Resting energy expenditure can be assessed by fat-free mass in female athletes regardless of body size

Energy requirements can be estimated from resting energy expenditure (REE). However, little is known about factors influencing REE in Japanese female athletes. This study was performed to evaluate the relationship between REE and body composition in Japanese female athletes with a wide range of body sizes. Ninety-three athletes (age 20.3±1.2 y, height 162.8±6.4 cm, body weight (BW) 57.0±9.2 kg, fat-free mass (FFM) 45.4±6.2 kg) were classified into three groups according to BW: small-size (S) (n=34), medium-size (M) (n=34), and large-size (L) (n=25). Systemic and regional body compositions (skeletal muscle (SM), fat mass (FM), bone mass (BM), and residual mass (RM)) were estimated by dual energy X-ray absorptiometry (DXA). Measured resting energy expenditure (REEm) was evaluated by indirect calorimetry. Marked differences were found in REEm (S: 1,111±150, M: 1,242±133, L: 1,478±138 kcal/d), and systemic and regional body compositions among the three groups. REEm was strongly correlated with FFM, and absolute values of RM and SM increased significantly according to body size. There was good agreement between REEm and estimated REE (REEe) from the specific metabolic rates of four major organ tissue level compartments. These data indicate that REE for female athletes can be attributed to changes in organ tissue mass, and not changes in organ tissue metabolic rate. That is, change in REE can be explained mainly by the change in FFM, and REE can be assessed by FFM in female athletes regardless of body size.     

Regional lean body mass and resting energy expenditure in non-obese adults

Summary Objective To study the effect of regional lean body mass (LBM) on resting energy expenditure (REE). Design Cross-sectional study in a homogenous group of 26 young healthy non-obese subjects. Methods Regional body composition was assessed by dual-energy X-ray absorptiometry (DEXA). REE was measured by indirect calorimetry. Results REE showed positive relationships with whole body LBM (LBM_b; r=0.89) as well as with regional LBM (LBM_trunk = LBM_t, r = 0.88, and LBM_arms+legs = LBM_e for LBM_extremities, r = 0.89) with non-zero intercepts (between 1.86 and 2.83 MJ/d). REE per kg LBM_b falls as LBM_b increases (r = 0.77). By contrast, REE adjusted for regional distribution of LBM (i. e. the ratio of LBM_t to LBM_e) increases as LBM_b increases (r = 0.91) showing a near-zero intercept (i. e. 0.048 MJ/d). Adjusting REE for LBM_b as well as for the ratio of LBM_t to LBM_e can be used for comparison between subjects. Conclusions Our data suggest that regional distribution of LBM is a determinant of REE. Assessment of LBM_t and LBM_e by DEXA provides a possibility to adjust for the non-linearity of REE on LBM_b. Received: 11 April 2001, Accepted: 12 June 2001     

Intra- and interindividual variability of resting energy expenditure in healthy male subjects -- biological and methodological variability of resting energy expenditure

The objective of the present study was to investigate the contribution of intra-individual variance of resting energy expenditure (REE) to interindividual variance in REE. REE was measured longitudinally in a sample of twenty-three healthy men using indirect calorimetry. Over a period of 2 months, two consecutive measurements were done in the whole group. In subgroups of seventeen and eleven subjects, three and four consecutive measurements were performed over a period of 6 months. Data analysis followed a standard protocol considering the last 15 min of each measurement period and alternatively an optimised protocol with strict inclusion criteria. Intra-individual variance in REE and body composition measurements (CV(intra)) as well as interindividual variance (CV(inter)) were calculated and compared with each other as well as with REE prediction from a population-specific formula. Mean CV(intra) for measured REE and fat-free mass (FFM) ranged from 5.0 to 5.6 % and from 1.3 to 1.6 %, respectively. CV(intra) did not change with the number of repeated measurements or the type of protocol (standard v. optimised protocol). CV(inter) for REE and REE adjusted for FFM (REE(adj)) ranged from 12.1 to 16.1 % and from 10.4 to 13.6 %, respectively. We calculated total error to be 8 %. Variance in body composition (CV(intra) FFM) explains 19 % of the variability in REE(adj), whereas the remaining 81 % is explained by the variability of the metabolic rate (CV(intra) REE). We conclude that CV(intra) of REE measurements was neither influenced by type of protocol for data analysis nor by the number of repeated measurements. About 20 % of the variance in REE(adj) is explained by variance in body composition.     

