and reported that higher systolic BP in girls 2
9 years of age and boys 2 12 years of age was positively associated with
family history of hypertension.184 Further, boys with the DD genotype had
higher systolic BP than those heterozygous or homozygous II.184

The ACE I/D polymorphism association with increased BP has also

been shown in other ethnic samples, such as African Americans. In a case-

21

 

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aHe

 

 

 

the
Car
BPI
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ass:
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2653

ITIOTB

 

Funh
hYDer
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theat
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Israel
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ACE

control study of 89 Black Americans from the Detroit Metropolitan area, the 0
allele frequency was more prevalent in hypertensive participants compared to
the norrnotensive controls.‘54 In a separate study of participants of Afro-
Caribbean decent, the frequency of the 0 allele increased, with increases in
BP from normal (52%) to hypertensive (70%) participants.28 In a case-control
study of Chinese participants > 40 years of age, the 0 allele was signiﬁcantly
associated with hypertension."9 In the study sample, the hypertensive group
was 1.46 (95%CI=1.08-1.99) times more likely to have a 0 allele then the
norrnotensive group.49 In subgroup analysis of elderly participants, deﬁned as
265 years of age, the hypertensive group was 2.28 (95%CI=1.17-4.45) times
more likely to have the 0 allele and the DD genotype than normotensives.“
Further, when the elderly subgroup was stratiﬁed by gender, the odds of the
hypertensive group having the 0 allele was 4.57 (95%CI=1.58-13.21) times
that of the nomiotensive group.49 A unique study by Uemura et aI.195 looked at
the association between ACE l/D polymorphisms and BP patients post
hospitalization. Patients showed a signiﬁcantly higher systolic BP after being
discharged from the hospital when they had the 0 allele.195 In 197 healthy
Israeli men between the age of 40 and 70 years, those who possessed the 0
allele had higher 3P, both systolic and diastolic, than the lligenotype group.142
This result was unchanged even after controlling for age and BMI.142

The studies in this section illustrate a positive association between the
ACE I/D polymorphism and BP or hypertension. This association is found in

various ages and populations. Further, this association was found when using

22

 

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bet

L3.

and

and
whne

hype

nota

 

Hung
assoc
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(14-2
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being
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hyper
VS-47
respe

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several research strategies and various samples sizes. Thus, an association

between BP and the ACE I/D Polymorphism is evident.

L3.2 Evidence against an association between ACE ID Polymorphism
and BPIHypertension

Not all studies have shown an association between the ACE genotype
and BP or hypertension. In a cross-sectional study of 1975 non-Hispanic
whites from Minnesota, the ACE I/D polymorphism was not associated with
hypertension.194 In separate Finnish adults studies the ACE genotypes were
not associated with systolic or diastolic 337591

In various case-control studies with participants including Germans,
Hungarians, and Greeks ranging in age from 14 to 65, there was no
association found between the ACE l/D polymorphism and BP or
hypertension.”2‘27”“199 Within 178 Hungarian adolescents and young adults
(14-20 years of age), the distribution of ACE genotypes did not differ between
the hypertensive (N=120) and norrnotensive (n=58) groups.144 Both groups
had similar genotypic frequencies, with approximately 25% being 00, 45%
being ID and 33% being ”.1“ In 182 Greek participants, 98 of whom were
hypertensive, the ACE l/D polymorphism had no association with presence of
hypertension.199 The genotype distribution (00: 30.6% vs. 34.5%, ID: 45.9%
vs. 47.6%, II: 23.5% vs. 17.8% for hypertensive vs. norrnotensive participants,
respectively) and allele frequency (0.45 in hypertensive and 0.42 in

normotensive participants) was similar between groups‘gg, establishing that

23

the ACE polymorphism was not involved in causing the hypertension in the
case group. Mondorf et al.126 found that in 246 Germans (53.6% male), 121 of
whom were hypertensive, no difference was seen in genotype distribution
between the hypertensive cases and healthy controls. The ACE concentration
levels were graded within hypertensive patients based on genotype, with 00
having the highest (46.7 :I: 12.5 UIL), ID being intermediate (30.5 :I: 9.3 U/L),
and II having the lowest (20.3 d: 13.4 U/L) value.126 In a separate study of 1358
Germans (48.4% male), 638 of whom were hypertensive, the genotypic
frequency between the two groups, hypertensive and norrnotensive groups,
was identical with 26% DD, 49% I0, and 23% H.127

I.“ utilized the four corners approach, which involves

Harrap eta
creating four groups representing different conditions to assess measured BP
of both parental and adolescent participants. After grouping the participants
based on high/low parental and high/low participants 3P, no difference in
genotype distribution was present among groups.85 When regrouped by
genotype, the 00 group had the highest concentration of ACE enzyme while
the II group had the lowest.85

In a study of 853 Hong Kong Chinese (41.6% male), the ACE I/D
polymorphism had no relationship with 33188 In a Japanese cohort of
approximately 1200 participants (36.1% male), the researchers looked at the
association of the ACE l/D polymorphism and self-monitored home BP as well

as ambulatory BP.116 Neither the 3P measures nor the prevalence of previous

CVD differed among the three genotypes (00, ID, II).116 A Cross-sectional

24

study involving 3P screening of Japanese college students indicated that
those who were found to be hypertensive did not differ in genotype distribution
(17% DD, 33% ID, 50% II) or allele frequencies from that of the general
Japanese population (10% DD, 43% ID, 47% II).'23

Results of the various studies in this section, which were conducted
using differing populations based on age and nationality, do not support the
hypothesis that the ACE l/D polymorphism is associated with BP or
hypertension. Inconsistencies in these negative ﬁndings compared to studies
showing positive relationships may result from differing subject characteristics

such as ethnicity, age, and sex.

L3.3 ACE ID Polymorphism and BPIHypertension Summary

Based on the current literature, it is unclear whether the ACE l/D
polymorphism is associated with BP or hypertension. In studies that declare
an association, the nature of the relationship is the same with the 0 allele or
the DD genotype having the highest BP value, the ID genotype being
intermediate and the Iallele or II genotype with the lowest BP values. There
are numerous studies supportingZ7'2"'49'5‘"7‘°"138'142'184'195 this association.
However, there is also evidence among a variety of populations that
refutes75""""9“16'1”“12‘5'127"“"""’"9‘"199 an association that the ACE l/D
polymorphism may have with BP or hypertension. The association between
ACE polymorphism and BP remains ambiguous, possibly due to unidentiﬁed

confounders and/or effect modiﬁers. Further work must be undertaken in this

25

 

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MW

area to conﬁrm or disprove the biologically plausible association between the
ACE I/D polymorphism and BP or hypertension. By examining this association
in young healthy, college-aged adults it may be possible to deﬁne the role of
genetics with less chance of co-morbidities such as age, chronic disease, and

other co-morbidities confounding the results.

L4 ACE ID Polymorphism and Lipids

Adverse levels of lipids and lipoproteins are recognized risk factors for
CVD. Increases in TC, speciﬁcally through increases in low-density
Iipoproteins (LDL) and decreases in HDL concentrations, contribute to the
development of CVD through the development of atherosclerosis. While
hypercholesterolemia can lead to the development of atherosclerosis, this is
not the only way for cholesterol to enhance the atherosclerotic process.
Adverse molecular changes, such as oxidation of Iipoproteins can also
stimulate atherogensis. The ACE I/D polymorphism maybe associated with
atherosclerotic process through increasing levels and/or changes made to the
molecular structure of lipids.

At birth, TC, LDL and triglyceride levels are very low and increase
sharply during the ﬁrst year of life. By age two, cholesterol levels are
equivalent to those of young adults,31 with the bulk of this cholesterol
consisting of LDL and HDL.31 Total cholesterol and sub-fractions are similar in
boys and girls until puberty, but a marked sex difference is observed during

maturation. Boys tend to have reduced plasma HDL levels during puberty that

26

are not observed in girls.62 While increases in lipid concentration are normal
with growth, adverse levels, even at younger ages, are highly associated with
atherosclerosis.

Elevations in lipids are associated with the development of
atherosclerosis through mechanisms of initiation and progression.35'145 By 15

18° which continues to

years of age virtually all individuals have atherosclerosis,
develop silently for decades before becoming clinically signiﬁcant.149
Atherosclerosis is marked by cholesterol-Iipid-calcium deposits in the intimal
layer of the arterial wall.4 Atherosclerotic lesions are established as ﬂat fatty
streaks and progress to raised lesions with intermediate raised fatty streaks.117
The Pathobiological Determinates of Atherosclerosis in Youth (PDAY)
examined the relationship of adult CVD risk factors to the development of
atherosclerosis as autopsies were performed on approximately 3000 people
between the age of 15 and 34 who died of external causes. Results from
PDAY showed the amount of intimal surface with evidence of atherosclerosis
was positively correlated with LDL concentration and negatively associated
with HDL.1 Raised fatty streaks were present in 20% of 15 - 19 year old
subjects and increased to 40% by ages 30 - 34.117 In the Bogalusa Heart
Study, researchers examined children between the ages of 5 and 14 and
followed them through their late 30s. The prevalence of fatty streaks in
coronary arteries increased with age from about 8% at ages 2-15 to 69% at

ages 21-39.31 Thus, the percent of intimal surface involved with fatty streaks

increases with age and is appreciably related to concentrations of lipoprotein

27

cholesterol.117 Thus, fatty buildup in arteries or atherosclerosis begins in
childhood, is associated with CVD risk factors in adolescents, and these CVD
risk factors accelerate atherogensis in later decades.

Genetic factors affect the lipid levels and ultimately, atherogensis.
Heritability estimates of TC and LDL cholesterol range from 0.43-0.97,88
though this decreases as people age due to the increase in environmental
variance.35 Genetic inﬂuence appears to be stable after adolescence,35
though genetics may still dictate molecular changes in cholesterol. It is
estimated that genetics affects 1/4 - 1/2 of the total variance in cholesterols
levels.115 The ACE l/D polymorphism may participate in the development of
atherosclerosis through its critical role in RAS. Evidence exists for an
interaction between RAS and hypercholesterolemia in the atherogenic
process.107 Angiotensin II, the major vasoconstrictor component of RAS
controlled by ACE, has been associated with arterial wall lipid deposition, lipid
peroxidation, and modiﬁcation/uptake of L0L.72'9€"97 Angiotensin II facilitates
oxidized LDL uptake by endothelial cell through the increases in scavenger
receptors, thus increasing the macrophage content of foam cells."’7'115
Angiotensin II interaction with LDL leads to a modiﬁed LDL form that is taken
up at an enhanced rate, increasing cellular accumulation of cholesterol.:"7'72'96
Further, angiotensin It displays pro-atherogenic properties as it has been
found to stimulate smooth muscle cell proliferation, increased expression of

inﬂammatory molecules and increased platelet activation.”97 Thus, the effect

of the ACE IID polymorphism on ACE and angiotensin II in turn effects

28

 

cholesterol content,37 providing evidence for a possible association between

the ACE l/D polymorphism and lipids.

L4.1 Evidence for an association between ACE ID Polymorphism and
Lipids

Few studies have examined the association of the ACE I/D
polymorphism with lipids. Of the studies that have assessed this association,
only four have found positive associations between the ACE polymorphism
and lipid levels. While all found a positive association there were few
consistencies in the associations. In a cross-sectional study, Oren et al.142
examined the correlation of the ACE polymorphism with CVD risk factors in
197 healthy Israeli men between the ages of 40 and 70. The investigators
found a signiﬁcant association between the ACE I/D polymorphism and LDL
cholesterol, with the highest LDL levels in those who were 00 homozygous. In
a sample of 675 healthy Korean men, Choi et al.50 found that TC and LDL
levels where higher in men with the DD genotype. Both studies were
conducted in apparently healthy, middle aged men, suggesting the ACE I/D
polymorphism does play a role in the amount of cholesterol in the blood and
that common genotypic variations could play a role in disease development.
In a study of ovenlveight or obese Canadian men between the ages of 25 and
55 years, Bosse et al.37 reported that HDL levels were higher in those
possessing the DD allele. This sample consisted of dyslipidemic young to

middle age men. Thus, there may be some evidence that the ACE l/D

29

 

the

Stu

99
the

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Ieve
CTOS
aSSC
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flIldil

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polymorphism was associated with lipid level, but it may be environment
dependent. In a study comparing the association of ACE with cardiovascular

I.196 compared 105 women from an urban community

risk factors, Vallejo eta
to 59 women from a rural community. Women residing in an urban community
with the II genotype were more likely to have abnormal LDL levels compared
to the urban community counterparts that had the ID genotype. The authors
recommend caution when interpreting these results, as they found an
association that is opposite (II allele being associated with abnormal lipids
levels) too much of the literature. There explanation for this opposite finding is
their samples ethnicity and small sample size.196 While slightly different, these
studies showed an associated with the ACE polymorphism and lipids.

In middle-aged, healthy men there was an association between the DD
genotype and increase LDL levels. In slightly younger but dyslipidemic men,
the DD allele was associated with higher HDL. Results may imply that the DD
allele is protective due to high HDL level being protective, but the high HDL ,
level could just be a result of their dyslipidemic state. The studies were all
cross-sectional in design and limited to males. The only study finding an
association in females was a case-control design looking at the ACE l/D
polymorphism in Mexican women from urban and rural environments. The

finding of the II genotype being associated with abnormal LDL levels is

contrary to the finding in men.

30

L4.2 Evidence against an association between ACE ID Polymorphism
and Lipids

Other studies have failed to ﬁnd an association between the ACE
polymorphism and lipid levels. Cross-sectional, case control and cohort
studies involving participants from Asia, Europe, South and North American
have not shown signiﬁcant variation in lipid levels based on ACE genotype.

A cross sectional study of 136 Japanese participants, mostly male
(n=106), found that males had higher levels of triglycerides but lower levels of
HDL than females.141 While participants having the 0 allele tended to have
smaller LDL particle size, there was no difference between genotypes and little
association was found between the ACE I/D polymorphism and lipids.141 In a
study of 274 American Samoan participants, Crews et al.55 found that the ACE
polymorphism was not associated with cholesterol levels. In a larger cohort
sample, 1552 middle aged, healthy French participants were assessed for
genetic and lipid proﬁles. Pallaud et al.145 found no association between lipid
levels and the ACE gene.

In a case control study of 147 Mexican coronary artery disease patients
and 100 healthy controls between the ages of 20 and 84 years, Vargas-

I.198 reported an increased frequency of 0 allele in the patient

Alarcon et a
group compared to controls. Further, the patient group had a slight, but non-
signiﬁcant increase in TC and LDL when the 0 allele was present.198 Similar
results were found in a case control study of post-menopausal Italian women

with and without coronary artery disease. The 0 allele was found to be in

31

higher frequency in the coronary artery disease group, but there was no
difference in TC, LDL, or HDL levels between cases and controls.53 Thus, the
ACE genotype had no association with lipid levels.

There are inconsistent ﬁndings for an association between the ACE I/D
polymorphism and blood lipids. Differences in study participants, ethnicity and
gender, may explain some of the variability in study results. Study participants’
age (mostly middle-aged and older) may also explain some of the
inconsistencies. With increased age comes an increase in confounders, like
heart disease, that may mask any association. Further, increases in age may
lead to changes in the contribution of genetic and/or environmental factors

leading to increased variability in study ﬁnding.“

L4.3 ACE ID Polymorphism and Lipids summary

In summary, the current literature is inconsistent regarding a possible
association between the lipid levels and the ACE I/D polymorphism. It is
possible for the ACE I/D polymorphism to be associated with lipids levels
through the affects of angiotensin II, but the true biological mechanism and it
strength of association have yet to be ascertained.

