Fig. 1: Organ-specific proteomic aging clocks and their associations with disease and mortality across diverse populations.

a, Performance of the organ aging models across the discovery cohort (UKB, n = 43,616) and external validation cohorts (CKB, n = 3,977; NHS, n = 800). Models were trained on organ-enriched proteins from the Olink Explore 3072 panel, which were identified by GTEx tissue expression data. Performance was assessed using Pearson correlations between predicted organ age and chronological age. The top 20 proteins included in each model are detailed in Extended Data Fig. 2. b, Cross-cohort consistency of the performance of proteomic organ aging clocks, assessed using Pearson correlation (left, UKB versus CKB; middle, UKB versus NHS; right, CKB versus NHS). c, Distribution of proteomic organ age gap across cohorts. Box bounds indicate the first quartile (Q1), median and Q3; whiskers extend to Q1 − 1.5 × interquartile range (IQR) and Q3 + 1.5 × IQR. d, Pairwise correlations among organismal and organ-specific age gaps in the UKB (mean r = 0.16), CKB (mean r = 0.19) and NHS (mean r = 0.10). e, Overlap in constituent proteins among the organismal aging clock and three representative organ-specific clocks (brain, artery and heart). f, Associations between organ-specific age gaps and the incidence of five NDs, five psychiatric disorders, seven other chronic physical diseases and all-cause mortality in the UKB (n = 43,616). Associations were externally validated in the CKB and NHS (Extended Data Fig. 3). HRs per 1-s.d. change in the organismal and ten organ-specific age gaps are shown for significant associations, estimated using separate Cox proportional hazards models for each outcome, with adjustments for age, sex, ethnicity, Townsend deprivation index, smoking, physical activity level and recruitment center. The number of incident cases is presented. Mean differences in organ age gaps at baseline between participants with and without corresponding ‘incident’ diseases are visualized. The right panel shows the relative contributions of organ age gaps to each outcome, calculated by scaling z-scores for significant organs so that they sum to 1. g, Association between organ age gaps and years since disease diagnosis in participants with prevalent diseases at the baseline proteomic assessment, assessed by Pearson correlation. h, Visualization of the brain age gap after prevalent diseases before baseline (reflecting disease progression) or before incident diseases (reflecting prodromal disease). Participants with incident diseases were matched by age (±2 years) and sex with five healthy controls without corresponding incident diseases during the follow-up. The associations of brain age gaps with CKD (n = 11,890), ACD (n = 5,760) and depression (n = 9,710) are shown as examples. Trajectories were fitted using Loess regression, with error bands indicating 95% confidence intervals (CIs). All regression models were adjusted for age, sex, ethnicity, Townsend deprivation index, smoking, physical activity level and recruitment center. All statistical tests are two-sided. The Benjamini–Hochberg FDR was used to correct for multiple comparisons in f and g. The asterisks denote FDR-adjusted P-value thresholds: *q < 0.05; **q < 0.01; ***q < 0.001. ProtAge, proteomic age; COPD, chronic obstructive pulmonary disease.