New Methods for Analysis and Interpretation of Obesity Experiments

Abstract

We report here the first data repository for experiments of obesity and body weight change. Modern obesity research depends upon indirect calorimetry systems to allow for state‐of‐the‐art experimental measurement of energy expenditure and food intake. These systems generate a flood of raw data that require time‐consuming manual manipulation for formatting, outlier detection, statistical interpretation, and visualization. Analysis of calorimetry experiments requires an onerous and specialized statistical treatment to account for differences in body mass and body composition. As such, controversies have emerged on the appropriate treatment of the data generated by these experiments, fundamentally challenging some published conclusions. We recently described a free web‐based tool, CalR, for analysis of experiments using indirect calorimetry to measure physiological energy balance. CalR simplifies the process to import raw data files, generate plots, and determine the most appropriate statistical tests for interpretation of diverse experimental designs including those of obesity and thermogenesis. Critically, CalR can read in data files from all experimental setups and produce a standardized output file.A recent effort to promote transparency and increase the rigor of the scientific process, especially regarding biostatistical analysis and improving reproducibility, has created an atmosphere receptive towards novel tools that assist in achieving these goals. Many forms of large scale datasets have central repositories, including that for microarray, RNA‐seq, and proteomics experiments. Here describe the first repository for indirect calorimetry experiments, populated by reference data from more than 30,000 individual mice from the International Mouse Phenotype Consortium (IMPC). By uploading data, in the standard CalR format to the repository, users can see how their experiments compare to the reference data. Furthermore, the user‐supplied data can be shared with the public as a lasting resource. This new system will provide the transparency necessary to enhance consistency, rigor, and reproducibility in studies of obesity and energy balance.Support or Funding Information‘Financial support for this work was provided by the NIDDK Mouse Metabolic Phenotyping Centers (MMPC, www.mmpc.org) under the MICROMouse Program, grants DK076169 and DK115255.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.