World Health Organization Equation Research Articles

Resting energy expenditure in adolescents with Down syndrome: a comparison of commonly used predictive equations.

Adolescents with Down syndrome (DS) are two to three times more likely to be obese than their typically developing peers. When preventing or treating obesity, it is useful for clinicians to understand an individual's energy intake needs. Predictive resting energy expenditure (REE) equations are often recommended for general use in energy intake recommendations; however, these predictive equations have not been validated in youth with DS. The aim of this study was to compare the accuracy of seven commonly used predictive equations for estimating REE in adolescents who are typically developing to REE measured by indirect calorimetry in adolescents with DS. Adolescents with DS participated in a 90-min laboratory visit before 10:00a.m. after a 12-h overnight fast and a 48-h abstention from aerobic exercise. REE was measured via indirect calorimetry, and estimated REE was derived using the Institute of Medicine, Molnar, Muller and World Health Organization equations. Mean differences between the measured and predicted REE for each equation were evaluated with equivalency testing, and P-values were adjusted for multiple comparisons using the Holm method. Forty-six adolescents with DS (age: 15.5±1.7years, 47.8% female, 73.9% non-Hispanic White) completed the REE assessment. Average measured REE was 1459.5±267.8kcal/day, and the Institute of Medicine equations provided the most accurate prediction of REE with a 1.7±11.2% (13.9±170.3kcal/day) overestimation. This prediction was not statistically different from the measured REE [P-value=0.582; 95% confidence interval (CI): -64.5, 36.7], and the difference between the measured and predicted REE was statistically equivalent to zero (P-value=0.024; 90% CI: -56.1, 28.3). The results suggest that the Institute of Medicine equation may be useful in predicting REE in adolescents with DS. Future research should confirm these results in a larger sample and determine the utility of the Institute of Medicine equation for energy intake recommendations during a weight management intervention.

Assessing the Efficacy of Predictive Resting Energy Expenditure in Strength and Power Athletes

Improve dietary prescriptions for strength-power athletes. Mifflin-St. Jeor (MSJ) and the World Health Organization (WHO) predictive equations are often used to gauge resting energy expenditure (REE). The Academy of Nutrition and Dietetics recommends using MSJ for healthy individuals whereas the WHO equation can be used against population norms. However, predictive equations often over or underestimate actual energy needs for populations such as athletes. Therefore, predictive equations should be examined further to determine precision compared to actual energy needs of athletic populations. To examine the relationships and precision of predictive REE (pREE) against measured (mREE) values in competitive strength-power athletes. Twelve male powerlifters and weightlifters (22.4±2.6y, 175.2±7.8cm, 92.0±22.1kg) participated in the study. Upon waking and in a fasted state, each participant followed standard hydration and body composition protocols followed by an REE assessment using a metabolic cart. Each participant laid motionless in a reclined position with a respiratory exchange mask for 40 minutes. The final 30 minutes was averaged to determine mREE. Pearson correlations were used to assess the relationships between mREE and pREE. Percent differences were calculated to determine over-underestimates between mREE and pREE values. Large relationships were observed between mREE and pREE for both predictive equations (MSJ [r=0.660]; WHO [r=0.685]). Relative to mREE values, the over-under percent difference was greatest for MSJ (11.0±12.3%) compared to the WHO (2.2±12.0%). When dealing with athletic populations the WHO equation should be considered for dietary prescriptions considering that the pREE appears to be more indicative of actual energy needs.

Measured Versus Estimated Resting Metabolic Rate in Heart Failure With Preserved Ejection Fraction.

