Machine learning model to predict obesity using gut metabolite and brain microstructure data
Vadim Osadchiy, Roshan Bal, Emeran A. Mayer, Rama Kunapuli, Tien S. Dong, Priten Vora, Danny Petrasek, Xiaoyue Cathy Liu, Jean Stains, Arpana Gupta
Abstract
A growing body of preclinical and clinical literature suggests that brain-gut-microbiota interactions may contribute to obesity pathogenesis. In this study, we use a machine learning approach to leverage the enormous amount of microstructural neuroimaging and fecal metabolomic data to better understand key drivers of the obese compared to overweight phenotype. Our findings reveal that although gut-derived factors play a role in this distinction, it is primarily brain-directed changes that differentiate obese from overweight individuals. Of the key gut metabolites that emerged from our model, many are likely at least in part derived or influenced by the gut-microbiota, including some amino-acid derivatives. Remarkably, key regions outside of the central nervous system extended reward network emerged as important differentiators, suggesting a role for previously unexplored neural pathways in the pathogenesis of obesity.