Correlation of Serum Ferritin With SLEDAI (SLE Disease Activity Index) and Its Relation With Different Disease Parameters in Systemic Lupus Erythematosus (SLE)

Abstract

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with fluctuating multisystem involvement and unpredictable inflammatory activity. Accurate quantification of disease activity is critical for timely therapeutic decisions, yet available clinical indices such as the SLE Disease Activity Index (SLEDAI) lack sensitive biochemical adjuncts. Serum ferritin, an iron-storage protein with acute-phase properties, may reflect inflammatory burden, but its role as a global biomarker of SLE activity across organ systems remains inadequately characterized. Objective: To evaluate the correlation between serum ferritin and SLEDAI and to determine ferritin’s relationship with specific organ involvement and its predictive capacity for severe disease activity. Methods: A prospective cross-sectional study was conducted among 112 consecutive SLE patients at a tertiary rheumatology center in Lahore, Pakistan (January–June 2025). Demographic, clinical, and laboratory data were collected, including SLEDAI scores and serum ferritin quantified by chemiluminescent immunoassay. Correlation and regression analyses assessed associations between ferritin and SLEDAI, while logistic regression and ROC analysis determined ferritin’s predictive ability for severe disease (SLEDAI ≥12). Results: The mean age was 29.1 ± 7.9 years; 89.3% were female. Serum ferritin correlated positively with SLEDAI (ρ = 0.34, p < 0.001) and remained significant after adjustment (β = 2.4, p = 0.002). Median ferritin levels were highest in nephritis (720 ng/mL) and hematologic involvement (810 ng/mL). Patients in the highest ferritin quartile (>1000 ng/mL) had markedly greater odds of severe disease (OR = 23.9, 95% CI 5.0–114, p < 0.001) with good discrimination (AUC = 0.82). Conclusion: Serum ferritin strongly correlates with global and organ-specific disease activity in SLE and serves as a cost-effective biomarker for identifying patients at risk of severe disease. Its incorporation into disease monitoring protocols may enhance precision in clinical decision-making.