Neuroimaging Predictors of Bariatric Surgical Outcome

Overview

Bariatric surgery is an important treatment option for morbidly obese patients who fail to lose weight through diet and exercise. Despite intervention, 20-50% of patients either fail to lose targeted amounts of weight or regain weight that was lost initially. Attempts at predicting degree of weight loss have had only modest success and none have long term (\>2 year) reliability. Moreover, research predicting weight loss beyond the 1st or 2nd year post-surgery and for outcomes other than weight loss including comorbidities common in the bariatric population is lacking. The investigators' pilot data in 45 patients suggest that individual differences on pre-surgical neural activity measured with functional MRI (fMRI) reliably explains 33% of the variance in weight loss up to 1 year post surgery, and over 50% of a multifaceted outcome measure, far outperforming many other indicators. These predictors implicate regions that closely conform to a theoretical model emphasizing both consummatory urges (a "Now" neural circuit) vs. regulation of craving and self-control (a "Later" circuit). The central hypothesis in this study is that individual differences in these neural pathways exert a powerful effect on the ability to sustain weight loss and achieve other key health outcomes. The study will replicate and refine this model over a longer timeframe and assess its predictive utility for key weight-related health outcomes. The investigators propose to replicate the model derived from their fMRI pilot data and secondarily to explore its predictive utility for changes in calorie intake, activity levels, liver fat, hemoglobin A1c, plasma lipids, blood pressure, and fasting glucose in a new, independent cohort of N=150 successively consenting, presurgical sleeve gastrectomy (SG) patients in study years 1-3. The study will follow the pilot cohort for up to 7 years and the new cohort for 3 or more years to determine if predictors replicated in Aim 1 retain their long-term predictive power, particularly when supplemented with non-brain imaging variables and using a larger longitudinal dataset. The study will use imaging and non-imaging data to develop multivariate statistical models incorporating energy balance, fMRI, and laboratory values with the variables described in Aim 1 to help to separate predictors vs. consequences of post-surgical outcomes. To help separate scan-to-scan variability from true post-surgical, trajectory-related brain changes, the study will enroll N=20 obese subjects who will not undergo bariatric surgery, and are individually matched with the above SG subjects. Finally, the study will evaluate whether several related, non-fMRI cognitive tests might potentially act as surrogates in clinical practice.