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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Oct 15;8(10):17418–17429.

Peroxisome proliferator-activated receptor gamma rs1801282 C>G polymorphism is associated with polycystic ovary syndrome susceptibility: a meta-analysis involving 7,069 subjects

Sheng Zhang 1,*, Yafeng Wang 2,*, Heping Jiang 3,*, Chao Liu 4, Bin Sun 4, Shuchen Chen 5, Mingqiang Kang 5, Weifeng Tang 4,5
PMCID: PMC4694232  PMID: 26770332

Abstract

In the peroxisome proliferator-activated receptor gamma (PPARG) gene, a polymorphism (rs1801282 C>G), has been shown to change an amino acid residue and then results in alternation of PPARG function. A number of studies have explored the relationship between PPARG rs1801282 C>G variants and polycystic ovary syndrome (PCOS) risk, but yielding inconsistent findings, especially in Asian population. This study aimed to assess the role of PPARG rs1801282 C>G polymorphism in susceptibility to PCOS. Databases of Pubmed, Embase and China National Knowledge Internet (CNKI) were searched until August 2, 2015. The association of PPARG 1801282 C>G polymorphism with PCOS risk was evaluated by crude odds ratios (ORs) with their 95% confidence intervals (CIs). Finally, there were twenty-three studies involving 3,458 PCOS cases and 3,611 controls included in our pooled analysis. Significant associations were identified between PPARG rs1801282 C>G variants and decreased PCOS risk in three genetic comparison models (OR, 0.78; 95% CI, 0.69-0.89; P < 0.001 for G vs. C; OR, 0.77; 95% CI, 0.68-0.89; P < 0.001 for GG+CG vs. CC and OR, 0.79; 95% CI, 0.68-0.91; P = 0.001 for CG vs. CC). In a subgroup analysis by race, significant correlation was also observed between PPARG rs1801282 C>G variants and decreased PCOS risk in three genetic models: G vs. C (OR, 0.83; 95% CI, 0.71-0.97; P = 0.019) and GG+CG vs. CC (OR, 0.83; 95% CI, 0.70-0.99; P = 0.033) among Caucasians and in one genetic models: G vs. C (OR, 0.72; 95% CI, 0.59-0.88; P = 0.001) among Asians. In summary, our results demonstrate that PPARG rs1801282 C>G polymorphism may be a protective factor for PCOS.

Keywords: Polycystic ovary syndrome, polymorphism, peroxisome proliferator-activated receptor gamma, meta-analysis

Introduction

Polycystic ovary syndrome (PCOS) is characterized by exaggerated production of androgens, ovulatory dysfunction and abnormalities in ovarian morphology, which affects about 5%-10% of women of reproductive age and is a leading cause of infertility [1,2]. PCOS is a common disease which is attributed to a handful of genetic and environmental risk factors. Various PCOS phenotypes may probably result from the interaction between a number of predisposing genomic mutations, each exerting only minor functions and strong environmental influences [3]. Of late, many candidate genes, such as insulin receptor substrate-1/2, follicle stimulating hormone receptor and insulin receptor, have been identified to contribute to PCOS risk [4-8].

Accumulating evidences demonstrate that impaired glucose tolerance, insulin resistance (IR) and type 2 diabetes mellitus (T2DM) are correlated with the development of PCOS [9-11]. Peroxisome proliferator-activated receptor gamma (PPARG), a known nuclear hormone receptor, regulates intracellular insulin-signaling events and controls adipocyte differentiation, lipid and glucose homeostasis. Prior studies have therefore explored the hypothesis that the mutation of PPARG gene involves in the development and progression of PCOS. Some single nucleotide polymorphisms (SNPs) of PPARG gene are deemed to influence the activity of PPARG. PPARG gene is polymorphic, and many SNPs have been identified, such as the rs1801282, rs4135247, rs3856806, rs1175543, rs2938395 and rs709158 polymorphisms, etc. Among these SNPs, the PPARG rs1801282 C>G polymorphism are the most widely studied for the relationship with PCOS susceptibility.

Recently, mounting studies have focused on the relationship of PPARG rs1801282 C>G polymorphism with PCOS. In several previous study, PPARG rs1801282 C>G polymorphism was correlated with decreased risk of PCOS [12,13]; however, an association between this SNP and the increased susceptibility of PCOS was found in another study [14]. Several meta-analyses suggested that PPARG rs1801282 C>G polymorphism was correlated with the decreased susceptibility of PCOS, especially in Caucasians [15,16]; however, in these studies, the included studies were seldom conducted in Asians populations. Now, more studies have focused on the association between PPARG rs1801282 C>G polymorphism and the risk of PCOS in Asians; nevertheless, the result remains inconclusive. Therefore, an updated meta-analysis was conducted to further clarify the role of the PPARG polymorphisms in PCOS risk.

