# Genetic Polymorphisms of Peroxisome Proliferator-Activated Receptors and the Risk of Cardiovascular Morbidity and Mortality in a Community-Based Cohort in Washington County, Maryland

Authors: L. Gallicchio; Bindu Kalesan; Han-Yao Huang; Paul Strickland; Sandra C. Hoffman; Kathy J. Helzlsouer
Journal: PPAR Research
 (2008)

---

## Abstract

The primary aim of this study was to examine prospectively the associations between 5 peroxisome proliferator-activated receptor (PPAR) single nucleotide polymorphisms (SNPs) and cardiovascular morbidity and mortality in a community-based cohort study in Washington County, Maryland.   Data were analyzed from 9,364 Caucasian men and women participating in CLUE-II.  Genotyping on 5 PPAR polymorphisms was conducted using peripheral DNA samples collected in 1989. The followup period was from 1989 to 2003.  The results showed that there were no statistically significant associations between the PPAR SNPs and cardiovascular deaths or events. In contrast, statistically significant age-adjusted associations were observed for PPARG rs4684847 with both baseline body mass and blood pressure, and for PPARG rs709158, PPARG rs1175543, and PPARD rs2016520 with baseline cholesterol levels. Future studies should be conducted to confirm these findings and to explore the associations in populations with greater racial and ethnic diversity.


---

## Body

## 1. Introduction

The  peroxisome  proliferator-activated receptors (PPARs)
are part of a superfamily of ligand-activated transcription factors involved in
fatty acid oxidation and lipid metabolism [1]. Three distinct
isoforms of PPARs that are encoded by
separate genes have been identified: PPAR-α, PPAR-γ,
and PPAR-δ [2]. The three isoforms play distinct
physiological roles depending on their tissue distribution. PPAR-α, which is expressed in the liver,
heart, skeletal muscle, and kidney, regulates lipid and lipoprotein metabolism. PPAR-γ is expressed in white and
brown adipose tissue and is involved in adipocyte differentiation, lipid storage, and glucose
metabolism. PPAR-δ is expressed in
many tissues and stimulates fatty acid oxidation [2, 3]. Beyond these major roles, PPARs also have been shown to play a
role in other biological processes, including the regulation of inflammatory
and oxidative pathways [2].PPARs are found in endothelial and vascular smooth muscle cells and have been shown
to influence inflammatory, fibrotic, and hypertrophic responses in the heart
and vascular wall [4]. Because of their location
and their involvement in fatty acid oxidation, lipid metabolism, and
inflammation, the role of PPARs in
cardiovascular disease and risk factors of cardiovascular disease has been of
great interest.  In general, activation
of the PPARs, both naturally and
synthetically, is considered beneficial for cardiovascular health [2]. Both PPAR-α and PPAR-γ play a role in modulating atherosclerosis; for
example, PPAR-γ
activation may promote monocyte apoptosis, contributing to the stabilization of
atherosclerotic lesions [5, 6]. Further, clinical trials have shown that the
use of pharmacological PPAR agonists such as fibrates (PPAR-α agonist) is antiatherogenic. Fibrates elevate high-density lipoprotein
(HDL) levels, decrease low-density lipoprotein (LDL) and triglyceride levels,
and reduce an individual’s risk of experiencing a cardiac event [7].A number ofPPAR polymorphisms have been identified within the 3 PPAR isoforms and there is a considerable
amount of literature on the associations between these polymorphisms and
cardiovascular risk factors (reviewed in Cresci [7]). There are less data, however, on the
associations between the PPAR polymorphisms and cardiovascular disease events (e.g., myocardial infarction
(MI), cardiovascular-related death). Further, findings with regard to these associations have been
inconsistent. For example, a
case-control study nested within the Physician’s Health Study suggested that
the PPARG Pro12Ala polymorphism,
located in exon B of PPAR-γ, is
associated with a reduced risk of MI [8]. In contrast, a recent publication using data
from the Health Professionals Followup Study showed that male carriers of the
Ala12 allele had an increased risk of MI or cardiac death [9] while other studies have
observed no statistically significant association between PPARG Pro12Ala and cardiovascular events or death [9, 10]. Thus, additional studies of these
polymorphisms are necessary to help us better understand the role of PPAR genetics in cardiovascular disease
especially in light of available pharmacological PPAR targeted agents.The primary aim of this study was
to examine prospectively the associations between 5PPAR polymorphisms (4 in PPAR-γ: rs4684847, rs709158,
rs1175543, and rs1801282; and 1 in PPAR-δ: rs2016520) and cardiovascular morbidity
and mortality in a community-based cohort study in Washington County, Maryland. As a secondary aim, we also examined the
associations between the PPAR polymorphisms and cardiovascular risk factors.


