Key points are not available for this paper at this time.
Abstract Objective Identification of those at high and low risk of disease relapse is a major unmet need in the management of patients with ANCA-associated vasculitis (AAV). Precise stratification would allow tailoring of immunosuppressive medication. We profiled the autoantibody repertoire of AAV patients in remission to identify novel autoantibodies associated with relapse risk. Methods Plasma samples collected from AAV patients in remission were screened for novel autoantibodies using in-house generated protein arrays including 42,000 protein fragments representing 18,000 unique human proteins. Patients were categorized based on the occurrence and frequency of relapses. We modelled the association between these antibodies and relapse occurrence using descriptive and high dimensional regression approaches. Results We observed nine autoantibodies at higher frequency in samples from AAV patients experiencing multiple relapses compared to patients in long-term remission off therapy (LTROT). LASSO analysis identified six autoantibodies that exhibited an association with relapse occurrence after sample collection. Antibodies targeting HFE and SYT5 were identified as associated with relapse in both analyses. Conclusion Through a broad protein array-based autoantibody screening, we identified two novel autoantibodies as candidate biomarkers of relapse in AAV. Key messages of this study Our multi-step screening based on high-throughput and high-multiplexing protein arrays allowed to identify novel autoantibodies in AAV patients. Our study identified two new autoantibodies as candidate biomarkers for predicting AAV patients at risk of relapse. The risk of relapse may be better reflected by the presence of specific autoantibodies than by the overall autoantibody load in patients with AAV.
Building similarity graph...
Analyzing shared references across papers
Loading...
Shaghayegh Bayati
Jamsheela Nazeer
James Ng
Nottingham University Hospitals NHS Trust
Trinity College Dublin
KTH Royal Institute of Technology
Science for Life Laboratory
Building similarity graph...
Analyzing shared references across papers
Loading...
Bayati et al. (Sat,) studied this question.
synapsesocial.com/papers/68e5ed4cb6db643587581e9a — DOI: https://doi.org/10.1101/2024.07.25.24310702
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: