Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling

Resting energy expenditure in young adults born preterm--the Helsinki study of very low birth weight adults

Background

Adults born preterm with very low birth weight (VLBW; <1500g) have higher levels of cardiovascular and metabolic risk factors than their counterparts born at term. Resting energy expenditure (REE) could be one factor contributing to, or protecting from, these risks. We studied the effects of premature birth with VLBW on REE.

Methodology/Principal Findings

We used indirect calorimetry to measure REE and dual x-ray absorptiometry (DXA) to measure lean body mass (LBM) in 116 VLBW and in 118 term-born control individuals (mean age: 22.5 years, SD 2.2) participating in a cohort study. Compared with controls VLBW adults had 6.3% lower REE (95% CI 3.2, 9.3) adjusted for age and sex, but 6.1% higher REE/LBM ratio (95% CI 3.4, 8.6). These differences remained similar when further adjusted for parental education, daily smoking, body fat percentage and self-reported leisure time exercise intensity, duration and frequency.

Conclusions/Significance

Adults born prematurely with very low birth weight have higher resting energy expenditure per unit lean body mass than their peers born at term. This is not explained by differences in childhood socio-economic status, current fat percentage, smoking or leisure time physical activity. Presence of metabolically more active tissue could protect people with very low birth weight from obesity and subsequent risk of chronic disease.

Analysis of energy utilization and body composition in kidney, bladder, and adrenal cancer patients

OBJECTIVE

To investigate resting energy expenditure (REE) and body composition and the relationship between substrate utilization and energy expenditure in urologic cancer patients.

PATIENTS AND METHODS

Measured resting energy expenditure (mREE) was detected by indirect calorimetry in 122 urologic cancer patients and 131 control subjects. Extracellular fluid (ECF), intracellular fluid (ICF), and total water (TW) were measured by bioelectrical impedance appliance. Fat oxidation rate (F-O), carbohydrate oxidation rate, fat mass (FM), and fat free mass (FFM) were further determined.

RESULTS

Compared with the controls, cancer patients showed significantly elevated mREE and mREE/FFM (P = 0.049; P < 0.001). Of all the cancer patients, 50% (n = 61) were hypermetabolic, 43.4% (n = 53) normometabolic, and 6.6% (n = 8) hypometabolic, whereas 35.1% (n = 46) of the controls were hypermetabolic, 56.5% (n = 74) normometabolic, and 8.4% (n = 11) hypometabolic. REE was correlated to substrate oxidation rate (R(2) = 0.710). Cancer patients exhibited no significant difference in FM, FM/body weight (BW) and FFM, compared with controls. Cancer patients presented no significant difference in TW compared with controls (P = 0.791), but they had increased ECF (P < 0.001) and decreased ICF (P < 0.001).

CONCLUSION

Aberrations in substrate utilization may contribute to the elevated energy expenditure in urologic cancer patients. Cancer type and pathologic stage are influential factors of REE.

Associations between energy demands, physical activity, and body composition in adult humans between 18 and 96 y of age

BACKGROUND

Associations between body composition and the energy expended on basal metabolism and activity are complex and age dependent.

OBJECTIVE

The objective was to examine associations between body composition and daily (DEE), basal (BEE), and activity energy expenditure (AEE) throughout the adult life span.

DESIGN

A cross-sectional study was conducted in 529 adults aged 18-96 y. DEE was measured by using doubly labeled water, BEE by using respirometry, and body composition by isotope dilution. AEE was calculated as DEE - BEE, and physical activity level (PAL) was calculated as DEE/BEE.

RESULTS

Up to age 52 y, fat-free mass (FFM) and fat mass (FM) were positively associated with age in men, but no significant effect was observed in women. No effects of age on DEE and AEE were observed. The average DEE in men (14.1 MJ/d) was 27% greater than that in women (10.7 MJ/d). PAL averaged 1.84 in men and 1.75 in women. Above and including the age of 52 y, FFM, FM, DEE, BEE, and AEE were all negatively associated with greater age. The effect of age on AEE was greater than on BEE; consequently, PAL by the age of 95 y was only 1.36. PAL and AEE were both unrelated to FFM (both age adjusted).

