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. 2015 Nov 5;25(4):383–390. doi: 10.18865/ed.25.4.383

Screening of Peroxisome Proliferator-Activated Receptors (PPARs) α, γ and α Gene Polymorphisms for Obesity and Metabolic Syndrome Association in the Multi-Ethnic Malaysian Population

Phee-Phee Chia 1,✉, Sook-Ha Fan 2, Yee-How Say 2
PMCID: PMC4671443  PMID: 26673968

Abstract

Objective

This study aimed to investigate the association of peroxisome proliferator-activated receptor (PPAR) genes PPARα L162V, PPARγ2 C161T and PPARδ T294C single nucleotide polymorphisms (SNPs) with obesity and metabolic syndrome (Met-S) in a multi-ethnic population in Kampar, Malaysia.

Methods

Socio-demographic data, anthropometric and biochemical measurements (plasma lipid profile, adiponectin and interleukin-6 [IL-6] levels) were taken from 307 participants (124 males; 180 obese; 249 Met-S; 97 Malays, 85 ethnic Chinese, 55 ethnic Indians).

Results

The overall minor allele frequencies were .08, .22 and .30 for PPAR α L162V, γ C161T, δ T294C, respectively. All SNPs were not associated with obesity, Met-S and obesity with/without Met-S by χ2 analysis, ethnicity-stratified and logistic regression analyses. Nevertheless, participants with V162 allele of PPARα had significantly higher IL-6, while those with T161 allele of PPARγ2 had significantly lower HOMA-IR.

Conclusions

All PPAR SNPs were not associated with obesity and Met-S in the suburban population of Kampar, Malaysia, where only PPARα V162 and PPARγ2 T161 alleles were associated with plasma IL-6 and HOMA-IR, respectively.

Keywords: Peroxisome Proliferator-Activated Receptors, Single Nucleotide Polymorphism, Obesity, Metabolic Syndrome, Malaysia

Introduction

Metabolic syndrome (Met-S) is a group of risk factors including insulin resistance, central obesity, dyslipidemia and hypertension.1Peroxisome proliferator activated receptors (PPAR) are composed of three isotypes, PPARα, PPARδ/β and PPARγ. They are ligand-activated nuclear transcription factors that modulate the expression of an array of genes involved in regulating glucose, lipid and cholesterol metabolism.2 Considering this, any dysregulation of these metabolic pathways caused by single nucleotide polymorphisms (SNPs) can lead to Met-S and obesity.

Three common SNPs that have been variably associated with obesity and Met-S in different populations are PPARα L162V, PPARγ2 C161T and PPARδ T294C. PPARα L162V SNP (rs1800206) is identified as 484C/G transversion in exon 5, which results in a missense mutation of leucine to valine at codon 162.3,4 PPARγ2 C161T SNP (rs3856806) is a silent His477His polymorphism in exon 6,5 while PPARδ T294C SNP (rs2016520) is located at the 5’- untranslated region (UTR) of exon 4, 87 nucleotides upstream of the start codon.6

We previously performed a pilot study investigating the association of PPARα L162V and PPARγ2 C161T SNPs with overweight among university students (majority were of the Chinese ethnicity, n = 256), and found no association.7 Expanding the study to a relatively larger and more multi-ethnic sample population, we screened the above two SNPs and included the PPARδ T294C SNP, and also measured additional anthropometrics and biochemical variables related to obesity and Met-S.

Methods

Study Population, Questionnaire and Clinical Measurements

Based on the population of 88,638 in Kampar (Department of Statistics Malaysia, 2010),8 a minimum sample size of 288 had to be recruited, assuming a margin of error of 5%, CI 95% and response distribution of at least 25% (Raosoft® Sample Size Calculator Software, Raosoft Inc, USA). A total of 307 participants was recruited (age range: 21-80; mean age 53.3 ± 14.2 years; 124 males, 183 females; 127 non-obese, 180 obese; 58 non-Met-S, 249 Met-S; 97 Malays, 85 Chinese, 55 Indians) from patients who attended the Kampar Health Clinic from June to December 2011. Patrons of the clinic who had fasted overnight to have their blood drawn were recruited by convenience sampling. The exclusion criteria included medical conditions such as hyperthyroidism, pituitary diseases, chronic liver/renal diseases, acute infection hematologic diseases, pregnancy, patients undergoing dialysis, body builders or highly trained athletes, people with a fever or swelling and patients with osteoporosis. Clinical and anthropometric measurements were taken as described in our previous study.9 The BMI cutoff point for obesity was ≥ 25 kg/m2.10 The presence of Met-S was based on having three of five criteria set by the US National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) for Asians, according to Tan et al1: waist circumference ≥ 90 cm for males and ≥ 80 cm for females; high triglyceride (TG) level with ≥ 1.7 mmol/L (150 mg/dL) or specific treatment for this lipid abnormality, low HDL-C level with < 1.03 mmol/L (40 mg/dL) for males and < 1.29 mmol/L (50 mg/dL) for females or specific treatment for this lipid abnormality; high systolic blood pressure (SBP)/diastolic blood pressure (DBP) of ≥ 130/85 mm Hg; and high fasting blood glucose (FBG) of ≥ 5.6 mmol/L (100 mg/dL); or previously diagnosed type 2 diabetes.

