Evidence map›Paper›PMID 41025730›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Diagnosis, Prognosis, and Drug Target Discovery for Chronic Widespread Pain: A Large Proteogenomic Study.

Li Chen, Eoin Kelleher, Ruogu Meng, Duanke Liu, Yuchen Guo, Yunhe Wang, Yaqing Gao, Zhe Huang, Zhu Liang, Shuai Yuan and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

17 authors.

Li ChenCentre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, OX3 7LD, UK.ORCID https://orcid.org/0000-0002-8607-8997
Eoin KelleherNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK.
Ruogu MengNational Institute of Health Data Science, Peking University, Beijing, 100191, China.
Duanke LiuBeijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
Yuchen GuoCentre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, OX3 7LD, UK.
Yunhe WangNuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
Yaqing GaoNuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
Zhe HuangNuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
Zhu LiangNuffield Department of Medicine, Target Discovery Institute, Center for Medicines Discovery, University of Oxford, Oxford, OX3 7FZ, UK.
Shuai YuanDepartment of Surgery, Perelman School of Medicine at the University of Pennsylvania, PA, 19104, USA.
Chao ZengDepartment of Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410013, China.
Jun MaInstitute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China.
Yanhui DongInstitute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China.
Anushka IraniCentre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, OX3 7LD, UK.
Guanghua LeiDepartment of Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410013, China.
Junqing XieCentre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, OX3 7LD, UK.ORCID https://orcid.org/0000-0002-0040-0042
Daniel Prieto-AlhambraCentre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, OX3 7LD, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic widespread pain (CWP) remains challenging due to its heterogeneous causes and complex mechanisms. A total of 2920 plasma proteins are analyzed from 29,254 UK Biobank participants. A total of 256 proteins are identified as cross-sectionally correlated with CWP. A simple (top 10 proteins) and comprehensive (all significant proteins) proteomic-based score (ProtS) is created for CWP diagnosis, both outperforming and improving the existing clinical score (area under the curve, AUC: 0.801, 0.723, and 0.791 alone, and 0.856 and 0.880 in combination). In addition, the protein score predicted 13-years risk of pain-related traits over the body, including pain onset, progression, and intensity; Moreover, it has stronger associations with nociplastic pain and fibromyalgia compared to nociceptive and neuropathic pain, implying a unique protein signature of different pain mechanisms. Finally, among 434 candidate proteins prioritized in the observational analysis, 18 are corroborated with causal relevance by Mendelian randomization, and importantly, four (CA14, DPEP1, LGALS3, and TNF) showed potential as novel drug targets repurposed for treating CWP.

Indexed as

Chronic PainDrug DiscoveryProteogenomicsAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedPrognosisProteomicsbiomarkerdrug‐repurposingnociplastic painproteomicwide spread pain

Identifiers

PMID41025730
PMCPMC12713070

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.