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Using deep body composition phenotypic data from a Chinese cohort, researchers developed BodycomBA to estimate chronological age and BodycomBAA to quantify aging acceleration. Validation across two independent cohorts (n=9,256 and n=3,403) showed strong performance, including discriminating early-onset age-related disease and improving diagnosis when combined with conventional risk factors. Multi-omics integration identified DNAm, proteins, metabolites, and gut microbiota linked to BodycomBAA and uncovered pathway modules and cross-omics molecular networks.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-body-composition-aging-clock-and-its-multiomic-profiles-session-9150-late-breaking-translational-geroscience-i/427050/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-body-composition-aging-clock-and-its-multiomic-profiles-session-9150-late-breaking-translational-geroscience-i/427050.png","ImageObject",300,407,{"name":92,"@type":93},"CatatanPagi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-12","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What are BodycomBA and BodycomBAA designed to measure?","Question",{"text":112,"@type":113},"BodycomBA estimates chronological age from body composition data, while BodycomBAA assesses aging acceleration.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the aging clock validated?",{"text":117,"@type":113},"Performance was validated in two independent large cohorts with sample sizes of 9,256 and 3,403.",{"name":119,"@type":110,"acceptedAnswer":120},"What multiomic components were linked to BodycomBAA?",{"text":121,"@type":113},"Integrating multiomics revealed significant associations involving 350 DNA methylation sites, 68 proteins, 41 metabolites, and 257 gut microbiota features.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},427050,1791026023,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},962090894170,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","SESSION 9150 (PAPER)  LATE BREAKING: TRANSLATIONAL GEROSCIENCE I  \nAbstract citation ID: igaf122.4402  \nA BODY COMPOSITION AGING CLOCK AND ITS MULTIOMIC PROFILES  \nLiming Zhang1, Xueqing Jia2, Maoyao Xia3, Shuli Jia4, Jesse Poganik5, Xia Jiang3, Meiling Ge3, and Zuyun Liu1,  \n1. Zhejiang University, Hangzhou, Zhejiang, China (People’s Republic), 2. Zhejiang university, Hangzhou, Zhejiang, China (People’s Republic), 3. Sichuan University, Chengdu, Sichuan, China (People’s Republic), 4. Sichuan University, Chengdu, Zhejiang, China (People’s Republic), 5. Harvard Medical School, Boston, Massachusetts, United States  \nBody composition represent a fundamental and physiologically relevant dimension of aging. Leveraging deep body composition phenotypic data from a Chinese cohort, we developed BodycomBA, a novel aging clock, and BodycomBAA for assessing aging acceleration. BodycomBA showed robust correlations with chronological age (CA), and BodycomBAA exhibited strong efficacy in capturing a wide spectrum of physical health conditions. These findings were validated in two large independent cohorts (n=9,256 and n=3,403). Additionally, BodycomBAA was effective in discriminating early-onset cases of age-related diseases, and incorporating BodycomBA into models of conventional risk factors (e.g., CA, smoking, and drinking) enhanced the diagnostic power for these diseases. By integrating multiomic data, we identified 350 DNAm sites, 68 proteins, 41 metabolites, and 257 gut microbiota significantly associated with BodycomBAA and elucidated their related biological pathways. Calculating the similarity network between these pathways further identified 2 connected (integrin binding-focal adhesion pathways act as bridges between the 2 modules) and 8 independent functional modules for proteome and DNA methylome, respectively. Subsequent cross-omics analysis revealed interconnected networks of molecular pairs and core molecules (e.g., PLOD1 protein) underlying BodycomBAA. Interestingly, the omic-inferred BodycomBA models demonstrated enhanced capability in elucidating the heterogeneity of physical health conditions (e.g., functions of liver, kidney, and metabolism) compared to the actual BodycomBA. Collectively, this study presents a highly adaptable and valid aging clock derived from body composition data. Its multiomic profiles provide insights into aging biology and a valuable framework for precisely stratifying individuals at risk of accelerated aging, paving the way for personalized anti-aging interventions.","cbCaie02HWyAzuKa","https://ap.wps.com/l/cbCaie02HWyAzuKa","pdf",1167609,"English","# Background\n## BodycomBA and BodycomBAA development\n## Validation in independent cohorts\n# Multiomics integration and findings\n## Molecular associations and pathway modules\n## Cross-omics networks and key molecules\n# Implications","[{\"question\":\"What are BodycomBA and BodycomBAA designed to measure?\",\"answer\":\"BodycomBA estimates chronological age from body composition data, while BodycomBAA assesses aging acceleration.\"},{\"question\":\"How was the aging clock validated?\",\"answer\":\"Performance was validated in two independent large cohorts with sample sizes of 9,256 and 3,403.\"},{\"question\":\"What multiomic components were linked to BodycomBAA?\",\"answer\":\"Integrating multiomics revealed significant associations involving 350 DNA methylation sites, 68 proteins, 41 metabolites, and 257 gut microbiota features.\"}]","A BODY COMPOSITION AGING CLOCK AND ITS MULTIOMIC PROFILES - Session 9150 Late Breaking Translational Geroscience I | PDF",1790642621]