The effect of ACE ID polymorphism on ACE and angiotensin It may
affect cholesterol levels,” but evidence supporting this claim are minimal and
inconsistent. Further research on the ACE I/D polymorphism is warranted to
conﬁrm or disprove the biological plausible association between the ACE I/D

polymorphism and lipids. Researching this association in young healthy,

32

college-aged adults may assist in deﬁning the role of genetics; as there is less
chance of co-morbidities such as age, chronic disease, and other co-

morbidities confounding the results.

L5 ACE ID Polymorphism and Overweighthbesity

The prevalence of overweight and obesity is a growing epidemic, with
estimates of more than 1 billion people globally considered overweight (BMI 2
25 kg/mz), and at least 300 million considered obese (BMI 2 25 kglmz).13 In the
US the estimated prevalence of oven/veight is 64%, while the estimated
prevalence of obesity is 32%.109 The upward trend in overweight and obesity is
startling since they are independent risk factors for increased morbidity and
mortality, throughout the lifespan.60

Overweight and obesity occurs due to a complex interaction between
environmental and genetic factors. Physical activity, an environmental
exposure, generally shows an inverse relationship with overweight and
obesity, with those with more PA or increased ﬁtness being less likely to be
overweight or obese.‘53 While environmental exposures are pivotal to the
development of ovenlveight or obesity, there is signiﬁcant interindividual
variation, most likely due to genetics. It has been suggested that genetics
accounts for approximately 5% of the variance in BMI and subcutaneous fat,
while accounting for approximately 25% of the variance of percent body fat
and fat mass measures.38 ACE IID is one of many polymorphisms associated

with overweight or obesity. The genetic effect that the ACE I/D polymorphism

33

would have on being oven/veight or obese is thought to be related to the role
that it plays in regulating RAS.

It is suggested that modulation of RAS lead to metabolic and
biochemical changes in fat cells.45 ACE, a functional enzyme of RAS, is
associated with fat cell growth,30 with stronger ACE expression being found in
human visceral fat than subcutaneous fat.68 Additionally, adipose tissue
contains a functional RAS, with angiotensin II participating in adipocyte growth

and differentiation.‘58'89

L5.1 Evidence for an association between ACE ID Polymorphism and
OvenlveightIObesity

Limited research is available on the association between the ACE I/D
polymorphism and overweight or obesity, but there are a few studies that show
a positive relationship. Moran et al.131 studied 1016 Greek adolescents (11-18
years of age) to assess the inﬂuence of different variants of the ACE l/D
polymorphism on measures of the subcutaneous fat mass. They reported in
females, but not males, that the ACE 0 allele was strongly associated with
increased fatness.131 Further, the relationship was strongest in females who
participated in little PA.'31 Strazzulo et' al.179 studied 959 Italian males (25-75
years) to determine the association between genetic polymorphisms and
ovenlveight lobesity and body fat distribution. The authors found that BMI was
higher in participants with the DD genotype.179 Additionally, those with the DD

genotype had higher odds, 1.82 and 1.76 respectively, of increased abdominal

34

 

 

 

 

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Obese
2 40 l
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adiposity or being overweight compared to those with I allele.179 Similarly, in a
study of 457 Saudi men and women the DD genotype and the 0 allele
occurred more frequently in overweight and obese participants.‘57 Riera-
Fortuny et al.157 studied 185 participants, of Spanish decent, who had suffered
at least one episode of coronary heart disease and compared them to 182
controls. Subjects with coronary heart disease and the 0 allele were heavier
with larger measures of waist circumference and had a higher prevalence of
obesity.

Finding from these few studies indicate that there may be some form of
an association between the ACE ID polymorphism and overweight and
obesity. These studies, by no means provide conclusive evidence for this
association, but they suggest that further work should be done in determining

the relationship the ACE IID polymorphism and overweight‘and obesity.

L5.2 Evidence against an association between ACE ID Polymorphism
and OverweightIObesity

In contrast to the above studies, there are studies showing no
relationship between the ACE l/D polymorphism and being overweight and

obese. Bell et al.30

studied 1054 severely obese adolescents and adults (BMI
2 40 kg/m2 or > 97% for age and sex) and compared them to 918 non-obese
controls to assess the effect of the ACE genotype. They reported no difference
in allele frequency between cases and control groups. In a separate study of

24 obese females, Suchanek et aI.183 assessed the effect of regular aerobic

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PA on changes in BMI and body composition when stratiﬁed by genotype.
There were no differences in BMI and no association to changes in body
composition among any of the genotype groups,183 suggesting that the ACE
polymorphism has no effect on BMI or body composition. In a group of 449
Jamaican participants, Cooper et al.52 examined the relationship between RAS
and obesity. The ACE polymorphism was not associated with BMI, but was
associated with levels of ACE which was found to be higher in obese
individuals.52

These three studies found no association between the ACE I/D
polymorphism and overweight and obesity. Inconsistencies between ﬁndings
are unclear, but could be due to different study designs, sampling techniques

and/or subject sampling.

L5.3 Associations between ACE ID Polymorphism and
Overweight/Obesity Summary

In summary, overweight and obesity is a growing epidemic affecting
billions of people worldwide. There is a small amount of research that
suggests that the 0 allele may be associated with fatness or BMI,67'”""57'179
but this body of research is small and limited. Further, there are a handful of
studies that refute this association.3°'52"83. Further research needs to be
conducted to discern the role, if any, that the ACE I/D polymorphism has with
fatness and/or BMI. Understanding the etiology of this disease is paramount to

slowing the progression of overweight and obesity and associated oo-

36

morbidities. Understanding the genetic and environmental variables that
individually and through interaction affect ovenNeight and obesity can help to

slow this growing epidemic.

L6 ACE IID polymorphism and Physical Activity/Aerobic Fitness

The development of chronic disease depends on many factors including
the environment and genetics. It has been stated that physical inactivity is an
initiating factor in the mechanism of disease.36 Physical inactivity is an
independent risk factor for a variety of health outcomes and the relative risk of
CVD associated with physical inactivity ranges from 1.5 to 2.5.151160 In
contrast, regular PA provides substantial protective effects against CVD and
its risk factors.36

It is possible that our genome was programmed to expect PA, thus low
levels of PA is an abnormal event that results in a differing phenotypic
expression.36 The response to PA and/or exercise is variable among
individuals and it is suggested that the genetic make-up of the individual is a
potential reason for this variability.39'152 Genetic factors may account for up to
30% of individual difference in the level of habitual PA.1°‘

There are numerous studies identifying an association between the
ACE I/D polymorphism and PA and aerobic ﬁtness.‘9° Some of these studies
have found an association between the ACE I allele and PA and ﬁtness, while
other have reported an association between the ACE 0 allele and PA and

24.25.51 ,54.78.1 30,135,189,205,211 Finally
1

ﬁtness. there are studies that have found no

37

association with the ACE l/D polymorphism and PA or aerobic
ﬁtness.59'75'15°'151

While there are inconsistencies in the strength and speciﬁcs of
relationships found between ACE l/D polymorphism and PA and aerobic
ﬁtness, there are a number of biologically plausible mechanisms for their

U58 were the ﬁrst to show that the ACE I/D polymorphism

existence. Rigat et a
was associated with ACE availability, with the 0 allele linked to higher enzyme
activity. ACE is responsible for converting angiotensin I into the active
angiotensin II,“56 and inactivating bradykinins and other vasodilator
peptides.58 The ACE II genotype is related to lower ACE levels which in turn
means potentially higher kinin levels.205 Kinins are a group of polypeptides that
inﬂuence smooth muscle contraction and increase blood ﬂow and capillary
density, all of which lead to increased oxygen and substrate delivery to
muscles.205 These effects may improve the efﬁciency of muscle aerobic
metabolism and the contractile function of both skeletal and cardiac muscle.94
The 00 genotype is associated with increases in angiotensin II, which is
reported to inhibit glucose uptake of smooth muscle, inhibit and Iipolysis in
both skeletal and adipose tissue, and cause hypertrophy in cardiac
myocytes.25'29'81'164 The hypertrophic response to physical training may be
attenuated by the presence of the ACE 0 allele, resulting in left ventricular
hypertrophy, which in turn would increase VOzmax via increased stroke

volume.211 The ACE 0 allele has been associated with increases in left

ventricular mass as a response to training.129 Further, angiotensin II has been

38

 

LE

Ph

totc

gel

be

 

shown to regulate oxygen consumption and affect muscle energy
expenditure.25 Thus, there a number of biologically plausible reasons the ACE

l/D polymorphism could be related to PA or aerobic ﬁtness.

L6.1 Evidence for an association between ACE ID Polymorphism and
Physical Activity/Aerobic Fitness

In a Portuguese twin study, Maia et al.112 reported that 63% and 32% of
total variance in leisure time PA in males and females was explained by
genetic factors.112 Speciﬁcally, the ACE l/D polymorphism has been shown to
be associated with PA. Winnicki et al.205 studied a group of 355 young (mean
age ~33 years), mildly hypertensive Italian adults. They found that the 0 allele
was associated with lower levels of PA.”5 Further, those participating in sports
had an increased frequency of the II allele as did those who were
sedentary.205 They also stated that the ACE l/D polymorphism was the
strongest contributor to PA status.”5 The authors assessed PA through a
questionnaire that dichotomized PA (sedentary and exerciser) based on
relative intensity while also assessing hypertensive adults.205

Numerous studies have shown associations between the ACE Iallele
and aerobic ﬁtness.2“'51'7‘3'130'135'189 Alvarez et al.24 studied a group of 60
professional Spanish, male athletes that participated in cycling, long distance
running, and hand ball and compared them to 400 healthy controls. They

found an increased frequency of the ACE I in athletes compared to healthy

controls?4 This result implies that there is an association between those with

39

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higher levels of aerobic ﬁtness, the “endurance athletes”, and the ACE I allele.
Collins et al.51 studied 447 Caucasian male triathletes who completed the
2000 or 2001 South Africa lronman and compared them to 199 Caucasian
healthy controls. After subdividing the triathletes into homogenous groups
(fastest 100 ﬁnishers, slowest 100 ﬁnishers, South African born ﬁnishers, 100
fastest South African born ﬁnishers and the slowest 100 South African born
ﬁnishers) they reported that frequency of the ACE I allele was higher in the
fastest South African born ﬁnishers compared to controls? However, there
was no difference in allele frequency or genotype between the fasted 100
ﬁnishers and the slowest 100 ﬁnishers or controls.51 These results suggest
that the ACE Iallele is associated with performance in the highly aerobic event
of triathlons, but the association between the ACE I allele and aerobic
performance or ﬁtness may be dependent on genetic origin. Regardless, the

I51 show that there is an association between the ACE I

results of Collins eta
allele and ﬁtness. Gayagay et al. 78 performed a case-control study, in which
they assessed the association of the ACE I/D polymorphism with
cardiovascular adaptations to exercise in Olympic level rowers (n=64) and
matched controls (n=118). They found that the rowers had an increased
frequency of the ACE I allele and the II genotype compared to controls.78
Myerson et al.135 assessed 495 Olympic level athletes from varying sport
disciplines. They found in runners, as the distance of the event increased so

did the frequency of the I allele.135 Further, there was no association reported

in athletes who participated in sporting disciplines that were not aerobic in

40

nature. Tsianos et al.193 studied 35 elite swimmers and found that the ACE I
allele was more prevalent in those competing in long distance events (25 km
races), while the ACE 0 allele was more prevalent in those competing in short
distance events (1-10km distance races). Montgomery et al.130 compared 25
male, British high-altitude mountaineers, considered to have high aerobic
ﬁtness, to 1,906 healthy British, male controls. They found the genotype

. distribution to be signiﬁcantly different between the mountaineers and controls,
with more mountaineers having the II genotype.13o Additionally, none of the
climbers who ascended greater than 8,000 meters were homozygous for the
00 genotype.130 Thompson et al.189 also studied high-altitude mountaineers
(n=141) to assess the association of the I allele with the success of ascending
greater that than 8000 meters. The authors determined that the distribution of
the ACE genotype was signiﬁcantly different between mountaineers who
successfully ascended 8000 meters and those who were unsuccessful.189
These studies demonstrate an association between the I allele of the ACE I/D
polymorphism and aerobic ﬁtness. A caveat to these results is that none of
these studies included actual measured or estimated aerobic ﬁtness, but
rather aerobic ﬁtness was implied based on the type of athlete chosen,
including cyclist, triathletes, Olympic level runners and rowers, as well as high
altitude mountaineers. While these studies did ﬁnd an association between the
ACE I allele and aerobic ﬁtness, they speciﬁcally looked at the genotype

frequency of elite athletes and the ﬁndings could potentially be due to

selection bias.

41

Other studies have found an association between aerobic ﬁtness and
the ACE l/D polymorphism, but instead of the I allele these studies report an
association with the D allele.2""5“'2°5'211 Costa et al.54 reported that the allele
distribution was signiﬁcantly different between Portuguese elite short distance
swimmers, considered anaerobic, and elite middle distance swimmers,
considered a mix between anaerobic and aerobic. They reported that the 0
allele was more prevalent in the long distance swimmers, who may have had
higher aerobic ﬁtness levels.54 Amir et al.25 compared the ACE allele
frequency in 121 (male and female) Israeli elite endurance athletes, elite
power athletes and healthy controls. The 0 allele was more frequent in
endurance sport athletes than in power sports athletes or controls.25 Again,
the endurance athletes with an assumed higher aerobic ﬁtness, had a
signiﬁcantly higher frequency of the 0 allele. Similar to the association
between the ACE Iallele and aerobic ﬁtness, these associations between the
ACE 0 allele and aerobic ﬁtness are reported in selected groups of athletes
who are assumed, but not measured, to have high levels of aerobic ﬁtness.

Only one study was found that included a direct measure of aerobic
ﬁtness. In a sample of 63 young (23 years of age) Chinese men, Zhao et al.211
assessed aerobic ﬁtness using a treadmill running test to volitional exhaustion.
In this study, the DD genotype was found to be associated with higher levels
of measured aerobic capacity, 2" thus providing measured evidence that the

ACE 0 allele may be associated with aerobic ﬁtness.

42

The ACE l/D polymorphism has been shown to be associated with PA
or aerobic ﬁtness. The majority of these studies have been conducted in
selected athletes to assess the association with aerobic ﬁtness. While there
are a number of studies showing an association, some show an association

24,51 ,78,130,135,189

with the I allele while others show an association with the 0

25.54.2052" and PA or aerobic ﬁtness.

allele
L6.2 Evidence against an association between ACE ID Polymorphism
and Physical ActivityIAerobic Fitness

In contrast to ﬁndings reported in the previous section, there are studies
that report no association between the ACE l/D polymorphism and PA or

L75

aerobic ﬁtness. Fuentes eta assessed moderate intensity leisure time PA

in 721 middle-aged Finnish adults and found no association with the ACE I/D

L75

polymorphism. In this study, Fuentes eta assessed leisure time PA

through the use of a single self-reported question. A singular question may not
provide the validity needed to investigate gene effects.203
Other investigators have also reported no association between aerobic
ﬁtness and the ACE l/D polymorphism. In a study of 60 untrained women
(mean age 24 years), Day et al.59 assessed the ACE l/D polymorphism and
maximal oxygen uptake and found no association. Similarly, Rankinen et al.150
found no association between measured VOzmax values and the ACE I/D

polymorphism in 724 sedentary adults. In a comparison of 192 aerobically ﬁt

male athletes, VOzmax 2 75 ml/kg/min, and 182 sedentary males controls

43

there was no difference in genotype or frequency between groups.151 Further,
there was no difference in genotype frequency among the athlete quartiles.151
In 147 US army recruits, Sonna et al.174 performed pre and post basic training
ﬁtness tests, including a timed 2 mile run. The authors found no difference in
pre basic training or post basic training estimated VOzmax values among the

ACE l/D polymorphism. Taylor et al.186

assessed frequency of the ACE
polymorphism in a group of 120 aerobically trained, Australian national
athletes and compared them to 685 healthy controls. Their analysis revealed
no differences in genotype distribution between the aerobic athletes and the

healthy controls.

