Evidence map›Paper›PMID 42860408›Full record

ArticleNature communications2026

Atlas of genetic architecture and exploratory temporal interaction networks of plasma protein-based organ and system aging.

Fan Pu, Xueqing Jia, Xiaoxi Hu, Youheng Wu, Ciyun Zhao, Yanjie Zhao, Jiening Yu, Shuming Wang, Shuhui Wu, Shenghan Wang and 22 more

Abstract read
In one paragraph

Article in Nature communications, 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

32 authors.

Fan Pu *Center for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0002-9498-6936
Xueqing Jia *Center for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xiaoxi Hu *School of Life Sciences, Fudan University, Shanghai, China.ORCID http://orcid.org/0000-0002-8696-9001
Youheng Wu *Center for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Ciyun Zhao *Center for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yanjie ZhaoCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jiening YuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Shuming WangHuman Phenome Institute, Fudan University, Shanghai, China.
Shuhui WuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Shenghan WangSchool of Life Sciences, Fudan University, Shanghai, China.
Duoduo FuDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0009-0000-1482-0390
Dianqing ChenDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Yishan WangDepartment of Clinical Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Zhiyong ZhaoKey Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
Dan WuKey Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.ORCID http://orcid.org/0000-0003-4703-4040
Nan LiuBone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0002-8314-0900
Junfang ChenCenter for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, China.
Xiaofeng WangSchool of Life Sciences, Fudan University, Shanghai, China.
Juulia JylhäväDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0003-0250-4491
Chiara HerzogEuropean Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria.
Alan A CohenButler Columbia Aging Center and Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.ORCID http://orcid.org/0000-0003-4113-3988
Huaidong DuClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-9814-0049
Derrick BennettClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-9170-8447
Robin WaltersClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-9179-0321
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-0019-0014
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID http://orcid.org/0000-0001-7916-3870
Zhengming ChenClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-6423-105X
Liming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID http://orcid.org/0000-0001-5873-7089
Li JinSchool of Life Sciences, Fudan University, Shanghai, China.ORCID http://orcid.org/0000-0001-9201-2321
Dianjianyi SunDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China. dsun1@bjmu.edu.cn.ORCID http://orcid.org/0000-0003-3651-6693
Zixin HuShanghai Academy of Artificial Intelligence for Science, Shanghai, China. huzixin@fudan.edu.cn.
Zuyun LiuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. zuyunliu@zju.edu.cn.ORCID http://orcid.org/0000-0001-6120-5913

Funding

Wellcome Trust
6 · The paper itself

Abstract

Organ-level investigations provide insights into inter-individual heterogeneity in health and disease, yet aging across organs and systems remains incompletely understood. Leveraging plasma proteomic data from 53,014 UK Biobank participants, we construct 16 organ- and system-specific aging clocks that show 5436 significant associations with 1059 diseases (i.e., 655 incident and 404 prevalent diseases) and widespread responses to 841 diverse environmental factors, highlighting organ-specific and pan-organ patterns. These clocks are benchmarked against two existing models and then externally validated in the China Kadoorie Biobank (n = 3977), supporting their robustness and generalizability. Genetic analyses identify 261 genomic loci associated with these clocks, revealing organ-specific and pleiotropic genetic architectures. Notably, the immune system and liver exhibit the strongest genetic connectivity with other organ systems, closely mirroring their phenotypic correlations. Using longitudinal data from 1006 participants, we apply a multitask Bayesian framework to explore potential temporal interaction networks across organs and systems, identifying temporal precedence of the immune system, liver and stomach, with sex-specific patterns characterized by stronger arterial precedence in females and stomach precedence in males. Together, this study provides a comprehensive atlas integrating genetic, environmental, and interactive insights into multiple plasma protein-based organ and system aging, informing future research on targeted anti-aging strategies.

Indexed as

AgingBlood ProteinsAgedBayes TheoremFemaleHumansLiverMaleMiddle AgedOrgan SpecificityProteomicsUK BiobankBlood Proteins

Identifiers

PMID42860408
PMCPMC13655183

What OpenQuestion holds

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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.