Yi Shen1, Yulong Jia1, Yuandong Li2, Xuefeng Gu3, Guoqing Wan3, Peng Zhang4, Yafeng Zhang5, Liying Jiang6. 1. Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu Province, People's Republic of China. 2. School of Management, Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China. 3. Shanghai Key Laboratory for Molecular Imaging, University of Medicine and Health Sciences, Shanghai, People's Republic of China. 4. School of Clinical Medicine, University of Medicine and Health Sciences, Shanghai, People's Republic of China. 5. Affiliated Hospital of Nantong University, Nantong University, Nantong, Jiangsu Province, People's Republic of China. fcius2004@hotmail.com. 6. Shanghai Key Laboratory for Molecular Imaging, University of Medicine and Health Sciences, Shanghai, People's Republic of China. j_meili@126.com.
Abstract
BACKGROUND: Genetic risk score (GRS) is more informative to identify the complicated associations between variants of genes and disease. Considering similar pathogenesis and shared genetic predispositions between gestational diabetes mellitus (GDM) and type 2 diabetes/obesity, we conducted this study to explore whether the GRS model integrating variants related to type 2 diabetes/obesity is also associated with GDM risk. METHODS: A population-based case-control study that included 1429 subjects was conducted to investigate the association between the GRS model and GDM risk, which were analyzed employing stratified logistic regression analysis with the adjustment for age, BMI, parity and family history of diabetes. RESULTS: We have screened 23 SNPs and further filtered six SNPs that were significantly associated with the risk of GDM: four risk SNPs (MTNR1B: rs10830963, rs1387153, rs2166706; MC4R: rs2229616) and two protective SNPs (MTNR1B: rs1447352 and rs4753426). The GRS model with a higher score indicated a higher genetic predisposition to develop GDM, especially in the highest quartile of GRS (all P < 0.001) and the strata of advanced maternal age (all P < 0.001) and obesity (all P = 0.005). CONCLUSION: In this study, six SNPs were explored and further identified to be associated with GDM risk, which suggested GRSs including these polymorphisms might participate in facilitating GDM risk. These findings offer the potential to improve our understanding of the etiology of GDM.
BACKGROUND: Genetic risk score (GRS) is more informative to identify the complicated associations between variants of genes and disease. Considering similar pathogenesis and shared genetic predispositions between gestational diabetes mellitus (GDM) and type 2 diabetes/obesity, we conducted this study to explore whether the GRS model integrating variants related to type 2 diabetes/obesity is also associated with GDM risk. METHODS: A population-based case-control study that included 1429 subjects was conducted to investigate the association between the GRS model and GDM risk, which were analyzed employing stratified logistic regression analysis with the adjustment for age, BMI, parity and family history of diabetes. RESULTS: We have screened 23 SNPs and further filtered six SNPs that were significantly associated with the risk of GDM: four risk SNPs (MTNR1B: rs10830963, rs1387153, rs2166706; MC4R: rs2229616) and two protective SNPs (MTNR1B: rs1447352 and rs4753426). The GRS model with a higher score indicated a higher genetic predisposition to develop GDM, especially in the highest quartile of GRS (all P < 0.001) and the strata of advanced maternal age (all P < 0.001) and obesity (all P = 0.005). CONCLUSION: In this study, six SNPs were explored and further identified to be associated with GDM risk, which suggested GRSs including these polymorphisms might participate in facilitating GDM risk. These findings offer the potential to improve our understanding of the etiology of GDM.
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