CONCLUSIONS

PAL and AEE were not associated with age in subjects aged <52 y. AEE, BEE, and PAL were all negatively associated with age in subjects aged ≥52 y. An absence of a relation between age-adjusted PAL and FFM suggested that greater physical activity was not associated with higher FFM in the elderly.

Poor prediction of resting energy expenditure in obese women by established equations

The objective of the study was to evaluate the accuracy of established prediction equations that calculate resting energy expenditure (REE) in obese women. This was a cross-sectional study. In 273 mildly to severely obese women (age, 41.7 +/- 13.2 years; body mass index, 42.8 +/- 7.0 kg/m(2)), REE was measured by indirect calorimetry (mREE), along with fat mass (FM) and fat-free mass (FFM) by bioelectrical impedance analysis. Eleven established equations were used to predict REE (pREE), with 9 equations basing on the anthropometric parameters body weight and height and 2 equations including body composition parameters (FM, FFM). All equations provided pREE values that significantly correlated with mREE (r > 0.66, P < .001), although 8 equations systematically underestimated mREE (P < .05). Of note, even the best equation was not able to accurately predict mREE with a deviation of less than +/-10% in more than 70% of the tested women. Furthermore, equations using body composition data were not superior in predicting REE as compared with equations exclusively including anthropometric variables. Multiple linear regression analyses revealed 2 new equations--one including body weight and age and another including FM, FFM, and age--that explained 56.9% and 57.2%, respectively, of variance in mREE. However, when these 2 new equations were applied to an independent sample of 33 obese women, they also provided an accurate prediction (+/-10%) of mREE in only 56.7% and 60.6%, respectively, of the women. Data show that an accurate prediction of REE is not feasible using established equations in obese women. Equations that include body composition parameters as assessed by bioelectrical impedance analysis do not increase the accuracy of prediction. Based on our results, we conclude that calculating REE by standard prediction equations does not represent a reliable alternative to indirect calorimetry for the assessment of REE in obese women.

Brain and high metabolic rate organ mass: contributions to resting energy expenditure beyond fat-free mass

BACKGROUND

The degree to which interindividual variation in the mass of select high metabolic rate organs (HMROs) mediates variability in resting energy expenditure (REE) is unknown.

OBJECTIVE

The objective was to investigate how much REE variability is explained by differences in HMRO mass in adults and whether age, sex, and race independently predict REE after adjustment for HMRO.

DESIGN

A cross-sectional evaluation of 55 women [30 African Americans aged 48.7 +/- 22.2 y (mean +/- SD) and 25 whites aged 46.4 +/- 17.7 y] and 32 men (8 African Americans aged 34.3 +/- 18.2 y and 24 whites aged 51.3 +/- 20.6 y) was conducted. Liver, kidney, spleen, heart, and brain masses were measured by magnetic resonance imaging, and fat and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry. REE was measured by indirect calorimetry.

RESULTS

REE estimated from age (P = 0.001), race (P = 0.006), sex (P = 0.31), fat (P = 0.001), and FFM (P < 0.001) accounted for 70% (adjusted (2)) of the variability in REE. The addition of trunk HMRO (P = 0.001) and brain (P = 0.006) to the model increased the explained variance to 75% and rendered the contributions of age, sex, and race statistically nonsignificant, whereas fat and FFM continued to make significant contributions (both P < 0.05). The addition of brain to the model rendered the intercept (69 kcal . kg(-1) . d(-1)) consistent with zero, which indicated zero REE for zero body mass.

CONCLUSIONS

Relatively small interindividual variation in HMRO mass significantly affects REE and reduces the role of age, race, and sex in explaining REE. Decreases in REE with increasing age may be partly related to age-associated changes in the relative size of FFM components.

Resting energy expenditure in manic episode

OBJECTIVE

We aimed to assess the resting energy expenditure in bipolar I disorder, manic episode patients.

