B lymphocyte subset-based stratification in primary Sjögren’s syndrome: implications for lymphoma risk and personalized treatment

Abstract

<p>Objective: This study aimed to perform a detailed stratification analysis of B lymphocyte subsets in patients with primary Sjögren’s syndrome (pSS) and to investigate their associations with lymphoma risk, clinical phenotypes, and disease activity. Methods: In this retrospective study, we analyzed data from 137 patients with pSS. We employed machine learning approaches, specifically principal component analysis (PCA) and k-means clustering, to examine B lymphocyte subset distributions from flow cytometry data and immunoglobulin IgG and complement (C3, C4) levels. The optimal cluster number was determined using the Elbow Method in R software. Based on these 10 variables, patients were categorized into distinct subgroups. We then comprehensively compared clinical characteristics, laboratory parameters, and disease activity indices among these identified subgroups. Results: Four distinct subgroups were identified. Cluster A exhibited a significantly higher lymphoma incidence rate of 20%, compared to 3.39% in Cluster B and 0% in Clusters C and D (p = 0.007). Cluster A also had the highest percentage of double-negative B cells (32.26 ± 17.96%) and plasma cells (2.02 ± 1.92%). ESSDAI scores indicated that disease activity was highest in Cluster A (9.00, 6.00–20.00), followed by Clusters B (7.00, 3.50–14.00), C (6.00, 1.25–17.50), and D (5.00, 1.50–9.00), respectively. Conclusion: This innovative stratification method revealed the critical role of B cell subset imbalance in the pathogenesis of pSS and provided new evidence for predicting lymphoma risk and guiding personalized treatment. (Table presented.)</p>