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.     

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.     

The age-related decline in resting energy expenditure in humans is due to the loss of fat-free mass and to alterations in its metabolically active components

There is conflicting evidence as to whether the age-related decline in resting energy expenditure (REE) can be attributed to i) absolute changes in fat-free mass (FFM), ii) alterations in the composition of FFM or iii) decreasing organ metabolic rates. This study directly addressed the first and second hypotheses by quantification of metabolically active components of FFM assuming constant tissue respiration rates to calculate REE (REEc). REE was measured (REEm) in 26 young (13 females, 13 males, age 22-31 y) and 26 elderly subjects (15 females, 11 males, age 60-82 y) by indirect calorimetry and detailed body composition analysis was obtained using bioelectrical impedance analysis (BIA), dual energy X-ray absorptiometry (DXA), and MRI. Specific organ metabolic rates were taken from the literature. REEm adjusted for differences in FFM was lower in older subjects than in younger control subjects (5.43 +/- 0.61 MJ/d compared with 6.37 +/- 0.48 MJ/d; P < 0.001). Skeletal muscle mass plus liver mass accounted for 86% and 48% of the variance in REE in young and elderly subjects, respectively. The difference between REEm and REEc was 0.03 +/- 0.40 MJ/d and -0.36 +/- 0.70 MJ/d in young and elderly subjects, respectively. In the elderly 58% of the difference in variance was attributed to heart mass. REEm - REEc was -1.40 +/- 0.44 MJ/d in subjects with hypertensive cardiac hypertrophy, i.e., heart mass > 500 g, suggesting a decrease in heart metabolic rate with increasing heart mass. Excluding five elderly subjects with cardiac hypertrophy resulted in agreement between REEm and REEc in the elderly (-0.10 +/- 0.48 MJ/d). We concluded that the age-related decline in REE is attributed to a reduction in FFM as well as in proportional changes in its metabolically active components. There is no evidence for a decreasing organ metabolic rate in healthy aging.     

Small organs with a high metabolic rate explain lower resting energy expenditure in African American than in white adults

BACKGROUND: African Americans have a lower resting energy expenditure (REE) relative to fat-free mass (FFM) than do whites. Whether the composition of FFM at the organ-tissue level differs between African Americans and whites and, if so, whether that difference could account for differences by race in REE are unknown. OBJECTIVE: The objectives were to quantify FFM in vivo in women and men at the organ-tissue level and to ascertain whether the mass of specific high-metabolic-rate organs and tissues differs between African Americans and whites and, if so, whether that difference can account for differences in REE. DESIGN: The study was a cross-sectional evaluation of 64 women (n = 34 African Americans, 30 whites) and 35 men (n = 8 African Americans, 27 whites). Magnetic resonance imaging measures of liver, kidney, heart, spleen, brain, skeletal muscle, and adipose tissue and dual-energy X-ray absorptiometry measures of fat and FFM were acquired. REE was measured by using indirect calorimetry. RESULTS: The mass of selected high-metabolic-rate organs (sum of liver, heart, spleen, kidneys, and brain) after adjustment for fat, FFM, sex, and age was significantly (P < 0.001) smaller in African Americans than in whites (3.1 and 3.4 kg, respectively; x +/- SEE difference: 0.30 +/- 0.06 kg). In a multiple regression analysis with fat, FFM, sex, age, and race as predictors of REE, the addition of the total mass rendered race nonsignificant. CONCLUSIONS: Racial differences in REE were reduced by >50% and were no longer significant when the mass of specific high-metabolic-rate organs was considered. Differences in FFM composition may be responsible for the reported REE differences.     

The reconstruction of Kleiber's law at the organ-tissue level.

