Human Microbiome-Based Prediction of Health Effects of Foods via Machine Learning

Food absorption is dependent on the activities of internal microorganisms. When exploring food functionality, considering the food compounds and their metabolites produced by microbial metabolism is crucial. In this study, we developed a machine learning method to predict food functionalities using microorganism and metabolic data. The prediction was performed on the chemical properties of 70,478 constituent compounds and 24,255 metabolites and their interactions with target proteins in disease-related pathways. This identified potential functional associations between 941 foods and 83 diseases, providing insights into the mechanisms involved, particularly those related to microorganisms. The effects of microorganism-mediated foods on diseases can be categorized based on food type. For example, the microorganisms associated with diseases within each food category indicated the potential involvement of the Bacteroidetes phylum in dyslipidemia and the Firmicutes phylum in Parkinson's disease. This method could aid in identifying disease-associated microorganisms, identifying prebiotic foods, and highlighting the potential of food interventions in the preventive medicine field. Furthermore, this approach is expected to be useful for elucidating novel mechanisms of food-disease interactions mediated by microbial metabolites.