Topic: Mathematical Models of Metabolism and Body Weight Regulation
Meeting dates: July 12-15, 2011
Kevin D. Hall (NIDDK, National Institutes of Health)
Steven B. Heymsfield (Global Director, Scientific Affairs, Obesity, Merck & Co., Inc.)
Diana M. Thomas (Assoc. Professor of Mathematical Sciences, Montclair State Univ.)
Objectives: The mechanisms regulating human body weight are extraordinarily complex, and the ongoing obesity epidemic makes it imperative that we improve our understanding of these processes. In the engineering and physical sciences, there is a long history of using mathematical models and computer simulations to better understand the behavior of complex systems. This approach is now becoming more widely used in the biological and clinical sciences and a small but important field is emerging that uses mathematical and computational methods to address key questions about human metabolism and body weight regulation. This work is highly interdisciplinary and researchers entering this field are posed with communication challenges arising from the disparate backgrounds of researchers in mathematical and medical sciences. While this challenge is typical of truly interdisciplinary research, a second unique challenge arises from the intense media coverage of obesity and weight loss that often misinforms as much as it educates. The goal of this workshop is to bring together researchers in the fields of obesity and metabolism with investigators expert in mathematical and computational modeling to facilitate communication and collaboration between these researchers. The workshop will provide background on the physiology of human body weight regulation, highlight some of the recent progress applying such methods to modeling human metabolism, food intake, and body composition, and pose open mathematical modeling problems originating from metabolism and body weight regulation research. We believe that it will act as a catalyst for future research on this important topic.
Central Theme: With more than two-thirds of the United States considered overweight and more than one-third categorized as obese, understanding mechanisms behind weight gain, loss, and maintenance is a major national goal. Mathematical modeling of the metabolism and body weight regulation is an important and growing subfield of obesity research which serves to understand these mechanisms. Models aid in understanding changes in body composition during weight loss or gain, the degree of individual adherence to a diet or exercise plan, and long‐term effects of changes in diet and exercise on an individual’s weight. Currently, mathematical models developed in collaboration by the PIs and clinicians are being used to develop strategies for dietary interventions during illnesses such as cancer, foster participant adherence to target diet and exercise protocols, and help understand differences between surgical interventions, drug interventions, and dietary interventions to achieve desired weight change. Mathematical models have been applied to understand how metabolic rate varies among animal species and the contribution of reduced physical activity and increased food consumption to our current obesity epidemic. The central theme of this workshop is to provide a formal venue to bring together researchers in nutrition, physiology, and mathematics to circulate the latest advances and pose open challenges in the field.
Summary Report. Discussion focused on open problems along with completed applications that integrate mathematics and obesity. Lectures on energy balance models that predict weight change, application of energy balance models to facilitate positive behavior modification, and energy balance models applied to children were presented. Small group discussions on measurements, use and limitations of portable devices, and energy balance model validation were held. Small groups have formed since the meeting to develop a spread of obesity infection model, include portable sensor results inside an energy balance model, and develop an exercise energy balance model. These groups were formed at the workshop and integrated the mathematicians with the obesity researchers.
This workshop is a satellite conference to the ICIAM 2011: 7th International Congress on Industrial and Applied Mathematics to be held July 18-22, 2011, in Vancouver, British Columbia, Canada.
Li Y, Gaillard JR, McLaughlin T, Sorensen TIA, Periwal V. 2015. Macro fat and micro fat: Insulin sensitivity and gender dependent response of adipose tissue to isocaloric diet change. Adipocyte.
Dhurandhar EJ, Kaiser KA, Dawson JA, Alcorn AS, Keating KD, Allison DB. 2014. Predicting adult weight change in the real world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure. International Journal of Obesity. [Online]
Dhurandhar et al. 2014. Energy balance measurement: when something is not better than nothing. International Journal of Obesity. [Online]
Thomas DM, Weedermann M, Fuemmeler BF, Martin CK, Dhurandhar NV, Bredlau C, Heymsfield SB, Ravussin E, Bouchard C. 2014. Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends. Obesity, 22(2): 590-597. [Online]
Steffen et al. 2013. Development and validation of a risk score predicting substantial weight gain over 5 years in middle-aged European men and women. PLoS One, 8(7): e67429. [Online]
Baracos V, Caserotti P, Earthman CP, Fields D, Gallagher D, Hall KD, Heymsfield SB, Muller MJ, Napolitano Rosen A, Pichard C, Redma LM, Shen W, Shepherd JA, Thomas D. 2012. Advances in the science and application of body composition measurement. Journal of Parenteral & Enteral Nutrition, 36(1): 96-107. [Online]
Dong Y, Rivera DE, Thomas DM, Navarro-Barrientos JE, Downs DS, Savage JS, Collins LM. 2012. A dynamical systems model for improving gestational weight gain behavioral interventions. American Control Conference Published Proceedings from 27-29 June 2012, pp. 4059-4064. [Online]
Heitmann BL et al. 2012. Obesity: lessons from evolution and the environment. Obesity Reviews, 12(10): 910-922. [Online]
Heymsfield SB, Muller MJ, Bosy-Westphal A, Thomas D, Shen W. 2012. Human brain mass: Similar body composition associations as observed across mammals. American Journal of Human Biology, 24(4): 479-485. [Online]
Heymsfield SB, Thomas D, Martin CK, Redman LM, Strauss B, Bosy-Westphal A, Muller MJ, Shen W, Martin Nguyen A. 2012. Energy content of weight loss: Kinetic features during voluntary caloric restriction. Metabolism, 61(7): 937-943. [Online]
Sorensen TI. 2012. Is obesity a healthy active response to an expected future lack of energy rather than a passive storage of surplus energy? Obesity Facts, 5(3): 431-435. [Online]
Thomas DM, Navarro-Barrientos JE, Rivera DE, Heymsfield SB, Bredlau C, Redman LM, Martin CK, Lederman SA, Collins LM, Butte NF. 2012. Dynamic energy-balance model predicting gestational weight gain. The American Journal for Clinical Nutrition, 95(1): 115-122. [Online]
Thomas DM, Bouchard C, Church T, Slentz C, Kraus WE, Redman LM, Martin CK, Silva AM, Vossen M, Westerterp K, Heymsfield SB. 2012. Why do individuals not lose more weight from an exercise intervention at a defined dose? An energy balance analysis. Obesity Reviews, 13(10): 835-847. [Online]
Caja Rivera RM, Barradas I. May 2016. To protect female sandflies to protect humans?: Control policies in Cutaneous leishmaniasis. BAMM, Biology and Medicine Through Mathematics Conference, Virginia CommonWealth University, Richmond, VA.
Weedermann M. May 2014. Dynamic Obesity Prevalence, 1st Short Course on Mathematical Sciences in Obesity Research, University of Alabama.
Kaiser KA, Dhurandhar EJ, Allison DB. 2012. Using empirical observations to update theoretical predictions of the effects of manipulations on components of energy balance. The Obesity Society Annual Meeting, San Antonio, TX.
Grants & Proposals
Kaiser KA, Dhurandhar EJ. 2012-2013. Grant: Empirically informed predictions of human adult body weight change in response to energetic perturbations. International Life Sciences Institute, North America. $129,789. Accepted.
Software, Data, Models
Dhurandhar EJ, Kaiser KA, Dawson JA, Alcorn AS, Keating KD, Allison DB. 2014. Simple factor calculator based on compensation estimates from the paper published. [Online]
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