{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhao R"],"funding":["Shanghai Municipal Science and Technology Major Project","National Key Research and Development program of China","Science and Technology Innovation 2030 Major Projects"],"pagination":["1411-1423"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11978558"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["47(2)"],"pubmed_abstract":["Individual's aging rates vary across organs. However, there are few methods for assessing aging at organ levels and whether they contribute differently to mortalities remains unknown. We analyzed data from 45,821 adults in the UK Biobank, using plasma proteomics and machine learning to estimate biological ages for 12 major organs. The differences between biological age and chronological age, referred to as \"age gaps,\" were calculated for each organ. Partial correlation analyses were used to assess the association between age gaps and modifiable factors. Adjusted multivariable Cox regression models were applied to examine the association of age gaps with all-cause mortality, cause-specific mortalities, and cancer-specific mortalities. We reveal a complex network of varied associations betwe"],"journal":["GeroScience"],"pubmed_title":["Plasma proteomics-based organ-specific aging for all-cause mortality and cause-specific mortality: a prospective cohort study."],"pmcid":["PMC11978558"],"funding_grant_id":["2023SHZDZX02","2022ZD0211600","2023YFC2508000"],"pubmed_authors":["Jiang Y","Xu K","Liu Z","Zhang T","Zhao R","Lu H","Suo C","Chen X","Yuan H","Chen S"],"additional_accession":[]},"is_claimable":false,"name":"Plasma proteomics-based organ-specific aging for all-cause mortality and cause-specific mortality: a prospective cohort study.","description":"Individual's aging rates vary across organs. However, there are few methods for assessing aging at organ levels and whether they contribute differently to mortalities remains unknown. We analyzed data from 45,821 adults in the UK Biobank, using plasma proteomics and machine learning to estimate biological ages for 12 major organs. The differences between biological age and chronological age, referred to as \"age gaps,\" were calculated for each organ. Partial correlation analyses were used to assess the association between age gaps and modifiable factors. Adjusted multivariable Cox regression models were applied to examine the association of age gaps with all-cause mortality, cause-specific mortalities, and cancer-specific mortalities. We reveal a complex network of varied associations betwe","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2025-07-12T03:04:11.712Z","creation":"2025-07-12T03:04:11.712Z"},"accession":"S-EPMC11978558","cross_references":{"pubmed":["39477866"],"doi":["10.1007/s11357-024-01411-w"]}}