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Genetic variants in FTO associated with metabolic syndrome: a meta- and gene-based analysis

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Abstract

The objective of this study was to examine the effect of genetic variants in fat mass and obesity associated (FTO) gene on metabolic syndrome (MetS). A systematic literature search was performed and random-effects meta-analysis was used to evaluate genetic variants in FTO with MetS. A gene-based analysis was conducted to investigate the cumulative effects of genetic polymorphisms in FTO. A total of 18 studies from 13 published papers were included in our analysis. Random-effects meta-analysis yielded an estimated odds ratio of 1.19 (95% CI 1.12–1.27; P = 1.38 × 10−7) for rs9939609, 1.19 (95% CI 1.05–1.35; P = 0.008) for rs8050136, and 1.89 (95% CI 1.20–2.96; P = 0.006) for rs1421085. The gene-based analysis indicated that FTO is strongly associated with MetS (P < 10−5). This association remains after excluding rs9939609, a SNP that was frequently reported to have strong association with obesity and MetS. In this study, we concluded that the FTO gene may play a critical role in leading to MetS. Targeting this gene may provide novel therapeutic strategies for the prevention and treatment of metabolic syndrome.

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Acknowledgments

This study was supported by the Independent Innovation Foundation of Shandong University (No. 2010TS071). The project described was supported by Award Number P50DA010075-15 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health.

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Authors and Affiliations

  1. College of Pharmaceutical Sciences, Shandong University, Jinan, Shandong, China

    Haina Wang

  2. Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, Hunan, China

    Shuqian Dong

  3. Department of Obstetrics and Gynecology, The Second Hospital of Shandong University, Jinan, Shandong, China

    Hui Xu

  4. Department of Orthopedics, Peking Union Medical College Hospital, CAMS & PUMC, Beijing, China

    Jun Qian

  5. Methodology Center, Pennsylvania State University, 204 E. Calder Way, Suite 400, State College, PA, 16801, USA

    Jingyun Yang

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  1. Haina Wang
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  4. Jun Qian
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Correspondence to Jingyun Yang.

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Wang, H., Dong, S., Xu, H. et al. Genetic variants in FTO associated with metabolic syndrome: a meta- and gene-based analysis. Mol Biol Rep 39, 5691–5698 (2012). https://doi.org/10.1007/s11033-011-1377-y

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