ORIGINAL ARTICLE

Polymorphisms in PPARγ (Pro12Ala, C1431T), IRS1(G972R), IRS2 (G1057D) and Coronary artery disease

Shivani Vats & Kawaljit K. Matharoo & Ajinder P. Singh &

A. J. S. Bhanwer & Vasudha Sambyal

Received: 28 March 2013 /Accepted: 11 September 2013 /Published online: 6 October 2013# Research Society for Study of Diabetes in India 2013

Abstract Defect in insulin receptors or insulin signalingpathway is a hallmark of T2DM as it directly affects theendothelium. Coronary artery disease (CAD) being acomorbidity of T2DM, polymorphisms in genes of the insulinsignaling pathway may affect an individual’s susceptibility toCAD. The objective of present study was to assess the asso-ciation of CAD with polymorphisms in three genes of insulinsignalling pathway; IRS1 (G972R), IRS2 (G1057D) andPPARγ (Pro12Ala, C1431T). Blood samples were collectedfrom 416 subjects of Punjabi origin; 200 CAD patients and216 normal healthy controls matched for age and sex. ForG972R polymorphism, Arg972 (A) allele carriers (GA+AA)had increased risk of CAD (OR: 1.92, CI: 1.13–3.29, P=0.01)in overall population and in individuals with lowBMI, HDL-C,high WHR, waist circumference, normal range of cholesteroland triglycerides. D allele of G1057D polymorphism wasassociated with increased risk of CAD (OR: 1.44, CI-1.08–1.92, P=0.01) in overall population as well as in individualswith high BMI, WC, WHR and normal range of triglyceridesand cholesterol. For PPARγ variants (Pro12Ala,C1431T), nostatistical significant association was observed in the allelic andgenotypic frequencies of cases and controls but TT genotype(C1431T) andG allele ( Pro12Ala) conferred protection againstCAD in individuals with high cholesterol and normal HDL-Crespectively. Statistically significant difference in the differentgenotypic combinations of IRS1 (G972R) with IRS2 (G1057D)and PPARγ (C1431T) confirmed their role in susceptibility toCAD in Punjabi population from North-west India.

Keywords Type 2 diabetes . Coronary artery disease . Insulinsignaling . IRS1 . IRS2 .PPARγ

Introduction

Coronary artery disease (CAD) is the major comorbidity oftype 2 diabetes mellitus (T2DM). It is characterized by thepresence of atherosclerotic plaques in epicardial coronaryarteries, which progressively narrow the coronary arterylumen and impair blood flow, often leading to myocardialinfarction [1]. Defect in insulin signaling leading to insulinresistance characterizes obesity, T2DM and cardiovasculardiseases. It is associated with 2–3 fold risk of cardiovas-cular mortality in T2DM patients, totally independent ofhyperglycemia [2]. Thus, the genes involved in insulinsignalling pathway may also be important candidate genesfor CAD.

On stimulation of receptors for insulin or growth factor,tyrosyl phosphorylation of insulin receptor substrate 1(IRS1 )[3] and insulin receptor substrate 2 (IRS2 )[4] takesplace and activates phosphatidylinositol 3-kinase (PI3-K )which phosphorylates Akt; the phosphorylated Akt phosphor-ylates eNOS, resulting in nitric oxide (NO) production. Perox-isome proliferator activated receptor gamma (PPARγ) pro-motes insulin stimulated tyrosine phosphorylation of IRS1 ,IRS2 and PI3K activity associated with insulin receptor sub-strate proteins[5]. Thus, among genes of PI3-K/Akt pathway,IRS1 , IRS2 and PPARγ are important genes for both endothe-lial function and NO production.

Genetic variants in PPARγ, IRS1 and IRS2 have beenstudied in T2DM with varying results [6–8]. For CAD thereare few studies on IRS1 [9, 10] and PPARγ [11, 12]and tilldate only one study is available on IRS2 variant [13].

G972R of IRS1 gene is a common variant that lies in exon 1between two potential sites of tyrosine phosphorylation in-volved in binding the p85 subunit of PI-3 kinase. It impairsinsulin signaling and contributes to insulin resistance [14]. Ithas been studied in relation to CAD with conflicting results[9, 10].

