Disclosure: The authors declare no conflict of interest.
Pro12Ala variant of the PPARG2 gene increases body mass index: An updated meta-analysis encompassing 49,092 subjects
Article first published online: 13 MAY 2013
Copyright © 2012 The Obesity Society
Volume 21, Issue 7, pages 1486–1495, July 2013
How to Cite
Galbete, C., Toledo, E., Martínez-González, M.A., Martínez, J.A., Guillén-Grima, F. and Marti, A. (2013), Pro12Ala variant of the PPARG2 gene increases body mass index: An updated meta-analysis encompassing 49,092 subjects. Obesity, 21: 1486–1495. doi: 10.1002/oby.20150
Funding agencies: Research relating to this paper was funded by grant from Spanish Ministry of Health and Consumption (Grants PI01/0619, PI030678, PI040233, PI042241, PI050976, PI070240, PI070312, PI081943, PI080819, PI1002658, PI1002293, RD06/0045, G03/140 and 87/2010), the Navarra Regional Government (36/2001, 43/2002, 41/2005, 36/2008) and the University of Navarra, Línea Especial, Nutrición y Obesidad (University of Navarra), Carlos III Health Institute (CIBER project, CB06/03/1017) and RETICS network.
- Issue published online: 12 AUG 2013
- Article first published online: 13 MAY 2013
- Accepted manuscript online: 6 NOV 2012 08:16AM EST
- Manuscript Accepted: 18 OCT 2012
- Manuscript Received: 15 SEP 2011
The peroxisome proliferator-activated receptor gamma 2 (PPARG2) gene has been intensively studied with relation to obesity and metabolic disorders. Indeed, a large number of studies assessing the association between the PPARG2 polymorphism Pro12Ala (rs1801282) and body mass index (BMI) have been published with some controversial results. In this meta-analysis, the effects of Pro12Ala polymorphism of the PPARG2 gene on BMI were investigated.
Design and Methods
Externally published data were collected and we included our own novel data from a study in the elderly participants (>55 years) of a Mediterranean cohort, the SUN (“Seguimiento Universidad de Navarra”) Project (n = 972). A total of 75 independent studies with 49,092 subjects (39,806 with the genotype Pro12Pro and 9,286 carrier subjects of the Ala allele) were included.
The meta-analysis revealed a higher BMI with an overall estimation of +0.065 kg/m2 (95%CI = 0.026-0.103, P = 0.001) for homo-/heterozygous carriers of the Ala allele of the PPARG2 gene in comparison to non-carriers. The analysis also showed that there was heterogeneity (P for heterogeneity <0.001), but funnel plots did not suggest apparent publication bias. Furthermore, the association between the Pro12Ala polymorphism of the PPARG2 gene and increased BMI was stronger in Caucasian. Thus, carriers of the Ala allele had significantly higher BMI than non-carriers in a subsample of 6,528 Caucasian male subjects (standardized mean difference = 0.090, 95%CI=0.032-0.148, P = 0.002, P for heterogeneity = 0.121).
This updated meta-analysis showed that carriers of the Ala12 allele of the PPARG2 gene had a higher average BMI.
The peroxisome proliferator-activated receptor gamma 2 (PPARG2) is a nuclear receptor expressed mainly in adipose tissue that exerts an essential role in the regulation of adipocyte differentiation, lipid storage, and insulin sensitization . PPARG2 also plays a key role in the entraining of adipose tissue lipid metabolism to nutritional state. The PPARG2 gene activation leads to upregulation of genes that mediate fatty acid uptake and trapping .
The PPARG2 gene is located in the chromosome 3p25; the Pro12Ala gene variant (rs1801282) of this gene, a missense mutation on exon B highly prevalent in the Caucasian population, has been controversially associated with obesity risk . This mutation is a CG substitution that results in the conversion of proline to alanine at residue 12 of the PPARG2 protein . Functional analysis revealed that the receptor expressing this allele displays reduced deoxyribonucleic acid (DNA)-binding affinity and impaired transcriptional activity in target genes .
