Utilizing Machine Learning Approaches to Understand the Interrelationship of Diet, the Human Gastrointestinal Microbiome, and Health

BackgroundA growing body of literature supports the ability of specific foods and nutrients to impact the gastrointestinal microbiome. However, there is a dearth of knowledge on the interplay of dietary components (e.g. foods and nutrients), gastrointestinal bacteria, and bacterial metabolites. Current analytical approaches limit investigation of these complex interrelations; therefore, further research utilizing modern machine learning methods is needed. ObjectiveWe aimed to fill the gap in knowledge about the interrelationship of diet, the gastrointestinal microbiome, and health by utilizing multivariate approaches that address P>>N, many features but few samples to 1) validate results generated by prototype software with previously published results; and 2) identify novel associations among relevant foods, nutrients, bacteria, and bacterial metabolites. MethodsData generated from a human dietary intervention study that included habitual food and nutrient intake patterns (NHANES food frequency questionn...