USING GENETIC ADMIXTURE AND LIPOPROTEIN LIPASE POLYMORPHISMS
TO EXPLAIN VARIATIONS IN PLASMA TRIGLYCERIDES AND HIGH DENSITY
LIPOPROTEIN CHOLESTEROL IN PREMENOPAUASAL AFRICAN AND
EUROPEAN AMERICAN FEMALES
by
NIHAL OMAR NATOUR
JOSE R. FERNANDEZ, CHAIR
BARBARA A. GOWER
M. AMANDA BROWN
A Thesis
Submitted to the graduate faculty of The University of Alabama at Birmingham,
in partial fulfillment of the requirements for the degree of
Master of Science
BIRMINGHAM, ALABAMA
2007
Copyright by
NIHAL OMAR NATOUR
(2007)
USING GENETIC ADMIXTURE AND LIPOPROTEIN LIPASE POLYMORPHISMS
TO EXPLAIN VARIATIONS IN PLASMA TRIGLYCERIDES AND HIGH DENSITY
LIPOPROTEIN CHOLESTEROL IN PREMENOPAUASAL AFRICAN AND EUROPEAN AMERICAN FEMALES
NIHAL OMAR A NATOUR
ABSTRACT
Although African Americans (AA) are well known to be more obese and more insulin resistant than European Americas (EA), several studies have shown that AA have
lower plasma triglyceride (TG) and higher high density lipoprotein cholesterol (HDL-C).
The reasons for these levels of TG and HDL-C are not understood, and scientists have
suggested a role of lipoprotein lipase (LPL), a key regulator of TG rich lipoproteins metabolism that is also thought to indirectly affect HDL-C metabolism. To investigate the
extent to which these observed differences in TG and HDL-C are due to genetic makeup,
we investigated the role of genetic admixture and two polymorphisms of the lipoprotein
lipase gene (LPL RS285 and LPL RS250) in a sample of 255 AA and EA premenopausal
females. To control for the confounding effect of other variables known to affect plasma
TG and HDL-C and to differ between AA and EA, measures of adiposity, insulin resistance and secretion, and socioeconomic status were included as covariates in statistical
models testing for the contributions of African genetic admixture to plasma levels of TG
and HDL-C and for the presence of the gene polymorphism after adjusting for ancestral
genetic background. Our results showed that both African genetic admixture and LPL
RS285 explained significant portion of plasma TG variation (p< 0.001, p = 0.02, respectively). No significant association was found between African genetic admixture or LPL
RS285 and HDL-C, neither between LPL RS250 or the outcomes of interest. In conclu-
ii
sion, genetic admixture and LPL RS 285 seem to explain TG variation between
premenopausal AA and EA females.
iii
DEDICATION
This work is dedicated to my beloved parents, Sobheyyeh and Omar, and to my
sisters and brothers, especially, Salam and Basma, for the continuous support, love, and
inspiration they provided for me from overseas.
iv
ACKNOWLEDGEMENT
I acknowledge all the people who guided me through my graduate program. This
work was a big challenge for me, English was my second language and it was my first
time to study abroad away from my family and friends, but my goal was achieved by the
continuous support, patience and understanding of my mentor Dr. Jose Fernandez, who
believed in me even when I was at the edge, listened to my complaints with kind heart
and highly valued every small effort I was doing. Working with Dr. Jose was a rich experience for me, although challenging. I would like also to acknowledge Dr. Amanda
Brown, who guided me in every step through my program, her sweet heart and trust in
me tremendously increased my confidence and inspired me. My mentor, Dr. Barbara
Gower guidance was very valuable, her notes and directions provided me with clues to
dramatically improve my scientific researching. Dr. Krista Casazza, my officemate, read
every draft I wrote, she made the corrections, then read the corrected drafts again. I felt
many times I was distracting her from her work, but she was gentler than I imagined. I
cannot be more thankful to her. Finally, my gratitude goes to my parents, my sisters
Basma and Salam, and my very dear friend Areeg who were my resort when I thought I
could not go on any more.
v
TABLE OF CONTENTS
Page
ABSTRACT........................................................................................................................ ii
DEDICATION................................................................................................................... iv
ACKNOWLEDGEMENT ...................................................................................................v
List of Contents.................................................................................................................. vi
LIST OF TABLES........................................................................................................... viii
LIST OF ABBREVIATIONS..............................................................................................x
INTRODUCTION ...............................................................................................................1
SPECIFIC AIMS AND HYPOTHESIS ..............................................................................5
ARTICLE REVIEW ............................................................................................................6
Genetic Admixture..........................................................................................................7
Triglyceride Rich Lipoprotein .......................................................................................8
Lipolysis.........................................................................................................................9
HDL-C Metabolism and its Relation to Lipolysis .......................................................10
Lipases and Lipoprotein Lipase ...................................................................................11
Association of LPL Mutation with Altered Metabolism .............................................13
S447X Polymorphism............................................................................................13
N291S Polymorphism............................................................................................13
D9N Polymorphism ...............................................................................................14
LPL RS250 (-93TG ) Polymorphism.....................................................................14
LPL RS285 (PvuII ) Polymorphism ......................................................................15
Factors Confounding the Association between Race, Plasma TG, and HDL-C..........16
Obesity ...................................................................................................................16
Insulin Resistance ..................................................................................................17
Fat Distribution ......................................................................................................17
Lifestyle Factors.....................................................................................................19
METHODS ........................................................................................................................20
vi
Subjects ........................................................................................................................20
Study Design................................................................................................................21
Intravenous Glucose Test.............................................................................................21
Juliet Subjects .........................................................................................................22
Romeo Subjects ......................................................................................................22
Lipid Assessment .........................................................................................................23
Body Composition .......................................................................................................23
Computed Tomography ...............................................................................................24
Genotyping...................................................................................................................24
LPL RS250 ............................................................................................................24
LPL RS285 ............................................................................................................25
Admixture Analysis ...............................................................................................26
Socioeconomic Status ...........................................................................................26
Statistical Analysis.................................................................................................26
RESULTS ..........................................................................................................................28
Polymorphism LPL RS250 ..........................................................................................28
Polymorphism LPL RS285 ..........................................................................................29
Triglycerides ................................................................................................................30
Correlations............................................................................................................31
Multiple Linear Regression....................................................................................31
High Density Lipoprotein Cholesterol.........................................................................32
Correlations............................................................................................................32
Multiple Regression Models ..................................................................................32
DISCUSSION ....................................................................................................................49
Study Limitations.........................................................................................................56
Future Studies ..............................................................................................................57
Implications..................................................................................................................57
REFERENCE LIST ...........................................................................................................59
IRB APPROVAL CERTIFICATE ....................................................................................66
vii
LIST OF TABLES
Table
Page
1 Descriptive Statistics of AA and EA Premenopausal Females for the
Total Sample and According to Race. .........................................................................35
2
Genotypic Frequency of LPL Polymorphisms (LPL RS250 and LPL
RS 285) in a Total Sample of AA and EA Premenopausal Females. ..........................36
3
Comparison of the Study Variables between Carriers and Noncarriers of LPL RS250, G Allele, in a Total Sample of AA and EA Premenopausal Females. ...................................................................................................37
4
Comparison of the Variables of the Study between Carriers and Noncarriers of LPL RS250, G Allele, According to Race in a Sample of
AA and EA Premenopausal Females...........................................................................38
5
Comparison of the Variables of the Study between Carriers of LPL
RS285, T Allele, in a Total Sample of AA and EA Premenopausal
Females. .......................................................................................................................39
6
Comparison of the Variables of the Study between Carriers and Noncarriers of LPL RS285, T allele, According to Race in a Sample of
AA and EA Premenopausal Females...........................................................................40
7
Correlations of TG with Various Variables of the Study for the Total
Sample of AA and EA Premenopausal Females, and According to
Race..............................................................................................................................41
8
Parameter Estimates for the Model testing the Association of TG with
African Genetic Admixture after Adjusting for Covariates in Sample of
AA and EA Premenopausal Females (n= 182; R = 0.54). ...........................................42
9
Parameter Estimates for the Model testing the Genotypic Association of
TG with RS250, after Adjusting for Covariates in Sample of AA and EA
Premenopausal Females (n=158, R= 0.52)..................................................................42
viii
10
Parameter Estimates for the Model Testing the Allelic Association of
TG with RS250, After Adjusting for Covariates in Sample of AA and EA
Premenopausal Females (n=158; R= 0.54)...............................................................43
11
Parameter Estimates for the Model testing the Genotypic Association of
TG with RS285 After Adjusting for Covariates in Sample of AA and EA
Premenopausal Females (n=158; R= 0.54)...............................................................43
12
Parameter Estimates for the Model Testing the Allelic Association of TG
with RS285 after Adjusting for Covariates in Sample of AA and EA Premenopausal Females (n=158; R= 0.54). .......................................................................44
13
Correlations of HDL-C with Various Variables of the Study for the Total
Sample of AA and EA Premenopausal Females, and According to Race.................45
14
Parameter Estimates for the Model Testing the Association between African Genetic Admixture and HDL-C After Adjusting for Covariates in a
Sample of AA and EA Premenopausal Females (n=184; R= 0.40)...........................46
15
Parameter Estimates for the Association between African Genetic Admixture and HDL-C After Adjusting for TG and Other Covariates in Sample of AA
and EA Premenopausal females (n=184)...................................................................46
16
Parameter Estimates for Genotypic Association between HDL-C and RS250
After Adjusting for Covariates in Sample of AA and EA Premenopausal
Females (n=160; R= 0.44 ). .......................................................................................47
17
Parameter Estimates for Allelic Association between HDL-C and RS250 After Adjusting for Covariates in Sample of AA and EA Premenopausal Females
(n=160, R= 0.45 ).......................................................................................................47
18
Parameter Estimates for the Genotypic Association between HDL-C and RS285
After Adjusting for Covariates in Sample of AA and EA Premenopausal Females
(n=160; R= 0.44 ).......................................................................................................48
19
Parameter Estimates for the Allelic Association between HDL-C and RS285
After Adjusting for Covariates in Sample of AA and EA Premenopausal
Females (n=160; 0.44 ). .............................................................................................48
ix
ABBREVIATIONS
A
Adenine
A0
Angstrom
AA
African American
AFADM
African Admixture
AIMS
Ancestry Informative Markers
AIRg
Acute Insulin Response to glucose
AMADM
Amarindian Admixture
BMI
Body Mass Index
C
Cytosine
O
C
Degree centigrade
CE
Cholesteryl Ester
CHD
Coronary Heart Disease
cm2
Squared Centimeter
CT
Computed Tomography
CVD
Cardiovascular Disease
CV
Coeffecient of Variation
DM
Diabetes Mellitus
DNTP
Deoxy Nucleotide Triphosphate
DXA
Dual X-ray Absorptiometry
x
EA
European American
EUADM
European Admixture
FSIGT
Frequently sampled Intravenous Glucose Tolerance Test
GCRC
General Clinical Research Centre
G
Guanine
HDL-C
High Density Lipoprotein Cholesterol
HL
Hepatic Lipase
IDL
Intermediate Density Lipoprotein
Kg/m2
Kilogram per squared meter
JULIET
Exercise Training in Obesity_Prone Black and White Women
LDL
Low Density Lipoprotein
LPL
Lipoprotein Lipase
McSNP
Melting Curve Single Nucleotide Polymorphism
min
Minute
ml
Millileter
µl
Microliter
mmol/l
Millimol per Liter
NHANESIII
National Health and Nutritional Examination Survey 1999-2000
PCR
Polymerase Chain Reaction
PL
Phospholipids
SCAT
Subcutaneous Adipose Tissue
SES
Socioeconomic Status
SI
Insulin sensitivity Index
xi
T
Thiamine
ROMEO
Role of Metabolism and Exercise in Obesity
TF
Total Fat
TG
Triglyceride
US
United States
VAT
Visceral Adipose Tissue
VLDL
Very Low Density Lipoprotein
WHR
Waist to Hip Ratio
UAB
University of Alabama at Birmingham
U
Unit
xii
INTRODUCTION
Health disparities in the prevalence and outcomes of chronic diseases such as obesity, hypertension, diabetes mellitus (DM), and cardiovascular diseases (CVD) between
racial groups continue to be a burden in the United States. These health problems are
more common among African Americans (AA), especially women, than their European
American (EA) counterparts (1-5). AA are known to have lower serum triglycerides
(TG) and higher high density lipoprotein cholesterol (HDL-C) than EA, which, in theory,
should decrease the risk factors for the aforementioned diseases in this group (4, 6, 7).
