Original Research Article
Association of PPAR-g Gene Polymorphisms with Obesity and
Obesity-Associated Phenotypes in North Indian Population
JAI PRAKASH,1NEENA SRIVASTAVA,1SHALLYAWASTHI,1C. AGARWAL,1S. NATU,1NARESH RAJPAL,2AND BALRAJ MITTAL3
1Chatrapati Shahuji Maharaj Medical University, Lucknow, U.P., India
2Vivakanand Polyclinic, Lucknow, U.P., India
3SGPIMS, Lucknow, U.P., India
proliferator-activated receptor gamma (PPAR-g) controls adipocyte differentiation and regulates a number of genes
associated with energy homeostasis. In this study, we investigated the association of PPAR-g gene Pro12Ala
(rs1801282) and C1431T (rs3856806) polymorphisms with morbid obesity and related phenotypes, in north Indian pop-
A total of 6,42 subjects, 309, obese and 333 nonobese individuals were included in this case–control study.
Insulin, adiponectin, glucose, and lipid levels were estimated using standard protocols. All subjects were genotyped by
PCR restriction fragment length polymorphism (PCR-RFLP) method.
The ProAla1AlaAla genotypes of PPAR-g Pro12Ala were significantly associated with higher risk of obe-
sity while C1431T polymorphism did not show any significant association. None of the haplotypes showed association
with morbid obesity. However, a strong association of variant genotypes was observed with higher levels of insulin,
HOMA-IR, and lower serum adiponectin concentrations.
Conclusion:PPAR-g gene polymorphisms influence obesity and obesity phenotype in a complex manner, probably
involving insulin resistance in north Indian population. Am. J. Hum. Biol. 24:454–459, 2012.
The worldwide increasing prevalence of obesity is considered as a major health problem. Peroxisome
' 2012 Wiley Periodicals, Inc.
Obesity is a multifactorial disorder, with genetic factors
playing a significant pathogenic role (Perusse and Bou-
chard, 1999). Products of related genes may be involved in
the regulation of food intake and energy expenditure. The
association with Type 2 diabetes, dyslipidemia and hyper-
tension create increased morbidity and mortality risks for
obese individuals (Mokdad et al., 2000).
(PPAR-g) gene is a member of nuclear hormone receptor
super family (Kliewer et al., 1995). It regulates the tran-
scription of several genes involved in glucose metabolism,
adipocyte differentiation, lipid oxidation, angiogenesis,
and inflammation (Tontonoz et al., 1995). PPAR-g gene is
an important regulator of adipocyte differentiation and a
modulator of intracellular insulin-signaling events (Spie-
gelman, 1998). The functional role of PPAR-g is well docu-
mented, and its alterations have been widely associated
with metabolic diseases, such as obesity (Beamer et al.,
1998; Rosen and Spiegelman, 2001). The Pro12Ala
(rs1801282) polymorphism has been associated with obe-
sity risk and measures of obesity (Beamer et al., 1998;
Meirhaeghe et al., 2000).
Another silent mutation in exon 6 of the PPAR-g gene,
C1431T (rs3856806), which has been studied in several
populations, was found to be in linkage disequilibrium
(LD) with the Pro12Ala SNP (Valve et al., 1999). Two stud-
ies in large European cohorts have shown the modifying
effect of these SNPs on each other (Doney et al., 2002).
Rhee et al. (2006) have reported an association of
Pro12Ala and C1431T SNPs with some parameters of
metabolic syndrome in Korean females but no association
of this polymorphism was observed in a South Indian pop-
ulation (Vimaleswaran et al., 2007).
Because of population specific variations and lack of
studies in north Indians, we investigated the influence of
the PPAR-g Pro12Ala and C1431T polymorphisms on the
risk of obesity and obesity-related phenotypes like insulin
resistance in north Indian population.