The relationship between resting energy expenditure (REE) (kJ/d) and body mass (M) (kg) is a cornerstone in the study of energy physiology. By expressing REE as a function of body mass observed across mammals, Kleiber formulated the now classic equation: REE = 293M(0.75). The biological processes underlying Kleiber's law have been a topic of long-standing interest and speculation. In the present report we develop a new perspective of Kleiber's law by developing an organ-tissue level REE model consisting of five components: liver, brain, kidneys, heart and remaining tissues. The resting thermal output of each component is the product of the component's specific resting metabolic rate (K) and mass (T). With increasing body size, the K values for all five components had negative exponents and were directly proportional to M(-0.08--0.27), and all component T values were directly proportional to M(0.76-1.01). The resulting exponents of the product (K x T) were M(0.60-0.86) for the five components. Although the (K x T) values of individual components do not scale equally, their combined formula (286M(0.76)) is similar to that observed by Kleiber on the whole-body level. Modeling mammalian REE at the organ-tissue level provides new insights and pathways for future mechanistic explorations of REE-body composition relationships.     

Lack of adaptation to severe malnutrition in elderly patients

BACKGROUND & AIMS: Sarcopenia is a common feature in the healthy elderly. However, little is known on age-related modifications of body composition in malnourished patients. The aims of this cross-sectional study were to evaluate the effects of aging per se on body composition and resting energy expenditure (REE) in malnourished patients. METHODS: Ninety-seven non-stressed patients referred for chronic malnutrition (C-reactive protein <5 mg/l) were separated into two groups: middle-aged (26 female, 19 male, 48+/-15 yr), and elderly (26 female, 26 male, 79+/-6 yr). Body composition was assessed by bioelectrical impedance analysis and REE by indirect calorimetry. RESULTS: In middle-aged patients, body composition remained stable between moderate (body-mass index [BMI; in kg/m(2)] 16-18.5) and severe (BMI < 16) malnutrition, with similar values of fat-free mass (FFM), body cell mass (BCM) and fat mass (FM) as percentages of body weight, whereas in elderly patients malnutrition occurred at the expense of FFM and BCM, with unchanged FM absolute values. REE/FFM values remained stable in middle-aged patients at every stage of malnutrition, whereas they increased in elderly patients along with their degree of malnutrition. In multivariate analysis, both body composition and REE/FFM were influenced by sex, age, BMI and mid-arm circumference. CONCLUSION: Compared to younger patients, weight loss in the elderly leads to cachexia, with a preferential loss of FFM and BCM that may participate in the more severe outcomes observed in these patients. They also show elevated REE/FFM values that induce higher energy needs.     

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.     

Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation

Differences in body composition have often been examined in conjunction with measurements of energy expenditure in men and women. Numerous studies during the past decade examined the relationship between resting energy expenditure (REE) and the components of a two-compartment model of composition, namely the fat-free mass (FFM) and the fat mass (FM). A synthetic review of these studies confirms a primary correlation between REE and FFM in adults over a broad range of body weights. A generalized prediction equation is proposed as REE = 370 +/- 21.6 x FFM. This equation explains 65-90% of the variation in REE. Several studies suggest, further, that FFM predicts total daily energy expenditure (TDEE) equally well. An independent contribution by FM to the prediction of either REE or TDEE is not supported for the general population, perhaps reflecting the relative constancy of the absolute FM in nonobese individuals. In the subset of obese women, FM may be a significant predictor.     

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.     

Sex differences in resting energy expenditure and their relation to insulin resistance in children (EarlyBird 13)