Understanding the racial differences in biomarkers for chronic diseases such as serum TG
and HDL-C is important because it may help in the development of tools that have the
potential to decrease the morbidity and mortality associated with conditions like obesity,
diabetes mellitus (DM), and CVD in AA.
Variations in serum lipids between AA and EA can be affected by several factors,
including amount of visceral adipose tissue(VAT) (8, 9), insulin sensitivity (SI) (7, 10,
11), genetic make-up (12), socioeconomic status (SES) and lifestyle factors (diet, physical activity) (9, 13). Greater accumulation of VAT has been shown to be more prevalent
in EA than their AA counterparts when matched for BMI, insulin sensitivity, fat mass,
and fat free mass (9, 14, 15). VAT is associated with increased serum TG and decreased
serum HDL-C, and it has been identified as a greater contributor to morbidity associated
with obesity than the degree of obesity itself (9, 14, 15). Insulin resistance also plays a
1
2
role in the variation in serum lipids between AA and EA. Under ordinary circumstances,
the release of insulin stimulates adipose tissue lipoprotein lipase (LPL), which should decrease serum TG. However, insulin resistance stimulates the production of very low density lipoprotein (VLDL) by the liver, causing an increase in serum TG. As such, insulin
resistant adipose tissue fails to produce enough LPL, causing these lipoproteins to accumulate in the blood (11). AA are well known to be more insulin resistant, a fact that does
not support the racial difference between AA and EA in regard to serum TG and HDL-C
(7, 9). Lifestyle factors (9) and SES (13) have been shown to explain significant portions
of the variation in serum TG and HDL-C as well. It has been suggested that SES affects
an individual’s choice to adopt healthy lifestyle behaviors, which includes the adoption of
a healthy diet and engaging in regular daily physical activity (16). Other lifestyle factors, such as alcohol consumption, also have been shown to affect serum HDL-C and TG
(17, 18).
Nevertheless, the above discussed factors cannot fully explain the racial discrepancy in serum TG and HDL-C between AA and EA. Although AA are more insulin resistant, more likely to be obese, and tend to adopt unhealthy lifestyles more frequently
than EA(1, 3, 5), AA continue to have a tendency towards lower serum TG and higher
HDL-C. This physiological paradox seems to be confounded by ethnic categorization
and lack of understanding of the biological and environmental factors underlying racial
classification (19, 20) . The search for the explanation of these differences in metabolic
parameters has therefore shifted focus toward polymorphisms and genetic variants that
affect lipoprotein metabolism. Some of these variants have been shown to exhibit un-
3
equal frequency between the two racial groups. One example of such a mutation is the
gene that codes for the lipoprotein lipase (LPL) enzyme.
LPL is an enzyme involved in the process of lipolysis (9, 21, 22) and exhibits a
wide range of genetic variation. Heterozygous polymorphisms of LPL are frequent (occurring at a rate of 1 out of 500) (21) and have been shown to affect serum TG and HDLC (21-24). Most of the well studied mutations such as D9N and N291S have been
shown to cause an increase in serum triglycerides and a decrease in HDL-C (21-28).
Other variants such as S447X and -93TG (LPL RS250) cause increased LPL activity, decreased TG, and increased HDL-C (21, 23-26, 28-30).
Different ethnic groups may have different frequencies of certain LPL polymorphisms. One prominent example of an LPL polymorphism showing frequency differences between races is the LPL promoter mutation - LPL RS250, also referred as -93TG
(24, 25, 29, 31). Ehrenborg et al. (1997) found that in European, African, and Chinese
subjects, the polymorphism LPL RS250 occurs at rate of 1.7%, 76.4% and, 0%, respectively (25). Another LPL variant that shows differences in the frequency between racial
groups is LPL RS285. It also occurs at different frequencies between different ethnic
groups (http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=285). This LPL RS285 polymorphism (described as Pvu II in the literature) occurs at intron 6, involves C→T transition, and has been associated with TG and HDL-C disturbances (32). Carriers of the allele T have been shown to have lower serum TG (32-34) and higher serum HDL-C (34).
The greatest limitation of racial/ethnic dependent genetic association studies is the
confounding effects of population stratification (35), evident in racial categorization. It is
generally accepted that simply classifying subjects based upon race does not account for
4
the intermixing between ancestral parental populations during the colonial period that
characterize populations in the United States. To more accurately study the genesis of
variation in serum TG and HDL-C in individuals of diverse backgrounds, the proportion
that each parental population contributes to the subject should be measured and accounted for (20). This can be done using Ancestry Informative Markers (AIMs), where
alleles of varying distinctive frequencies between parental populations (19) are used to
calculate an estimate of genetic admixture for each subject. This value is then included in
any statistical model that relates TG or HDL-C to independent variables, in this case LPL
polymorphisms (20). By adding environmental covariates to the models, the effect of genetic polymorphisms on serum TG and HDL-C are evaluated in a context that considers
the complex biological and non-biological factors underlying the relationship between
race and variations in TG and HDL-C, reducing the effect of population stratification by
the inclusion of genetic admixture estimates (19).
In this study, we evaluated the effect of genetic admixture and LPL on serum levels of TG and HDL-C. We hypothesized that as the proportion of African genetic admixture increased, the TG would decrease and the HDL-C would increase after adjusting for
possible covariates (total body fat, VAT, measures of insulin effect and secretion, income). We compared the serum TG and HDL-C between carriers and noncarriers of the
LPL RS285 mutation. We hypothesized that carriers of the allele T would have lower
serum TG and high serum HDL-C after accounting for possible covariates. We also
compared the TG and HDL-C between carriers and noncarriers of the mutation LPL
RS250. We hypothesized that this mutation would be associated with lower TG and
higher HDL-C after accounting for possible covariates.
5
SPECIFIC AIMS AND HYPOTHESES
1. To determine the effect of genetic admixture on the serum concentrations of TG and
HDL-C in a cohort of pre-menopausal African and European American females.
a. We hypothesize that as the African genetic admixture component increases, serum TG will decrease, after accounting for possible covariates.
Covariates include total body fat, VAT, SI, acute insulin response to glucose (AIRg), and income.
b.
We hypothesize that as the African genetic admixture component increases, serum HDL-C will increase after the consideration of possible covariates.
2. To evaluate the effect of LPL genotypes on serum TG and HDL-C levels in a cohort
of premenopausal African and European American females.
a.
We hypothesize that women with the mutant allele T of the marker LPL
RS285 will have lower serum TG and higher HDL-C, after accounting for
genetic admixture and possible covariates.
b. We hypothesize that women with the mutant allele G of the marker LPL
RS250 will have lower serum TG and higher HDL-C after accounting for
genetic admixture and possible covariates.
6
ARTICLE REVIEW
African American (AA) females experience a higher incidence of coronary heart
diseases (CHD) than European Americans (EA) (4). AA females also have higher mortality and morbidity rates related to CHD than their EA counterparts (2). Research has
documented that the disparity in CHD incidence and outcomes between AA and EA results from the aggregation of several prevalent risk factors in AA, including higher blood
pressure, higher incidence of diabetes mellitus, higher prevalence of obesity, and the likelihood of adopting an unhealthy lifestyle (i.e. poor diet, physical inactivity) (1, 3, 5).
Observed lower levels of serum TG and higher levels of serum HDL-C in AA,
despite their higher levels of obesity and insulin resistance, seems to account for the
lower predisposition of AA for CHD (6, 7, 9). It has been proposed that racial/ethnic
variations in serum TG and HDL-C are a result of both genetic and environmental components among races. Therefore, any study investigating the genesis of racial/ethnic differences in serum lipids requires an effective design considering those genetic and environmental factors underlying racial classification that might have an effect on the variations of serum TG and HDL-C. The use of Ancestry Informative Markers (AIMs), genetic markers of distinctive frequency in each racial group to estimate genetic admixture
for subjects of the study (6, 20, 36), and the careful consideration of environmental parameters have been considered an appropriate design to study racial differences in serum
lipids. Clarification of factors underlying discrepancies in serum TG and HDL-C between
7
AA and EA is imperative to the development of preventive measures toward eliminating
health disparities between races in the US.
Genetic Admixture
European, African, and Amerindian parental populations intermixed in the New
World during the colonial period, forming “admixed populations”. The individuals of the
newly formed populations inherited their genetic makeup from these parental populations
with some DNA sequences having alleles with higher frequencies in some parental populations compared to others. These alleles are called AIMS, and they are used to estimate
the proportion of European, African, and Amerindian genetic ancestry of individual
members of admixed populations in the New World (20, 36).
Using genetic admixture is of great value in research. First, it provides the framework to detect genetic influences on certain diseases that are prevalent in specific populations, serving as a gene mapping tool for disease risk (20). Second, it provides a tool to
control the studies more efficiently than simply assigning individuals to racial categories.
Third, it controls for the effect of population stratification in genetic association studies.
On the other hand, the genetic admixture approach is limited by the number of markers
used and the frequency difference of the alleles of these markers between parental populations (36). The genetic information from the AIMs is converted into quantitative value
by the use of maximum likelihood methods or Bayesian methods, as discussed elsewhere
(37). The individual estimate of genetic admixture is a function of the frequency of genotypic combinations in an individual, the frequency of alleles in the parental populations
and the proportion of admixture at each genotypic locus (19). Genetic admixture esti-
8
mates can also be used as covariates in regression models testing for genetic association
to reduce Type I errors on the association between risk factors and disease (38).
Genetic admixture has been used in various studies to explore racial differences in
complex health traits, such as obesity (36), insulin resistance (20), resting metabolic rate
(19), and allergies in the respiratory pathways (38). To appropriately apply this tool to
substitute racial categorization it is important to assess and understand cultural and/or environmental characteristics of the study populations. In this study we use genetic admixture to explore the extent to which differences between AA and EA women in serum levels of TG and HDL-C are due to ancestral background. We also use genetic admixture as
a genomic control factor when testing for the association between the LPL gene polymorphisms and levels of TG and HDL-C.
Triglyceride Rich Lipoproteins
Serum TG is a combined measure of the serum concentration of chylomicrons,
very low density lipoproteins (VLDL), and free fatty acids. The different classification
of these lipids is a result of their chemical structure. Lipoproteins are spherical particles
ranging in sizes between 80-500A that transport lipids from the sites of their production
to the sites of their usage, catabolism, and excretion. The variability of their size is due to
various combinations of proteins (Apoproteins) and lipids. The lipid part includes
triglycerides (TG), cholesterol esters (CE), and phospholipids (PL) (12, 39).
Chylomicrons are TG rich lipoproteins made of 1% protein, 88% TG, 7% phospholipids, and 4% CE (12, 39). They are formed when the fats released from the catabolism of dietary fat by digestive lipases are combined with apoB48 in the enterocytes. Af-
9
ter more apoproteins are added to the newly formed chylomicrons, (mainly ApoA1,
ApoA2, which are thought to come from intestinal HDL-C) chylomicrons then transfer
the dietary fat from the digestive tract to the blood via the lymphatic system (39).
The other TG rich lipoprotein is VLDL, which is endogenously produced by the
liver. VLDL is produced when the body has a surplus of energy. Once synthesized,
VLDL is transferred from liver to extra hepatic tissues. VLDL is made of 8% protein,
54%TG, 16%PL, and 22% CE (12, 39).
Both chylomicrons and VLDL are catabolized in a process that involves LPL,
lipid transfer proteins, receptors, and phospholipid rich lipoproteins, such as HDL-C
(composed of 50% protein, 4% TG, 20 % CE and 26% PL). This catabolic process is
called lipolysis and leads to the formation of CE rich lipoproteins (12, 39). CE rich lipoproteins include low density lipoprotein (LDL) and intermediate density lipoprotein
(IDL), which are atherogenic lipoproteins. LDL is comprised of 21% protein, 11% TG,
and 46% CE, and 22%PL (12, 39).