Overnight fasting blood samples of participants were collected under routine phlebotomy by professional nurses. Our study was registered under the National Medical Research Registry (NMRR-09-826-4266) and the protocol was approved by the Medical Research and Ethnics Committee, Ministry of Health Malaysia. All participants in this study signed informed consent forms and all samples were taken in accordance with the Declaration of Helsinki (revised in Seoul, 2008).

Clinical Biochemistry

The fasting plasma concentration of total cholesterol (TC), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides (TG) and total cholesterol/HDL cholesterol ratio (TC/HDL-C) were measured via an outsourced service (Pathology and Clinical Laboratory (M) Sdn. Bhd.). Fasting blood glucose concentration of the subjects was determined using One Touch® Ultra-Easy blood glucose monitoring system (LifeScan, Inc. USA). Fasting plasma insulin, adiponectin and interleukin-6 (IL-6) levels were measured using commercial ELISA kits (Cortez Diagnostics Inc., USA or R&D Systems, USA). Insulin resistance was calculated via homeostatic model assessment (HOMA-IR), where the product of fasting glucose (mmol/L) and fasting insulin (μU/mL) was divided by 22.5.11

Genotyping

Genomic DNA of the participants was extracted from the leukocytes using the Genomic DNA Mini Kit (Blood/Cultured Cell) (Geneaid, USA). The genotyping of PPARα L162V, PPARγ2 C161T and PPARδ T294C SNPs was performed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), as described in our previous study7 and Miao et al.12 The annealing temperatures for PPARα L162V, PPARγ2 C161T and PPARδ T294C were 55°C, 56˚C and 60°C, respectively and their respective PCR products were digested with 3U of restriction enzymes BglI (New England Biolabs, USA) and PmlI (Thermo Scientific, USA) at 37ºC, or BslI (Thermo Scientific, USA) at 55ºC. All PCR reactions were carried out on the MJ MiniTM Personal Thermal Cycler (Biorad, US). Gels were then stained withethidium bromide and visualized under UVlight after electrophoresis. Three genotypes from each SNP were verified by direct DNA sequencing of PCR products (First BASE Laboratories Sdn. Bhd., Malaysia).

Statistical Analysis

SPSS statistics software was used to analyze the data of the study. Allelic frequencies for each SNP were estimated by gene counting and the distribution of genotypes was tested for Hardy-Weinberg equilibrium using the Chi-square (χ2) test. Data for continuous variables were presented as means ± standard deviations (SD) or adjusted means ± standard error of the mean (SEM) and as frequency for categorical variables. The normality of distributions of continuous variables was tested with the Kolmogorov-Smirnov test and variables that were not distributed normally were log-transformed prior to statistical analysis. Genotype and allele frequencies of the SNPs with respect to BMI status, sex, and ethnicity were assessed for association using χ2 test while univariate analysis of variance using General Linear model (adjusted for age and ethnicity) was performed for continuous variables. Logistic regression analysis (enter method) was performed with adjustment for age, gender and ethnicity to evaluate the association between obesity, metabolic syndrome and obese with/without metabolic syndrome with the three SNPs. A P of <.05 was considered statistically significant.

Results

The distribution of all the PPARα L162V, PPARγ2 C161T and PPARδ T294C alleles according to ethnicities and sex is shown in Table 1, which did not deviate from the Hardy-Weinberg equilibrium. The overall minor allele frequencies (MAFs) for PPARα L162V, PPARγ2 C161T and PPARδ T294C SNPs were .08, .22 and .30, respectively. PPARα V162 allele was the least common allele due to the absence of subjects carrying PPARα V162V genotype. According to Malay/Chinese/Indian ethnicities, their MAFs were .07/.08/.08; .21/.27/.13; .31/.32/.22, respectively, where only the allele distribution of PPARγ2 C161T was significantly associated with ethnicity – MAF of Indians being significantly lower. The allele distribution of all the SNPs was also not significantly different between the sexes (Table 1).