BACKGROUND: Insulin resistance is believed to be the process underlying type 2 diabetes and premature cardiovascular disease. We have established that a relation between body mass and insulin resistance calculated by homeostasis model assessment (HOMA-IR) exists by 5 y of age in contemporary UK children. Resting energy expenditure (REE) is variable among individuals and is one of many factors controlling body mass. OBJECTIVE: The objective was to investigate the relations between REE, body mass, and HOMA-IR in young children. DESIGN: EarlyBird is a nonintervention prospective cohort study of 307 healthy 5-y-olds that asks the question: Which children develop insulin resistance and why? REE by indirect calorimetry and HOMA-IR were measured in addition to total body mass, fat-free mass (FFM) by bioimpedance, body mass index (BMI; in kg/m(2)), and skinfold thickness when the mean age of the cohort was 5.9 +/- 0.2 y. RESULTS: Whereas the BMI of the boys was lower than that of the girls (x +/- SD: boys, 15.9 +/- 1.9; girls, 16.5 +/- 1.9; P = 0.03), their REE was higher by 6% (x +/- SD: 4724 +/- 615 compared with 4469 +/- 531 kJ/d; P = 0.002). This difference persisted after adjustment for FFM and other anthropometric variables (P = 0.04). In boys, there was a weak, although significant, inverse correlation between REE and HOMA-IR, independent of fat mass and FFM (boys: r = -0.21, P = 0.03; girls: r = 0.12, P = 0.34). CONCLUSION: There is a sex difference in REE at 6 y of age that cannot be explained by body composition. The difference appears to be intrinsic, and its contribution to sex differences in adiposity and HOMA-IR in children merits further exploration.     

Body composition and energy expenditure in Duchenne muscular dystrophy

OBJECTIVE: To investigate the relationship between resting energy expenditure (REE) and body composition in Duchenne Muscular Dystrophy (DMD). DESIGN: An observational study. SETTING: University Research Centre. SUBJECTS: Nine Duchenne children (age range 6-12 y), mean relative weight 128%, agreed to undergo the investigation and all of them completed the study; INTERVENTIONS: Assessment of body composition (total body fat and skeletal muscle mass) by magnetic resonance imaging and resting energy expenditure by indirect calorimetry. MAIN OUTCOME MEASURES: Fat mass (FM; kg and percentage weight), fat-free mass (FFM; kg and percentage weight), muscle mass (kg and percentage weight), resting energy expenditure (kJ/kg body weight and kJ/kg fat-free mass). RESULTS:: In Duchenne children fat mass averages 32% and total skeletal muscle mass 20% of body weight. Resting energy expenditure per kg of body weight falls within the normal range for children of the same age range, while when expressed per kg of FFM is significantly higher than reference values. No relationship was found between REE and total skeletal muscle mass. CONCLUSIONS: Our results do not demonstrate a low REE in DMD boys; on the contrary REE per kg of FFM is higher than normal, probably due to the altered FFM composition. We suggest that the development of obesity in DMD children is not primarily due to a low REE but to other causes such as a reduction in physical activity and or overfeeding.     

健康老年人静息能量消耗

目的 :　探讨老年人 REE与性别、年龄 ,人体测量学指标的相关性。方法 :　用间接能量测定仪测试 82名 (男 3 0、女 5 2 )平均年龄 80岁的中国健康汉族老年人的静息能量消耗 (rest-ing energy expenditure,REE)的水平 ,并将 REE测试值与根据 Harris- Benedict公式算出的基础能量消耗值 (basal energy expenditure,BEE)进行比较。同时应用生物电阻抗分析法 (bioelectricalimpedance analysis,BIA)测定去脂体重 (fatfree mass,FFM)和体脂重量 (fat mass,FM)等人体测量学数据。结果 :　 82名健康老人的 REE平均值为 (4.44± 0 .5 2 ) MJ/2 4 h,与公式计算的 BEE比无统计学差异 ,但比 FAO/WHO/UNU(1 985 )公式值低 9% ,比 Owen公式值低 1 9%。本研究观察到我国健康老年人的 REE与去脂体重、体重、体表面积 (body surface area,BSA)、年龄、身高、性别和体重指数 (body mass index,BMI)之间有相关性。老年男女的每公斤体重、每公斤去脂体重和单位体表面积所产生的 REE间无统计学差异。结论 :　 Harris- Benedict公式、FAO/WHO/UNU(1 985 )公式与 Owen公式都过高估计了我国健康老年人的基础能量消耗。由于老年人的REE存在较大的个体差异 ,其 REE值宜实测而不宜用公式预测。我国健康老年人的 REE与去脂体重、体?     

