Development and multi-center validation of machine learning models based on targeted metabolomics for rheumatoid arthritis

Rheumatoid arthritis (RA) remains in urgent need of more effective biomarkers to improve diagnostic accuracy. In this study, we conducted a comprehensive analysis of 2,863 blood samples obtained from seven cohorts comprising RA, osteoarthritis (OA), and healthy control (HC) subjects, recruited across five medical centers spanning three geographically diverse regions. Candidate biomarkers were first identified through untargeted metabolomic profiling, and subsequently validated using targeted approaches. Metabolite-based classification models were then developed employing a range of machine learning algorithms. Six metabolites were ultimately identified as promising diagnostic biomarkers, including imidazoleacetic acid, ergothioneine, N-acetyl-L-methionine, 2-keto-3-deoxy-D-gluconic acid, 1-methylnicotinamide and dehydroepiandrosterone sulfate. Based on these metabolites, we constructed classification models to differentiate RA from both HC and OA groups, and evaluated their performance across multiple independent validation cohorts. In three geographically distinct cohorts, RA vs. HC classifiers demonstrated robust discriminatory power, with an area under the receiver operating characteristic curve (AUC) ranging from 0.8375 to 0.9280, while RA vs. OA classifiers achieved moderate to good accuracy (AUC range: 0.7340–0.8181). Importantly, analysis of the seronegative RA subgroup indicated that the classifier’s performance was independent of serological status. Furthermore, validations conducted across different sample types and analytical platforms confirmed the reproducibility and stability of the models. Taken together, these findings highlight the utility of metabolomics as a complementary approach for improving RA diagnosis and establish a broadly applicable framework for the development of metabolite-based classifiers across diverse and clinically heterogeneous disease contexts.