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.     

Effect of acclimation on resting energy expenditure measurements.

BACKGROUND: Development of an acclimation protocol for use when measuring resting energy expenditure (REE) would simplify and standardize data collection. The purpose of this study was to determine if our 2 metabolic carts could be used interchangeably and to determine if excluding the first 3 or 5 minutes of data collected as an acclimation period would significantly improve the coefficients of variation (CVs) for oxygen consumed (VO(2)) and carbon dioxide produced (VCO(2)) when performing REE assessments with our metabolic cart systems. METHODS: Thirteen healthy, nonsmoking adults ranging in age from 32 to 45 years, with activity levels ranging from sedentary to highly active, participated. Indirect calorimetry was performed twice in the morning after 30 minutes of supine resting. Subjects had fasted for 12 hours, and did not exercise within the last 24 hours. The system order for testing was randomized for the first measurement. When the first measurement was completed, subjects were crossed over for measurement using a second metabolic cart. RESULTS: The CVs for VO(2) and VCO(2) were significantly lower when excluding the first 3 (VO(2), p = .0005), (VCO(2), p = .0024) or 5 minutes (VO(2), p = .0001, VCO(2), p = .0021) of data compared with no exclusions. No significant differences in CVs between the 3- and 5-minute exclusions were found for VO(2) (p = .3224) or VCO(2) (p = .2255). CONCLUSIONS: Clearly, our machines cannot be used interchangeably within a study. An acclimation period improves CVs of VO(2) and VCO(2.) The similarities in CVs led us to adopt a 3-minute acclimation period for measuring REE.     

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.     

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.     

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.     

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.     

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.     

Measured and predicted resting energy expenditure in clinically stable patients

Resting energy expenditure (REE), measured by bedside indirect calorimetry, was compared to estimated REE by the Harris-Benedict and Kleiber predictors in 200 clinically stable hospitalized patients (100 males, 100 females) and 72 healthy control subjects (20 males, 52 females).Mean predicted values were not significantly different from measured REE for the male patients and control subjects, but measured REE was significantly overestimated by the Kleiber formula in female patients and controls (p<0.01). In comparison to control subjects, a substantially larger range of individual differences between measured REE and resting energy expenditure as estimated by the Harris-Benedict and Kleiber formulae existed among the male and female patient samples. Measured REE was over or underestimated by greater than 10% via the Harris-Benedict predictors in 40% of the patients but only 20% of the healthy controls. The Kleiber formulae were inappropriate for 46% of the individual patients and 33% of the normal subjects.Since no method exists for identifying the clinically stable patient for whom REE cannot be estimated via these commonly employed predictors, the bedside measurement of resting energy expenditure is the most appropriate method for deriving caloric expenditure and designing subsequent caloric provision regimens for adequate and safe nutritional repletion or maintenance.     

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.     

An evaluation of resting energy expenditure in hospitalized, severely underweight patients.

A prospective trial was conducted with 14 hospitalized patients who were severely underweight with a mean weight of 40.9+/-5.1 kg and 70.7+/-7.8% of ideal body weight, to compare estimates of resting energy expenditure (REE) with measured values. The 9 women and 3 men, whose mean age was 66.5+/-13.9 y, underwent nutritional assessment and measurement of their REE by indirect calorimetry using the Sensormedics Deltatrac MBM100 indirect calorimeter. Their REE was also estimated by the Harris-Benedict formula (mean 1032+/-66 kcal/d) as well as a previously established empirical formula where REE = 25 x body weight in kg (mean 1023+/-129 kcal/d). Results by both estimates were significantly lower than the measured resting energy expenditure (MREE) in this group of patients (P<0.0001). The percentage difference between MREE and estimated REE by the Harris-Benedict formula was 18.4+/-9.4% and 20.9+/-7.5% by the empirical formula. The MREE exceeded the estimated REE in each individual. The correlation between MREE and body weight (r2 = 0.558, r = 0.005) was better than that between MREE and estimated REE by Harris-Benedict formula (r2 = 0.275, P = 0.08) suggesting that weight was the principal determinant rather than the other components (height, age, sex) of the Harris-Benedict formula. Our data shows that commonly employed formulae routinely underestimate the energy needs of severely underweight patients below 50 kg in body weight. The Harris-Benedict equation had limited predictive value for the individual, explaining approximately 25% of the variance in energy expenditure. Given the particular importance of matching energy intake to needs in this group of patients with limited reserves, many of whom are critically ill, we suggest an empirical equation using 30-32 kcal/kg be used to estimate the energy requirements of severely underweight patients when direct measurements are unavailable or clinically less imperative.     

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     

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     

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.     

Increased resting energy expenditure in patients with end-stage renal disease

BACKGROUND: Protein-calorie malnutrition is a significant problem for patients with end-stage renal disease. Increased resting energy expenditure may be an important contributing factor. We postulate that resting energy expen diture in the different stages of renal disease and treatments may be different. METHODS: Resting energy expenditure was measured using a whole-room indirect calorimeter (metabolic chamber) along with nutritional parameters and body composition after 12-hour fasting in 15 patients with advanced chronic renal failure patients, 15 patients on chronic hemodialysis, and 10 patients on peritoneal dialysis. Patients on hemodialysis were assessed on a non-dialysis day. A 2-day dietary recall was used to assess energy intake. RESULTS: Resting energy expenditure, adjusted for fat-free mass, was similar in patients on hemodialysis and peritoneal dialysis but significantly higher than in patients with chronic renal failure (p < .05). Resting energy expenditure in all patients were generally higher (10% to 20%) than predicted values using standard equations derived in normal and obese populations, whereas daily energy intake was less (26% to 34%) than energy expenditure for all groups, adjusted for light daily activity. CONCLUSIONS: End-stage renal disease patients displayed increases in resting energy expenditure over the predicted values derived using normal populations. Resting energy expenditure was significantly higher in patients receiving dialysis, regardless of the modality, than patients with chronic renal failure. Daily energy intake was substantially less than required in all patient groups studied, suggesting that patients with renal failure could develop protein-calorie malnutrition because of increased resting energy expenditure, which is exacerbated by dialysis.