Table 1. PPARα L162V, PPARγ2 C161T and PPARδ T294C allele distribution according to demographic and anthropometric-clinical classes.

Alleles Ethnicity Sex Obesity Met-S Obese with Met-S
Malay n (%) Chinese n (%) Indian n (%) Male n (%) Female n (%) Non-obese n (%) Obese n (%) Absent n (%) Present n (%) Absent n (%) Present n (%)
PPARαL162V
L162 181 (93.3) 284 (91.6) 101 (91.8) 225 (9.7) 341 (93.2) 234 (92.1) 332 (92.2) 105 (9.5) 461 (92.6) 32 (94.1) 300 (92.0)
V162 13 (6.7) 26 (8.4) 9 (8.2) 23 (9.3) 25 (6.8) 20 (7.9) 28 (7.8) 11 (9.5) 37 (7.4) 2 (5.9) 26 (8.0)
χ2; P .50; .78 1.23; .27 .002; .964 .550; .458 NP
PPARγ C161T
C161 154 (79.4) 226 (72.9) 96 (87.3) 186 (75.0) 290 (79.2) 193 (76.0) 283 (78.6) 84 (72.4) 392 (78.7) 25 (73.5) 258 (79.1)
T161 40 (2.6) 84 (27.1) 14 (12.7) 62 (25.0) 76 (2.8) 61 (24.0) 77 (21.4) 32 (27.6) 106 (21.3) 9 (26.5) 68 (2.9)
χ2; P 1.18; .01a 1.52; .22 .590; .443 2.144; .143 .577; .448
PPARδ T294C
T294 133 (68.6) 212 (68.4) 86 (78.2) 166 (66.9) 265 (72.4) 174 (68.5) 257 (71.4) 80 (69.0) 351 (7.5) 23 (67.6) 234 (71.8)
C294 61 (31.4) 98 (31.6) 24 (21.8) 82 (33.1) 101 (27.6) 80 (31.5) 103 (28.6) 36 (31.0) 147 (29.5) 11 (32.4) 92 (28.2)
χ2; P 4.09; .13 2.11; .15 .592; .441 .103; .748 .257; .612
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NP, χ2 test not performed due presence of cell having the count of less than 5, χ2 and P by Pearson’s χ2 test.

a. P significant at <.05.

Participants were then categorized based on obesity, Met-S and obese with Met-S status (Table 2), and when analyzed for their association with PPARα L162V, PPARγ2 C161T and PPARδ T294C SNPs, no association was found. When stratified analysis was performed according to ethnicities, similar absence of association was also found within each ethnicity (data not shown). Logistic regression analysis also revealed that participants with the mutant alleles for the three PPAR SNPs did not have a significantly higher odds ratio to develop obesity, Met-S or obesity with Met-S (data not shown). Similar for anthropometric and clinical measurements, their means were not significantly different among the alleles of the three PPAR SNPs, except for IL-6 and HOMA-IR, where they were significantly higher in subjects with PPARα V162 alleleand PPARγ2 C161 allele, respectively (Table 3). Furthermore, when the three PPAR SNPs were analyzed together, subjects with all the homozygous wild-type genotypes did not have significantly different anthropometric and clinical measurements compared to those without (Table 4).

Table 2. Logistic regression analysis for the association of PPARα L162V, PPARγ2 C161T and PPARδ T294C genotypes/alleles with anthropometric-clinical classesa.

Genotypes and allele Obesity Met-S Obese with Met-S
OR Pb 95% CI OR Pb 95% CI OR Pb 95% CI
PPARα L162V
L162V .977 .942 .519, 1.838 .944 .897 .396, 2.250 1.732 .507 .342, 8.774
V162 1.021 .945 . 559, 1.867 .950 .903 .417, 2.163 1.647 .530 .348, 7.804
PPARγ2 C161T
C161T/T161T .783 .303 .491, 1.248 .625 .165 .322, 1.214 .775 .638 .269, 2.239
T161 .845 .391 .574, 1.243 .693 .178 .407, 1.182 .764 .534 .327 , 1.785
PPARδ T294C
T294C/C294C .714 .155 .448, 1.136 1.109 .761 .571, 2.153 1.120 .834 .387, 3.244
C294 .850 .369 .596, 1.211 1.008 .976 .607, 1.672 .910 .816 .409, 2.022
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OR, odds ratio; CI, confidence interval.

a. The wild-type genotypes and alleles were set as reference or OR 1.0. Genotypes for PPARγ2 and PPARδ were combined for analysis.

b.Adjusted for age, sex and ethnicity.