## 2. Methods

### 2.1. Study Sample

In 1974 and 1989, two cohorts named CLUE I and CLUE II (“Give us a Clue to Cancer and Heart Disease”) were established in Washington County, Maryland. CLUE I
and CLUE II enrolled 20 305 and 25 081 Washington County
residents, respectively.  At baseline for
both cohorts, participants provided informed consent, completed a brief
questionnaire, and donated a blood sample. The questionnaire ascertained data on age, gender, marital status,
education, height and weight (CLUE II only), cigarette smoking, and medication
and vitamin supplement use within the 48 hours prior to blood donation. In addition, in both 1974 and 1989, blood
pressure was measured by a study nurse with a blood pressure cuff while the
participant was in a seated position. Blood pressure was assessed three times in succession and the third
blood pressure value was recorded. In 1989, total cholesterol (nonfasting) was
assayed. Individuals who donated blood
to both CLUE I and CLUE II constitute the Odyssey cohort (N = 8394) [11, 12].In addition to the Odyssey cohort,
a CLUE II subcohort was selected for case-cohort studies that would be
conducted using the CLUE II cohort data. The subcohort was identified by taking an approximate 10% age- and sex-stratified
random sample of CLUE II participants who donated a blood specimen and were
adult residents of Washington County, Maryland. Of the 2460 participants identified for the subcohort, 807
were also in the Odyssey cohort. Therefore, 10 047 unique participants were part of either the Odyssey
cohort or the randomly selected CLUE II subcohort.Of the participants in the Odyssey
Cohort and the CLUE II subcohort, DNA was successfully extracted from the buffy
coat samples of 9960 individuals (99.1%). DNA from these participants was genotyped for polymorphisms in genes
controlling biological processes such as inflammation that have been associated
with multiple diseases. For the study presented here, 5%  of the Odyssey and subcohort participants who had
no data on all of the chosenPPAR SNPs
(n = 475) were excluded from the analysis. Further, all non-Caucasians (n = 121) were excluded from the analysis
because previous studies have shown that race is an important effect modifier
in investigations of polymorphisms and disease and there were not a sufficient
number of non-Caucasians in the cohort to analyze the associations among this
group. With the exception of race, excluded
and included participants did not differ with respect to baseline
characteristics. This study was approved by the Institutional Review Board of
the Johns Hopkins Bloomberg School of Public Health.


### 2.2. Outcome Assessment

Mortality
All participants were followed from the date of blood draw to the date of death
or the end of follow up (August 31, 2003), whichever came first. In the CLUE cohorts, deaths are identified
through daily searches of obituaries, cross-linkage with death certificates for
Washington County, and through searches of the
Social Security Administration for individuals aged 65 or older and the
National Death Index. Cause of death is
ascertained from the underlying cause on Maryland State
death certificates as coded by state nosologists. Of specific interest in this study were
cardiovascular disease deaths, for which the underlying cause was coded as
ICD-9 390–459 or ICD-10 I00–I99. During
the followup period, 2159 deaths were documented in the Odyssey cohort and the
CLUE II subcohort, and of these, 791 (36.6%) were cardiovascular
deaths. Approximately 4% (n = 334) of
the Odyssey cohort and the CLUE II subcohort participants were lost to follow up. Since these individuals were not documented
to have died during the followup period, they were considered alive at the end
of follow up and censored at August 31, 2003.Morbidity
Information on cardiovascular events was
obtained using participant self-report beginning with questionnaires
administered in 1996 and about every 2 years thereafter. On these questionnaires, participants were
asked whether a “doctor had told them they ever had” a specific condition and
at what age the condition was first diagnosed. The cardiovascular-related outcomes queried were: diabetes, high blood
pressure, high cholesterol, heart attack (MI), angina pectoris, stroke,
transient ischemic attack, peripheral artery disease, arrhythmia, and blood
clots.
Data were examined across the questionnaires for consistency; 5% of
the participants had inconsistent data with regards to self-reported events. However,
exclusion of these participants did not change the results and, therefore,
these participants were not excluded. For this analysis, we examined
any self-reported nonfatal cardiovascular event as an outcome. A nonfatal
cardiovascular event was defined as consistent reporting from 1996 to 2003
of any one of the following cardiovascular conditions: MI, angina, stroke,
transient ischemic attack, peripheral arterial disease, arrhythmia, or blood
clots. We also considered a composite variable including only MI, stroke,
transient ischemic attack, and peripheral arterial disease; however, the
results were similar to the composite variable including all seven outcomes
and, therefore, the seven outcome variable was used in all analyses. Individual
diagnoses were also examined separately. Because of the inconsistency in the
collection of data on the
age at which a condition occurred as well as the large amount of missing data
for the age variables, age at diagnosis data were not used in the analysis.


