POS0827 AUTOANTIBODY-COMBINED SIGNATURES DELINEATING IMMUNOLOGIC CLUSTERS RELATED TO HIGH SYSTEMIC ACTIVITY IN PATIENTS WITH SJÖGREN DISEASE

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Background: Patients with Sjögren disease (SjD) often present with multiple serum autoantibodies, most commonly ANA, RF, anti-Ro, and anti-La antibodies. Traditional approaches often examine a potential predictive role of these autoantibodies in isolation. However, the predictive power of specific combinations of autoantibodies for systemic disease presentation remains unclear. Objectives: To determine whether a particular autoantibody combination—or "immunologic signature"— can help stratify patients into high vs. no-high systemic disease expression at the onset of SjD, investigating which specific combinations of ANA, RF, anti-Ro, and anti-La antibodies are associated with a more aggressive disease, and identifying and validating immunologic "signatures" for stratifying patients by disease severity. Methods: The global dataset comprised 17,416 patients fulfilling the 2002/2016 classification criteria (Sjögren Big Data Registry). The study analysed the distinct patterns resulting from all the possible combinations of the four key SjD-related autoantibodies (ANA, RF, anti-Ro, and anti-La), each treated as a dichotomous variable (positive/negative). We employed a genAI-assisted hierarchical clustering and network analysis to clarify the relationships between these autoantibody patterns and the DAS. Contingency tables were constructed to assess the frequencies of each autoantibody pattern in relation to the Disease Activity Score (DAS) categories. Associations between autoantibody combinations and DAS categories were explored using Chi-square tests, followed by the calculation of odds ratios (ORs). An ordinal logistic regression was used to evaluate how various antibody combinations influence disease severity (low → moderate → high). Results: The individual frequencies of positive autoantibodies at diagnosis were 83.78% for ANA, 75.88% for anti-Ro, 42.82%for RF and 41.89% for anti-La. DAS categories were distributed as follows: Low (53.59%), Moderate (34.18%), and High (12.24%). Analysis of the 16 possible autoantibody-positive combinations showed that 6 of these accounted for the majority of cases in the cohort. Quadruple positivity (ANA+Ro+RF+La+) was the most frequently immunological signature, found in 3,586 patients (20.59%), followed by ANA+Ro+ in 2,556 (14.68%), ANA+RF+Ro+ in 1743 (10%) and ANA+Ro+La+ in 1706 (7.94%) patients. An ordinal logistic regression indicated significant associations between most autoantibody clusters and high DAS, with odds ratios ranging from 1.45 to 11.51 (p<0.001). However, reduced ordinal logistic regression confirmed that only 4 specific clusters were robustly associated with high DAS, with ORs ranging from 1.86 to 3.57. Among the identified clusters, patients positive for ANA+RF+Ro+ had the highest OR (3.57, 95% CI: 3.06–4.15, p<0.001), followed by those positive for ANA+Ro+ (OR 2.80, 95% CI: 2.37–3.31, p<0.001), those for ANA+Ro+La+ (OR 2.53, 95% CI: 2.20–2.91, p<0.001) and finally, patients presenting with the quadruple positivity ANA+RF+Ro+La+ (OR 1.85, 95% CI: 1.81–1.91, p<0.001) (Figure 1). Figure 1 Conclusion: The presence of multiple positive autoantibodies correlated with more severe systemic disease in patients diagnosed with SjD, but only those presenting with 4 specific autoantibody-positive clusters (ANA+RF+Ro+, ANA+Ro+, ANA+Ro+La+ and ANA+RF+Ro+La+) were 2-3 times as likely to present with high systemic activity compared to the immunonegative control cluster (negative results for the 4 autoantibodies), bolstering the hypothesis of synergy in immune-mediated damage. By identifying these 4 high-risk, combined autoantibody signatures at diagnosis, clinicians can better anticipate systemic complications guiding early intensified follow-up. This approach can accelerate early, biomarker-driven interventions tailored to immunologic risk profiles, ultimately helping clinicians triage patients and initiate more aggressive therapeutic strategies when necessary. This study pioneers the use of hierarchical clustering and network analysis to dissect complex autoantibody interactions in Sjögren's disease, a novel approach that moves beyond traditional single-biomarker analyses. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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