METHOD

Forty-two bipolar I disorder, manic episode patients that were treated in the inpatient psychiatry clinic of Trakya University Hospital and had met the necessary study criteria were included along with 27 controls. DSM-IV criteria and the Bech-Rafaelsen Mania Rating Scale were used to evaluate patients' diagnosis and severity of the manic episodes. The indirect calorimetry device was used to measure resting energy expenditure values.

RESULTS

Resting energy expenditure values of manic patients were found to be higher than those of the controls. Controls showed significant correlations between body mass index and resting energy expenditure, but manic patients did not exhibit similar correlations. There was also no relation between Bech-Rafaelsen Mania Rating Scale scores and resting energy expenditure values in manic patients.

CONCLUSIONS

We found significantly increased resting energy expenditure values in bipolar I disorder, manic episode patients. These findings suggest a possible clinical use of resting energy expenditure for evaluation of bipolar I disorder manic episode and also suggest resting energy expenditure as a possible biological marker.

Do patients with amyotrophic lateral sclerosis (ALS) have increased energy needs?

BACKGROUND AND AIMS

Nutritional status is a prognostic factor for survival in amyotrophic lateral sclerosis (ALS) patients. We investigated the contribution of some of the components contributing to resting energy expenditure (REE) in order to determine whether potentially higher energy needs should be considered for these patients.

METHODS

Thirty three ALS patients and 33 age- and gender-matched healthy controls participated. REE was measured by an open-circuit indirect calorimeter, body composition by dual energy X-ray absorptiometry, and estimated caloric intake by 7-day food records.

RESULTS

Patients had lower body mass indices and lower lean body mass (LBM) than healthy controls. REE values (as a percentage of predicted) was similar but increased when normalized by LBM (P<0.001). LBM and REE decreased while REE/LBM increased in ten patients who were reassessed 6 months later. A model for predicting measured REE was constructed based on the different components, with 86% prediction of its variability.

CONCLUSIONS

ALS is associated with increased REE. Various factors, such as poor caloric intake and mechanical ventilation, may mask this tendency. All the above parameters need to be considered during nutritional intervention to prevent additional muscle loss.

Body Composition and Resting Energy Expenditure of Individuals With Duchenne and Becker Muscular Dystrophy

Purpose: The relationship between body composition and resting energy expenditure (REE) was investigated in two boys and two men with Duchenne muscular dystrophy (DMD) (ages 11 to 22.4 years) and two boys with Becker muscular dystrophy (BMD) (ages 7.75 and 13.75 years).

Methods: The REE was assessed by indirect calorimetry; body composition indices (weight, height, skinfolds, and mid-arm circumference) were measured using standardized techniques and compared with healthy reference data.

Results: Those with DMD had reduced corrected mid-upperarm muscle area (C-MUMA) in comparison with healthy peers, and approximately twice the subcutaneous fat levels of subjects with BMD and healthy peers. Boys with BMD had remarkably lower muscle status than did boys with DMD and healthy peers. In both groups, REE was lower than in healthy peers; REE was associated with body weight (r=0.85), height (r=0.92), mid-upper arm fat area (MUFA) (r=0.97), and C-MUMA (r=0.65).

Conclusions: Individuals with muscular dystrophy (MD) exhibit considerable disease-specific alterations in body composition. The REE had a stronger relationship with growth (weight and height) and subcutaneous body fat composition and a weaker association with C-MUMA. Understanding the effect of MD on body composition and REE will allow dietitians to individualize energy recommendations.

