ArticleFrontiers in molecular biosciences2026
Untargeted metabolomics reveals immune-metabolic signatures in established cases of rheumatoid arthritis.
Article in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Metabolic control of RNA methylation in rheumatoid arthritis: from synovial stress to pathogenic cellular adaptation.Frontiers in immunology · 2026Review
- Metabolic-epigenetic rewiring in rheumatoid arthritis: from pathogenic memory to precision restoration.Frontiers in immunology · 2026Review
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Authors and funding
11 authors.
Funding
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Abstract
Introduction: Rheumatoid arthritis (RA) is a complex progressive autoimmune disorder wherein chronic inflammation is tightly coupled to metabolic reprogramming. The known diagnostic markers are not sensitive and specific enough to reflect disease activity. Finding a metabolomics-based biomarker specific for established cases of RA is important. This study aimed to investigate the metabolomic profiles of patients with established RA compared to those of controls. Methods: An untargeted high resolution mass spectrometry (MS)-based metabolomics approach with bioinformatics analysis was used to analyze 122 plasma samples, patients (n = 60), and controls (n = 62). Results: A total of 300 significantly dysregulated metabolites (unpaired t-test with FDR q value < 0.05, FC cut off 1.5) were identified between RA and controls, where 147 were upregulated and 153 downregulated. From among these, 182 metabolites were identified and annotated and after excluding the exogenous metabolites 60 endogenous metabolites were successfully identified. Results from the OPLSDA model showed a clear separation between patients with RA and controls (Q2 = 0.736, R2 = 0.988), indicating significant metabolic differences between the groups. The plasma metabolomics profile revealed statistically significant changes in metabolites belonging to different classes including those involved in lipid (including Succinyladenosine, CDP- DG (PGE Conclusion: Our findings support the potential of plasma metabolomics for phenotyping and highlight potential candidate biomarkers for disease prognosis and monitoring in RA.
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