L6.3 ACE ID polymorphism and Physical Activity/Aerobic Fitness
Summary

Based on the current literature it is unclear how the ACE I/D
polymorphism is associated with PA or aerobic ﬁtness. In the studies that have
shown an association, the nature of the relationship is still ambiguous with a
number of studies reporting that the I allele is associate with PA or aerobic

24-51'78-‘30'135'189 with others report that the 0 allele is associated with PA

ﬁtness
or aerobic ﬁtness.25'5“"°'°5'211 Further, there is evidence that refutes an
association between the ACE l/D polymorphism and PA and aerobic
ﬁtness.59'7""“’°'151 Further work must be completed in this area to conﬁrm or

disprove the biologically plausible association between the ACE llD

polymorphism and PA and aerobic ﬁtness.

44

L7 ACE ID Polymorphism and Cardiovascular Disease Risk Factor
Literature Review Summary

In summary, the ACE I/D polymorphism is highly researched, but few
deﬁnitive results exists in terms of its relationship with CVD risk factors, like
BP, lipids, and fatness. There are several biologically plausible mechanisms
for how the ACE I/D polymorphism could be related to modiﬁable CVD risk
factors, which is why further studies must be completed to discern this
relationship. Further, understanding the relationship that the ACE I/D
polymorphism has with PA or aerobic ﬁtness is necessary as they both have
been shown to be associated with CVD risk factors. Understanding an
interaction between these genetic and environmental factors is important,
since this could affect risk factors or disease progression and/or severity. By
examining this association in young healthy adults it may be possible to deﬁne
the role of genetics with less chance of co-morbidities such as age, chronic

disease, or other co-morbidities confounding the results.

45

CHAPTER 3

METHODOLOGY

M1 Research Design

A cross-sectional study design was used to assess PA, aerobic ﬁtness,
ACE genotype, and other CVD risk factors of college students enrolled in a
healthy lifestyles class at Michigan State University (MSU). All students
enrolled in class during fall, 2007 and spring, 2008 semesters were eligible for
the study. The research protocol had three major elements: 1) completion of
an online survey with standardized questions, 2) completion of CVD risk factor
assessment including measures of BP, cholesterol, BMI and percent fat, and
3) collection of mouth buccal cells for genetic testing. Written informed consent
from all study participants was obtained prior to data collection. This study was
approved by the Michigan State University Biomedical and Health Institutional

Review Board.

M2 Subject recruitment

Study investigators attended the lecture portion of one class period for
each of the healthy lifestyle class sections (total of 6 sections). A lecture on
CVD, associated risk factors, and prevention strategies was presented to each
class and followed by a detailed description of study objectives, requirements,
and incentives. Students interested in the study completed and signed a

consent form that included a randomly assigned survey ID number and

46

 

a l

pa

 

crl

 

 

 

 

 

ii A i

instructions for accessing the online data collection instrument (Appendix A).
For those who consented, DNA was collected (explained beloW) for data

analysis.

M3. Eligibility Criteria (inclusion/exclusion)

All students enrolled in the healthy lifestyles class during the fall 2007
and spring 2008 semesters were eligible for participation in this study. All
participants had to be 18 years of age, but there were no other exclusion

criteria applied to the undergraduate enrollees.

M4 Survey data collection
The majority of data collection utilized the Longitudinal Study Engine

(LSE), an internet-based surveillance system developed at MSU, within the

ﬁrst two weeks of each semester. The LSE is a menu-driven web page,

accessible by any major internet browser, with main features detailed below:

a) Each study participant was provided online access to the LSE
(http://lse.commtechlab.msu.edu/Iogin.php)

b) LSE software uses a unique survey ID number (provided to participants) to
keep track of each individual survey protocol and also to keep track of each
individual designated participant's responses at baseline and/or over time.
(The LSE builds a unique ID number that tracks both the survey protocol

and the individual participant.)

47

c) At the time of completion of the initial baseline assessment, the designated

01)

participant accesses the LSE website, keys in his/her LSE ID number, and
is prompted to key in a personal user_id, and password. Prompting
questions will be asked to help the individual recall the user_id and
password if these should be lost or forgotten (e.g., What is the ﬁrst initial of
your mother's maiden name (surname before marriage)?')

Thereafter, the web browser displays the study’s disclosure statement,
which invites participation, and the participant can key a'NEXT button to
proceed to complete the survey, or can log out at any time. The LSE is
designed to allow the participant to terminate participation and to skip over
any survey item.

Survey elements used in this investigation included true/false, multiple
choice, and short answer items.

The LSE captures keyed responses as well as time elements (e.g., time of
log-in, time of log-out), with storage of these responses within a protocol-

speciﬁc EXCEL spreadsheet stored on the LSE secure server.

M5 Demographics and Physical Activity Data

Participant demographic information such as age, gender, race and

academic major were collected using the LSE. Physical activity during the past

month was assessed using a questionnaire, based on questions taken from

previously validated instruments,

1649“” and was administered through the

LSE. If the participant indicated that s/he performed leisure-time PA, a series

48

 

 

 

rec

Em

 

of three questions were asked to assess type (mode), freqUency (sessions per
week), and duration (average minutes per session) of PA. The same series of
questions were repeated for up to two more activities. Each activity was
assigned a metabolic equivalent value (MET) using the Compendium of
Physical Activities.19 One MET is equivalent to one kilocalorie expended per
week indexed by body weight (kcal/kg/week). Weekly energy expenditure
(kcal/kg/week) was extrapolated using the MET values for each activity, the
number of sessions per week, and the number of minutes per session.
Sedentary behaviors were assessed by determining the number of hours per
day spent watching television, playing video games, using the computer, or

reading/studying (Appendix 3).

M6 Cardiovascular Disease Risk Factors and Fitness

As part of enrollment in healthy lifestyles class, each student was
required to complete a health assessment at the beginning of the semester.
Employees of Olin Health Center at MSU conducted the health assessment.
The health assessment included measures of height (cm) via stadiometer,
weight (kg) via a triple beam balance, and a BMI (kg/m2) calculation. Seated
resting systolic and diastolic BP were assessed via an automated cuff placed
on the right arm. Values that exceed 140 (systolic) or 90 (diastolic) millimeters
of mercury were repeated for accuracy. Body fatness was assessed by four
site skinfold (triceps, abdomen, ilium, and thigh). Skinfold measures were used

because of their feasibility, reliability, and validity of estimating body

49

 

 

 

3T

ole

pla

est

flint

 

composition in the ﬁeld.132 Skinfold sites were marked and measured on the
right side of the body in accordance to American College of Sports Medicine
guidelines (ACSM).8 AII measures were taken in a triplicate series by the same
person using a Lange caliper (Beta Technology Incorporated, Santa Cruz,
CA). Average of the three measures taken at each site was calculated and
then summed. The sum of four site skinfold was then used to estimate
percent fat using a standard equation (Appendix C).132 Nurses from Sparrow
Hospital (Lansing, Michigan) collected non-fasting blood samples to assess
total cholesterol and total high-density Iipoproteins. Smoking was assessed by
a speciﬁc question via the LSE. Aerobic capacity (ﬁtness) was estimated via
an instructor administered one and half mile endurance test completed during
class. The one and half mile endurance test is a valid ﬁeld tool (correlation of
R=0.79-0.928°"°3"19'212 ) in predicting VOzmax and a very expeditious
technique to use with large groups.8"°3 The objective of the endurance test is
to cover one and half miles in the shortest time possible, with time being
placed in an equation (V02max (ml/kg/min) = 3.5 + 483/(time in minutes) to
estimate aerobic capacity.8 Following the completion of the health/aerobic
ﬁtness assessment, students recorded their results on a data sheet that was

returned to the principal investigator (Appendix 0).
M7 Genetic Sampling
Indicator FTA® cards (Whatman Plc.) were used to collect and store

participants’ deoxyribonucleic acid (DNA), obtained on the day of recruitment.

50

 

 

ch
Fc
re;

Pa

COT

 

Indicating FTA® cards are saturated with a chemical that lyses cell membranes
and denature proteins on contact while trapping, stabilizing and protecting
nucleic acids in a cellulose ﬁber. Fresh buccal epithelial cells were removed,
non-invasively, from the participant’s mouth using a foam tipped applicator and
placed onto the indicating circle (changes color when sample is applied) of the
FTA® card. Once the sample was applied to the indicating FTA® card, the card
was individually sealed in a plastic bag to be stored at room temperature until

further use.122

Illl8 Genetic Testing

Immobilized DNA on each FTA® card was prepared for polymerase
chain reaction (PCR) via modiﬁed manufacture’s protocol (Whatman Plc.).10
Four discs per card (one card per subject) were punched into a 0.2 ml PCR
reaction tube using a 1.58 mm hole punch (approximately 1/16 inch).
Participant samples were prepared in batches of 24. To minimize
contamination the punch was cleaned between samples using a clean lint free
wipe moistened with 70% ethanol and allowed to air dry for 30 seconds
between samples. After punching the FT A® card, samples were washed per
manufacture’s protocol (Whatman Plc.)10 with a ﬁve minute incubation being
completed on a MuIti-Microplate Genie Rotator (Scientiﬁc Industries) at
approximately 1800-1900 RPM. After completing the ﬁnal wash all liquid was

aspirated off the disc. This step was performed in duplicate to ensure removal

51

of all buffers. At this point, 5 ul of double distilled water was add to each PCR
tube and held at 80°C for approximately 30 minutes.

DNA was then ampliﬁed by PCR according to modiﬁed methods of
Lindpainter et al.108 as described in detail. After a 30 minute hold, a 50 pL
PCR reaction master mix containing: 1 uM of each primer (hace3s,
5’GCCCTGCAGGTGTCTGCAGCATGT3’; hace3as,
5’GGATGGCTCTCCCCGCCTTGTCTC3’), 200 (M each of deoxynucleotide
triphosphates, 1.3mM magnesium chloride, 50mM potassium chloride, 10 mM
TRIS-hydrochloric acid (pH 8.4 at 23°C) and 0.35 units of Thomas aquaticus
polymerase (Applied Biosystems). DNA was ampliﬁed for 35 cycles with
denaturing at 94°C for 30 seconds, annealing at 56°C for 45 seconds, and
extension at 72°C for 2 minutes using a GeneAmp PCR system 9700 (Applied
Biosystems).

Ten uL of PCR product was combined with 0.5 uL of 5X glycerol based
loading buffer and loaded into a 1.5% agarose gel submerged in TAE buffer.
The products were then electrophoresed to separate DNA fragments of
differing length (597 bp for I allele and 319 bp for 0 allele) for 0.7 hours at
approximately 5Vlcm. The gel was then stained with a 0.02% to 0.1% solution
of ethidum bromide (0.1 ml diluted in 500-750 ml H20) for ten to ﬁfteen
minutes and visualized with UV transillumination.

Due to the preferential ampliﬁcation of the 0 allele in heterozygous

172

samples, all samples found to be the DD genotype were subjected to a

second independent PCR ampliﬁcation using an insertion speciﬁc primer pair

52

(hace5a, 5’TGGGACCACAGCGCCCGCCACTAC3’, hace50,
5’TCGCCAGCCCTCCCATGCCCATAA3’). PCR conditions were the same
with the exception of an annealing temperature of 67°C. Positive reaction
produced ampliﬁcation of a 335 base pair amplicon only in the presence of an
I allele. All assays were performed in the Histocompatibilty and
Immunogenetics Laboratory in the Department of Medicine at Michigan State

University.

M9.1 Statistical Analysis - Data Management

Collected FTA® cards and completed health assessment forms were
separated from identifying information and stored in locked ﬁling cabinets.
Data from the FTA® card and health assessment forms were entered into in
ExcelTM database and stored on a password protected computer. Responses
from the LSE online questionnaire were abstracted directly into the data ﬁle.

Data ﬁles were backed up regularly and stored in secure locations.

M9.2 Statistical Analysis - Data Linkage

Each participant was assigned a unique coupon number that was
recorded on the study questionnaire, health assessment form, and FTA® card.
Data were linked in a single anonymous database using this coupon number.
No personal identiﬁcation could be accessed from the database. The de-

identiﬁed database was used for statistical analysis.

53

M9.3 Statistical Analysis - Data Cleaning

All data were evaluated for miscoded, erroneous, and missing values.
Individual erroneous variables were replaced by imputing the sample mean.
Frequency distributions were assessed indicating that no recoding and/or

transformations were necessary.

M9.4 Statistical Analysis - Variable Deﬁnitions
Main exposure variables:

Angiotensin Converting Enzyme l/D Polymorphism: ACE I/D
polymorphism data were obtained through the genetic testing. For analysis,
ACE I/D polymorphism was categorized by genotype (II, ID, 00).

Physical activity: Participation in leisure time PA was assessed by
asking each participant if they participated in leisure-time PA or exercise in the
past month. If yes, participants where then asked to assess the type,
frequency, and duration of the activity. Physical activity was expressed as a
continuous variable calculated as kilocalories expended per week indexed by

body weight (kcaI/kg/week).

Primary outcome variables:
Blood pressure: BP was recorded as millimeters of mercury (mm Hg)

and analyzed as a continuous variable.
Total cholesterol: TC was recorded in units of milligrams per deciliter

(mgldL) and analyzed as a continuous variable.

54

High Density Lipoprotein cholesterol: HDL cholesterol was recorded as
mg/dL and analyzed as a continuous variable.

Body Mass Index: Weight (kg) and height (m) were collected and body
mass index (kg/m2) calculated. For analysis, BMI was assessed as a
continuous variable as well as was a categorical variable deﬁnitionsof normal
weight (5 24.9 kg/mz), overweight (25-29.9 kg/mz), and obese (230 kg/mz).

Body fatness: Percent body fatness was measured via skinfolds and

analyzed as a continuous variable.

Additional variables:

Gender: Gender was be deﬁned as a dichotomous variable
(male/female).

Age: Because of the age range of recruited participants, age was
deﬁned as a categorical variable (18 years, 19 years, 20 years and 2 21
years).

Race: Due to the relatively low numbers of non-Caucasian minorities,
race was dichotomized as Caucasian or Other

Total seat time: A variable composed of various sedentary behaviors
including study time, television viewing time, playing of video games and
computer use. This variable was analyzed as a continuous variable (hours)

Smoking: Current smoking was deﬁned as a yes/no dichotomous

variable

55

Estimated VOgmax: An estimate of maximal oxygen consumption
(VOzmax) was determined based on the time it took to cover one and half
miles (Appendix C for formula). The VOgmax (ml/kg/min) value was analyzed

as a continuous variable.

M9.5 Statistical Analysis - Sample Size Calculation

Linear regression was used to evaluate the relationships between PA
and ACE |/D polymorphism on the presence of CVD risk factors (blood
pressure, total cholesterol, HDL cholesterol, BMI, and percent body fat).
Power analysis to detect significant linear regression coefficients for the
prediction of outcome variables was performed with the conservative
assumption that regressions would include nine predictors. Based on the
means and standard deviations of the study sample, and a fairly large effect
size of 0.30 will enable us to detect a difference beyond measurement error for
all outcome variables.