Clinical, agricultural, and evolutionary biology of myostatin: a comparative review

The discovery of myostatin and our introduction to the "Mighty Mouse" over a decade ago spurred both basic and applied research and impacted popular culture as well. The myostatin-null genotype produces "double muscling" in mice and livestock and was recently described in a child. The field's rapid growth is by no means surprising considering the potential benefits of enhancing muscle growth in clinical and agricultural settings. Indeed, several recent studies suggest that blocking myostatin's inhibitory effects could improve the clinical treatment of several muscle growth disorders, whereas comparative studies suggest that these actions are at least partly conserved. Thus, neutralizing myostatin's effects could also have agricultural significance. Extrapolating between studies that use different vertebrate models, particularly fish and mammals, is somewhat confusing because whole genome duplication events have resulted in the production and retention of up to four unique myostatin genes in some fish species. Such comparisons, however, suggest that myostatin's actions may not be limited to skeletal muscle per se, but may additionally influence other tissues including cardiac muscle, adipocytes, and the brain. Thus, therapeutic intervention in the clinic or on the farm must consider the potential of alternative side effects that could impact these or other tissues. In addition, the presence of multiple and actively diversifying myostatin genes in most fish species provides a unique opportunity to study adaptive molecular evolution. It may also provide insight into myostatin's nonmuscle actions as results from these and other comparative studies gain visibility in biomedical fields.

Effect of a polymorphism in the ND1 mitochondrial gene on human skeletal muscle mitochondrial function

OBJECTIVE

A non-silent polymorphism in the mitochondrial coding region of the ND1 gene, a subunit of reduced nicotinamide adenine dinucleotide (NADH) dehydrogenase is associated with resting metabolic rate (RMR) in 245 non-diabetic Pima Indians. The purpose of this investigation was to determine the effect of the ND1 gene polymorphism on mitochondrial function in 14 male Pima Indians.

METHODS AND PROCEDURES

Seven subjects with an A at site 3547 of the ND1 gene (Ile at amino acid 81), and seven with a G at this site (Val) were studied. Mitochondria were isolated from 0.8 to 1.5 g of skeletal muscle obtained by needle biopsy of the lateral quadriceps muscle. In intact mitochondria, maximal (state-3) and resting (state-4) respiration rates were measured polarographically at 37 degrees C with a variety of single substrates or substrate combinations. Disrupted mitochondria were analyzed for maximal capacities through the entire electron transport chain (ETC) (NADH oxidase (NADHOX)), as well as through a segment of Complex I that is independent of the ND1 component (NADH-ferricyanide (NADH-FeCN) reductase).

RESULTS

Mitochondria were well coupled and exhibited higher respiratory control ratios (RCRs) than rodent muscle. There were no differences between the two groups for any of the measured parameters.

DISCUSSION

These results indicate that the cause of the observed association between RMR and the ND1 polymorphism is not related to in vitro mitochondrial function.

Prediction of resting energy expenditure in severely obese Italian males

The objectives of the present study were to develop and cross-validate new equations for predicting resting energy expenditure (PREE) in severely obese Italian males, and to compare their accuracy with those of the Harris-Benedict, WHO/ FAO/UNU, Huang, Owen, Mifflin, Livingston, Nelson, Bernstein, and Cunnimgham equations in order to predict resting energy expenditure (REE), using the Bland-Altman method. One hundred and sixty-four severely obese males [mean body mass index (BMI): 45.4 kg/m2; 50.2% fat mass), aged 20 to 65 yr participated in this study. REE was measured by indirect calorimetry and body composition by bioelectrical analysis. Equations were derived by stepwise multiple regression analysis using a calibration group and tested against the validation group. Two new specific equations, based on anthropometric [REE=Weight x 0.048 + Height x 4.655 - age x 0.020 - 3.605 (R2=0.68, SE=1.14 MJ/d)] or body composition parameters [REE=fat free mass (FFM) x 0.081 + fat mass (FM) x 0.049 - age x 0.019 - 2.194 (R2=0.65, SE=1.15 MJ/d)], were generated. Mean PREE were not different from the mean measured REE (MREE) (<1%, p<0.001), REE being predicted accurately (95-105% of MREE) in 66 and 62% of subjects, respectively. The Harris-Benedict, WHO/FAO/UNU, Huang and Owen equations showed mean differences lower than 5% and PREE was accurate in less than 30% of subjects. The Mifflin, Livingston, and Nelson equations showed a mean PREE underestimation >7% (p<0.001) and PREE was accurate in less than 25% of subjects. The Bernstein and Cunningham equations showed a greater PREE underestimation (>22%, p<0.001) in more than 85% of subjects. The new prediction equations allow an accurate estimation of REE in groups of severely obese males and result in lower mean differences and lower limits of agreement between PREE and MREE than the commonly used equations.

