Evolving concepts on adjusting human resting energy expenditure measurements for body size

Abstract

Establishing if an adult's resting energy expenditure (REE) is high or low for their body size is a pervasive question in nutrition research. Early workers applied body mass and height as size measures and formulated the Surface Law and Kleiber's Law, although each has limitations when adjusting REE. Body composition methods introduced during the mid-20th century provided a new opportunity to identify metabolically homogeneous 'active' compartments. These compartments all show improved correlations with REE estimates over body mass-height approaches, but collectively share a common limitation: REE-body composition ratios are not 'constant' but vary across men and women and with race, age and body size. The now-accepted alternative to ratio-based norms is to adjust for predictors by applying regression models to calculate 'residuals' that establish if an REE is relatively high or low. The distinguishing feature of statistical REE-body composition models is a 'non-zero' intercept of unknown origin. The recent introduction of imaging methods has allowed development of physiological tissue-organ-based REE prediction models. Herein, we apply these imaging methods to provide a mechanistic explanation, supported by experimental data, for the non-zero intercept phenomenon and, in that context, propose future research directions for establishing between-subject differences in relative energy metabolism.

Figure 1

Resting energy expenditure (REE) versus fat-free mass (FFM) in 131 men and 158 women reported in an earlier study. The respective REE/FFM ratios±SD for men and women are also presented in the figure.

Figure 2

Major whole body compartments representing body mass across the tissue-organ, cellular, and molecular body composition levels as cited in this review.

Figure 3

Resting energy expenditure (REE) versus body mass, stature, and composition measures in A. men (n=154) and B. women (N=208) from the New York Obesity Research Center (NYORC) study (Supplementary Material). CM, cell mass; FFM, fat-free mass; LST, lean soft tissue; SA, surface area. Data are fit with simple power functions and all are statistically significant at p<0.001.

Figure 3

Resting energy expenditure (REE) versus body mass, stature, and composition measures in A. men (n=154) and B. women (N=208) from the New York Obesity Research Center (NYORC) study (Supplementary Material). CM, cell mass; FFM, fat-free mass; LST, lean soft tissue; SA, surface area. Data are fit with simple power functions and all are statistically significant at p<0.001.

Figure 4

Powers of Kiel Study (Supplementary Material) tissue-organ level components adjusted for significant age and adipose tissue mass effects observed when scaled to adipose-tissue free mass. The presence of a significant positive (+) or negative (−) age or adipose tissue covariate is noted in the figure. Results are shown ±SE. RM, residual mass; SM, skeletal muscle mass.

Figure 5

Characteristics of multiple regression models predicting REE in Kiel Study (Table 2 and Supplementary Material) participants. The simplest model predicts REE from adipose-tissue free mass (ATFM) with subsequent addition of age, adipose-tissue (AT), sex, brain mass (Br), and liver mass (Liv) as potential covariates. The final model included brain, liver, kidneys, spleen, heart, skeletal muscle, bone, adipose tissue, and residual mass as potential covariates with brain, liver, skeletal muscle, bone, adipose tissue, residual mass, and age remaining as significant predictor variables. Of the added potential predictor variables, brain, liver, skeletal muscle, bone, adipose tissue, residual mass, and age added as significant covariates. The age panel depicts the β values for age observed across the seven models.