Table 3. Means of anthropometric and clinical measurements according PPARα L162V, PPARγ2 C161T and PPARδ T294C allelesa.

Variables PPARα L162V PPARγ2 C161T PPARδ T294C
L162 M:F = 1:.66 Mean age = 53.40 ± 14.00 V162 M:F = 1:.92 Mean age = 52.08 ± 15.89 P C161 M:F = 1:.64 Mean age = 53.38 ± 14.02 T161 M:F = 1:.82 Mean age = 53.03 ± 14.64 P T294 M:F = 1:.63 Mean age = 53.70 ± 14.16 C294 M:F = 1:.81 Mean age = 52.36 ± 14.14 P
Weighta, kg 67.33 ± .60 68.07 ± 2.05 .796 67.67 ± .65 66.38 ± 1.21 .363 67.32 ± .68 67.54 ± 1.05 .781
BMIa, kg/m2 26.87 ± .23 26.86 ± .80 .890 27.03 ± .25 26.31 ± .47 .207 26.95 ± .27 26.66 ± .41 .540
WC, cm 91.88 ± .53 91.65 ± 1.83 .903 92.00 ± .58 91.37 ± 1.08 .606 91.76 ± .61 92.09 ± .94 .769
WHRa .91 ± .01 .90 ± .02 .362 .91 ± .01 .92 ± .01 .448 .91 ± .01 .91 ± .01 .954
TBFa, % 33.07 ± .31 32.54 ± 1.05 .815 33.26 ± .33 32.23 ± .62 .194 33.23 ± .35 32.56 ± .54 .546
SF, % 27.27 ± .36 25.70 ± 1.23 .221 27.46 ± .39 26.06 ± .73 .091 27.48 ± .41 26.38 ± .63 .146
VFLa, % 12.05 ± .27 12.03 ± .94 .962 12.16 ± .30 11.66 ± .55 .396 12.10 ± .31 11.92 ± .48 .691
RMa, kcal 1416.70 ± 1.06 145.34 ± 34.57 .355 1422.78 ± 1.98 1407.45 ± 2.40 .493 1416.41 ± 11.55 1426.21 ± 17.74 .658
SMa, % 24.82 ± .17 25.25 ± .59 .394 24.82 ± .19 24.96 ± .35 .630 24.72 ± .20 25.17 ± .30 .171
SBPa, mm Hg 14.82 ± .87 139.45 ± 2.97 .589 141.34 ± .94 138.56 ± 1.75 .198 14.92 ± .99 14.22 ± 1.52 .811
DBP, mm Hg 81.41 ± .52 82.13 ± 1.80 .703 81.69 ± .57 8.72 ± 1.06 .424 81.66 ± .60 81.03 ± .92 .567
FBG a, mg/dL 7.73 ± .14 7.10 ± .50 .318 7.74 ± .15 7.49 ± .28 .267 7.76 ± .16 7.52 ± .25 .597
FI a, μIU/mL 12.65 ± .66 13.63 ± 2.31 .627 13.17 ± .72 11.09 ± 1.35 .088 13.17 ± .75 11.61 ± 1.18 .700
HOMA-IRa 4.55 ± .28 4.33 ± .98 .997 4.75 ± .30 3.74 ± .57 .042b 4.73 ± .32 4.04 ± .50 .487
TC, mmol/L 4.44 ± .04 4.21 ± .15 .139 4.42 ± .045 4.42 ± .08 .954 4.42 ± .05 4.41 ± .07 .889
TG a, mmol/L 1.49 ± .05 1.46 ± .17 .646 1.47 ± .05 1.53 ± .10 .900 1.51 ± .06 1.43 ± .09 .845
HDL-C a, mmol/L 1.21 ± .01 1.21 ± .05 .445 1.20 ± .01 1.25 ± .03 .223 1.22 ± .01 1.19 ± .02 .168
LDL-C, mmol/L 2.55 ± .03 2.35 ± .12 .107 2.55 ± .04 2.49 ± .07 .420 2.52 ± .04 2.58 ± .06 .430
TC/HDL-C ratioa 3.76 ± .04 3.66 ± .14 .429 3.78 ± .04 3.69 ± .08 .297 3.73 ± .04 3.83 ± .07 .228
Plasma Adiponectina, ng/mL 6326.88 ± 216.39 524.79 ± 761.52 .120 614.54 ± 235.79 6618.95 ± 445.36 .342 6319.63 ± 247.68 6064.10 ± 387.83 .458
Plasma IL-6a, pg/mL 8.90 ± .78 1.59 ± 2.75 .041b 9.13 ± .85 8.64 ± 1.61 .615 9.78 ± .89 7.18 ± 1.39 .254
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BMI, body mass index; WC, waist circumference; WHR, waist-hip ratio; TBF, total body fat; SF, subcutaneous fat; VFL, visceral fat level; RM, resting metabolism; SM, skeletal muscles; SBP, systolic blood pressure: DBP, diastolic blood pressure; FBG, fasting blood glucose; FI, fasting insulin; HOMA-IR, homeostasis model assessment-estimated insulin resistance; TC, total cholesterol; TG, triglycerides; HDL-C, high density lipoprotein-cholesterol; LDL-C, low density lipoprotein-cholesterol; IL-6, Interleukin-6.

