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We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20\u201350% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer\u2019s disease (AD)\u00a0progression independently from and as strongly as plasma pTau-181 (ref.\n                    <jats:sup>5<\/jats:sup>\n                    ), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.\n                  <\/jats:p>","DOI":"10.1038\/s41586-023-06802-1","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T12:02:55Z","timestamp":1701864175000},"page":"164-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":701,"title":["Organ aging signatures in the plasma proteome track health and disease"],"prefix":"10.1038","volume":"624","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8192-7593","authenticated-orcid":false,"given":"Hamilton 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