IRS-2 , one of the major substrates of the insulin receptor,has a crucial role in insulin signaling, beta cell development

S. Vats :K. K. Matharoo :A. J. S. Bhanwer :V. Sambyal (*)Department of Human Genetics, Guru Nanak Dev University,Amritsar, Indiae-mail: vasudhasambyal@yahoo.co.in

A. P. SinghA.P. Heart Care Clinic, Amritsar, India

Int J Diabetes Dev Ctries (October–December 2013) 33(4):192–201DOI 10.1007/s13410-013-0150-2

and survival. Among its known polymorphisms , G1057D hasbeen studied as a risk factor for T2DM due to its higherprevalence in different populations [6, 15, 16]. Its relationwith T2DM is still not clear [6, 15]. There is only one reportfor the relation of this polymorphism with CAD [13].

The Peroxisome proliferator-activated receptor (PPARs)comprises an important subfamily of the nuclear hormonereceptor (NHR) superfamily. PPARγ2 is a PPAR isoformmainly expressed in adipose tissue which participates in bio-logical pathways of differentiation, insulin sensitivity, T2DM,atherosclerosis, and cancer [17]. Among the reported poly-morphisms ofPPARγ, Pro12Ala is a missense polymorphism,located on the domain that enhances ligand- independentactivation. It results in a proline to alanine amino acid substi-tution at codon 12 and is a less active transcription factor,resulting in lower transcription levels of target genes [18].Though pathophysiological role of PPARγ in cardiovasculardiseases (CVD) is apparent, there is a lot of variation in theresults from different studies regarding association of CVDwith PPARγ polymorphisms [11, 19, 20]. Another polymor-phism in exon 6 at nucleotide 1431 of PPARγ results in asilent substitution C to T (C1431T). There are conflictingreports for its association with CAD [21–23].

Indians are a high risk population for T2DM and prema-ture CAD [24]. The polymorphisms in PPARγ, IRS1 andIRS2 have been studied in various Indian populations inrelation to T2DM with conflicting results[6, 8, 25–27] butdata for CAD is scarce. The only reported study from India forthe relationship of PPARγ (Pro12Ala) and CAD [19] in SouthIndian population found no statistical significant difference inallelic and genotypic frequencies between cases and controls.Association of G972R and G1057D with CAD has not beenstudied from North India.

Amritsar city in North-West India has Punjabi popula-tion of Caucasian, Indo-Scythian racial admixture as itsmain inhabitants [28]. The population has higher preva-lence of obesity, T2DM and CAD as compared to otherNorth Indian and South Indian populations [29]. Insulinresistance being one of the hallmarks of CAD, the presentstudy was proposed with a hypothesis ; Pro12Ala andC1431T of PPARγ, G972R of IRS1 and G1057D of IRS2are among the genetic factors predisposing Punjabi pop-ulation from Amritsar to CAD.

Material and methods

The present case–control study was undertaken after dueclearance by institutional ethical committee of Guru NanakDev University, Amritsar as per declaration of Helsinki andICMR guidelines. Power calculation for effective sample sizein the present study was based on a previous study [30] andsignificant levels were set on 5 % (Power of study 90 %).

Blood samples were collected after informed consent from atotal of 416 subjects of Punjabi origin; 200 CAD patients (87female and113 male) and 216 (103 female and 113 male)normal healthy controls matched for age and sex. The sampleswere collected betweenAugust, 2010 and February, 2012. Thepatients, pre-diagnosed by the physician were selected fromDr AP Singh Heart Care Centre, Amritsar. Controls wererandomly selected from different parts of Amritsar city. Therelevant information of all subjects was collected on apredesigned proforma. Inclusion Criteria for CAD patients:definite history of an episode of myocardial infarction and ofangina pectoris with documented electrocardiography (ECG)findings, namely the Minnesota codes 1–1, 4–1, 5–2 or 9–2.Exclusion criteria: presence of T2DM or any other chronicdisease as confirmed by the physician. Inclusion criterion forcontrols: normal glucose tolerance, absence of angina, myo-cardial infarction or history of any vascular disease, normalresting 12-lead ECG. Exclusion criteria for controls: history ofany vascular or chronic disease, presence of any congenitalmalformation/disease.

