A practical approach to estimate resting energy expenditure in frail elderly people

Objectives: Some prediction equations of resting energy expenditure (REE) are available and can be used in clinical wards to determine energy requirements of patients. The aim of the present study was to assess the accuracy of those equations in sick elderly patients, using the Bland & Altman methods with our database of 187 REE measurements.Design: The 3 equations tested were Harris & Benedict equation of 1919, WHO/FAO/UNU equation of 1985 and Fredrix et al. equation of 1990. In addition, three models developed from the present data were tested.Results: The present study shows that the Fredrix et al equation gave an accurate prediction of REE without significant bias along the whole range of REE. It also shows that under-weight sick elderly patients (BMI ≤ 21 kg/m²) had a greater weight-adjusted REE than their normal weight counterparts.Conclusion: A simple formula using a factor multiplying body weight, i.e. 22 kcal/kg/d in under-weight and 19 kcal/kg/d in normal weight sick elderly was accurate to predicting REE and bias was not influenced by the level of REE. This model included half of the group in the range of ±10% of the difference between predicted REE and measured REE, but the confidence interval of the bias was ±400 kcal/d. Conversely, the Harris & Benedict and WHO formulae did accurately predict REE.     

Failure of preoperative resting energy expenditure in predicting weight loss after gastroplasty

OBJECTIVE: To evaluate the predictive efficacy of preoperative resting energy expenditure (REE) on weight loss after vertical banded gastroplasty (VBG). When subjected to a gastric restriction procedure of similar extent, the patients with higher energy expenditure should experience a greater negative energy balance than those with lower-energy expenditure, and thus, lose more weight, thereby making REE a reliable predictor of weight loss after VBG. RESEARCH METHODS AND PROCEDURES: This was a prospective investigation after VBG, taking into account the relationship between preoperative REE values and the results at 1-year follow-up in terms of weight loss and success of the procedure. The correlations were evaluated by multiple and logistic regression analysis. RESULTS: The weight loss and the outcome at 1 year after VBG seemed to be completely independent of preoperative energy expenditure. DISCUSSION: These findings suggest that, despite gastric restriction, patients may voluntarily adjust their energy intake, and that the weight outcome after VBG is influenced more by behavioral and cognitive variables than by biological or surgical factors.     

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.     

A genome scan among Nigerians linking resting energy expenditure to chromosome 16

Energy requirements at rest account for 50% to 75% of total energy expenditure. Interindividual variation in resting energy expenditure (REE) has been studied for potential links to obesity and hypertension. REE is a modestly heritable trait, and yet virtually nothing is known about the genetic factors that might influence the familial patterns. The objectives of this study were to identify the genomic regions showing genetic linkage to REE variation in a Nigerian population. For linkage analysis across the genome, three hundred seventy-seven microsatellite markers were typed on DNA from 995 individuals in 153 families. A genome scan was performed using a multipoint variance component method. Heritability of REE was 0.30 after adjustment for body size. The strongest linkage signal was detected on chromosome 16 (16q22.3) with a likelihood of odds of 2.96 (p = 0.08). Linkage evidence (likelihood of odds > 1) was detected on another three chromosomal regions, namely 2q12.1, 8q21.2, and 15p11.2.     

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.     

Positive association between resting energy expenditure and weight gain in a lean adult population

BACKGROUND: Weight gain in adulthood is common, from modest gains in developing countries to substantial increases in Western societies. Evidence of the importance of energy expenditure in adult weight change has been limited to studies conducted in Pima Indians, in whom resting energy expenditure (REE) was found to be inversely associated with weight gain. OBJECTIVE: The aim was to determine whether REE was predictive of weight change in lean Nigerian adults. DESIGN: Weight was measured in 744 adults on 2-4 occasions over 5.5 y. REE was measured in the second follow-up examination. Sex-specific, mixed-effects models with REE, fat-free mass, and age as fixed effects were used to test the association between REE and weight change. RESULTS: Adults aged >19 y (n = 352 men and 392 women) were included in these analyses. At baseline, the mean (+/-SD) age was 45.9 +/- 16.1 y for the whole population; the mean weight was 61.4 +/- 10.7 and 58.1 +/- 12.1 kg and body mass index (in kg/m(2)) was 21.4 +/- 3.2 and 23.1 +/- 4.0 for men and women, respectively. Over a mean 5.5 y of follow-up, the age-adjusted weight gain was 0.42 kg/y for the men and 0.59 kg/y for the women. In mixed-effects models, REE was positively associated with weight gain in both men and women (P < 0.001). No significant association was observed in participants who lost weight. CONCLUSIONS: In contrast with observations in overweight Pima Indians, REE adjusted for body size and composition was positively associated with weight gain in lean Nigerian adults. This suggests either that the potential for differential regulation of body weight in lean compared with overweight populations exists or that the increased REE in this population was the result, rather than cause, of weight gain.     