Materials and methods

Search strategy

We extensively searched literatures from PubMed, Embase and China National Knowledge Internet (CNKI) databases (published up to August 2, 2015) using the following words ‘Peroxisome proliferator-activated receptor gamma’, ‘PPARG’ ‘PPARγ’, ‘polymorphism’, ‘SNP’, ‘variant’, ‘polycystic ovary syndrome’, ‘PCOS’. The relevant publications in the references were also manually scanned. If there were overlapping data, only the latest investigation with the larger subjects was recruited.

Inclusion and exclusion criteria

In the current analysis, all publications included had to meet the following criteria: (a) designed as a case-control or a cohort study; (b) assessed the relationship of PPARG rs1801282 C>G polymorphism with PCOS risk; (c) the available frequencies of genotypes or alleles must be provided. The major reasons for exclusion were: (a) incomplete data; (b) overlapping data; (c) only relevant to PCOS treatment; (d) review, editorial, comment, meta-analysis or letter.

Data extraction

In a uniform table, three reviewers (S. Zhang, Y. Wang and H. Jiang) independently extracted the relevant data from all included studies. The following characteristics were extracted: (a) first author, (b) year of publication, (c) country of study, (d) ethnicity, (e) the allele and genotype frequencies, (f) genotyping method, (g) sample size and (H) the evidence of HWE in controls. If disputes generated, they were solved by consulting the third author (W. Tang).

Statistical analysis

In our study, the pooled odds ratios (ORs) with their 95% confidence intervals (CIs) were measured for dominant model, recessive model, heterozygote comparison, homozygote comparison and allelic comparison. A stratified analysis was performed by ethnicity. Heterogeneity among the included studies was evaluated by using a χ 2-test-based Q statistic test. The value of P < 0.1 was considered as a substantial heterogeneity across the publications, then the data were pooled by using the random-effects model (DerSimonian and Laird) [17]; otherwise, the fixed-effects model was harnessed (Mantel-Haenszel) [18]. Sensitivity analysis was conducted to check the stability of this meta-analysis. Potential publication bias was assessed by a funnel plots and Egger’s linear regression test [19]. A P < 0.1 was considered as statistical significance. The distribution of the genotypes in controls was tested for Hardy-Weinberg equilibrium (HWE) using a web-based χ 2 test program (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). Data analysis was conducted with STATA 12.0 software package (Stata Corp LP, College Station, Texas).

Results

Study characteristics

As summarized in Figure 1, a total of eighty-six potentially relevant publications were retrieved based on the search words from PubMed, Embase and CNKI databases. Finally, there were twenty-three publications [12-14,20-39] involving 3,458 PCOS cases and 3,611 controls included in our analysis. In additional, all subjects were female. As for subjects in these studies, sixteen studies focused on Caucasians [13,20-34] and seven studies focused on Asians [12,14,35-39]. As for HWE test, eighteen studies conformed to HWE [13,22-34,37-39], while five studies deviated from the HWE [14,20,21,35,36]. Characteristics of the included studies and distribution of the PPARG rs1801282 C>G variants as well as alleles are listed in Tables 1 and 2, respectively.

Figure 1.

Figure 1

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Flow chart shows studies included procedure for meta-analysis.

Table 1.

Characteristics of the included studies

Study Year Country Ethnicity Sample size Genotype method
Baldani et al. 2014 Croatia Caucasians 151/179 TaqMan
Yang et al. 2013 China Asians 120/118 RFLP
Shaikh et al. 2013 Indian Asians 450/300 Direct sequencing
Hemimi et al. 2013 USA Caucasians 50/96 RFLP
Dasgupta et al. 2012 Indian Asians 250/299 PCR
Bidzińska-Speichert et al. 2011 Poland Caucasians 54/51 RFLP
Christopoulos et al. 2010 Greece Caucasians 183/148 RFLP
San-Millán et al. 2010 Spain Caucasians 161/113 RFLP
Chae et al. 2009 Korea Asians 184/256 RT-PCR
Xita et al. 2009 Greece Caucasians 180/140 RFLP
Gu et al. 2009 Korea Asians 238/125 RFLP
Koika et al. 2009 Greece Caucasians 156/56 RFLP
Zheng et al. 2008 China Asians 150/135 RFLP
Knebel et al. 2008 German Caucasians 21/120 RFLP
Antoine et al. 2007 USA Caucasians 285/187 TaqMan
Guzman et al. 2007 Chile Caucasians 50/75 RFLP
Yilmaz et al. 2006 Turkey Caucasians 100/100 RFLP
Wang et al. 2006 China Asians 201/147 RFLP
Haap et al. 2005 German Caucasians 57/567 PCR
Hahn et al. 2005 German Caucasians 102/104 RFLP
Tok et al. 2005 Turkey Caucasians 60/60 PCR
Oroi et al. 2004 Italy Caucasians 120/120 RFLP
Korhonen et al. 2003 Finland Caucasians 135/115 SSCP
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RT-PCR: real-time PCR; RFLP: restriction fragment length polymorphism; SSCP: single strand conformation polymorphism.