For this study there were 600 potential participants. Two hundred and
seventy five participants completed all data collection (PA questionnaire, CVO
data sheet, and a FT A® card). Based on a PCR success rate of 80%,122 our
study sample size would be 220. Assuming alpha equal to P s 0.05 and at
sample size of 220, we will be adequately powered (Power > 0.99) to detect
an effect size of 0.30. This sample size should be large enough to provide the

power necessary to effectively assess study aims two and three.

56

In assessing study aim four, the interaction of PA and the ACE l/D
polymorphism on CVD risk factors, the allele frequency and the association
between the exposure and outcome variables must be considered. It is
suggested that to detect interactive effects that you need at least four times as
many participants than needed for the evaluation of main effects.90 With a
power of 0.8 and signiﬁcance 0.05, it is estimated that a minimum of 1279
participants would be need to complete this portion of the study. Due to that
fact that the maximal sample size could only be 600 participants, study aim #4

will be exploratory.

M9.6 Statistical Analysis

Aim One: To assess the PA and CVD risk factor proﬁle of college students,
basic descriptive analysis were used. Means, standard deviations, medians,
intra-quartile ranges and histograms were calculated for each of the following:
systolic and diastolic BP, TC, HDL, BMI, percent body fat, PA, and estimated
VOzmax. Percent distributions were calculated for gender, age groups, race,
PA quartiles and BMI categories. Due to the fact that previous
investigatorsam4 have shown male students to be more likely than female
students to report high levels of PA, we used univariate logistic regression
analysis to develop odds ratios for differences in categorical variables between
genders. Further, an analysis of variance was used to assess differences in

continuous variables between genders and among the PA quartiles.

57

Aim Two: A student’s test was conducted to assess mean differences of
continuous variables between each dichotomized variable (gender, race,
major, and smoking). To determine the associations between PA and BP, TC,
HDL, BMI, and percent body fat, linear regression analysis was utilized.
Univariate linear regression was used to assess the relationships between PA
and a single dependent variable (BP, TC, HDL, BMI, or percent body fat). Five
separate multiple linear regressions models were developed to assess both
the main effects and interaction effects (based on post hoc statistical analysis)
of PA on each dependent variable while controlling for covariates (Appendix
E). The first step in the development of these multiple linear regression models
was the generation of a baseline model which included the main effect of PA,
plus two a-priori confounders - gender and race. All other covariates were then
tested by adding them to this base model and then removing them using
backwards elimination methods if their associated p-value was > 0.10. Non-
significant variables were not removed if their removal changed the estimate
for the remaining variables by more than 10%. Modeling was completed with
interactions being assessed for significance first, followed by main effect
terms. No main effect term was eliminated if it was included in a significant
interaction term. The final regression model was tested for significance (p-
value 5 0.05) and included all variables that had a p-value s 0.10. The same
modeling procedure was repeated for each of the five multiple linear

regressions.

58

Aim Three: Univariate linear regression models were developed to assess the
relationship between the ACE l/D polymorphism and a single dependent
variable (BP, TC, HDL, BMI, percent body fat, PA or evogmax). Multiple linear
regression models were developed to assess the impact of the ACE l/D
polymorphism on a dependent variable (BP, TC, HDL, BMI, percent body fat,
PA or eV02max) while controlling for covariates (Appendix E). The first step in
the development of these multiple linear regression models was the
generation of a baseline model which included the main effect of the ACE l/D
polymorphism. All other covariates were then tested by adding them to this
base model and then removing them using backwards elimination methods if
their associated p-value was > 0.10. Non-significant variables were not
removed if their removal changed the estimate for the remaining variables by
more than 10%. Modeling was completed with interactions being assessed for
significance first, followed by main effect terms. No main effect term was
eliminated if it was included in a significant interaction term. The final
regression model was tested for significance (p-value s 0.05) and included all
variables that had a p-value s 0.10.The same modeling procedure was

repeated for each of the five multiple linear regressions.

AIM Four: Multiple linear regression analysis was used to determine whether
PA affects the association between ACE I/D polymorphism and BP, TC, HDL,
BMI and percent body fat in college students. Multiple linear regression

models assessing the effects of ACE I/D polymorphism and PA on individual

59

dependent variables (3P, TC, HDL, BMI, or percent body fat) was developed.
To obtain the most parsimonious model, all covariates (Appendix E) were
entered into the model and removed in a backwards stepwise fashion based
on the level of the p-value. Finally, ﬁve multiple regression models were
developed, containing both the main and interactive effects of the ACE IID
polymorphism and PA on each dependent variable while controlling for the

covariates (Appendix E).

60

CHAPTER 4

RESULTS

R1 Descriptive Data (Study Aim #1)

Among the 600 students eligible to participate, a total of 191 (32%)
completed both the online assessment and the CVD risk factor proﬁle, while
149 (24.8%) completed all aspects of data collection (i.e., online assessment,
CVD risk factor proﬁle and genotyping). The sample was similar to an earlier
sample of 388 participants from the same class studied during the 2005-2006
academic year.139 The only signiﬁcant difference between the two study
samples was that systolic BP was higher in the 2005-2006 cohort
(SBP=124t15 bpm) as compared to the 2007-2008 sample (SBP=118114
bpm). For the sample, 90% reported meeting the American College of Sports
Medicine (ACSM) minimal PA recommendation of 7.5 kcals/kg/wk (I' able
1A).133 Descriptive data for age, race, major, smoking status, BMI categories,
PA quartiles, and genotype are shown in Table 1A. The majority of the sample
was female (69%), Caucasian (84%), non-smokers (85%) and of normal
weight (72%) based on BMI. Chi square analysis was used to assess gender
differences for BMI categories, PA levels and genotype. A signiﬁcant
difference (p<0.05) was found between genders for BMI categories and PA
levels. Males were 3.3 times and 1.8 times more likely than females to be
overweight or obese, respectively. Further, males were 3.7 time and 6.4 times

more likely than females to be in the 3rd (28.0-54.7 kcaIs/kg/week) and 4th

61

(>54.7 kcalslkg/week) PA quartile, respectively. The ACE polymorphism was
found to be in Hardy-Weinberg equilibrium (p=0.08). There was no difference
in genotype based on gender (p=0.097), although when Hardy-Weinberg
equilibrium was analyzed by gender, the genotype in males deviated
(p=0.032) from Hardy-Weinberg, while the females remained in equilibrium
(p=0.36).

Cardiovascular disease risk factor measures for the sample and by
gender are found in Table 13. Systolic and diastolic BP, eVOzmax and PA
were signiﬁcantly (p<0.05) higher in men (SBP:130113 mmHg; DBP:7617
mmHg; eVOzmax 41 .916.4 ml/kg/min; PA 48.21289 kcalslkglwk) than women
(SBP:114111 mmHg; DBP:7219 mmHg; eVOzmax 34517.2 mI/kg/min; PA
31.5 kcalslkglwk). Body mass index had a small (1.7 kg/mz) but signiﬁcant
(p<0.05) mean difference between the genders (males: 24813.7; females:
23.1137). Conversely, HDL and percent fat was signiﬁcantly (p<0.05) lower in
men (HOL:53110 mgldL; %fat:15.415.4) than women (HDL:64114 mgldL;
%fat:26.715). No gender differences were found for other variables.

Further descriptive characteristics for the sample by PA quartile are
shown in Table 10. Systolic BP was signiﬁcantly (p<0.05) lower in PA
quartiles 1 (0—14.8 kcalslkglwk) and 2 (14.9-28.0 kcalslkglwk) when compared
to the highest quartile (>54.7 kcalslkglwk). Percent fat decreased as PA
activity quartiles increased with percent fat in quartiles 1 and 2 being
signiﬁcantly (p<0.05) higher than both quartiles 3 and 4. Body mass index in

quartile 1 was signiﬁcantly (p<0.05) higher than all other quartiles. Estimated

62

maximal oxygen consumption increased linearly with increased PA quartiles,
with those in quartiles 1 and 2 being signiﬁcantly (p<0.05) lower than all other

quartiles. Total seat time was not signiﬁcantly different among quartiles.

R2.1 Association between Physical Activity and Systolic BP (Study aim
#2)

Descriptive data for systolic BP (SBP) in terms of gender, race, age,
major and smoking are shown in Table 2.1A. A Student’s t-test was used to
assess differences in mean SBP between each dichotomized variable
(gender, race, major, smoking) while analysis of variance (ANOVA) was used
to assess differences in SBP among age groups. The only signiﬁcant ﬁnding
was between SBP and gender, with males (SBP=130113 mmHg) having a
signiﬁcantly (p<0.05) higher SBP than females (SBP=114111 mmHg).
Univariate linear regression showed that PA was signiﬁcantly related to SBP
(B=0.09 [0.20-0.159], p<0.05). Other variables including HDL, percent fat, BMI
and eVOzmax were signiﬁcantly related to SBP in univariate analysis (table
2.13). Multiple regression was conducted to assess the outcome variable of
SBP with a base model of PA, gender, and race. The remaining covariates
(TC, HDL, percent fat, BMI, eVOzmax, total seat time, and smoking risk) were
entered into the model and then removed if non-signiﬁcant (p>0.10) via
backwards elimination. A model with four predictors estimated 40% (F4452
=25.12; p<0.05) of the total variance for SBP (Table 2.1C). Body mass index

and gender were signiﬁcantly related to SBP. Speciﬁcally, participants who

63

were male and lor had an increased BMI had higher SBP. Total PA was not a

signiﬁcant contributor to the model when controlling for covariates.

R2.2 Association between Physical Activity and Diastolic BP (Study aim
#2)

Descriptive data for diastolic BP (DBP) are shown in Table 2.2A.
Diastolic BP was signiﬁcantly different (p<0.05) between genders (male:7617
mmHg; femalesz7219 mmHg). Univariate linear regression showed that PA
was not signiﬁcantly related to DBP ([3=-0.008 [-0.046-0.031], p=0.702). Of the
covariates, only BMI was signiﬁcantly related to DBP ([3=0.692 [0394-0990],
p=0.00) during univariate analysis (Table 2.23). Further, a ﬁve predictor
multiple regression model (F5,151=6.104, p < 0.05) estimated 17% of DBP.
Percent fat (B=0.485 {0.23-0.74}, p<0.05) and gender (B=-9.65 {-13.39- —5.91],
p<0.05) were signiﬁcant predictors of DBP (Table 2.2C). PA remained a non-

signiﬁcant predictor of DBP when controlling for covariates.

R2.3 Association between Physical Activity and TC (Study aim #2)
Descriptive data for TC is shown in Table 2.3A. A signiﬁcant difference
(p<0.05) in mean TC between races (Caucasianz172130 mgldL; Otherz151139
mgldL) was present. Physical activity was not related to TC (3 =0.03 {-0.157-
0.164], p=0.968). No other variables were related to TC during univariate
analysis (Table 2.33). Multivariate analysis revealed that all variables were not

signiﬁcant predictors of TC (Table 2.30).

64

R2.4 Association between Physical Activity and HDL Levels (Study aim
#2)

Males (53139 mgldL) had (p<0.05) lower mean HDL levels than
females (64139 mgldL) (Table 2.4A). High density Iipoprotein levels were
different among age groups. Speciﬁcally, participants 221 year of age had a
lower mean HDL concentration than all other age groups. Physical activity was
not signiﬁcantly related to HDL (B=-0.017 [-0.09-0.056], p=0.644) (refer to
Table 2.43). Univariate analysis found that both SBP and BMI were
signiﬁcantly related to HDL. On further analysis, BMI (B=-1.206 [-1.82- -0.59],
p<0.05), smoking risk ([3 =7.64 [082-1446], p<0.05) and gender ([3 =9.16
[4.59-13.73], p<0.05) were signiﬁcant predictors of HDL cholesterol while SBP

and PA where non-signiﬁcant predictors Table 2.4C).

R2.5 Association between Physical Activity and Body Composition
(Study aim #2)

Descriptive data for the sample related to body composition are
displayed in Table 2.5A. Body composition (%fat) was signiﬁcantly different
(p<0.05) between genders (malez1515; females22715). Univariate liner
regression analysis indicated that PA was signiﬁcantly related to percent fat
(l3=-0.098 {-0.133 - -0.063], p<0.05). Further, univariate linear regression
indicated that SBP, HDL, and eVOzmax also had a signiﬁcant (p<0.05)
relationship with percent fat (Table 2.53). In controlling for covariates, a

multiple regression model with six predictors (DBP, BMI, eVOzmax, PA, race

65

and gender) estimated 82% (F5,150=112.07; p<0.05) of the total variance in
percent fat values. Due to issues of multicollinearity between PA and
eVOzmax, data were re-analyzed with the removal of eVOzmax and a more
parsimonious model with ﬁve predictors estimated 81% (F5,154=132.27;
p<0.05) of the total variance in percent fat values (Table 2.5C). Physical
activity, BMI and gender were all signiﬁcant contributors to the model. While
controlling for other variables, PA had an inverse association with percent fat
values, indicating that as PA participation increased percent fat value

decreased.

R2.6 Association between Physical Activity and BMI (Study aim #2)

As shown in Table 2.6A, males (BMI =24.8) had a signiﬁcantly higher
(p<0.05) BMI values than females (BMI =23.3). There were no differences in
BMI among the other categorical variables. Univariate linear regression
indicated that PA activity was not signiﬁcantly related with BMI (B=-0.01 [-
0.028 - 0.08], p=0.259). Systolic BP, DBP and percent fat had signiﬁcant
positive relationship with BMI, while HDL and eVOzmax had inverse
relationship with BMI (Table 2.63). Using multivariate analysis to control for
covariates, SBP ([3 =0.05 [0.02 - 0.08], p=0.003) and percent fat (8 =0.48 [0.40
- 0.57], pS0.05) remained positive determinants of BMI while HDL (B =-0.03 [-
0.06 - -0.01], p=0.015) remained a signiﬁcant inverse determinate of BMI.
Thus, being male, having increased SBP, and/or increased percent fat value

increased the BMI values while having a higher HDL level lowered BMI, while

66

controlling for other factors (Table 2.6C). PA remained a non-signiﬁcant

predictor of BMI.

R2.? Summary of the association between Physical Activity and
Cardiovascular Disease Risk Factors (Study aim #2)

The purpose of study aim #2 was to determine the association between
PA levels and BP, blood lipid concentration, BMI and body fatness in young
adults. It was hypothesized that PA would be inversely related with SBP, DBP,
TC, BMI, and percent fat while be directly related with HDL. However, results
showed that only percent fat values were signiﬁcantly and inversely associated

with PA.

R3. Association between ACE ID Polymorphism and Cardiovascular
Disease Risk Factors (Study aim #3)

Of the 600 eligible study participants usable ACE I/D genotype data
were obtained in 149 (24.8%). The genotype frequency for the sample was
24= II, 84=ID, and 41=DD (Figure 3.1). This was in Hardy-Weinberg
equilibrium. Of those classiﬁed as II, 12.5% were male, 90.9% were non-
smokers, and 75% were white. In the ID classiﬁcation, 33.3% were male,
87.8% were non-smokers, and 85.7% were white. Finally, the DD genotype
was 36.6% male, 94% non-smokers, and 85.4% white. There was no
difference among genotype groups for gender, race, smoking, or BMI category

(Table 3.1).