MATERIALS AND METHODS
All individuals were of north Indian origin and the pop-
ulation was homogeneous with regard to ethnic back-
ground. A total of 821 subjects were enrolled initially from
the out patients department of Chatrapati Shahuji Maha-
raj Medical University, Lucknow, and volunteers from
general population of Lucknow (North India). Out of these
88.20% were Hindus (Hindi spoken and residing in Luck-
now for at least two generations) while 21.8% belonged to
other religions. Individuals of South, East, and Central
Indian origin were excluded. A process of disproportionate
stratified and systematic sampling was used to select indi-
viduals between 19 and 60 years old, oversampling of the
majority groups to ensure that prevalence estimates for
the majority groups were reliable and to allow statistical
comparison. Every individual has been classified as Hindu
north Indian (Hindi speaking) depending on self-reported
family origin from two generations. Moreover, the possibil-
ity of population admixture is slight, because in this part of
country, interreligion marriage or consanguity is rare.
Additional Supporting Information may be found in the online version of
Contract grant sponsors: Department of Biotechnology (DBT) and In-
dian Council of Medical Research, New Delhi, India.
*Correspondence to: Neena Srivastava, Department of Physiology, Cha-
trapati Shahuji Maharaj Medical University, Lucknow, U.P., India. E-mail:
Received 9 October 2011; Revision received 24 December 2011; Accepted
2 January 2011
Published online 12 March 2012 in Wiley Online Library (wileyonlinelibrary.
AMERICAN JOURNAL OF HUMAN BIOLOGY 24:454–459 (2012)
C2012 Wiley Periodicals, Inc.
Out of these subjects, only 309 obese (BMI > 30 kg/m2)
and 333 nonobese (BMI ? 30 kg/m2)individuals were
selected befitting the strict inclusion criteria. In all indi-
viduals, body height, body weight, waist circumferences,
and hip circumferences were measured for calculation of
BMI and WHR. Hypertension was diagnosed when the
systolic or diastolic BP was 5140/590 mm Hg on a
repeated single-day measurements or when the individual
was a known hypertensive. Diabetes was diagnosed when
a subject provided history of previously diagnosed diabe-
tes or the fasting blood glucose was 5126 mg/dl.
Subjects with established diabetes mellitus, coronary
artery disease, congestive heart failure, and pregnant
women were excluded. Informed consent was obtained
from each participant and the study was carried out in
accordance with the local ethics committee. All study par-
ticipants were subjected to a thorough screening program
that included assessment of a detailed personal and fam-
ily history, physical examination, determination of anthro-
pometric indices and measurement of various biochemical
parameters. In general, Smokers (12.6 %) included sub-
jects with present or past smoking or any other tobacco
use. 87.4% were nonsmokers, 88.6% were nonalcoholic,
88.3% do not have betel chewing habit and 56.1% of
participants self-reported to be vegetarians.
Venous blood was collected after an overnight fast, and
plasma and serum samples were either used immediately
for analysis or were stored frozen at 2808C. Commercial
enzymatic test kits were used for determining total choles-
terol, HDL (high density lipoprotein) cholesterol and tri-
glyceride concentrations but LDL (low density lipoprotein)
cholesterol and VLDL (very low density lipoprotein) cho-
lesterol were calculated by the formula of Friedewald
(LDL-cholesterol 5 total cholesterol 2 HDL cholesterol –
triglyceride/5 mg/dl). Insulin levels were determined by
Research). The degree of insulin sensitivity/resistance
was calculated according to the homeostasis model assess-
ment (HOMA) which is a good index for assessing insulin
sensitivity/resistance. Insulin resistance (IR) was calcu-
lated as follows: IR 5 FI 3 g/22.5; where FI 5 fasting in-
sulin (lu/ml) and g 5 fasting glucose (mmol/1) (Matthews
et al., 1985).
Adiponectin was assayed with ELISA kits (Linco
Research, USA). The fasting glucose concentration was
measured by Glucose oxidase-Peroxidase (GOD-POD)
method (Young et al., 1975). Systolic (SBP) and diastolic
(DBP) blood pressure were measured twice on the right
arm, after a 15-min rest, using a mercury sphygmoma-
nometer (Parker et al., 1988). All protocols were approved
by the Institutional Review Board or Ethical Committee
at CSM Medical University (Chatrapati Shahuji Maharaj
Medical University) UP, Lucknow, and all the subjects
gave informed consent.