Validation of energy intake by dietary recall against different methods to assess energy expenditure

OBJECTIVES: To compare the validity of dietary recalls and physical activity recalls and investigate some factors influencing this validity. To provide an example showing how procedures based on recalls of physical activity can assess the validity of dietary recalls and identify subjects constantly underreporting their energy intake (EI). DESIGN AND SUBJECTS: Thirty-seven women were studied using three 24-h dietary recalls, two kinds of physical activity recalls, indirect calorimetry and the doubly labelled water method. RESULTS: The EI obtained using dietary recalls were biased with respect to body mass index (BMI) and attitudes towards body weight and dieting, whereas results obtained using a physical activity recall were not. Eighteen women produced underreports (UR), i.e. their average EI was below 76% of total energy expenditure (TEE), whereas 24 women reported an EI that was lower than TEE on all three recall days, i.e. constantly underreporting subjects. A physical activity recall identified 13 URs and 20 of the constantly underreporting subjects. CONCLUSIONS: In contrast to estimates of EI, TEE assessed using physical activity recalls was not biased with respect to BMI or attitudes towards body weight and dieting. Recalls of physical activity represent potentially useful procedures for identifying URs and constantly underreporting subjects but are not accurate enough for individuals.     

Characteristics of body composition and resting energy expenditure in lean young women

The number of lean young women has been increasing. Fear of being fat may induce unnecessary attempts to reduce body weight, which can cause several types of illness. Many investigations have demonstrated dysfunction of the hypothalamus and metabolic differences in patients with anorexia nervosa. However, it is unclear whether there are any differences in physical characteristics between women with lower body weight and no illness compared to those of normal body weight. In this study, we investigated the differences in body composition, biochemical parameters, and resting energy expenditure (REE) between young women with low and normal body mass index (BMI). Twenty lean women (BMI<18.5 kg/m(2)) and 20 normal women (18.5≤BMI<25 kg/m(2)) were recruited for this study. Body composition, biochemical parameters, and REE (REEm: measurement of REE) were measured, and the REE (REEe: estimation of REE) was estimated by using a prediction model. Marked differences were found in body composition. All of the values of blood analysis were in the normal ranges in both groups. REEm (kcal/d and kcal/kg BW/d) was significantly lower in lean than in normal women, but there were no significant differences in the REEm to fat free mass (FFM) ratio between the two groups. In addition, there was good agreement between REEm and REEe obtained from the specific metabolic rates of four tissue organs. These data indicate that the lean women without any illness have normal values of biochemical parameters and energy metabolism compared to women with normal BMI.     

Anthropometric measured fat-free mass as essential determinant of resting energy expenditure for pregnant and non-pregnant women

Objective

There is conflicting evidence as to whether anthropometric parameters are related to resting energy expenditure (REE) during pregnancy. The aim of this prospective longitudinal study was to precisely assess a major anthropometric determinant of REE for pregnant and non-pregnant women with verification of its use as a possible predictor.

Methods

One hundred fifty-two randomly recruited, healthy, pregnant Czech women were divided into groups G1 and G2. G1 (n = 31) was used for determination of the association between anthropometric parameters and REE. G2 (n = 121) and a group of non-pregnant women (G0; n = 24) were used for verification that observed relations were suitable for the prediction of REE during pregnancy. The women in the study groups were measured during four periods of pregnancy for REE by indirect calorimetry and anthropometric parameters after 12 h of fasting.

