1399-P: Quantitation of the Energy Cost of Physical Activity in Mice

Abstract

Mice are widely used for diabetes and obesity research, but the quantitative contribution of physical activity to mouse energy metabolism is poorly understood. The energy cost of activity (PAEE) is typically determined by regression of total energy expenditure (TEE) versus physical activity. Confounding factors are 1) that muscle-produced heat reduces cold induced thermogenesis by brown fat and 2) that core body temperature (Tb) increases during physical activity. Minimizing cold induced thermogenesis addresses the first problem. Prior studies used housing at 30°C, but 30°C is now known to be below thermoneutrality in the dark phase. Furthermore, the Tb increase accompanying physical activity increases energy expenditure by non-activity processes; this was not considered previously. To account for these factors, we studied mice by indirect calorimetry at an ambient temperature of 35 °C while measuring Tb by telemetry and physical activity by beam break. We developed a model, partitioning TEE into PAEE and resting energy expenditure (REE) . Significant features of the model are i) physical activity measurements are aligned to the measured TEE using the time constant of the indirect calorimetry system, ii) the REE term is adjusted for the measured Tb using a Q10 (van’t Hoff coefficient) of 3.0, and iii) the REE term is scaled for body mass. In this model, the within mouse PAEE was robustly fit by linear regression, yielding PAEE of 22.5 ± 0.8 (light) and 21.7 ± 1.0 (dark) cal/1000 beam breaks (n=17) . The correlations between measured and model-calculated TEE were r = 0.888 ± 0.013 (light) and 0.888 ± 0.0 (dark) . Under usual housing conditions (ambient temperature of 23 °C) , the average physical activity was 1390 ± 130 (light) and 4940 ± 390 (dark) beam breaks/h. Based on these data, PAEE accounts for 8.6 ± 0.9 % (light) and 21.7 ± 1.8 % (dark) of TEE. This rigorous determination of PAEE advances understanding the role of physical activity in mouse energy metabolism, improving the predictive value of the mouse as a model for human disorders. Disclosure V.Skop: None. J.Guo: n/a. N.Liu: None. K.D.Hall: None. O.Gavrilova: None. M.L.Reitman: None. Funding Intramural NIDDK