### 2.3. Genotyping

ThePPAR SNPs analyzed in this study were a part of a larger group of 210 SNPs selected
for investigation within the Odyssey cohort. SNPs were selected based on the following criteria: (a) the minor allele
frequency was estimated to be ≥5% among Caucasians in the published
literature or databases; (b) the polymorphism was in a gene of known or of
promising importance in the development of cancer, cardiovascular diseases,
and/or longevity; and
(c) the polymorphism was either known to be functional or was likely to alter
function based on the published literature. PPAR polymorphisms selected
for analysis were rs2016520 (PPARD Ex4 + 15C > T), rs709158 (PPARG IVS9 + 4523A > G), rs1175543 (PPARG IVS9 + 7780A > G), rs1801282 (PPARG Pro12Ala), and rs4684847
(PPARG IVS3-6622C > T). To note,
none of the other 210 SNPs selected for investigation in the Odyssey cohort
were located in PPAR-α, PPAR-γ, or PPAR-δ.DNA extracted from the preserved buffy coat
samples collected in 1989 were used for genotyping. Within 6 hours of collection, the heparinized
blood sample was centrifuged at 1500g for 30 minutes at room temperature. Blood samples were separated into plasma,
buffy coat, and red blood cells and frozen at−70ºC within 24 hours of
collection. The buffy coat remained
frozen until DNA extraction was performed. The DNA extraction procedures used the alkaline lysis method [13].  Genotyping was performed by Celera Genomics Co.
(Rockville, Md, USA)
for rs4684847, rs709158, and rs1175543 and by Applied Biosystems Inc. (Foster City, Calif, USA)
for rs2016520 and rs1801282. All
polymorphisms were genotyped using TaqMan technology. Laboratory technicians were masked to disease
status. Of the 9,364 participants in
the analytic cohort, approximately 90% had data on all five genotypes; 6.7% had
data on four, 2.4% had data on three, 0.7% had data on two, and 0.07% had data
on only one.


### 2.4. Statistical Analysis

The Hardy-Weinberg equilibrium for each
SNP was tested by a goodness-of-fit approach. As reported in separate publication,
all of thePPAR SNPs were in Hardy-Weinberg equilibrium [12]. The
cohort characteristics were stratified by gender and compared using chi-square
tests or student t-tests. Blood pressure at baseline was categorized into 3
groups independent of antihypertensive medication use as follows: normal,
individuals with a systolic pressure less than 120 and diastolic pressure less
than 80; hypertensive, those with systolic pressure greater than 140 or
diastolic pressure greater than 90; and prehypertensive, those with a systolic
pressure between 120 and 140 or diastolic pressure between 80 and 90. The age-adjusted associations between the PPAR SNPs and cardiovascular risk
factors (i.e., baseline BMI, cholesterol levels, blood pressure) were examined
using logistic regression models. Age was adjusted for in all analyses as there
were statistically significant age differences for several of the SNPs. Gender was not adjusted for  in these analyses
because it was not associated with SNP prevalence. Cox-proportional hazard
ratios were calculated for both all-cause and cardiovascular mortality after
adjustment for age. Since
the nonfatal cardiovascular outcomes (including MI), followed a Poisson
distribution, and age at diagnosis data were not used in the analysis,
age-adjusted relative risks for nonfatal
cardiovascular events and for only MI were obtained using Poisson regression
methods; this type of analysis was also used when analyzing fatal and nonfatal
outcomes combined.  Premature death (both
overall and due to cardiovascular disease) was also examined as an outcome
variable and defined as death prior to the age of 65. All analyses were done separately for the 5
SNPs and stratified by gender, diabetes diagnosis, and body mass index (BMI) at
baseline. No differences in the risk
estimates were observed in these strata and, therefore, only results for the
entire cohort are presented.To address the issue of multiple
testing in this study,P values for the associations between SNPs and
the cardiovascular risk factors were adjusted for the false discovery rate utilizing
Fisher’s combination method using bootstrap resampling.  All statistical analysis was carried out
using SAS software, version 9.1 (SAS Institute, Inc., Cary, NC, USA). A two-sided P value ≤.05 was considered statistically significant.