Lipolysis
Lipolysis is the mechanism by which LPL releases fatty acids from the cores of
chylomicrons and VLDL. These fatty acids are transferred to the tissues that need them
or re-esterified into TG in the HDL-C lipoprotein core. After ingestion of a diet rich in
fat, chylomicrons travel through the lymphatic system into the blood and interact with
HDL-C to acquire additional apoproteins (most importantly, ApoC2, ApoC3). LPL is
then activated by ApoC2 and acts on the chylomicrons’ ester bonds to release fatty acids
(21, 39, 40) and chylomicron remnants that are removed from blood by hepatocytes
10
later. The process of removal of the chylomicrons remnants is mediated by LDL, ApoE2
and LDL related protein receptors.
It has been hypothesized that lipoprotein lipase (LPL) works as a ligand, mediating the clearance of chylomicrons remnants from blood (21, 39, 40) and hydrolyzing the
TG core of VLDL. The hydrolyzation of VLDL is followed by the conversion of VLDL
to IDL. IDL are either cleared by hepatocytes in the same way as chylomicrons remnants
are cleared, or converted into LDL following a change of surface proteins (39, 40). Consequently, any defect in LPL is expected to cause inefficiency in the breakdown of chylomicrons and VLDL particles, at either the first step in lipolysis or in the process of hepatic clearance of the remnants.
HDL-C Metabolism and its Relation to Lipolysis
The biosynthesis of HDL-C requires proteins from the surface of chylomicron
remnants. HDL-C exchanges lipids with TG rich lipoproteins in a process where HDL-C
accepts the TG and donates CE to TG rich lipoprotein. When the process of lipolysis is
impaired, as it is in the case when LPL is mutated, the hydrolysis of TG rich lipoproteins
is reduced, resulting in an extended period of CE and TG exchange. This extended exchange period reduces the production of remnants necessary for HDL-C biosynthesis and
creates an oversaturation of serum HDL-C with TG, resulting in a less stable HDL-C
molecule that can be easily hydrolyzed by hepatic lipase (39, 41, 42).
In summary, LPL is the regulation core of the process of lipolysis. This is the
main metabolic pathway for TG rich lipoproteins. LPL also directly and indirectly affects
the concentration of HDL-C and metabolism of HDL-C by hepatic lipase (HL) (39).
11
Lipases and Lipoprotein Lipase
Lipases are enzymes that are responsible for hydrolyzing serum TG into free fatty
acids and glycerol. The most important lipases in the regulation of serum TG and HDL-C
are HL and LPL. Hepatic lipase is secreted from the hepatocytes and hydrolyzes TG, PL
in IDL, and TG rich HDL-C (producing LDL and TG poor HDL-C). Many studies have
shown that in hypertriglyceridemia, TG rich HDL-C is metabolized by hepatic lipase to
produce an unstable and easily degraded HDL-C, resulting in low serum levels of HDL-C
(43). Lipoprotein lipase is the key regulator of the lipolysis process. LPL is secreted
from parenchymal tissues of mainly adipose and muscular tissues. Other tissues like the
mammary glands, adrenal glands, ovaries, kidneys, lungs and spleen can secrete lower
amounts of LPL (40, 42, 44). The main substrates for this enzyme are chylomicrons and
VLDL. LPL acts on the TG rich core of these substrates hydrolyzing the ester bond of
the TG molecule and producing two fatty acids and one 2-monoglyceride molecule (40,
42, 44).
LPL activity requires activation by Apo C11 (40, 42, 44, 45). Studies have suggested that LPL plays a non-enzymatic role in lipid metabolism that is part of the process
of lipoprotein internalization, where LPL acts as a bridge to internalize chylomicrons,
VLDL, or their remnants into different cells (for example hepatocytes and monocytes).
This internalization of lipoprotein remnants controls TG and HDL-C concentrations in
the blood. On the other hand, VLDL rich monocytes, which are a result of remnant internalization into monocytes, contribute to the formation of the atherosclerotic plaques
(40, 42, 44-46).
12
Because of its importance in the process of lipoprotein metabolism, LPL has been
under extensive research. Structurally, LPL is made of 475 amino acids, 27 of which are
signal amino acids. The enzyme is coded for by a gene located at position 8p22, it is
32kb long, and is made of 10 exons and 9 introns (40, 42, 44, 45). Four important structural sites of LPL are relevant for LPL activity: a) the site where hydrolysis of the ester
bond takes place (the triad), b) the site that receives the substrate, c) the site responsible
for dimeration of the enzyme (active LPL is a dimeric protein), and d) the site for Nglycosylation, which is needed to connect LPL to heparin proteoglycan sulfate, is involved in secretion of LPL and is needed for its anchoring to endothelial surfaces (40,
44).
Approximately 221 mutations in the gene coding for the LPL enzyme have been
reported and classified as missense, nonsense, frame shift (insertion, deletion), gross,
splicing, and promoter mutations (42). Most of these mutations can cause partial defects
in LPL function because they may affect one or more of its 4 structural sites. For example, D9N has been hypothesized to affect the site of N-glycosylation and the site where
LPL interacts with ApoC11 (28). The rare LPL null mutation which leads to complete
absence of the enzyme (occurs at a rate of 1 per million) causes familial LPL deficiency
(22, 47). Heterozygous mutations that produce partially defective enzymes are more frequent, occurring at a rate of 1 out of 500, and including, for example, the D9N, N291S,
S447X mutations, the promoter -93TG mutation, and Hind111, Pvu II restriction fragment polymorphism cutting sites (22). Mutations of the LPL gene have also been considered risk factors for CVD. For example, Kastelein, et al. ( 1998) found that the carrier
frequency of the two alleles 9N/-93G was significantly higher in patients with MI than
13
the controls (p=0.01) (27). Another study demonstrated a significant association between
LPL HindІІІ polymorphism and CHD risk and severity (48). Furthermore, Wittrup et al.
(2002) found that the presence of LPL Asn291Ser lead to a two-fold increase in the risk
of ischemic heart disease in females (30).
Association of LPL Mutation with Altered Lipids Metabolism
Many LPL polymorphisms have been studied in association with serum lipids.
Many LPL mutations cause partially defective function of LPL and lead to impaired
lipolysis. Impaired lipolysis consequently affects serum TG and HDL-C. A brief discussion of the most studied LPL variants follows.
S447X Polymorphism
S447X mutation, which introduces a premature stop codon at position 447 of LPL
gene (49), occurs at a frequency of 20% in healthy subjects. Although conflicting results
have been documented, there is some evidence showing that this polymorphism increases
HDL-C and decreases TG (23, 31).
N291S Polymorphism
The frequency of N291S polymorphism, which involves A→G transition at position nt 1127 in the coding region of the LPL gene (50), occurs in the general population
at frequency of 0-6.7%. N291S has been associated with a 32-50% reduction in LPL activity, a 31% increase in TG and a 0.12mmol/L decrease in HDL-C (51, 52).
14
D9N Polymorphism
Carrier frequency of the D9N polymorphism, which involves G→A transition at
position nt 280 in the coding region (50), in different ethnic populations is from 1.64.1%. D9N has been associated with an 11-30% reduction in LPL mass and an 8-34%
reduction in its activity, and, therefore, is associated with an increase in serum TG by
20% and a decrease of HDL-C by 0.8mmol/L (42). The association among TG, HDL-C
and D9N has also been documented by other studies (24, 52). D9N has also been studied
in association with the mutation -93 TG (LPL RS250), showing a strong linkage disequilibrium between the D9N and LPL RS250 mutations in Europeans and Hispanic Americans but not in Africans or African Americans (25, 26, 29).
LPL RS250 (-93TG) Polymorphism
The LPL RS250 polymorphism, which involves T→G transmission, has received
particular interest in the attempt to explain the observed differences in serum TG and
HDL-C between different ethnic groups. LPL RS250 occurs at different frequencies in
different populations. Ehrenborg et al. (1997) observed that in a multiethnic sample, 93TG mutation occurred at a rate of 1.7% in Europeans, 76.4% in Africans, and 0% in
Chinese (25). Talmud et al. (1998) studied 66 healthy Hispanics and 42 healthy African
Americans for -93TG and D9N mutations, finding that the rate of the mutation in Hispanics was 0.09 and in African Americans it was 0.28 (29).
The effects of the haplotype unit formed by the LPL RS250 and the D9N mutations on serum lipids have been investigated in different ethnic groups. For example, in a
study by Hall et al. (1997), the haplotype D9N/LPL RS250 was studied in 3 ethnic
15
groups (523 Europeans, 549 Africans, and 505 South Asians) to assess the haplotype effect on serum lipids. This study showed no difference among different haplotypes in the
serum lipids, and the authors justified their results by the lack of exclusion of individuals
taking lipid lowering agents and the measurement of serum lipids during the fasting state
(D9N mutation effect is more prominent in the non-fasting state) (51). In another study,
Hall et al. (1997) also studied the D9N/LPL RS250 haplotype combination in a cohort of
1575 Europeans and 93 Afro-Caribbeans. The association between serum lipids and the
haplotypes TG/DD, TG/DN and TT/DN was found to be significant (p = 0.01), TG/DD
was mainly found in Afro-Caribbeans who also had the lowest TG level (mean =
1.36mmol/ml), TT/DN had a middle level TG value of 1.78 mmol/dl, and TG/DN was
most frequent in Europeans and was associated with the highest TG (mean 1.93mmol/dl).
From this study one can infer that although the mutant G allele is associated with lower
TG levels, when it is combined with other mutations (like the D9N in Europeans) it may
synergize the effect of the defective mutation (26, 29). Kastelein et al. (1998) found this
“synergetic” phenomenon in a case control myocardial infarction study in a Dutch population. The N9/-93G carriers had significantly lower HDL-C (0.88±0.03 mmol/ml) than
noncarriers (0.98±0.01 mmol/ml), (p= 0.02)(27). This haplotype group again had a tendency towards higher TG. The mean serum TG for the carriers was (1.96±0.14
mmol/ml), while the noncarriers had TG mean of (1.73±0.03 mmol/ml) (p=0.08) (27).
LPL RS285 (Pvu Ⅱ) Polymorphism
The LPL RS285 mutation occurs at intron 6 in the LPL gene and involves a C→T
transition that causes the deletion of the cut site for the restriction enzyme Pvu II. This
16
mutation has been found to be associated with many diseases, including CVD and diabetes mellitus (32). The mutation has also been associated with serum TG and HDL-C (3234, 53). For example, Chamberlain et al. (1989) found a significant dose effect of this
polymorphism on serum TG in 93 healthy Europeans (33). This mutation has also been
associated with HDL-C in a group of Japanese children, where mutations on Pvu II were
associated with variations on serum levels of TG and HDL-C (34). The relationship between Pvu II and TG has also been validated by other investigators (32).
In conclusion, racial differences in the frequency of LPL polymorphisms may explain part of the fact that African Americans have lower serum TG and higher HDL-C
than European Americans when matched for BMI and adiposity (51). Nevertheless, other
factors seem to contribute to the relationship between race and serum TG and HDL-C.
Factors Confounding the Association between Race, Serum TG and HDL-C
Scientific evidence has demonstrated that the relationship between race, serum
TG, and HDL-C is mediated by other factors, including obesity, insulin resistance, fat
distribution, and lifestyle. A brief description of the contributions of these factors follows.
Obesity
Data from the National Health and Nutritional Examination Survey (NHANESIII)
1999-2000 indicates an overall increase in the prevalence of obesity among US populations, particularly in AA females. It has been previously shown that although obesity or
high percentage body fat are related to disturbances in serum TG and HDL-C, this asso-
17
ciation is not constant among all overweight subjects. Rather, it is higher in the male
(android) abdominal pattern of obesity or central obesity (54), which is less frequent in
AA than EA.
