Using a Model of Human Energy Metabolism to Estimate Energy Costs of Turnover

Abstract

The goal of this research is to use a computational model of nutrient digestion and metabolism in a human to predict energy use and quantify effects of increased futile cycling on energy metabolism. The model is composed of 6 state variables representing amino acids, body protein, visceral protein, glucose, triglyceride, and fatty acids. Differential equations represent carbohydrate, amino acid, and fatty acid uptake and output by tissues. The model estimates metabolizable energy, heat production (HP), respiratory quotient, P/O ratio, energy balance and glucose and fatty acid oxidation based on ATP creation and use, O2 use, CO2 production, heats of combustion, and nutrient flux. Protein deposition is a function of maximum genetic potential for protein accretion. Adipose deposition is a function of energy balance. To examine effects of increasing protein and adipose turnover, protein turnover increased from 2 to 14 %/day and adipose turnover increased from 1.5 to 3.0 %/day for a 70 kg male. Increasing protein turnover increased HP from 11.2 to 20.5 MJ/day, increased glucose oxidation from 1.88 to 2.29 mole/day, increased fatty acid oxidation from 0.132 to 0.873 mole/day and decreased body weight by 6 kg over a 30 day simulation. Increasing adipose turnover decreased HP from 10.8 to 10.7 MJ/day, decreased glucose oxidation from 2.18 to 2.04 mole/day, increased fatty acid oxidation from 0.0200 to 0.0543 mole/day, and decreased body weight by 4.5 kg over a 30 day simulation. Therefore the model is able to predict body weight loss due to increased heat production from increased energy use as a result of changes in muscle and adipose turnover. Since humans are genetically diverse with a range of basal metabolic rates, activity levels and diets, a computational model will quantify diversity and predict metabolic function for a wide range of individuals.