## 2.1. Study Sample

In 1974 and 1989, two cohorts named CLUE I and CLUE II (“Give us a Clue to Cancer and Heart Disease”) were established in Washington County, Maryland. CLUE I
and CLUE II enrolled 20 305 and 25 081 Washington County
residents, respectively.  At baseline for
both cohorts, participants provided informed consent, completed a brief
questionnaire, and donated a blood sample. The questionnaire ascertained data on age, gender, marital status,
education, height and weight (CLUE II only), cigarette smoking, and medication
and vitamin supplement use within the 48 hours prior to blood donation. In addition, in both 1974 and 1989, blood
pressure was measured by a study nurse with a blood pressure cuff while the
participant was in a seated position. Blood pressure was assessed three times in succession and the third
blood pressure value was recorded. In 1989, total cholesterol (nonfasting) was
assayed. Individuals who donated blood
to both CLUE I and CLUE II constitute the Odyssey cohort (N = 8394) [11, 12].In addition to the Odyssey cohort,
a CLUE II subcohort was selected for case-cohort studies that would be
conducted using the CLUE II cohort data. The subcohort was identified by taking an approximate 10% age- and sex-stratified
random sample of CLUE II participants who donated a blood specimen and were
adult residents of Washington County, Maryland. Of the 2460 participants identified for the subcohort, 807
were also in the Odyssey cohort. Therefore, 10 047 unique participants were part of either the Odyssey
cohort or the randomly selected CLUE II subcohort.Of the participants in the Odyssey
Cohort and the CLUE II subcohort, DNA was successfully extracted from the buffy
coat samples of 9960 individuals (99.1%). DNA from these participants was genotyped for polymorphisms in genes
controlling biological processes such as inflammation that have been associated
with multiple diseases. For the study presented here, 5%  of the Odyssey and subcohort participants who had
no data on all of the chosenPPAR SNPs
(n = 475) were excluded from the analysis. Further, all non-Caucasians (n = 121) were excluded from the analysis
because previous studies have shown that race is an important effect modifier
in investigations of polymorphisms and disease and there were not a sufficient
number of non-Caucasians in the cohort to analyze the associations among this
group. With the exception of race, excluded
and included participants did not differ with respect to baseline
characteristics. This study was approved by the Institutional Review Board of
the Johns Hopkins Bloomberg School of Public Health.


## 2.2. Outcome Assessment

Mortality
All participants were followed from the date of blood draw to the date of death
or the end of follow up (August 31, 2003), whichever came first. In the CLUE cohorts, deaths are identified
through daily searches of obituaries, cross-linkage with death certificates for
Washington County, and through searches of the
Social Security Administration for individuals aged 65 or older and the
National Death Index. Cause of death is
ascertained from the underlying cause on Maryland State
death certificates as coded by state nosologists. Of specific interest in this study were
cardiovascular disease deaths, for which the underlying cause was coded as
ICD-9 390–459 or ICD-10 I00–I99. During
the followup period, 2159 deaths were documented in the Odyssey cohort and the
CLUE II subcohort, and of these, 791 (36.6%) were cardiovascular
deaths. Approximately 4% (n = 334) of
the Odyssey cohort and the CLUE II subcohort participants were lost to follow up. Since these individuals were not documented
to have died during the followup period, they were considered alive at the end
of follow up and censored at August 31, 2003.Morbidity
Information on cardiovascular events was
obtained using participant self-report beginning with questionnaires
administered in 1996 and about every 2 years thereafter. On these questionnaires, participants were
asked whether a “doctor had told them they ever had” a specific condition and
at what age the condition was first diagnosed. The cardiovascular-related outcomes queried were: diabetes, high blood
pressure, high cholesterol, heart attack (MI), angina pectoris, stroke,
transient ischemic attack, peripheral artery disease, arrhythmia, and blood
clots.
Data were examined across the questionnaires for consistency; 5% of
the participants had inconsistent data with regards to self-reported events. However,
exclusion of these participants did not change the results and, therefore,
these participants were not excluded. For this analysis, we examined
any self-reported nonfatal cardiovascular event as an outcome. A nonfatal
cardiovascular event was defined as consistent reporting from 1996 to 2003
of any one of the following cardiovascular conditions: MI, angina, stroke,
transient ischemic attack, peripheral arterial disease, arrhythmia, or blood
clots. We also considered a composite variable including only MI, stroke,
transient ischemic attack, and peripheral arterial disease; however, the
results were similar to the composite variable including all seven outcomes
and, therefore, the seven outcome variable was used in all analyses. Individual
diagnoses were also examined separately. Because of the inconsistency in the
collection of data on the
age at which a condition occurred as well as the large amount of missing data
for the age variables, age at diagnosis data were not used in the analysis.