Obesity is common in heart failure with preserved ejection fraction (HFpEF), and a hypocaloric diet can improve functional capacity. Malnutrition, sarcopenia, and frailty are also frequently present, and calorie restriction could harm some patients. Resting metabolic rate (RMR) is an essential determinant of caloric needs; however, it is rarely measured in clinical practice. The accuracy of commonly used predictive equations in HFpEF is unknown. RMR was measured with indirect calorimetry in 43 patients with HFpEF undergoing right heart catheterization at the University of Michigan, and among 49 participants in the SECRET trial (Study of the Effects of Caloric Restriction and Exercise Training in Patients With Heart Failure and a Normal Ejection Fraction); SECRET patients also had dual-energy X-ray absorptiometry body composition measures. Measured RMR was compared with RMR estimated using the Harris Benedict, Mifflin-St Jeor, World Health Organization, and Academy for Nutrition and Dietetics equations. All predictive equations overestimated RMR (by >10%, P<0.001 for all), with mean (95% CI) differences Harris Benedict equation +250 (186-313), Mifflin-St. Jeor equation +169 (110-229), World Health Organization equation +300 (239-361), and Academy for Nutrition and Dietetics equation +794 (890-697) kcal/day. Results were similar across both patient groups, and the discrepancy between measured and estimated RMR tended to increase with body mass index. In SECRET, measured RMR was closely associated with lean body mass (ρ=0.74; by linear regression adjusted for age and sex: β=27 [95% CI, 18-36] kcal/day per kg, P<0.001; r2=0.56). Commonly used predictive equations systematically overestimate measured RMR in patients with HFpEF. Direct measurement of RMR may be needed to effectively tailor dietary guidance in this population. Registration: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT00959660.

Resting Energy Expenditure in CrossFit® Participants: Predictive Equations versus Indirect Calorimetry

Background: CrossFit® involves high-intensity functional movements and research has shown that the program increases metabolic rates in participants. Objective: To measure resting energy expenditure (REE) in CrossFit® participants using indirect calorimetry (IC) and to verify the most appropriate predictive equation to estimate REE. Methods: Overall, 142 CrossFit® participants (18–59 years; 91 [64.1%], women) underwent weight, height, waist circumference, and body mass index (BMI) measurements. Body composition was evaluated using a portable ultrasound system (BodyMetrix®). REEs were measured (mREE) by IC and predicted by six different equations (pREE): Harris-Benedict, World Health Organization (WHO), Henry and Rees, Cunningham (1980 and 1991), and Mifflin–St. Jeor. Results: The mean age was 33.0 (6.3) years, with no significant difference between men and women; mean mREE, 1583.2(404.4) kcal/d; and pREE, 1455.5(230.9) to 1711.3(285.5) kcal/d. The best REE predictive equations for this population were Cunningham (1991) (P=0.338), WHO (P=0.494), and Harris-Benedict (P=0.705) equations. The Harris-Benedict equation presented a smaller difference compared with IC [12.9(307.6) kcal], the Cunningham (1991) equation showed improved adequacy (102.5%), and the WHO equation presented highest accuracy (59.9%). The equations that were closest to the mREE were the Harris-Benedict for women and the WHO equation for men. Conclusion: Therefore, for CrossFit® participants, the REE can accurately be predicted with the Cunningham (1991), WHO, and Harris-Benedict equations.

Abstract 15864: Measured versus Estimated Resting Metabolic Rate in Heart Failure With Preserved Ejection Fraction

Introduction: Obesity is common in heart failure with preserved ejection fraction (HFpEF) and dietary weight loss can improve functional capacity, but sarcopenia and frailty are also frequently present. Little evidence is available regarding resting metabolic rate (RMR) or how commonly used equations to estimate RMR compare to measured RMR in HFpEF. This information is vital for counseling patients on individual caloric needs. Hypothesis: Commonly used estimation equations do not accurately reflect measured RMR in patients with HFpEF. Methods: Resting metabolic rate (RMR) was measured with a metabolic cart for consecutive patients with HFpEF (EF ≥50%) referred for right heart catheterization at the University of Michigan from 2011-2015. Patients with congenital, infiltrative, hypertrophic, or restrictive cardiomyopathy were excluded. The RMR was calculated using the Weir formula: RMR= 1440*[3.94 VO 2 (l/min) + 1.11*VCO 2 (l/min)] kcal/day. Measured RMR and estimations of RMR using the Harris Benedict Equation (HBE), Mifflin-St Jeor Equation (MSJE), and World Health Organization (WHO) equation were compared using paired t-tests and Bland-Altman plots. Results: Patients (n=43) were aged (mean ± SD) 62 ± 11.6 years, 53% female, and BMI 34.9 ± 11. Mean measured RMR from Weir equation was 1514 ± 479 kcal/day. Estimated RMR by HBE was 1784 ± 530, MSJE was 1685 ± 457, and WHO equation was 1816.8 ± 485 kcal/day. All estimations significantly overestimated RMR when compared to the Weir method (&gt;10% difference and p&lt;0.01 for all; Figure A-C). The MSJE had the closest range of agreement to the measured RMR. Conclusions: Estimations of resting metabolic rate in patients with HFpEF demonstrated a fixed bias towards overestimation when compared to measured RMR by metabolic cart. Given HFpEF populations are often obese and are counseled routinely on weight loss, understanding the implicit bias of equations estimating RMR is vital when providing nutritional and dietary counseling.

