01667nas a2200289   4500000000100000000000100001008004100002260001500043653001300058653002000071100001900091700001500110700002000125700001800145700001400163700001600177700001900193700001400212700001600226700001700242245009700259856005500356300000900411490000700420520093600427022001401363       2023                            d  c2023-04-0410aDiseases10asystems biology1 aVadim Osadchiy1 aRoshan Bal1 aEmeran A. Mayer1 aRama Kunapuli1 aTien Dong1 aPriten Vora1 aDanny Petrasek1 aCathy Liu1 aJean Stains1 aArpana Gupta00aMachine learning model to predict obesity using gut metabolite and brain microstructure data  uhttps://www.nature.com/articles/s41598-023-32713-2  a54880 v133 aA 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.  a2045-2322