a. Values were log transformed before analysis; values are presented as adjusted mean ± SEM (estimated marginal means ± standard error of the mean) by univariate analysis of variance (General Linear Model), adjusted for co-variates: age and ethnicity.

b. P significant at <.05.

Table 4. Means of anthropometric and clinical variables according to PPAR genotype combinations.

Variable Subjects with homozygous wild type for all three SNPsa M:F = 1:.42 Mean age: 55.70 ± 11.49 Subjects without homozygous wild type for all three SNPs M:F = 1:.78 Mean age: 52.54 ± 14.86 p
Weightb, kg 66.21 ± 1.68 67.87 ± .94 .438
BMIb, kg/m2 26.80 ± .65 26.88 ± .36 .977
WC, cm 9.03 ± 1.48 92.44 ± .83 .157
WHRb .90 ± .01 .91 ± .01 .479
TBFb, % 33.51 ± .86 32.88 ± .48 .632
SF, % 28.52 ± 1.00 26.71 ± .56 .117
VFLb, % 11.64 ± .76 12.17 ± .43 .927
RMb, kcal 1378.83 ± 28.04 1429.18 ± 21.57 .269
SMb, % 24.46 ± .48 24.98 ± .27 .278
SBPb, mm Hg 141.97 ± 2.41 14.32 ± 1.35 .698
DBP, mm Hg 81.27 ± 1.46 81.53 ± .82 .874
Pulse rate, bpm 78.41 ± 1.57 77.40 ± .88 .573
FBGb, mg/dL 7.79 ± .38 7.65 ± .22 .581
FIb, μIU/mL 13.02 ± 1.78 12.61 ± 1.04 .839
HOMA-IR v 4.81 ± .75 4.44 ± .44 .957
TC, Mmol/L 4.42 ± .11 4.42 ± .07 .987
TGb, Mmol/L 1.38 ± .13 1.52 ± .08 .566
HDL-Cb, Mmol/L 1.24 ± .04 1.20 ± .02 .144
LDL-C, Mmol/L 2.56 ± .09 2.53 ± .54 .809
TC/HDL-Cb 3.65 ± .11 3.79 ± .06 .237
Plasma adiponectinb, ng/mL 6414.12 ± 586.59 6186.87 ± 344.19 .913
Plasma IL-6b, pg/mL 11.83 ± 2.10 8.05 ± 1.23 .318
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BMI, body mass index; WC, waist circumference; WHR, waist-hip ratio; TBF, total body fat; SF, subcutaneous fat; VFL, visceral fat level; RM, resting metabolism; SM, skeletal muscles; SBP, systolic blood pressure: DBP, diastolic blood pressure; FBG, fasting blood glucose; FI, fasting insulin; HOMA-IR, homeostasis model assessment-estimated insulin resistance; TC, total cholesterol; TG, triglycerides; HDL-C, high density lipoprotein-cholesterol; LDL-C, low density lipoprotein-cholesterol; IL-6, Interleukin-6.

a. Participants with homozygous wild-type genotypes for all three PPAR SNPs: LL for PPARα L162V, CC for PPARγ2 C161T and TT for PPARδ T294C.

