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New WUR model improves interpretation of respiratory data
To identify, which food substrate is utilized by an organism, the Respiratory Exchange Ratio (RER) is a qualitative indicator. Respiratory data are for example used to characterize metabolic diseases in humans or to get deeper insights in metabolism in animal models for such human diseases.
A new mathematical model designed by researchers from Wageningen University & Research (WUR) improves the value of obtained respiration data. “Our model enables better interpretation, enhances data quality and offers a simpler way to quantify respiratory data”, says Christian Klein, PhD researcher at WUR chair group Human and Animal Physiology (HAP).
The Respiratory Exchange Ratio (RER) is the ratio of total carbon dioxide produced over total oxygen consumed by an organism and can be measured in breath. This ratio serves as a qualitative measure to determine the substrate usage of a particular organism. For example, a RER of 1.0 means that a body is burning (oxidizing) 100% carbohydrates (glucose) to generate energy. A factor of around 0.7 indicates that fat is the main substrate, and any value in between represents a mix thereof. Respiratory data are crucial to characterize and identify metabolic diseases in humans or to get deeper insights into (energy) metabolism using animal models for such human diseases, and can also be applied in target animals ranging from insects to production animals.
The way quantification of RER is done until now, has its limitations, according to the WUR research team. “RER data are mostly continuous, however, currently calculations are based on conversion tables published more than 30 years ago. These tables lack the use of continuous data. To cover the full range of RER data, we aimed to think of a new interpolation approach for the existing data tables”, explains PhD researcher Christian Klein of the chair group Human and Animal Physiology (HAP). The mathematical model designed by Zhuohui Gan and further validated by Klein has recently been published in the American Journal of Physiology, Endocrinology & Metabolism.
Approach
Researchers Gan and Klein started by determining that linear interpolation of the existing data tables would lead to incorrect values. “The observed non-linearity stressed the need for an improved interpolation method. Therefore, we constructed a new mathematical model as a strategy to translatecontinuous RER valuesinto correct valuesof relative glucose and lipid oxidation.”
Validation
The new mathematical model was validated against the original data table by Péronnet & Massicotte, and against a linear and exponential interpolation of these data. For this purpose, a datasetof a previously performed and published nutritional intervention study in mice were used. “This showed that our model outperforms the other methods,providing more accuratedata”, says Klein. He and his colleague Gan conclude that applying their mathematical model will lead to an increase in data quality. “Furthermore, the model will offer a very simple,straightforward approach to obtain best levels of relative glucose and lipid oxidation from continuous RER values.”
New tool
According to the research team, the mathematical model they designed offers a new tool to convert continuous RER data into more accurate estimations of relative glucose and lipid oxidation. “One of the major benefits of our model is that it circumvents the use of non-protein respiratory quotient tables and simplifies calculations by automating the conversions.” The model can be implemented into software commonly used for indirect calorimetry (respiratory) measurements, is applicable for a wide range of animals (including humans), and provides real-time data of glucose and lipid oxidation during a running experiment.
Funding
The research into the new mathematical model for respiratory data conducted by Zhuohui Gan and Christian Klein was funded by the Next Level Animal Sciences (NLAS) program of Wageningen University & Research, and the INSPIRE European Training Network, respectively. INSPIRE received funding from the EU Horizon 2020 Research and Innovation program, under the Marie Skłodowska-Curie program (GA 858070).