The effects of undernutrition and refeeding on metabolism and digestive function

PURPOSE OF REVIEW

An intimate interrelationship exists between nutritional status and gut function. This review focuses on the consequences of a poor nutritional state on metabolism and digestive function, and evaluates the effects of refeeding.

RECENT FINDINGS

Severe undernutrition has been associated with increased fat and protein catabolism, reflected by a decreased respiratory quotient. Resting energy expenditure assessed in relationship to body weight was increased, probably as a consequence of changes in body composition. Protein synthesis, expressed per kg body weight, was decreased in undernourished patients with coexistent disease, but not in anorexia nervosa patients, indicating the detrimental effects of disease states. Severe undernutrition is associated with malabsorption, which improves following refeeding. Despite a high prevalence of villous atrophy in the duodenal mucosa in undernourished patients, mucosal protein fractional synthesis rates appeared normal. Refeeding resulted in a potent trophic response, and normalization of the mucosal morphology. Gastric and pancreatic secretion was significantly impaired by the undernourished state, with significant improvement following refeeding.

SUMMARY

Undernutrition is associated with impairment of digestive function, which is likely to further aggravate the nutritional state. Refeeding corrects this dysfunction, and results in disruption of this vicious circle.

Some mathematical and technical issues in the measurement and interpretation of open-circuit indirect calorimetry in small animals

Indirect calorimetry is increasingly used to investigate why compounds or genetic manipulations affect body weight or composition in small animals. This review introduces the principles of indirect (primarily open-circuit) calorimetry and explains some common misunderstandings. It is not widely understood that in open-circuit systems in which carbon dioxide (CO2) is not removed from the air leaving the respiratory chamber, measurement of airflow out of the chamber and its oxygen (O2) content paradoxically allows a more reliable estimate of energy expenditure (EE) than of O2 consumption. If the CO2 content of the exiting air is also measured, both O2 consumption and CO2 production, and hence respiratory quotient (RQ), can be calculated. Respiratory quotient coupled with nitrogen excretion allows the calculation of the relative combustion of the macronutrients only if measurements are over a period where interconversions of macronutrients that alter their pool sizes can be ignored. Changes in rates of O2 consumption and CO2 production are not instantly reflected in changes in the concentrations of O2 and CO2 in the air leaving the respiratory chamber. Consequently, unless air-flow is high and chamber size is small, or rates of change of O2 and CO2 concentrations are included in the calculations, maxima and minima are underestimated and will appear later than their real times. It is widely appreciated that bigger animals with more body tissue will expend more energy than smaller animals. A major issue is how to compare animals correcting for such differences in body size. Comparison of the EE or O2 consumption per gram body weight of lean and obese animals is misleading because tissues vary in their energy requirements or in how they influence EE in other ways. Moreover, the contribution of fat to EE is lower than that of lean tissue. Use of metabolic mass for normalisation, based on interspecific scaling exponents (0.75 or 0.66), is similarly flawed. It is best to use analysis of covariance to determine the relationship of EE to body mass or fat-free mass within each group, and then test whether this relationship differs between groups.

Resting energy expenditure in patients with solid tumors undergoing anticancer therapy

OBJECTIVE

Few studies have investigated the resting energy expenditure (REE) of, or determined the individual predictive accuracy of prediction equations in, cancer patients undergoing anticancer therapy. This study compared the measured REE of patients with cancer undergoing anticancer therapy with (1) healthy subjects and (2) REE estimated from commonly used prediction methods.

METHODS

Resting energy expenditure was measured in 18 cancer patients and 17 healthy subjects by using indirect calorimetry under standard conditions and was estimated from seven prediction methods. Fat-free mass (FFM) was measured by bioelectrical impedance analysis. Data were analyzed with regression modeling to adjust REE for FFM. Agreement between measured and predicted REE values was analyzed using the Bland-Altman approach.

