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Fat Mass and Obesity Associated Gene Polymorphism and the Risk of Polycystic Ovary Syndrome: A Meta-analysis.


ABSTRACT: We aimed to elucidate the association between fat mass and obesity associated gene (FTO) polymorphism and the risk of polycystic ovary syndrome (PCOS) by meta-analysis.We searched PubMed and Embase databases to find the relevant studies. Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were used for pooled analysis. Statistical analyses were carried out by using R 3.12 software. Heterogeneity was assessed using I 2 and Q statistics. I 2 >50% or P<0.05 was considered as heterogeneity statistically, and random effects model was used for pooled analysis. Otherwise, fixed-effect model was used.Twelve eligible studies that published from 2008 to 2015 were included in this meta-analysis. The pooled analyses showed that rs9939609 polymorphism of FTO gene was significantly associated with risk of PCOS under A vs. T, AT vs. TT, AA vs. TT, AA vs. AT+TT and AA+AT vs. TT genetic models. However, for rs8050136 and rs1421085, significant association was only found under recessive genetic model.rs9939609 variation of FTO gene is significantly associated with risk of PCOS. However, the association between rs8050136, rs1421085, and PCOS is still unclear and needs further confirmation.

SUBMITTER: Liu Y 

PROVIDER: S-EPMC5401934 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Fat Mass and Obesity Associated Gene Polymorphism and the Risk of Polycystic Ovary Syndrome: A Meta-analysis.

Liu Ying Y   Chen Yongxia Y  

Iranian journal of public health 20170101 1


<h4>Background</h4>We aimed to elucidate the association between fat mass and obesity associated gene (FTO) polymorphism and the risk of polycystic ovary syndrome (PCOS) by meta-analysis.<h4>Methods</h4>We searched PubMed and Embase databases to find the relevant studies. Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were used for pooled analysis. Statistical analyses were carried out by using R 3.12 software. Heterogeneity was assessed using <i>I <sup>2</sup></i> and  ...[more]

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