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The Dynamics of Human Body Weight Change

Overview of attention for article published in PLoS Computational Biology, March 2008
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Title
The Dynamics of Human Body Weight Change
Published in
PLoS Computational Biology, March 2008
DOI 10.1371/journal.pcbi.1000045
Pubmed ID
Authors

Carson C. Chow, Kevin D. Hall

Abstract

An imbalance between energy intake and energy expenditure will lead to a change in body weight (mass) and body composition (fat and lean masses). A quantitative understanding of the processes involved, which currently remains lacking, will be useful in determining the etiology and treatment of obesity and other conditions resulting from prolonged energy imbalance. Here, we show that a mathematical model of the macronutrient flux balances can capture the long-term dynamics of human weight change; all previous models are special cases of this model. We show that the generic dynamic behavior of body composition for a clamped diet can be divided into two classes. In the first class, the body composition and mass are determined uniquely. In the second class, the body composition can exist at an infinite number of possible states. Surprisingly, perturbations of dietary energy intake or energy expenditure can give identical responses in both model classes, and existing data are insufficient to distinguish between these two possibilities. Nevertheless, this distinction has important implications for the efficacy of clinical interventions that alter body composition and mass.

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Mendeley readers

The data shown below were compiled from readership statistics for 182 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
United Kingdom 2 1%
Switzerland 1 <1%
Netherlands 1 <1%
Colombia 1 <1%
Chile 1 <1%
Canada 1 <1%
Luxembourg 1 <1%
Unknown 170 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 43 24%
Student > Ph. D. Student 33 18%
Student > Master 20 11%
Student > Bachelor 13 7%
Other 12 7%
Other 34 19%
Unknown 27 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 18%
Medicine and Dentistry 31 17%
Engineering 18 10%
Mathematics 11 6%
Sports and Recreations 8 4%
Other 49 27%
Unknown 33 18%