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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月6日

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

摘要

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.