中国科学院北京基因组研究所汪敏先小组的一项最新发现揭示了用于预测肥胖症、2型糖尿病及相关疾病的代谢性多基因风险评分。相关论文于2026年3月16日发表在《细胞—代谢》杂志上。

研究小组获得了一个生物富集代谢PRS (MetPRS)，这是一个综合评分，主题是来自850多万人的20个代谢特征的多祖先全基因组关联研究。MetPRS通过优化预测肥胖（O-MetPRS）和T2D (D-MetPRS)，在六个不同祖先人群中预测肥胖和2型糖尿病的表现均优于现有PRS。O-MetPRS和D-MetPRS可有效识别代谢多病高风险个体并预测临床结果，包括GLP-1受体激动剂启动。O-MetPRS和D-MetPRS显示GLP-1受体激动剂启动的风险在前十分位数比中五分位数高2倍。生物富集的MetPRS有可能为代谢性疾病的疾病预测和管理方法增加额外的信息层。

据介绍，肥胖和2型糖尿病（T2D）是具有共同病理生理的代谢性疾病。传统的多基因风险评分（PRSs）侧重于这些单独的疾病，但单一疾病的方法在捕捉代谢功能障碍的全面方面存在不足。

附：英文原文

Title: Metabolic polygenic risk scores for prediction of obesity, type 2 diabetes, and related morbidities

Author: Min Seo Kim, Qiuli Chen, Yang Sui, Xiong Yang, Shaoqi Wang, Lu-Chen Weng, So Mi Jemma Cho, Satoshi Koyama, Xinyu Zhu, Kang Yu, Xingyu Chen, Rufan Zhang, Wanqing Yin, Shuangqiao Liao, Zhaoqi Liu, Fowzan S. Alkuraya, Pradeep Natarajan, Patrick T. Ellinor, Akl C. Fahed, Minxian Wang

Issue&Volume: 2026-03-16

Abstract: Obesity and type 2 diabetes (T2D) are metabolic diseases with shared pathophysiology. Traditional polygenic risk scores (PRSs) have focused on these conditions individually, yet the single-disease approach falls short in capturing the full dimension of metabolic dysfunction. We derived a biologically enriched metabolic PRS (MetPRS), a composite score that uses multi-ancestry genome-wide association studies of 20 metabolic traits from over 8.5 million individuals. MetPRS, optimized to predict obesity (O-MetPRS) and T2D (D-MetPRS), outperformed existing PRSs in predicting obesity and T2D across six ancestries. O-MetPRS and D-MetPRS effectively identify individuals at high risk for metabolic multimorbidity and predict clinical outcomes, including GLP-1 receptor agonist initiation. O-MetPRS and D-MetPRS showed an ～2-fold increased risk of GLP-1 receptor agonist initiation for the top decile versus the middle quintile. The biologically enriched MetPRS has the potential to add an extra layer of information to disease prediction and management approaches for metabolic diseases.

DOI: 10.1016/j.cmet.2026.02.009

Source: https://www.cell.com/cell-metabolism/abstract/S1550-4131(26)00052-5