{"title":"Clinically useful obesity subtypes revealed by harnessing deviations from population-average risk","authors":"","doi":"10.1038/s41591-024-03477-7","DOIUrl":null,"url":null,"abstract":"Obesity is associated with many life-threatening comorbidities. Its heterogeneous risk profile makes the prevention of obesity and its pathogenic consequences challenging. In this study, the heterogeneous relationships between body mass index and ten cardiovascular risk markers were quantified using machine learning, from which powerful clinical prediction models were developed and validated.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"49 1","pages":""},"PeriodicalIF":58.7000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41591-024-03477-7","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Obesity is associated with many life-threatening comorbidities. Its heterogeneous risk profile makes the prevention of obesity and its pathogenic consequences challenging. In this study, the heterogeneous relationships between body mass index and ten cardiovascular risk markers were quantified using machine learning, from which powerful clinical prediction models were developed and validated.
期刊介绍:
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