Comprehensive Program on Obesity Research Studies
Research studies led by the Comprehensive Program on Obesity at NYU Langone yield new data that enable our experts to better understand the complex factors that contribute to obesity.
Translational Research: Weight and Metabolic Change
Goal: To determine predictors of weight and metabolic outcomes in bariatric surgery patients.
Hypothesis: The dramatic weight loss after bariatric surgery serves as a powerful tool to study mechanisms of weight and metabolic change in obesity and diabetes. Comprehensive sample collection, combined with individual, clinical, and population health data and big data analytics, will allow us to better understand these mechanisms and their implications and improve our ability to predict the outcomes of the surgery.
Plan: We are recruiting bariatric surgery patients and following them longitudinally for two years after surgery. In addition to collecting stool, blood, and adipose tissue samples, we conduct continuous glucose monitoring several times pre- and post-bariatric surgery.
Enriched Database Research: Pediatric Obesity
Goal: Standard methods allow us to predict 7 to 10 percent of the variance in childhood obesity. Our goal is to predict 75 percent of variance with currently available data.
Hypothesis: We are currently unable to accurately predict which children will experience problems with obesity later in life. We will combine comprehensive clinical and population health data, along with advanced analytic techniques, to better calculate obesity risk for children. This will set the stage for prevention and treatment of the disease for individuals and communities.
Plan: We are gathering a diverse set of data on a large sample of children in multiple NYU Langone sites over time. Using machine learning, we will employ big data techniques to predict obesity at the individual level and population level.
Association of Obesity and COVID-19
Given the impact of obesity on 2019 coronavirus disease (COVID-19) outcomes, we are pursuing studies that will allow us to better understand this association.