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Background: Biological ageing is known to vary among different organs within an individual, but the extent to which advanced ageing of specific organs increases the risk of age-related diseases in the same and other organs remains poorly understood., Methods: In this observational cohort study, to assess the biological age of an individual's organs relative to those of same-aged peers, ie, organ age gaps, we collected plasma samples from 6235 middle-aged (age 45-69 years) participants of the Whitehall II prospective cohort study in London, UK, in 1997-99. Age gaps of nine organs were determined from plasma proteins via SomaScan (SomaLogic; Boulder, CO, USA) using the Python package organage. Following this assessment, we tracked participants for 20 years through linkage to national health records. Study outcomes were 45 individual age-related diseases and multimorbidity., Findings: Over 123 712 person-years of observation (mean follow-up 19·8 years [SD 3·6]), after excluding baseline disease cases and adjusting for age, sex, ethnicity, and age gaps in organs other than the one under investigation, individuals with large organ age gaps showed an increased risk of 30 diseases. Six diseases were exclusively associated with accelerated ageing of their respective organ: liver failure (hazard ratio [HR] per SD increment in the organ age gap 2·13 [95% CI 1·41-3·22]), dilated cardiomyopathy (HR 1·65 [1·28-2·12]), chronic heart failure (HR 1·52 [1·40-1·65]), lung cancer (HR 1·29 [1·04-1·59]), agranulocytosis (HR 1·27 [1·07-1·51]), and lymphatic node metastasis (HR 1·23 [1·06-1·43]). 24 diseases were associated with more than one organ age gap or with organ age gaps not directly related to the disease location. Larger age gaps were also associated with elevated HRs of developing two or more diseases affecting different organs within the same individual (ie, multiorgan multimorbidity): 2·03 (1·51-2·74) for the arterial age gap, 1·78 (1·48-2·14) for the kidney age gap, 1·52 (1·38-1·68) for the heart age gap, 1·52 (1·12-2·06) for the brain age gap, 1·43 (1·16-1·78) for the pancreas age gap, 1·37 (1·17-1·61) for the lung age gap, 1·36 (1·26-1·46) for the immune system age gap, and 1·30 (1·18-1·42) for the liver age gap., Interpretation: Advanced proteomic organ ageing is associated with the long-term risk of age-related diseases. In most cases, faster ageing of a specific organ increases susceptibility to morbidity affecting multiple organs., Funding: Wellcome Trust, UK Medical Research Council, National Institute for Aging, Academy of Finland., Competing Interests: Declaration of interests We declare no competing interests. Hamilton Se-Hwee is Co-founder and Scientific Advisor of Tealomics., (Copyright © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Importance: The clinical value of current multifactorial algorithms for individualized assessment of dementia risk remains unclear., Objective: To evaluate the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk., Design, Setting, and Participants: This prospective population-based UK Biobank cohort study assessed 4 dementia risk scores at baseline (2006-2010) and ascertained incident dementia during the following 10 years. Replication with a 20-year follow-up was based on the British Whitehall II study. For both analyses, participants who had no dementia at baseline, had complete data on at least 1 dementia risk score, and were linked to electronic health records from hospitalizations or mortality were included. Data analysis was conducted from July 5, 2022, to April 20, 2023., Exposures: Four existing dementia risk scores: the Cardiovascular Risk Factors, Aging and Dementia (CAIDE)-Clinical score, the CAIDE-APOE-supplemented score, the Brief Dementia Screening Indicator (BDSI), and the Australian National University Alzheimer Disease Risk Index (ANU-ADRI)., Main Outcomes and Measures: Dementia was ascertained from linked electronic health records. To evaluate how well each score predicted the 10-year risk of dementia, concordance (C) statistics, detection rate, false-positive rate, and the ratio of true to false positives were calculated for each risk score and for a model including age alone., Results: Of 465 929 UK Biobank participants without dementia at baseline (mean [SD] age, 56.5 [8.1] years; range, 38-73 years; 252 778 [54.3%] female participants), 3421 were diagnosed with dementia at follow-up (7.5 per 10 000 person-years). If the threshold for a positive test result was calibrated to achieve a 5% false-positive rate, all 4 risk scores detected 9% to 16% of incident dementia and therefore missed 84% to 91% (failure rate). The corresponding failure rate was 84% for a model that included age only. For a positive test result calibrated to detect at least half of future incident dementia, the ratio of true to false positives ranged between 1 to 66 (for CAIDE-APOE-supplemented) and 1 to 116 (for ANU-ADRI). For age alone, the ratio was 1 to 43. The C statistic was 0.66 (95% CI, 0.65-0.67) for the CAIDE clinical version, 0.73 (95% CI, 0.72-0.73) for the CAIDE-APOE-supplemented, 0.68 (95% CI, 0.67-0.69) for BDSI, 0.59 (95% CI, 0.58-0.60) for ANU-ADRI, and 0.79 (95% CI, 0.79-0.80) for age alone. Similar C statistics were seen for 20-year dementia risk in the Whitehall II study cohort, which included 4865 participants (mean [SD] age, 54.9 [5.9] years; 1342 [27.6%] female participants). In a subgroup analysis of same-aged participants aged 65 (±1) years, discriminatory capacity of risk scores was low (C statistics between 0.52 and 0.60)., Conclusions and Relevance: In these cohort studies, individualized assessments of dementia risk using existing risk prediction scores had high error rates. These findings suggest that the scores were of limited value in targeting people for dementia prevention. Further research is needed to develop more accurate algorithms for estimation of dementia risk.