Abstract
ObjectivesPatients with Rheumatoid Arthritis (RA) are increasingly achieving stable disease remission, yet the mechanisms that govern ongoing clinical disease and subsequent risk of future flare are not well understood. We sought to identify serum proteomic alterations that dictate clinically important features of stable RA, and couple broad-based proteomics with machine learning to predict future flare.MethodsWe studied baseline serum samples from a cohort of stable RA patients (RETRO, n = 130) in clinical remission (DAS28<2.6) and quantified 1307 serum proteins using the SOMAscan platform. Unsupervised hierarchical clustering and supervised classification were applied to identify proteomic-driven clusters and model biomarkers that were associated with future disease flare after 12 months of follow-up and RA medication withdrawal. Network analysis was used to define pathways that were enriched in proteomic datasets.ResultsWe defined 4 proteomic clusters, with one cluster (Cluster 4) displaying a lower mean DAS28 score (p = 0.03), with DAS28 associating with humoral immune responses and complement activation. Clustering did not clearly predict future risk of flare, however an XGboost machine learning algorithm classified patients who relapsed with an AUC (area under the receiver operating characteristic curve) of 0.80 using only baseline serum proteomics.ConclusionsThe serum proteome provides a rich dataset to understand stable RA and its clinical heterogeneity. Combining proteomics and machine learning may enable prediction of future RA disease flare in patients with RA who aim to withdrawal therapy.
Highlights
Rheumatoid Arthritis (RA) is a systemic autoimmune disease that is characterized by inflammation of synovial joints [1]
Clustering did not clearly predict future risk of flare, an XGboost machine learning algorithm classified patients who relapsed with an area under of the curve (AUC) of 0.80 using only baseline serum proteomics
Combining proteomics and machine learning may enable prediction of future RA disease flare in patients with RA who aim to withdrawal therapy
Summary
Rheumatoid Arthritis (RA) is a systemic autoimmune disease that is characterized by inflammation of synovial joints [1]. Modern RA therapy is initiated early and escalated aggressively using a treat-to-target approach to try an obtain disease remission [2]. The development of both targeted treatments and combination regimens continues to improve expected outcomes for patients. Clinical remission, defined by multiple measures of disease activity [3], has become a realistic expectation for most patients with RA. Patients with RA who are able to achieve disease remission using standard therapy are not well studied, given their lack of disease activity and need for treatment changes. Given the limited understanding of the pathological mechanisms that drive subclinical disease, clinicians are left to guess which of their patients might sustain remission using less aggressive therapy
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