## 2.3. Genotyping

ThePPAR SNPs analyzed in this study were a part of a larger group of 210 SNPs selected
for investigation within the Odyssey cohort. SNPs were selected based on the following criteria: (a) the minor allele
frequency was estimated to be ≥5% among Caucasians in the published
literature or databases; (b) the polymorphism was in a gene of known or of
promising importance in the development of cancer, cardiovascular diseases,
and/or longevity; and
(c) the polymorphism was either known to be functional or was likely to alter
function based on the published literature. PPAR polymorphisms selected
for analysis were rs2016520 (PPARD Ex4 + 15C > T), rs709158 (PPARG IVS9 + 4523A > G), rs1175543 (PPARG IVS9 + 7780A > G), rs1801282 (PPARG Pro12Ala), and rs4684847
(PPARG IVS3-6622C > T). To note,
none of the other 210 SNPs selected for investigation in the Odyssey cohort
were located in PPAR-α, PPAR-γ, or PPAR-δ.DNA extracted from the preserved buffy coat
samples collected in 1989 were used for genotyping. Within 6 hours of collection, the heparinized
blood sample was centrifuged at 1500g for 30 minutes at room temperature. Blood samples were separated into plasma,
buffy coat, and red blood cells and frozen at−70ºC within 24 hours of
collection. The buffy coat remained
frozen until DNA extraction was performed. The DNA extraction procedures used the alkaline lysis method [13].  Genotyping was performed by Celera Genomics Co.
(Rockville, Md, USA)
for rs4684847, rs709158, and rs1175543 and by Applied Biosystems Inc. (Foster City, Calif, USA)
for rs2016520 and rs1801282. All
polymorphisms were genotyped using TaqMan technology. Laboratory technicians were masked to disease
status. Of the 9,364 participants in
the analytic cohort, approximately 90% had data on all five genotypes; 6.7% had
data on four, 2.4% had data on three, 0.7% had data on two, and 0.07% had data
on only one.


## 2.4. Statistical Analysis

The Hardy-Weinberg equilibrium for each
SNP was tested by a goodness-of-fit approach. As reported in separate publication,
all of thePPAR SNPs were in Hardy-Weinberg equilibrium [12]. The
cohort characteristics were stratified by gender and compared using chi-square
tests or student t-tests. Blood pressure at baseline was categorized into 3
groups independent of antihypertensive medication use as follows: normal,
individuals with a systolic pressure less than 120 and diastolic pressure less
than 80; hypertensive, those with systolic pressure greater than 140 or
diastolic pressure greater than 90; and prehypertensive, those with a systolic
pressure between 120 and 140 or diastolic pressure between 80 and 90. The age-adjusted associations between the PPAR SNPs and cardiovascular risk
factors (i.e., baseline BMI, cholesterol levels, blood pressure) were examined
using logistic regression models. Age was adjusted for in all analyses as there
were statistically significant age differences for several of the SNPs. Gender was not adjusted for  in these analyses
because it was not associated with SNP prevalence. Cox-proportional hazard
ratios were calculated for both all-cause and cardiovascular mortality after
adjustment for age. Since
the nonfatal cardiovascular outcomes (including MI), followed a Poisson
distribution, and age at diagnosis data were not used in the analysis,
age-adjusted relative risks for nonfatal
cardiovascular events and for only MI were obtained using Poisson regression
methods; this type of analysis was also used when analyzing fatal and nonfatal
outcomes combined.  Premature death (both
overall and due to cardiovascular disease) was also examined as an outcome
variable and defined as death prior to the age of 65. All analyses were done separately for the 5
SNPs and stratified by gender, diabetes diagnosis, and body mass index (BMI) at
baseline. No differences in the risk
estimates were observed in these strata and, therefore, only results for the
entire cohort are presented.To address the issue of multiple
testing in this study,P values for the associations between SNPs and
the cardiovascular risk factors were adjusted for the false discovery rate utilizing
Fisher’s combination method using bootstrap resampling.  All statistical analysis was carried out
using SAS software, version 9.1 (SAS Institute, Inc., Cary, NC, USA). A two-sided P value ≤.05 was considered statistically significant.