Table 2.

Distribution of PPARG rs1801282 C>G polymorphism genotypes and alleles

Study Year Case Control Case Control HWE
CC CG GG CC CG GG G C G C
Baldani et al. 2014 106 43 2 129 47 3 47 255 53 305 Yes
Yang et al. 2013 111 9 0 101 17 0 9 231 17 219 Yes
Shaikh et al. 2013 381 65 4 219 79 2 73 827 83 517 Yes
Hemimi et al. 2013 43 7 0 74 22 0 7 93 22 170 Yes
Dasgupta et al. 2012 197 36 10 211 53 17 56 430 87 475 No
Bidzińska-Speichert et al. 2011 35 13 6 30 15 6 25 83 27 75 Yes
Christopoulos et al. 2010 166 14 3 131 15 2 20 346 19 277 Yes
San-Millán et al. 2010 141 20 0 92 21 0 20 302 21 205 Yes
Chae et al. 2009 171 11 2 230 23 3 15 353 29 483 No
Xita et al. 2009 150 30 0 122 17 1 30 330 19 261 Yes
Gu et al. 2009 222 16 0 125 0 0 16 460 0 250 No
Koika et al. 2009 136 19 1 48 6 2 21 291 10 102 No
Zheng et al. 2008 140 10 0 127 8 0 10 290 8 262 Yes
Knebel et al. 2008 17 4 0 99 19 2 4 38 23 217 Yes
Antoine et al. 2007 213 52 2 134 29 5 56 478 39 297 No
Guzman et al. 2007 42 7 1 59 14 2 9 91 18 132 Yes
Yilmaz et al. 2006 85 15 0 78 22 0 15 185 22 178 Yes
Wang et al. 2006 183 18 0 136 10 1 18 384 12 282 Yes
Haap et al. 2005 43 9 1 407 133 6 11 95 145 947 Yes
Hahn et al. 2005 79 22 1 80 24 0 24 180 24 184 Yes
Tok et al. 2005 54 6 0 47 13 0 6 114 13 107 Yes
Oroi et al. 2004 113 7 0 115 5 0 7 233 5 235 Yes
Korhonen et al. 2003 104 28 3 76 34 5 34 236 44 186 Yes
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HWE: Hardy-Weinberg equilibrium.

Association between PPARG rs1801282 C>G polymorphism and the risk of pcos

A total of 3,458 PCOS cases and 3,611 controls from twenty-three eligible studies were relevant to the relationship between PPARG rs1801282 C>G polymorphism and PCOS risk. In overall meta-analysis, significantly decreased PCOS risk was associated with PPARG rs1801282 C>G variants in three genetic models: G vs. C (OR, 0.78; 95% CI, 0.69-0.89; P < 0.001), GG+CG vs. CC (OR, 0.77; 95% CI, 0.68-0.89; P < 0.001) and CG vs. CC (OR, 0.79; 95% CI, 0.68-0.91; P = 0.001) (Table 3). Additionally, in a subgroup analysis by ethnicity, a significantly decreased PCOS risk was also identified among Caucasians in three genetic models: G vs. C (OR, 0.83; 95% CI, 0.71-0.97; P = 0.019) and GG+CG vs. CC (OR, 0.83; 95% CI, 0.70-0.99; P = 0.033) (Table 3; Figures 2 and 3) and among Asians in one genetic model: G vs. C (OR, 0.72; 95% CI, 0.59-0.88; P = 0.001) (Table 3; Figure 2).

Table 3.

