Prediction of resting energy expenditure from fat-free mass and fat mass

Nelson, Karl M.; Weinsier, Roland L.; Long, Calvin L.; Schütz, Y.

doi:10.1093/ajcn/56.5.848

Cited by 322 publications

(277 citation statements)

References 28 publications

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“…The relationship of age and gender with metabolic rates disappeared after adjustment for fat mass and fat-free mass, except for SMR-8h. Similar results have been reported showing that the effect of age and gender on metabolic rates is mainly due to fat-free mass (Ravussin et al, 1986;Astrup et al, 1990;Cunningham, 1991;Nelson et al, 1992) and fat mass (Dionne et al, 1999).…”

Section: Discussionsupporting

confidence: 88%

Interindividual variability in sleeping metabolic rate in Japanese subjects

et al. 2007

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Introduction: Basal metabolic rate (BMR) or sleeping metabolic rate (SMR) is the largest component of total energy expenditure (EE). An accurate prediction of BMR or SMR is needed to accurately predict total EE or physical activity EE for each individual. However, large variability in BMR and SMR has been reported. Objectives: This study was designed to develop prediction equations using body size measurements for the estimation of both SMR and BMR and to compare the prediction errors with those in previous reports. Methods: We measured body size, height, weight and body composition (fat mass and fat-free mass) from skinfold thickness in adult Japanese men (n ¼ 71) and women (n ¼ 66). SMR was determined as the sum of EE during 8 h of sleep (SMR-8h) and minimum EE during 3 consecutive hours of sleep (SMR-3h) measured using two open-circuit indirect human calorimeters. BMR was determined using a human calorimeter or a mask and Douglas bag. Results: The study population ranged widely in age. The SMR/BMR ratio was 1.0170.09 (range 0.82-1.42) for SMR-8h and 0.9470.07 (range 0.77-1.23) for SMR-3h. The prediction equations for SMR accounted for a 3À5% larger variance with 2-3% smaller standard error of estimate (SEE) than the prediction equations for BMR. Discussion: SMR can be predicted more accurately than previously reported, which indicates that SMR interindividual variability is smaller than expected, at least for Japanese subjects. The prediction equations for SMR are preferable to those for BMR because the former exhibits a smaller prediction error than the latter.

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Section: Discussionsupporting

confidence: 88%

Interindividual variability in sleeping metabolic rate in Japanese subjects

et al. 2007

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“…Despite the significant differences we observed in PAL between young and elderly subjects, these were very small (elderly vs younger women: 1.69 vs 1.64; elderly vs younger men: 1.66 vs 1.70) and PAL was not a significant predictor of RMR in our subjects. The influence of FM, body fat distribution, PAL and smoking on RMR has been discussed in detail elsewhere (Hofstetter et al, 1986;Weststrate et al, 1990;Moffat & Owens, 1991;Leenen et al, 1992;Nelson et al, 1992;Svendsen et al, 1993;Pannemans & Westerterp, 1995;Ryan et al, 1996;Withers et al, 1998;Nielsen et al, 2000;Kimm et al, 2001;Lührmann et al, 2001).…”

Section: Discussionmentioning

confidence: 99%

Lower resting metabolic rate in the elderly may not be entirely due to changes in body composition

et al. 2004

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Objective: To investigate whether or not the lower resting metabolic rate (RMR) in the elderly is entirely due to changes in body composition. Design: Cross-sectional data of 132 female (age 69.975.5 y, body mass index (BMI) 26.574.0 kg/m 2 ) and 84 male (age 68.975.1 y, BMI 26.172.8 kg/m 2 ) participants of the longitudinal study on nutrition and health status in an aging population of Giessen, Germany, as well as that of 159 young women (age 24.873.0 y, BMI 21.172.5 kg/m 2 ) and 67 young men (age 26.873.4 y, BMI 23.372.4 kg/m 2 ) were analysed. RMR was measured by indirect calorimetry after an overnight fast and body composition was estimated by bioelectrical impedance analysis and predictive equations from the literature. Analysis of covariance was used to adjust RMR for body composition, body fat distribution and smoking habits. Additionally, RMR that is to be expected theoretically, was calculated on the basis of the subjects' body composition and the specific metabolic rate of the different organs and was compared to measured RMR. Results: Compared to young subjects adjusted RMR was significantly lower in elderly women (5432782 vs 5809770 kJ/day, Po0.01) and men (6971799 vs 75587121 kJ/day, Po0.001). In both elderly women and men, measured RMR was markedly lower than calculated RMR (À6257404, À5157570 kJ/day). By contrast, measured and calculated RMR were nearly the same in young men (1597612 kJ/day); in young women the difference between measured and calculated RMR was only À3007457 kJ/day. In both sexes, these differences are significantly larger in the elderly when compared to young adults. Conclusion: These results support the point of view that the decline in RMR with advancing age cannot be totally due to changes in body composition.

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“…When regression models are used, the contribution of fat mass appears to be around 0.15-0.2 of the contribution of the same mass of lean tissue. 24,25 Studies in humans show that in this species as well, fat mass makes a contribution to EE that is a 0.15-0.2, 5,26,27 or a greater 28 proportion of that of the equivalent lean mass.…”

Section: Comparison Of Energy Expenditure In Lean and Obese Animalsmentioning

confidence: 94%

Some mathematical and technical issues in the measurement and interpretation of open-circuit indirect calorimetry in small animals

et al. 2006

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Indirect calorimetry is increasingly used to investigate why compounds or genetic manipulations affect body weight or composition in small animals. This review introduces the principles of indirect (primarily open-circuit) calorimetry and explains some common misunderstandings. It is not widely understood that in open-circuit systems in which carbon dioxide (CO 2 ) is not removed from the air leaving the respiratory chamber, measurement of airflow out of the chamber and its oxygen (O 2 ) content paradoxically allows a more reliable estimate of energy expenditure (EE) than of O 2 consumption. If the CO 2 content of the exiting air is also measured, both O 2 consumption and CO 2 production, and hence respiratory quotient (RQ), can be calculated. Respiratory quotient coupled with nitrogen excretion allows the calculation of the relative combustion of the macronutrients only if measurements are over a period where interconversions of macronutrients that alter their pool sizes can be ignored. Changes in rates of O 2 consumption and CO 2 production are not instantly reflected in changes in the concentrations of O 2 and CO 2 in the air leaving the respiratory chamber. Consequently, unless air-flow is high and chamber size is small, or rates of change of O 2 and CO 2 concentrations are included in the calculations, maxima and minima are underestimated and will appear later than their real times. It is widely appreciated that bigger animals with more body tissue will expend more energy than smaller animals. A major issue is how to compare animals correcting for such differences in body size. Comparison of the EE or O 2 consumption per gram body weight of lean and obese animals is misleading because tissues vary in their energy requirements or in how they influence EE in other ways. Moreover, the contribution of fat to EE is lower than that of lean tissue. Use of metabolic mass for normalisation, based on interspecific scaling exponents (0.75 or 0.66), is similarly flawed. It is best to use analysis of covariance to determine the relationship of EE to body mass or fat-free mass within each group, and then test whether this relationship differs between groups.

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Prediction of resting energy expenditure from fat-free mass and fat mass

Cited by 322 publications

References 28 publications

Interindividual variability in sleeping metabolic rate in Japanese subjects

Interindividual variability in sleeping metabolic rate in Japanese subjects

Lower resting metabolic rate in the elderly may not be entirely due to changes in body composition

Some mathematical and technical issues in the measurement and interpretation of open-circuit indirect calorimetry in small animals

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