ArticleTranslational vision science & technology2022
A Blood Protein Signature Stratifies Clinical Response to csDMARD Therapy in Pediatric Uveitis.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Elevated Plasma Complement Factors in CRB1-Associated Inherited Retinal Dystrophies.Investigative ophthalmology & visual science · 2025Article
- Serum Biomarkers of Vascular Involvement in Childhood Uveitis.Translational vision science & technology · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo identify a serum biomarker signature that can help predict response to conventional synthetic disease-modifying antirheumatic drug (csDMARD) therapy in pediatric noninfectious uveitis.
methodsIn this case-control cohort study, we performed a 368-plex proteomic analysis of serum samples of 72 treatment-free patients with active uveitis (new onset or relapse) and 15 healthy controls. Among these, 37 patients were sampled at diagnosis before commencing csDMARD therapy. After 6 months, csDMARD response was evaluated and cases were categorized as "responder" or "nonresponder." Patients were considered "nonresponders" if remission was not achieved under csDMARD therapy. Serum protein profiles were used to train random forest models to predict csDMARD failure and compared to a model based on eight clinical parameters at diagnosis (e.g., maximum cell grade).
resultsIn total, 19 of 37 (51%) cases were categorized as csDMARD nonresponders. We identified a 10-protein signature that could predict csDMARD failure with an overall accuracy of 84%, which was higher compared to a model based on eight clinical parameters (73% accuracy). Adjusting for age, sex, anatomic location of uveitis, and cell grade, cases stratified by the 10-protein signature at diagnosis showed a large difference in risk for csDMARD failure (hazard ratio, 12.8; 95% confidence interval, 2.5-64.6; P = 0.002).
conclusionsMachine learning models based on the serum proteome can stratify pediatric patients with uveitis at high risk for csDMARD failure. TRANSLATIONAL RELEVANCE: The identified protein signature has implications for the development of clinical decision tools that integrate clinical parameters with biological data to better predict the best treatment option.
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