Estimation of resting energy expenditure by anthropometry

The role of anthropometry in estimating resting energy expenditure (REE) has been assessed in 142 clinically stable patients. Ninety eight patients had cancer (54 weight stable, 44 weight losing) and 44 patients had nonmalignant illness (27 weight stable, 17 weight losing). Mid-arm muscle circumference (MAMC) measurements correlated significantly with REE measured by indirect calorimetry in each of the groups studied. Weight loss significantly affected this correlation whereas cancer did not. The correlation in weight stable patients was poorer than that in weight losing patients, possibly reflecting inaccuracy of anthropometric measurements due to subcutaneous adipose tissue. Significant correlations were also observed between mid-arm circumference (MAC) and REE, and between MAMC and whole body oxygen consumption.REE can be estimated from MAMC measurements in weight stable and weight losing patients with benign or malignant disease. This simple method may be of value in estimating REE where indirect calorimetry facilities are unavailable.     

中等手术创伤对能量代谢的影响

为了解中等手术创伤对能量代谢的影响 ,作者采用计算机控制的间接热能监测仪测定择期胆囊切除术病人手术前后的静息能量消耗 ( REE)。结果表明 ,男性病人术后第 1、2、3、4天和第 7天的 REE与术前的 REE比 (称应激因子 ,stress factor,SF)分别为 1.18± 0 .0 8、1.15± 0 .0 8、1.0 9± 0 .0 7、1.0 2± 0 .0 5和 1.0 1± 0 .0 6;女性病人为 1.13± 0 .0 7、1.11± 0 .0 5、1.0 8± 0 .11、1.0 2± 0 .0 7和 0 .99± 0 .0 4 ;术后前 3d明显高于术前 ( P<0 .0 1) ,前 3d SF平均值男女病人分别为1.14± 0 .0 9和 1.11± 0 .0 8,术后第 4天开始与术前无明显差异。提示中等手术创伤后早期能量代谢会有一定程度的增高 ,男、女病人约增加 14%和 11%。这为中等手术创伤病人营养支持时的能量供给提供参考     

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.     

Effect of daytime on resting energy expenditure and thermic effect of food in obese adolescents.

The aim of this study was to investigate the effect of daytime on resting energy expenditure (REE) and thermic effect of food (TEF), REE and TEF were measured in 11 obese boys (mean age +/- SD 11.9 +/- 1.6 years; body mass index 30.1 +/- 4.0 kg/m2) by indirect calorimetry (SensorMedics 2900) on two consecutive days: early in the morning (7:30 a.m.) on one day and at 12 noon on the other day or vice versa. REE was measured for 30 minutes and TEF for 180 minutes after a 600 kcal liquid meal containing 13% protein, 39% fat and 48% carbohydrates. REE measured in the morning was not statistically different from that measured at noon (2191 +/- 358 vs 2223 +/- 319 kcal/24 hours) and morning values were highly correlated with afternoon values (r2 = 0.805). Therefore we conclude that the effect of daytime is negligible for measurements of REE if the patients are in a postabsorptive state and avoid physical activity and stress 10-12 hours prior to measurements. The thermic effect of food calculated in the morning also was not statistically significantly from values found in the afternoon (8.2 +/- 8.8% in the morning and 6.6 +/- 7.5% in the afternoon). However, because of very high within-patient variability the correlation between morning and afternoon values was weaker than for REE (r2 = 0.289).     

Estimating energy requirements in burned children: a new approach derived from measurements of resting energy expenditure

We examined the determinants of resting energy expenditure (REE) in 127 observations in 56 burned children. Predicted basal energy expenditure (PBEE), body surface area (BSA), and body weight correlated significantly with REE (r2 = 0.76). Days postburn and burn size (% BSA burned) only accounted for 21%, and 24% of the variation in the elevation in REE above PBEE. The single most powerful predictor of REE was PBEE (REE = 1.29 x PBEE); addition of other variables did not improve the prediction. When our recently described activity factor of 1.2 for burn patients is used, the data predict that the average energy requirement to maintain energy balance is 1.55 x PBEE, which is significantly lower than commonly used recommendations, especially for larger burns. The energy required to ensure that 95% of patients achieve energy balance was (1.55 x PBEE) + (2.39 xoff+PBEE0.75), approximately equal to 2 x PBEE. Because the equations presented are derived from measurements of energy expenditure, they represent the most valid approach to estimating energy requirements.     

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.     

Comparability of resting energy expenditure in Nigerians and U.S. blacks

OBJECTIVE: To determine the influence of environmental factors on resting energy expenditure (REE) and its relationship to adiposity in two populations of West African origin, Nigerians and U.S. blacks. RESEARCH METHODS AND PROCEDURES: REE and body composition were measured in a cross-sectional sample of 89 Nigerian adults (39 women and 50 men), and 181 U.S. black adults (117 women and 65 men). Both groups represent randomly selected population samples. REE was measured by indirect calorimetry after an overnight fast in both sites using the same instrument. Body composition was estimated using bioelectrical impedance analysis (BIA) in 72 Nigerians and 156 U.S. participants. Multivariate regression analysis was used to determine the significant predictors of REE. The analyses were repeated in a set of 17 Nigerians and 28 U.S. blacks in whom body composition was measured using deuterium dilution. RESULTS: U.S. black adults were significantly heavier and had both more fat-free mass (FFM) and body fat than Nigerians. FFM was the only significant determinant of REE in both population groups, whether body composition was measured using BIA or deuterium dilution. The relationship between REE and body composition did not differ by site. There was no relationship between REE and adiposity. DISCUSSION: Differences in current environmental settings did not impact REE. The differences observed in mean levels of body fat between Nigerians and U.S. blacks were not the result of differences in REE adjusted for body composition.     

