| Title: | A machine learning-derived polygenic risk score reveals that healthy lifestyle counteracts obesity-related mortality |
| Journal: | npj Digital Medicine |
| Published: | 6 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41495413/ |
| DOI: | https://doi.org/10.1038/s41746-025-02314-6 |
| URL: | http://dx.doi.org/10.1038/s41746-025-02314-6 |
Publication 16870
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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.</p>
15 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 92668 | Development and validation of enhanced prediction models based on polygenic analysis and clinical factors for chronic diseases |
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