BACKGROUND: To identify serum metabolic biomarkers that distinguish corticosteroid and cyclosporin A (CS 3O, and SPB 20:0;2O) exhibited AUC values of 0.934, 0.953, and 0.904, respectively, and were all upregulated in CS & CsA resistant patients. In contrast, N-acetylaspartic acid showed an AUC of 0.934 and was downregulated in CS & CsA resistant patients. The combined classification model incorporating these 4 metabolites achieved an AUC of 1.0. Validation in an independent internal cohort confirmed the model's excellent performance, with AUC values of 0.971 for NNET, 0.971 for LASSO, and 0.957 for XGBoost. CONCLUSION: We have established a classification model capable of effectively discriminating CS & CsA-resistant from -sensitive PIU patients. The machine learning model leveraging metabolic biomarkers demonstrates exceptional classification accuracy and generalizability, offering potential for clinical subtype classification.
Chang et al. (Sat,) studied this question.