Assessment of resting energy expenditure in mechanically ventilated patients

BACKGROUND: Usual equations for predicting resting energy expenditure (REE) are not appropriate for critically ill patients, and indirect calorimetry criteria render its routine use difficult. OBJECTIVE: Variables that might influence the REE of mechanically ventilated patients were evaluated to establish a predictive relation between these variables and REE. DESIGN: The REE of 70 metabolically stable, mechanically ventilated patients was prospectively measured by indirect calorimetry and calculated with the use of standard predictive models (Harris and Benedict's equations corrected for hypermetabolism factors). Patient data that might influence REE were assessed, and multivariate analysis was conducted to determine the relations between measured REE and these data. Measured and calculated REE were compared by using the Bland-Altman method. RESULTS: Multivariate analysis retained 4 independent variables defining REE: body weight (r(2) = 0.14, P < 0.0001), height (r(2) = 0.11, P = 0.0002), minute ventilation (r(2) = 0.04, P = 0.01), and body temperature (r(2) = 0.07, P = 0.002): REE (kcal/d) = 8 x body weight + 14 x height + 32 x minute ventilation + 94 x body temperature - 4834. REE calculated with this equation was well correlated with measured REE (r(2) = 0.61, P < 0.0001). Bland-Altman plots showed a mean bias approaching zero, and the limits of agreement between measured and predicted REE were clinically acceptable. CONCLUSION: Our results suggest that REE estimated on the basis of body weight, height, minute ventilation, and body temperature is clinically more relevant than are the usual predictive equations for metabolically stable, mechanically ventilated patients.     

Normal value of resting energy expenditure in healthy neonates

OBJECTIVE: We investigated the value of resting energy expenditure (REE) in healthy neonates and evaluated the impact factors on REE. METHODS: One hundred eighty healthy neonates (95 boys and 85 girls) with birth weights above 2500 g were measured by indirect calorimetry, and the effect of birth weight evaluated. Measured and predicted REEs were compared, and the effects of sex and delivery method on REE were examined in 154 newborn infants with birth weights of approximately 2500 to 4000 g. RESULTS: Birth weight had a significant effect on REE. There was a negative relation between REE and birth weight (r = -0.289). The REEs of newborn infants weighing more than 4000 g were statistically lower than those of infants weighing 2500 to 4000 g (44.5 +/- 5.9 versus 48.3 +/- 6.1 kcal x kg(-1) x d(-1), P = 0.01). The measured and predicted REEs of 154 newborn infants were 48.3 +/- 6.1 and 54.1 +/- 1.1 kcal x kg(-1) x d(-1), respectively. There was a significant difference between the two values. Sex and delivery methods had no effect on REE in healthy neonates. CONCLUSIONS: The value from the predicted equation is not suitable for neonatal energy supplementation in clinical practice. The normal REE value for healthy neonates with birth weights of 2500 to 4000 g is 48.3 +/- 6.1 kcal x kg(-1) x d(-1).     

Measurement of resting energy expenditure in a clinical setting

In this study indirect calorimetry for the measurement of a patient's resting energy expenditure (REE) was assessed in clinical practice. REE measured early in the morning after an overnight fast was highly reproducible. REE measured in the afternoon, when patients had consumed their meals, was 15% higher than REE measured in the morning. REE measured at mid-morning was not different from that measured early in the morning, except for patients who had breakfast between the two measurements. Therefore, to avoid the effect of diet-induced thermogenesis in the measurement a patient must be measured in the morning in the post-absorptive state. Variations because of limited physical activities may be neglected, including a short travel from home to the hospital, which implies that REE may be measured on an out-patient basis. The effect of total parenteral nutrition (TPN) on energy expenditure (EE) was a 12% increase. The respiratory quotient (RQ) rose to almost 1.0. Nine days of enteral nutritional support showed only a 3% increase in REE, while RQ increased from 0.78 to 0.87, indicating restored glycogen stores.     