## 3. Results

Baseline characteristics of the study
sample, overall and by gender, are shown on Table1. In 1989, males were significantly more likely
than females to have some college education, to report being a current or
former smoker, and to be categorized as prehypertensive or hypertensive. In
addition, males had a significantly higher mean BMI than females. In contrast, females were more likely to have
cholesterol levels greater than 200 mg/dL, either with or without cholesterol
medication use, than males. There were
no gender differences in baseline age or PPAR SNP prevalence.Table 1
Characteristics of study sample by gender,N = 9364, P value derived from
χ2 test for categorical variables, Student’s t-test for continuous variables.
FemaleN = 5776 (%)MaleN = 3588 (%)Total (%)P valueAge, mean(SD)53.2 (15.4)52.9 (15.5)53.1 (15.4).3974Education.0029<1224.525.024.71247.344.046.0>1228.231.029.3Missing,n415BMI, kg/m2, mean
(SD)26.1 (5.3)26.6 (4.0)26.3 (4.9)<.0001BMI, kg/m2<.0001<2548.834.043.125–3030.848.837.7>3020.417.319.2Missing,n11112Smoking status<.0001Never62.540.354.0Former21.442.729.6Current16.117.016.4Missing,n0Cholesterol no Rx, mg/dL<.0001≤20041.348.243.9200–23937.136.937.0≥24021.614.919.1Cholesterol with Rx, mg/dL<.0001≤20015.736.023.1200–23943.936.541.2≥24040.427.535.7Blood pressure<.0001Normal30.215.624.6Prehypertensive56.467.660.7Hypertensive13.416.814.7Missing,n5611PPARG rs4684847.0910CC78.877.378.2CT/TT21.222.721.8Missing,n188120308PPARG rs709158.4741AA39.640.339.8AG/GG60.459.760.2Missing,n13498232PPARG rs1175543.9218AA40.240.140.2AG/GG59.859.959.8Missing,n13691227PPARD rs2016520.5240CC64.664.064.4CT/TT35.436.035.6Missing,n146104250PPARG rs1801282.2728Pro/Pro78.477.578.1Pro/Ala or Ala/Ala21.622.521.9Missing,n156106262Table2 shows the age-adjusted
associations between the PPAR SNPs
and cardiovascular risk factors examined at baseline and at follow up. After adjustment for age, BMI at baseline was
significantly associated with the PPARG rs4684847 SNP such that individuals with at least one of the less common T
alleles were significantly more likely to have a higher BMI than individuals
carrying the CC genotype. Cholesterol level (without medication use) at
baseline was significantly associated with the PPARG rs1175543, the PPARG rs709158, and the PPARD rs2016520 SNPs.  Specifically, individuals
carrying the PPARG rs1175543 AA, the PPARG rs709158 AA, or the PPARD rs2016520 CT or TT genotypes
were significantly more likely to be categorized as having baseline cholesterol
levels of 240 mg/dL or greater compared to those with the PPARG rs1175543 AG or GG, the PPARG rs709158 AG or GG, or the PPARD rs2016520 CC genotypes,
respectively.  Further, participants
carrying the PPARG rs4684847 CT or TT
genotypes were more likely to be categorized as being prehypertensive or
hypertensive at baseline compared to participants carrying the CC genotype. No statistically significantly associations
were observed between any of the PPAR sNPs and high blood pressure, high cholesterol, or diabetes diagnoses over the
entire followup period. To note, after adjustment for multiple testing, none of
the associations between the SNPs and the cardiovascular risk factors were
statistically significant.Table 2
The associations between PPAR SNPs and cardiovascular risk factors. Goodness of fit
likelihood ratioP value using logistic regression modeling adjusted for
age; P value for age based on Student’s t-test. Combined outcomes had
condition at baseline or reported on followup questionnaires (for diabetes,
self-reported on followup questionnaires only).
(a)
Risk factorsPPARG rs4684847PPARG rs709158PPARG 1175543CCCT/ TTP valueAAAG/GGP valueAAAG/GGP valueBaseline characteristicsAge, mean(SD)53.0 (15.5)53.4 (15.4).357152.7 (15.4)53.3 (15.4).078052.9 (15.5)53.3 (15.3).1910BMI, kg/m2, n (%).0203.8458.9777<253097 (79)815 (21)1562 (40)2372 (60)1575 (40)2366 (60)25–302645 (78)745 (22)1392 (40)2048 (60)1393 (41)2037 (59)>301331 (76)412 (24)681 (39)1067 (61)696 (40)1059 (60)Cholesterol no Rx, mg/dL,.7144.0370.0198n