Results

Associations were found in all groups between measured REE by indirect calorimetry and anthropometric parameters such as weight, fat mass, fat-free mass (FFM), body surface area, and body mass index (P < 0.0001). The best derived predictor, REE/FFM (29.5 kcal/kg, r = 0.70, P < 0.0001), in group G1 was statistically verified in group G2 and compared with G0.

Conclusion

Anthropometrically measured FFM with its metabolically active components is an essential determinant of REE in pregnancy. REE/FFM can be used for the prediction of REE in pregnant and non-pregnant woman.     

Larger mass of high-metabolic-rate organs does not explain higher resting energy expenditure in children

BACKGROUND: Children have a high resting energy expenditure (REE) relative to their body weight. The decline in REE during growth may be due to changes in body composition or to changes in the metabolic rate of individual organs and tissues. OBJECTIVES: The goals were to quantify body-composition components in children at the organ-tissue level in vivo and to determine whether the observed masses 1) account for the elevated REE in children and 2) account, when combined with specific organ-tissue metabolic constants, for children's REE. DESIGN: This was a cross-sectional evaluation of 15 children (aged 9.3 +/- 1.7 y) and 13 young adults (aged 26.0 +/- 1.8 y) with body mass indexes (in kg/m(2)) < 30. Magnetic resonance imaging-derived in vivo measures of brain, liver, kidney, heart, skeletal muscle, and adipose tissue were acquired. REE was measured by indirect calorimetry (REE(m)). Previously published organ-tissue metabolic rate constants were used to calculate whole-body REE (REE(c)). RESULTS: The proportion of adipose-tissue-free mass as liver (3.7 +/- 0.5% compared with 3.1 +/- 0.5%; P < 0.01) and brain (6.2 +/- 1.2% compared with 3.3 +/- 0.9%; P < 0.001) was significantly greater in children than in young adults. The addition of brain and liver mass significantly improved the model but did not eliminate the role of age. REE(c) with published metabolic coefficients underestimated REE(m) (REE(c) = 3869 +/- 615 kJ/d; REE(m) = 5119 +/- 769 kJ/d; P < 0.001) in children. CONCLUSION: The decline in REE during growth is likely due to both a decrease in the proportion of some of the more metabolically active organs and tissues and changes in the metabolic rate of individual organs and tissues.     

Prediction of daily energy expenditure during a feeding trial using measurements of resting energy expenditure, fat-free mass, or Harris-Benedict equations

BACKGROUND: During feeding trials, it is useful to predict daily energy expenditure (DEE) to estimate energy requirements and to assess subject compliance. OBJECTIVE: We examined predictors of DEE during a feeding trial conducted in a clinical research center. DESIGN: During a 28-d period, all food consumed by 26 healthy, nonobese, young adults was provided by the investigators. Energy intake was adjusted to maintain constant body weight. Before and after this period, fat-free mass (FFM) and fat mass were assessed by using dual-energy X-ray absorptiometry, and DEE was estimated from the change (after - before) in body energy (DeltaBE) and in observed energy intake (EI): DEE = EI - DeltaBE. We examined the relation of DEE to pretrial resting energy expenditure (REE), FFM, REE derived from the average of REE and calculated from FFM [REE = (21.2 x FFM) + 415], and an estimate of DEE based on the Harris-Benedict equation (HB estimate) (DEE = 1.6 REE). RESULTS: DEE correlated (P < 0.001) with FFM (r = 0.78), REE (r = 0.73), average REE (r = 0.82), and the HB estimate (r = 0.81). In a multiple regression model containing all these variables, R(2) was 0.70. The mean (+/-SEM) ratios of DEE to REE, to average REE, and to the HB estimate were 1.86 +/- 0.06, 1.79 +/- 0.04, and 1.02 +/- 0.02, respectively. CONCLUSIONS: Although a slightly improved prediction of DEE is possible with multiple measurements, each of these measurements suggests that DEE equals 1.60-1.86 x REE. The findings are similar to those of previous studies that describe the relation of REE to DEE measured directly.     