Estimated Vs. Measured Resting Metabolic Rate to Guide Nutrition Recommendations for Patients with Heart Failure with Reduced Ejection Fraction

Introduction Malnutrition and weight loss among patients with heart failure with reduced ejection fraction (HFrEF) are associated with poor functional status, hospitalization, and death. Nutritional interventions might benefit patients with HFrEF, but few data on resting metabolic rate (RMR) are available to guide such programs. We sought to clarify the relationship between heart failure (HF) severity and RMR and the accuracy of commonly used estimations of RMR in this population. Hypothesis We hypothesized that worsening HF severity would impact RMR and that commonly used estimations of RMR would be inaccurate. Methods We measured RMR with a metabolic cart in 548 consecutive patients with HFrEF at the University of Michigan from 2011 to 2015. The primary outcome of interest was measured RMR using the Weir formula: RMR= 1440*[3.94 VO2 (l/min) + 1.11* VCO2 (l/min)] kcal/day. The primary independent variable was HF severity determined by New York Heart Association function class (NYHA FC). Linear regression adjusted for age, gender, weight, and height was performed to evaluate the impact of HF severity on RMR. Measured RMR and estimations of RMR using the Harris Benedict, Mifflin-St Jeor, and World Health Organization equations, both overall and by NYHA FC, were compared using paired t-tests. Results Patients were aged (mean ± SD) 59 ± 13 years, 73% male, and had an ejection fraction of 19 ± 7%; measured RMR was 1488 ± 416 kcal per day. Worsening HF severity was associated with reduced RMR. In the multivariable model, the predicted RMR for NYHA FC IV patients was -177.5 (95% CI, -246.7 to -108.3, p Conclusions Worsening HF severity is associated with decreasing RMR in patients with HFrEF and discordance of estimations increased with worsening NYHA FC. Estimations of RMR have the potential to introduce clinically meaningful (>10%) error in RMR. Improved understanding of these issues, including their relationship with other measures of HF severity and with body composition, will be necessary to promote adequate nutritional intake for patients with HFrEF.

Resting Energy Expenditure In Crossfit® Practitioners: Indirect Calorimetry Versus Predictive Equations

BACKGROUND: Indirect calorimetry (IC) is the gold standard method to assess individual resting energy expenditure (REE). However, due to its high cost and time demand, predictive equations are largely used to estimate energy requirements, which may vary according to different body compositions and health status. Crossfit® is a training program created by Greg Glessman in 1995 and consists of performing high intensity functional movements. Some studies have already reported increase of metabolic rates in this population. PURPOSE: To measure REE in Crossfit® practitioners, using IC, and verify the most appropriate predictive equation to estimate this variable. METHODS: 142 Crossfit® practitioners, 91 female (64.1%), aged between 16-59 years, underwent nutritional assessment, including weight, height, waist circumference (WC) and body mass index (BMI). Body composition was measured by a portable ultrasound. REE was measured by IC (mREE) and predicted by six different equations (pREE): Harris-Benedict, World Health Organization (WHO), Henry and Rees, Cunningham (1980), Cunningham (1991), and Mifflin-St.Jeor. Statistical analysis were carried out by Kolmogorov-Smirnov Test, Student’s t test and Bland and Altman plots. RESULTS: The mean age was 33.0 ± 6.3 years of age, without difference between men and women. The mean BMI was 24.7 ± 3.5 kg/m2. The mean mREE was 1583.2 ± 404.4 kcal, and the pREE ranged from 1455.5 ± 230.9 kcal to 1711.3 ± 285.5. The best REE predictive equations for Crossfit® practitioners were the Cunningham (1991) (P=0.338), WHO equation (P=0.494) and Harris-Benedict (P=0.705). Harris-Benedict presented the smaller difference comparing with IC (12.9 ± 307.6 kcal), Cunningham (1991) showed better adequacy (102.5%) and WHO equation presented greater percentage of accuracy (59.9%). The same equations remained adequate when data were stratified by gender. CONCLUSIONS: This study showed that Cunningham (1991), WHO (1985) and Harris-Benedict (1919) equations were the most appropriated REE equations for Crossfit® practitioners. Further studies should investigate more suitable methods to determine the energy requirements in Crossfit® and should, perhaps, create and propose a specific equation for this population.