Insulin Resistance
Insulin resistance is more prevalent among AA than EA (9). A causal relationship
has been established between insulin resistance and dyslipidemia, which is believed to
occur from an increased production of VLDL by the liver (which is stimulated by insulin)
or a possible decrease of LPL secretion and activity that will limit the degradation of serum TG (7). In 26 non-diabetic males and females, it was shown that insulin resistance is
negatively associated with adipose tissue LPL m-RNA ( P< 0.02) and heparin releasable
LPL activity (p< 0.01) (11). Hanyu et al. (2004) showed that pre-heparin LPL mass can
be used as a marker for insulin resistance in the general population and in patients with
diabetes mellitus (55). Moreover, carriers of genes that lead to a partial deficiency in
LPL are more likely to be insulin resistant than noncarriers (10). However, the relationship between serum TG and insulin resistance is different between AA and EA. For example, Sumner et al. (2005) showed that hypertriglyceridemia could be indicative of insulin resistance in Europeans but not Africans (7).
Fat Distribution
Central obesity, which can be measured by waist hip ratio (WHR), has two components: subcutaneous adipose tissue (SCAT) and visceral adipose tissue (VAT). Albu et
al. (1997) showed that VAT is associated with serum TG levels independent of total adi-
18
posity, whereas SCAT is not significantly associated with serum TG (14). It has been
well documented that AA of the same total body fat (TF) and WHR, have less VAT than
European counterparts (8), regardless of AA females having higher central adiposity (9).
However, it has to be emphasized that African females that have higher central adiposity
than their European counterparts, tend to have significantly higher SCAT (which has not
been shown to cause dyslipidemia). Moreover, Conway et al. (1999) was able to show
that WHR is less correlated with VAT in AA females than EA females (8, 9).
Moreover, Despres et al. (2000) has shown that although Canadian African
women had higher body weight, percentage of body fat, fat mass, waist girth, and SCAT
than their European counterparts, VAT was similar for both African and European
women. However, the African women cohort had lower TG and higher HDL-C. Also,
African women had higher post heparin LPL and lower post heparin HL. TG was found
to be best predicted by VAT. On the other hand, HDL-C was best predicted by post
heparin LPL (15). VAT has been positively associated with serum insulin resistance,
mainly because VAT depot is highly resistant to the effect of insulin. Also, VAT depot is
highly responsive to any stimulus that indicates the body’s need for energy. When glucose is not used efficiently, VAT depot releases abundant free fatty acids into blood that
stimulate VLDL biosynthesis (8, 9). These observations suggest that VAT might be serving as a protective factor in AA from the effect of insulin resistance on serum TG and
HDL-C levels (15).
19
Lifestyle Factors
Differences in cigarette smoking and levels of physical activity between AA and EA have
been documented. These factors are also associated with variations in serum lipids (9).
Socioeconomic status (SES) has also been shown to be associated with serum HDL-C
(13). Pradeepa et al. (2003) measured the patterns of dyslipidemia between two different
socioeconomic groups (middle vs. low income) in an urban Indian population. It was
found that the lower socioeconomic group had lower HDL-C. However, both males and
females of low income had lower TG than the middle income group (p< 0.01, p< 0.001
respectively) (56). Nevertheless, Gaillard et al. (1997) were not able to detect a relationship between SES and serum lipids (57). The relationship between SES and serum TG
and HDL-C could be a result of better lifestyle choices and better control of diseases associated with high income. For example, Rennie et al. (2003) indicated a strong association between vigorous activity (in males and females of European population from London) with SES. Vigorous activity has been shown to lower serum TG and increase serum
HDL-C in the same study (58). Another example is the study by Chaturvedi et al. (1996)
who showed that European men who had DM and were college educated were more
likely to participate in vigorous activity (p< 0. 01), to be non-smokers (P< 0. 01), and
have lower TG (p= 0. 01) than their counterparts with lower education. This study illustrated the poorer control of symptoms of dyslipidemia associated with DM in lower socioeconomic groups (59). In conclusion, racial differences in serum TG can be a result of
various factors including genetic make-up, insulin sensitivity, adiposity, VAT, and lifestyle.
20
METHODS
Subjects
Data for this study was the baseline data obtained from a total of 255 women who
participated in two ongoing longitudinal research protocols, JULIET study (Exercise
Training in Obesity-Prone Black and White Women), and ROMEO (Role of Metabolism
and Exercise in Obesity). Both of the studies were longitudinal studies conducted at the
Department of Nutrition Sciences at the University of Alabama at Birmingham (UAB).
JULIET subjects were overweight, normoglycemic, premenopausal African and European American women, aged 20-41 years. Moreover, JULIET subjects had BMI between
27-31 Kg/m2, sedentary lifestyles, family history of obesity (defined as having at least
first degree relative of BMI>27 Kg/m2), and no history of diabetes mellitus. All subjects
were nonsmokers, and not taking any medications that would affect energy expenditure,
insulin level, heart rate, or thyroid function. However, some of ROMEO subjects had a
BMI between (21-25) Kg/m2 and the rest had a BMI between (27-30) Kg/m2. Other criteria of ROMEO subjects were similar to the aforementioned for JULIET subjects. All recruits were informed of the experimental design and oral and written informed consents
were obtained. The Institutional Review Board at UAB approved the protocols for the
ROMEO and JULIET studies.
21
Study Design
The objective of this study was to measure the effect of genetic admixture and
LPL polymorphisms (LPL RS285 and LPL RS250) on serum lipids after accounting for
possible covariates. The baseline biochemical, physiological, and physical tests were
done under tightly controlled conditions by the personnel of the General Clinical Research Centre (GCRC) following a four week stabilization period. During the first two
weeks, subjects followed their customary diet at home. Each subject was instructed to
record a 4-day food record, reflecting 2 weekdays and 2 weekend days. During the last
two weeks, the UAB GCRC provided the subjects with a diet adequate in calories for
weight maintenance (as calculated by the Harris Benedict equation, with an activity factor of 1.35). Upon completion of these four weeks, each participant was admitted to the
GCRC as an inpatient for a total of three nights. During the admission period several
tests were performed while GCRC continued to provide weight maintenance food for a
period of testing on patients. The tests included: measurement of insulin resistance using
insulin sensitivity index (SI), measurement of insulin secretion using acute insulin response to glucose (AIRg), serum TG and HDL-C, adiposity which was measured with
total body fat, central adiposity which was measured by VAT, genotyping, admixture
analysis, and socioeconomic status which was measured by median household income.
Intravenous Glucose Tolerance Test
JULIET Subjects
Insulin modified, frequently sampled intravenous glucose tolerance (FSIGT) test
was used to measure the whole body insulin sensitivity. Flexible intravenous catheters
22
were placed in the antecubital veins of both arms of each subject. Basal insulin was determined by taking three blood samples after an overnight fast at the GCRC. Then, glucose (50% dextrose, 11.4g/m2) was administered at time 0. A blood (2 ml) sample was
withdrawn after 32 time points following the glucose administration. Bolus insulin (0.02
U/kg, Humulin, Eli Lilly and Co., Indianapolis) was injected 20-25 minutes after the glucose administration. Glucose was assayed in 10µl sera using the Ektachem DT II System
(Johnson and Johnson Clinical Diagnostics). The mean intra-assay coefficient of variation (CV) of this analysis is 0.61%, while the mean interassay CV was 1.45% in the Core
Laboratory. Insulin was measured in duplicate 100 µl aliquots with Linco Research
Products Inc. (St. Charles, MO) reagents; the intra-assay and inter-assay CV for this assay are 3.49%µlU/ml, 5.57% and a sensitivity of 3.35 µU/ml, respectively.
ROMEO Subjects
Tolbutamide modified, frequently sampled glucose tolerance test (FSIGT) was
used for ROMEO subjects. 11.4gm/m2 of glucose was administered for each subject.
Then, 125mg/m2 of Tolbutamide was administered. 2ml blood samples were collected 33
times -20-to+180 minutes relative to glucose administration. Insulin and glucose were
assayed as discussed above, but DPC Research Products reagents were used to assay the
insulin.
The obtained values for insulin and glucose assays were entered into the MINMOD computer program (Millennium ver., © Richard N. Bergman) to obtain measures
for SI (which reflects the rate at which glucose declines for a given amount of insulin)
23
and AIRg (which refers to the area under the curve of insulin during the first 10 minutes
following glucose injection).
Lipids Assessment
After four weeks of baseline stabilization, blood samples for lipids assessment
were collected during GCRC admission. For lipids measurement, the Ektachem DT 11
system was used. Total triglyceride and HDL-C were assayed using this system after
precipitation of LDL and VLDL using dextran sulfate and magnesium chloride. Ektachem DT 11 system in the GCRC is calibrated using reagents supplied from the manufacturer. Control sera of low and high substrate concentrations were analyzed with each
group of samples, ensuring that values for the controls fell within accepted ranges before
the samples were analyzed.
Body Composition
Total body fat was measured by Dual-energy X-ray Absorptiometry (DXA). The
lunar DPX-L densitometer along with Adult Software Version 1.33 (Lunar Corp) was
used to measure the total and regional body composition for the ROMEO subjects,
whereas Prodigy DPX-L was used for the JULIET subjects who went through a total
body scan for about 25 minutes. Prior to the scanning process, subjects were told to take
off all metal objects, wear a hospital grown, and lie on their backs on a padded table.
24
Computed Tomography (CT)
Visceral adipose tissue (VAT) was measured using CT. GE HiLight/Advantage
scanner in the University Hospital Radiology Department was used in the measurement.
Radiographic factors were 120 kVp and 40 mA. Subjects were scanned to obtain 5 mm
scans for 2 seconds while they were in the supine position with their arms stretched over
their heads. The scan was taken between the 4th and 5th lumbar vertebrae corresponding to
the umbilical level. After obtaining the scans for our subjects, an automated computer
was used to calculate the intra-abdominal fat areas (expressed in cm2). If the automated
program incorrectly outlined the areas to be measured, it was manually corrected. To
avoid variation in measurement, the scans were analyzed by the same person. Our estimate of CV for repeated measurements of cross-sectional scans of 40 subjects was less
than 2% in our laboratory.
Genotyping
LPL RS 250
DNA was extracted from leukocytes as it was described by Kunkel et al. (1977)
(60). For the detection of the LPL RS250 polymorphism, polymerase chain reaction
(PCR) amplification of genomic DNA for promoter polymorphism was performed in 25
µl of reaction solution in the presence of 1.5 mmol/L MgCl2, 0.75 mmol DNTPs, and 2.5
ml Triton Buffer 10x, 1 units of Taq DNA polymerase 0.2 mmol/L of the primer prLPL-1
(5`- GTGTTTGGTGCTTAGACAGG, located at positions-58 to -239) and primer
prLPL-1(5` GCTAGAAGTGGGCAGCTTTC, located at positions +37 to +56). The re-
25
action was incubated at 940C for 5 minutes, followed by 35 cycles at 940C for 30 seconds, 580 C for 30 seconds, 720C for 30 seconds, 720 C for 5 minutes, and the reaction
was held at 40C. The PCR products were digested with 0.5 units of Hae111 restriction
enzyme for detection of -93 T/G substitution using 3.5 ml of the buffer NEB#2 at 370C.
Then the digested fragments were separated on 2% agarose gel.
LPL RS285
DNA was extracted from leukocytes as it was described by Kunkel et al. (1977)
(60). For the genotyping of RS285, polymerase chain reaction (PCR) amplification of
genomic DNA was performed in 25 µl of reaction solution in the presence of 1.5 mmol/L
MgCl2, 0.75mmol DNTPs, and 2.5 ml Buffer 10x, 1unit of Taq DNA polymerase, 0.2
mmol/L OF the forward primer (5’ TGCAAGGGTTTTGCTTAATTCT) and reverse
primer (5` CAACAACAAAACCCCACAGC). The reaction was incubated at 940C for 5
minutes, followed by 35 cycles at 940C for 35 seconds, 550 C for 30 seconds, 720C for 30
seconds, 720 C for 5 minutes, and the reaction was held at 40C. The PCR products were
digested with 0.5 units of Pvu II restriction enzyme for detection of C/T substitution at
introns 6 using 3.5 ml of the buffer NEB#2 at 370C. Then the polymorphisms were detected using Melting Curve analysis of single nucleotide polymorphism (McSNP) as described elsewhere (61).
