Measuring energy metabolism in the mouse – theoretical, practical and analytical considerations

• Main Author: John Roger Speakman
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2013-03-01
• Series: Frontiers in Physiology
• Subjects: Adiposity; Body Composition; Energy Metabolism; Obesity; energy balance; indirect calorimetry
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fphys.2013.00034/full

The mouse is one of the most important model organisms for understanding human genetic function and disease. This includes characterisation of the factors that influence energy expenditure and dysregulation of energy balance leading to obesity and its sequalae. Measuring energy metabolism in the mouse presents a challenge because the animals are small, and in this respect it presents similar challenges to measuring energy demands in many other species of small mammal. This paper considers some theoretical, practical and analytical considerations to be considered when measuring energy expenditure in mice. Theoretically total daily energy expenditure is comprised of several different components: basal or resting expenditure, physical activity, thermoregulation and the thermic effect of food. Energy expenditure in mice is normally measured using open flow indirect calorimetry apparatus. Two types of system are available – one of which involves a single small Spartan chamber linked to a single analyser, which is ideal for measuring the individual components of energy demand. The other type of system involves a large chamber which mimics the home cage environment and is generally configured with several chambers per analyser. These latter systems are ideal for measuring total daily energy expenditure but at present do not allow accurate decomposition of the total expenditure into its components. The greatest analytical challenge for mouse expenditure data is how to account for body size differences between individuals. This has been a matter of some discussion for at least 120 years. The statistically most appropriate approach is to use ANCOVA with individual aspects of body composition as independent predictors.