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Circulating metabolites, genetics and lifestyle factors in relation to future risk of type 2 diabetes

2型糖尿病 代谢组 肥胖 胰岛素抵抗 糖尿病 尿素循环 医学 生物 生物信息学 代谢组学 代谢综合征 遗传学 全基因组关联研究 2型糖尿病 遗传变异 遗传关联 遗传变异 内分泌学 生理学 风险因素 内科学 代谢途径 基因 胰岛素 基因检测 生物标志物
作者
Jun Li,Jie Hu,Huan Yun,Zhendong Mei,Xingyan Wang,Kai Luo,Marta Guasch-Ferré,Xikun Han,Buu Truong,Jordi Merino,Chengyong Jia,Miguel Ruiz-Canela,Casey M. Rebholz,Eun Hye Moon,Taryn Alkis,Guning Liu,Jie Yao,Xiyuan Zhang,Bianca C. Porneala,Jordi Salas‐Salvadó
出处
期刊:Nature Medicine [Nature Portfolio]
卷期号:32 (2): 660-670 被引量:7
标识
DOI:10.1038/s41591-025-04105-8
摘要

The human metabolome reflects complex metabolic states affected by genetic and environmental factors. However, metabolites associated with type 2 diabetes (T2D) risk and their determinants remain insufficiently characterized. Here we integrated blood metabolomic, genomic and lifestyle data from up to 23,634 initially T2D-free participants from ten cohorts. Of 469 metabolites examined, 235 were associated with incident T2D during up to 26 years of follow-up, including 67 associations not previously reported across bile acid, lipid, carnitine, urea cycle and arginine/proline, glycine and histidine pathways. Further genetic analyses linked these metabolites to signaling pathways and clinical traits central to T2D pathophysiology, including insulin resistance, glucose/insulin response, ectopic fat deposition, energy/lipid regulation and liver function. Lifestyle factors—particularly physical activity, obesity and diet—explained greater variations in T2D-associated versus non-associated metabolites, with specific metabolites revealed as potential mediators. Finally, a 44-metabolite signature improved T2D risk prediction beyond conventional factors. These findings provide a foundation for understanding T2D mechanisms and may inform precision prevention targeting specific metabolic pathways. Researchers studied the blood-based metabolome of over 23,000 people from ten ethnically diverse cohorts. They identified 235 metabolites associated with future risk of type 2 diabetes (T2D). By integrating genetic and modifiable lifestyle factors, their findings provide insights into T2D mechanisms and could improve risk prediction and inform precision prevention.
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