Admixture Analysis
Three 5-ml blood samples were obtained from each subject and sent to the laboratory core at UAB to establish the lymphoblast cell lines and DNA extraction. Extracted
26
DNA was sent to Dr. Mark Shiver at Pennsylvania State University for genotyping of
Ancestry Informative Markers (AIM). Molecular techniques used for genotyping include
the melting curve analysis of single nucleotide polymorphisms (McSNP) described by
Akey et al. (2001) (62), and electrophoresis markers and techniques used for the identification of ancestry informative DNA sequences as was described earlier (63), and are
available through dbSNP (http:www.ucli.nlm.nih.gov/SNP) using the handle PSAANTH. All genotypic data was translated into individual values of admixture using the
maximum likelihood method to obtain estimates of African (AFADM), Amerindian
(AMADM) and European admixture (EUADM).
Socioeconomic Status
Socioeconomic status was measured using median household income by zip code
as obtained from US 2000 Census (http://factfinder.census.gov/servlet/SAFFFacts?). Zip
code information was obtained from participants at the time of enrollment in the studies.
Statistical Analysis
Descriptive statistics were given for all dependent and independent variables. Exploratory analyses were performed to detect differences in the insulin-related measures
because of the use of different methods between ROMEO and JULIET studies using ttest. T-tests were also used as preliminary analysis to compare the study variables by racial category and also to compare the variables according to carriers and noncarriers of
LPL RS250 and LPL RS285 mutants in the total sample and for each racial group. Sim-
27
ple correlations were also conducted to study the relationship between TG and variables
of the study. Similarly, simple correlations were run between variables of the study and
HDL-C. The simple correlations were also run for each racial group. Multiple regression
models were analyzed to test the contributions of genetic admixture and the polymorphisms of interest to TG and HDL-C, accounting for total body fat, VAT, SI, AIRg, and
income as covariates. A similar model was used for HDL-C with AIRg excluded from the
regression model and TG included as covariate instead. Two different models were considered when evaluating the contributions of the polymorphisms: an allelic model testing
the presence or absence of the mutant allele (equivalent to a dominant genetic model) and
a genotypic model where genotypes were coded as 0, 1 and 2; 1 being the code for the
heterozygous (equivalent to an additive genetic model). Both allelic and genotypic models were analyzed when testing the contributions of LPL RS250 and LPL RS285 to TG
and HDL-C, including African and Amerindian admixture as covariates. All statistical
models were evaluated for residual normality, and those values above and below three
standard deviations were removed from each model. Variables were logarithmically
transformed when residuals were not normally distributed. All data was analyzed using
SAS 9.1 software.
28
RESULTS
A total of 254 women (114 European American and 140 African American) were
included in the analysis, and genotyping data was available for 215 subjects of the total
sample. Descriptive statistics for the total study group and by racial group are listed in
Table 1. No significant differences were obtained between ROMEO and JULIET participants in any of the insulin-related outcomes. However, significant differences were
found between races in various measures. African American women had significantly
greater (AIRg) (p<0.001), HDL-C (p<0.001), and AFADM (p<0.001) than European
American women, whereas European American women had greater SI (p<0.05), total
body fat (p<0.05), VAT (<0.001), TG (p< 0.001), income (p<0.001), and EUADM (p<
0.001) than African American women. There was no significant difference in age and
AMADM across racial groups.
Polymorphism LPL RS250
The genotypic frequency for LPL RS250 polymorphism of the entire group was
62.3%, 27.4%, and 10.2% for TT, TG, and GG genotypes, respectively. As shown in Table 2, African American females were more frequently carriers of the mutant allele G
(61%) as opposed to European American women (5%); (χ2= 70,df=1, p<0.001). The frequency of the genotypes TT, TG, and GG was 39%, 44%, and 17%, respectively in African Americans, whereas it was 95% for TT, 4% for TG, and 1% for GG in European
29
American women. Descriptive statistics of the study variables for carriers and noncarriers
of the mutant allele G are shown in Table 3. The carriers of the G allele had a greater
AIRg (P< 0.001) and African admixture (p< 0.001) than noncarriers, whereas noncarriers
of the mutant allele G had greater total body fat (p< 0.05), VAT (p< 0.001), TG (p<
0.001), income (p< 0.001), European admixture (p< 0.001), and Amerindian admixture
(p<0.05) than the carriers. There was no significant difference between the carrier and
noncarrier groups in HDL-C. Furthermore, the variables did not differ significantly between carriers and noncarriers of the mutant allele G when each group was analyzed for
each racial group (Table 4).
Polymorphism LPL RS285
The genotypic frequency of the RS285 polymorphism for the entire group was
50%, 36%, and 14% for TT, CT, and CC genotypes, respectively. As shown in Table 2,
the frequency of the allele T was greater in AA (97.6%) than in EA women (70.3%); (χ2=
32.5,df=1, 213 p<0.001). The frequency of the genotypes TT, CT, and CC was 75%,
23%, and 2%, respectively in African Americans, whereas it was 16%, 54%, and 30%,
respectively for European Americans. Descriptive statistics of the study variables for carriers and noncarriers of the mutant allele T are shown in Table 5. The carriers of the allele
T had greater AIRg (p< 0.05), and AFADM (p<0.001), whereas noncarriers had a greater
TG (p<0.01), income (p< 0.05), AMADM (p< 0.05), and EUADM (p< 0.001). There was
no significant difference between the groups in terms of total body fat, VAT, SI, and
HDL-C. Also, the variables did not differ significantly between carriers and noncarriers
of the allele C when each group was analyzed separately (Table 6).
30
Triglycerides
Correlations
Simple correlations between TG and variables of the study are shown in Table 7.
Triglycerides were found to be significantly correlated with AIRg (p< 0.05), total body fat
(p< 0.05), VAT (p< 0.01), HDL-C (p< 0.001), income (p< 0.01), AFADM (p< 0.001),
and EUADM (p< 0.001). On the other hand, AMADM and SI were not significantly correlated with TG. Also, when correlations were analyzed based on racial group. TG was
found to significantly correlate with VAT and HDL-C for both African American (p<
0.01, 0.05 respectively) and European American women (p< 0.01, 0.05 respectively),
while insulin sensitivity was only significantly correlated with TG only in European
Americans (p< 0.05), and age was correlated with TG in AA ( p< 0.05 ). Other variables
including AIRg, total body fat, income, AFADM, EUADM, and AMADM were not significantly correlated with TG in either African or European American women.
Multiple Linear Regression Models
Multiple linear regression was used to test three different models evaluating the
individual effect of admixture, LPL RS250, and LPL RS285 independently. AFADM
and AMADM were used to explain variations in log TG (n=182). The model testing the
association between genetic admixture and TG was found to be significant (r2=0.29,
F=11.8, df (7,174), p <0.001), accounting for 54% of the variation in logTG. African
Admixture had a significant effect on the model (p<0.001), whereas Amerindian admixture did not play a significant role on the model (p =0.7). No significant contributions
31
were identified between TG levels and covariates total body fat (p=0.6), income (p = 0.4)
and log AIRg (p =0.09). However, VAT was significantly associated with TG (p <
0.001), as well as SI (p=0.03). Results from the model are summarized in Table 8.
Although the model evaluating the genotypic association of the RS250 polymorphism as an independent variable (n=158) was significant (r2= 0.27, F=8.4, df (8,149), p<
0.001), LPL RS250 was not a significant contributor to TG variation (p= 0.5). SI, AIRg,
VAT and African admixture showed significant contributions (p =0.03, p =0.02, p
=0.001, and p= 0.016, respectively). On the other hand, no significant contributions were
detected for total body fat (p= 0.5), income (p= 0.5), and AMADM (p = 0.03). The total
model explained 52% in the variation in TG (Table 9). No significant contributions were
detected when evaluating the allelic model for RS250 in the entire sample (Table 10).
When the relationship between TG and LPL RS250 was evaluated by race, no significant
genotypic or allelic contributions were observed for either AA or EA women.
The results of the model evaluating the relationship between TG and the polymorphism LPL RS285 in the entire sample was significant (r2=0.30, F=9.11, df (8,151),
p< 0.001), after accounting for the confounding effects of total body fat, VAT, SI, AIRg,
AMADM, AFADM, and income (Table 11). LPL RS285 genotype was found to be significantly associated with log TG (p=0.02), and significant contributions to this model
were observed from VAT (p<0.001) and SI (p=0.008). AIRg, AFADM, AMADM, income, and log total body fat were found not to be significant variables in the model (p=
0.08, 0.051, p=0.77, p=0.80 and p=0.38, respectively). Analyzing the same model by race
revealed that the significance of LPL RS285, for both European Americans (n= 75) and
African American (n=84), disappeared. Moreover, when the model was analyzed for the
32
allelic contributions of RS285, the model remained significant (r2= 0.3, F= 8.7, df
(8,149), p< 0. 001), but no significant contribution of RS285 allele to TG was observed
(Table 12). No significant contributions were observed when the models were analyzed
according to race.
High Density Lipoprotein Cholesterol
Correlations
The correlations between HDL-C and various variables of the study are shown in
Table 13. HDL-C was found to be significantly correlated with SI (p< 0.05), VAT (p<
0.001), TG (p< 0.001), AFADM (p< 0.001), and EUADM (p< 0.001), but not with AIRg,
total body fat, income, or AMADM. When the correlations were analyzed by racial
group, HDL-C was found to be significantly correlated with AIRg(p< 0.01) in African
American, whereas in European American women, HDL-C was significantly correlated
with SI (p< 0.05) and VAT (p< 0.05). Total body fat, income, AFADM, EUADM, and
AMADM were not significantly correlated with HDL-C in either African or European
American women.
Multiple Regression Models
Genetic admixture for both African and Amerindian ancestry were used as predictors of HDL-C in an overall significant multiple linear regression model (r2= 0.16, F=
6.8, df (6,177), p< 0.001) that included total body fat, VAT, SI and income as possible
covariates. African admixture was found to have a significant independent contribution to
levels of HDL-C (p= 0.026) whereas Amerindian admixture did not contribute signifi-
33
cantly (p= 0.97). The covariates that significantly influenced HDL-C were SI (p= 0.024)
and VAT (p= 0.002); neither income nor total for fat were significantly associated with
TG in the model (p=0.6 for both of them). Results for this model are shown in Table 14.
This model was able to predict 40% of variation in log HDL-C. When TG was considered as a covariate, the African Admixture was found not significant (p= 0.07), but both
VAT and SI remained significant ( p= 0.007, p= 0.03, respectively), as shown in Table
15.
Another multiple regression model was analyzed to assess the genotypic relationship between HDL-C and the polymorphism RS 250. The model was significant (r2=0.2,
F= 6.3, df= (8,151), p< 0.001) and explained 45% of variation in HDL-C. However, no
significant independent contribution from LPL RS250 was detected (p=0.2), nor were
VAT (p=0.13), total body fat (p=0.8), TG (p=0.12), AMADM (p= 0.9), and income
(p=0.06). On the other hand, both SI and AFADM were significant (p=0.001, 0.01 respectively), as shown in Table 16. When the model was evaluated for the allelic contributions of RS250, the no association results remained, but income had a trend (p= 0.06)
(Table 17). When the RS250 allelic model was evaluated according to race, RS250 allele
remained not significant in EA but demonstrated a trend in AA women (p=0.07).
A last model was used to assess the relationship between LPL RS285 genotype
and HDL-C. The model also included the variables total body fat, VAT, SI, TG,
AFADM, and AMADM. Although the full model was significant (r2=0.19, F= 5.8, df
(8,152), p< 0.001), and explained 44% of variation in HDL-C (Table 18), only SI and
AFADM showed significant contributions to HDL-C (p= 0.001, 0.04 respectively). A
similar model evaluated the genotypic contributions of LPL RS285 for AA and EA
34
women; the results of the analysis revealed that RS285 genotype was not significant in
AA or EA (p= 0.3, p= 0.09, respectively). No significant contributions of RS285 were
detected in the allelic association for the total sample, but income became significant (p<
0.05) (Table 19). Moreover, no significant associations between LPL RS285 and HDL-C
were found when the models were evaluated according to race.
35
Table 1
Descriptive Statistics of AA and EA Premenopausal Females for the Total Sample and
According to Race.
