Integrating genetic, epigenetic, and clinical signatures via machine learning for robust prediction of leflunomide response in rheumatoid arthritis: a multi-center validation study

ObjectiveTo develop and validate a machine learning(ML)-based integrated predictive model combining genetic, epigenetic, and clinical factors for predicting leflunomide (LEF) treatment response in rheumatoid arthritis (RA) patients.MethodsA total of 357 RA patients (231 in the model development cohort [MDC], 126 in the external validation cohort [EVC]) were recruited from multiple centers in China. Whole-exome sequencing(WES), genome-wide DNA methylation profiling, and comprehensive clinical data were integrated for model development.

Feature selection was performed via univariate analysis, Least Absolute Shrinkage and Selection Operator(LASSO) regression, and clinical feasibility filtering. Ten ML algorithms were tested, with SHapley Additive exPlanations (SHAP) for interpretability, and external validation to assess generalizability.ResultsThe final integrated model included 3 single nucleotide polymorphisms (SNPs: ESR1-rs2813563, ABCC2-rs4148396, LMO4-rs983332), 7 differentially methylated positions (DMPs: cg13568171-MECR, cg07694252-ANGPT1, cg13401893-RNF39, cg19814518-UHMK1, cg26370237-HSF5, cg11136343-intergenic, cg15961042-intergenic), and 3 clinical variables (IgG, course of disease, baseline Disease Activity Score 28(DAS28)).