Authors

Sarah Berry, Ana Valdes, Nicola Segata, Andrew Chan, Richard Davies, David Drew, Paul Franks and Tim Spector. 

Abstract

Glycemic, insulinemic and lipemic postprandial responses are multi-factorial and contribute to diabetes, obesity and CVD. The aim of the PREDICT I study is to assess the genetic, metabolic, metagenomic, and meal-context contribution to postprandial responses, integrating the metabolic burden and gut microbiome to predict individual responses to food using a machine learning algorithm. 

The integration of the information provided by different omics shows, in this article, how the metabolic response to the same meal in healthy people is highly variable and also modifiable, since the genetic component of the variation is limited. The large volume of data generated would allow the application of artificial intelligence to predict individual responses to specific foods.