Comparison of Measured and Estimated Resting Energy Expenditure in Adolescents and Young Adults With Severe Obesity Before and 1 Year After Sleeve Gastrectomy.

Introduction: Resting energy expenditure (REE) is often evaluated in adults and adolescents with obesity to estimate caloric requirements when advising dietary changes. However, data are lacking regarding the accuracy of methods used to clinically assess REE in adolescents with severe obesity. Moreover, there are no data regarding the effects of sleeve gastrectomy (SG) on REE in adolescents. We evaluated the accuracy and error rate between estimated and measured REE in adolescents with severe obesity and changes in REE following (SG).Materials and Methods: Cross-sectional study (CSS): 64 adolescents and young adults, 14–22 years old, with moderate to severe obesity were enrolled. We measured REE (mREE) by indirect calorimetry and estimated REE (eREE) using Derumeaux (Deru), Mifflin-St Jeor (MS), Harris Benedict (HB), and World Health Organization (WHO) equations. DXA was used to determine body composition. Bland Altman analysis evaluated agreement between eREE and mREE. Longitudinal study: 12 subjects had repeat indirect calorimetry and DXA 1 year after SG. Longitudinal analysis was used to assess changes in REE and body composition.Results: CSS: Median BMI was 45.2 kg/m2 and median age was 18.0 (16.3–19.9) years. mREE correlated strongly with eREE . Bland Altman analysis demonstrated that only a few points were beyond the 1.96 SD limit of disagreement. However, there was considerable overestimation of mREE by most equations. Longitudinal Study: In the subset that underwent SG, after 12-months, absolute REE decreased from 1709 (1567.7–2234) to 1580.5 (1326–1862.5) Calories (p = 0.002); however, the ratio of REE/Total Body Weight (TBW) increased from 13.5 ± 2.3 at baseline to 15.5 ± 2.2 at 1 year (p = 0.043). When evaluating parameters affecting % total weight loss, we found that it correlated positively with REE/TBW at 12 months (R = 0.625; p = 0.03) and negatively with % fat mass at 12 months (R = −0.669; p = 0.024).Discussion: In adolescents with moderate-severe obesity, despite a correlation between mREE using indirect calorimetry and eREE using the Deru, MS, HB, and WHO equations, there is significant over-estimation of REE at the individual level, challenging their clinical utility. One year after SG, REE/TBW increased and strongly correlated with % total weight loss in adolescents.

Measurement of rest energy expanditure in pediatric oncological patients: concordance between indirect calorimetry and predictive equations

in childhood cancer, the disease impacts resting energy expenditure (GER) in a way that is not estimable by predictive equations. the aim of this study is to determine the concordance between the measurement of resting energy expenditure (REE) by indirect calorimetry in pediatric oncology patients versus the World Health Organization (WHO) and Schofield predictive equations. cross-sectional study in children aged 5-15 years receiving chemotherapy, in outpatient Clínica Las Condes and Hospital Dr. Sótero del Río, from July 2013 to July 2015. REE measurement was performed by indirect calorimetry and WHO and Schofield equations. Concordance analysis, with clinically relevant cut-off point and concordance coefficient of 90%. twenty-seven children were included and 27 calorimetries were performed; 66% of these children were diagnosed with leukemia, 15% with central nervous system tumor and 81% were in the maintenance stage of their treatment. There is no significant difference between indirect calorimetry measurement versus WHO (p 0.18) or Schofield (p 0.07), neither when stratifying by nutritional status or type of cancer diagnosis. Concordance was calculated between calorimetry and Schofield, with a concordance coefficient of Lin = 79.4% (95% CI = 65.2-93.6) and versus WHO = 78% (95% CI = 62.9-93.2). this level of agreement, less than 80% in both cases, is insufficient. With both equations for estimating REE, there is overestimation or underestimation of energy requirements in more than 20% of cases. There is no agreement between the measurement of REE measured with indirect calorimetry versus its estimation with Schofield's and the WHO equations. Consequently, indirect calorimetry is required as part of the nutritional assessment in a nutritionally at-risk population such as pediatric patients with oncological pathology.