Influence of methods used in body composition analysis on the prediction of resting energy expenditure

OBJECTIVE: There are considerable differences in published prediction algorithms for resting energy expenditure (REE) based on fat-free mass (FFM). The aim of the study was to investigate the influence of the methodology of body composition analysis on the prediction of REE from FFM. DESIGN: In a cross-sectional design measurements of REE and body composition were performed. SUBJECTS: The study population consisted of 50 men (age 37.1+/-15.1 years, body mass index (BMI) 25.9+/-4.1 kg/m2) and 54 women (age 35.3+/-15.4 years, BMI 25.5+/-4.4 kg/m2). INTERVENTIONS: REE was measured by indirect calorimetry and predicted by either FFM or body weight. Measurement of FFM was performed by methods based on a 2-compartment (2C)-model: skinfold (SF)-measurement, bioelectrical impedance analysis (BIA), Dual X-ray absorptiometry (DXA), air displacement plethysmography (ADP) and deuterium oxide dilution (D2O). A 4-compartment (4C)-model was used as a reference. RESULTS: When compared with the 4C-model, REE prediction from FFM obtained from the 2C methods were not significantly different. Intercepts of the regression equations of REE prediction by FFM differed from 1231 (FFM(ADP)) to 1645 kJ/24 h (FFM(SF)) and the slopes ranged between 100.3 kJ (FFM(SF)) and 108.1 kJ/FFM (kg) (FFM(ADP)). In a normal range of FFM, REE predicted from FFM by different methods showed only small differences. The variance in REE explained by FFM varied from 69% (FFM(BIA)) to 75% (FFM(DXA)) and was only 46% for body weight. CONCLUSION: Differences in slopes and intercepts of the regression lines between REE and FFM depended on the methods used for body composition analysis. However, the differences in prediction of REE are small and do not explain the large differences in the results obtained from published FFM-based REE prediction equations and therefore imply a population- and/or investigator specificity of algorithms for REE prediction.     

Comparison of resting energy expenditure in bronchopulmonary dysplasia to predicted equation

OBJECTIVE: Children with bronchopulmonary dysplasia (BPD) often suffer from growth failure because of disturbances in energy balance with an increase of resting energy expenditure (REE). Evaluation of REE is a useful tool for nutritional management. Indirect calorimetry is an elective method for measuring REE, but it is time consuming and requires rigorous procedure. The objective of this study was to test accuracy of prediction equation to evaluate REE in BPD children. PATIENTS AND METHODS: Fifty-two children aged 4-10 years with BPD (30 boys and 22 girls) and 30 healthy lean children (20 boys and 10 girls) were enrolled. In this study, indirect calorimetry was compared to four prediction equations (Schoffield-W, Schoffield-HW, Harris-Benedict and Food and Agriculture Organization equation) using Bland-Altman pair wise comparison. RESULTS: The Harris-Benedict equation was the best equation to predict REE in children with BPD, and Schoffield-W was the best in healthy children. For the children with chronic lung disease of prematurity the Harris-Benedict equation showed the lowest mean predicted REE-REE measured by indirect calorimetry difference (difference = 15 kcal/day; limits of agreement -266 and 236 kcal/day; 95% confidence interval for the bias -207 to 177 kcal/day), and graphically, the best agreement. For the group of healthy children, it was the Schofield-W equation (-2.9 kcal/day; limits of agreement -275 and 269 kcal/day; 95% confidence interval for the bias -171 to 165 kcal/day), and graphically, the best agreement. CONCLUSION: Differences in prediction equation are minimal compared to calorimetry. Prediction equation could be useful in the management of children with BPD.     

