Metabolically active components of fat free mass and resting energy expenditure in nonobese adults

Abstract

Resting energy expenditure (REE) and components of fat-free mass (FFM) were assessed in 26 healthy nonobese adults (13 males, 13 females). Detailed body composition analyses were performed by the combined use of dual-energy X-ray absorptiometry (DEXA), magnetic resonance imaging (MRI), bioelectrical impedance analysis (BIA), and anthropometrics. We found close correlations between REE and FFM(BIA) (r = 0.92), muscle mass(DEXA) (r = 0.89), and sum of internal organs(MRI) (r = 0.90). In a multiple stepwise regression analysis, FFM(BIA) alone explained 85% of the variance in REE (standard error of the estimate 423 kJ/day). Including the sum of internal organs(MRI) into the model increased the r(2) to 0.89 with a standard error of 381 kJ/day. With respect to individual organs, only skeletal muscle(DEXA) and liver mass(MRI) significantly contributed to REE. Prediction of REE based on 1) individual organ masses and 2) a constant metabolic rate per kilogram organ mass was very close to the measured REE, with a mean prediction error of 96 kJ/day. The very close agreement between measured and predicted REE argues against significant variations in specific REEs of individual organs. In conclusion, the mass of internal organs contributes significantly to the variance in REE.

Validation of the BodyGem™ hand-held calorimeter

“…FFM, representative of the most highly metabolically active tissue, is a strong predictor of RMR, explaining B65-90% of the variance in RMR. [14][15][16] In the current sample, FFM was strongly correlated with RMR measured by the BG adj (r ¼ 0.86, Po0.0001) and the SM-2900 (r ¼ 0.90, Po0.0001).…”

Effect of organ and tissue masses on resting energy expenditure in underweight, normal weight and obese adults

“…A detailed description of the procedure, its accuracy, precision and stability is reported elsewhere. 8,16 Briefly, measurements were performed between 0700 and 0900 h in a metabolic ward at constant humidity (55%) and room temperature (221C). Continuous gas exchange measurements (GEM, gas exchange measurement, Europa Scientific, Crewe, England) were made for 1 h. Data obtained during the first 15 min were omitted.…”

“…Slice thickness ranged from 6 mm for brain to 7 mm for the heart and 8-10 mm for abdominal organs with no interslice gaps. For a detailed description of the method, see Müller et al 2 and Illner et al 8 Volume data were transformed into organ weights using the following densities: 1.036 g/cm 3 for brain, 1.06 g/cm 3 for heart and liver, 1.05 g/cm 3 for kidneys and 1.054 g/cm 3 for spleen. 21 Residual mass was calculated as body mass minus the sum of brain MRI , heart MRI , liver MRI , kidneys MRI , MM DXA , skeletal bone DXA and AT DXA .…”

“…The impact of metabolically active FFM components on REE has been investigated only recently [5][6][7][8] (for a review see Müller et al 2 ). In normal-weight young adults, REE was calculated from the masses of the different tissue compartments (assessed by magnetic resonance imaging (MRI) or computer tomography (CT), and dual energy X-ray absorptiometry (DXA)) times their specific metabolic rates taken from the literature (REEc).…”

“…In normal-weight young adults, REE was calculated from the masses of the different tissue compartments (assessed by magnetic resonance imaging (MRI) or computer tomography (CT), and dual energy X-ray absorptiometry (DXA)) times their specific metabolic rates taken from the literature (REEc). 1 REEc was then compared to REE measured by indirect calorimetry (REEm), and had a mean prediction error of 83 6 or 96 kJ/day, 8 respectively. REEc has thus been proven to be a valid predictor of REE.…”