275-LB: Connectome Predictive Modeling May Identify Brain Connectivity Signatures to Help Predict Who Will Benefit from Low-Calorie Diet

While studies have investigated changes in brain connectivity following diet and weight loss, it has not yet been explored whether baseline brain connectivity can be used to predict weight loss during diet or to identify individual subjects for which a particular diet may be effective. The following study uses Connectome-based predictive modeling (CPM), a data-driven analysis approach for producing predictive models of individual brain-behavior relationships from brain connectivity data using cross-validation, to help identify individual participants who will lose weight after an 8-week low calorie diet. 16 Healthy OB subjects (10F/6M, age 44.4±8 years, BMI 32.7±2) and 9 T2DM subjects (5F/4M, age 48±9, BMI 33.9±2) underwent functional MRI (fMRI) during a hyperglycemic-euglycemic clamp. Blood-oxygen-level-dependent (BOLD) brain activity was assessed while subjects viewed food (high-calorie, low-calorie) pictures and non-food images. The study was repeated after 8 weeks of a reduced calorie diet. CPM was applied to BOLD activity during hyperglycemia and euglycemia. Analyses show that models built on individual subject brain connectivity matrices averaged between hyperglycemia and euglycemia before an 8-week low calorie diet are able to predict 50.2% and 53.4% of the variance in BMI and weight loss (kg), respectively (p Disclosure D. Groskreutz: None. W. Lam: None. C. Lacadie: None. A. Elshafie: None. J.J. Hwang: None. D. Seo: None. M. Savoye: None. R. Sinha: None. T. Constable: None. R. Sherwin: Other Relationship; Self; ICON plc., IQVIA, MannKind Corporation. R. Belfort-DeAguiar: Research Support; Self; Silver Palate Kitchens, Inc. Funding National Institutes of Health