Machine learning identifies novel blood protein predictors of penetrating and stricturing complications in newly diagnosed paediatric Crohn's disease.

There is a need for improved risk stratification in Crohn's disease. To identify novel blood protein biomarkers associated with future Crohn's disease complications METHODS: We performed a case-cohort study utilising a paediatric inception cohort, the Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn's disease (RISK) study. All patients had inflammatory disease (B1) at baseline. Outcomes were development of stricturing (B2) or penetrating (B3) complications. We assayed 92 inflammation-related proteins in baseline plasma using a proximity extension assay (Olink Proteomics). An ensemble machine learning technique, random survival forests (RSF), selected variables predicting B2 and B3 complications. Selected analytes were compared to clinical variables and serology only models. We examined selected proteins in a single-cell sequencing cohort to analyse differential cell expression in blood and ileum. We included 265 patients with mean age 11.6 years (standard deviation [SD] 3.2). Seventy-three and 34 patients, respectively, had B2 and B3 complications within mean 1123 (SD 477) days for B2 and 1251 (442) for B3. A model with 5 protein markers predicted B3 complications with an area under the curve (AUC) of 0.79 (95% confidence interval [CI] 0.76-0.82) compared to 0.69 (95% CI 0.66-0.72) for serologies and 0.74 (95% CI 0.71-0.77) for clinical variables. A model with 4 protein markers predicted B2 complications with an AUC of 0.68 (95% CI 0.65-0.71) compared to 0.62 (95% CI 0.59-0.65) for serologies and 0.52 (95% CI 0.50-0.55) for clinical variables. B2 analytes were highly expressed in ileal stromal cells while B3 analytes were prominent in peripheral blood and ileal T cells. We identified novel blood proteomic markers, distinct for B2 and B3, associated with progression of paediatric Crohn's disease. © 2020 John Wiley & Sons Ltd.

Citation

Ryan C Ungaro, Liangyuan Hu, Jiayi Ji, Shikha Nayar, Subra Kugathasan, Lee A Denson, Jeffrey Hyams, Marla C Dubinsky, Bruce E Sands, Judy H Cho. Machine learning identifies novel blood protein predictors of penetrating and stricturing complications in newly diagnosed paediatric Crohn's disease. Alimentary pharmacology & therapeutics. 2021 Jan;53(2):281-290

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PMID: 33131065

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