RESULTS

There was no significant difference in FFM-adjusted REE between cancer patients and healthy subjects (mean difference 10%). Limits of agreement were wide for all prediction methods in estimating REE as much as 40% below and up to 30% above measured REE.

CONCLUSIONS

REE in cancer patients undergoing anticancer therapies does not appear to be as high as commonly thought. None of the prediction equations examined were acceptable for predicting REE of individual cancer patients or healthy subjects.

Estimation of Total-Body Skeletal Muscle Mass in Children and Adolescents

PURPOSE

The estimation of total-body skeletal muscle mass (SMM) has been predicted in healthy adults using anthropometric measurements and urine creatinine excretion. SMM measurement is compulsory to evaluate exercise performance and the influence of physical training on muscle mass. However, there is a lack of information on children and adolescents when quantifying appendicular skeletal muscle mass.

METHODS

Thirty-nine Caucasian children and adolescents (male and female, 7-16 yr old) and 20 adults (men and women, 20-24 yr old) were tested for total-body SMM using dual-energy x-ray absorptiometry measurement (DEXA), anthropometric measurements (ANTHR), and urine creatinine (UCrn) determination. Skinfold thickness and circumference were measured at mid-arm (CAG), mid-thigh (CTG), and mid-calf (CCG) and the skin-corrected circumferences (cm), together with height (Ht; m), age (yr), and sex (0 for female, 1 for male). The UCrn excretion (g.24 h(-1)) was also determined in all subjects. The ANTHR and UCrn measurements were then compared with DEXA as reference value.

RESULTS

The multiple linear regression from anthropometric measurements gave the following equation to evaluate the total-body skeletal muscle mass (SMM) in children and adolescents: SMM (kg) = Ht x [(0.0064 x CAG) + (0.0032 x CTG) + (0.0015 x CCG)(2)] + (2.56 x sex) + (0.136 x age). The prediction of SMM from a 24-h urine collection was obtained with the following equation: SMM (kg) = (10.62 x Crn) + 6.63. The correlation coefficient (r(2)) was 0.966 and 0.710 for the anthropometric and creatinine methods, respectively (P < 0.001).

CONCLUSION

Besides DEXA technique, the determination of total-body skeletal muscle mass in children and adolescents can be highly validated with satisfactory confidence by simple anthropometric measurements or 24-h urine creatinine excretion.

Análise de taxa metabólica basal e composição corporal de idosos do sexo masculino antes e seis meses após exercícios de resistência

O objetivo deste trabalho foi o de comparar a taxa metabólica basal e a composição corporal antes e após um programa de exercício de resistência. Foram selecionados 46 voluntários do sexo masculino com idade entre 60 e 75 (66,97 ± 4,80 anos), que foram distribuídos aleatoriamente em dois grupos: 1) grupo controle, que foi orientado a não alterar seus hábitos rotineiros e não se engajar em nenhum programa de exercício físico; e 2) grupo experimental, que participou de um programa de exercícios em cicloergômetro três vezes por semana (60 minutos) em dias alternados por seis meses, com intensidade prescrita referente à freqüência cardíaca do limiar ventilatório 1 (LV-1). Os voluntários foram submetidos a avaliação da composição corporal (DEXA); calorimetria indireta, análise sanguínea e teste ergoespirométrico. Após o período de estudo, foram observados decréscimo significativo nos hormônios tireoidianos e mudanças no metabolismo basal em ambos os grupos, mas não foram constatadas alterações na composição corporal. No entanto, o grupo experimental apresentou aumento significativo no consumo de oxigênio pico e na carga de trabalho referente à intensidade do LV-1. Os dados sugerem que um programa de exercícios aeróbios na intensidade do LV-1 não é suficiente para provocar alterações favoráveis no metabolismo basal e composição corporal de idosos, embora promova benefícios cardiovasculares.

