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Dynamic coordination of macronutrient balance during infant growth: insights from a mathematical model
1Laboratory of Biological Modeling, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-5621, USA.
Background:
Complex dynamic changes in body composition, dietary intake, energy expenditure, and macronutrient oxidation occur during infant growth. Although previous investigators have focused on energy requirements for normal growth, little is known about the dynamic coordination of macronutrient balance.
Objective:
Our objective was to develop a mathematical model of the dynamic relations between diet, macronutrient oxidation, and energy expenditure during normal infant growth.
Design:
We developed a mathematical model that integrates longitudinal data on changes of body composition and carbon dioxide production determined with the doubly labeled water method to calculate both energy intake requirements and macronutrient oxidation rates during normal infant growth.
Results:
The calculated fat oxidation rate was initially <20 kcal x kg(-1) x d(-1), despite the consumption of >60 kcal x kg(-1) x d(-1) of dietary fat. This discrepancy was maintained until approximately 6 mo, after which fat intake was only slightly greater than fat oxidation. Nonfat oxidation closely followed nonfat dietary intake for the duration of the period studied. Model calculations of the energy intake requirements for normal growth were slightly lower than previous estimates. The calculations were robust to variations of body weight, body composition, and diet composition input data, but depended sensitively on variations of carbon dioxide production data.
Conclusions:
Our model presents a dynamic picture of how macronutrient oxidation adapts in concert with dietary changes and energy expenditure to give rise to normal tissue deposition. The model integrates a variety of data in a self-consistent way, simulating the complex metabolic adaptations occurring during normal growth while extracting important physiologic information from the data that would otherwise be unavailable.
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