Estimation of body fat composition
The body fat analyzer (Bioelectrical impedance was
obtained using a device, Tanita–TBF–310, Japan) was
used for assessing the percentage body fat, fat mass, and
fat-free mass (FFM).
The genomic DNA was extracted from peripheral blood
leucocytes pellet using the standard salting out method
(Miller et al., 1988). The analysis of Pro12Ala, C1431T
PPAR-g polymorphisms were determined by PCR-based
restriction fragment length polymorphism (PCR-RFLP) as
previously described (Liao et al., 2006) (Al-Shali et al.,
2004), respectively. A detailed description of the geno-
typing and quality control are provided in the Supporting
Genotype and allele distribution was compared between
obese and nonobese subjects using X2test. Haplotype
analysis was done by SNP analyzer version 1.2 by expec-
tation–maximization algorithm (Yoo et al., 2005). The
the Hardy–Weinberg equilibrium (HWE), comparing the
observed genotype frequencies with those expected (X2
test). Association analyses were performed assuming
additive, dominant, and recessive models. Binary logistic
regression analysis used to adjust odds ratios for age and
gender. We also performed multiple linear regression
analysis to examine the impact of these variants on quan-
titative risk variables of obesity, including % body fat, fat
mass, WHR (waist to hip ratio), fasting insulin, glucose,
HOMA-Index and lipid levels. b-correlation coefficients (P
values) were used as a correction for multiple statistical
analysis. Analyses were adjusted for the confounding
effects of age and sex in the combined analysis where
appropriate. Two-tailed tests were performed with the sig-
nificance level of 0.05. The results for continuous variables
are given as the mean 6 SD. The differences among three
groups were assessed by one-way ANOVA (analysis of var-
iance) for continuous variables. Effective sample sizes for
case–control study were calculated by Quanto 1.1 ver.
Software (Gauderman 2006).
Characteristics of study subjects
Demographic characteristics of study subjects are sum-
marized in Supporting Information Table 1. Levels of adi-
ponectin, insulin, HOMA-IR index, % body fat and fat
mass showed significant differences between obese and
nonobese subjects. Systolic blood pressure, diastolic blood
pressure, waist circumferences, hip circumference, WHR
(waist to hip ratio), and lipid profile data also showed sig-
nificant difference between both the group while other fac-
tor like fasting blood sugar did not show any significant
Distribution of PPAR-c Pro12Ala and C1431T genotypes,
alleles and haplotypes in obese and nonobese subjects
Frequencies of genotypes and haplotypes are summar-
ized in Supporting Information Table 2. Because of the
very low number of Ala/Ala genotypes at PPAR-g
Pro12Ala polymorphism, data from Pro/Ala and Ala/Ala
individuals were pooled and analyzed together. We
observed that the Pro12Ala polymorphism was signifi-
cantly associated with obesity [(P 5 0.006, OR, 1.655 CI,
(1.155–2.370)] in Dominant model (ORs were adjusted for
age and sex) but PPAR-g C1431T polymorphism showed
ASSOCIATION OF PPAR-? GENE POLYMORPHISMS
American Journal of Human Biology
no significant differences between obese and nonobese
In further analysis we constructed haplotype of PPAR-g
Pro12Ala and C1431T, four possible haplotypes were Pro-
C, Ala-T, Pro-T, and Ala-C whose frequencies of occur-
rence were 85.0, 9.0, 4.2, and 1.8, respectively in nonobese
subjects. However, on the basis of haplotypes, we did not
find any significant differences between obese and non-
obese subjects (Supporting Information Table 3).
Association of different metabolic parameters with
homozygous variant genotype of PPAR-c Pro12Ala and
C1431T gene polymorphism
We investigated whether PPAR-g Pro12Ala and C1431T
polymorphisms influenced insulin resistance by testing
for association with insulin, HOMA-IR index, % body fat,
fat mass and Adiponectin (Table 1 and Supporting Infor-
mation Table 4–6). ProAla1AlaAla genotype was associ-
ated with higher values of weight, % body fat, fat mass,
insulin, HOMA-IR, and lower value of adiponectin in obese
subjects only, and higher values of weight, % body fat, fat
mass, insulin, HOMA-IR, LDL cholesterol, and lower value
of adiponectin was associated in general population but
association was not observed with fasting glucose.
