A longitudinal cohort study uncovers plasma protein biomarkers predating clinical onset and treatment response of rheumatoid arthritis.

Journal: Nature communications
Published Date:

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.

Authors

  • Siyu He
    Department of R&D, Neusoft Medical Systems Co. Ltd, Beijing, China.
  • Chenxi Zhu
    Department of Rheumatology and Immunology and National Clinical Research Center for Geriatrics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
  • Yi Liu
    Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
  • Zhiqiang Xu
    Engineering Research Center of Large Scale Reactor Engineering and Technology, Ministry of Education, State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology Shanghai 200237 China wying@ecust.edu.cn +86 21 64252192 +86 21 64252151.
  • Rui Sun
    The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, China.
  • Bin Yang
    School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, PR China. Electronic address: yangbin@dlut.edu.cn.
  • Xin Guo
    Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.
  • Martin Herrmann I
    Department of Rheumatology and Immunology and National Clinical Research Center for Geriatrics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
  • Luis E Muñoz
    Department for Internal Medicine 3, University Hospital Erlangen, and Deutsches Zentrum für Immuntherapie; Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Inger Gjertsson
    Department of Rheumatology and Inflammation Research, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Rikard Holmdahl
    Section of Medical Inflammation Research, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden.
  • Lunzhi Dai
    Department of Rheumatology and Immunology and National Clinical Research Center for Geriatrics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. lunzhi.dai@scu.edu.cn.
  • Yi Zhao
    Department of Biostatistics and Health Data Science, Indiana University School of Medicine.