ArticleArthritis research & therapy2025
A DNA methylation-based algorithm for diagnosing rheumatoid arthritis.
Article in Arthritis research & therapy, 2025. 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.
- Hypomethylation of the HumanArchives of Iranian medicine · 2026Article
- Epigenetic alterations in rheumatoid arthritis: multilayer mechanisms and translational opportunities.Frontiers in immunology · 2026Review
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16 authors.
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Abstract
backgroundRheumatoid arthritis (RA), particularly seronegative disease, is difficult to diagnose early, which can delay treatment initiation. This study aims to develop a binary DNA methylation (DNAm)-based algorithm to diagnose RA.
methodsThree datasets (discovery, training, holdout) were constructed from DNAm profiles from 1366 persons (treatment-naïve RA, other inflammatory/autoimmune diseases, healthy individuals). DNAm features that differentiate RA from other inflammatory/autoimmune diseases and healthy individuals were identified using the discovery set. Our classification algorithm was developed using machine learning techniques in the training set. Its diagnostic performance, with and without serological status, was evaluated in the holdout set containing RA cases (15 seropositive, 6 seronegative) and controls (14 other arthritides, 11 healthy individuals).
resultsOur algorithm included 391 DNAm features. Combined with serological status, it classified RA from controls in the holdout set with the following performance: sensitivity 0.90 [95% CI: 0.70-0.99], specificity 0.88 [95% CI: 0.69-0.97], and AUC 0.96 [95% CI: 0.91-1.00]. Its performance in classifying patients with seronegative RA versus those with other arthritides was: sensitivity 0.83 [95% CI: 0.36-1.00], specificity 0.79 [95% CI: 0.49-0.95], and AUC 0.81 [95% CI: 0.61-1.00].
conclusionsThe present DNAm-based classification algorithm may be clinically useful for the early diagnosis of RA, especially in seronegative patients, which currently often poses a diagnostic challenge.
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