What this means
Researchers built tests that estimate organ age from proteins in blood. Older-looking organs were linked to later disease and death, but this does not show the tests change outcomes.
Plain-English summary written by an AI model from the abstract. The original is below.
- Who it was tested in
- 43,616 UK adults, plus 3,977 people from China and 800 from the USA
- Kind of research
- A study
The science
- Original title
- Organ-specific proteomic aging clocks predict disease and longevity across diverse populations.
- Authors
- Wang Y, Xiao S, Liu B et al.
- Journal
- Nature aging
- Published
- Nov 26, 2025
- Study type
- Study · in humans
- PMID
- 41299092 ↗
Abstract
Aging and age-related diseases share convergent pathways at the proteome level. Here, using plasma proteomics and machine learning, we developed organismal and ten organ-specific aging clocks in the UK Biobank (n = 43,616) and validated their high accuracy in cohorts from China (n = 3,977) and the USA (n = 800; cross-cohort r = 0.98 and 0.93). Accelerated organ aging predicted disease onset, progression and mortality beyond clinical and genetic risk factors, with brain aging being most strongly linked to mortality. Organ aging reflected both genetic and environmental determinants: brain aging was associated with lifestyle, the GABBR1 and ECM1 genes, and brain structure. Distinct organ-specific pathogenic pathways were identified, with the brain and artery clocks linking synaptic loss, vascular dysfunction and glial activation to cognitive decline and dementia. The brain aging clock further stratified Alzheimer's disease risk across APOE haplotypes, and a super-youthful brain appears to confer resilience to APOE4. Together, proteomic organ aging clocks provide a biologically interpretable framework for tracking aging and disease risk across diverse populations.
Wang Y, Xiao S, Liu B et al. Organ-specific proteomic aging clocks predict disease and longevity across diverse populations. Nature aging. 2025. doi:10.1038/s43587-025-01016-8. PMID: 41299092. https://pubmed.ncbi.nlm.nih.gov/41299092/