Measurements

Blood pressure was measured by standard auscultatory meth-od. Three readings were taken at 5 min interval, mean of thethree were taken as subject’s blood pressure. Anthropometricmeasurements (height, weight, waist and hip circumference),were taken on cases and controls as per the standard method[31]. Obesity was estimated from calculated body mass index(BMI), Waist circumference (WC) and Waist Hip ratio(WHR). As per WHO (2004) criteria [32] subjects havingBMI ≥25 Kg/m2 were considered as obese in both malesand females. For WC; 85 cm and 80 cm were cut-offs usedfor males and females respectively, whereas for WHR, 0.81in females and 0.89 in males were the cut offs used [33].Plasma obtained from blood samples was used for theestimation of lipid profile using automated blood analyzer(Erba Mannheim). Following constituents were estimated:Cholesterol, High density lipoprotein cholesterol (HDL-C),Low density lipoprotein cholesterol ( LDL-C), Very Lowdensity lipoprotein cholesterol (VLDL-C) and Triglycerides(TG). Friedewald (1972) [34] formula was used to calculateVLDL- C and LDL-C concentrations. Reference values forthe lipid profile were as recommended by NCEP III [35].

Genotyping

DNA was isolated by standard inorganic method [36] withminor modifications according to the laboratory conditionsand quantified by agarose gel electrophoresis andspectophotometric analysis. Amplification Refractory muta-tion system (ARMS) PCR was used to detect Pro12Ala(rs1801282) polymorphism according to previously

Int J Diabetes Dev Ctries (October–December 2013) 33(4):192–201 193

reported method [37]. The other three polymorphismswere analysed with PCR- RFLP method using previous-ly reported primers .i.e. PPARγ (C1431T,rs3856806) [38],IRS1 (G972R,rs1801278) [39], IRS2 (G1057D,rs1805097)[6].

Appropriate quality control measures were used to elimi-nate the genotypic error. Samples were randomly selected andgenotyped again to remove all the ambiguity in genotyping.For all the polymorphisms, based on the analysis of 100 blindduplicates (>20 %) there was 100 % concordance in thegenotyping.

Statistical analysis

Statistical analysis was done using SPSS 13.0 (SPSSInc., Chicago,IL,USA). The Hardy Weinberg equilibriumtest was performed using the chi square test. All thecontinuous variables in the data were represented asmean ± standard deviation. Continuous clinical charac-teristics between cases and controls were compared withstudent’s t-test whereas non continuous variables werecompared with chi-squared statistics. Bonferroni correc-tion was used to adjust P values. Since the number ofhomozygous individuals for minor allele were small inpatients as well as control group, we combined CG+GG( G allele carriers), GA+AA ( A allele carriers) and

GD+DD ( D allele carriers) for Pro12Ala, G972R andG1057D polymorphisms respectively to increase thepower of the study. Binary logistic regression was usedfor stratified analysis. Different allelic and genotypicfrequencies of all the polymorphisms in CAD patientsand controls were compared using chi-squared statistics(χ2). ODDs ratio (OR) and 95 % confidence interval(CI) were calculated to estimate the association of differentgenotypes with CAD risk. Statistical significance was definedat P <0.05.

Results

The clinical characteristics of patients and control group werecompared according to the gender (Table 1). Among 226males (113 patients and 113 controls) SBP, DBP, BMI, LDLwere found out to be significantly higher (P <0.05) in patientsthan controls. Among 190 females subjects (87 patientsand 103 controls), female patients had significantly higher(P <0.05) value of DBP than controls.

Genotypic and allelic frequencies of all the four polymor-phisms; Pro12Ala, C1431T of PPAR γ, G972R of IRS1 andG1057D of IRS2 were compared for patients and controls(Table 2).

For IRS1 (G972R) polymorphism , the A allele was foundout to be significantly higher(12.0 %) in patients (P <0.05)

Table 1 Statistical comparison of clinical characteristics of CAD patients and controls

Males (N =226) Females (N =190)

Variable Cases Controls P-value Cases Controls P-value(units) N=113 N=113 N =87 N =103

Age(y) 55.24 ±11.98 52.97±12.92 0.17 54.79±12.02 52.74±11.04 0.22

SBP (mm Hg) 134.29±23.66 125.36±16.17 0.00,0.00* 129.76±21.19 122.99±11.38 0.01,0.14

DBP (mm Hg) 89.33±13.47 83.98±8.64 0.00,0.00* 85.45±11.24 81.09±7.76 0.00,0.00*

Height (cm) 166.08±8.95 169.07±7.93 0.01,0.14* 159.95±9.61 157.72±7.23 0.07

Weight (Kg) 74.37±13.70 72.02±13.74 0.20 69.72±14.71 65.78±12.01 0.04,0.56*

BMI (Kg/m2) 27.00±4.67 25.17±4.38 0.00,0.00* 27.34±5.81 26.48±4.87 0.27

WC (cm) 93.58±11.08 95.26±12.95 0.30 91.60±13.05 90.88±17.64 0.75

HC (cm) 100.03±10.13 107.30±84.77 0.37 99.70±12.83 103.82±10.93 0.08

WHR 0.94±0.084 0.95±0.13 0.34 0.93±0.15 0.87±0.14 0.02,0.28*

Choles (mg/dL) 191.87±61.68 173.06±40.42 0.01,0.14* 182.06±55.07 184.30±56.81 0.78

