Development and cross-validation of prediction equations for estimating resting energy expenditure in severely obese Caucasian children and adolescents

The objectives of the present study were to develop and cross-validate new equations for predicting resting energy expenditure (REE) in severely obese children and adolescents, and to determine the accuracy of new equations using the Bland-Altman method. The subjects of the study were 574 obese Caucasian children and adolescents (mean BMI z-score 3.3). REE was determined by indirect calorimetry and body composition by bioelectrical impedance analysis. Equations were derived by stepwise multiple regression analysis using a calibration cohort of 287 subjects and the equations were cross-validated in the remaining 287 subjects. Two new specific equations based on anthropometric parameters were generated as follows: (1) REE=(Sex x 892.68)-(Age x 115.93)+(Weight x 54.96)+(Stature x 1816.23)+1484.50 (R(2) 0.66; se 1028.97 kJ); (2) REE=(Sex x 909.12)-(Age x 107.48)+(fat-free mass x 68.39)+(fat mass x 55.19)+3631.23 (R(2) 0.66; se 1034.28 kJ). In the cross-validation group, mean predicted REE values were not significantly different from the mean measured REE for all children and adolescents, as well as for boys and for girls (difference <2 %) and the limits of agreement (+/-2 sd) were +2.06 and -1.77 MJ/d (NS). The new prediction equations allow an accurate estimation of REE in groups of severely obese children and adolescents. These equations might be useful for health care professionals and researchers when estimating REE in severely obese children and adolescents.     

A new predictive equation for resting energy expenditure in healthy individuals

A predictive equation for resting energy expenditure (REE) was derived from data from 498 healthy subjects, including females (n = 247) and males (n = 251), aged 19-78 y (45 +/- 14 y, mean +/- SD). Normal-weight (n = 264) and obese (n = 234) individuals were studied and REE was measured by indirect calorimetry. Multiple-regression analyses were employed to drive relationships between REE and weight, height, and age for both men and women (R2 = 0.71): REE = 9.99 x weight + 6.25 x height - 4.92 x age + 166 x sex (males, 1; females, 0) - 161. Simplification of this formula and separation by sex did not affect its predictive value: REE (males) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) + 5; REE (females) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) - 161. The inclusion of relative body weight and body-weight distribution did not significantly improve the predictive value of these equations. The Harris-Benedict Equations derived in 1919 overestimated measured REE by 5% (p less than 0.01). Fat-free mass (FFM) was the best single predictor of REE (R2 = 0.64): REE = 19.7 x FFM + 413. Weight also was closely correlated with REE (R2 = 0.56): REE = 15.1 x weight + 371.     

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.     

Predicted and measured resting energy expenditure in healthy young women

Basal energy expenditure (BEE) was either measured by indirect calorimetry or predicted by different formulae in 104 young women: 74 lean and overweight subjects (normal weight, NWt) and 30 obese subjects. The predictive equations were based on weight alone (Owen, FAO-1, Schofield-1) or on weight and height (Harris-Benedict, Mifflin, Kleiber, and again FAO-2 and Schofield-2). With the exception of the Owen equation all the equations over-estimated measured BEE in both study groups. The ratio between measured and predicted value (% MP) varied between 102.3 (Owen) and 87.7 (Kleiber) in the NWt subjects and between 113.2 (Owen) and 89.3 (Schofield-1) in the obese subjects. The range including 95% of the predicted-measured differences (PMdiff) was larger than 1700 kJ/d in the NWt group and 2300 kJ/d in the obese group. In both study groups most of the equations showed a significant relationship between PMdiff and/or % MP with body weight and the magnitude of BEE. In conclusion, these equations are of little help in predicting BEE in a single subject and should be used with caution when assessing energy requirements in populations or groups of subjects.     

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).     

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.     

Resting energy expenditure in the obese: A cross-validation and comparison of prediction equations

Objective To examine the accuracy and precision of 12 equations or tables for predicting resting metabolic rate (RMR) in obese persons.Design Observational (correlational) study.Setting Obesity Research Center, St Luke's/Roosevelt Hospital, New York, NY.Subjects/samples One hundred twenty-six (73 women, 53 men) healthy, obese subjects recruited through the Obesity Research Center's Weight Control Unit.Measures RMR by indirect calorimetry. Weight and height were measured to the nearest 0.1 kg and to the nearest 1 cm.Statistical analyses performed Bivariate regression of predicted RMR on measured RMR; paired t tests for the difference between means of predicted RMR and measured RMR.Results Of the 12 prediction equations, 6 had intercepts or slopes that were significantly different from 0 and 1, respectively. With two exceptions, the equations accounted for between 56% and 63% of the variance in measured RMR. The Robertson and Reid (1952) equation and the Fleisch (1951) equation performed best with our obese sample.Applications/conclusions The Robertson and Reid (1952) and the Fleisch (1951) equations are recommended for clinical use with obese patients. J Am Diet Assoc. 1993; 93: 1031–1036.     

