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 Float="No" ID="Taba"> <tgroup cols="2"> <colspec align="left" colname="c1" colnum="1" /> <colspec align="left" colname="c2" colnum="2" /> <tbody> <row> <entry nameend="c2" namest="c1"> 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. </entry> </row> </tbody> </tgroup> </Table>

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

期刊论文
发表日期：

2025年4月29日

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Xuan Qi,
Doudou Zhao,
Naidi Wang,
Yipeng Han,
Bo Huang,
Ruiling Feng,
Yuebo Jin,
Ruoyi Wang,
Xiang Lin,
Jing He

摘要

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.