Variability in results from predicted resting energy needs as compared to measured resting energy expenditure in Korean children

Energy needs are influenced by many factors, including ethnicity. Multiple studies have shown that the accuracy of an energy prediction equation varies with the ethnic background of the study population. Therefore, it is crucial to identify the most accurate energy prediction equation to use for a given population. This study compared measured resting energy expenditure to results from commonly-used energy prediction equations to identify the most accurate equation to use for Korean children. Based on previous literature showing wide variation in accuracy of energy prediction equations in different ethnic groups, we hypothesized that results from measured- vs. predicted energy needs would be significantly different in this population. Subjects were 92 South Korean children (38 boys, 54 girls) age 7.7 ± 2.7 years (mean ± SD). Measurements included: resting metabolic rate (TrueOne 2400 metabolic cart), weight/height (digital scale/stadiometer); body fat (BIA, Inbody720), blood pressure (sphingomanometer), triceps skinfold thickness (MD-500 skinfold calipers), muscle mass (Heymsfield's formula) and body surface area (Dubois formula) calculations. Resting energy needs were predicted using the Harris-Benedict, WHO/NAO/FAO, Altman and Dittmer, Maffeis, and Schofield-HW equations, and the Dietary Reference Intake recommendations. Measured and predicted energy needs were significantly correlated (P < .001 for all; range R² = 0.54-0.56), yet significantly different for all equations studied (P < .05) except the Maffeis and Schofield-HW equations. Differences (means ± SD) between measured vs. predicted energy needs ranged from 9.5 ± 123.2 (Schofield-HW) to 199.6 ± 132.7 (WHO/NAO/FAO) kcal/day, where a value closer to zero indicates increased accuracy of the prediction equation to correspond to measured energy needs. Although results from equations studied were significantly correlated with measured resting energy needs, notable discrepancies existed which, over time, could produce undesirable weight changes in Korean children.     

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.     

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.     

Energy intake and resting energy expenditure in adult male rats after early postnatal food restriction

Both in man and in animal models, changes in food intake and body composition in later life have been reported after alterations in perinatal nutrition. Therefore, we hypothesised that early postnatal undernutrition in the rat induces permanent changes in energy balance. Food restriction (FR) during lactation was achieved by enlarging litter size to twenty pups, whereas control animals were raised in litters containing ten pups. Energy intake and resting energy expenditure were determined in adult males. Early postnatal FR resulted in acute growth restriction followed by incomplete catch-up in body weight, body length and BMI. At the age of 12 months, middle-aged FR males had significantly lower absolute resting energy expenditure (200 v. 216 kJ/24 h, P = 0.009), absolute energy intake (281 v. 310 kJ/24 h, P = 0.001) and energy intake adjusted for BMI (284 v. 305 kJ/24 h, P = 0.016) than controls, whereas resting energy expenditure adjusted for BMI did not differ significantly between the groups (204 v. 211 kJ/24 h, P = 0.156). The amount of energy remaining for other functions was lower in FR males (80 v. 94 kJ/24 h, P = 0.044). Comparable data were obtained at the age of 6 months. These results indicate that in rats energy balance can be programmed by early nutrition. A low early postnatal food intake appears to programme these animals for a low energy intake and to remain slender in adult life.     

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.     

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, activity energy expenditure and total energy expenditure at age 91-96 years

There is a limited knowledge concerning energy requirements of the elderly, especially the oldest old (> 80 years). Energy requirements should be estimated from measurements of energy expenditure. For this purpose twenty-one free-living individuals (eight males, thirteen females) aged 91-96 years living in G?teborg, Sweden were studied. Total body water (TBW) measured by the doubly-labelled-water (DLW) technique was 29.5 (sd 5.4) kg in females and 35.6 (sd 4.3) kg in males. TBW measured using bioelectric impedance (BIA) was 31.6 (sd 6.4) kg in females and 42.0 (sd 7.4) kg in males. The mean difference between TBW measured by BIA and that measured by DLW was 3.54 (sd 3.6) kg (P = 0.0002). Resting metabolic rate (RMR) was measured using a ventilated-hood system and averaged 5.36 (sd 0.71) MJ/d in females (n 12) and 6.09 (sd 0.91) MJ/d in males (n 8). Difference between measured RMR and predicted BMR (n 20) was 0.015 (sd 0.86) MJ/d (NS). Total energy expenditure (TEE) measured by DLW averaged 6.3 (sd 0.81) MJ/d in females and 8.1 (sd 0.73) MJ/d in males. Activity energy expenditure (TEE - RMR), thus including diet-induced thermogenesis (DIT), averaged 0.95 (sd 0.95) MJ/d in females (n 12) and 2.02 (sd 1.13) MJ/d in males. Physical activity level (TEE/BMR) averaged 1.19 (sd 0. 19) in females and 1.36 (sd 0.21) (P = 0.08) in males. If DIT is assumed to be 10 % of the TEE, energy spent on physical activity will be very low in this population.