TG (mg/dL) 149.56±70.75 168.65±107.96 0.12 175.82±95.73 141.50±75.9 0.01,0.14*

HDL-C (mg/dL) 39.11±24.05 45.89±18.42 0.02,0.28* 41.88±22.78 44.64±14.47 0.31

VLDL-C (mg/dL) 29.65±14.37 33.08±21.53 0.10 35.16±19.14 28.30±15.0 0.01,0.14*

LDL-C (mg/dL) 121.76±68.50 93.90±48.74 0.00,0.00* 103.76±51.64 111.36±53.95 0.32

Values are mean±SD, * P-values after boneferroni correction

N number of individuals, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, WC waist circumference, HC hipcircumference, WHR waist hip ratio, Choles cholesterol, Trig riglycerides, HDL-C high density lipoprotein cholesterol, VLDL-C very low densitylipoprotein cholesterol, LDL-C low density lipoprotein cholesterol

194 Int J Diabetes Dev Ctries (October–December 2013) 33(4):192–201

than controls (4.9 %). For IRS2 , G1057D polymorphism thefrequency of D allele was significantly higher (p <0.05) inpatients (34.0 %) than controls (25.6 %). Whereas, no statis-tically significant difference was observed in the allelic andgenotypic frequencies among patients and controls forPro12Ala and C1431T polymorphisms (Table 2).

On comparing the anthropometric, obesity parameters andlipid profile in relation to the genotypes of the studied

polymorphisms in patients and controls, for G972R polymor-phism, GG genotype was found to be associated with signif-icantly higher triglycerides and VLDL-C (P <0.05) amongpatients. For G1057D polymorphismGG genotype was foundout to be associated with significantly higher DBP (P >0.05)in controls. For Pro12Ala polymorphism, CC genotype wasfound out to be associated with significantly higher waistcircumference and WHR as compared to G allele carriers

Table 2 Statistical comparison of genotype distribution of different polymorphisms in CAD patients and controls

Gene Patients Controls P-value Dominant Recessive Co-Dominant

(polymorphism) N (%) N (%) Genotypic Model Model model

Allelic p p P

(OR:95% (OR:95% (OR:95%

CI) CI) CI)

IRS1

(G972R)GA/AA vs GG

AAvsGG/GA

AA vs GA=GA vs GG

GG 157(78.5) 189(87.5) 0.01 0.21 0.01

GA 38(19.0) 25(11.6) 0.04,0.12* (1.92:1.13- (2.74:0.53- (1.80:1.13-2.88)

)03.41)42.3)9.0(2)5.2(5AA

Frequency of G allele 88.0% 93.3% 0.002*,0.006*

Frequency of A allele 12.0% 6.7%

IRS2

(G1057D svDDGGsvDD/DG) DDvs GD=GD vs

GG+GD GG

GG 92(46) 123(57.2) 0.02 0.07 0.01

GD 80(40) 74(34.4) 0.04,0.12* (1.57:1.07- (1.78:0.95- (1.44:1.08-1.92)

)33.3)13.2)4.8(81)0.41(82DD

Frequency of G allele 66.0% 74.4% 0.007,0.021*

Frequency of D allele 34.0% 25.6%PPARγ

(Pro12Ala) CG/GG GG vs GG vs CG=CG vs

vs CC CC/CG CC

CC 168 (84) 173(80.1) 0.30 0.83 0.34

CG 28(14) 38(17.6) 0.58 (0.77:0.46- (0.86:0.23- (0.81:0.53-1.25)

)52.3)72.1)3.2(5)2(4GG

Frequency of Callele 91.0%88.9%

0.31

Frequency of Gallele 9.0% 11.1%

(C1431T)

CC 0 0 0.12 - - -

CT 182(91) 186(86.1)

TT 18(9) 30(13.9)

Frequency of C allele 45.5% 43.1% 0.48

Frequency of T allele 54.5% 56.9%

n number of individuals with a particular genotype. * P value after boneferroni correction

Int J Diabetes Dev Ctries (October–December 2013) 33(4):192–201 195

(GG+GC) ( P <0.05) in controls. In case of C1431T polymor-phism, height, weight, cholesterol and LDL-C were found outto be significantly higher (P <0.05) in TT genotype as com-pared to CT genotype among controls (Data not shown)(Tables 3, 4, 5 and 6).

