Nuno R Zilhao, Jie Zhang, Dorret I Boomsma, Thorkild I A Sørensen, Christina C Dahm
{"title":"偏离基因预测的BMI和全因死亡率:英国生物银行的一项队列研究。","authors":"Nuno R Zilhao, Jie Zhang, Dorret I Boomsma, Thorkild I A Sørensen, Christina C Dahm","doi":"10.1002/oby.70042","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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><p><strong>Methods: </strong>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><p><strong>Results: </strong>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/m<sup>2</sup>, but this nadir varied by genetic predisposition; thus, for individuals with high gBMI, lowest mortality occurred at higher observed BMI (24-26 kg/m<sup>2</sup>), while those with low or medium gBMI showed sharper increases in mortality at higher BMI.</p><p><strong>Conclusions: </strong>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>","PeriodicalId":94163,"journal":{"name":"Obesity (Silver Spring, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deviation From Genetically Predicted BMI and All-Cause Mortality: A Cohort Study in the UK Biobank.\",\"authors\":\"Nuno R Zilhao, Jie Zhang, Dorret I Boomsma, Thorkild I A Sørensen, Christina C Dahm\",\"doi\":\"10.1002/oby.70042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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><p><strong>Methods: </strong>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><p><strong>Results: </strong>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/m<sup>2</sup>, but this nadir varied by genetic predisposition; thus, for individuals with high gBMI, lowest mortality occurred at higher observed BMI (24-26 kg/m<sup>2</sup>), while those with low or medium gBMI showed sharper increases in mortality at higher BMI.</p><p><strong>Conclusions: </strong>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>\",\"PeriodicalId\":94163,\"journal\":{\"name\":\"Obesity (Silver Spring, Md.)\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Obesity (Silver Spring, Md.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/oby.70042\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Obesity (Silver Spring, Md.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/oby.70042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deviation From Genetically Predicted BMI and All-Cause Mortality: A Cohort Study in the UK Biobank.
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.
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.
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.
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.