Evidence map›Paper›PMID 41381541›Full record

ArticleNature communications2025

Revealing the genetic architectures underlying organ-specific aging based on proteomic data.

Ren-Jie Zhu, Yan Guo, Jia-Hao Wang, Ke Yu, Wei Shi, Wen Pan, Xian-Lin Yu, Wei Chen, Shan-Shan Dong, Tie-Lin Yang

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

10 authors.

Ren-Jie Zhu *Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.ORCID http://orcid.org/0000-0002-9266-2209
Yan Guo *Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.ORCID http://orcid.org/0000-0002-7364-2392
Jia-Hao WangBiomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
Ke YuBiomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
Wei ShiBiomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.ORCID http://orcid.org/0000-0003-4751-9275
Wen PanDepartment of Health Management Center, Hospital of Chengdu Office of People's Government of Xizang Autonomous Region (Hospital.C.X.), Chengdu, Sichuan, PR China.
Xian-Lin YuBiomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
Wei ChenDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
Shan-Shan DongBiomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China. dongss@xjtu.edu.cn.ORCID http://orcid.org/0000-0001-6976-4576
Tie-Lin YangBiomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China. yangtielin@xjtu.edu.cn.ORCID http://orcid.org/0000-0001-7062-3025

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Organ-specific plasma protein signatures identified via proteomics profiling could be used to quantitatively track organ aging. However, the genetic determinants and molecular mechanisms underlying the organ-specific aging process remain poorly characterized. Here we integrated large-scale plasma proteomic and genomic data from 51,936 UK Biobank participants to uncover the genetic architectures underlying aging across 13 organs. We identified 119 genetic loci associated with organ aging, including 27 shared across multiple organs, and prioritized 554 risk genes involved in organ-relevant biological pathways, such as T cell-mediated immunity in immune aging. Causal inference analyses indicated that accelerated heart and muscle aging increase the risk of heart failure, whereas kidney aging contributes to hypertension. Moreover, smoking initiation was positively linked to the aging of the lung, intestine, kidney, and stomach. These findings establish a genetic foundation for understanding organ-specific aging and provide insights for promoting healthy longevity.

Indexed as

AgingOrgan SpecificityProteomicsAdultAgedAged, 80 and overFemaleGenome-Wide Association StudyHumansKidneyLongevityMaleMiddle AgedSmoking

Identifiers

PMID41381541
PMCPMC12804865

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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.