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.     

Body movement and physical activity energy expenditure in children and adolescents: how to adjust for differences in body size and age

BACKGROUND: Physical activity data in children and adolescents who differ in body size and age are influenced by whether physical activity is expressed in terms of body movement or energy expenditure. OBJECTIVE: We examined whether physical activity expressed as body movement (ie, accelerometer counts) differs from physical activity energy expenditure (PAEE) as a function of body size and age. DESIGN: This was a cross-sectional study in children [n = 26; (+/-SD) age: 9.6 +/- 0.3 y] and adolescents (n = 25; age: 17.6 +/- 1.5 y) in which body movement and total energy expenditure (TEE) were simultaneously measured with the use of accelerometry and the doubly labeled water method, respectively. PAEE was expressed as 1) unadjusted PAEE [TEE minus resting energy expenditure (REE); in MJ/d], 2) PAEE adjusted for body weight (BW) (PAEE. kg(-1). d(-1)), 3) PAEE adjusted for fat-free mass (FFM) (PAEE. kg FFM(-1). d(-1)), and 4) the physical activity level (PAL = TEE/REE). RESULTS: Body movement was significantly higher (P = 0.03) in children than in adolescents. Similarly, when PAEE was normalized for differences in BW or FFM, it was significantly higher in children than in adolescents (P = 0.03). In contrast, unadjusted PAEE and PAL were significantly higher in adolescents (P < 0.01). CONCLUSIONS: PAEE should be normalized for BW or FFM for comparison of physical activity between children and adolescents who differ in body size and age. Adjusting PAEE for FFM removes the confounding effect of sex, and therefore FFM may be the most appropriate body-composition variable for normalization of PAEE. Unadjusted PAEE and PAL depend on body size.     

DXA: potential for creating a metabolic map of organ-tissue resting energy expenditure components

OBJECTIVE: This study tested the hypothesis that tissue-organ components can be derived from DXA measurements, and in turn, resting energy expenditure (REE) can be calculated from the summed heat productions of DXA-estimated brain, skeletal muscle mass (SM), adipose tissue, bone, and residual mass (RM). RESEARCH METHODS AND PROCEDURES: Subjects were divided into five groups of adults <50 years of age. The specific metabolic rate of RM was developed in 13 Group I healthy subjects and a DXA-brain mass prediction formula in 52 Group II subjects. SM, adipose tissue, and bone models were developed based on earlier reports. The composite REE prediction model (REEp) was tested in 154 Group III subjects in whom REEp was compared with measured REE (REEm). Features of the developed model were determined in 94 normal-weight men and women (Group IV) and seven spinal cord injury patients and healthy matched controls (Group V). RESULTS: REEp and REEm in Group III were highly correlated (y = 0.85x + 233; r = 0.82, p < 0.001), and no bias was detected. Both REEm (mean +/- SD, 1,579 +/- 324 kcal/d) and REEp (1,585 +/- 316 kcal/d) were also highly correlated (r values = 0.85 to 0.98; p values < 0.001) and provided similar group values to REE estimated by the Harris-Benedict equations (1,597 +/- 279 kcal/d) and Wang's composite fat-free mass-based REE equation (1,547 +/- 248 kcal/d). New insights into the sources and distribution of REE were provided by analysis of the demonstration groups. DISCUSSION: This approach offers a new practical and educational opportunity to examine REE in subject groups using modeling strategies that reveal the magnitude and distribution of fundamental somatic heat-producing units.     

