{"title":"通过利用人群平均风险的偏差揭示临床有用的肥胖亚型","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":"{\"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}","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}
Clinically useful obesity subtypes revealed by harnessing deviations from population-average risk
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：
Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine. The publication focuses on originality, timeliness, interdisciplinary interest, and the impact on improving human health. In addition to research articles, Nature Medicine also publishes commissioned content such as News, Reviews, and Perspectives. This content aims to provide context for the latest advances in translational and clinical research, reaching a wide audience of M.D. and Ph.D. readers. All editorial decisions for the journal are made by a team of full-time professional editors.
Nature Medicine consider all types of clinical research, including:
-Case-reports and small case series
-Clinical trials, whether phase 1, 2, 3 or 4
-Observational studies
-Meta-analyses
-Biomarker studies
-Public and global health studies
Nature Medicine is also committed to facilitating communication between translational and clinical researchers. As such, we consider “hybrid” studies with preclinical and translational findings reported alongside data from clinical studies.
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