A systematic review of genetic variants associated with metabolic syndrome in patients with schizophrenia

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• 作者：Stefanie Malan-Mü ; ller^a ; ^b ; ^{smalan@sun.ac.za" class="auth_mail" title="E-mail the corresponding author} ; Sanja Kilian^a ; Leigh L. van den Heuvel^a ; Soraya Bardien^b ; Laila Asmal^a ; Louise Warnich^c ; Robin A. Emsley^a ; Sî ; an M.J. Hemmings^a ; ^b ; Soraya Seedat^a
• 关键词：Schizophrenia ; Metabolic syndrome ; Gene variants ; Genetic association ; Comorbidity ; Pleiotropy
• 刊名：Schizophrenia Research
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：170
• 期：1
• 页码：1-17
• 全文大小：806 K

文摘

Metabolic syndrome (MetS) is a cluster of factors that increases the risk of cardiovascular disease (CVD), one of the leading causes of mortality in patients with schizophrenia. Incidence rates of MetS are significantly higher in patients with schizophrenia compared to the general population. Several factors contribute to this high comorbidity. This systematic review focuses on genetic factors and interrogates data from association studies of genes implicated in the development of MetS in patients with schizophrenia. We aimed to identify variants that potentially contribute to the high comorbidity between these disorders. PubMed, Web of Science and Scopus databases were accessed and a systematic review of published studies was conducted. Several genes showed strong evidence for an association with MetS in patients with schizophrenia, including the fat mass and obesity associated gene (FTO), leptin and leptin receptor genes (LEP, LEPR), methylenetetrahydrofolate reductase (MTHFR) gene and the serotonin receptor 2C gene (HTR2C). Genetic association studies in complex disorders are convoluted by the multifactorial nature of these disorders, further complicating investigations of comorbidity. Recommendations for future studies include assessment of larger samples, inclusion of healthy controls, longitudinal rather than cross-sectional study designs, detailed capturing of data on confounding variables for both disorders and verification of significant findings in other populations. In future, big genomic datasets may allow for the calculation of polygenic risk scores in risk prediction of MetS in patients with schizophrenia. This could ultimately facilitate early, precise, and patient-specific pharmacological and non-pharmacological interventions to minimise CVD associated morbidity and mortality.