Evidence map›Paper›PMID 39805987›Full record

ArticleNature metabolism2025

Longitudinal serum proteome mapping reveals biomarkers for healthy ageing and related cardiometabolic diseases.

Jun Tang, Liang Yue, Ying Xu, Fengzhe Xu, Xue Cai, Yuanqing Fu, Zelei Miao, Wanglong Gou, Wei Hu, Zhangzhi Xue and 10 more

Abstract read
In one paragraph

Article in Nature metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Review
  2. Article
  3. Proteogenomics in human populations.Nature reviews. Genetics · 2026
    Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Observational
  10. Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Review
  16. New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 2026
    Review
  17. Article
  18. Article
  19. Review
  20. Review
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

20 authors.

Jun Tang *Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Liang Yue *Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Ying Xu *Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Fengzhe Xu *Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Xue CaiWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Yuanqing FuWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.ORCID http://orcid.org/0000-0002-3955-9376
Zelei MiaoWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Wanglong GouWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Wei HuGuangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Zhangzhi XueWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Kui DengWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Luqi ShenWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Zengliang JiangWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Menglei ShuaiWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Xinxiu LiangWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Congmei XiaoWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Yuting XieWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Tiannan GuoWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China. guotiannan@westlake.edu.cn.ORCID http://orcid.org/0000-0003-3869-7651
Yu-Ming ChenGuangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China. chenyum@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0003-1658-5528
Ju-Sheng ZhengWestlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China. zhengjusheng@westlake.edu.cn.ORCID http://orcid.org/0000-0001-6560-4890

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82073529National Natural Science Foundation of China (National Science Foundation of China) 82073546National Natural Science Foundation of China (National Science Foundation of China) 82103826National Natural Science Foundation of China (National Science Foundation of China) 82103828National Natural Science Foundation of China (National Science Foundation of China) 82204161
6 · The paper itself

Abstract

The blood proteome contains biomarkers of ageing and age-associated diseases, but such markers are rarely validated longitudinally. Here we map the longitudinal proteome in 7,565 serum samples from a cohort of 3,796 middle-aged and elderly adults across three time points over a 9-year follow-up period. We pinpoint 86 ageing-related proteins that exhibit signatures associated with 32 clinical traits and the incidence of 14 major ageing-related chronic diseases. Leveraging a machine-learning model, we pick 22 of these proteins to generate a proteomic healthy ageing score (PHAS), capable of predicting the incidence of cardiometabolic diseases. We further identify the gut microbiota as a modifiable factor influencing the PHAS. Our data constitute a valuable resource and offer useful insights into the roles of serum proteins in ageing and age-associated cardiometabolic diseases, providing potential targets for intervention with therapeutics to promote healthy ageing.

Indexed as

AgingBiomarkersBlood ProteinsCardiovascular DiseasesHealthy AgingMetabolic DiseasesProteomeAgedFemaleGastrointestinal MicrobiomeHumansLongitudinal StudiesMachine LearningMaleMiddle AgedProteomicsBiomarkersBlood ProteinsProteome

Identifiers

PMID39805987
PMCPMC11774760

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.