Total
( n= 207-255 )
Mean ± SD
RACE
Age (year)
34.50 ± 5.40
AA
( n= 110-140 )
Mean ± SD
34.50 ± 6.14
AIRg
(µlU/ml×10)
SI (×10-4.min1
/(µlU/ ml))
Total body fat
(kg)
VAT (cm2)
754.10 ± 544.00
950.30 ± 611.50
530.00 ± 337.90***
3.60 ± 2.50
3.30 ± 2.60
4.00 ± 2.30*
31.20 ± 6.70
30.20 ± 6.60
32.30 ± 6.60*
75.90 ± 31.90
63.30 ± 26.90
90.80 ± 31.00***
TG (mg/dl )
88.80 ± 47.90
68.70 ± 26.30
113.60 ± 56.30***
HDL-C (mg/dl )
40.30 ± 10.60
43.40 ± 10.80
36.60 ± 9.00***
Income (dollars)
43708.70 ± 16582.80
37838.20 ±
14600.20
50186.00 ±
16229.20***
AFADM
0.44 ± 0.29
0.70 ± 0.12
0.13 ± 0.04***
EUADM
0.49 ± 0.28
0.24 ± 0.10
0.80 ± 0.08***
AMADM
0.06 ± 0.05
0.06 ± 0.04
0.07 ± 0.05
Variable
EA
( n= 96-114 )
Mean ± SD
33.60 ± 5.70
Note. * p< 0.05. ** р< 0.01. *** р < 0.001. AIRg = acute insulin response to glucose;
SI= insulin sensitivity; BMI= body mass index; VAT= visceral adipose tissue; TG= serum triglycerides, HDL-C= high density lipoprotein cholesterol; AFADM= African;
EUADM= European admixture; AMADM= Amerindian admixture.
36
Table 2
Genotypic Frequency of LPL Polymorphisms (LPL RS250 and LPL RS 285) in a Total
Sample of AA and EA Premenopausal Females.
Genotype
AA
(n=124)
% AA
RACE
EA
(n= 91)
% EA
LPL RS250 (n= 215)*
T/T
38.7
94.5
T/G
44.4
4.4
G/G
16.9
1.1
LPL RS285 (n= 215)**
C/C
2.4
29.7
T/C
22.6
53.8
T/T
75.0
16.5
Note. *Chi-square=70, p-value< 0.001. ; **Chi-square= 78, p-value< 0.001; T= thymine;
G= guanine; C= cytosine.
37
Table 3
Comparison of the Study Variables between Carriers and Noncarriers of LPL RS250, G
Allele, in a Total Sample of AA and EA Premenopausal Females.
Variable
Age (year)
Carriers
( TG or GG )
( n= 71-81 )
33.80 ± 5.60
Noncarriers
( TT )
( n= 110-134)
34.50 ± 6.10
AIRg (µlU/ml×10)
930.90 ± 660.10
632.10 ± 437.60***
SI (×10-4.min-1/(µlU/ ml))
3.70 ± 3.20
3.90 ± 2.20
Total body fat (kg)
29.30 ± 7.10
31.70 ± 6.60*
VAT (cm2)
65.40 ± 30.30
79.10 ± 29.10***
TG (mg/dl )
72.70 ± 31.60
97.00 ± 55.30***
HDL-C (mg/dl )
41.00 ± 10.80
38.50 ±9.60
Income (dollars)
37194.10 ± 12333.10
45299.60 ±
16991.60***
AFADM
0.70 ± 0.20
0.33 ±0.28***
EUADM
0.27 ± 0.15
0.60 ± 0.28***
AMADM
0.06 ± 0.04*
0.07 ± 0.06*
Note. * р< 0.05. ** р< 0.01. *** p< 0.001. AIRg = acute insulin response to glucose; SI=
insulin sensitivity; BMI= body mass index; VAT= visceral adipose tissue; TG= serum
triglycerides, HDL-C= high density lipoprotein cholesterol; AFADM= African;
EUADM= European admixture; AMADM= Amerindian admixture.
38
Table 4
Comparison of the Variables of the Study between Carriers and Noncarriers of LPL
RS250, G Allele, According to Race in a Sample of AA and EA Premenopausal Females.
Race
Genotype
Age (year)
AA
Mean ±SD
Carriers
Noncarriers
(GG,TG)
( TT )
( n= 66-76 )
( n= 39-48 )
33.50 ± 5.5
34.00 ± 5.90
EA
Mean ±SD
Carriers
Noncarriers
( GG, TG )
( TT )
( n= 5 )
( n= 73-86)
39.30 ± 4.10
34.70 ± 6.20
AIRg (×10.min-1/(µlU/
ml)
SI (×10-4.min1
/(µlU/ ml)
Total body fat
(kg)
VAT (cm2)
963.30 ±
665.20
868.60 ± 533.90 457.60 ± 350.60 519.50 ±
331.80
3.60 ± 3.10
3.20 ± 1.90
5.90 ±3.40
4.20 ± 2.30
30.90 ± 6.70
30.60 ± 5.80
29.00 ± 7.90
32.20 ± 7.00
62.90 ± 28.40
62.20 ± 20.20
102.10 ± 35.60
88.50 ± 29.10
TG (mg/dl )
69.30 ± 26.10
68.10 ± 27.90
123.00 ±
58.6.00
113.20 ±
60.10
HDL-C (mg/dl )
41.50 ± 10.50
44.20 ± 10.00
33.60 ± 13.7.00
35.30 ± 7.80
Income (dollars)
36864.20 ±
12632.70
36997.30 ±
16431.70
41549.80 ±
6584.10
49507.600 ±
15.00
AFADM
0.71 ± 0.10
0.67 ± 0.15
0.15 ± 0.04
0.13 ± 0.04
EUADM
0.24 ± 0.08
0.26 ± 0.14
0.75 ± 0.04
0.79 ± 0.08
AMADM
0.05 ± 0.04
0.06 ± 0.04
0.1 ± 0.04
0.08 ± 0.06
4
Note. No significant effects were found; AIRg = acute insulin response to glucose; SI=
insulin sensitivity; BMI= body mass index; VAT= visceral adipose tissue; TG= serum
triglycerides, HDL-C= high density lipoprotein cholesterol; AFADM= African;
EUADM= European admixture; AMADM= Amerindian admixture.
39
Table 5
Comparison of the Study Variables between Carriers and Noncarriers of LPL RS285, T
Allele, in a Total Sample of AA and EA Premenopausal Females.
Variable
Age (year)
Carriers
(CT or TT)
( n= 159-185 )
34.20 ± 5.90
Noncarriers
(CC)
( n= 22-30 )
34.50 ± 6.00
AIRg (µlU/ml×10)
778.30 ± 564.30
571.00 ± 448.10*
SI (×10-4.min-1/(µlU/ ml))
3.80 ± 2.60
3.90 ± 2.40
Total bod fat (kg)
30.60 ± 6.90
32.10 ± 7.10
VAT (cm2)
72.70 ± 30.50
82.40 ± 27.50
TG (mg/dl )
82.50 ± 45.30
120.90 ± 59.00**
HDL-C (mg/dl )
39.80 ± 10.10
36.90 ± 9.90
Income(dollars)
41047.40 ±
15677.20
49872.80 ± 14825.40*
AFADM
0.50 ± 0.30
0.18 ± 0.15***
EUADM
0.44 ± 0.28
0.73 ± 0.2***
AMADM
0.06 ± 0.05
0.09 ± 0.06*
Note. * р < 0.05. ** р < 0.01. *** р < 0.001. AIRg = acute insulin response to glucose;
SI= insulin sensitivity; BMI= body mass index; VAT= visceral adipose tissue; TG= serum triglycerides, HDL-C= high density lipoprotein cholesterol; AFADM= African;
EUADM= European admixture; AMADM= Amerindian admixture.
40
Table 6
Comparison of the Variables of the Study between Carriers and Noncarriers of LPL
RS285, T allele, According to Race in a Sample of AA and EA Premenopausal Females.
RACE
Genotype
Age (year)
AIRg (µlU/ml×10)
AA
Mean ±SD
Carriers
Noncarriers
(CT or TT)
(CC)
( n= 103-121) ( n= 0-3 )
33.60 ± 5.60
37.90 ± 7.60
EA
Mean ±SD
Carriers
Noncarriers
(CT or TT)
(CC)
( n= 56-64)
( n= 22-27 )
35.30 ± 6.30
34.20 ± 5.90
926.80 ±
625.20
1037.00 ±
531.60
514.70 ±
289.60
519.70 ±
417.40
2.50 ± 1.30
4.30 ± 2.30
4.10 ± 2.50
33.10 ± 5.60
32.10 ± 6.90
32.00 ± 7.30
62.70 ± 25.70
59.20 ± 17.90
91.10 ± 30.30
85.00 ± 27.40
68.80 ± 26.90
70.00 ± 29.60
108.40 ±
59.60
126.50 ±
59.30
42.40 ± 10.00
47.30 ± 22.20
34.90 ± 8.40
35.80 ± 7.60
36912.00 ±
1383.00
-
48653.60 ±
15804.20
49872.80 ±
14825.40
0.70 ± 0.11
0.54 ± 0.28
0.13 ± 0.04
0.14 ± 0.06
0.24 ± 0.10
0.32 ± 0.19
0.79 ± 0.07
0.78 ±0.08
0.06 ± 0.04
0.14 ± 0.09
0.08 ± 0.06
0.07 ±0.08
SI ×10-4.min-1/(µlU/ 3.50 ± 2.80
ml))
29.70 ± 6.70
Total body fat (Kg)
VAT (cm2)
TG (mg/dl )
HDL-C (mg/dl )
Income
(dollars)
AFADM
EUADM
AMADM
Note. * р < 0.05. ** р < 0.01. AIR= acute insulin response to glucose; SI= insulin sensitivity; BMI= body mass index; VAT= visceral adipose tissue; TG= serum triglycerides,
HDL-C= high density lipoprotein cholesterol; AFADM= African; EUADM= European
admixture; AMADM= Amerindian admixture.
41
Table 7
Correlations of TG with Various Variables of the Study for the Total Sample of AA and
EA Premenopausal Females, and According to Race.
Total R
Variable
RACE
Age (year)
( n= 206-255)
0.09
AA R
( n = 110-140 )
0.15*
EA R
( n= 110-114 )
0.05
AIRg (µlU/ml×10)
-0.13*
-0.08
-0.08
SI (×10-4.min-1/(µlU/ ml)
-0.04
-0.08
-0.20*
Total body fat (kg)
0.10*
< 0.01
0.05
VAT (cm2)
0.17**
0.06**
0.07**
HDL-C (mg/dl )
-0.30***
-0.17*
-0.22*
Income (dollars)
0.21**
0.02
0.09
AFADM
-0.44***
-0.04
0.04
EUADM
0.44***
0.04
0.02
AMADM
0.01
<0. 01
0.02
Note. * р < 0.05. ** р< 0.01. *** р< 0.001. AIRg = acute insulin response to glucose; SI=
insulin sensitivity; VAT= visceral adipose tissue; TG= serum triglycerides, HDL-C= high
density lipoprotein cholesterol; AFADM= African; EUADM= European admixture;
AMADM= Amerindian admixture
42
Table 8
Parameter Estimates for the Model testing the Association of TG with African Genetic
Admixture after Adjusting for Covariates in Sample of AA and EA Premenopausal Females (n= 182; R =0.54)
Variable
Parameter estimate ± SE
p-value
Intercept
4.150 ± 0.620
<0.001
Log AIRg
-0.090 ± 0.050
0.090
Log SI
-0.130 ± 0.060
0.030
Log Total body Fat -0.060 ± 0.130
0.600
Log VAT
0.300 ± 0.080
<0.001
AFADM
-0.490 ± 0.130
<0.001
AMADM
0.280 ± 0.66
0.700
0.390
Income
1.300 x 10-6 ± 2.040 x 10-6
Note. AIRg= acute insulin response to glucose; SI= insulin sensitivity; VAT= visceral
adipose tissue; TG= serum triglycerides; AFADM= African; AMADM= Amerindian admixture.