Association of energy intake and expenditure with obesity: A cross-sectional study of 150 pediatric patients following treatment for leukemia

ABSTRACTIncreased obesity in leukemia survivors has been attributed to chemotherapy and radiation. Data on total energy intake (TEI) and total energy expenditure (TEE) are lacking in obese childhood leukemia patients after completion of therapy from India. We conducted a cross-sectional study in pediatric acute leukemia patients after completion of therapy wherein energy intake was assessed by 24-hour recall method. TEE was calculated using Harris–Benedict equation, by assessing the physical activity level using Physical Activity Questionnaire for children and basal metabolic rate by World Health Organization equation. Indian Academy of Pediatrics 2015 guidelines for BMI were used for defining overweight and obesity. Nutritional status was assessed in 150 leukemia patients after completion of therapy. Twenty-five percent of leukemia patients after completion of therapy were overweight and obese versus 11% of healthy controls (p = 0.042). The mean ratio of TEI/required energy intake (REI), TEE/required energy expenditure (REE), and (TEI:REI)/(TEE:REE) were significantly higher in overweight and obese group versus nonobese survivors (p < 0.001, p = 0.091, p < 0.001, respectively). Multivariate analysis showed higher income (HR-2.3, p = 0.04), increased TEI/REI (HR-4, p = 0.049) and higher (TEI:REI)/(TEE:REE) (HR-3.1, p = 0.039) to be significant factors predicting obesity. Obesity in leukemia patients after completion of therapy is associated with increased energy intake, causing imbalance between energy intake and TEE in these patients.

Arm-associated measurements as estimates of true height in black and white young adults of both genders: an exploratory study, Pietermaritzburg, KwaZulu-Natal, South Africa

Objective: To determine the accuracy of arm-associated anthropometric measurements as estimates of true height.Design: This was a cross-sectional descriptive survey.Setting: The setting was Pietermaritzburg, KwaZulu-Natal, South Africa.Subjects: The study population included a convenience sample (n = 200) of young adults aged 18 to 24 years, which included an equal number (n = 50) of white males and females, and black males and females.Outcome measures: The following measurements were taken in accordance with international standards for anthropometric kinanthropometry: (i) stretch stature; (ii) armspan; (iii) half-armspan; and (iv) demi-span. Adjustment equations used to convert arm-associated measurements to true height included that of the World Health Organization equation, half-armspan multiplied by two and, the demi-span equation.Results: None of the existing height estimation equations accurately predicted true height in the study sample. Significant differences in the accuracy of estimates were also measured between race groups (p < 0.001) and for gender (p < 0.001). In black males the demi-span male-specific equation provided results that did not differ from true height, as was also the case for armspan in white males. Black females and white females had identical outcomes where all height estimates differed significantly from true height.Conclusion: Findings indicate the need for gender and race-specific height estimation methods. It would seem that armspan is suitable for use in white males and demi-span male equation suitable for use in black males. None of the height estimation methods accurately predicted true height in females.