(%)≤2002956 (79)791 (21)1469 (39)2295 (61)1481 (39)2284 (61)200–2392443 (78)702 (22)1274 (40)1897 (60)1281 (40)1904 (60)≥2401275 (79)346 (21)674 (41)964 (59)688 (42)943 (58)Cholesterol with Rx, mg/dL,.8997.4595.3418n (%)≤20082 (76)26 (24)40 (36)71 (64)37 (34)71 (66)200–239141 (74)50 (26)78 (39)121 (61)79 (40)119 (60)≥240125 (75)42 (25)70 (41)102 (59)70 (41)102 (59)Blood pressure,n (%).0248.0513.0509Normal1777 (79)460 (21)875 (39)1369 (61)882 (39)1362 (61)Prehypertensive4290 (78)1193 (22)2209 (40)3338 (60)2221 (40)3322 (60)Hypertensive1005 (76)320 (24)549 (41)782 (59)561 (42)779 (58)Combined outcomesDiabetes,n (%).7845.8617.5499No4504 (78)1243 (22)2325 (40)3466 (60)2350 (41)3451 (59)Yes821 (78)225 (22)420 (40)643 (60)418 (39)648 (61)High cholesterol,n (%).3452.4527.5670No2502 (78)706 (22)1280 (40)1944 (60)1296 (40)1938 (60)Yes2712 (79)730 (21)1408 (40)2081 (60)1414 (41)2075 (59)High blood pressure,n (%).6258.2150.2724No2441 (79)660 (21)1287 (41)1834 (59)1293 (41)1829 (59)Yes3379 (78)969 (22)1720 (39)2675 (61)1741 (40)2663 (60)(b)
Risk factorsPPARD rs2016520PPARG rs1801282CCCT/TTP valuePro/ProPro/Ala or
Ala/AlaP valueAge, mean(SD)53.0 (15.4)53.0 (15.5).964052.9 (15.4)53.2 (15.3).4587BMI, kg/m2, n
(%).9138.1280<252534 (64)1389 (36)3085 (79)832 (21)25–302190 (64)1246 (36)2668 (78)757 (22)>301136 (65)609 (35)1346 (77)403 (23)Cholesterol no Rx, mg/dL,n
(%).0161.7401≤2002475 (66)1290 (34)2953 (78)811 (22)200–2392011 (63)1161 (37)2467 (78)705 (22)≥2401019 (63)604 (37)1261 (78)354 (22)Cholesterol with Rx, mg/dL,n (%).3821.6233≤20074 (67)37 (33)86 (80)22 (20)200–239135 (69)62 (31)148 (76)48 (24)≥240106 (63)63 (37)128 (76)40 (24)Blood pressure,n (%).8076.2636Normal1441 (64)812 (36)1769 (79)483 (21)Prehypertensive3578 (65)1939 (35)4312 (78)1199 (22)Hypertensive842 (63)491 (37)1016 (77)312 (23)Combined outcomesDiabetes,n (%).6185.1278No3747 (65)2058 (35)4517 (78)1275 (22)Yes675 (64)383 (36)841 (80)212 (20)High cholesterol,n (%).3178.3691No2060 (64)1175 (36)2516 (78)713 (22)Yes2263 (65)1225 (35)2732 (79)740 (21)High blood pressure,n (%).9412.8331No2006 (64)1121 (36)2462 (78)676 (22)Yes2821 (64)1564 (36)3384 (78)971 (22)There were no statistically
significant associations between thePPAR SNPs and cardiovascular deaths or events (including nonfatal events combined
and MI alone) (see Table 3). A 40%
reduction in the risk of premature cardiovascular death was observed for
individuals with the PPARG rs4684847
CT or TT genotype compared to the CC genotype; however, the confidence interval
was wide due to a small number of deaths (see Table 4). There were no statistically significant
associations for premature death (all cause and cardiovascular-related) and PPARG rs709158, PPARG rs1175543, PPARG Pro12Ala,
and PPARD rs2016520.Table 3
Age-adjusted hazard ratios (HR) and 95% confidence intervals (95% CI) of
all-cause and cardiovascular mortality and nonfatal cardiovascular events for 5
PPAR polymorphisms. Person years: cumulative person years of follow up. RR (95% CI): relative risk using poisson regression.
All-cause
deathCV-related
deathNonfatal
CV eventsFatal and nonfatal
CV eventsSNPsPerson yearsnHR (95% CI)nHR (95% CI)nRR (95% CI)RR (95% CI)PPARG rs4684847CC9282717051.00
(reference)4161.00
(reference)23511.00
(reference)1.00
(reference)CT/TT258804740.95 (0.85–1.05)1140.90
(0.73–1.11)6791.00
(0.73–1.37)0.88 (0.67–1.18)PPARG rs709158AA478128501.00
(reference)2051.00
(reference)12021.00
(reference)1.00
(reference)AG/GG7197213280.99 (0.91–1.09)3241.02
(0.86–1.20)18430.98 (0.75–1.28)0.98
(0.77–1.24)PPARG rs1175543AA481728671.00
(reference)2131.00
(reference)11831.00
(reference)1.00
(reference)AG/GG7162713311.0 (0.92–1.09)3160.99 (0.83–1.18)18641.03
(0.79–1.34)1.00
(0.79–1.28)PPARD rs2016520CC7704213861.00
(reference)3421.00
(reference)20271.00
(reference)1.00 (reference)CT/TT425937661.03 (0.94–1.12)1781.02 (0.85–1.23)10030.91
(0.69–1.19)0.98
(0.76–1.26)PPARG rs1801282Pro/Pro9325416961.00
(reference)4081.00
(reference)23501.00
(reference)1.00
(reference)Pro/Ala or Ala/Ala261964560.96 (0.86–1.06)1120.97
(0.79–1.20)6560.98
(0.71–1.35)0.96
(0.72–1.28)Table 4
Age-adjusted hazard ratios (HR) and 95% confidence intervals (95% CI) of
premature (age <65 years) all-cause and cardiovascular mortality for 5 PPAR
SNPs. Person years: cumulative person years of follow up.
All-cause deathCV-related deathSNPsPerson yearsnHR (95% CI)nHR (95% CI)PPARG rs4684847CC434452311.00 (reference)271.00 (reference)CT/TT11743581.05 (0.78–1.40)40.60 (0.21–1.72)PPARG rs709158AA230781151.00 (reference)131.00 (reference)AG/GG328221751.05 (0.83–1.33)180.95 (0.47–1.95)PPARG rs1175543AA228751151.00 (reference)141.00 (reference)AG/GG328591751.04 (0.82–1.32)170.83 (0.41–1.68)PPARD rs2016520CC363891861.00 (reference)211.00 (reference)CT/TT19940990.98 (0.77–1.25)90.79 (0.36–1.71)PPARG rs1801282Pro/Pro442632361.00 (reference)251.00 (reference)Pro/Ala or Ala/Ala12247500.90 (0.66–1.23)50.79 (0.30–2.09)


