| Title: | Deviation From Genetically Predicted BMI and All-Cause Mortality: A Cohort Study in the UK Biobank |
| Journal: | Obesity |
| Published: | 2 Oct 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41036672/ |
| DOI: | https://doi.org/10.1002/oby.70042 |
| URL: | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/oby.70042 |
Publication 19308
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
OBJECTIVE: The relation between genetically predicted BMI (gBMI) and actual BMI may have health effects. This study examines the relationship between deviations from gBMI and all-cause mortality in 208,146 UK Biobank participants.</p>
METHODS: We derived gBMI from polygenic risk scores, with deviations calculated as the difference between observed and predicted BMI. Cox proportional hazards models are adjusted for confounders and current BMI.</p>
RESULTS: Downward deviations (> 2 SD below gBMI) were associated with significantly increased mortality (HR: 1.25, 95% CI: 1.01-1.55), whereas upward deviations (> 2 SD above) showed no significant effect (HR: 1.10, 95% CI: 0.93-1.29). The mortality exhibited the known nonlinear J-shaped association with observed BMI, here lowest at BMI ~22 kg/m2, but this nadir varied by genetic predisposition; thus, for individuals with high gBMI, lowest mortality occurred at higher observed BMI (24-26 kg/m2), while those with low or medium gBMI showed sharper increases in mortality at higher BMI.</p>
CONCLUSIONS: These findings highlight the possible importance of aligning current BMI to genetic predisposition, and future research should examine BMI deviations and their long-term health effects. This perspective may inform personalized obesity management strategies to optimize health outcomes.</p>
5 Authors
- Nuno R. Zilhao
- Jie Zhang
- Dorret I. Boomsma
- Thorkild I. A. Sørensen
- Christina C. Dahm
1 Application
| Application ID | Title |
|---|---|
| 81520 | Identifying preventable risk factors and potential drug-targets in diabetes and associated diseases |
Enabling scientific discoveries that improve human health