67

Cardiovascular disease risk factors and other continuous measures by
genotype are found in Table 3.2. Physical activity, percent fat, and eVOzmax
showed signiﬁcant differences among the genotype groups. Speciﬁcally, those
classiﬁed as 00 had signiﬁcantly greater amounts of PA (50.4133 kcal/kg/wk)
and higher eVOzmax (3917 mI/kg/min) values than those classiﬁed as I0
(PA:33.1126 kcal/kg/wk; eVOzmaxz3618 mI/kg/min), who had signiﬁcantly
higher values than the II group (PA:25.5117 kcal/kg/wk; eVOzmaxz3415
mllkg/min). Conversely, participants possessing the II genotype had
signiﬁcantly higher (p=0.04) percent fat (26.515%fat) values than those with
the DD genotype (21 .717%fat), but there was no difference between
homozygous groups and the heterozygous group. When similar analysis was
conducted on the sample stratiﬁed by gender there were no signiﬁcant
differences among genotype groups, except for PA in females (Table 3.3). In
females, there was a graded response with the DD genotype having the
highest PA levels (50.9137), the ID group being intermediate (26.6122) and Il
group having the lowest PA levels (24.0118). All were signiﬁcantly different
from each other (p50.05). The ACE I/D polymorphism was not signiﬁcantly
related to any other cardiovascular risk factors for the entire sample (Table

3.2).

68

R3.1 Association between ACE ID Polymorphism and Systolic BP (Study
aim #3)

Univariate analysis showed no differences in SBP between genotype
groups (F1144 =1.252; p=0.289). Further analysis indicated that race was not a
signiﬁcant predictor of SBP, but when gender was added to the genotype
model it predicted 31% of the variance in SBP (F3443 =21.051; p=0.000).
There was a difference between the genders (B=-17.205 [-21.610-12.801],
p=0.000) with females having lower SBP than males, but there were no
signiﬁcant differences in SBP among the genotypes. The ﬁnal multivariate
model included ﬁve predictors that accounted for 41.5% (F5115 =16.41;
p<0.00) of the variance in SBP (Table 3.1A). When controlling for variables
that remained in the model (Gender: B=~14.660 {-18.972- -10.347], p=0.000;
BMI: B=1.271 [0.687 - 1.856], p=0.000; TC: B=0.059 {-0.007- 0.126], p =
0.079) there was no difference among the genotypes. Additional analysis
revealed no signiﬁcant interaction between the ACE genotype and gender.

When the data were stratiﬁed by gender, there was no difference in
SBP among the genotypes in males or females. When controlling for
covariates, SBP remained similar between genotype groups. Thus, there was
no difference in SBP values between the II, ID, or 00 genotype in males or

females.

69

R3.2 Association between ACE IID polymorphism and Diastolic BP
(Study aim #3)

The ACE l/D polymorphism was not associated with DBP, as there was
no difference in DBP values among the genotype groups. Further regression
analysis with genotype and gender in the model predicted 8% (F3,143=4.395;
p=0.005) of the total variance for DBP. Similar to SBP, gender was a
signiﬁcant ([3=-4.77 [-7.5- -2.02], p=0.000) predictor of DBP, with females
having lower values than males. Additional analysis controlling for other
covariates revealed a model that accounted for 19.5% (F4,115=7.02; p=0.000)
of the total variance in DBP with four predictors (Table 32A). Percent fat
(B=0.56 {0.26-0.86}, p<0.05) and gender (8:40.47 [-14.76- —6.18], p<0.05)
were signiﬁcant predictors of DBP, but there was no difference in DBP among
genotype groups. Thus, females had a lower DBP while increases in percent
body fat increased DBP regardless of genotype.

Similar to SBP, genotype was a non-signiﬁcant predictor of DBP when
stratiﬁed by gender. In males, there was no difference in DBP among
genotypes. Interestingly, while controlling for covariates in females the ID
heterozygote ([3=—4.88 {-8.58- -1.18], p<0.05) group was signiﬁcantly different
from the DD genotype group (referent group). Speciﬁcally, those classiﬁed as
ID were more likely to have a lower DBP as compared to the homozygote
groups. Further, percent fat (B=0.54[0.15- 0.93], p<0.05]) was a signiﬁcant
predictors of DBP. As percent fat increased so did DBP, which is similar to the

ﬁnding for the overall sample.

70

R3.3 Association between ACE ID Polymorphism and TC (Study aim #3)
Similar to the ﬁnding for BP, there was no association (F11 32 =0.077;
p=0.926) between genotype and TC. When gender or racewas added to the
model neither were signiﬁcant predictors of TC and there was no difference in
TC among the ACE genotype. Further analysis controlling for covariates found
similar non-signiﬁcant results (Table 3.3A). Additional analysis on gender

stratiﬁed data revealed a non-signiﬁcant difference between genotype and TC.

R3.4 Association between ACE ID Polymorphism and HDL Levels (Study
aim #3)

No differences in HDL were found between ACE I/D polymorphism
groups in univariate analysis. Race was a non-signiﬁcant predictor when
added to the genotype model, but gender was a signiﬁcant predictor of HDL
(B=10.45 [567-1522], p=0.000). Women had higher levels of HDL compared
to men, independent of genotype. Further, analysis controlling for covariates
revealed a model where both BMI (IS-1.086 {-1.78 - -0.39], p=0.002) and
gender ([3=8.3 [3.19- 13.41], p=0.002) were signiﬁcantly associated with HDL,
but there were no differences among genotype groups (Table 3.4A). Thus,
there was no difference in HDL among genotypes, but the female tended to
have higher levels of HDL while those with increased BMI had decreasing
levels of HDL.

When stratiﬁed by gender, the only signiﬁcant results were found in

females. Speciﬁcally, BMI ([3=-1.33 {-2.20- -0.45], p < 0.05) was a signiﬁcant

71

predictor of HDL, but there was no difference in HDL among the genotypes.
This result is similar to the overall sample in that as BMI increased, HDL

cholesterol decreased.

R3.5 Association between ACE ID Polymorphism and Body Composition
(Study aim #3)

Univariate analysis revealed that percent fat values were signiﬁcantly
different among the genotype groups. Speciﬁcally, the II group had the highest
percent fat (26.5%) with those classiﬁed as ID being intennediate (23.3%) and
those classiﬁed as 00 having the lowest percent fat (21.7%). However, only
those classiﬁed as II had a signiﬁcantly different value from the referent group
(II %fat=26.5, DD %fat=21]; F2,143=3.25; p=0.042). Analysis conducted with
genotype and gender in the model accounted for 47% of the variance found in
percent fat. While gender was a signiﬁcant (3:10.614 [8.69— 12.54], p=0.000)
predictor of percent fat, it also eliminated any signiﬁcant difference that
occurred in percent fat among genotypes. Specifically, females had higher
percent fat values (26.215) than males (15415.4) and this was independent of
genotype. A multivariate regression model with ﬁve predictors accounted for
65% (F5,115=42.025; p=0.000) of the variance in percent fat values (Table
3.5A). Speciﬁcally, females had higher percent fat values. In addition, percent
fat was directly related to DBP and inversely related to eVOzmax. There was

no interaction effect found between gender and genotype.

72

When stratiﬁed by gender, there was no signiﬁcant difference in percent
fat among the ACE l/D genotypes. When controlling for covariates in males
there was no difference in percent fat among the genotypes. While non-
signiﬁcant, there was a trend for the II genotype to have the highest percent fat
(18.3%fat) values with those classiﬁed as ID being intermediate (16.5%fat)
and the DD genotype having the lowest percent fat (14.3%fat). While
controlling for covariates in females, a similar but non-signiﬁcant trend was
seen with the II genotype group having the highest percent fat (27.6%fat) and
the DD genotype having the lowest percent fat (25.9%fat). This is similar to the

ﬁnding for the entire sample.

R3.6 Association between ACE IID polymorphism and BMI (Study aim #3)
There was no difference in BMI among the genotypes. This did not
change when the analysis controlled for race or gender. Gender was a
signiﬁcant predictor of BMI (B=-1.560 {-2.813- -0.307], p=0.015), with females
having lower levels than males. In a multiple regression model controlling for
other covariates, a signiﬁcant difference (B=—1.781[—3.1 1- -0.451], p=0.009)
was found between the II group and the DD (referent) group. Speciﬁcally,
those classiﬁed as II had lower BMI than those of the 00 group. This model
contained six predictors and accounted for 60% (F6,114=27.246; p=0.000) of
the variance in BMI. Systolic 3P (B=0.50 [0.013— 0.086], p=0.008), HDL (B=-
0.040 [-0.71- -0.008], p= 0.014), percent fat (3:0.481 [0.387- 0.576], p=0.000),

and gender (B=-5.311[-6.926- -3.695], p = 0.000)) were found to be signiﬁcant

73

predictors of BMI (Table 3.6A). Speciﬁcally, females and/or having higher HDL
lowered your BMI, while increases in percent fat and SBP increased BMI.

In gender stratiﬁed analysis, there was no difference in BMI among the
genotype groups. When controlling for other covariates, BMI values remained

similar among the genotypes for both males and females.

R3.7 Association between ACE ID Polymorphism and PAIeVOzmax
(Study aim #3)

Univariate analysis revealed that PA and eVOzmax were signiﬁcantly
different among the genotype groups. Speciﬁcally, the II group had the lowest
PA and eVOzmax (PA:25.53; kcalslkg/week; eVOzmax: 33.6 mI/kg/min) with
those classiﬁed as ID being intermediate (PA:33.05 kcalslkg/week; eVOzmax:
36.08 ml/kg/min) and those classiﬁed as 00 having the highest values (PA:
50.36 kcalslkg/week; eVOzmaxz39.13 ml/kg/min). Analysis conducted with
genotype and gender in the modes accounted for 14% and 21.7% of the
variance found in PA and eVOzmax, respectively. Gender was a signiﬁcant
predictor of both PA (3:42.33 {-21.72- -2.944) and eVOzmax (B=-6.89 {-9.41-
-4.38). Speciﬁcally, females had lower PA and eVOzmax than males. For PA,
a multivariate regression model with four predictors accounted for 22% (F4419
= 8.38; p=0.00) of the variance (Table 3.7A). A signiﬁcant difference remained
between the genotype while controlling for females having lower PA and PA
being inversely related to DBP. For eVOzmax, a multivariate model with ﬁve

predictors accounted for 38.8% of the total variance (Table 3.73). While

74

controlling for percent fat and race, the ACE II genotype remained signiﬁcantly
different from the referent group (00 genotype), but the ID genotype was not
longer signiﬁcantly different. There was no interaction effect found between
gender and genotype for either model.

When stratiﬁed by gender, there was no signiﬁcant difference in PA or
eVOzmax among the ACE l/D genotypes for males, when controlling for
covariates. In females, there was a signiﬁcant difference inPA among the
genders, with It having the lowest (PA:24.0 kcalslkg/week) and DD (PA:50.9
kcalslkg/week) have the highest. Further, when controlling for covariates
(Race) a signiﬁcant difference in PA among the genotypes remained. This is

similar to the ﬁnding for the entire sample.

R3.8 Summary of associations between the ACE ID Polymorphism and
Cardiovascular Disease Risk Factors (Study aim #3)

The speciﬁc aim for this section was to determine the association
between ACE I/D polymorphism and BP, blood lipid concentrations, BMI, and
percent body fat in young adults. It was hypothesized that the DD genotype
would be associated with increased 3P, TC, BMI, percent body fat and
inversely associated with HDL cholesterol. The data from this sample did not
support the hypothesis. There was no association between SBP, DBP, TC,
HDL, or BMI and the ACE genotype. There was a signiﬁcant difference among

genotype groups and percent fat, but this was not maintained when controlling

75

for covariates. There was a trend for percent fat to be higher in the II versus ID

versus 00 genotype groups.

R4. Determination of interaction effect between ACE ID Polymorphism
and Physical Activity on Cardiovascular Disease Risk Factors (Study aim
#4)

Cardiovascular disease is a multifactorial disease that can be affected
by genotype and the environment factors. The focus of this section is to
assess the interaction between the ACE l/D polymorphism and PA levels on
CVD risk factors. Of the 600 eligible participants, usable PA assessment, CVD
risk factor proﬁle and ACE genotype data was obtained in 149 (24.8%)

participants.

R4.1 Association between ACE ID Polymorphism and Physical Activity
on Systolic BP (Study aim #4)

Linear regression with both genotype and PA in the model predicted
only 4.3% (F3,143=2.155; p=0.096) of the variance in SBP. There was no
difference in SBP among genotype groups when controlling for PA. Further,
when controlling for genotype, there was no difference in SBP by PA level.
When gender was added to the base model of PA and genotype, the model
predicted 31 % (F4I142=18.885; p=0.000) of the total variance in SBP. Genotype
and PA remained non-signiﬁcant predictors, while gender was signiﬁcant

(p=0.00). Speciﬁcally, females (113111 mmHg) had lower SBP than males

76

(130113 mmHg) independent of genotype and PA (B=—16.852 {-21.36- -12.35],
p = 0.000). When assessed for covariates, BMI was a signiﬁcant ([3=1.262
[0.67- 1.85], p=0.000) predictor of SBP. There was no signiﬁcant interaction
between genotype and PA, but gender (B=—16.023[-20.53- -11.52], p = 0.000)
and BMI ([3= 1.442 [0.85- 2.03], p = 0.000) remained signiﬁcant predictors of
SBP (Table 4.1A). Speciﬁcally, when controlling for the interaction between PA
and the ACE llD genotype, females had a lower SBP while. BMI was

associated with higher SBP independent of all other variables.

R4.2 Association between ACE ID Polymorphism and Physical Activity
on Diastolic BP (Study aim #4)

There was no difference among ACE I/D genotype groups when
controlling for PA. Similar to SBP, gender was a signiﬁcant predictor of DBP
([3=-5.246 {-8.04- -2.45], p=0.000) independent of genotype and PA, with
females (7219 mmHg) having lower DBP than males (7617 mmHg) (Table
4.2A). There was no signiﬁcant interaction between the ACE I/D genotype and
PA levels. Thus, no interaction effect was found between ACE l/D

polymorphism and PA levels on DBP.

R4.3 Association between ACE ID Polymorphism and Physical Activity
on TC (Study aim #4)
Similar to 3P, there were no differences in TC among genotype groups

when controlling for PA levels. Further, there was no difference among

77

genotype groups when controlling for both gender and PA. However, race was
a signiﬁcant predictor of TC (B=-15.485 {-30.39— -0.58], p=0.042) independent
of PA and genotype. Speciﬁcally, Caucasians (172129 mgldL) had higher TC
levels than other races (156130 mgldL) regardless of genotype group and PA
level (Table 4.3A). Further, analysis revealed no interaction effect between

genotype and PA for TC.

R4.4 Association between ACE ID Polymorphism and Physical Activity
on HDL Levels (Study aim #4)

No differences among genotype groups were revealed for HDL levels
when controlling for PA, though there was a trend for the II group to have a
lower HDL level (57.31121 mgldL) as compared to those with the 0 allele (ID:
60.71126 mgldL; 00: 60.61159 mgldL). Independent of genotype and PA,
gender was a signiﬁcant predictor of HDL cholesterol, with males (52.81103
mgldL) having lower HDL levels in female (63.71137 mgldL) (Table 4.4A). For
HDL cholesterol, there was no interaction effect found between the ACE IID

genotype and PA level.

R4.5 Association between ACE ID Polymorphism and Physical Activity
on Body Composition (Study aim #4)

There were no differences in percent fat among genotype groups when
controlling for PA level, though there was a trend for those with the II genotype

to have greater percent fat (2715 %fat) than those having the 0 allele (ID:

78

2318 %fat; 00: 2217 %fat). Independent of genotype, PA was a signiﬁcant
predictor ([3=-0.079 {-0.12- -0.036], p=0.000) of percent fat values, with higher
PA being associated with lower percent fat. Gender was found to be a
signiﬁcant predictor (B=10.093 [8.16- 12.03, p=0.000) of percent fat
independent of both genotype and PA level. Speciﬁcally, males (1515 %fat)
had lower levels of percent fat than females (2715 %fat). When controlling for
covariates there was no differences among genotype, nor was PA a signiﬁcant
predictor of percent body fat. Gender, diastolic BP and eVOzmax were all
signiﬁcant predictors of percent body fat when controlling for PA and genotype
(Table 4.5A). There was no interaction observed between PA levels and

genotype on percent fat values.

