ASSESSMENT OF SERUM INTERLEUKIN-6, FAMILIAL, AND INFECTIOUS FACTORS ASSOCIATED WITH RHEUMATOID ARTHRITIS: A CASE–CONTROL STUDY
Abstract
Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease involving interactions between genetic susceptibility, infectious exposures, and immune dysregulation. This study assessed familial and infectious factors associated with RA and evaluated serum interleukin-6 as a potential biomarker. Methods: A case–control study included 47 participants: 28 patients with RA and 19 healthy controls. Demographic characteristics, family history of RA and autoimmune disease, severe viral infection, environmental and reproductive factors, and COVID-19 vaccination status were assessed. Serum IL-6 concentrations were compared between RA patients and controls. Results: Mean age was comparable between RA patients and controls (49.89 ± 15.38 vs 49.63 ± 4.52 years, P = 0.935), whereas females were significantly more frequent among RA patients (96.4% vs 57.9%, P = 0.0016). Family history of RA was significantly associated with RA (50.0% vs 5.3%; OR = 18.00, P = 0.0013), while family history of autoimmune disease showed a stronger association (71.4% vs 5.3%; OR = 45.00, P < 0.0001). Severe viral infection was also significantly associated with RA (39.3% vs 5.3%; OR = 11.65, P = 0.0149). COVID-19 vaccination was lower among RA patients than controls (35.7% vs 72.2%; OR = 0.21, P = 0.0331). Serum IL-6 concentrations did not differ significantly between RA patients and controls (87.61 ± 9.64 vs 86.88 ± 13.03 pg/mL), with identical median values (90.34 pg/mL). The estimated ROC AUC of approximately 0.50–0.60 indicated weak discriminatory performance. Conclusion: Familial autoimmune susceptibility and severe viral infection were significantly associated with RA. Despite its biological importance in RA inflammation, serum IL-6 demonstrated limited standalone diagnostic discrimination in this cohort. Larger studies are needed to validate these associations and develop integrated clinical and immunological models for RA assessment.