Two previous meta-analyses assessing the role of Pro12Ala of the PPARG2 gene on BMI and diabetes-related traits have been published . In 2003 Masud et al.  conducted a meta-analysis to explore the effect of this Pro12Ala gene variant (rs1801282) on BMI in 19,136 subjects from 30 studies. They found a stronger association between the Ala12 allele and BMI in subjects with BMI≥27 kg/m2, whereas this association was not detected in individuals with BMI<27 kg/m2. In 2006, Tönjes et al.  performed another meta-analysis on the effect of the same SNP (rs1801282) of PPARG2 on diabetes-related traits in pre-diabetic subjects. They showed a direct association between carrying the Ala allele and greater BMI in 28,734 subjects from 45 studies.
A number of studies evaluating the association between the Pro12Ala polymorphism of the PPARG2 gene and BMI have been reported since 1998. In the present study, we pooled data obtained after a comprehensive and systematic literature review to overcome the limitations of single-study research work. Meta-analysis provides more reliable results by reducing the probability that random errors will produce false-positive or false-negative associations . In our meta-analysis, we included novel data (n = 972) from a Mediterranean study (older participants of the SUN “Seguimiento Universidad de Navarra” cohort).
Subjects and Methods
This study was conducted within the framework of the SUN Project . This is a dynamic cohort study, including only university graduates initiated in December 1999 in Spain. The survey and procedures have been previously described elsewhere . For this study, elderly participants of the SUN project, those aged >55 years old when the basal questionnaire was completed, were invited to participate. Each volunteer received a kit designed to collect saliva (Oragene®ADN Self-Collector kit-OG250). Anthropometric data were collected from the baseline questionnaire. Self-reported information on BMI had been previously validated in a subsample of the SUN Project .
Voluntary completion of the first questionnaire was considered to imply informed consent to participate in the SUN Project, and written informed consent was requested to collaborate in this study. The study protocol was performed in accordance with the ethical standards of the Declaration of Helsinki (as revised in Hong Kong in 1989, in Edinburgh in 2000, and in South Korea in 2008) and was approved by the Institutional Ethical Review Board of the University of Navarra.
DNA was extracted from saliva samples according to the instructions of the kits manufacture (Oragene®ADN Self-Collector kit-OG250). The genotyping for the Pro12Ala polymorphism of the PPARG2 gene (rs1801282) was performed using a TaqMan assay with allele-specific probes on the ABI Prism 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA) according to the standardized laboratory protocols. Replicate quality control samples were included in each genotyping plate with >99% of concordance.
A chi-square test was use to evaluate the Hardy-Weinberg equilibrium. The association of Pro12Ala polymorphism of the PPARG2 gene on BMI was analyzed with Student's t-test. The threshold for statistical significance was set a priori at P < 0.05.
A logistic regression model was performed to calculate Odds Ratios for obesity risk in the SUN cohort after adjustment for confounding factors, such as sex, age (years), physical activity practice (METs/h-week), and total energy intake (kcal/day).
Raw and adjusted geometric means for BMI have also been calculated to take into account skewed data. Linear general models were used to estimate adjusted BMI differences after controlling for the indicated confounding variables.
In our meta-analysis, the presence/absence of the Ala allele (dominant effect) was considered as the exposure, whereas differences in BMI between carriers and non-carriers of the Ala allele were considered as the outcome . To systematically review differences in BMI across the presence/absence of the Pro12Ala polymorphism of the PPARG2 gene, we used a formal meta-analysis and updated the existing literature review . A Pubmed search was done to find articles concerning the influence of Pro12Ala polymorphism of the PPARG2 gene on BMI using different combinations of the following search criteria: “BMI”, “obesity”, “Pro12Ala”, “rs1801282”. A total of 75 studies, comprising 109 samples and published before January 2011 were identified. Thirty of them were previously included in the meta-analisis by Masud (5), with a total sample of 19,136 subjects. The meta-analysis by Tönjes (6) provided 19,041 new subjects from 27 new studies, and finally, our work contributes with 11,243 novel individuals from 19 studies. They are mainly Caucasian, but also some Asian, Mexican-Hispanic and African-American subjects. Studies conducted in children or adolescents were not included as well as populations in which the allele prevalence of the Pro12Ala polymorphism of PPARG2 was not similar to those described in the HapMap database [11-14].