The TT genotype of C1431T gene polymorphism also
associated with higher values of % body fat, fat mass, and
higher value of insulin in obese subjects only while % body
fat and fat mass was associated in general population.
The main aspect of this study was to assess the associa-
tion of PPAR-g polymorphisms with obesity and obesity
related phenotypes like hypertension, insulin resistance,
and dyslipidemia. In this study, we observe significant
association of PPAR-g Pro12Ala polymorphism with risk
of obesity and insulin resistance while PPAR-g C1431T
polymorphism did not show any association with obesity.
These results are in agreement of the previous study in
which PPAR-g ProAla12 associated with obesity (Koch
et al., 1999). The PPAR-g genetic variant is a polymor-
phism replacing alanine with proline at codon 12
(Pro12Ala) in exon B, which encodes part of the PPAR-g
transactivation domain (Semple et al., 2006). Semple
et al. (2006) reported that multiple gene–gene interactions
of PPAR-g associated with increased risk of obesity and
diabetes. Bhagat et al. (Bhagat et al., 2010) showed that
obese Asian Indians residing in north India have greater
prevalence of polymorphism of Pro12Ala in PPARG gene.
Sanghera et al. (2010) reported PPAR-g Pro12Ala alone
the strongest predictor of the development of T2D in
Indian Sikhs. It is interesting that the same variant of
PPAR-g was not shown to be association with T2D and did
not improve insulin sensitivity and/or decrease risk for
type 2 diabetes in South Indians, as it does in Caucasians
(Radha et al., 2006) and South Indians living in Singapore
(Tai et al., 2004). Barroso et al. (1999) reported that
PPAR-g receptor is important in the control of insulin sen-
sitivity, glucose homeostasis and blood pressure in man.
Meirhaeghe et al. (2000) suggested that genetic variability
of PPAR-g affects body weight.
Valve et al. (1999) found that frequency of Ala12Ala ge-
notypes in the PPAR-g gene in obese women was higher
as compared to Pro12Pro genotypes. Similarly, in WHO-
MONICA population, the presence of the Ala allele was
significantly associated with obesity defined by BMI
(Meirhaeghe et al., 2000). In two Caucasian populations,
the Ala12 variant was also shown to be associated with
obese subjects (Beamer et al., 1998). However, in some
studies, the Pro12Ala polymorphism was not associated
with obesity (Ringel et al., 1999; Swarbrick et al., 2001).
In addition, only among obese, individuals carrying the
Pro/Ala or Ala/Ala genotype had higher levels of systolic
blood pressure, weight, % body fat, fat mass, fasting insu-
lin, and HOMA-IR, lower values of adiponectin thus indi-
cating insulin resistance (IR). Robitaille et al. (2003)
observed that Ala/ Ala variant genotype carriers had a
higher BMI, waist circumference, fat mass, as well as sub-
cutaneous adipose tissue, and visceral adipose tissue
areas, than Pro/Pro homozygotes and this study also
showed that this polymorphism can modulate the associa-
tion between dietary fat intake and components of the
metabolic syndrome). Waden et al. (2007) reported that,
sedentary Ala12 carriers had more frequent metabolic
TABLE 1. Phenotypes and genotypes classes for PPAR-c Pro12Ala in all study subjects
ProPro (477)ProAla1AlaAla (165)p*
SIS BP* (mm Hg)
DIAS BP* (mm Hg)
% body fat*
Fasting sugar* (mg/dl)
Fasting insulin* (lU/ml)
T Cholesterol* (mg/dl)
LDL* Cholesterol (mg/dl)
P values were derived from multiple linear regression analysis using the best genetic model.
