PREDICTION OF SLE FLARES BY MEASURING AUTOANTIBODY DYNAMICS: A NOVEL APPROACH FOR EARLY DETECTION AND MONITORING

PV027 / #769 Poster Topic: AS04 - Biomarkers Background/Purpose Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by a highly variable disease course, including unpredictable flares that can lead to significant morbidity. Early identification of impending flares could improve patient management and outcomes. This study investigates whether measuring dynamic levels of autoantibodies can predict the occurrence of SLE flares. Methods A cohort of 100 SLE patients was prospectively followed for 2 years, with visits scheduled every 3 months at the outpatient clinic of the University Medical Center, Utrecht, the Netherlands. At each visit, clinical parameters were recorded, including the presence or absence of a flare, assessed with the SELENA-SLEDAI Flare Index. Additionally, blood samples were collected, and blood biomarker levels, including antibodies associated with SLE as well as calprotectin as a marker of neutrophil activation, were semiquantitatively measured using the EliATM technology (Phadia AB, Sweden) on citrate plasma. To assess whether changes in these autoantibodies predicted the occurrence of a flare 3 months later, binary logistic regression analysis was performed. For patients who experienced a flare during follow-up, changes (Δ) in biomarker levels were calculated between baseline and the preflare timepoint. For patients without flares, the highest Δ value observed between any 2 time points was used (Figure). Figure. For no-flare patients, change in autoantibody values was calculated between all consecutive visits and the maximum Δ (ie, the maximum background dynamics in patients with stable disease) used. For flare patients, Δ between baseline and the visit prior to flare was calculated to assess dynamics preflare. aB2: anti-beta-2-glycoprotein-1; aCL: anti-cardiolipin. p<0.05 statistically significant. Results The cohort consisted of 100 SLE patients with a median age of 50 years (IQR 39-57); 88 women and 12 men. Regarding ethnic distribution, 77 patients were White, 9 Asian, and 4 Black. Median disease duration was 18 years (IQR 8-28). Median SLEDAI score at baseline was 4 (IQR 2-6). During follow-up, 132 flares were registered, of which 119 moderate and 13 severe. In the binary logistic regression analysis changes in La/SSB (OR 1.48, 95% CI 1.026-2.129), Ro52 (OR 1.03, 95% CI 1.004-1.047), and Ro60 (OR 1.04, 95% CI 1.007-1.076) were identified as significant predictors of a flare occurring within the following 3 months. RA33 IgA was inversely associated with flare risk (OR 0.20, 95% CI 0.053-0.757) (Table 1). Table 1: Results of binary logistic regression analysis assessing whether changes in autoantibody titers predict the occurrence of flares 3 months later. Conclusions These findings suggest that the dynamics over time of specific autoantibody levels, particularly La/SSB, Ro52, and Ro60, can serve as predictors for impending disease flares in SLE patients. Additionally, the inverse association of RA33 IgA with flare risk indicates a potential modulatory role in disease activity. Further research is needed to elucidate the underlying mechanisms of this findings and explore their clinical applicability in personalized disease management.

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