|BMI ≥ 30 (kg/m2)||BMI < 30 (kg/m2)||ORa||CI 95%||P|
|Pro12Pro||75 (0.76)||739 (0.84)||1 (ref)|
|Pro12Ala||22 (0.22)||134 (0.15)||1.60||0.955-2.671||0.074|
|Ala12Ala||2 (0.02)||7 (0.01)||3.01||0.602-15.053||0.179|
|Ala carriers||24 (0.24)||141 (0.16)||1.66||1.011-2.738||0.045|
No BMI differences were observed between carrier and non-carrier subjects of the Ala allele of the PPARG2 gene (Table 2).
|Pro12Pro (n = 814)||Ala carriers (n = 164)||P|
|Sex (% male)||70||73||0.329|
|Age (years)||68.8 (68.4-69.2)||70.0 (68.8-70.8)||0.071|
|BMI (kg/m2)||25.73 (25.51-25.95)||26.19 (25.69-26.69)||0.091|
|Adjusted BMI (kg/m2)a||25.74 (25.53-25.92)||26.13 (25.66-26.60)||0.139|
|BMI (kg/m2)b||25.54 (25.33-25.51)||25.99 (25.52-26.48)||0.089|
|Adjusted BMI (kg/m2)a, b||25.56 (25.51-25.76)||25.93 (25.48-26.39)||0.141|
|No. of cases||No. of controls||SMD||95% CI||P||I2 (%)||Pheterogeneity|
|All studies||9,286||39,806||0.065||0.026-0.103||0.0010||54.1||< 0.001|
|Study||Masud's meta- analysis||Tonjes' meta- analysis||Galbete's meta-analysis||N Controls||N Cases||% Ala12||Subject description|
|Beamer et al., 1998||141||28||16.57||Obese, non diabetic Caucasian men and women|
|Beamer et al., 1998||408||109||21.08||Non obese, non diabetic Caucasian men and women|
|Deeb et al., 1998||257||76||22.82||Non diabetic Caucasian|
|Deeb et al., 1998||695||278||28.57||Caucasian|
|Mori et al., 1998||203||12||5.58||Asian (Japanese)|
|Be et al., 1999||540||212||28.19||Obese Caucasian men|
|Be et al., 1999||641||228||26.24||Non obese Caucasian|
|Koch et al., 1999||75||33||30.56||Non obese, non diabetic Caucasian|
|Mancini et al., 1999||114||17||12.98||Non obese, diabetic Caucasian men|
|Mancini et al., 1999||255||57||18.27||Non obese, non diabetic, Caucasian|
|Ringel et al., 1999||388||134||25.67||Non obese Caucasian|
|Ringel et al., 1999||372||131||26.04||Non obese, diabetic Caucasian Subjects|
|Valve et al., 1999||107||34||24.11||Obese, non diabetic, Caucasian women|
|Clement et al., 2000||246||49||16.61||Diabetic Caucasian|
|Clement et al., 2000||294||78||20.97||Non obese, non diabetic Caucasian|
|Clement et al., 2000||339||63||15.67||Obese, non diabetic Caucasian|
|Cole et al., 2000||711||210||22.80||Diabetic Caucasian|
|Hara et al., 2000||496||45||8.32||Asian (Japanese)|
|Hara et al., 2000||400||15||3.61||Asian (Japanese)|
|Hegele et al., 2000||90||29||24.37||Diabetic Oji-Cree (Canadian) women|
|Hegele et al., 2000||148||23||13.45||Non diabetic Oji-Cree (Canadian) women|
|Lei et al., 2000||553||43||7.21||Asian (Taiwanese)|
|Meirhaeghe et al., 2000||661||111||21.12||Non obese Caucasian|
|Meirhaeghe et al., 2000||136||34||20.00||Obese Caucasian|
|Oh et al., 2000||211||18||7.86||Asian (Korean)|
|Poirier et al., 2000||507||168||24.89||Non obese, non diabetic Caucasian men|
|Ek et al.,2001||456||160||25.97||Non obese, non diabetic Caucasian men|
|Ek et al.,2001||270||94||25.82||Non obese, non diabetic Caucasian men|
|Hseuh et al., 2001||234||66||22.00||Mexican-American|
|Lindi et al., 2001||93||26||21.85||Non obese Caucasian|
|Luan et al., 2001||203||56||21.62||Non obese, non diabetic Caucasian men|
|Luan et al., 2001||265||68||20.42||Non obese, non diabetic Caucasian women|
|Nicklas et al., 2001||56||14||20.00||Obese, non diabetic Caucasian women|
|Schaffler et al., 2001||276||83||23.12||Non obese Caucasian|
|Swarbrick et al., 2001||215||77||26.37||Obese Caucasian|
