Abstract
The epidemics of obesity and diabetes have aroused great interest in the analysis of energy balance, with the use of organisms ranging from nematode worms to humans. Although generating energy-intake or -expenditure data is relatively straightforward, the most appropriate way to analyse the data has been an issue of contention for many decades. In the last few years, a consensus has been reached regarding the best methods for analysing such data. To facilitate using these best-practice methods, we present here an algorithm that provides a step-by-step guide for analysing energy-intake or -expenditure data. The algorithm can be used to analyse data from either humans or experimental animals, such as small mammals or invertebrates. It can be used in combination with any commercial statistics package; however, to assist with analysis, we have included detailed instructions for performing each step for three popular statistics packages (SPSS, MINITAB and R). We also provide interpretations of the results obtained at each step. We hope that this algorithm will assist in the statistically appropriate analysis of such data, a field in which there has been much confusion and some controversy.
Highlights
The twin epidemics of obesity and diabetes have placed a great premium on understanding more about the regulation of energy balance and how its dysregulation affects fat deposition and glucose homeostasis
Research in this area is being carried out using many organisms, including invertebrates such as Drosophila melanogaster and Caenorhabditis elegans, small mammals such as mice and rats, non-human primates, and humans
It was agreed that the best way forward was not to perform simple ratio calculations because these approaches do not adequately normalise for the mass effect (Allison et al, 1995; Poehlman and Toth, 1995)
Summary
The ‘39 steps’: an algorithm for performing statistical analysis of data on energy intake and expenditure. The algorithm can be used to analyse data from either humans or experimental animals, such as small mammals or invertebrates. It can be used in combination with any commercial statistics package; to assist with analysis, we have included detailed instructions for performing each step for three popular statistics packages (SPSS, MINITAB and R). We provide interpretations of the results obtained at each step We hope that this algorithm will assist in the statistically appropriate analysis of such data, a field in which there has been much confusion and some controversy
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