R4.6 Association between ACE ID Polymorphism and Physical Activity
on BMI (Study aim #4)

Body Mass Index was not shown to be affected by genotype or PA
level. Gender was a signiﬁcant predictor ([3=-1.817[-3.09- 0.55], p=0.005) of
BMI independent of genotype and PA levels, with females (2314 kglmz) having
lower BMI values than males (2514 kg/mz) (Table 4.6A). There was no

interaction effect on BMI found between PA and genotype.

79

R4.7 Summary of association between ACE ID Polymorphism and
Physical Activity on Cardiovascular Disease Risk Factors (Study aim #4)
The purpose of this speciﬁc‘ aim was to determine whether PA modiﬁed
the effect of the ACE I/D polymorphism on 3P, lipid concentration, BMI and
body fatness in cOlIege student. It was hypothesized that PA would suppress
any relationship between the DD genotype and the CVD risk factors. Data
from this sample did not support this hypothesis. There was no interaction

observed between PA and genotype for any CVD risk factor.

80

CHAPTER 5

DISCUSSION

D1 Descriptive Data (Study Aim #1)

The reported PA for the sample was high, with 90% .of the participants
reporting meeting the minimal requirements (7.5 kcalslkg/week) of the ACSM.
Our reported amount of physical activity is much higher than other reported
values for young adults. According to Behavioral risk Factor Surveillance
System Survey data, 64.5% of participants 18-24 years of age residing in
Michigan reported meeting recommended PA guidelines.12 Further, the US.
National Center for Health statistics reported in 2007 that 37% and 30% of
participants 18-24 years of age reported regular leisure-time activity and some
leisure-time activity, respectively.15 Additional, the National College Health
Assessment reports in 2009 that 43.6% of participants meet recommended
PA guidelines. This suggests that are sample of college adults was more
active than other national and state reports. A potential reason for the increase
in reported PA is that our sample was a convenience sample from a
Kinesiology class. These participants may be participating in more PA activity
due to the fact that they are in a class that promotes being PA.

The results of this study illustrate a gender effect for young adult PA
participation. The impact of gender and PA participation in college students
has been studied previously. Previous investigators have shown that male

students were more likely to report higher levels of PA (43.7%) than female

81

students (33.0%)."'106 However, others have found no differences in PA
participation between males and females.176 Inconsistencies in PA
participation results could be due to data collection differences. Physical
activity has been reported as the number of days per week of vigorous PA, the
number of days of structured exercise, or as a categorical variable based on

I.106 used a detailed assessment of

national PA recommendations. Leslie eta
PA which included frequency, intensity, and duration to determine weekly PA
energy expenditure (kcals/week) in college aged students. They reported a
higher proportion of females than males in the sedentary (< 100kcals/week)
and low activity (100-799 kcals/week) categories, whereas males were more
likely to be moderately active (>800 kcaIs/week of non-vigorous PA) or
vigorous activity (>1600 kcaIs/week of PA including vigorous activity). Our
results appear to support this finding of Leslie et al.106

Percent fat values in men (1515%) were significantly lower than in
women (2715%) for our sample. This result is similar to a study conducted in
the upper Midwest region of the US showed female college students had
higher levels of percent fat (28.518), as measured by air displacement
plethysmography, as compared to males (17.718).140 Men typically have
higher lean mass and more central fat distribution, whereas women typically

have higher adiposity with more peripheral fat distribution.79 However, data on

participant fat distribution were not available in our sample for comparison.

82

02 Association between Physical Activity and Cardiovascular Disease
Risk Factors (Study aim #2)

Epidemiologic studies have demonstrated protective effects of PA,
exercise and physical fitness on the prevention of CVD and reduction of risk
factors. The relationship between physical fitness and CVD risk factors has
been reported to be stronger than the relationship of PA and CVD. In the
Quebec Family Study, Katzmarzyk et al.95 investigated the relationship
between in PA and CVD risk factors in 610 males and females between the
ages of 9-18. They reported that 5-20% percent of the variation in CVD risk
factors was explained by PA.95 Further, it is suggested that the relationship
between PA and CVD risk factors is not gender dependent.95 This is
supported by McGuire et al.118 who concluded, by assessing 5882 adUIts using
NHANES data, which PA was associated with a decrease in. CVD risk factors

but this association was not modified by gender.

02.1 Association between Physical Activity and BP (Study aim #2)

In our study, the adjusted regression model indicated that PA was not
associated with SBP (p=0.71) or DBP (p=0.98). However, SBP was predicted
by BMI and gender, while DBP was only predicted by BMI. Our results are
similar to those of Gaya et al.77 who found in 167 adolescents, higher BMI was
positively associated with both SBP and DBP. Gaya et al.77 also found BMI to

be the sole predictor of DBP. The fact that gender was a significant predictor

83

of SBP is not surprising considering that high BP is more common in males
than females until the age of 45.11

We hypothesized that PA would be inversely related to 3P, but this was
not supported by our results. The sample consisted of relatively healthy, young
adults who were enrolled in a kinesiology healthy lifestyles class. Selection
bias is a potential concern since students enrolled in an activity based class
may be more health conscious overall, and participate in other healthy
behaviors (e.g., healthy diet) that might help lower 3P. Also, the mean BP for
the sample (119114/7319 mmHg) is equal to, if not better than, the
recommended 120/80 mmHg. Therefore, it is possible that PA was not able to
inﬂuence 3P reductions any further in those whose values were already
optimal. Numerous studies have found that PA reduces the risk of
hypertension.20'32'4‘3'1“"143"154 Most of these studies included participants who

30'1“ or were prospective studies designed to examine participants

where olde
who developed hypertension.32"""143 For example, Leary et al.105 assessed BP
and PA in 5505 11-12 year-old children. The authors used accelerometers to
assess PA, a more objective measure of PA than self-report, and found that
higher levels of PA were associated with lower 3P. While it is widely accepted
that regular PA is effective in reducing SBP and DBP, this effect is most
beneﬁcial for those diagnosed with mild hypertension and not those who are

norrnotensive.63 Since our sample had relatively low BP values, it is possible

that any PA effect was minimized.

84

Another consideration is that for BP to be associated with PA there
must be an intrinsic biologic threshold for the amount of PA needed to effect a
change in BP.3‘6 Blair et al.32 found in the Aerobic Center Longitudinal Study
that those in the low ﬁtness category were more likely to develop hypertension
than those in the high ﬁtness category. In the Coronary Artery Risk
Development in Young Adults study, Carnethon et al.44 reported that
individuals with high cardiorespiratory ﬁtness, as measured by treadmill test,
had a lower relative risk of hypertension than those of low ﬁtness. These
studies differ from our study in that they examined ﬁtness levels, where as our
primary goal was to assess the PA level and association with BP. It could be
possible that the level of PA activity was not enough to meet the intrinsic
threshold needed for an association in this young adult sample. Further, since
our study was cross-sectional and consisted of apparently healthy, young
adults it is possible that the environmental variability necessary to ﬁnd an
association between PA and BP is still masked by a relatively high genetic
component.

It is possible that while PA and BP are not associated currently in our

l.32 in their

sample, that an association will occur later in life. Blair eta
prospective study of over 6000 norrnotensive men and women declared that
the risk of developing hypertension increases substantially with increased

higher baseline BP. This idea may be important for our study because while

the mean BP was normal (119114/7319 mmHg), individual variability existed.

Further, when BP was assessed by gender, males had a mean BP value

85

(130113/7717) that is in the pre-hypertensive range, while females had a
mean BP value of (114111/7219) which is considered normal. In additional
analysis, there were no trends for an association between PA and SBP by
gender. Finally, it is possible that 3P values measures for this study may not
have represented true resting values. The testing was done as a part of health
and wellness screening, thus due to moving from one station to another, the

resting BP could have been elevated.

02.2 Association between Physical Activity and Blood Lipid
Concentration (Study aim #2)

Our results showed that PA was not associated with TC or HDL levels.
On average, TC (169132 mgldL) and HDL (60114 mgldL) results for this
sample were considered normal, and potentially favorable, based on the
recommendation set by the National Cholesterol Education program for
cholesterol management.5 The HDL mean for the sample (60114 mg/dL) and
for females'(63114 mgldL) would be considered protective, while the values
for men (53110 mgldL) were normal. The TC and HDL levels found in this
study are similar to those reported by National Health and Nutrition
Examination Survey (NHANES) 2003-2004 among adolescents 12 to 19 years
of age (TC:162 mgldL; HDL: 55 mg/dL).‘°° The similarity between the
NHANES national TC and HDL statistics and our results are understandable

considering that the majority of our sample was 20 years of age or younger.

86

The fact that PA was not associated with TC nor HDL is surprising
considering that PA is frequently associated with increases in HDL levels. In

L125

the Atherosclerosis Risk in Communities study, Monda eta assessed the

longitudinal effect of PA on lipids and reported that increases in PA level were
associated with increases in HDL. In a review of literature, Tambalis et al.185
concluded that the most frequently observed result of exercise training was
increased HDL, with decreases in TC being less frequent.

It is possible that our results represent an age and PA level
dependence. Ainslie et al.18 found in a group of men, that the older group
(mean age 5614 years) had a greater reduction in TC and greater increase in
HDL as compared to the younger group (mean age 2414 years) after ten days
of prolonged walking. Our study participants were young adults with favorable
levels of TC and HDL, thus associations with PA could be hidden by other
factors, such as diet and age.

Our results indicated that there was a racial difference in TC, with
Caucasians having higher TC (172130 mgldL) than other races (157130

I.120 who reported, from

mgldL). Our results differ from McWilliams eta
NHANES data, that TC levels did not differ by race. It is possible that our
results are due to small sample size resulting in a spurious distribution, since
26% of our study participants were not Caucasian.

We found gender to be a signiﬁcant predictor of HDL values. Females

had signiﬁcantly higher levels of HDL (64139 mgldL) than did males (53139

mgldL). This ﬁnding is supported by the fact that boys tend to have a reduction

87

 

in plasma HDL levels during puberty that is not observed in girls.62 Our data is
similar to the NHANES 2003-2004 data for adolescent aged 12-19 years,
where females had higher HDL levels (56 mg/dL) than males (50 mg/dL).160
Further, NHANES data in adults supports the general trend that females have
higher HDL levels than males.160

In summary, our results indicate that in a sample of young, apparently
healthy adults PA was not associated with TC or HDL cholesterol. This result
is most likely due to the optimal levels of HDL and TC found in most of our

sample.

. 02.3 Association between Physical Activity and BMI and Body
Composition (Study aim #2)

Our results show that as PA quartile increased, percent fat values
decreased, regardless of gender (refer to Table 13). This ﬁnding supports our
hypothesis that PA would be inversely associated with percent fat values. Our
results are similar those of Zeno et al.2°9 who found in a study of 142 African
American and Caucasian adults that ﬁtness was inversely associated with
percent fat in both males and females. Zanovec et al.208 found in 278 young
adult students that prediction equations of percent fat are enhanced when PA
is accounted for, implying that PA has an association with percent fat. Gregory
et al.82 reported that lower levels of PA in Guatemalan young adults were

associated with higher percent fat values. Our result that PA is associated with

88

percent fat is consistent with the belief that PA plays a signiﬁcant role
determining body composition.63

While our data showed a signiﬁcant association between PA and
percent fat, we did not ﬁnd a similar relationship between PA and BMI for
either gender. This ﬁnding is in contrast to the results of Zeno et al.209 who
reported a signiﬁcant association between PA and BMI. Our results are

supported by the ﬁndings of Janssen et al.93

which showed that for a given
BMI, physically active individuals have lower levels of total fat mass than those
who were less active. Further, in two separate randomized control trials, Ross

I16"162 observed that exercise without weight loss was associated with

eta
reductions in total and abdominal fat, even in individuals who did not lose
weight. In our sample, both men and women had normal BMI values
(men=24.8; women=23.3).14 Since mean BMI was in the normal range, a
signiﬁcant PA effect would likely be minimal.

In summary, our results supported our hypothesis that PA would be

inversely associated with percent fat but did not support an association

between PA and BMI.

03 Associations between the ACE ID Polymorphism and Cardiovascular
Disease Risk Factors (Study aim #3)

The overall frequencies of genotypes II, ID, and 00 were 16.1, 56.4
and 27.5, respectively for the entire sample. The observed genotype

frequencies were in agreement with those predicted by Hardy-Weinberg

89

equilibrium. Our study is similar to studies done in Scottish young adults
(mean age approximately 20 years) and Finish young adults (mean age
approximately 34 years)”91 When stratiﬁed by gender, the overall
frequencies for the II, ID and DD genotype were 20.3, 54.3 and 25.2 in women
and 6.5, 60.8 and 32.6 in men, respectively. For women, the genotype
frequencies were similar to results from a study done with US adolescents and
young adults that reported a genotype frequency of 19.4 for II, 49.1 for ID, and
31.5 for 00.194 In our study the gene frequency in males deviated from Hardy-
Weinberg equilibrium for this sample, but this is most likely due to the low
number of males (n=3) classiﬁed as having the II genotype.

In our sample we found a difference in the amount of PA based on
genotype. Participants classiﬁed as 00 reported higher levels of PA, with ID
being intermediate and II having the lowest levels of PA. This ﬁnding does not

L75 who found no association between the

support the results of Fuentes eta
ACE I/D polymorphism and moderate intensity leisure time PA in middle aged
(mean age 44 year) Finish adults. This difference may be due to differences in
measured PA. Fuentes et al.75 asked a single self-reported question on
moderate leisure time PA. This single question may not have the precision to
determine PA at the level needed to result in a relationship with gene
effects.203 We assessed PA through self-report, but were much more speciﬁc
in terms of frequency, intensity and duration. An additional difference could be
the age of the sample. Our sample consisted of young healthy adults, whereas

I.75

the Fuentes eta studied middle aged Finnish adults. Our study results are

90

opposite of those found in a study conducted on young, Italian adults.
Speciﬁcally, Winnicki et al.205 found that the 0 allele was associated with lower
levels of PA.205 In contrast to our sample, Winnicki et al.205 assessed the
association between PA and the ACE l/D polymorphism in a group of
hypertensive young (mean age ~33 years) adults. Further, they assessed PA
through a questionnaire that dichotomized PA (sedentary and exerciser)
based on relative intensity.205
We also found a graded response among genotypes for estimated
VOzmax, with the DD genotype (39 mllkglmin) having the highest estimated
values and the II genotype having the lowest (33 mllkg/min). Our ﬁnding of the
ACE DD genotype being associated with higher VOzmax levels is in contrast
to previous studies performed on elite and extremely ﬁt
athletes.2“'5‘'7'3'130'135'189 In studies of elite athletes, including Spanish runners
and cyclists”, 100 fastest South African born lronman ﬁnishers,51 and
Australian rowers,78 the Iallele occurred at a higher frequency compared to
healthy controls. In a study of potential British Olympic athletes there was a
significant linear trend between the distance of the event and the frequency of
the Iallele; those who participated in longer distance running events had
higher frequencies of I allele than athletes who participated in shorter distance
events.135 In a study of British high mountaineers, there was a signiﬁcant
difference in genotype distribution, with mountaineers having an increased

representation of the Iallele as compared to normal controls.‘3°'189

91

 

The potential mechanism for the association between the ACE I allele
and elite endurance performance may be due to increased substrate

k,128 increased

delivery,130 modiﬁed absorptive function of the intestinal trac
anabolic response for fat-free mass,24 and/or an increase in percentage of
slow-twitch ﬁbers.210 These studies focused on the genotype frequency of elite
athletes or high level mountaineers and the ﬁnding could potentially be due to
selection bias. The difference in sample characteristics could explain why our
results differed from these previous studies.