Correction of anemia in patients with congestive heart failure increases resting energy expenditure

BACKGROUND & AIM

Congestive heart failure (CHF) and anemia were reported to affect resting energy expenditure (REE). The aim of this study was to evaluate the effect of the correction of anemia on REE in subjects with CHF.

PATIENTS AND METHODS

Nine anemic patients with compensated CHF and CRF were studied before and after correction of anemia. REE was studied by an open circuit indirect calorimeter, body composition by dual-energy-X-ray absorption and total body and extracellular water by multi-frequency bioelectrical impedence. Four anemic and 5 non-anemic CHF patients who did not receive any new treatment served as controls.

RESULTS

After the correction of their anemia patients tended to increase weight (P<0.06), but no significant changes were observed in body composition. Daily caloric intake increased significantly (P<0.02). Ejection fraction increased (P<0.05) and pulse rate decreased significantly (P<0.001). REE and REEPP were in the normal range before correction but increased significantly afterwards (1402+/-256 vs. 1496+/-206 kcal/d, and 101+/-9 vs. 109+/-8, P<0.023 and P<0.006, respectively).

CONCLUSION

Correction of anemia in patients with CHF increases their REE. This can be related either to improved tissue oxygenation and/or to increased caloric intake.

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.

A new handheld device for measuring resting metabolic rate and oxygen consumption

OBJECTIVE

The purpose of this study was to test a new handheld device, the BodyGem (HealtheTech Inc., Golden, CO), that measures resting metabolic rate (RMR).

SUBJECTS/DESIGN

Sixty-three adults (43 women, 20 men) (mean+/-SD, age 41.3+/-11.2 years and body mass index, 26.5+/-6.6 kg/m(2)) were tested during two separate sessions within a 2-week period, and, in each session, two BodyGem and two Douglas bag RMR measurements were made in a random and counterbalanced order.

MAIN OUTCOME MEASURES

Resting oxygen consumption and energy expenditure.

STATISTICAL ANALYSES PERFORMED

A 2 (methods) x 4 (time points) repeated measures ANOVA, Pearson product-moment coefficients, Bland-Altman plots.

RESULTS

Within session reliability for measurement of O(2), consumption was high on both days for the BodyGem (r=0.97). During the 4 single tests, BodyGem and Douglas bag O(2) consumption values were significantly correlated (r=0.81-0.87), with SEEs ranging from 22 to 28 mL. min(-1). Mean O(2) consumption and RMR values for all 4 tests were 241+/-46 and 240+/-45 mL x min(-1) (r=0.91, SEE 18.7 mL x min(-1)) and 1,657+/-324 and 1,650+/-307 kcals x day(-1) (r=0.91, SEE 134 kcals x day(-1)) for the BodyGem and Douglas bag methods, respectively.

APPLICATIONS/CONCLUSIONS

These data indicate that the BodyGem is an accurate and reliable device for measuring oxygen consumption and calculating RMR during repeated tests within a day, single tests on separate days, or when measurements are averaged.

Body composition and energy expenditure: relationship and changes in obese subjects before and after biliopancreatic diversion

Changes in total and segmental body composition were studied in 101 obese women before and 2, 6, 12, and 24 months after biliopancreatic diversion (BPD) and data 24 months after surgery were matched to 53 control subjects. The patients were studied by anthropometry, indirect calorimetry, and double-emission x-ray absorptiometry (DXA). The combination of calorimetry and body composition analysis allowed estimation of visceral and muscle lean mass. We observed a significant (analysis of variance [ANOVA]: P <.05) progressive reduction of fat and lean body mass (LBM) following BPD, with stabilization of both parameters between 12 and 24 months at levels not different from controls. Fat loss was significant in the arms, legs, and trunk segments. After 24 months, there was no significant difference in segmental fat mass between post-BPD patients and controls. Calorimetric data seem to confirm lean body mass (LBM) reduction. Visceral lean mass (kg) was significantly reduced from 8.1 +/- 2.2 in obese subjects to 6.5 +/- 1.8 in post-BPD patients at 24 months (P <.05); the control value was 7.2 +/- 1.8. Muscular lean mass (kg) was also significantly reduced, from 50.2 +/- 5.8 to 39.8 +/- 5.7 in the same subjects (P <.05), with a control value of 42.5 +/- 5.9. The decrease in muscle and visceral LBM reached control values without significant differences. Viscera/muscle ratio in pre-BPD patients was preserved in post-BPD patients at 24 months, but it was reduced during weight loss. Body composition studies showed a logarithmic relationship between fat and lean mass and a physiological contribution of lean mass to weight loss in the BPD patients. In conclusion, weight loss after BPD was achieved with an appropriate decline of LBM and with all parameters reaching, at stable weight, values similar to weight-matched controls.