## 4. Discussion

In general, the findings from this
prospective, community-based cohort study indicate that the selected SNPs inPPAR genes are not associated with
overall and premature cardiovascular morbidity and mortality. Further, with a few exceptions, we found that
the selected PPAR SNPs were not
associated with risk factors of cardiovascular disease. These findings were consistent among both men
and women, among those with a diagnosis of diabetes, and in strata defined by baseline
BMI.Data onPPAR polymorphisms and cardiovascular disease and cardiovascular
disease risk factors are limited and inconsistent. The most studied PPAR polymorphism, PPARG Pro12Ala,
has been shown to be associated with reduced PPARG activity [14], and initial studies reported
a lower risk of type 2 diabetes associated with the Ala12 allele [14]. This association was observed in some [15–19], but not all [20–23], subsequent studies. The lack
of consistency also extends to studies examining the PPARG Pro12Ala polymorphism and cardiovascular disease events: one
prospective cohort study examining the PPARG Pro12Ala polymorphism and cardiovascular disease suggested a decreased risk of
CHD among carriers of the Ala12 allele [8], while others have shown no
association [9, 10, 24] or an increase in risk [9, 25]. Although our study was limited by the small
number of cardiovascular events, our results are consistent with those previous
studies that have shown no association. This finding, as well as the data showing a lack of association between PPARG Pro12Ala and cardiovascular risk
factors, suggests that this SNP is not involved in the development of
cardiovascular disease in this population.While most of the analyses
conducted resulted in null findings, statistically significant age-adjusted associations
were observed forPPARG rs4684847
with both baseline BMI and blood pressure, and for PPARG rs709158, PPARG rs1175543
and PPARD rs2016520 with baseline cholesterol
levels. These associations were not
statistically significant when self-reported blood pressure or high cholesterol
at any time point over the study period were considered as outcome variables. Further,
the statistical significance of the associations disappeared after correction
for multiple testing.  However, this may
be due to a lack of statistical power. To our knowledge, these polymorphisms have not been examined in relation
to cardiovascular disease or cardiovascular disease risk factors. Further, the functionalities of these
polymorphisms are unknown. Although located in intron regions, these
polymorphisms may affect enzyme distribution or other physiological functions
related to cardiovascular health. Alternatively, it may be that the associations observed with these SNPs in this study are
due to the polymorphisms being in linkage disequilibrium with other functional
polymorphisms in the respective regions.Several limitations of
this study must be considered when interpreting the results. First, all of the followup data on
cardiovascular risk factors and events were based on self-report. Because of this, there were some missing data
for those who did not complete any of the followup questionnaires. Further,
among those who did complete the questionnaires, there is the possibility of
misclassification; this would likely be nondifferential, resulting in a
dilution of the true risk estimate. However,
in another investigation of this population, self-report of incidence of MI was compared to data
gathered from on ongoing county-wide surveillance of MI events documented by
hospital review and the sensitivity and specificity of reports were in excess
of 94% (HY Huang, personal communication). A second limitation is that because of the racial homogeneity of the
sample we were not able to explore potential racial differences in the
associations between thePPAR polymorphisms and cardiovascular disease. In a previous publication [12], we did find, however, racial
differences in the prevalence of the PPAR polymorphisms in this study that are consistent with those published in the
SNP500 and dbSNP databases.


## 5. Conclusions

The results from this prospective,
community-based cohort study showed no association between the selectedPPAR polymorphisms and cardiovascular
morbidity and mortality. In addition, in
general, no statistically significant associations were observed between the PPAR polymorphisms and cardiovascular
risk factors. Future studies should be
conducted to confirm these findings and to explore the associations in
populations with greater racial and ethnic heterogeneity.



---

*Source: 276581-2007-12-04.xml*