A Longitudinal Study Reveals Metabolomic Markers for Individuals at Risk, Disease Severity, and Treatment Response in Rheumatoid Arthritis

Abstract

Rheumatoid arthritis (RA) is a systemic inflammatory joint disease characterized by heterogeneous clinical manifestations, which requires deeper exploration in identifying reliable biomarkers for early diagnosis, monitoring, and treatment assessment. The aim is to discover plasma metabolomic markers to predict RA onset, assess disease activity, and forecast treatment efficacy. The study includes 209 established RA patients who are disease-modifying antirheumatic drugs-free for six months prior to enrollment, with 197 of them followed for 3-6 months to assess treatment response. Additionally, 56 individuals at risk are recruited, with 34 completing a 5-7-year follow-up. Analysis reveals that metabolites related to methylation and redox imbalance, such as S-adenosylmethionine, sarcosine, nicotinamide adenine dinucleotide, glutathione, etc., are associated with RA development and severity, and contribute to its heterogeneity across age, sex, and anti-citrullinated protein autoantibody status. Ridge regression models are constructed using metabolite and clinical features for the response to methotrexate (MTX) plus leflunomide, achieving an average receiver operating characteristic (ROC) score of 0.83, and for the MTX plus hydroxychloroquine, achieving an average ROC score of 0.92. In conclusion, our findings reveal RA metabolomic alterations, aiding early diagnosis and treatment response.

Keywords: longitudinal profiling; machine learning; metabolomics; rheumatoid arthritis; therapy response.