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Using NMR-based metabolomic profiling from 209,144 UK Biobank participants and 6,820 German ESTHER participants, models were built and validated to link metabolite panels to 10-year all-cause mortality. The study identifies shared metabolite signals and sex- and age-stratified MetaboMR clocks that accelerate predicted mortality risk and improve model discrimination, supporting biological ageing measurement and personalized clinical risk stratification.",{"@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/development-of-age-and-sex-specific-metabolomics-based-biological-ageing-clocks-for-10-year-mortality-prediction/439604/",{"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/development-of-age-and-sex-specific-metabolomics-based-biological-ageing-clocks-for-10-year-mortality-prediction/439604.png","ImageObject",300,407,{"name":92,"@type":93},"Aditya","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-01","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data sources were used to build the metabolomics-based ageing clocks?","Question",{"text":112,"@type":113},"The study used NMR-based metabolomic profiling from 209,144 UK Biobank participants and 6,820 participants in the German ESTHER study.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were 10-year mortality risk models constructed?",{"text":117,"@type":113},"Mortality risk scores were derived using LASSO-regularized Cox regression, and metabolomics-based mortality risk clocks (MetaboMR clocks) were constructed using elastic net regression in sex- and age-stratified subgroups.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the MetaboMR clocks add beyond traditional prediction models?",{"text":121,"@type":113},"Sex- and age-specific metabolomic risk scores significantly enhance 10-year mortality prediction beyond traditional models, and age acceleration from MetaboMR clocks is associated with higher 10-year mortality risk in external validation.","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},439604,1790742100,{"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":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":36},962085564549,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","RESEARCH ARTICLE  \n[www.advancedscience.com](www.advancedscience.com)  \nDevelopment of Age-and Sex-Specific Metabolomics-Based Biological Ageing Clocks for 10-Year Mortality Prediction  \nLei Peng, Ruijie Xie, Bernd Holleczek, Hermann Brenner, and Ben Schöttker*  \nMetabolite concentrations vary by age and sex, yet age-and sex-specific metabolomic risk scores and biological ageing clocks for mortality prediction remain undeveloped. Nuclear magnetic resonance (NMR)-based metabolomic profiling is conducted in 209144 UK Biobank participants (12347 deaths) and 6820 from the German ESTHER study (804 deaths). Mortality risk scores are derived using least absolute shrinkage and selection operator  \n(LASSO)-regularized Cox regression, and metabolomics-based mortality risk clocks (MetaboMR clocks) are constructed using elastic net regression in sexand age-stratified subgroups (50–59 and 60–69 years). Models are trained in 70% of UK Biobank and validated internally (30%) and externally in ESTHER.  \n68 metabolites are significantly associated with 10-year all-cause mortality in both cohorts. 20, 18, 12, and 13 metabolites improved 10-year mortality prediction in younger and older men, younger and older women.  \nMetabolite-augmented models improved c-statistics by 0.036–0.084 across subgroups. In the external validation set, each year of age acceleration is associated with an 8% and 9% higher 10-year mortality risk for MetaboMR clock1 and clock2. Sex-and age-specific metabolomic risk scores significantly enhance 10-year mortality prediction beyond traditional models. The MetaboMR clocks may serve as measures of biological ageing and support personalized risk stratification in clinical settings.  \n1. Introduction  \nNumerous prognostic models have been developed based on routinely collected demographic, lifestyle, and clinical information to predict short- and long-term mortality in middle-aged adults, older adults, and high-risk populations. [1–3] While these models enable useful risk stratification, they rely primarily on non-biological variables. Although metabolic biomarkers are biologically informative and increasingly accessible,[4] they have yet to be systematically integrated into stratified mortality prediction.  \nMetabolomics also holds promise for elucidating biological mechanisms underlying premature mortality, offering insights beyond traditional risk factors. A growing body of evidence has linked individual metabolites, such as glycoprotein acetyls (GlycA), albumin, citrate, and various lipoproteins, that are independently associated with mortality.[4–12] Many of these metabolites can be reliably quantified using nuclear magnetic resonance (NMR)-based platforms, which enable highthroughput and reproducible measurement in large-scale epidemiologic studies.  \nL. Peng, R. Xie, H. Brenner, B. Schöttker  \nDivision of Clinical Epidemiology and Aging Research German Cancer Research Center  \nIm Neuenheimer Feld 581, 69120 Heidelberg, Germany E-mail: [b.schoettker@dkfz.de](b.schoettker@dkfz.de)  \nL. Peng, R. Xie  \nFaculty of Medicine University of Heidelberg  \nIm Neuenheimer Feld 672, 69120 Heidelberg, Germany  \nB. Holleczek  \nSaarland Cancer Registry  \nNeugeländstraße 9, 66117 Saarbrücken, Germany  \nH. Brenner  \nDivision of Preventive Oncology German Cancer Research Center  \nIm Neuenheimer Feld 460, 69120 Heidelberg, Germany  \n© 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nDOI: 10.1002/advs.202510189  \nPrior studies, particularly those based on the UK Biobank, have examined associations between metabolomic profiles and mortality in both the general population[11–16] and specific subgroups. [17–20] Recent advances include the development of metabolomic ageing clocks, which estimate biological age and predict mortality risk. [11,13","cbCaif40n3miQTL7","https://ap.wps.com/l/cbCaif40n3miQTL7","pdf",3364168,16,"English","# Introduction\n## Prognostic models and need for biological biomarkers\n## Metabolomics for mechanisms and premature mortality\n# Results\n## Traditional risk factors and 10-year mortality","[{\"question\":\"What data sources were used to build the metabolomics-based ageing clocks?\",\"answer\":\"The study used NMR-based metabolomic profiling from 209,144 UK Biobank participants and 6,820 participants in the German ESTHER study.\"},{\"question\":\"How were 10-year mortality risk models constructed?\",\"answer\":\"Mortality risk scores were derived using LASSO-regularized Cox regression, and metabolomics-based mortality risk clocks (MetaboMR clocks) were constructed using elastic net regression in sex- and age-stratified subgroups.\"},{\"question\":\"What do the MetaboMR clocks add beyond traditional prediction models?\",\"answer\":\"Sex- and age-specific metabolomic risk scores significantly enhance 10-year mortality prediction beyond traditional models, and age acceleration from MetaboMR clocks is associated with higher 10-year mortality risk in external validation.\"}]","Development of Age-and Sex-Specific Metabolomics-Based Biological Ageing Clocks for 10-Year Mortality Prediction | PDF",1790689440]