Relation of body mass index and body fatness to energy expenditure: longitudinal changes from preadolescence through adolescence

BACKGROUND: Although it is widely accepted that weight gain results when energy intake exceeds energy expenditure (EE), how reduced EE contributes to the development of obesity remains unclear. OBJECTIVE: We tested the hypothesis that reduced EE in the premenarcheal period in girls constitutes a risk factor for an increase in relative weight [body mass index (BMI) z score] and percentage of body fat (%BF) during adolescence. DESIGN: We measured EE at study entry in 196 premenarcheal nonobese girls. Resting metabolic rate (RMR) was measured by indirect calorimetry. Total energy expenditure (TEE) was measured by the doubly labeled water method. Activity energy expenditure (AEE) was calculated from RMR and TEE. After the baseline study, girls were followed annually until 4 y after menarche (x+/- SD: 7.1 +/- 2.6 y). At each visit, height, weight, and %BF by bioelectrical impedance were measured. Girls also completed annual food-frequency and activity questionnaires. Linear mixed effects modeling was used to evaluate the longitudinal relation between BMI z score and %BF and measures of baseline EE. RESULTS: We found no significant relation in change in %BF with RMR, AEE, or TEE. We observed a small positive relation between BMI z score and AEE and TEE (P < 0.05) but no significant relation with RMR. When we stratified by parental overweight, the findings were unchanged for RMR. TEE and AEE were positively related to BMI z score in girls of overweight parents. CONCLUSIONS: Our findings suggest that EE in the premenarcheal period is not a risk factor for increases in %BF or BMI z score in girls during adolescence.     

Actual or ideal body weight: which should be used to predict energy expenditure?

Assessing energy expenditure in obese people is problematic. Two questions arise: Can we predict energy expenditure accurately? Does actual or ideal body weight better predict energy expenditure? Two groups of obese subjects--65 hospitalized adults and 65 nonhospitalized adults--were studied. Both groups had actual body weights that were at least 30% above ideal body weights. For both groups, energy expenditure was measured by indirect calorimetry and calculated using the variables sex, actual and ideal body weight, age, and ventilatory status. All but three patients were receiving nutrition support by the enteral route (either orally or by tube) or by the parenteral route (with hypertonic dextrose, amino acid, and fat). The nonhospitalized subjects fasted during measurements of energy expenditure. Regression equations were derived to predict energy expenditure. Actual body weight better predicted energy expenditure than did ideal body weight. We conclude that actual weight should be used to predict energy expenditure in obese individuals.     

New equation for the prediction of resting energy expenditure during pregnancy

ObjectiveThe equation for the prediction of resting energy expenditure (REE) during pregnancy is unknown. The aim of this prospective longitudinal study was to determine a new equation for prediction of REE in pregnancy.MethodsA total of 152 randomly recruited healthy pregnant Czech women (nonsmokers, not users of chronic medications or abusers of alcohol or drugs, normoglycemic, euthyroid, and not anemic) were divided into two cohorts: group 1 (n = 31) was used for determination of the equation for calculation of pregnant REE and group 2 (n = 121) for cross-validation of this formula. The REE of the pregnant women in both study groups was examined by indirect calorimetry (REE-IC) along with anthropometry after 12 h of fasting in four periods of pregnancy. A statistical comparison of three basic equations (Harris Benedict, Schofield, and Kleiber) was used for the prediction of REE.ResultsThrough correlation analysis and linear regression, a new predictive equation of REE during pregnancy (P REE) was derived from the Harris Benedict equation. We observed high concordance between values from P REE and REE-IC in group 2. Analysis of alternative predictive equations of REE with the addition of kilocalories and a corrected multiplication factor for each stage of pregnancy expressed low concordance.ConclusionsThe equation for REE in kilocalories during pregnancy, P REE = 346.43943 + 13.962564 × W + 2.700416 × H ? 6.826376 × A (W, weight; H, height; A, age), with SD 116 kcal/d, corresponds closely to REE-IC and maternal changes in each phase of pregnancy. P REE can be applied for prediction of REE during gestation.