Evidence map›Paper›PMID 41299092›Full record

ArticleNature aging2026

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

Yunhe Wang, Sihao Xiao, Bowen Liu, Rongtao Jiang, Yuxi Liu, Yian Hang, Li Chen, Runsen Chen, Michael V Vitiello, Derrick Bennett and 15 more

Abstract read
In one paragraph

Article in Nature aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Review
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  8. Immune responses in aging adults.The Journal of clinical investigation · 2026
    Review
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  12. Review
  13. Observational
  14. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Yunhe Wang *Nuffield Department of Population Health, University of Oxford, Oxford, UK. yunhe.wang@channing.harvard.edu.ORCID http://orcid.org/0000-0002-0923-1441
Sihao Xiao *Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-1952-6494
Bowen LiuNuffield Department of Population Health, University of Oxford, Oxford, UK.
Rongtao JiangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.ORCID http://orcid.org/0000-0003-4657-0079
Yuxi LiuBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-2484-151X
Yian HangDepartment of Biology, University of Oxford, Oxford, UK.
Li ChenSchool of Public Health, Peking University, Beijing, China.
Runsen ChenVanke School of Public Health and Institute for Healthy China, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0003-3398-5750
Michael V VitielloDepartment of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.
Derrick BennettNuffield Department of Population Health, University of Oxford, Oxford, UK.
Baihan WangNuffield Department of Population Health, University of Oxford, Oxford, UK.
Jun LvSchool of Public Health, Peking University, Beijing, China.ORCID http://orcid.org/0000-0001-7916-3870
Canqing YuSchool of Public Health, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-0019-0014
Danielle E HaslamChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0144-3287
Qianyan ZhengDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Robert E GersztenDivision of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.ORCID http://orcid.org/0000-0002-6767-7687
Yanping BaoSchool of Public Health, Peking University, Beijing, China.
Jie ShiNational Institute on Drug Dependence, Peking University, Beijing, China.
Junqing XieCentre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-0040-0042
Lin LuNational Institute on Drug Dependence, Peking University, Beijing, China.ORCID http://orcid.org/0000-0003-0742-9072
Liming LiSchool of Public Health, Peking University, Beijing, China.
Cornelia M van DuijnNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-2374-9204
Dong D WangBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-0897-3048
Zhengming ChenNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-6423-105X
Andrew T ChanChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Funding

Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
RISK FACTORS FOR CVD IN WOMENR01HL034594 · NHLBI · TULANE UNIVERSITY OF LOUISIANA · PI JoAnn Elisabeth Manson, Lu Qi · 1985 to 2026
$13.8M
BIOCHEMICAL MARKERS IN THE NURSES'HEALTH STUDY COHORTR01CA049449 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI HANKINSON, SUSAN E · 1989 to 2008
$11.3M
Risk Factors for Ischemic Stroke in WomenR01HL088521 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI REXRODE, KATHRYN M · 2008 to 2025
$9.9M
Towards Precision Nutrition for Alzheimer's Dementia Prevention: A Prospective Study of Dietary Patterns, the Gut Microbiome and Cognitive FunctionR01AG077489 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI Dong Wang · 2022 to 2026
$4.4M
The Gut Microbiome and Personalized Mediterranean Diet Interventions for Cardiometabolic Disease PreventionR01NR019992 · NINR · BRIGHAM AND WOMEN'S HOSPITAL · PI WANG, DONG · 2021 to 2025
$3.8M
The Microbiome-Gut-Brain Axis and Personalized Mediterranean Diet Interventions for Alzheimer's Dementia PreventionRF1AG083764 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI CORELLA, DOLORES, RUIZ-CANELA, MIGUEL · 2024 to 2024
$2.5M
Proteomic and integrative omic profiles of sugar- and artificially sweetened beverage consumption and changes in type 2 diabetes risk factorsK01DK136968 · NIDDK · BRIGHAM AND WOMEN'S HOSPITAL · PI Danielle Haslam · 2023 to 2026
$634k
NCI NIH HHS R01 CA049449NCI NIH HHS UM1 CA186107NHLBI NIH HHS R01 HL034594NHLBI NIH HHS R01 HL088521NIA NIH HHS R01 AG077489NIA NIH HHS RF1 AG083764NIDDK NIH HHS K01 DK136968NINR NIH HHS R01 NR019992Wellcome Trust
6 · The paper itself

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.

Indexed as

AgingLongevityProteomeProteomicsAgedAged, 80 and overAlzheimer DiseaseBrainChinaFemaleHumansMachine LearningMaleMiddle AgedOrgan SpecificityRisk FactorsProteome

Identifiers

PMID41299092
PMCPMC12823432

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.