Table 9
Parameter Estimates for the Model testing the Genotypic Association of TG with RS250,
after Adjusting for Covariates in Sample of AA and EA Premenopausal Females (n=158,
R=0.52).
Variable
Intercept
Log AIRg
Log SI
Log Total body fat
Log VAT
AFADM
AMADM
Income
LPL RS250
Parameter Estimate ± SE
4.400 ± 0.650
-0.130 ± 0.060
-0.150 ± 0.070
-0.110 ± 0.150
0.300 ± 0.100
-0.390 ± 0.160
0.700 ± 0.680
4.500 x 10-7 ± 2.300 x 10-6
-0.040 ± 0.060
P-value
<0.001
0.02
0.030
0.500
0.001
0.020
0.300
0.700
0.500
Note. AIRg= acute insulin response to glucose; SI= insulin sensitivity; VAT= visceral
adipose tissue; TG= serum triglycerides; AFADM= African admixture; AMADM=
Amerindian admixture.
43
Table 10
Parameter Estimates for the Model Testing the Allelic Association of TG with RS250,
After Adjusting for Covariates in a Sample of AA and EA Premenopausal Females
(n=158; R=0.54).
Variable
Parameter estimate ± SE
p-value
Intercept
4.320 ± 0.670
<0.001
Log AIRg
-0.110 ± 0.060
0.050
Log SI
-0.050 ± 0.070
0.030
Log Total body Fat
-0.070 ± 0.150
0.660
Log VAT
0.300 ± 0.100
0. 003
AFADM
-0.480 ± 0.170
0.005
AMADM
0.490 ± 0.700
0.490
Income
8.930 x 10-7± 2.250 x 10-6
0.690
Allele RS250
-0.010 ± 0.090
0.890
Note. AIRg= acute insulin response to glucose; SI= insulin sensitivity; VAT= visceral
adipose tissue; TG= serum triglycerides, AFADM= African; AMADM= Amerindian admixture.
Table 11
Parameter Estimates for the Model testing the Genotypic Association of TG with RS285
After Adjusting for Covariates in Sample of AA and EA Premenopausal Females
(n=158; R=0.54).
Variable
Parameter estimate ± SE
p-value
Intercept
4.400 ± 0.680
<0.001
Log AIRg
-0.100 ± 0.050
0.080
Log SI
-0.350 ± 0.100
0.008
Log Total body Fat
-0.140 ± 0.150
0.380
Log VAT
0.330 ± 0.100
<0. 001
AFADM
-0.340 ± 0.170
0.051
AMADM
0.210 ± 0.710
0.770
Income
5.820 x 10-7± 2.300 x 10-6
0.800
LPL RS285
-0.130 ± 0.060
0.020
Note. AIRg = acute insulin response to glucose; SI= insulin sensitivity; VAT= visceral
adipose tissue; TG= serum triglycerides, AFADM= African; EUADM= European admixture; AMADM= Amerindian admixture.
44
Table 12
Parameter Estimates for the Model Testing the Allelic Association of TG with RS285
after Adjusting for Covariates in Sample of AA and EA Premenopausal Females (n=158;
R=0.54).
Variable
Parameter estimate ± SE
p-value
Intercept
4.450 ± 0.650
<0.001
Log AIRg
-0.120 ± 0.060
0.030
Log SI
-0.130 ± 0.070
0.040
Log Total body Fat
-0.110 ± 0.150
0.480
Log VAT
0.330 ± 0.100
0.001
AFADM
-0.370 ± 0.150
0.010
AMADM
0.580 ± 0.690
0.400
Income
7.400 x 10-7 ± 2.100 x 10-6
0.730
Allele RS285
-0.140 ± 0.100
0.140
Note. AIRg = acute insulin response to glucose; SI= insulin sensitivity; VAT= visceral
adipose tissue; TG= serum triglycerides, AFADM= African; EUADM= European admixture; AMADM= Amerindian admixture.
45
Table 13
Correlations of HDL-C with Various Variables of the Study for the Total Sample of AA
and EA Premenopausal Females, and According to Race.
Variable
Age (year)
AIRg (µlU/ml×10)
SI (×10-4.min-1/(µlU/ ml )
Total body fat (kg)
VAT (cm2)
Income (dollars)
AFADM
EUADM
AMADM
Total R2
( n= 236-255 )
RACE
2
0.02
AA R
( n= 110-140 )
0.060
EA R2
( n = 96-114 )
0.08
0.02
0.22**
0.06
0.12*
0.14
0.19*
0.09
-0.01
0.13
0.27***
0.09
0.21*
0.10
-0.09
0.13
0.28***
0.07
0.04
0.28***
-0.05
-0.05
0.02
-0.07
0.04
Note. * р < 0.05. ** р < 0.01. *** р < 0.001. AIRg = acute insulin response to glucose;
SI= insulin sensitivity; VAT= visceral adipose tissue; TG= serum triglycerides, HDL-C=
high density lipoprotein cholesterol; AFADM= African; EUADM= European admixture;
AMADM= Amerindian admixture.
46
Table 14
Parameter Estimates for the Model Testing the Association between African Genetic
Admixture and HDL-C After Adjusting for Covariates in a Sample of AA and EA
Premenopausal Females (n=184; R=0.40).
Variable
Parameter estimate ± SE
p-value
Intercept
4.010 ± 0.300
<0. 001
Log SI
0.070 ± 0.030
0.026
Log Total body Fat
0.035 ± 0.080
0.650
Log VAT
-0.150 ± 0.050
0. 002
AFADM
0.170 ± 0.070
0.026
AMADM
-0.020 ± 0.070
0.970
-7
-6
0.640
Income
-5.300 x 10 ± 1.100 x10
Note. SI= insulin sensitivity; VAT= visceral adipose tissue; TG= serum triglycerides,
HDL-C= high density lipoprotein cholesterol; AFADM= African; AMADM= Amerindian admixture.
Table 15
Parameter Estimates for the Association between African Genetic Admixture and HDL-C
After Adjusting for TG and Other Covariates in Sample of AA and EA Premenopausal
females (n=184, R=0.44).
Variable
Parameter estimate ± SE
p-value
Intercept
4.200 ± 0.340
<0. 001
Log SI
0.070 ± 0.030
0.030
Log TG
-0.040 ± 0.040
0.300
Log Total body Fat
0.030 ± 0.080
0.680
Log VAT
-0.130 ± 0.050
0. 007
AFADM
0.140 ± 0.080
0.070
AMADM
-0.010 ± 0.380
0.970
-7
-6
Income
-5.000 x 10 ± 1.100 x10
0.660
Note. SI= insulin sensitivity; VAT= visceral adipose tissue; TG= serum triglycerides,
HDL-C= high density lipoprotein cholesterol; AFADM= African; AMADM= Amerindian admixture.
47
Table 16
Parameter Estimates for Genotypic Association between HDL-C and RS250 After Adjusting for Covariates in Sample of AA and EA Premenopausal Females (n=160;
R=0.45).
Variable
Parameter Estimate ± SE
P-value
Intercept
4.100 ± 0.350
<0. 001
Log SI
0.110 ± 0.030
0. 001
Log TG
-0.070 ± 0.040
0.100
Log Total body fat
0.020 ± 0.080
0.800
Log VAT
-0.080 ± 0.060
0.100
AFADM
0.220 ± 0.090
0.010
AMADM
0.050 ± 0.380
0.900
Income
-2.200 x 10-6± 1.200x 10-6
0.060
LPL RS250
-0.050 ± 0.030
0.160
Note. .AIRg = acute insulin response to glucose; SI= insulin sensitivity; VAT= visceral
adipose tissue; TG= serum triglycerides, HDL-C= high density lipoprotein cholesterol;
AFADM= African; AMADM= Amerindian admixture.
Table 17
Parameter Estimates for Allelic Association between HDL-C and RS250 After Adjusting
for Covariates in Sample of AA and EA Premenopausal Females(n=160, R2=0.45 ).
Variable
Parameter Estimate ± SE
P-value
Intercept
4.050 ± 0.350
<0.001
Log SI
0.110 ± 0.030
0. 001
Log TG
-0.060 ± 0.040
0.140
Log Total body fat
0.030 ± 0.080
0.700
Log VAT
-0.090 ± 0.060
0.100
AFADM
0.210 ± 0.090
0.020
AMADM
0.050 ± 0.380
0.900
Income
-2.300 x 10-6± 1.200x 10-6
0.060
Allele RS250
-0.050 ± 0.030
0.290
Note. .AIRg = acute insulin response to glucose; SI= insulin sensitivity; VAT= visceral
adipose tissue; TG= serum triglycerides, HDL-C= high density lipoprotein cholesterol;
AFADM= African; AMADM= Amerindian admixture.
48
Table 18
Parameter Estimates for the Genotypic Association between HDL-C and RS285 After
Adjusting for Covariates in Sample of AA and EA Premenopausal Females (n=160;
R=0.44).
Variable
Parameter estimate ± SE
p-value
Intercept
4.100 ± 0.360
<0.001
Log SI
0.110 ± 0.030
0.001
Log TG
-0.060 ± 0.040
0.130
Log Total body Fat
0.040 ± 0.080
0.660
Log VAT
-0.070 ± 0.060
0.120
AFADM
0.190 ± 0.090
0.040
AMADM
0.040 ± 0.380
0.900
Income
2.300 x10-6 ± 1.200 x 10-6
0.050
LPL RS285
0.010 ± 0.030
0.630
Note. SI= insulin sensitivity; VAT= visceral adipose tissue; TG= serum triglycerides,
HDL-C= high density lipoprotein cholesterol; AFADM= African; AMADM= Amerindian admixture.
Table 19
Parameter Estimates for the Allelic Association between HDL-C and RS285 After Adjusting for Covariates in Sample of AA and EA Premenopausal Females (n=160;
R=0.44).
Variable
Parameter Estimate ± SE
P-value
Intercept
4.080 ± 0.350
<0.001
Log SI
0.110 ± 0.030
<0.001
Log TG
-0.070 ± 0.040
0.120
Log Total body fat
0.040 ± 0.080
0.670
Log VAT
-0.080 ± 0.060
0.150
AMADM
0.002 ± 0.370
0.900
AFADM
0.190 ± 0.080
0.020
Income
-2.400 x 10-6 ±1.200 x 10-6
0.046
Allele RS285
-0.060 ± 0.070
0.400
Note. SI= insulin sensitivity; VAT= visceral adipose tissue; TG= serum triglycerides,
HDL-C= high density lipoprotein cholesterol; AFADM= African; AMADM= Amerindian admixture.
49
DISCUSSION
Our study aimed to explain the extent to which variations in serum TG and HDLC between AA and EA responded to (a) ancestral genetic admixture and/or (b) to the effect of the polymorphisms for the markers LPL RS 250 (-93TG), and the marker LPL RS
285 (Pvu II). We hypothesized that as African genetic admixture increased, serum TG
would decrease, while HDL-C would increase. Additionally, we hypothesized that the
carriers of the G allele of the polymorphism LPL RS 250 would have lower TG and
higher HDL-C. Finally, we hypothesized that carriers of the T allele of the polymorphism LPL RS 285 would have lower TG and higher HDL-C. The results of our study
support that African genetic admixture significantly contributed to TG and that the LPL
RS285 polymorphism significantly contributed to serum levels of TG.
The experimental design of our study allowed the inclusion of relevant covariates
in the statistical models. Contrary to other studies investigating the relationship between
TG and LPL polymorphisms, our regression models for TG were improved by including
total body fat determined by DXA to account for adiposity, instead of the traditionally
used multifactorial BMI measure (64). We also adjusted our statistical models for visceral adipose tissue (which has been shown to have greater relationship to TG and HDLC than total body fat) (9, 14, 15), for SI as a measure for insulin resistance (which has
been previously shown to affect the serum TG levels through its effect on VLDL production, LPL mass, and activity) (11), and for AIRg as a measure for insulin secretion. Our
study was carefully designed to reduce spurious associations by including relevant
50
physiological covariates and exploring the contributions of genetic (accounted for by admixture), and environmental (accounted for by income) determinants of racial classification.
