A database of indirect calorimetry experiments for the study of energy homeostasis

Project Summary: A data repository capturing the inputs critical to body weight regulation in disease states is a much-needed resource for the biomedical community. The sheer volume of data recording food intake, metabolic rate, and physical activity is overwhelming. Currently, researchers have no appropriate place to deposit or locate relevant data. This proposal seeks funding to create a comprehensive and user-friendly curated data repository that will enable researchers to share their data as well as easily find and re-use, reanalyze, and review energy metabolism data from scientists worldwide. The repository will include any experiment involving indirect calorimetry in mice. Data will be available from studies on the effects of altered behaviors (such as eating and food consumption, sleep patterns, and exercise) on body weight and the role of various disease states in body weight regulation. It will also include experiments interrogating pathological states including cardiopulmonary disorders, aging, and cancer cachexia, which lead to altered food consumption and energy metabolism and thus body weight. By providing a centralized database for these studies, the repository will facilitate the development of new therapeutic interventions and help researchers understand the complex factors that influence weight gain and loss. This effort will serve as a template for a future repository for indirect calorimetry for human subjects or additional model organisms. Modern indirect calorimetry systems allow for high-frequency multi-dimensional time-series measurements of the components affecting energy balance—food intake, physical activity, and metabolic rates. Our group is at the forefront of efforts to simplify and standardize these types of data using a free online tool, CalR. However, no data repository for these types of experiments currently exists. Until this situation is remedied, NIH-funded investigators do not have the ability to comply with the 2023 NIH Data Management and Sharing Policy. We describe the creation of the CalRepository designed specifically to facilitate FAIR data principles. This project will standardize physiology data collected across NIDDK-funded sites including Diabetes Research Centers (DRC), Nutrition Obesity Research Centers (NORC), and Mouse Metabolic Phenotyping Centers (MMPC). The RC2 grant mechanism, designed to fund a research resource for the community, finally provides the support to make this possible. The large-scale analysis and re-use of these types of data will enable new insights into the complexities of mammalian physiology. Preparing the world’s physiological data for Artificial Intelligence-based applications will allow for new hypothesis-generating approaches to fundamental mammalian biology. In summary, this proposal will create a readily accessible resource for data storage, exploratory data visualization, and analysis focused on body weight regulation. This will be essential to effectively manage the rapidly growing volume of data on this topic and provide physiological context for future experiments. We believe that this project has the potential to significantly advance our understanding of metabolic regulation.