Abstract
Physical activity (PA) is a main determinant of total energy expenditure (TEE) and has been suggested to play a key role in body weight regulation. However, thus far it has been challenging to determine what part of the expended energy is due to activity in freely moving subjects. We developed a computational method to estimate activity related energy expenditure (AEE) and resting metabolic rate (RMR) in mice from activity and indirect calorimetry data. The method is based on penalised spline regression and takes the time dependency of the RMR into account. In addition, estimates of AEE and RMR are corrected for the regression dilution bias that results from inaccurate PA measurements. We evaluated the performance of our method based on 500 simulated metabolic chamber datasets and compared it to that of conventional methods. It was found that for a sample time of 10 minutes the penalised spline model estimated the time-dependent RMR with 1.7 times higher accuracy than the Kalman filter and with 2.7 times higher accuracy than linear regression. We assessed the applicability of our method on experimental data in a case study involving high fat diet fed male and female C57Bl/6J mice. We found that TEE in male mice was higher due to a difference in RMR while AEE levels were similar in both groups, even though female mice were more active. Interestingly, the higher activity did not result in a difference in AEE because female mice had a lower caloric cost of activity, which was likely due to their lower body weight. In conclusion, TEE decomposition by means of penalised spline regression provides robust estimates of the time-dependent AEE and RMR and can be applied to data generated with generic metabolic chamber and indirect calorimetry set-ups.
Highlights
In the last few decades a great deal of effort has been devoted to measuring energy expenditure in humans and animal models, with the aim of uncovering potential causes for obesity
Our method is based on the assumption that the slow time variations in the resting metabolic rate (RMR) can be modelled by a set of spline functions and that the activity related energy expenditure (AEE) linearly correlates with the intensity of physical activity (PA)
In order to account for the regression dilution bias that is introduced into the caloric cost of activity (CCA) estimate by inaccuracies in the PA measurements, a corrected estimator was devised based on a multiplicative errors-in-variables model
Summary
In the last few decades a great deal of effort has been devoted to measuring energy expenditure in humans and animal models, with the aim of uncovering potential causes for obesity. Since subtle differences in daily energy expenditure can lead to substantial changes in weight when integrated over extended periods of time, it is vital to have accurate estimates of AEE and RMR in order to gain a better and more quantitative understanding of their role in the development of overweight. The contribution of AEE and RMR to TEE is determined by linear regression [6] or by taking the TEE that is not associated with activity [5,12,13] Since these approaches do not take into account that the RMR varies with time – e.g. due to diurnal variations, the thermic effect of food, or nervous and hormonal changes – the estimates of the AEE and RMR that are obtained are relatively inaccurate
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