Meta-Analysis of PPARG rs1801282 C>G polymorphism with polycystic ovary syndrome risk

No. of study G vs. C GG vs. CC GG+CG vs. CC GG vs. CG+CC CG vs. CC
OR (95% CI) P P (Q-test) OR (95% CI) P P (Q-test) OR (95% CI) P P (Q-test) OR (95% CI) P P (Q-test) OR (95% CI) P P (Q-test)
Total 23 0.78 (0.69-0.89) < 0.001 0.458 0.66 (0.43-1.02) 0.060 0.962 0.77 (0.68-0.89) < 0.001 0.229 0.70 (0.46-1.07) 0.098 0.947 0.79 (0.68-0.91) 0.001 0.122
Ethnicity
    Asians 7 0.72 (0.59-0.88) 0.001 0.112 0.69 (0.36-1.32) 0.264 0.834 0.73 (0.50-1.06) 0.102 0.051 0.74 (0.39-1.41) 0.353 0.788 0.75 (0.49-1.13) 0.165 0.032
    Caucasians 16 0.83 (0.71-0.97) 0.019 0.842 0.64 (0.37-1.14) 0.128 0.871 0.83 (0.70-0.99) 0.033 0.720 0.67 (0.38-1.18) 0.167 0.849 0.85 (0.71-1.01) 0.071 0.595
HWE
    Yes 18 0.76 (0.66-0.88) < 0.001 0.493 0.81 (0.46-1.45) 0.481 0.980 0.74 (0.63-0.86) < 0.001 0.288 0.87 (0.49-1.54) 0.623 0.977 0.73 (0.62-0.86) < 0.001 0.206
    No 5 0.84 (0.66-1.06) 0.134 0.217 0.52 (0.27-0.99) 0.047 0.557 0.89 (0.69-1.16) 0.392 0.163 0.54 (0.29-1.02) 0.059 0.508 0.97 (0.73-1.29) 0.854 0.121
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HWE: Hardy-Weinberg equilibrium. Bold values are statistically significant (P < 0.05).

Figure 2.

Figure 2

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Forest plot of PCOS risk associated with PPARG rs1801282 C>G polymorphism for the G vs. C (fixed effects model).

Figure 3.

Figure 3

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Forest plot of PCOS risk associated with PPARG rs1801282 C>G polymorphism for the GG+CG vs. CC (fixed effects model).

Publication bias and non-parametric ‘trim-and-fill’

Funnel plots and the Egger’s linear regression test were conducted to measure potential publication bias among literatures [19]. A slight publication bias was identified in some genetic comparison models (G vs. C: Begg’s test P = 0.635, Egger’s test P = 0.084; GG vs. CC: Begg’s test P = 0.767, Egger’s test P = 0.925; GG+CG vs. CC: Begg’s test P = 0.492, Egger’s test P = 0.081; GG vs. CG+CC: Begg’s test P = 0.843, Egger’s test P = 0.961; CG vs. CC: Begg’s test P = 0.369, Egger’s test P = 0.090; Figure 4). Thus, non-parametric ‘trim-and-fill’ method was used to calculate the adjusted ORs and CIs. The results also suggested that PPARG rs1801282 C>G variants may be a protective factor for PCOS risk (G vs. C: adjusted pooled OR = 0.76, 95% CI: 0.67-0.86, P < 0.001; GG vs. CC: adjusted pooled OR = 0.67, 95% CI: 0.43-1.04, P = 0.073; GG+CG vs. CC: adjusted pooled OR = 0.74, 95% CI: 0.65-0.85, P < 0.001; GG vs. CG+CC: adjusted pooled OR = 0.71, 95% CI: 0.46-1.09, P = 0.119; CG vs. CC: adjusted pooled OR = 0.713, 95% CI: 0.622-0.818, P < 0.001; Figure 5).

Figure 4.

Figure 4

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Begg’s funnel plot analysis of PPARG rs1801282 C>G polymorphism with PCOS risk for the G vs. C (fixed-effects model).

Figure 5.

Figure 5

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Filled funnel plot of PPARG rs1801282 C>G polymorphism with PCOS risk for the G vs. C (fixed-effects model).

Sensitivity analyses

One-way sensitivity analysis was harnessed to check the stability of our findings. The results demonstrated that there were not significantly altered when anyone was omitted at a time, attesting the robustness of our findings (Figure 6).

Figure 6.

Figure 6

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One-way sensitivity analysis of PPARG rs1801282 C>G polymorphism with PCOS risk for the G vs. C (fixed-effects model).

Tests for heterogeneity

Heterogeneity across studies was listed in Table 3. There was no significant heterogeneity in five genetic models, suggesting the robustness of our findings.

