Utility of machine learning for serum metabolomic data analysis in pediatric Crohn disease

Abstract:

Background

The pathogenesis of pCD remains poorly understood, but evidence suggests roles for genetics, environment, immune response, and gut microbes. Microbial changes can contribute to chronic inflammation and correlate with disease severity. Metabolomics reflects interactions between host immune and gut microbial function by quantifying compounds in biological samples. Therefore, metabolomics provides a unique opportunity to gain insight into pCD pathogenesis.

Aims

To correlate disease severity, metabolites, and clinical data by applying machine learning algorithms in pediatric Crohn Disease (pCD).

Methods

ImageKids is a multicenter, prospective, cohort observational study, conducted to develop magnetic resonance enterography (MRE) indices for pCD. Paired serum specimens were collected at study initiation (Visit One; V1) and completion (Visit Four; V4; 18 months) for 120 pCD patients. Serum from patients with representative clinical scenarios and paired samples was analyzed at The Metabolomics Innovation Centre (TMIC; University of Alberta) and 131 metabolites were identified. Metabolites were analyzed via Unsupervised (U.ML) and Supervised (S.ML) Machine Learning algorithms based on Scikit-learn library in Python. Principal Component Analysis (PCA) was used to identify the variation pattern of the patients’ metabolome. Classifiers and regression algorithms were trained to assess correlation with disease activity.