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.