|Swarbrick et al., 2001||277||94||25.34||Non obese Caucasian|
|Ahluwalia et al., 2002||139||44||24.04||Non obese, diabetic Caucasian|
|Doney et al., 2002||869||238||21.50||Obese, diabetic Caucasian|
|Eriksson et al., 2002||324||152||31.93||Non obese Caucasian|
|Frederiksen et al., 2002||1,671||574||25.57||Non obese, non diabetic Caucasian|
|Gonzalez-Sanchez et al., 2002||37||14||27.45||Caucasian men|
|Gonzalez-Sanchez et al., 2002||82||12||12.77||Caucasian woman|
|Gonzalez-Sanchez et al., 2002||137||22||13.84||Non obese Caucasian men|
|Gonzalez-Sanchez et al., 2002||127||31||19.62||Non obese Caucasian women|
|Lindi et al., 2002||337||153||31.22||Obese Caucasian|
|Masud et al., 2002||813||271||25.00||Non obese Caucasian|
|Schneider et al., 2002||156||38||19.59||Non obese, non diabetic Caucasian|
|Schneider et al., 2002||87||13||13.00||No obese, diabetic Caucasian|
|Stumvoll et al., 2002||135||42||23.73||Non obese, non diabetic Caucasian|
|Stumvoll et al., 2002||391||128||24.66||Non obese, non diabetic Caucasian|
|Thamer et al., 2002||73||25||25.51||Non obese Caucasian men|
|Yamamoto et al., 2002||454||24||5.02||Asian (Japanese)|
|Yamamoto et al., 2002||109||8||6.84||Asian (Japanese)|
|Yamamoto et al., 2002||77||4||4.94||Asian (Japanese)|
|Baratta et al., 2003||296||42||12.43||Non diabetic Caucasian|
|Eurlings et al., 2003||57||22||27.85||Familiar combined hyperlipidemia Caucasian|
|Eurlings et al., 2003||93||31||25.00||Non diabetic Caucasian|
|Kahara et al., 2003||117||6||4.88||Asian (Japanese)|
|Kolehmainen et al., 2003||22||8||26.67||Obese Caucasian|
|Lindi et al., 2003||114||36||24.00||Caucasian|
|Muller et al., 2003||678||117||14.72||Pima Indian|
|Poulsen et al., 2003||161||47||22.60||Caucasian|
|Poulsen et al., 2003||268||77||22.32||Caucasian|
|Robitaille et al., 2003||586||134||18.61||Caucasian|
|Rosmond et al., 2003||186||82||30.60||Non obese Caucasian men|
|Thamer et al., 2003||500||148||22.84||Caucasian|
|Andrulionyte et al., 2004||592||178||23.12||Obese Caucasian|
|Buzzetti et al., 2004||1,008||207||17.04||Obese, non diabetic Caucasian|
|Franks et al., 2004||86||27||23.89||Non diabetic Caucasian women|
|Franks et al., 2004||114||26||18.57||Non diabetic Caucasian women|
|Franks et al., 2004||91||22||19.47||No diabetic Caucasian men|
|Franks et al., 2004||108||32||22.86||No diabetic Caucasian men|
|Kim et al., 2004||977||74||7.04||Asian (Korean)|
|Pihlajamaki et al., 2004||208||103||33.12||Non obese Caucasian|
|Pisabarro et al., 2004||39||6||13.33||No diabetic Caucasian|
|Tai et al., 2004||2,796||284||9.22||Non diabetic Asian (Chinese, Malaya, Indian)|
|Tai et al., 2004||499||39||7.25||Impair glucose Asian (Chinese, Malaya, Indian)|
|Tai et al., 2004||374||46||10.95||Diabetic Asian (Chinese, Malaya, Indian)|
|Takata et al., 2004||139||7||4.79||Non diabetic Asian men|
|Takata et al., 2004||90||11||10.89||Non diabetic Asian|
|Barbieri et al., 2005||362||67||15.62||Non obese Caucasian|
|Danawati et al., 2005||196||7||3.45||Non diabetic Indian|
|Danawati et al., 2005||330||7||2.08||Diabetic Indian|
|Fornage et al., 2005||1,765||79||4.28||African-americans|
|Fornage et al., 2005||1,581||473||23.03||Non obese Caucasian|
|Ghoussaini et al., 2005||673||192||22.20||Non obese, non diabetic Caucasian|
|Ghoussaini et al., 2005||397||110||21.70||Obese Caucasian|