When the subjects were stratified according to obesity anddyslipidemia, for G972R, A allele carriers ( GA+AA) werefound out to be associated with increased risk of CAD inindividuals with BMI<25 (OR:2.75.CI-1.18-6.39,P=0.02),high WC (OR:1.85,CI-1.01- 3.34,P=0.05), normal range ofcholesterol (<200) (OR:2.85,CI-1.43-5.67), triglycerides(<150) (OR:2.18,CI-1.08-4.43,P =0.03) and low HDL(<40)( OR:2.82,CI-1.22-6.51,P=0.02) (Table 5).

For G1057D polymorphism, D allele carriers (GD+DD)were found out to be associated with increased risk of CAD insubjects having BMI(≥25) (OR:1.79,CI-1.06-3.03,P=0.03),high WC(OR:1.70,CI-1.14-2.77,P =0.02), higher WHR(OR:1.78,CI-1.14-.2.77,P=0.01), normal range of cholester-ol(<200) (OR=1.78, CI=1.07-2.97,P=0.03) andtriglycerides(<150) ( OR=1.77,CI-1.02-3.08,P=0.04) (Table 5).

G allele carriers of Pro12Ala were found out to be associ-ated with reduced risk of CAD in normal range of HDL (≥40)group (OR: 0.36, CI-0.14-0.92,P=0.03). In case of C1431Tpolymorphism TT genotype conferred protection againstCAD in higher cholesterol (≥200) group (OR: 0.34, CI:0.12-0.93,P=0.04) (Table 6).

On analyzing various genotypic combinations, GA-GDgenotypic combination in IRS1 (G972R) and IRS2(G1057D) polymorphisms respectively was found out be

associated with significantly high risk of CAD (OR: 3.32,CI-1.29-8.47,P=0.012,GG-GG as reference) as 9 % patientsand 3.2 % controls had this combination (Table 3).

The genotypic combinations, AA-CT and GA- CT in IRS1(G972R) and PPARγ (C1431T) polymorphisms were alsofound out to be significantly different in patients and controls.AA-CT combination was found in higher percentage in pa-tients (16.5 %) than controls (0.9 %) and conferred 17.78 foldrisk against CAD (OR:17.78,CI:4.15 76.18,P=0.00). WhereasGA-CT combination was found at higher percentage in con-trols (9.3 %) than patients (3 %) and conferred a reduced riskagainst CAD(OR:0.34,CI:0.13-0.88,P=0.03,GG-CT as refer-ence) (Table 4). When all the other possible genotypic combi-nations of Pro12 Ala, C1431T, G972R and G1057D werestudied together, no statistical significance was observed, butsome combinations were found to be associated with CAD i.e.GG-TT-GA-GD and CC-CT-GG-DD were higher in patientsthan controls. CC-CT-GG-GG and CG-CT-GG-GD and GG-CT-GG-GG were lower in patients as compared to controls.

Discussion

Complex interplay of genetic and environmental factors playscrucial role in multifactorial diseases such as CAD. In thepresent study, the association of CAD with four selectedpolymorphisms in genes of insulin signaling pathway wasassessed; G972R (IRS1), G1057D (IRS2) and Pro12Ala,C1431T (PPARγ) along with traditional risk factors like

Table 3 Genotypic combinationsof IRS1 (G972R) and IRS2(G1057D) polymorphismsin patients and controls

statistically significant at P<0.05

Genotype combinations Patients Controls P-value OR(95 % CI)

GG-GG 76(38.0 %) 106(49.1 %) reference –

GG-DD 20(10 %) 17(7.9 %) 0.28 1.49(0.72–3.07)

GG-GD 61(30.5 %) 66(30.6 %) 0.29 1.29(0.80–2.045)

GA-GG 13(6.5 %) 17(7.9 %) 0.88 0.94(0.421–2.1)

GA-DD 7(3.5 %) 1(0.5 %) 0.05 8.27(0.98–69.73)

AA-GD 1(0.5 %) 1(0.5 %) 0.97 1.06(.06–17.65)

AA-GG 3(1.5 %) 1(0.5 %) 0.17 5.15(0.50–5.85)

AA-DD 1(0.5 %) – – –

GA-GD 18(9 %) 7(3.2 %) 0.01* 3.32(1.29–8.47)

