A longitudinal cohort study uncovers plasma protein biomarkers predating clinical onset and treatment response of rheumatoid arthritis
Siyu He, Chenxi Zhu, Yi Liu, Zhiqiang Xu, Rui Sun, Bin Yang, Xin Guo, Martin Herrmann, Luis E. Muñoz, Inger Gjertsson, Rikard Holmdahl, Lunzhi Dai, Yi Zhao
Abstract
Rheumatoid arthritis (RA) is a systemic inflammatory condition posing challenges in identifying biomarkers for onset, severity and treatment responses. Here we investigate the plasma proteome in a longitudinal cohort of 278 RA patients, alongside 60 at-risk individuals and 99 healthy controls. We observe distinct proteome signatures in at-risk individuals and RA patients, with protein levels alterations correlating with disease activity, notably at DAS28-CRP thresholds of 3.1, 3.8 and 5.0. The combination of methotrexate (MTX) and leflunomide (LEF) modulates proinflammatory pathways, whereas MTX plus hydroxychloroquine (HCQ) impact energy metabolism. A machine-learning model is trained for predicting responses, and achieves average receiver operating characteristic (ROC) scores of 0.88 (MTX + LEF) and 0.82 (MTX + HCQ) in the testing sets. The efficiency of these models is further validated in independent cohorts using enzyme-linked immunosorbent assay data. Overall, our study unveils distinct plasma proteome signatures across various stages and subtypes of RA, providing valuable biomarkers for predicting disease onset and treatment responses. Rheumatoid arthritis (RA) mainly impacts the joints, which are difficult to sample, so identifying peripheral biomarkers may aid in diagnosis and treatment response predictions. Here the authors pursue a longitudinal study profiling plasma protein and use machine learning to train an algorithm for predicting RA onsets and responses to antirheumatic drugs.