|Meirhaeghe et al., 2005||893||240||21.18||Caucasian|
|Mousavinasab et al., 2005||173||79||31.35||Non diabetic Caucasian|
|Ostergard et al., 2005||61||18||22.78||Non diabetic, non obese Caucasian|
|Rhee et al., 2005||226||27||10.67||Asian (Korea)|
|Tanko et al., 2005||1,088||386||26.19||Non obese, non diabetic Caucasian women|
|Weiss et al., 2005||24||8||25.00||Non diabetic Caucasian men|
|Weiss et al., 2005||37||4||9.76||Non diabetic Caucasian women|
|Stefanski et al., 2006||154||60||28.04||Obese, diabetic Caucasian|
|Canizales-Quinteros et al., 2007||105||26||19.85||Non diabetic Amerindian-Mexican (BMI<25)|
|Canizales-Quinteros et al., 2007||76||32||29.63||Non diabetic Amerindian-Mexican (BMI>25)|
|Helwig et al., 2007||515||193||27.26||Non obese, non diabetic Caucasian men|
|Kim et al., 2007||115||14||10.85||Asian(Korea) women|
|Mattevi et al., 2007||130||23||15.03||Non obese, non diabetic Caucasian men|
|Mattevi et al., 2007||153||29||15.93||Non obese, no diabetic Caucasian women|
|Vaccaro et al., 2007||301||42||12.24||Diabetic, obese Caucasian|
|Morini et al., 2008||501||65||11.48||Non obese, non diabetic Caucasian men and women|
|Ben Ali et al., 2009||197||18||8.37||Tunisian women|
|Ben Ali et al., 2009||151||21||12.21||Tunisian men|
|Ereqat et al.,2009||179||23||11.39||Palestinian|
|Milewicz et al., 2009||222||96||30.19||Non obese, non diabetic Caucasian women|
|Razquin et al., 2009||837||138||14.15||Caucasian (high cardiovascular risk)|
Our meta-analysis was carried out using the STATA 10.0 software (Stata-Corp, College Station, TX). We estimated a standardized mean difference (SMD) as the weighted effect size. This variable is the pooled estimate across studies for the BMI difference (kg/m2) in homo-/heterozygous carriers versus non-carriers of the Ala allele (dominant effect). We also calculated a 95% confidence interval (CI) for the pooled difference in BMI. A test of heterogeneity was also calculated, estimating the Cochran's Q statistic (Cochran 1954). A random-effect model was used when heterogeneity among studies was observed and a fixed-effect model when studies were homogenous. The I2 statistic was also used to evaluate heterogeneity among studies . I2 describes the percentage of total variation across all the studies due to heterogeneity rather than chance and does not rely on the number of studies. Thus, it can be used for comparisons across meta-analyses with different number of studies. Percentages around 25% (I2=25), 50% (I2=50), and 75% (I2=75) would indicate low, medium, and high heterogeneity, respectively .
The meta-analysis was carried out first in the total sample, 9,286 carrier subjects of the Ala allele compared with 39,806 non-carrier subjects (dominant model) and because significant heterogeneity was evident, the DerSimonian and Laird's random effect model was used. To evaluate potential sources of heterogeneity, we separated samples into sub-groups using the following criteria: sex, ethnic group (Asian or Caucasian), and presence or absence of type 2 diabetes. Within the Caucasian samples, we studied separately men and women, lean and obese, and diabetic and non-diabetic subjects. When homogeneity between samples was observed, the fixed-effects model was used. The false discovery rate method from Benjamini and Hochberg was used to control for multiple testing in the sub-groups analyses .