Discussion

PCOS is a common cause of infertility, which affects 6-10% of reproductive-aged women [40]. The etiology of PCOS is very complex and was not completely understood. PCOS is characterized by hyperandrogenism, peripheral IR, glucose-stimulated hyperinsulinemia, abnormalities of energy expenditure and dyslipidemia and chronic anovulation [41-43]. Additionally, a higher risk of developing impaired glucose tolerance at a relatively younger age and probably T2DM later in life is found in PCOS cases [44,45]. It is also reported that PCOS patients accompanied with significant IR in a few classic insulin target tissues, including adipocytes and skeletal muscle [46]. In view of these primary findings, the PPARG rs1801282 C>G polymorphism have been extensively studied for the relationship with PCOS susceptibility. Results of previous meta-analyses demonstrated that the PPARG rs1801282 G allele modified the risk of PCOS in Caucasians [15,16]. Recently, more case-control studies were conducted on the association of PPARG rs1801282 C>G polymorphism with PCOS susceptibility; however, the results were inconclusive, especially in Asian population. In this study, we summarized data for 3,458 PCOS cases and 3,611 controls from twenty-three included studies and attempted to examine the risk of PPARG rs1801282 C>G variants to PCOS by an updated meta-analysis. Our findings suggested that PPARG rs1801282 G allele might significantly decrease the risk of PCOS in both Caucasians and Asians.

PPARG, a nuclear hormone receptor, recognizes and binds to the certain PPARG response elements, then regulates the target genes in their promoter region. PPARG plays a vital role in insulin sensitization, lipogenesis, glucose homeostasis, inflammatory cytokine production and cell differentiation [47]. The PPARG rs1801282 C>G variant, a common polymorphism in exon 2 of PPARG gene, encodes a Pro→Ala substitution (Pro12Ala) [48]. As demonstrated in a previous study, the missense substitution of PPARG Pro12Ala could cause less transcriptional activation of target genes in vitro [49], it may presumably affect the risk of PCOS. In combination with this pooled analysis, our findings suggested that PPARG rs1801282 C>G polymorphism may decreased the risk of PCOS, probably through changing binding capacity for PPARG response elements, and then promoting anti-proliferative, pro-differentiation and pro-apoptotic properties. In our study, five studies deviated from the HWE, which showed the presence of population stratification and/or genotyping errors [14,20,21,35,36]. When we omitted these studies, the relationship between PPARG rs1801282 C>G polymorphism and PCOS was also significant with respect to the three genetic models (OR, 0.76; 95% CI, 0.66-0.88; P < 0.001 for G vs. C; OR, 0.74; 95% CI, 0.63-0.86; P < 0.001 for GG+CG vs. CC and OR, 0.73; 95% CI, 0.62-0.86; P < 0.001 for CG vs. CC; Table 3), attesting the robustness of our findings.

Some merits should be mentioned in our study. First of all, the sample sizes of our study were larger than several previous studies [15,16,50]. Secondly, for the first time, we highlighted PPARG rs1801282 G allele decreased the susceptibility of PCOS in Asians. Finally, in current study, there was no significant heterogeneity in all genetic models, suggesting the robustness of our findings. But, some limitations need to be acknowledged. Only published studies were included in our pooled analysis, certain bias may inevitably exist. Moreover, in this study, most of publications were conducted mainly in Caucasians. Only seven studies focused on Asians, which limited the power to measure a real influence. Hence, large-scale studies in Asian populations are needed. Furthermore, due to lack of sufficient information, further evaluation of potential interactions, such as age, family history, and body mass index, was not carried out. In consideration of the complex etiology of PCOS, these factors should not be ignored. Finally, the correlation of other important SNPs of PPARG gene (e.g., rs4135247, rs1175543, rs709158, rs3856806 and rs2938395) with PCOS was seldom studied, these SNPs were not considered in current study.

In summary, our findings highlight that PPARG rs1801282 C>G variants are correlated with a decreased risk of PCOS in both Caucasians and Asians. In the future, larger and well-designed epidemiological studies are definitely demanded to further confirm our findings.

Acknowledgements

This study was supported in part by Jiangsu University Clinical Medicine Science and Technology Development Fund (JLY20140012), National Natural Science Foundation of China (81472332, 81341006), Fujian Province Natural Science Foundation (2013J01126, 2013J05116), Fujian Medical University professor fund (JS12008), The Fund of Union Hospital (2015TC-1-048 and 2015TC-2-004) and Fujian Province Science and Technology Programmed Fund (2012Y0030).

Disclosure of conflict of interest

None.

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