Table 4 Genotypic combinationsof IRS1 (G972R) and PPARγ(C1431T) polymorphismsin Patients and controls

*statistically significant atP<0.05

Genotype combinations Patients Controls p-value OR( 95%CI)

GG-CT 143(71.5 %) 164(75.9 %) reference –

GG-TT 14(7.0 %) 25(11.6 %) 0.16 0.61(0.30–1.22)

GA-TT 4(2.0 %) 5(2.3 %) 0.92 0.93(0.24–3.62)

AA-CT 33(16.5 %) 2(0.9 %) 0.00* 17.78(4.15–76.18)

GA-CT 6(3.0 %) 20(9.3 %) 0.03* 0.34(0.13–0.88)

196 Int J Diabetes Dev Ctries (October–December 2013) 33(4):192–201

obesity, blood pressure and dyslipidemia. This was the firstcase–control study in India to evaluate the association of thesefour polymorphisms with CAD risk.

In IRS1, G972R polymorphism, has been shown toimpair IRS-1 function, associated with T2D and lipid ab-normalities. In the present study, the Arg 972 (A) allelecarriers (GA+AA) conferred nearly 2 fold increased risk ofCAD. The frequency of minor allele A (4.9 %) was foundout to be nearly double than previously reported for SouthIndians (2 %) [8] but was comparable with Caucasians(5.8 %) [40]. On stratifying the data with traditional riskfactors i.e. obesity and dyslipidemia, A allele carriers werefound to be at increased risk of CAD; 2.75 fold for low

BMI(<25), 1.85 fold for high waist circumference andWHR, 2.85 fold for normal range of cholesterol(<200)and triglycerides <150 (2.18 fold) and 2.82 fold for lowHDL(<40). Whereas in CAD patients, A allele was asso-ciated with low triglycerides and VLDL. This indicates anassociation of IRS1 (G972R) polymorphism with lipids. Ameta-analysis of 27 studies for the association of thispolymorphism with T2D [41] found that individuals car-rying the Arg 972 variant (A allele) were at 25 % increasedrisk of T2D as compared with non-carriers. Similar to aprevious report on CAD [9] an association of this poly-morphism with an increased risk of CAD was observed inthe present study but in contrast the A allele was associated

Table 5 Stratified analyses for IRS1 (G972R) and IRS2 (G1057D) genotypes in Patients and Controls

Variable G972R Crude P, adjusted G1057D Crude P, adjusted(GA+AA/GG) P(OR: 95 % CI) (GD+DD/GG) P(OR: 95 % CI)

Obesity Patients Controls Patients Controls

BMI (Kg/m2)

≥25 25/105 14/105 0.11,0.17 56/74 68/51 0.03,0.03

(1.66:0.81–3.43) (1.79:1.06–3.03)*

<25 18/52 13/84 0.04,0.02 36/34 56/41 0.42,0.41

(2.75:1.18–6.39)* (1.30:0.70–2.41)

WC(cm) High

(≥85men,≥80 women) 35/18 22/156 0.02,0.05 74/89 103/75 0.02,0.02

(1.85:1.01–3.36)* (1.705:1.11–2.64)*

Low 8/29 5/33 0.33,0.31 18/19 21/17 0.57,0.69

(<85men,<80 women) (1.96:0.54–7.12) (1.21:0.47–3.11)

WHR High

(≥0.89men,≥0.81 women) 34/125 22/148 0.04,0.05 70/89 100/70 0.01,0.01

(1.83:1.01–3.34)* (1.78:1.14–2.77)*

Low 9/32 5/41 0.160,0.41 22/19 24/22 0.89,0.98

(<0.89men,<0.81 women) (1.73:0.48–6.31) (1.01:0.40–2.55)

Dyslipidemia Cholesterol (mg/dL)

≥ 200 15/76 9/48 0.91,0.82 45/46 33/24 0.32,0.34

(0.90:0.35–2.28) (1.39:0.71–.74)

<200 27/81 18/141 0.00,0.00 47/61 91/68 0.03,0.03

(2.85:1.43–5.67)* (1.78:1.07–2.97)*

Triglyceride (mg/dL)

≥150 16/80 10/82 0.25,0.25 44/52 49/53 0.31,0.42

(1.68:0.69–4.01) (1.27:0.70–2.31)

<150 26/77 17/107 0.03,0.03 48/55 75/49 0.04,0.04

(2.18:1.08–4.43)* (1.77:1.02–3.08)*

HDL-C (mg/dL)

≥ 40 13/64 17/108 0.52,0.63 37/40 75/50 0.09,0.09

(1.22:0.54–2.71) (1.65:0.92–2.95)

<40 29/93 9/79 0.01,0.02 55/67 46/42 0.30,0.43

(2.82:1.22–6.51)* (1.26:0.71–2.22)

P- value adjusted for age and sex for groups based on BMI and for other groups adjusted for age sex and BMI. * statistically significant at P<0.05

Int J Diabetes Dev Ctries (October–December 2013) 33(4):192–201 197

with reduced triglycerides and VLDL, similar to anotherstudy [42].