The visual Funnel plots and the Egger's test were used to detect evidence of possible bias, resulting from selective publication of positive studies .
Effect of the Pro12Ala polymorphism on the body mass index in an elderly SUN population
The frequency of the Ala allele of the PPARG2 gene was 0.19 in our elderly SUN population. Specifically, 83% of the subjects carried the Pro12Pro genotype (wild type), 16% of the subjects were heterozygous for the mutation (Pro12Ala), and only 1% was homozygous (Table 1). The allele distribution fulfilled the Hardy-Weinberg equilibrium. The presence of the Ala allele of the PPARG2 gene significantly increased obesity risk (OR=1.664, 95%CI=1.011-2.738, P = 0.045).
We pooled 109 comparisons from 75 independent studies, comprising 49,092 subjects: 39,806 subjects had the genotype Pro12Pro and 9,286 were carrier subjects of the Ala allele (Tables 3 and 4).
The meta-analysis revealed a higher BMI with an overall estimation of +0.065 kg/m2 (95% CI = 0.026-0.103, P = 0.001, Figure 1) for carriers of the Ala allele of the PPARG2 gene in comparison to non-carriers (dominant effect). The analysis also showed that there was significant heterogeneity (P for heterogeneity < 0.001) among the different studies. The Funnel plot for all samples and the Egger's test (P = 0.249) showed symmetrical distribution, indicating that there was no apparent publication bias (Figure 2).
We investigated possible sources of heterogeneity such as sex, ethnic group (Asian or Caucasian population), and type 2 diabetes (presence or absence). Within the Caucasian samples, we studied separately men and women, lean and obese, and also diabetic and non-diabetic subjects.
As it has been reported the Pro12Ala polymorphism of the PPARG2 gene is linked to lower type 2 diabetes risk. We performed a meta-analysis, including 4,698 diabetic patients, but no effect for the Ala allele on BMI was apparent.
A significant association between Pro12Ala polymorphism of the PPARG2 gene and increased BMI was detected in studies performed separately in Caucasian men, Caucasian women, and in obese Caucasian subjects. Following the hypothesis that the Pro12Ala polymorphism of the PPARG2 gene may have a stronger effect on BMI in markedly obese individuals, we restricted the meta-analysis to Caucasian individuals and conducted two separate assessments: in obese (6,602 subjects with BMI ≥ 30 kg/m2) and non-obese subjects (21,438 subjects with BMI < 30 kg/m2). A significant association between the Pro12Ala polymorphism of the PPARG2 gene and BMI in obese subjects was observed (SMD = 0.156, 95%CI = 0.041-0.271, P = 0.008, P for heterogeneity < 0.001). Moreover, in a fixed-effects model meta-analysis with 6,528 Caucasian men, carriers of the Ala allele had significantly higher BMI than non-carriers (SMD = 0.090, 95%CI = 0.032-0.148, P = 0.002, P for heterogeneity = 0.121). Similar results were observed in Caucasian women (SMD = 0.082, 95%CI = 0.010-0.155, P = 0.026, P for heterogeneity = 0.246) although after the Benjamini-Hochberg multiple comparison the results did not remain statistically significant.
PPARG2 is one of the most studied genes as potentially linked to obesity phenotypes. Indeed, a large number of human studies have shown that the Ala12 allele was associated with increased adiposity .
In our meta-analysis, we compiled previously reported studies and have also included novel data from a study in 972 older participants of the SUN Project. To reduce heterogeneity, different analyses were conducted for Caucasian or Asian population, men and women, diabetic and non-diabetic subjects, and also obese and non-obese subjects.
Our meta-analysis with a total of 49,092 subjects (39,806 Pro12Pro subjects and 9,286 subjects) revealed a significantly higher BMI with an overall estimation of +0.065 kg/m2 for homo-/heterozygous carriers of the Ala allele of the PPARG2 gene when compared to non-carriers (Pro12Pro subjects).
A similar effect of the Pro12Ala gene variant of the PPARG2 gene on BMI changes (+0.066 kg/m2) was reported in a former meta-analysis . They found heterogeneity but no apparent publication bias.
