Clinical rheumatology · 2025
B lymphocyte subset-based stratification in primary Sjögren's syndrome: implications for lymphoma risk and personalized treatment.
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Qi Xuan, Zhao Doudou, Wang Naidi, Han Yipeng, Huang Bo, Feng Ruiling, Jin Yuebo, Wang Ruoyi, Lin Xiang, He Jing
Аннотация
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. 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.
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. 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.