Evidence map›Paper›PMID 42526095›Full record

ArticleEBioMedicine2026

Proteomic organ-specific signatures, ageing traits, disease risks and genetic architecture in an East Asian population.

Derrick A Bennett, Baihan Wang, Sihao Xiao, Neil Wright, Ahmed Edris, Yunhe Wang, Iona Y Millwood, Robin G Walters, Huaidong Du, Ling Yang and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

21 authors.

Derrick A BennettClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK. Electronic address: derrick.bennett@ndph.ox.ac.uk.
Baihan WangClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Sihao XiaoBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Neil WrightClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Ahmed EdrisClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Yunhe WangNational Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China.
Iona Y MillwoodClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Robin G WaltersClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Huaidong DuClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Ling YangClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Yiping ChenClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Daniel AveryClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Dan Valle SchmidtClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Canqing YuDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
Dianjianyi SunDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
Jun LvDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
Michael HillClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Liming LiDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
Robert ClarkeClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Zhengming ChenClinical Trial Service Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK. Electronic address: zhengming.chen@ndph.ox.ac.uk.
China Kadoorie Biobank Collaborative Group

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundProtein-based ageing clocks can be used to help identify individuals at high risk of death or morbidity. There is a lack of evidence in non-European populations on clocks derived from combining different proteomic platforms and their relationship with age-related traits, diseases and genetic architecture.

methodsThe prospective China Kadoorie Biobank assayed proteins via Olink and SomaScan in ∼4000 individuals with a mean age of 58 years. We used organ-enriched plasma proteins identified from the Genotype-Tissue Expression Project and trained Light Gradient Boosting models on chronological age (ChronAge) to derive protein organ age and age gaps (ProtAgeGap) for 18 organs. We then investigated their relationship with age-related traits and incident diseases after adjustment for multiple testing. Moreover, we conducted genome-wide association studies to identify genetic variants for overall and organ-specific ProtAgeGaps.

findingsThe overall proteomic and organ-specific ageing clocks for each platform combined for all participants were strongly related with ChronAge. The organ-specific and overall proteomic age were associated with age-related traits and a range of incident diseases independent of ChronAge. In particular, the kidney organ-specific ageing clock was associated with higher risk of stroke (1.03; 1.02-1.05), chronic liver disease (CLD, 1.11; 1.05-1.18); chronic kidney disease (CKD, 1.19; 1.11-1.27) per 1-year higher ProtAgeGap. GWAS of overall proteomic age identified AFF3 and IL1RAPL1 as associated with biological ageing.

interpretationAdvanced proteomic ageing, in particular kidney organ ageing, was associated with age-related traits and diseases and, pending further validation, may have utility as a potential biomarker for clinical trials.

fundingBritish Heart Foundation.

Indexed as

AgingProteomeProteomicsAgedEast Asian PeopleFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMiddle AgedOrgan SpecificityRisk FactorsProteomeBiological ageingDiseaseEast AsianOrgan-specificProteomicsTraits

Identifiers

PMID42526095
PMCPMC13450583

What Socratic holds

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LicenceCC BY
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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 Socratic graph.