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A machine learning-derived polygenic risk score reveals that healthy lifestyle counteracts obesity-related mortality

Lushan Xiao,Shengxing Liang,Lin Zeng,Shumin Cai,Jiaren Wang,C. Hong,Yan Li,Ruining Li,Pu Jiang,Zebin Xie,Ting Li,Shanshan Wu,Li Liu,Gongfa Wu,Weinan Lai

Published 2026 in npj Digital Medicine

ABSTRACT

Obesity is influenced by genetic predisposition and lifestyle. The associations among genetic susceptibility to obesity, lifestyle, and all-cause mortality remain unexplored. Our goal is to develop and validate a machine learning model to assess the genetic risk of obesity and examine its association with lifestyle and all-cause mortality. We integrated genetic data from 482,700 UK Biobank participants and 8,607 Nanfang Hospital participants to create and validate a stacked machine learning model, which generates an obesity-related polygenic risk score (OPRS), to evaluate the relationships among genetic risk of obesity, lifestyle, and all-cause mortality. The model achieved area under the receiver operating characteristic curve values of 0.621, 0.616, and 0.565 for the training, internal, and external test cohorts, respectively. A high OPRS is associated with increased all-cause mortality, with a linear relationship observed among individuals with normal weight or overweight. Among individuals with a high genetic risk of obesity, adhering to four healthy lifestyle factors reduced the risk of all-cause mortality by 59% compared to those who did not. Thus, high genetic risk of obesity is associated with higher risk of all-cause mortality, but a healthy lifestyle mitigates this risk.

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