The total number of participants with homozygous mutated genotypes for all variables was 74/307, except for FBP, TC, TG, HDL-C, LDL-C, TC/HDL-C (70/280) and FI, HOMA-IR (67/260).

b. Values were log transformed before analysis; values are presented as adjusted mean ± SEM (estimated marginal means ± standard error of the mean) by univariate analysis of variance (General Linear Model), adjusted for co-variates: age and ethnicity.

Discussion

The PPARα V162V genotype was found to be absent in our study, consistent with our previous study7 and previous studies among Caucasians13 and African Americans,14 while the V162 allele was found to be absent among African Senegalese.15 Meanwhile, despite having similar demographics with Malaysia, Chan et al16 reported lower MAFs for this SNP - .005/.004/.02 in Singaporean Malays/Chinese/Indians. In contrast, a much higher MAF of .13 was reported by Gu et al17 among the Chinese Han population. Meanwhile, in Caucasian populations, MAF ranged from .06 to .08.13,18-21 In agreement with our current and previous findings,7 PPARα L162V SNP was not associated with obesity in the Chinese Han,17 Brazilians22 and Caucasians.4,23-25 Furthermore, a study conducted by Robitaille et al23 reported no association of this SNP with Met-S among Caucasian men, consistent with our current finding. The current study which also found that the lipid profile, glucose metabolism parameters and adiponectin levels were not affected by PPARα L162V SNP, is further supported by some studies, 4,18,22,24,26,27 but not others.13,19

For PPARγ2 C161T SNPs, difference in the prevalence of MAF between ethnicities was noted in our current and previous7 studies, with Indians showing significant lower MAF than Chinese. The MAF of Malaysian Indians was found to be similar with South Indians28 and Caucasians.5 Meanwhile, the Han Chinese from other countries portrayed similar MAF with us, for example the MAF of .25 in Taiwan29 and .21 in Beijing30 populations. Similar with our study, this SNP was previously found to have no significant association with obesity28-35 and Met-S5,28-30,36 in various populations, while others found positive association with obesity37,38 and Met-S.39,40 As with PPARα L162V, this SNP was also not associated with lipid profile, glucose metabolism parameters and adiponectin, consistent with previous studies in other populations.31,32,36,37,41 However, Gu et al42 and Yilmaz-Aydogan et al43 reported that the T161 allele carriers had higher serum TG.

For PPARδ T294C SNP, the MAF varies from .16 to .25 among Caucacians6,44,45 and .28 among African Americans.45 The MAF in Chinese Han population was reported as.30,42,46 which is similar to the MAF of Malaysian Chinese population in our study. Similar with our study, several studies from other populations reported the lack of association of PPARδ T294C SNP with obesity,38,47 Met-S and their related clinical variables.47 Meanwhile, other studies found significant association with obesity,46,48-50 increased plasma LDL-C,6,51 and decreased plasma HDL-C.6,49

When participants with homozygous wild type for all the three SNPs were clustered together to compare with those with at least one mutant allele, no significant differences in the anthropometric and clinical measurements were observed. This finding suggests that polygenic obesity and Met-S have a multi-factorial etiology that involves complex interactions between many candidate genes, hormonal status, dietary habits, and other environmental factors.

One of the major limitations of our study is our relatively small sample size, thus limiting statistical power. As our participants were recruited from a health clinic, the findings on the SNPs prevalence and association may not be fully representative of the general Malaysian population. In addition, our study did not assess in detail the dietary habits and lifestyle factors, which are factors that may also affect the biochemical parameters in our study.

Conclusion

PPARα L162V, PPARγ2 C161T and PPARδ T294C SNPs are not the major genetic determinants of obesity and metabolic syndrome in the multi-ethnic Malaysian population. Nevertheless, we found that PPARα V162 allele carriers were associated with significantly higher plasma IL-6 level, while participants with PPARγ2 T161 allele had significantly lower HOMA-IR, suggesting that the latter SNP may confer some protective effect on insulin sensitivity.

Acknowledgments

This project was funded by the Universiti Tunku Abdul Rahman Research Fund (IPSR/RMC/UTARRF/C111/C03). We would like to extend our deepest gratitude to the Kampar District Health Office for granting us permission to carry out this study at the Kampar Health Clinic, the nurses who assisted with the blood sampling, and all the respondents who have volunteered to participate in this study.

Statement of Human Rights

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.

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