MeNu GUIDE - a metabolite nutrition graph to uncover interactions with disease etiology

Abstract

AO_SCPLOWBSTRACTC_SCPLOWThe relationship between diet and disease is well-documented, yet the complex interactions among foods, metabolites, and genetics makes research challenging. This study explores the potential insights offered by a knowledge graph that connects nutrition and diseases on a metabolic level. Ten ontologies and data from six databases were merged, resulting in a graph with over 25 million triple statements, stored in a Turtle file and added to a GraphDB repository. SPARQL queries revealed biases towards specific foods and conditions within the integrated databases. Despite these biases, this knowledge graph serves as a proof-of-concept, demonstrating the feasibility of integrating information from diverse resources to yield valuable insights and enabling the drawing of meaningful conclusions. The graph allows efficient identification of disease-related compounds and their food sources and enables the exploration of changes in metabolite concentrations, such as those occurring during food processing. Researchers could use such a knowledge graph to identify biomarkers, help generate new hypotheses, and improve experimental designs. Expanding the graph with automated text-mining and recipe data would further enhance its utility for nutrition research. Such a resource could advance understanding of the molecular mechanisms behind diet-disease relationships, guiding more targeted interventions.