Use of bioelectrical impedance analysis to predict energy expenditure of hospitalized patients receiving nutrition support

BACKGROUND: The energy requirement of a patient receiving nutrition support is typically estimated by calculating the basal energy expenditure (BEE) using the Harris-Benedict equations and multiplying by stress and activity factors. Because fat-free mass (FFM) and fat mass (FM) are important determinants of BEE, we hypothesized that body composition estimates derived from bioelectrical impedance analysis (BIA) could be used to develop predictive equations for resting energy expenditure (REE) that were more accurate than those calculated using the Harris-Benedict equations. METHODS: Seventy-six adults referred to the nutrition support service were studied. REE was measured by indirect calorimetry, and single-frequency BIA was used to estimate FFM and FM. Using the first 20 male and 20 female patients, predictive equations for REE were developed by multiple regression analysis, using BIA-derived body composition values, age, and gender. The next 36 patients were used to compare the accuracy of these equations with the Harris-Benedict equations in estimating REE. RESULTS: Using BLA-derived body composition values, gender, and age, predictive equations were developed for REE that explained approximately 65% of the variance. Inclusion of other BIA or anthropometric parameters did not improve the equations. When compared with the Harris-Benedict equations, the equations developed in this study were significantly more accurate, providing an REE estimate that was closer to the measured value in about 75% of patients. CONCLUSIONS: These results indicate that BLA-derived body composition estimates may be used to more accurately predict the energy requirements of patients receiving nutrition support than calculations based on the Harris-Benedict equations.     

Comparison between measured and predicted resting energy expenditure in mechanically ventilated patients with COPD

The aim of this study was to compare resting energy expenditure (REE) obtained by indirect calorimetry (IC) and Harris-Benedict (H-B) equations, and to examine whether hypocaloric nutrition support could improve protein nutritional status in mechanically ventilated patients with chronic obstructive pulmonary disease (COPD). Thirtythree COPD patients (20 males, 13 females) were recruited and REE was measured by IC. Measured REE (REEm) was compared to predictive REE by H-B equations (REEH-B) and its corrected values. Correlation between REEm and APACHE II score was also analyzed. Patients were randomly divided into hypocaloric energy group (50%-90% of REEm, En-low) and general energy group (90%-130% of REEm, En-gen) for nutrition support. The differences of albumin, prealbumin, transferrin, hemoglobin, and lymphocyte count before and after 7 days nutrition support were observed. Results show that REEH-B and REEH-B×1.2 were significantly lower than REEm (p<0.01). REEm positively correlated with APACHE II score (p<0.05 or p<0.01). After nutrition support, hemoglobin decreased significantly in En-gen group (p<0.05); lymphocyte count in both groups, and transferrin and prealbumin in the En-low group increased significantly (p<0.05 or p<0.01). Our data suggest that 1) these patients' REE were increased; 2) since IC is the best method to determine REE, in the absence of IC, H-B equations (with standard body weight) can be used to calculate REE, but the value should be adjusted by correction coefficients derived from APACHE II; 3) low energy nutrition support during mechanical ventilation in COPD patients might have better effects on improving protein nutritional status than high energy support.     

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.     

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.     

Time-dependent variation in weight and body composition in healthy adults

OBJECTIVES: To define the limits of change in body weight and body composition after different time intervals in healthy, normal adults. METHODS: Prospective and retrospective analyses of paired body composition studies in a total of 326 healthy adults, ages 18 to 97. Measurements included body weight, fat and fat-free mass (FFM) by dual x-ray absorptiometry (DXA) and bioimpedance analysis (BIA), plus body cell mass (BCM) by whole-body counting of 40K and BIA. RESULTS: Time interval between studies was a significant predictor of the differences in paired studies. The 95% confidence intervals for percent difference were lowest for body weight, intermediate for BCM and FFM, and highest for fat, in part because of the differences in sizes of these body compartments. There were significant associations among the changes in body composition by BIA and by criterion methods, suggesting that the observed changes are real. CONCLUSIONS: The normal variation in body weight and body composition increases over time. Time-dependent criteria may increase the sensitivity in diagnosing malnutrition. Interpreting changes in body compartments requires consideration of the size of each compartment.