A guide to analysis of mouse energy metabolism
By Matthias Tschöp, J. Speakman, J. R. Arch, Johan Auwerx, Jens C. Brüning, L. Chan, R. Eckel, Robert V Farese, J. Galgani, C. Hambly and 15 more. Nature Methods, 2011
784 citations recorded by the source corpus.
We present a consolidated view of the complexity and challenges of designing studies for measurement of energy metabolism in mouse models, including a practical guide to the assessment of energy expenditure, energy intake and body composition and statistical analysis thereof. We hope this guide will facilitate comparisons across studies and minimize spurious interpretations of data. We recommend that division of energy expenditure data by either body weight or lean body weight and that presentation of group effects as histograms should be replaced by plotting individual data and analyzing both group and body-composition effects using analysis of covariance (ANCOVA).
Subfields: Cell Biology, Ecology, Physiology
View Paper: https://www.semanticscholar.org/paper/5f7931dc188bf43547a74c55a7c1e0ad8200614a
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In Physiology
Citations in the corpus
- CalR: A Web-based Analysis Tool for Indirect Calorimetry Experiments (2017, methodology): “Following decades of debate, analysis of covariance (ANCOVA) has become the consensus approach to the treatment of indirect calorimetry EE data when comparing animals of different body composition as occurs in obesity [5, 9-11].”
- LY3298176, a novel dual GIP and GLP-1 receptor agonist for the treatment of type 2 diabetes mellitus: From discovery to clinical proof of concept (2018, methodology): “com [43,44] was used to assess potential effects on energy expenditure, independent of changes in body weight.”