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.     

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.     

Relationship between blood adipocytokines and resting energy expenditure in young and elderly women

It has been demonstrated in a previous study that resting energy expenditure (REE) is associated with adiponectin levels in the blood. However, body composition was not taken into consideration in that study. The purpose of the present study was to again investigate the relationship between blood adipocytokines and REE, adjusted by body composition, in both young and elderly women. REE and blood adipocytokines were measured in 115 young (age: 22.3+/-2.1 y, BMI: 21.3+/-1.9 kg/m(2)) and 71 elderly (63.4+/-6.5 y, 22.9+/- 2.3 kg/m(2)) women. Dual energy X-ray absorptiometry was used to measure percent body fat. Fat mass and fat free mass (FFM) were calculated. REE (kcal/d and kcal/kg BW/d) was lower in elderly women than in young women, but no significant difference was observed in REE, expressed as kcal/kg FFM/d, between the two groups. Although elderly women had a higher percent body fat and higher serum leptin concentrations than young women, plasma adiponectin concentrations did not differ between young and elderly women. In elderly women, REE (kcal/d) was significantly and inversely correlated with plasma adiponectin concentration (r=-0.386, p<0.001), but REE expressed per kilogram of BW or FFM was not significantly correlated. Furthermore, no significant correlation was observed between REE (kcal/d) and concentrations of plasma adiponectin or serum leptin, after adjusting for potential confounders such as body composition and hormones, in either age group. These results suggest that adipocytokines do not influence REE in adult women.     

Validation of a new method for estimating resting energy expenditure of non-ambulatory tube-fed patients with severe neurodevelopmental disabilities

OBJECTIVE: We assessed the bias and precision of the Arlington Developmental Center (ADC) equations derived from our previous study and the Harris-Benedict equations for estimating resting energy expenditure in non-ambulatory, tube-fed patients with severe neurodevelopmental disabilities. METHODS: Fifteen non-ambulatory patients with neurodevelopmental disabilities referred to the nutrition consult service for evaluation of enteral tube feeding via a permanent ostomy who had a steady-state resting energy expenditure measurement performed by indirect calorimetry were included in the study. The predicted energy expenditure values were compared with the measured resting energy expenditure values and evaluated for bias and precision. RESULTS: Both ADC equations were more precise (95% confidence interval [CI]: 9-22% and 10-18% error, respectively) for the total population than the Harris-Benedict equations (95% CI: 17-40% error). The ADC-2 equation was precise (95% CI: 7-15% error) and unbiased (95% CI: -5 to 139 kcal/d) in contrast to the Harris-Benedict equations (95% CI: 23-54% error; bias, +230 to 365 kcal/d) for patients with cerebral palsy and fixed upper extremity contractures. The Harris-Benedict equations were precise and unbiased (95% CI: 3-14% error; bias, -182 to 39 kcal/d) for patients with cerebral palsy with preservation of upper body movement, whereas the ADC equations were biased toward underprediction and associated with greater error (95% CI: -367 to -73 kcal/d and 7-26% error; 95% CI: -379 to -109 kcal/d and 9-27% error, respectively). CONCLUSIONS: The ADC-2 equation was unbiased and more precise in non-ambulatory adult patients with severe neurodevelopmental disabilities and fixed upper extremity contractures, whereas the Harris-Benedict equations were more precise and unbiased for those with preservation of limited functional and non-functional upper extremity movement.     

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.     

Simple kcal/kg formula is comparable to prediction equations for estimating resting energy expenditure in older cognitively impaired long term care residents

Objective

Assessment of energy needs is a critical step in developing the nutrition care plan, especially for individuals unable to modulate their own energy intakes. The purpose of this study was to assess precision and accuracy of commonly used prediction equations in comparison to measured resting energy expenditure in a sample of “oldest old” adults residing in long term care (LTC).

Subjects and Design

Resting energy expenditure (mREE) was measured by indirect calorimetry in 45 residents aged 86.1 ± 7.3 years, and compared to frequently used prediction equations (pREE): Mifflin St.Jeor, Harris Benedict, World Health Organization and Owen. Precision and accuracy were determined by concordance correlation coefficients and number of individuals within ± 10% of mREE. Bland Altman plots with linear dependence trends were constructed to visualize agreement. To complete analyses, the common 25 kcal/kg formula was assessed and alternative formulas were determined for best fit by regressing adjusted mREE on body weight.