Our data indicated that African genetic admixture was independently and inversely related to serum TG (p<0.001) in the entire sample. Amerindian admixture,
which was included in all models to account for the effects of residual confounding and
to reduce the rate of false positive associations, did not have any significant contributions
to serum TG. The relationship between admixture and TG was significant even after accounting for the independent significant effects of insulin resistance and visceral fatness.
The significant contribution of insulin resistance is not surprising, particularly when taking into account that insulin resistance has been associated with serum TG disturbances
either by stimulating the production of VLDL (9), or, possibly, by affecting postprandial
chylomicron metabolism by LPL (65, 65). Insulin resistance also regulates the expression, activity, and secretion of LPL, such that changes in the level and function of LPL
could influence how dietary lipids are partitioned towards storage or utilization (66).
Visceral fatness has been reported to be greater in EA than in AA and has also been associated with increased serum TG (9, 14, 15). The effect of African genetic admixture on
insulin sensitivity has been previously investigated by Gower et al. (2003) showing that
genetic admixture significantly explained part of the variation in SI (p< 0.001) (20), and
recent research has demonstrated that visceral adiposity is inversely related to African
genetic admixture (67). Future investigations might be needed to fully understand the
mechanisms for which the mediation among TG, VAT, SI and admixture might occur.
51
The results for HDL-C were different from the results for TG. Admixture was not
a significant independent contributor of HDL-C levels in our sample after adjusting for
significant covariates. This lack of association suggests no direct contribution of genetic
admixture to HDL-C; however, previously reported significant associations of genetic
admixture with insulin outcomes, visceral adiposity and TG levels (all of which are factors influencing serum HDL-C and accounted for in the model) might support an indirect
effect of genetic admixture on serum HDL-C. Our results indicated that visceral adiposity and insulin sensitivity were the best predictors of HDL-C. These findings are in concordance with the work of Hansel and colleagues who demonstrated that insulin resistance, waist circumference, and variables reflecting lifestyle significantly impacted variation in serum HDL-C (17). In the models where HDL-C was the predicted variable, TG
was included as an additional covariate. As previously discussed, TG is known to have an
influence on the biosynthesis of HDL-C mediated by the lipolysis process (39, 41, 41,
42). In our study, the addition of TG as a covariate in statistical models did not show a
significant contribution to HDL-C; on the contrary, it eliminated the significant contributions of AFADM (Tables 15 and 16). This change of significance in admixture reflects a
multicollinear relationship between admixture and TG, an observation that should not be
surprising, taking into account that AFADM is a significant contributor to levels of TG,
as shown on Table 8. Although the results of our study are unique and represent the first
attempt to understand the relationship between serum TG, HDL-C and genetic admixture
within a comprehensive framework that accounts for genetic, environmental and physiological contributors, it is evident that further investigations are needed to understand the
52
putative mediating direct and indirect effects of ancestral background in metabolic parameters and the mechanisms underlying these possible mediations.
The evaluation of the LPL RS250 polymorphism supported a significant difference in the frequency of this polymorphism between AA and EA (p-value< 0.01), as previously reported by others (25, 29, 51). Although the carriers of the mutant allele had
significantly lower serum TG (p< 0.01) and higher serum HDL-C (p< 0.05) than the noncarriers in the whole sample, there was no independent effect of LPL RS250, on TG or
HDL-C (p= 0.5, p=0.16, respectively) when accounting for covariates in the full statistical models. As previously mentioned, LPL RS250 did not have any significant contributions to TG or HDL-C when analyzed according to race.
An interesting finding in the association between LPL RS250 and TG was the significance of AIRg in the statistical models. Physiologically, this significant result is not
surprising. Insulin secretion measured by AIRg has been shown to have an inverse relationship with serum TG such that an increase in AIRg results in decreased TG. Since free
fatty acids are used in the biosynthesis of TG and elevated serum insulin inhibits lipolysis
resulting in a decreased flux of free fatty acids into blood, increased AIRg may cause a
decreased amount of substrate for TG formation. Among AA, who have been shown to
have higher AIRg, and consequently a lower flux of free fatty acids, the relationship between AIRg and free fatty acids might serve as an explanation of the observed lower levels of TG in this population (68). The lack of association of AIRg in other models could
be due to power limitations that prevented this influence to be detected in other models
evaluating TG levels.
53
In contrast to work completed by other researchers, we did not study the haplotype effect of LPL RS250 and D9N polymorphisms which has been found to be in linkage disequilibrium in European populations (25, 29, 51). In people of European ancestry,
the mutant allele G of the marker LPL RS250 (-93G) cosegregates with the mutant allele
A of the marker D9N, forming the -93G/9N haplotype. This haplotype creates a “synergetic” relationship that results in overall higher levels of TG. In African descendents,
there is no evidence of linkage disequilibrium between the two LPL variants, LPL RS250
and D9N (26). The presence of mutant allele A of D9N produces a defective form of LPL
that results in higher levels of TG and lower HDL-C. The G mutation of -93TG overexpresses the production of LPL, which typically has a lowering effect on serum TG. The
haplotype -93G/9N results in the overexpression of a defective LPL that consequently,
produces increased levels of TG. In our study we did not observe a significant association
between the LPL RS250 mutation and higher levels of TG. It appears that the small
sample size resulting from the separation according to race might explain this lack of association. Further investigation is needed to explore the relationship between LPL RS250
and levels of TG in individuals of European descent.
The independent effect of the LPL RS250 on TG levels in African descendents
(where no linkage disequilibrium occurs with D9N) has been previously studied, showing
contradictory results. While the mutant allele G was shown to have a TG lowering effect
but no effect on HDL-C in some studies(25, 29), Hall et al. (1997) did not detect any effect of this polymorphism on either serum TG or HDL-C (26). Ehrenburg et al. (1997)
reported that carriers of the GG phenotype of LPL RS250 had mildly lower serum TG
than carriers of the TT genotype in a South African cohort (p=0.04), whereas no signifi-
54
cant association with HDL-C was observed. Our results did not support an association
between LPL RS250 and TG levels in AA, a finding that could be explained by differences in experimental design. For example, where other studies have accounted for adiposity by the use of BMI and/or WHR, our design included more sophisticated and precise measures for body composition, including total body fat measured by DXA and visceral adiposity. In addition, while other studies utilized the category “race” to classify
individuals, we decomposed racial categorization by the inclusion of income and genetic
admixture in our statistical models. Contrary to other studies, our experimental design
included outcomes related to insulin secretion and action, which have been shown to have
an important role in the biosynthesis of TG and HDL-C. On the other hand, our study was
limited by sample size, particularly when separated according to race; by the use of a preselected sample that might not be completely representative of the entire population
(many of our subjects were recruited based on BMI category); by the use of a crude
measure of income; and our study only included premenopausal females. Estrogen, in
females, and testosterone, in males, has been shown to regulate LPL expression in opposite ways (69, 70). Further experiments addressing the limitations of our study are needed
to confirm the important findings of this investigation.
In all models for HDL-C and LPL RS250, income had a trend of significance for
both genotypic and allelic models (p=0.061 and 0.05 respectively). Although a significant
effect of income has been documented by Zoratti and colleagues (9), further investigation
is warranted to understand the role of socio-environmental factors in HDL-C measures,
particularly when taking into account that median income according to zip code might
not be the most accurate measure of socioeconomic status in biomedical investigations.
55
The evaluation of the LPL RS285 polymorphism supported a genotypic association with TG (p = 0.02) only when the entire sample was considered in the analysis. This
association has been previously studied in various ethnic populations, yet the results in
this regard lack consistency. Our genotypic results support previous studies considering
the association of LPL RS285 polymorphism and serum TG. For example, a study by
Wang et al. (2004) (32) and a study by Chamberlain et al. (1989) (33) found a significant
association of LPL RS285 with serum TG in European cohorts. This association was also
replicated in a Chinese population (71) and in a study that included Japanese schoolchildren (34). However, several studies in Croatian (72), Swedish (73), and Taiwanese populations (74) have failed to detect the association between LPL RS285 and serum TG.
This lack of association might be a consequence of differences in the frequency of the
polymorphisms in different populations. In many studies, for instance, the presence of the
allele C has been the principal variable of interest; however, allele C tends to be in higher
frequency in populations of Asian and European descent than to populations of African
descent. In these populations, allele C has been associated with increased levels of TG.
Our interest was to explore factors influencing variations between AA and EA populations, and our hypothesis was focused on the evaluation of the T allele as a predisposing
factor for lower serum TG. Although the genotypic association supported lower levels of
TG in those individuals with the T allele, the lack of association when the models were
evaluated according to race did not allow further clarifications of which allele (C or T) is
the main factor affecting serum TG. Nevertheless, the inclusion of genetic admixture in
our statistical models provides validity to our findings, particularly when taking into ac-
56
count the control for population stratification and the reduction of Type I errors obtained
by the inclusion of ancestral background.
The LPL RS285 polymorphism was not significant in the models analyzed for
HDL-C, either when included as a genotypic or as an allelic variable, or when analyzed
for the total sample or by racial group. Again, the literature regarding the HDL-C is quite
contradictory. In accordance with our results, Wang et al. (1996) was unable to find any
effect of this polymorphism on HDL-C in a European cohort (p=0.26) (32), whereas Yamana et al. (1998) found that this polymorphism was significantly associated with serum
HDL-C (p< 0.01) in a sample of Japanese children (34). Our inability to detect a significant relationship between HDL-C and the RS285 polymorphism could be due to power
limitations, to the consideration of different populations, or to the definitions of the phenotypic variables (we utilized a quantitative value as our phenotype, whereas in the study
by Yamana and colleagues, they considered a category based on high or low levels of the
phenotype). Interestingly, AFADM was significant in both the genotypic and allelic
models evaluating LPL RS285, suggesting that HDL-C serum levels might be mediated
by other genetic variants that are not RS285. Furthermore, it could be suggested that the
effect of LPL RS285 on HDL-C might be mediated through its effect on serum TG or
insulin sensitivity; this is a hypothesis that might be worth exploring in another study.
Study Limitations
Our study was limited by the small number of subjects it included, especially
when stratified by race. We also used a convenience sample of healthy premenopausal
females who volunteered to participate in our study, limiting its ability to generalize for
57
the general population. In addition, we only considered two polymorphisms for LPL, excluding other polymorphismic DNA segments within the gene that may have a contribution to the phenotypes of interests. Furthermore, the use of median income as a socioeconomic variable is limiting because it might not reflect the variability of social and financial factors that might occur within a similar zip code.
Future Studies
Studies investigating other LPL variants, controlling for genetic admixture, insulin sensitivity, income, and visceral adiposity would be of special significance. These
studies should include both females and males from various ethnic backgrounds. Also,
the relationship between LPL, its interaction with other genes, and various complex traits
like dyslipidemia, insulin resistance, and visceral fatness should be investigated using
genetic admixture and environmental components. Finally, the mechanisms and specific
role of insulin sensitivity, visceral fatness, genetic admixture, LPL activity, and environmental components of race in the variation and mediation of serum lipids require further
study.
Implications
In conclusion, according to our study, genetic admixture explained a significant
portion of serum TG variation, as did LPL RS285, after controlling for VAT, AIRg, SI,
total body fat, and income. Our study showed that LPL RS250 is not a significant predictor of serum TG in the same model. Moreover, our study showed that none of genetic
admixture, LPL RS250, or LPL RS285 directly affected serum HDL-C after adjusting for
58
serum TG, SI, VAT, total body fat, and income in a group of premenopausal AA and EA
women. However, identification of carriers of LPL RS285 may be used to screen
premenopausal females for dyslipidemia. Moreover, the negative relationship between
African admixture and serum TG indicates that genetic background in AA premenopausal females might protect them from developing dyslipidemia. However, this genetic
protection from dyslipidemia does not seem to translate into a protection from CVD, suggesting that CVD in the AA population is not mediated by serum lipids, or that any genetic protection against dyslipidemia is overruled by environmental and socioeconomic
factors that will eventually cause serious comorbidities that will have an effect on CVD.
Further investigation is needed to identify and understand the gene-environment interactions that affect health_related outcomes and in developing protective measures to prevent dyslipidemia and associated comorbidities.
59
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APPENDIX A
IRB APPROVAL CERTIFICATE
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