Weekly measurements accurately represent trends in resting energy expenditure in children undergoing hematopoietic stem cell transplantation

BACKGROUND: Resting energy expenditure (REE) measurements are optimal for accurate assessment of energy requirements and precise provision of parenteral nutrients. We previously observed significant reduction in REE during a 4-week period in children undergoing hematopoietic stem cell transplantation (HSCT). The goal of this study was to determine if weekly REE measurements could accurately represent changes in REE in the peritransplant period compared with a more frequent standard of daily measurements. METHODS: Data are presented from a previously described cohort of 37 children undergoing HSCT. We performed weekly indirect calorimetry on 25 patients; of those 25, we performed daily measurements on a convenience sample of 5 children. The time course of REE was analyzed in each sample by repeated measures regression. RESULTS: The REE trend of the 20 weekly participants was similar to that of the 5 daily participants, reaching about 80% of predicted REE at 4 weeks posttransplant, with an average decline of 3.4% per week during 4 weeks. CONCLUSION: The results suggest that weekly REE measurements accurately characterize REE changes 4 weeks after HSCT compared with daily measurements. Characterization of these trends using weekly measurements may help guide clinical and nutrition care of these patients.     

Effects of exercise training on resting energy expenditure during caloric restriction

Resting energy expenditure (REE), maximum oxygen uptake (VO2max), and body composition were measured in seven moderately obese women during 9 wk of dietary restriction (800 kcal/d). During weeks 4-6, subjects underwent exercise training (30 min cycling/d, 5 d/wk, at 70% VO2max). The first 3 wk of caloric restriction decreased REE by 13% (from 1437 +/- 76 to 1254 +/- 66 kcal/24 h, means +/- SEM, p less than 0.05). Exercise training increased VO2max (from 1717 +/- 108 to 1960 +/- 120 mL/min, means +/- SEM, p less than 0.05) but did not elevate the dietary-depressed REE (from 1254 +/- 66 to 1262 +/- 62 kcal/24 h). The greatest decrease in body fat (3.7 +/- 0.4 kg) occurred during exercise training, resulting in a small apparent increase in REE when expressed per kilogram total body weight. However, expressed per unit lean body mass, REE remained suppressed throughout the period of caloric restriction. We conclude that exercise training of sufficient intensity to substantially increase VO2max does not reverse the dietary-induced depression of REE.     

Lean-body-mass composition and resting energy expenditure before and after long-term overfeeding.

This report deals with the association between the constituents of lean body mass (LBM) and resting metabolic rate (RMR) before and after a 100-d overfeeding period. Computed-tomography (CT) scan of 22 young adult males at nine different body levels were used to estimate adipose tissue mass (ATMCT), LBMCT, skeletal-muscle mass (SMMCT), and non-muscular LBMCT (NM-LBMCT). Before overfeeding, all body constituents, except ATMCT, were significantly correlated with RMR. Only body mass changes were significantly correlated with RMR changes. Comparison of these results with those of several studies in the literature reveals that the relationship between RMR and fat-free mass is highly influenced by the size of the SD for the latter variable. In stepwise-multiple-regression analysis, only SMMCT could be used to predict RMR. It was concluded that SMMCT and ATMCT, but not NM-LBMCT, increased during overfeeding and that the best correlates of RMR remain LBMCT, SMMCT, and body mass.     

Predicting resting energy expenditure in patients with musculoskeletal deformities

The aim of this study was to assess the validity of the commonly used equations (Harris-Benedict (HB), Schofield (S) and equations based on midarm circumference (MAC) and midarm muscle circumference (MAMC) in predicting resting energy expenditure (REE) in a population of patients with musculoskeletal deformities. 20 kyphoscoliotic patients (15 female (F); 5 male (M); mean age 59.6 years) and 10 controls (7 F; 3M; 59.8 years) were studied. REE measured by indirect calorimetry (IC) with a ventilated canopy system (Deltatrac metabolic monitor) was not significantly different between patients and controls (Mean (SD) REE (MJ/24 h): Patients: 5.48 (1.1); controls: 5.28(0.8)). In patients with deformities the Schofield equation gave values which were closest to measured REE (mean difference and limits of agreement IC vs S: 0.098 MJ/24 h; -0.822 and 1.018). The Harris-Benedict equation using height (Ht) and armspan (AS) in lieu of height also gave acceptable results (IC vs HB (Ht): 0.34; -0.638 and 1.318; IC vs HB (AS): 0.255; -0.683 and 1.253). Equations based on MAC and MAMC compared poorly (IC vs MAC equation: 0.398; -1.530 and 2.326; IC vs MAMC equation 0.687; -0.911 and 2.285). On regression analysis the equation REE = 0.295 (MAMC) + 0.0483 (AS) -0.0324 (age) -6.25 predicted REE best in the patient population (r(2) = 0.861).     

Human resting energy expenditure in relation to dietary potassium

This study investigates the putative effect of potassium on energy expenditure. Eight young adult men were submitted to two different normocaloric mixed diets in a randomized order, containing either 163 +/- 9 or 69 +/- 2 mmol potassium/d. On the fifth day of each diet, after an overnight fast, resting metabolic rate (RMR) was measured over a 1-h period. After these measurements, either a potassium load (50 mmol) or a placebo were given to subjects submitted to the low- or the high-potassium diet, respectively. RMR was then measured again for 3 h and the last hour was kept for further analysis. Results showed that acute and chronic variations in potassium intake do not induce significant changes in RMR, and chronic but not acute changes in serum potassium concentration were significantly correlated with changes in energy expenditure (r = 0.74, P less than 0.05) by mechanisms that remain to be elucidated.