Evidence map›Paper›PMID 40345707›Full record

ArticleRMD open2025

Metabolomic profiles underlying gout flares: a prospective study of people with gout.

Wenyan Sun, Rui Li, Nicola Dalbeth, Lingling Cui, Zhen Liu, Can Wang, Lin Han, Hui Zhang, Jie Lu, Huiyong Yin and 2 more

Abstract read
In one paragraph

Article in RMD open, 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. Article
  2. Review
  3. Article
  4. 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

12 authors.

Wenyan Sun *The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.ORCID 0000-0003-2265-3566
Rui Li *Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences (CAS), Shanghai, China.
Nicola Dalbeth *University of Auckland, Auckland, New Zealand.ORCID 0000-0003-4632-4476
Lingling CuiThe Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Zhen LiuThe Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Can WangThe Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.ORCID 0000-0003-0719-3952
Lin HanThe Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Hui ZhangQingdao University, Qingdao, Shandong, China.
Jie LuThe Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Huiyong Yin *Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences (CAS), Shanghai, China lichanggui@medmail.com.cn hbchen@tongji.edu.cn huiyoyin@cityu.edu.hk.
Haibing Chen *Shanghai Tenth People's Hospital, Shanghai, Shanghai, China.
Changgui Li *The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China lichanggui@medmail.com.cn hbchen@tongji.edu.cn huiyoyin@cityu.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo identify specific metabolomic profiles associated with gout flares in people with gout.

methodsParticipants with gout were sequentially enrolled. In cross-sectional analysis, data were analysed according to the presence of gout flare (acute group) or absence of gout flare (intercritical group) at the time of enrolment. Participants in the intercritical group were prospectively followed and analysed according to the development of gout flares (recurrent flare group) or no gout flare (no flare group) over 1 year. Relative abundances of metabolites in serum obtained at the baseline visit were measured by untargeted liquid chromatography-mass spectrometry. Risk of incident flare was analysed using least absolute shrinkage and selection operator (LASSO)-Cox regression and time-receiver operating characteristic (ROC). Machine learning models were performed to identify biomarkers in cross-sectional and longitudinal analysis, which was further optimised using quantitative targeted metabolomics in an independent validation cohort.

resultsParticipants in the acute and intercritical groups showed distinct metabolic profiles, including carbohydrate, lipid and nucleotide metabolism. Many metabolites were associated with recurrent gout flare in the prospective analysis. The metabolic risk score with six LASSO-derived metabolites, including 5-methoxytryptamine, differentiated well for gout flare risk, yielding an area under the ROC curve (AUC) of 0.82 (95% CI 0.74 to 0.90). Machine learning models achieved an AUC of 0.828 for comparison between the acute and intercritical groups. For the prediction of recurrent flare, AUC reached 0.807-0.867 with combined metabolites and clinical measurements.

conclusionsMetabolic reprogramming differentiates between the acute and intercritical stages of gout, and implicated metabolites may serve as biomarkers for future gout flares.

Indexed as

GoutMetabolomeMetabolomicsAdultAgedBiomarkersCross-Sectional StudiesFemaleHumansMachine LearningMaleMiddle AgedProspective StudiesROC CurveSymptom Flare UpBiomarkersGoutInflammationMachine Learning

Identifiers

PMID40345707
PMCPMC12067777

What Socratic holds

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LicenceCC BY-NC
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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.