Organ-specific proteomic aging clocks predict disease and longevity across diverse populations

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.

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Publication Details

Journal
Nature Aging
Published
2025-11-26
DOI
https://doi.org/10.1038/s43587-025-01016-8
Citations
6
Primary Topic
Alzheimer's disease research and treatments
Type
article
Field-Weighted Citation Impact
6.99
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Organ-specific proteomic aging clocks predict disease and longevity across diverse populations

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6 citations
Nature Aging
Alzheimer's disease research and treatments
6.99
article

Organ-specific proteomic aging clocks predict disease and longevity across diverse populations

Jie Shi, Junqing Xie, Rongtao Jiang, Runsen Chen, Cornelia M. van Duijn, Yanping Bao, Yue‐Yu Hang, Canqing Yu, Qianyan Zheng, Robert E. Gerszten, Sihao Xiao, Derrick Bennett, Baihan Wang, Jun Lv, Lin Lü, Danielle E. Haslam, Dong D. Wang, Li Chen, Liming Li, Zhengming Chen, Yunhe Wang, Andrew T. Chan, Michael V. Vitiello, Yuxi Liu, Bo‐Wen Liu
article en
6 citations

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.

Nature AgingVol. 6(1)
Broad Institute (US), Brigham and Women's Hospital (US), Beth Israel Deaconess Medical Center (US), Harvard University (US), University of Washington (US), Peking University (CN), Beijing Normal University (CN), Yale University (US), University of Oxford (GB), Massachusetts General Hospital (US), Chinese Institute for Brain Research (CN), Vanke (China) (CN), National Institutes for Food and Drug Control (CN), Oxford BioMedica (United Kingdom) (GB), Center for Life Sciences (CN), Peking University Sixth Hospital (CN), Tsinghua University (CN)
Openalex Percentile: Top 3%
Alzheimer's disease research and treatments
6.99
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