Our results are also in contrast to three previous studies reporting no
association between the VOzmax and the ACE IID polymorphism. In a study of
60 untrained women59 (age= 24 years), and in 724 sedentary adults150 there
was no association between measured VOzmax values and the ACE l/D
polymorphism. In another study, 192 aerobically ﬁt male athletes (VOzmax 2
72 ml/kg/min) showed no difference in genotype distribution from 182
sedentary controls.151

In support of our ﬁndings, Costa et al.54 reported that the allele
distribution was signiﬁcantly different between Portuguese elite short distance
swimmers, considered anaerobic athletes, and elite middle distance
swimmers, considered a mix between anaerobic and aerobic athletes. The
authors reported that the 0 allele was more prevalent in the long distance
swimmers, which is likely the group with the higher VOzmax values.54 Amir et

al.25 compared the ACE allele frequency in Israeli elite endurance athletes,

elite power athletes and healthy controls. The 0 allele was more frequent in

92

endurance athletes than in power athletes or controls.25 Similar to our ﬁndings,
endurance athletes with an assumed higher VOzmax had a signiﬁcantly higher
frequency for the 0 allele. In a sample of young (23 years of age) Chinese
men, the DD genotype was found to be associated with higher levels of
measured aerobic capacity.211

Rigat et al.158 were the ﬁrst to show that the ACE IID polymorphism was
associated with serum ACE levels, with the 0 allele linked to higher enzyme
levels. ACE is responsible for converting angiotensin I into the active

I,“56 which reportedly causes hypertrophy in cardiac

angiotensin I
myocytes.164 The hypertrophic response to physical training may be
attenuated by the presence of the ACE 0 allele, resulting in left ventricular
hypertrophy which in turn would increase VOzmax via increased stroke
volume.211 The ACE 0 allele has been associated with increased in left
ventricular mass as a response to training.129 Thus, our ﬁnding that the ACE
DD genotype was associated with estimated VOzmax levels is biologically
plausible.

In summary, we found the DD genotype to be associated with highest
eVOzmax values and the II genotype having the lowest. Our ﬁndings are in
agreement with other studies that have reported that the 0 allele is associated
with higher VOzmax values,25'5“'211 but in opposition to those reporting that the

24,51 ,78,130,135,189

I allele is associated with higher VOzmax values or has no

association. 59'15°'151

93

03.1 Association between ACE ID Polymorphism and BP (Study aim #2)
In our sample there was no association between the ACE l/D
polymorphism and BP. This result is not supported by studies that have
shown an association between ACE I/D polymorphism and
hypertension.27'49'“'83'1“ Case-control studies, including younger Croatian
adults (mean age ~35 years)”, older Chinese men (mean age ~57 years),49
older Mongolian men and women (mean age ~52)83 and African American
men and women (age not reported)“, have reported that the 0 allele of the
ACE l/D polymorphism was associated with hypertension. Explanations of
these ﬁndings include the regulation of BP through increased angiotensin II, a
potent vasoconstrictor, as well as the degradation of bradykinin, a potent

2749““ assessed participants who were

vasodilator.27"9 The above studies
greater than 35 years of age with hypertension. We assessed increased 3P as
a risk factor in young adults instead of hypertension in middle age to older
adults, potentially explaining differences between the ﬁndings.

Our ﬁnding of no association between the ACE I/D polymorphism and
BP is supported by other studies.75'“'“"‘6'126'127'144'194'199. In 885 Caucasian
young (5-29 years of age) participants as well as in 721 young (mean age 34
years) Finnish adults, the ACE l/D polymorphism was not associated with SBP
or DBP.75'“"“ In a variety of case-control studies, including German,
Hungarian and Greek participants ranging from youth to late adulthood, there

was no association between the ACE IID polymorphism and BP or

hypertension.12"6'127'144'199 Two meta-analyses, conducted to assess the

94

inﬂuence of the ACE IID polymorphism on BP, reported no relationship
between the ACE genotype and 3317"" These studies support our ﬁnding of
no association between ACE IID polymorphism and BP in a sample of young
aduﬂs.

While the ACE polymorphism has been shown to increase plasma ACE
‘ activity,“"15"'191 it is plausible that increased ACE levels do not result in
increased production of angiotensin II. Such a result would provide some
mechanistic evidence for the lack of association between the ACE
polymorphism and BP.17 Animal studies support the idea that hypertension
genes may be activated only under certain conditions or during speciﬁc
periods throughout the life span. This could explain why the ACE genotype
has been found in association with hypertension in older adults, but not with
BP in younger individuals. Further, it is possible that the ACE gene does not
play a role in the initiation of increased 3P but may contribute to the
maintenance of high 3P under situations of hypertension or older age.49
Finally, multiple biochemical and physiological controls regulate 3P levels
through interactions with various genetic and environmental factors.‘“ Thus, it

is likely that variation in the ACE gene may have only small effects on

BP 83,194,199

95

03.2 Association between ACE IID genotype and Blood Lipid
Concentrations (Study aim #2)

We found that the ACE I/D polymorphism was not associated with TC
or HDL cholesterol. Our ﬁndings differ from those who have found an
association between impaired lipid levels and the DD genotype. Results from
studies of Japanese and Israeli adolescents with type II diabetes showed
those with the 0 allele had increased levels of TC and H0L.23'99 In Polish
adults (age~43 years) with coronary artery disease, the DD genotype was
associated with elevated TC and low density lipoprotein cholesterol.137 In two
additional non-US samples (Korean and Israeli men) TC and low density
Iipoprotein cholesterol were highest in the those with the DD genotype
compared the those with the II genotype.“142 In treatment intervention
studies, researchers have found a gene by treatment effect, with those having
the DD genotype having greater reduction in lipid levels in response to
pharmaceutical treatment.“115 Current data do not provide a clear plausible
mechanism for the association between ACE l/D polymorphism and lipid
levels,115 though it has been demonstrated that angiotensin II modiﬁes LDL
causing an enhanced rate of cellular uptake.“96 Thus, increases in ACE
activity associated with the DD genotype could potentially increase
angiotensin II production which could lead to increased modiﬁed LDL uptake.
Our study results may differ from those that that showed an association
because we did not assess older individuals, nor those with disease, and our

design did not include an intervention. It is possible that the ACE l/D

96

polymorphism is a variability gene which contributes to the variability of lipid
levels only under variation in environmental factors, such as age,
pharmacological treatment or disease.37

Our results are supported by studies which have reported no
association between ACE genotype and lipid proﬁles. For example, case-
control studies of older (255 years of age) Italian and Mexican adults found no
association of lipid levels among the genotypes.”198 In addition, studies of
American Samoans and Japanese adults showed the ACE l/D polymorphism
was not associated with lipid levels.55'“‘ A lack of association between the
ACE genotype and lipids levels in our study is likely due to our study
participants having low TC (169 mgldL) and high HDL (60 mgldL) levels.
Speciﬁcally, TC levels were well below the cut point, 2200 mgldL, set by the
National Cholesterol Education Program.5 Further, the mean HDL level for the
sample would be considered protective since they are equal to the
recommended level (60 mgldL) set by the National Cholesterol Education
Program.5 Thus, TC and HDL levels in our sample may have limited our ability
to ﬁnd an association since they are already optimal. It is plausible that the
ACE gene does not play a role in the initiation of high TC or decreased HDL,
but may contribute to the maintenance of adverse lipid levels.49 Further, the
interaction of multiple genetic and physiologic factors may account for variable

levels of blood lipids.55

97

03.3 Association between ACE ID Polymorphism and BMI and Body
Composition (Study aim #2)

There was no association between ACE l/D polymorphism and CVD
risk factors in our sample, except for percent fat. Univariate analysis revealed
that those with the I allele had higher percent fat values compared to the
referent group of 00 homozygotes. A plausible explanation for this is that in
our sample of young adults those with the DD genotype were more physically
active (50.4133 kcalslkg/week) than those with the I allele (31.4124
kcalslkg/week). Thus, the association between the ACE l/D polymorphism and
percent fat values maybe driven by increased PA being inversely related to
percent fat. The result of I allele being associated with increases in percent fat
differs from others who have found the 0 allele to be associated with
increases in percent fat. In Greek adolescent and Italian adult males, the 0
allele was associated with increased fatness 1“ and increased incidence of

t,‘79 respectively. In Spanish coronary heart

being classiﬁed as overvveigh
disease patients, those with the 0 allele, had an increased prevalence of
obesity and abdominal fat.157 Inconsistencies between ﬁndings are unclear,
but could be due to different study designs, sampling techniques and/or
subject sampling. Further, many of the reported ﬁndings have associated the
ACE genotype with BMI, which is not a measure of body composition. We
found the ACE genotype to be associated with percent fat, not with BMI.

Support for our ﬁnding is seen in the results of Wacker et al.201 who

studied middle-aged men and women classiﬁed by BMI as obese or normal

98

weight. The authors found that the I allele was more frequent in the obese
males than in normal weight males.201 A similar ﬁnding was found in females
but it was not statistically signiﬁcant.201
In summary, univariate analysis revealed an association between the /
allele and percent fat level. However, this association disappeared after
controlling for gender. The ACE gene may contribute to the regulation of body

composition, but only through the complex interaction of multiple genes and

environmental exposures. 89

04 Determination of interaction effect between ACE ID Polymorphism
and Physical Activity on Cardiovascular Disease Risk Factors (Study aim
#4)

We hypothesized that PA would suppress any relationship between the
DD genotype and CVD risk factors, including increased 3P, lipids and percent
fat. However, we found no evidence of an interaction effect between PA levels,
the ACE I/D polymorphism and CVD risk factors in our sample.

For biologic interaction to exist, the gene and environment must
interdependently inﬂuence an expressed phenotype. There is abundant
evidence that PA inﬂuences CVD risk factors, although individual responses to
exercise differ. Thus, environmental exposure (PA) must interact to some
degree with genetics (ACE I/D polymorphism) to produce different phenotypic

responses. In our sample we were unable to substantiate this hypothesis.

99

There are a number of reasons why an interaction may not have occurred in
our sample. .

1) Cardiovascular disease and its risk factors are multifactorial in nature
and as such, are unlikely to be controlled signiﬁcantly by a 'single gene such
as the ACE polymorphism. There are several complicated and life-long gene
by environment interactions that may have to occur for PA to reduce the levels
of CVD risk factors or decrease the manifestation of CVD.39 Human diseases
and associated risk factors likely involve multiple genes with modest effects
and gene-environmental interactions.207 Bray et al.3’9 stated that ﬁndings of
gene-environment interaction studies may be dependent on the context in
which variables are analyzed, including assessing multiple polymorphisms in
each participant. Due to the nature of our study we were unable to analyze
more than one polymorphism, thus limiting our ability to assess potential
ﬁndings in multifactorial risk factors.

2) The possibility exists that PA is associated with the ACE IID
polymorphism and that the relationship does impact CVD risk factors, but it
could not be detected in this study. We assessed the relationship between PA
and the ACE polymorphism under the hypothesis that if the DD genotype was
present, high PA levels would be inversely associated with risk factor levels.
The relationship could exist in the context that at a currently undetermined
biologic threshold for PA, the interaction with ACE IID polymorphism ceases to
have biological signiﬁcance. Booth et al.36 stated that an intrinsic biologic

requirement for PA is likely and not meeting this threshold (aka, physical

100

inactivity), leads to a disruption of normal homeostasis. In our sample of young
adults, it could be possible that the high PA levels lead to no gene by

environment interaction. Mudd et al.133

reported that the minimum amount of
energy expenditure needed to meet the PA recommendations of the ACSM or
the Institutes of Medicine (IOM) was 7.5 kcalslkng and 21 kcals/kg/wk,
respectively. For our sample the median energy expenditure was 28
kcalslkglwk; which is above both ACSM and IOM recommended levels. When
stratiﬁed by gender, both males (43.0 kcalslkglwk) and females
(23.1kcals/kg/wk) reported median PA levels above the minimum required to
meet both recommendations. Thus, it is possible that our participants” high PA
levels were above the undetermined intrinsic PA threshold needed for an
interaction to exist between PA and the ACE IID polymorphism.

It is also possible that the PA and ACE IID polymorphism interaction
may be age dependent. Our participants were all under 25 years of age, which
may impede our ability to detect a gene-environment interaction effect for CVD
risk factors. Due to the sample’s age we may have missed the opportune
window of development that is needed for examining gene-environmental
interactions.207 After indentifying an age-varying association between ROBO1
and obesity, Lasky—Su et al.104 reported that inconsistencies in replication of
association ﬁndings may be due to age-genotype interactions. They stated
that age-dependent effects are easily missed during cross-sectional studies
because data collection may not occur when the genetic effect is strongest.104

While genetics are constant, environmental exposures are not. Thus, age may

101

be a necessary trigger that must be met to amplify the modest genetic effect of

__l'_'_”

the ACE l/D polymorphism or to allow for PA and the ACE polymorphism to
have a significant biologic effect.192
3) A Type II error occurred, as the power needed to detect gene-
environment interaction requires larger samples sizes than more basic
environmental studies.“61 In a review of statistical associations between

“3 stated that most gene association studies will

genes and disease, Manly
require hundreds or thousands of participants.113 Dempfle etal.61 stated that to
detect a gene-environment interaction a 7x increase in sample size may be
required over the sample size needed to assess genetic effects. Our final
sample size for the gene-environment interaction was 149 participants. Post
hoc power analysis suggested the power of our study to be 0.29, which
significantly reduced our ability to show an interactive effect between PA and
the ACE genotype. Thus, our Aim 4 was exploratory.

4) A final explanation for why an interaction between PA levels and the
ACE I/D polymorphism did not occur is that the precision of measure for PA
exposure or CVD risk factors may not been sufficient. A common complication
of gene by environmental studies is low precision of environmental exposures,
compared to genotype classification.11o Boks et al.33 stated that to prevent a
decreases in power, measures of traits and environmental exposures must be
completed as precisely as possible. In our study, PA was assessed via self-

reported questionnaire to capture duration, frequency and intensity of activity.

From these variables we were able to quantify PA into kcaIs/kg/week. This is a

102

valid measure of PA but is limited by report bias. Participants are likely to
overestimate their vigorous PA level and underestimate their moderate PA
level thus increasing measurement error.166 Thus, a more precise measure of
PA may be necessary when assessing PA as an environmental interaction
with genetics. In research there is often a tension between the precision of
measures and the size of the study. As the sample size increases selection of
precise assessment measure decreases due to location, cost, and time. Errors
in assessing the CVD risk factors such as participant BP potentially not being
measured in a completely rested state, not assessing low density Iipoprotein
levels, or using skinfold caliper to assess body composition all potentially lead
to increases in measurement error which subsequently could lead to
decreases in statistical power. Any compromise in precision of exposure or
outcome measures increases measurement error, and leads to a decreased
likelihood of finding a true effect.2°6'2°7 Thus, it is possible that no interaction
was found between the ACE I/D polymorphism and PA on CVD risk factors
due to the measures used to assess the various variables, despite the fact that
all measures were obtained with as much precision and attention to detail as
possible.

