{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["105"],"submitter":["Wang T"],"pubmed_abstract":["<h4>Background</h4>Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.<h4>Methods</h4>Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status.<h4>Findings</h4>Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have car"],"journal":["EBioMedicine"],"pagination":["105199"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11246009"],"repository":["biostudies-literature"],"pubmed_title":["A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age."],"pmcid":["PMC11246009"],"pubmed_authors":["Wu J","Kaddurah-Daouk R","Moses EK","Blangero J","Dakic A","Hui J","Salim A","Mellett NA","Magliano DJ","Meikle TG","Hung J","Giles C","Huynh K","Olshansky G","Meikle PJ","Wang T","Beyene HB","Cinel M","Beilby J","Yi C","Watts GF","Shaw JE"],"additional_accession":[]},"is_claimable":false,"name":"A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age.","description":"<h4>Background</h4>Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.<h4>Methods</h4>Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status.<h4>Findings</h4>Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have car","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2025-04-18T21:43:57.256Z","creation":"2025-02-19T02:34:44.839Z"},"accession":"S-EPMC11246009","cross_references":{"pubmed":["38905750"],"doi":["10.1016/j.ebiom.2024.105199"]}}