Fingerprinting Adiposity and Metabolic Function in the Brains of Overweight and Obese Humans

Abstract

The brain plays a central role in the pathophysiology of obesity. Connectome-based Predictive Modeling (CPM) is a newly developed, data-driven approach that exploits whole-brain functional connectivity to predict a behavior or trait that varies across individuals. We used CPM to determine whether brain "fingerprints" evoked during milkshake consumption could be isolated for common measures of adiposity in 67 overweight and obese adults. We found that a CPM could be identified for waist circumference, but not percent body fat or BMI, the most frequently used measures to assess brain correlates of obesity. In an exploratory analysis, we were also able to derive a largely distinct CPM predicting fasting blood insulin. These findings demonstrate that brain network patterns are more tightly coupled to waist circumference than BMI or percent body fat and that adiposity and glucose tolerance are associated with distinct maps, pointing to dissociable central pathophysiological phenotypes for obesity and diabetes.