To the best of our knowledge, this is the first studyfrom India and the second from the world to assess therole of IRS2 (G1057D) polymorphism in CAD. The minorallele frequency (25.6 %) observed in present study wasslightly lower than previously reported for Asian Indians(30 %) [6] Chinese (30 %) [16] and much lower than thatof Germans (37 %)[15]. In the present study, we found1.57 fold increased risk of CAD in D allele carriers. Onstratifying the data on obesity and dyslipidemia, obese Dallele carriers were at nearly 2 fold increased risk of CADas indicated by high BMI, WC and WHR. Similar riskwas apparent in individuals having even normal range ofcholesterol and triglycerides. It was reported earlier that

the presence of overweight and obesity modulate theeffect of D allele. In the absence of obesity, the risk ofT2DM decreased according to the dosage of D allele;conversely the interaction of obesity and genotype in-creased the risk of T2DM [6, 43]. The interaction ofobesity in modulating the effect of D allele is also clearfrom our results as D allele was associated with increasedrisk of CAD in obese group but not in the non-obesegroup. Further, high risk in individuals having normal rangeof cholesterol and triglycerides was suggestive of the possibleassociation of D allele with CAD irrespective of the classicalrisk factors. In the lone previous reported study on theassociation of G1057D with CAD done on Taiwanesepopulation, G allele increased risk 2 fold ,thus D allele con-ferred protection [13].

Table 6 Stratified analyses for PPARγ (Pro12Ala and C1431T) genotypes in Patients and Controls

Variable Pro12Ala Crude P, adjusted C1431T Crude P, adjusted(CG+GG/CC) P(OR: 95 % CI) (TT/CT) P(OR: 95 % CI)

Obesity Patients Control Patients Control

BMI (Kg/m2)

≥25 20/110 22/97 0.51,0.46 9/121 16/102 0.06,0.06

(0.77:0.39–1.52) (0.43: 0.18–1.02)

<25 12/58 21/76 0.47,0.47 9/61 13/84 0.92,0.94(0.97:0.38–2.45)

(0.72:0.32–1.62)

WC(cm) High

(≥85men,≥80 women) 26/137 29/149 0.93,0.71 15/148 27/151 0.09,0.09

(0.90:0.50–1.62) (0.56:0.28–1.10)

Low 6/31 14/24 0.04,0.05 3/34 3/35 0.97,0.94

(<85men,<80 women) (0.32:0.10–1.01) (1.07:0.18–6.54)

WHR High 26/133 28/142 0.98,0.76 16/143 27/143 0.12,0.10

(≥0.89men,≥0.81 women) (0.91:0.50–1.65) (0.57:0.29–1.11)

Low 6/35 15/31 0.05,0.12(0.39:0.12–1.22) 2/39 3/43 0.74,0.85(0.83:0.12–5.69)

(<0.89men,<0.81 women)

Dyslipidemia

Cholesterol (mg/dL)

≥200 15/76 11/46 0.66,0.74 8/83 12/45 0.03,0.04

(0.86:0.36–2.08) (0.34:0.12–0.93)*

<200 16/92 32/127 0.28,0.26 10/98 18/141 0.59,0.77

(0.68:0.35–1.33) (0.88:0.38–2.04)

Triglyceride (mg/dL)

≥150 12/84 16/76 0.35,0.32 7/89 13/79 0.13,0.17

(0.66:0.29–1.50) (0.49:0.18–1.35)

<150 19/84 27/97 0.54,0.51 11/92 17/107 0.49,0.18

(0.80:0.41–1.57) (0.56:0.24–1.30)

HDL (mg/dL)

≥ 40 6/71 24/101 0.03,0.03 5/72 18/107 0.09,0.09

(0.36:0.14–0.92)* (0.39:0.14–1.14)

<40 25/97 19/69 0.85,0.89(0.95:0.48–1.90) 13/109 10/78 0.87,0.64(0.81:0.33–1.98)

P- value adjusted for age and sex for groups based on BMI and for other groups adjusted for age sex and BMI. * statistically significant at P<0.05

198 Int J Diabetes Dev Ctries (October–December 2013) 33(4):192–201

In the present study, the difference in the susceptibility of Dallele with CAD can be attributed to the high prevalence ofobesity especially central body obesity in this population fromPunjab.