Results

mREE averaged 976.2 ± 190.3 kcal/day for females and 1260.0 ± 275.9 kcal/d for males. The strength of the relationships between pREE and mREE were only moderate (r = 0.41–0.72). In examining linear trends in the Bland Altman plots, significant systematic deviation from mREE was detected for all pREE. Two kcal/kg formulas were generated: 20.6 kcal/kg for females and 22.7 kcal/kg for males, which were not significantly different.

Conclusion

None of the prediction equations adequately estimated energy needs in this sample of the “oldest old.” A simple formula using 21–23 kcal/kg may be a more practical and reliable method to determine energy needs in the LTC setting.     

Comparison of energy prediction equations with measured resting energy expenditure in children with sickle cell anemia

OBJECTIVE: To determine the accuracy of energy prediction equations when compared with measured resting energy expenditure (REE) in children with sickle cell anemia. To develop a modified equation that more accurately estimates the energy needs of children with sickle cell anemia and to cross-validate these on a different set of patients (test patients). DESIGN: REE was measured in children using indirect calorimetry and compared with predicted values using the Harris-Benedict and the Food and Agriculture Organization/World Health Organization/United Nations University equations (WHO). SUBJECTS/SETTING: Eighteen patients participated in the original sample that compared predicted with measured energy expenditure. The modified equations were developed using the original 18 patients. A test population of 20 different patients was used to validate the modified equations. STATISTICAL ANALYSIS: Wilcoxon signed-rank test was performed to compare measured with predicted REE. The correlation analysis method and multiple linear regression method were used to develop 2 modified versions for the Harris-Benedict and WHO prediction equations. RESULTS: When compared with the mean predicted REE using the Harris-Benedict and WHO equations, the mean measured REE was 14% and 12% greater than both (P=.005 and P=.014, respectively). Two modified equations were developed from the Harris-Benedict and WHO equations. Based on the data from the test patients, the mean measured REE was 15% greater than the mean predicted REE based on the Harris-Benedict and WHO equations (P=.0001 for both). When the modified Harris-Benedict and WHO equations were used, there was almost no difference in the mean measured REE and the mean predicted REE (mean difference using Harris-Benedict = 14, P = .9273; mean difference using WHO = -13, P = .6215). CONCLUSION: Both energy prediction equations underestimated REE in children with sickle cell anemia. The 2 modified versions of the energy prediction equations that we propose predicted the energy needs of these children much more accurately; however, the modified equations need to be validated through application to other children with sickle cell anemia.     

Indirect calorimetry: comparison of hood and mask systems for measuring resting energy expenditure in healthy volunteers

Controversy exists as to the validity and reliability of hood and mask systems in measuring indirect calorimetry. The purpose of this study was to evaluate the accuracy and reproducibility of repeat measurements of resting energy expenditure (REE) in volunteers. Paired REE measurements were performed in 23 subjects after an overnight fast using hood and mask systems. Lean body mass was calculated from four skinfold measurements and body weight determinations. Data were normalized to body weight and lean body mass and were calculated as percent predicted REE in paired tests taken within 5 minutes on the same subject. No significant difference in mean REE was noted between hood and mask systems. Linear regression analysis showed a strong positive correlation (r = 0.91, p less than 0.001) between hood and mask measurements of REE.     

Validation of the BIOPAC indirect calorimeter for determining resting energy expenditure in healthy free-living older people

The BIOPAC indirect calorimeter for measuring resting energy expenditure (REE) is less cumbersome than many other calorimeters. We tested the hypothesis that the BIOPAC calorimeter is as well suited for determining REE in older people as traditional calorimeters. In 50 healthy persons (24 men and 26 women; age range, 61-83 years), REE by BIOPAC was validated against Vmax Spectra indirect calorimeter as a criterion method. Resting energy expenditure by BIOPAC was recorded at 2 different time intervals to find optimal conditions for older persons. Also, REE by 7 published equations was validated. The Bland-Altman procedure was used to test agreement between methods. Multiple regression analysis was applied to develop a new equation for predicting REE from BIOPAC. The BIOPAC calorimeter and most empirical equations significantly overpredicted mean REE in both sexes. Mifflin's equation best-predicted mean REE. Limits of agreement were wide for BIOPAC and narrow for most empirical equations. The Lührmann and Müller equations had smallest limits of agreement in men (±950 kJ/24 h) and the Harris-Benedict and Müller equations in women (±672 kJ/24 h). A new equation was developed for the BIOPAC device improving both predictions of mean and individual REE (R² = 0.671, standard error of the estimate = 136 kJ/24 h). Using this equation, 72.9% of subjects were lying within 10% of measured REE, compared with only 12.5% when using the manufacturer's algorithm. In conclusion, the BIOPAC calorimeter is suitable for measuring REE in healthy older adults when the new prediction equation is applied. This calorimeter is not applicable to frail older persons.