Data represents mean and its 95% confidence interval from ANOVA, * 5 data present in log-transformed. % body fat, percent body fat; FM, fat mass; SIS BP, systolic
blood pressure; DIAS BP, diastolic blood pressure; T cholesterol, total cholesterol; HDL-cholesterol, high density lipoprotein cholesterol; LDL-cholesterol, low density
lipoprotein cholesterol; VLDL cholesterol; very low density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment-insulin resistance.
SIS BP, DIAS BP, FM, % fat mass, fasting glucose, fasting insulin, HOMA-IR, adiponectin, total cholesterol, HDL cholesterol, triglyceride, LDL cholesterol and VLDL
cholesterol were analyzed on a transformed scale (log(adiponectin11) in order to obtain a normally distributed variable.
J. PRAKASH ET AL.
American Journal of Human Biology
syndrome than sedentary type 1 DM patients with the
Pro/Pro genotype. Pro12Ala polymorphism interacts with
the DIO2 T92A polymorphism which modulates deiodi-
nase activity and has been inconsistently associated with
IR (Fiorito et al., 2007). It was reported that in healthy
children, Ala12 was associated with an increased waist-to-
hip ratio, and carriers seemed to have higher DBP than
noncarriers (Sookoian et al., 2005).
Haseeb et al. (2009) indicate that both the Pro12Ala and
C1431T variants of PPARg do not exhibit any association
with metabolic syndrome and obesity. Vimaleswaran et al.
(2010) also observed that the Pro12Ala variants of the
PPARG2 gene did not associate with type 2 diabetes melli-
tus in South Indian population and MS in Asian Indian
Previous study linked insulin sensitivity with the
PPAR-g polymorphism in Caucasians (Deeb et al., 1998).
However, in this study, body composition with direct
measures of body fat content and distribution were done.
Case–control studies are usually underpowered to make
conclusions on the association between polymorphism and
the studied phenotype. Altshuler et al. (2000) reported a
significant effect of PPARG Pro12Ala on risk reduction for
type 2 diabetes in Caucasians. It is possible that genetic
interaction between PPAR-g Pro12Ala with other poly-
morphisms implicated in the regulation of insulin signal-
ing may conclude phenotypic expression of the ‘‘protec-
tive/risk’’ effect of the PPAR-g polymorphism.
PPAR-g C1431T polymorphism did not show any signifi-
cant association with obesity (Cecil et al., 2007; Costa
et al., 2009). Morini et al. (2008) reported no association
between C1431T polymorphism and obesity in the whole
population, although among men, carriers of TT geno-
types showed association with higher BMI. In obese sub-
jects, individuals carrying the CT or TT genotypes had
higher levels of systolic blood pressure, % body fat, fat
mass, fasting insulin, and HOMA-IR, thus indicating IR.
These observations suggest that the polymorphisms may
be a modulator of insulin resistance in the general popula-
tion. However, as in previous studies (Chuang et al., 2001;
Ek et al., 2001; Li et al., 2003), we also observed the asso-
ciation between Pro12Ala polymorphism and measures of
Our results are also supported by clinical and experi-
mental observations. Loss-of-function mutations in PPAR-
g have been associated with a phenotype resembling that
of the IR (Barroso et al., 1999). Because the Ala12 allele
has been associated with decreased transcriptional activ-
ity of PPAR-g, (Deeb et al., 1998; Masugi et al., 2000) we
hypothesize that the damaging effects of inflammatory
stimulus on adipose tissue are higher in obese subjects
carrying Ala12 compared with noncarriers, thus explain-
ing the significant associations found between the Ala12
allele (and the Pro/Ala genotype) and high risk for IR.
Tellechea et al. (2009) reported the explanation of the sig-
nificant associations found between the Ala/Ala genotype
(and the Pro/Ala genotype) and high risk for metabolic
syndrome and insulin resistance in nonsmokers. This is in
agreement with the therapeutic insulin sensitizer role of
PPAR-g agonists (Takano and Komuro, 2009; Zhang et al.,
2004), and PPARg-2-null mice exhibited IR (Zhang et al.,
2004). On the other hand, our results could be explained
by the fact that the Ala12 allele has been associated with
increased BMI and/or WC (He, 2009; Robitaille et al.,
2003; Tonjes and Stumvoll, 2007).