In summary, we found no interaction effect between PA and the ACE
I/D polymorphism on CVD risk factors. Potential reasons for this finding
include the multifactorial nature of CVD risk factors, the nature of the PA and
ACE I/D polymorphism relationship, the inadequate power given the final

sample size and the potential decrease in precision of variable measures.

103

While we did not find an interactive effect for the PA and the ACE l/0
polymorphism, studies that investigate the environment and generic factors
along with their interaction are necessary to determine the processes that lead

to complex diseases and their associated risk factors.“39

05 Conclusions

Overall, study results showed a gender effect for PA and percent fat,
with males having higher values of PA and lower values of percent fat than
females. Physical activity showed an inverse relationship with percent fat, but
was not associated with other CVD risk factors. The ACE I/D polymorphism
was found to have an association with PA and estimated VOgmax values.
Those with the DD genotype had the highest values of PA and estimated
VOzmax values. There was no association between the ACE l/D
polymorphism and other modifiable CVD risk factors. There was no interaction
effect of PA with the ACE l/D polymorphism on CVD risk factors.

Young adulthood is a critical time in the development of disease. While
young adults are typically considered healthy, lifestyle choices (e.g., exercise,
diet, smoking) affect their future risk of disease development. Future studies
should include larger samples, a wider range of health behavior profiles, and
tracking should continue through older adulthood. The future of health
research is dependent on the understanding of gene by environmental
interaction. The nature of the interaction as well as timing or triggers of the

interaction are critical to understanding disease progression and risk factors.

104

The study of gene by environmental interaction in young adults is central to

the future understanding of disease progression.

105

FIGURES AND TABLES

Table 1A: Descriptive categorical data for study participants based on
gender

Chi-
square
p-
Total Males Females value
n °/o n % n %
Race
Caucasian 162 84.4 49 83.1 113 85.0 N/A
Other 30 15.6 10 16.9 20 15.0
Age
18 40 20.8 8 13.6 32 24.1 N/A
19 49 25.5 13 22.0 36 27.1
20 62 32.3 25 42.0 37 27.8
221 41 21.4 13 22.0 28 21.1
Major
Kinesiology 84 43.8 23 39.0 61 45.9 N/A
Other 106 55.2 35 59.3 71 53.4
Smoking
Non-Smoker 164 85.4 47 79.7 117 88.0 N/A
Smoker 18 9.4 8 13.6 10 7.5
Physical Activity Cut point
5 7.5 kcalslkgMeek 18 9.4 3 5.1 15 11.4 N/A
> 7.5 kcalslkglweek 173 90.1 56 94.1 117 88.6
BMI
Obese 11 5.7 4 6.8 7 5.3 0.005
Overweight 39 20.3 20 33.9 19 14.3
Normal Weight 139 72.4 34 57.6 105 78.9
Physical Activity Quartile
0-14.8 kcalslkglweek 48 25.1 7 11.9 41 30.8 0.000
14.8-28.0 kcalslkglweek 48 25.1 9 15.3 39 29.3
28.0-54.7 kcalslkglweek 49 25.7 19 32.2 30 22.6
> 54.7 kcalslkglweek 46 24.1 24 40.7 22 16.5
Genotype
ll 24 12.5 3 6.5 21 20.4 0.097
ID 84 43.8 28 60.8 56 54.4
00 41 21.4 15 32.6 26 25.2

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127

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128

' Appendix A: Consent Form

A cross-sectional assessment of physical activity behaviors, cardiovascular
disease risk factors, and their association with genetic polymorphisms in
college aged adults

Consent Form

@mmary of the research protocol: You have been asked to participate in a
study assessing physical activity behaviors, cardiovascular disease (CVD) risk
factors, and their association with genetics in young adults. CVD has several
known modiﬁable risk factors, including high cholesterol, high blood pressure,
smoking, obesity, and a lack of physical activity. These CVD risk factors
develop through a complex relationship between the environment and
genetics. While CVD symptoms may not occur until later in life the risk factors
begin during childhood. Your participation in this study will enable us to
understand of how interactions between genetics, speciﬁcally the Angiotensin
Converting Enzyme Insertion/Deletion polymorphism, and the environmental,
speciﬁcally physical activity/physical ﬁtness, affect the development of CVD
risk factors in a young adult population. The ACE l/D polymorphism is shown
to be associated with the development of CVD risk factors such as obesity,
high cholesterol, and especially hypertension.

You are asked to complete an on-Iine survey and to donate, through
non-invasive measures, some mouth epithelia cells to determine your
Angiotensin Converting Enzyme Insertion/Deletion genotype.

Qtimate of subiect’s time: Please take your time and ﬁll out the survey
completely. It should take approximately 10 minutes. The collection of the
fresh mouth epithelia cells will take no longer than 5 minutes.

Experimental Procecﬁres:

On-line Survey: The on-line utilize the Longitudinal Surveillance Engine (LSE),
an internet-based surveillance system constructed at MSU. You will be
provided online access to the LSE through a web page accessible via any
major internet browser. A unique survey ID number will be given to you to
keep track responses. You will be asked questions pertaining to basic
demographics and your physical activity history.

 

Buccal cell collection: Buccal, relating to the check or mouth, epithelial cells
are being collected to analyze your DNA to see which of the Angiotensin

129

Converting Enzyme polymorphisms you inherited. Special indicator cards will
be used to collect and store your buccal epithelial cells. Fresh buccal epithelial
cells are removed, non-invasively, from your mouth using a foam-tipped
applicator, which is then placed onto the specially treated paper (changes
color when sample is applied) of the indicator card. Once the sample is
applied to the indicator card, it can be stored.

Risks/Discomforts: There are no known risks associated with participation in
this study. Outside of receiving an ice-cream cone from The Dairy Store for
your participation, you will not beneﬁt from participation in this study.
However, your participation in this study may contribute to the understanding
of how physical activity associates with speciﬁc genotypes to affect
cardiovascular risk factors.

Voluntary participation: Your participation in this study is completely voluntary,
dependent upon your consent and will no way effect your grade in KIN 121.
You may choose not to participate at all, you may refuse to answer certain
questions or you may withdraw from this study at any time. Furthermore, any
results of the testing that you complete will be available to you.

Conﬁclgntialiw anﬁd anonvmitv: Your privacy will be protected to the maximum
extent allowable by law. Presentation or publication of results will in no way
identify you personally. The records and results obtained will only be available
to the investigators of this study, Michigan State University biomedical
institutional review board and to yourself if you wish and will not be shared
with others.

Contact person: If you have any questions and/or concerns about your
participation in this study, you may contact:

Dr. James Pivarnik, PhD.
3 lM Sports Circle
Michigan State University
East Lansing, MI 48824
517-353-3520
iimpiv@msu.edu

If you have any questions pertaining to participant’s rights as human subjects
of research, please contact:

Dr. Peter Vasilenko, PhD.,

Director of Human Research Protection Programs
202 Olds Hall

Michigan State University

East Lansing, MI 48824

130

517-355-2180
irb@msu.edu

If you agree to join this study, please sign your name below. By completing
and returning this form, you indicate your voluntary agreement to participate.

E] I agree to allow my buccal epithelial cells to be stored and used for
determination of the Angiotensin Converting Enzyme Insertion/Deletion
polymorphism. Initials

[:l I agree to allow my buccal epithelial cells to be stored and used for future
research studies. Initials

 

 

Signature of participant Date

 

Subjects Ticket Number

131

Appendix B: Questionnaire

A cross-sectional assessment of physical activity behaviors, cardiovascular
disease risk factors, and their association with genetic polymorphisms in

college aged adults

Questionnaire

1. What is your age?

0.39 09 9.0.6!»

17
18
19
20
21
22
23
24
25 or greater

2. What is your gender?

a.
b.

Male
Female

3. How do you describe yourself?

90099-997!»

American Indian or Alaskan Native
Asian

Black or African American
Hispanic or Latino

Native Hawaiian or Paciﬁc Islander
White

Other

4. What best describes your current living status

9.0-9.0"?

On-campus

Off-campus apartment/house with friends
Off-campus with parents/guardians
Off-campus alone

Other

5. Are you a Kinesiology Major?

a.
b.

Yes
No

132

. During the past month, did you participate in any physical activities or
exercises such as running, calisthenics, golf, gardening, walking, etc.?
a. Yes
b. No

If yes, please continue to question #7

If no, please continue to question #18

. Using the list provided, click on the physical activity or exercise you
currently participate in most often?

. How many times per week do you usually participate in this activity
Less than 1

:9009990
meme-A

. Using 15 minute increments, how long do you usually participate in this
activity?

15

30

45

60

75

90

g. greater than 90

”@9939

10. Do you regularly participate in a second physical activity or exercise other

than what was previously mentioned?
a. Yes
b. No

If yes, please continue to question #11

If no, please continue to question #18

11.Using the list provided, click on the physical activity or exercise you

currently participate in the second most often?

12. How many times per week do you usually participate in this activity

133

Less than 1

#900599???
\IODO'l-hOON—k

13. Using 15 minute increments, how long do you usually participate in this
activity?

15

3O

45

60

75

90

greater than 90

000000

‘9

14. Do you regularly participate in a third physical activity or exercise other
than what previously mentioned?
a. Yes
b. No

If yes, please continue to question #15
If no, please continue to question #18

15. Using the list provided, click on the physical activity or exercise you
currently participate in the third most often?

16. How many times per week do you usually participate in this activity
Less than 1

#9050999?»
\ICDU'l-hQJKJ-db

134

17. Using 15 minute increments (15, 30, 45, 60, 75, etc.), how long do you
usually participate in this activity?

15

30

45

60

75

90

g. greater than 90

”SBP-9.0".”

18. On average, how many hours do you watch TV per day?
a. Less than 1
b. 1
c. 2
d. 3
e. 4
f. 5 or greater

19. On average, how many hours of video games do you play per day?
a. Less than 1
b. 1
c. 2
d. 3
e. 4
f. 5 or greater

20. On average, how many hours do you spend surﬁng the internet, writing
e-mails, or completing class work on a computer per day (do not include
playing video games)?

a. Less than 1
b. 1
c. 2

d. 3

e. 4

f. 5 or greater

21 . Did you participate in any sponsored and/or club sports or activities (i.e.,
basketball, cheerleading, marching band, etc.) during high school?

a. Yes
b. No

135

22. Using the list provided, click on all of the sponsored or club
sports/activities that you participated in.
Baseball
Basketball
Cheerleading
Cross Country
Dance
Field Hockey
Football
Golf
Ice Hockey
Marching Band
Pom Pons
Rugby
. Soccer
Soﬂbaﬂ
Swimming
Track
Volleyball
Other

T‘P'PPPSPFT'P'?‘P’”S°P-PF"P

23. How many times per week did you usually participate in these
sports/activities?
Less than 1

00"?90-‘909909

ACOQVOJU'IAOON—b

O

24. When you took part in these activities, how many minutes did you usually
participate?

30

60

90

120

150

09.900

136

25. During high school, did you participate in any non-school sponsored
leisure—time physical activities or exercises such as running, sports,
gardening, etc.?

a. Yes
b. No

26. Using the list provided, click on the leisure-time physical activities or

exercises you participated in most often during high school (choose no
more than 3 activities).

27. On average, how many times per week did you participate in these
activities combined? '

1
2
3
4
5
6
7
8
9
1

0000.009909-

O or greater

28.When you took part in these activities combined, how many minutes did
you usually participate?

15

30

45

60

75

90

greater than 90

0.009.900

29.What is your height in inches?

30.What is your weight in pounds?

31 .What section of KIN 121 are you currently enrolled?

137

009909

001
002
003
004
005
006

138

Appendix C: Equations

Generalized Skinfold Equations”2

4-site skinfold equation (abdomen, suprailiac, tricep, and thigh):
Male: %Fat = 0.29288 (sum of four skinfolds) - 0.0005 (sum of four
skinfolds)2 + 0.15845 (age) — 5.76377

Female: % Fat = 0.29669 (sum of four skinfolds) - 0.00043 (sum of four
skinfolds)2 + 0.02963 (age) + 1.4072

Field test eguation to estimate aerobic caQaciy
1.5 Mile Endurance test14

V02max (mllkg/min) = 3.5 + 483/(time in minutes)

139

Appendix D: Health assessment data sheet

5—DIGIT TICKET NUMBER
Please use your results from the OLIN Health Center General Fitness

 

Assessment test to answer the following questions. If you do not have a
result for a certain question, please leave it blank.
1. What was your weight in‘ pounds?

 

2. What was your height (example: 5’4")

 

3. What was your PERCENT BODY FAT according to the results of the
ﬁtness test? (example: PERCENT BODY FAT (%BF) = 28.8)

 

4. What was your dominant hand grip strength? (example: DOMINANT
HAND GRIP = 27)

 

5. What were the results for the SIT & REACH test (inches)? (example: SIT
8 REACH = 23)

 

6. What was your resting blood pressure? (example: RESTING BP =
132/74)

 

7. What was your V02 max? (example: V02 MAX mllkg/min = 41)

 

8. How many push-ups did you complete in one minute? (example: PUSH
UPS (608%) = 38)

 

140

9. How many sit-ups did you complete in one minute? (example: SIT UPS
(603ec) = 50)

 

10.What was your total cholesterol? (example: chol.= 208)

 

11.What was your HDL cholesterol? (example: HDL= 58)

 

12.What was your FVC? (example FVC = 3.6)

 

13.What was your FEV1? (example FEV1 = 3.4)

 

141

Appendix E: Description of Variables

 

Description of Main Variables

 

 

 

MAIN EXPOSURE VARIABLES
Variable Name Variable Type Analyses
Angiotensin Converting
Enzyme Polymorphism
o D or I
o Allele - ll, ID, DD
- Binary

o Genotype

0 Categories

 

Physical Activity

 

 

 

 

 

 

 

 

. Physical Activity (PA) . kcals-kg-wk‘1 . Quartiles
MAIN OUTCOME VARIABLES
Variable Name Variable Type Analyses
Blood pressure
. Blood pressure 0 mmHg Continuous
Normal (<120/<80 3 Categories
mmHg), pre-
hypertensive (120-
139/80-89 mmHg),
hypertensive (2140/290
mmHg)
Cholesterol
0 Total 0 mg/dI. Continuous
o Desirable (<200 3 Categories
mgldL), Borderline
(200-239 mgldL), High
(2240 mgldL)
. HDL Cholesterol , mg/dL Continuous
Low (<40 mgldL), High 3 Categories
(2 60 mg/dL
Overweight/Obesity
o Skinfolds o kgm'2 o 3 Categories

 

 

 

142

 

 

 

Body Mass Index (BMI)

 

Normal (18.6—24.9
kg-m'z), Overweight
(25-29.9 kg-m'z), Obese
(am-m4)

 

- Continuous
o 3 Categories

 

Description of Covariates

 

POSSIBLE THIRD VARIABLES

 

 

 

 

 

 

 

 

 

Variable Name Variable Type Analyses
- Gender o Male/Female o Dichotomous
0 Age 0 18 years (yrs), 19 yrs, 0 Categorical
20 yrs, 21 yrs, 222 yrs
0 Race 0 Caucasian, African 0 Categorical
American, Other
(Hispanic, Asian,
Paciﬁc Islander, Native
American)
0 Height 0 inches 0 Continuous
0 Weight 0 Pounds 0 Continuous
o Computer/TV time 0 Hour of use per day 0 Continuous
0 Low (<2 hrs/day), High 0 Dichotomous

(22 hrs/day)

 

 

Estimated VOzmax

 

mI-kg'1-m'1

0 Continuous

 

 

143

 

10.

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