Machine Learning-Based Analysis of Serum Interleukin-39 and Interleukin-40 Levels for Differentiating Rheumatoid Arthritis and Systemic Lupus Erythematosus.

Objective: Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are both severe autoimmune diseases characterised by immune dysregulation and systemic inflammation. Despite advances in diagnostic tools, distinguishing RA from SLE remains challenging due to overlapping clinical manifestations. Emerging evidence highlights the potential roles of novel cytokines, such as interleukin-39 [(IL)-39] and IL-40, in autoimmune pathogenesis. This study aimed to evaluate the diagnostic utility of serum levels of IL-39 and IL-40 for differentiating RA from SLE using several machine learning (ML) algorithms. Methods: Data from 66 patients with RA and 66 patients with SLE were analysed using previously published serum IL-39 and IL-40 datasets. ML algorithms, namely logistic regression, random forest, decision tree, and support vector machine, were applied. Model performance was evaluated using sensitivity, accuracy, specificity, and area under the receiver operating characteristic curve. Results: SLE patients exhibited significantly higher serum IL-39 and IL-40 levels than those of RA patients (p<0.001). The random forest model achieved an accuracy of 92.4% and an AUC of 0.95. Feature importance analysis revealed that IL-39 and IL-40 contributed 58% and 42%, respectively to the classification performance. Conclusions: ML models based on IL-39 and IL-40 serum levels can effectively differentiate RA from SLE. The findings suggest that integrating artificial intelligence-based analytical approaches with novel cytokine biomarkers may enhance diagnostic precision and support differential diagnosis in autoimmune diseases.