Age-related decrease in energy expenditure at rest parallels reductions in mass of internal organs

This study explores to what extent the mass of internal organs may impact the age-related decrease in energy expenditure at rest (EErest). The relationship between direct measurements of EErest in elderly women and predicted EErest based on equations deriving from the metabolic activity in tissue from younger women were also elucidated. Body composition of elderly women was measured by an impedance method. EErest was measured by the Douglas bag method after an overnight fast. These data were compared with predicted values of EErest based on equations derived from studies in younger women. The mass of internal organs was obtained from autopsy material. Young women (mean age 31.7 years, range 14-60, n = 104) and elderly women of 65 years (n = 22), 75 years (n = 26), and 85 years (n = 31) participated in this study. Autopsy data were obtained from women (n = 238) from the same birth cohorts as the elderly women who died at ages 42-87 years. EErest showed a progressive age-related decline, which appeared to parallel a similar reduction in the mass of internal organs derived from autopsy material of women who died at the same ages. In contrast, FFM was identical in the group of 65 and 75-year-old women, but was lower in the 85-year-old women. Predicted and measured EErest revealed a strong correlation in elderly women. Modest reductions in the mass of internal organs with a high metabolic rate appear to contribute markedly to the decline in EErest observed in aging. Further, it is also possible to predict EErest from the body composition of elderly women using equations developed from younger women.

Body-size dependence of resting energy expenditure can be attributed to nonenergetic homogeneity of fat-free mass

An enduring enigma is why the ratio of resting energy expenditure (REE) to metabolically active tissue mass, expressed as the REE/fat-free mass (FFM) ratio, is greater in magnitude in subjects with a small FFM than it is in subjects with a large FFM. This study tested the hypothesis that a higher REE/FFM ratio in subjects with a small body mass and FFM can be explained by a larger proportion of FFM as high-metabolic-rate tissues compared with that observed in heavier subjects. REE was measured by indirect calorimetry, FFM by dual-energy X-ray absorptiometry (DEXA), and tissue/organ contributions to FFM by whole body magnetic resonance imaging (MRI) in healthy adults. Four tissue heat-producing contributions to FFM were evaluated, low-metabolic-rate fat-free adipose tissue (18.8 kJ/kg), skeletal muscle (54.4 kJ/kg), and bone (9.6 kJ/kg); and high-metabolic-rate residual mass (225.9 kJ/kg). Initial evaluations in 130 men and 159 women provided strong support for two key, developed models, one linking DEXA FFM with MRI FFM estimates and the other linking REE predicted from the four MRI-derived components with measured REE. There was an inverse association observed between measured REE/FFM and FFM (r(2) = 0.17, P < 0.001). Allometric models revealed a similar pattern of tissue change relative to body mass across males and females with greater proportional increases in fat-free adipose tissue and skeletal muscle than in FFM and a smaller proportional increase in residual mass than in FFM. When examined as a function of FFM, positive slopes were observed for skeletal muscle/FFM and pooled low-metabolic-rate components, and a negative slope for residual mass. Our linked REE-body composition models and associations strongly support the hypothesis that FFM varies systematically in the proportion of thermogenic components as a function of body mass and FFM. These observations have important implications for the interpretation of between-individual differences in REE expressed relative to metabolically active tissue mass.