Online | The Role of Diet Composition on Energy Balance

"Biomedical Cross-Disciplinary Series - The George Washington University School of Medicine and Health Sciences"


Join us for the GW Biomedical Cross-disciplinary Seminar Series: Connecting Academic Research & Inquiry Across Disciplines. This virtual lecture series explores a new cross-disciplinary topic each year.  The goal is to promote networking and collaboration in translational health among researchers, health care providers, and policy makers from different disciplines to shift the paradigm—from seeking a cure to developing a strategy of prevention. The 2021-22 seminar series topic is metabolic syndrome. 

This lecture on The Role of Diet Composition on Energy Balance will be given by Kevin, D. Hall, PhD, Section Chief: Integrative Physiology Section, Laboratory of Biological Modeling at the NIH’s National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Trained as a physicist, he leads a team of mathematicians, neuroscientists, and dietitians who study metabolism and weight. His scientific focus areas are clinical research, computational biology, and neuroscience.  

Understanding the dynamics of human body weight change has important consequences for conditions such as obesity, diabetes, cardiovascular disease, starvation, and wasting syndromes such as anorexia nervosa and cancer cachexia. By using mathematical modeling to quantitatively integrate metabolism data with body weight and composition data, Dr. Hall's research aims to substantially improve our understanding of body weight regulation and develop practical tools for research and clinical use.

The Hall laboratory investigates how metabolism and the brain adapt in response to a variety of interventions to diet and physical activity. His team performs experiments in both humans and rodents to better understand the complex mechanisms regulating macronutrient metabolism, body composition, and energy expenditure. A unique aspect of the Hall laboratory involves the development of mathematical models to quantitatively describe, explain, integrate, and predict our experimental results.

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