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MTNR1B基因变异增加中国年轻孕妇患妊娠期糖尿病的风险。

MTNR1B variants increase gestational diabetes mellitus risk in young Chinese pregnant women.

作者信息

Zeng Qiaoli, Liu Xin, Liu Jia, Wu Yanying, Zhang Yuxuan, He Zhaotao, Wei Yue, Zeng Guofang, Zou Dehua, Guo Runmin

机构信息

Department of Internal Medicine, Shunde Women and Children's Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, Guangdong, China.

Key Laboratory of Research in Maternal and Child Medicine and Birth Defects, Guangdong Medical University, Foshan, Guangdong, China.

出版信息

Sci Rep. 2025 Jun 4;15(1):19643. doi: 10.1038/s41598-025-02248-9.

DOI:10.1038/s41598-025-02248-9
PMID:40467595
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12137604/
Abstract

The aim of this study is to explore the relationship between the MTNR1B gene variants rs1387153 and rs10830963 and the risk of gestational diabetes mellitus (GDM). Additionally, the study sought to investigate gene-environment interactions, assess the cumulative genetic risk through the application of Genetic Risk Scores (GRSs), and establish a predictive model for GDM. A case-control study was conducted with 500 GDM patients and 502 controls. MTNR1B gene variants were genotyped using SNPscan™. Associations between clinical data, genetic models, haplotype and GDM risk or blood glucose levels were analyzed using statistical tests. Gene-environment interactions were preliminarily analyzed with GMDR and logistic regression. SNP-age interactions were further explored through stratified analysis and GRS. A predictive model was developed using logistic regression, validated with bootstrap resampling, and its clinical utility was evaluated with decision curve analysis. The study has identified a significant association between the MTNR1B gene variants rs1387153 and rs10830963 and an increased risk of GDM, particularly in women under 30 years of age (all OR > 1, P < 0.05; rs1387153 TT vs. CC: OR = 2.969, P < 0.001, rs10830963 GG vs. CC: OR = 3.066, P < 0.001). The gene-age interaction was found to be statistically significant (P < 0.05). The analysis of the TC haplotype (OR > 1, P < 0.001) and the GRS, specifically in the top quartile of GRS (OR > 3, P < 0.001), further corroborates the cumulative impact of these variants on the risk of GDM among pregnant women under 30 years. The variants also significantly increase postprandial blood glucose levels in pregnant women under 30 years of age (P < 0.05). A predictive model that includes MTNR1B polymorphisms, maternal age, and pre-pregnancy BMI has shown good predictive accuracy for GDM risk (C-Statistics = 0.682, P < 0.001). The study highlights the key role of MTNR1B gene variants rs1387153 and rs10830963 in GDM risk among young pregnant women under the age of 30, with no correlation observed in pregnant women aged 30 and above. The gene-age interaction and GRS provide additional insights into GDM risk. These findings serve as a significant inspiration for future research on populations with MTNR1B gene variations, hopefully prompting more researchers to pay attention to adopting appropriate research, screening, prevention, and intervention strategies for pregnant women with diabetes at different age stages.

摘要

本研究的目的是探讨MTNR1B基因变异rs1387153和rs10830963与妊娠期糖尿病(GDM)风险之间的关系。此外,该研究还试图调查基因-环境相互作用,通过应用遗传风险评分(GRS)评估累积遗传风险,并建立GDM的预测模型。进行了一项病例对照研究,纳入500例GDM患者和502例对照。使用SNPscan™对MTNR1B基因变异进行基因分型。使用统计检验分析临床数据、遗传模型、单倍型与GDM风险或血糖水平之间的关联。用GMDR和逻辑回归初步分析基因-环境相互作用。通过分层分析和GRS进一步探索SNP-年龄相互作用。使用逻辑回归建立预测模型,通过自助重采样进行验证,并用决策曲线分析评估其临床实用性。该研究已确定MTNR1B基因变异rs1387153和rs10830963与GDM风险增加之间存在显著关联,尤其是在30岁以下的女性中(所有OR>1,P<0.05;rs1387153 TT与CC相比:OR=2.969,P<0.001,rs10830963 GG与CC相比:OR=3.066,P<0.001)。发现基因-年龄相互作用具有统计学意义(P<0.05)。对TC单倍型(OR>1,P<0.001)和GRS的分析,特别是在GRS的前四分位数中(OR>3,P<0.001),进一步证实了这些变异对30岁以下孕妇GDM风险的累积影响。这些变异还显著增加了30岁以下孕妇的餐后血糖水平(P<0.05)。一个包括MTNR1B多态性、母亲年龄和孕前BMI的预测模型对GDM风险显示出良好的预测准确性(C统计量=0.682,P<0.001)。该研究强调了MTNR1B基因变异rs1387153和rs10830963在30岁以下年轻孕妇GDM风险中的关键作用,而在30岁及以上孕妇中未观察到相关性。基因-年龄相互作用和GRS为GDM风险提供了更多见解。这些发现为未来对具有MTNR1B基因变异人群的研究提供了重要启示,有望促使更多研究人员关注为不同年龄阶段的糖尿病孕妇采取适当的研究、筛查、预防和干预策略。

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