The Pro12Ala and C1431T polymorphisms are in link-
age disequilibrium (Meirhaeghe et al., 2000; Valve et al.,
1999). Albanes et al. (1987) reported that both rare alleles
were associated with an increased BMI, and its overall
consequence was additive when they occurred mutually.
PPAR-g agonists can inhibit the proinflammatory cyto-
kines in monocyte–macrophages cell, which is involved in
the process of activation of the innate immunity most im-
portant to IR and adipose tissue inflammation (Heilbronn
and Campbell, 2008).
Adiponectin is a well-known anti-inflammatory adipo-
kine that increases PPAR-g and insulin sensitivity. How-
ever, the negative association between adiponectin and IR
(Hyun et al., 2008) has not always been reported (Yoshi-
naga et al., 2008). In addition, it is not clear whether hypo-
adiponectinemia is outcome or reason of obesity and IR
(Abbasi et al., 2004). Lower value of adiponectin associ-
ated with variant genotype of Pro12ala polymorphism. In
fact, insulin and TNF-a reduce adiponectin levels both in
vitro and in vivo (Lihn et al., 2003). Therefore, hyperinsu-
linemia caused by obesity induced IR, mutually with TNF-
a level, may contribute to hypoadiponectinemia. In fact,
insulin sensitivity is the main determinant of adiponecti-
nemia (Abbasi et al., 2004). The insulin-sensitizing effect
of adiponectin has been reported in various studies (Berg
et al., 2001; Yamauchi et al., 2001). Yamamoto et al. (2002)
also reported that the serum adiponectin levels were nega-
tively correlated with HOMA-IR and insulin. Moreover,
PPAR-g has been shown to increase or decrease the tran-
scription of target genes (Corton et al., 2000; Mangelsdorf
et al., 1995). The PPAR-g target genes together with ADI-
POQ encode proteins that contribute in the pathogenesis
of insulin resistance (Rangwala and Lazar, 2004).
The controversial findings linked to this polymorphism
may be due to population differences [i.e., the most reli-
able associations have been found in Japanese subjects
(Deeb et al., 1998; Hara et al., 2000; Mori et al., 2001)] or
environmental factors. On the other hand, the Pro12Ala
polymorphism may not be functional, but it may be in LD
with the causal mutation. This LD could vary between
populations, being higher in those in which the associa-
tion between the Pro12Ala polymorphism and obesity and
obesity-associated phenotype insulin resistance is higher.
India has immense diversity within the ethnic and lin-
guistic affiliations of the populations and always been a
debatable issue, whether some of them had originated
indigenously or were the results of earlier migrations
(Pattanayak, 1998; Risley, 1915; Sarkar, 1958). Kashyap
et al. (2006) have studied the genetic structure of Indian
populations on the basis of 15 autosomal loci. According to
their results, populations from southern and northeastern
India largely exhibited structuring while most other
Indian populations (North Indian, Western, and Central
India) shared similar membership in multiple clusters. In
this study, we have collected all cases and control samples
from a northern Indian population. Therefore, we can say
that study subjects represent relatively homogeneous pop-
ulation with less variation.
Although it is commonly believed that population strati-
fication is a serious threat in candidate gene studies, but
some reports indicate that population stratification has a
nonsignificant or relatively small influence in most case–
control studies published (Hutchison et al., 2004).
In conclusion, we report in an north Indian population
that individuals carrying the Ala12 allele of the functional
ASSOCIATION OF PPAR-? GENE POLYMORPHISMS
American Journal of Human Biology
polymorphism rs1801282 (Pro12Ala) of PPAR-g have a
higher risk of obesity and obesity associated phenotypes
like IR, while PPAR-g C1431T polymorphism did not show
any significant association but variant genotype associ-
ated with some component of the insulin resistance
(increased level % body fat, fat mass, and insulin) espe-
cially among obese subjects. Because of limited sample
size, our findings should be viewed as a preliminary for
future studies in larger sample sizes and different ethnic
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ASSOCIATION OF PPAR-? GENE POLYMORPHISMS
American Journal of Human Biology