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Updated: Feb 10, 2026

Determining Basal Energy Expenditure and the Capacity of Thermogenic Adipocytes to Expend Energy in Obese Mice
Published on: November 11, 2021
Normalizing resting energy expenditure across the life course in humans: challenges and hopes
Manfred J Müller1, Corinna Geisler2, Mark Hübers2
1Institute for Human Nutrition and Food Science, Christian-Albrechts-University, Kiel, Germany. mmueller@nutrfoodsc.uni-kiel.de.
Abstract:
Whole-body daily energy expenditure is primarily due to resting energy expenditure (REE). Since there is a high inter-individual variance in REE, a quantitative and predictive framework is needed to normalize the data. Complementing the assessment of REE with data normalization makes individuals of different sizes, age, and sex comparable. REE is closely correlated with body mass suggesting its near constancy for a given mass and, thus, a linearity of this association. Since body mass and its metabolic active components are the major determinants of REE, they have been implemented into allometric modeling to normalize REE for quantitative differences in body weight and/or body composition. Up to now, various size and allometric scale laws are used to adjust REE for body mass. In addition, the impact of the anatomical and physical properties of individual body components on REE has been quantified in large populations and for different age groups. More than 80% of the inter-individual variance in REE is explained by FFM and its composition. There is evidence that the impact of individual organs on REE varies between age groups with a higher contribution of brain and visceral organs in children/adolescents compared with adults where skeletal muscle mass contribution is greater than in children/adolescents. However, explaining REE variations by FFM and its composition has its own limitations (inter-correlations of organs/tissues). In future, this could be overcome by re-describing the organ-to-organ variation using principal components analysis and then using the scores on the components as predictors in a multiple regression analysis.
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