In PPARγ (Pro12Ala) polymorphism, the CAD patientshad lower frequency (9 %) of Ala allele than controls(11.1 %), but it was not statistically significant. The minorallele frequency in controls was comparable to the previouslyreported in South Indians (10.7 %) [25] and Caucasians [44],but was higher than other Asian populations from China(1.0 %) and Japan (2.0 %) [45]. This polymorphism resultsin reduction in both DNA binding and transcriptional activity,hence is associated with improved insulin sensitivity. Theprotective role of Ala12 allele in CAD has been documentedin previous studies on myocardial infarction in Americans[11] and CAD in Italian polulation [20] along with an associ-ation of Pro allele with high HDL cholesterol among CADpatients in Indian population [19]. Pro12Ala has been reportedto be protective against diabetes in Caucasians but not in southAsians [46]. In the present study, a higher frequency of Proallele was observed in both cases and controls (Table 3).Similar to previous studies on CAD from India [19] and korea[22] no significant association of Pro12Ala with CAD wasobserved in the present study. However, CC genotype wasassociated with high waist circumference and WHR in controls(p <0.05) and G allele conferred protection against CAD insubjects having high HDL.

Little is known about the PPAR γ (C1431T) polymorphism(silent substitution) and its association with CAD as it has notbeen as extensively studied as the Pro12Ala polymorphism. Inthe present study, no association in allelic and genotypicfrequencies of C1431T polymorphism with CAD was seen(Table 2). However, TT genotype was associated with in-creased body weight, height, and high levels of cholesteroland LDL among controls. On stratifying the data ondyslipidemia, in high cholesterol group, it apparently con-ferred protection against CAD. Our results are in line withthe previous studies, where no significant difference wasobserved in the allelic and genotypic frequencies of C1431Tpolymorphism in CAD patients and controls [47–49] but itssignificant association with lipid concentrations and bodyweight could not be neglected. The frequency of T allele inpresent study was higher (56.9 %) as compared other Asianpopulations from China (25.2 %), Malaysia (22.0 %) [49]South India (12.5 %) [25] and an endogamous Khatri sikhpopulation from four states of North India [7]. The highfrequency of CT heterozygotes could be attributed to thesignificant protection provided for increased body weight,high cholesterol and LDL in controls. The interaction of Tallele with environment (especially obesity) and otherfunctionally relevant genetic factors specific to Punjabi popu-lation could be one of the reasons for its high frequency in thepresent study.

PPAR γ , IRS1 and IRS2 are involved in PI3-Akt pathwayand have an important role in insulin signaling. The significantdifference in the different genotypic combinations of IRS1(G972R) with IRS2 (G1057D) and PPARγ (C1431T) empha-sized their cumulative effect in risk of CAD confirming thepolygenic inheritance of the CAD. Present study highlightedthe role of G972R and G1057D polymorphisms with theincreased susceptibility to CAD in combination with obesityand their possible association with lipid concentrations.According to the results of present study though the allelicand genotypic frequencies of polymorphisms of PPAR γ(Pro12Ala,C1431T) were not significantly associated withCAD, their role in the pathogenesis of CAD in context oflipid profile could not be ruled out. Further the association ofthese polymorphisms with obesity in controls is also one ofthe important indicator of their role in the pathogenesis ofCAD.

As India has an immense diversity within the ethnic affil-iations of the populations, a large number of genetic variationscan be seen. Population structure of Punjab differs from otherparts of India, as most of the invasions in India have occurredthrough Punjab and over the years high endogamy along withgenetic drift has kept the gene pool and its structure distinctfrom other populations. Our preliminary study confirms therole of the genes involved in the insulin signaling with CAD inPunjabi population of North-West India. Its validation mayrequire more studies with focus on obesity and insulinsignaling.

Acknowledgment Financial assistance in the form of grant (No. DST/INSPIRE Fellowship/2009(xv)) from Department of Science andTechnology to Shivani Vats and Ministry of Science and Technology,Department of Biotechnology (BT/PR8846/MED/12/327/2007) to Dr.AJS Bhanwer and Dr. Vasudha Sambyal is acknowledged.

Conflicts of interest The authors declare that they have no conflict ofinterest.

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