B lymphocyte subset-based stratification in primary Sjögren's syndrome: implications for lymphoma risk and...

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.

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.

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.

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. Key Points • Identifying a distinct patient subgroup with elevated lymphoma risk and increased disease activity could aid in risk prediction. • Applying machine learning techniques to stratify B cell populations provides insights into pSS pathogenesis. • A proposed framework for personalized treatment approaches based on B cell subset imbalances in pSS.