The magnitude of BMI change observed in our study is more consistent within Caucasian men. However, it is quite modest when compared with the average BMI increment per risk allele (+0.170 kg/m2 for 32 genetic variants with P-values < 5×10−8) in the largest meta-analysis (249,796 individuals) of GWAS for BMI thus far published [19, 20]. Moreover, the following information for the Pro12Ala SNP (rs1801282) was obtained from the GIANT consortium data files: a minor allele frequency equal to 0.075 and a P value equal to 0.0193 (after using regression coefficients and correction for inflation) in 123,856 subjects. Unfortunately, specific information for BMI according to the presence of the Ala12 allele is not available in the GIANT study.
Sex-differences in studies on obesity and genetic variants are extensively described in the literature. It is known that PPARG2 expression levels are higher in subcutaneous than visceral adipose tissue . And also it is worthy to mention that PPARG2 inhibits a key enzyme in estrogen biosynthesis the aromatase gene, suggesting that the action of PPARG2 could be modulated by sex steroids . This fact may be a possible explanation for the differences observed between men and women . Regardless of the biological pathways, it is important to consider that sex-specific lifestyle components (dietary intake, alcohol consumption, physical activity levels, or smoking habits) could act as modifiers for the effect of a given genetic variant on BMI.
Other studies have suggested that Pro12Ala polymorphism of the PPARG2 gene may exert its effect on BMI only in markedly obese individuals, for instance, those subjects with a BMI higher than 27 kg/m2 of the Masud's meta-analysis . Our study partially confirmed this point since statistical differences were only found for obese Caucasian (BMI ≥ 30 kg/m2) subjects in a random model meta-analysis, although the potential effect of publication bias should also to be taken into consideration.
Our meta-analysis has strengths and limitations. The lack of information on environmental factors and also gene-gene interactions is a limitation of our work. It has been described that dietary modification may influence the association between genetic variants and obesity, and they should be accounted for .
One advantage is that information for more 49,000 subjects is pooled together. It derives from 75 independent studies, including 109 samples of different size from 30 to 3,080 participants, which may account as a source of heterogeneity. We included populations with Ala12 frequency similar to those described in the HapMap database . The frequency of the Ala12 allele of the PPARG2 gene varies from 2% to 33% in our meta-analysis.
In some sub-groups analysis (i.e. after distributing subjects for the ethnic group or obesity status), heterogeneity remained, which suggests that there may be more than one source of heterogeneity at play. Heterogeneity usually accompanies genetic association studies. The level of heterogeneity in our meta-analyses by using the I2 test ranges from 13% to 69%, which corresponds to medium degree of heterogeneity .
Results from both random- and fixed-effects models have been provided throughout the article and did not differ substantially. The drawback of combining studies in the presence of heterogeneity is less related with the pooled estimate achieved and more with the explicit demonstration of this heterogeneity and the need for caution in interpretation.
Finally, to address the clinical relevance of the Ala allele of the PPARG2 gene information from weight-loss intervention studies is compiled here. Lindi et al.  and Franks et al.  showed that subjects with the Ala12 allele lost more weight after 3 years and 1-year follow-up period, respectively. But Adamo et al.  showed the opposite result: the Ala12 allele was more frequent in diet-resistant individuals. No effect was reported for Nicklas et al.  and Matsuo et al. . They attributed the lack of effect to specific characteristic of the population: only women and a low prevalence of the Ala12 allele, respectively. Other relevant point refers to the effect of genomic information as a behavioral health intervention. A recent review observed that simple communication of genetic information and disease susceptibility, in some motivated groups, might be sufficient to trigger lifestyle changes; for other groups, additional strategies may be required . Unfortunately, the effect of this genotype of PPARG2 on body weight regulation remains unclear.
In conclusion, the current meta-analysis showed that the Pro12Ala polymorphism of the PPARG2 gene has a modest role in increasing BMI, which cannot be considered very relevant from the clinical point of view; however, this positive association stronger among Caucasian men.
The scholarship to C. Galbete from the Asociación de Amigos de la Universidad de Navarra is fully acknowledged. E. Toledo was supported by a Rio Hortega post-residency fellowship of the Instituto de Salud Carlos III, Ministry of Economy and Competitiveness, Spanish Government.
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