Immunophenotyping and machine learning-based stratification reveal prognostic immune signatures in Behçet’s disease

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OBJECTIVE: The aim was to characterize peripheral blood lymphocyte subsets in BD patients, and to stratify patient subgroups and identify prognostic biomarkers. METHOD: Immunophenotyping of 11 immune cell subsets was performed using flow cytometry in peripheral blood samples from treatment-naive BD patients (n = 71) and age- and sex-matched healthy controls (HCs) (n = 71). XGBoost, PLS-DA and k-means clustering were employed to analyse the immune profiles and stratify BD patients based on their immunological subtypes and clinical parameters. Kaplan-Meier analysis and log-rank tests were used to compare relapse-free survival across BD patient clusters. Cox regression analysis was used to assess the clinical and immunological predictors of relapse. RESULTS: Our study reveals a T-cell dominant immune dysregulation in BD patients compared with HCs. Machine learning integration of immunophenotyping and clinical parameters identified two BD subgroups. Cluster 2 exhibited hyperactivated T cells (increased memory CD4+ T cells, CD8+DR+ T cells and CD8+CD38+ T cells), higher inflammatory markers (hsCRP, ESR, NLR and PLR), higher disease severity scores and relapse risk. Multivariate Cox regression analysis revealed that high hsCRP and CD8+DR+T-cells proportion were independent predictors of relapse. The HR-weighted combinations of CD8+DR+T%, hsCRP and disease severity scores exhibited superior relapse predictive performance (AUC = 0.815). CONCLUSION: This study provided a comprehensive immunophenotypic profile of treatment-naive BD patients. Machine learning integration of lymphocyte subsets and clinical parameters enabled the identification of subgroups at high risk of relapse at an early stage, providing a rationale for personalized treatment strategies.

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