Evidence map›Paper›PMID 40664692›Full record

ArticleNature communications2025

Linear and non-linear proteome-wide association studies provide novel insight into venous thromboembolism.

Yifan Kong, Wangxia Tang, Haonan Kang, Yunlong Guan, Si Li, Xi Cao, Zhonghe Shao, Yi Jiang, Chaolong Wang, Xingjie Hao

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Proteomics-Driven Strategies for Proximity-Inducing Drug Discovery.Angewandte Chemie (International ed. in English) · 2026
    Review
  2. Review
  3. Article
  4. Proteomic Insights into Venous Thromboembolism.Medical sciences (Basel, Switzerland) · 2026
    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

10 authors.

Yifan KongDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Wangxia TangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Haonan KangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Yunlong GuanDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Si LiDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.ORCID http://orcid.org/0009-0003-1556-1249
Xi CaoDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Zhonghe ShaoDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Yi JiangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China. jiangyi3029@hust.edu.cn.
Chaolong WangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China. chaolong@hust.edu.cn.ORCID http://orcid.org/0000-0003-3945-1012
Xingjie HaoDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China. xingjie@hust.edu.cn.ORCID http://orcid.org/0000-0003-1535-9860

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32470658National Natural Science Foundation of China (National Science Foundation of China) 82325044
6 · The paper itself

Abstract

Venous thromboembolism is a life-threatening vascular event with high prevalence and genetic determinants. PWAS has become a popular strategy to identify therapeutic targets of complex diseases. However, the current PWAS model only considers the linear relationship between protein and disease. Here, we propose a novel non-linear PWAS pipeline and identify 43 proteins exhibiting non-linear associations with venous thromboembolism in the UK Biobank, of which eight proteins cannot be captured by linear PWAS. We further conduct prospective cohort replication in the UK Biobank Pharma Proteomics Project, and replicate eight proteins with similar non-linear trends, including ULBP2, IL18BP, MAN1A2, CCL25, ICAM2, LGALS4, VSIG2 and ABO. Pathway enrichment analysis suggests that the identified non-linear proteins are involved in endothelium development, fluid shear stress and atherosclerosis pathways. In summary, we develop a novel non-linear PWAS analysis pipeline, and identify 43 non-linear proteins with venous thromboembolism, highlighting the importance of incorporating non-linear analysis in PWAS.

Indexed as

ProteomeProteomicsVenous ThromboembolismFemaleGenome-Wide Association StudyHumansMaleMiddle AgedNonlinear DynamicsProspective StudiesUnited KingdomProteome

Identifiers

PMID40664692
PMCPMC12264037

What Socratic 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 Socratic graph.