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Background <p>Rheumatoid 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.</p> Methods <p>Three 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).</p> Results <p>Our 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].</p> Conclusions <p>The 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.</p>

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A DNA methylation-based algorithm for diagnosing rheumatoid arthritis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月17日

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  • Espen Riskedal,
  • Astanand Jugessur,
  • Silje Watterdal Syversen,
  • Cathrine Lund Hadley,
  • Jennifer R. Harris,
  • Maria Dahl Mjaavatten,
  • Joe Sexton,
  • Janis Neumann,
  • Gina Hetland Brinkmann,
  • Guro Løvik Goll,
  • Grethe-Elisabeth Stenvik,
  • Håkon Bøås,
  • Arne Søraas,
  • Karl Trygve Kalleberg,
  • Siri Lillegraven,
  • Espen A. Haavardsholm

摘要

Background

Rheumatoid 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.

Methods

Three 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).

Results

Our 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].

Conclusions

The 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.