Cross-Validation of Resting Metabolic Rate Prediction Equations

BackgroundResting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence the accuracy of RMR predictions will help to revise existing, or develop new and improved, equations. ObjectiveOur aim was to test the validity of RMR predicted in healthy adults by the Harris-Benedict, World Health Organization, Mifflin-St Jeor, Nelson, Wang equations, and three meta-equations of Sabounchi. DesignPredicted RMR was tested for agreement with indirect calorimetry. Participants/settingMen and women (n=30) age 18 to 65 years from Grand Forks, ND, were recruited and included for analysis during spring/summer 2014. Participants were nonobese or obese (body mass index range=19 to 39) and primarly white. Main outcome measureAgreement between measured (indirect calorimetry) and predicted RMR was measured. Statistical analysisThe methods of Bland and Altman were employed to determine mean bias (predicted minus measured RMR, kcal/day) and limits of agreement between predicted and measured RMR. Repeated-measures analysis of variance was used to test for bias in RMR predicted from each equation vs the measured RMR. ResultsBias (mean±2 standard deviations) was lowest for the Harris-Benedict (−14±378 kcal/24 h) and World Health Organization (−25±394 kcal/24 h) equations. These equations also predicted RMR that were not different from measured. Mean RMR predictions from all other equations significantly differed from indirect calorimetry. The 2 standard deviation limits of agreement were moderate or large for all equations tested, ranging from 314 to 445 kcal/24 h. Prediction bias was inversely associated with the magnitude of RMR and with fat-free mass. ConclusionsAt the group level, the traditional Harris-Benedict and World Health Organization equations were the most accurate. However, these equations did not perform well at the individual level. As fat-free mass increased, the prediction equations further underestimated RMR.

Energy Requirements of a Pediatric Lung Transplant Population

Background: Comparison of estimated energy requirements using predictive equations (PBMR) to measured resting energy requirements (MREE) using indirect calorimetry is not widely investigated in the pediatric lung transplant (LTx) population. In this population, optimal nutrition delivery may promote appropriate weight gain post-transplantation. The purpose of this case series was to evaluate the difference between predicted and measured energy requirements pre- and post- transplant by comparing PBMR calculated by the World Health Organization (WHO) equation and MREE by indirect calorimetry. The secondary aim was to determine if the diagnosis of Cystic Fibrosis (CF) influenced energy expenditure. It was predicted that the majority of LTx children would have higher MREE than PBMR (denoted as % PBMR). The % PBMR value is relative to MREE. Methods and findings: After exclusion, there were 7 LTx patients (n=5 male, n=4 CF, median age 11.55, age range: 4-16 y) and 14 measurements (7 repeated measures). Measurements were conducted pre- or post- transplant. Pre-LTx patients (n=4 patients, n=2 CF) exhibited a median 106% PBMR (interquartile range: 24%). Post-LTx patients (n=3 patients, n=2 CF) exhibited a median 105% PBMR (24%). The median % PBMR did not differ significantly between CF and non-CF LTx children irrespective of transplant status (113% (22%), 103% (19%), respectively, P=0.12). Conclusion: These results suggest that MREE was higher than PBMR; the WHO equation may underestimate energy requirements for pre- and post-LTx children. There was a trend towards decreasing energy requirements post-LTx. In children post-LTx, the diagnosis of CF may not affect energy requirements.

Validity of Wearable Activity Monitors for Estimation of Resting Energy Expenditure in Adults

Accurate assessment of resting energy expenditure (REE) is a key to estimating total daily energy expenditure since REE contributes to 60-70% of daily energy expenditure. Commercially available wearable activity monitors have the function to estimate REE by utilizing their own proprietary algorithms. However, no evidence exists about the relative accuracy of the monitors in estimating REE in relation to measured REE. PURPOSE: The purpose of this study was to evaluate the validity of the Fitbit Flex (FF) and SenseWear Mini Armband (SWA) in REE estimates in adults. METHODS: Sixty healthy (26.4±5.7 yrs) males (n=30) and females (n=30) volunteered to participate in the study. The REE measurement was performed in the morning (i.e., 6:00-9:00am) after a 10-hour fast, following previously published guidelines. Estimates of REE from the FF and SWA monitors were obtained from the corresponding software and website. These REE estimates were compared to REE measured from open-circuit indirect calorimetry (IC) and estimated using the Institute of Medicine (IOM) and World Health Organization (WHO) prediction equations. RESULTS: Analyses of covariance (ANCOVA) showed no significant effects of gender for any of the comparisons with REE from IC; therefore, males and females were combined for all analyses. REE from FF, SWA, IOM, and WHO were (kcals/day) (means±SD): 1554.3±249.3, 1587.1±247.7, 1528.0±213.0, and 1559.0±232.0, respectively. Mean absolute percentage errors were: 10.85±8.8%, 9.53±8.2%, 9.31±8.4%, and 10.8±8.7% for the FF, SWA, IOM, and WHO, respectively. Pearson correlation coefficients for the FF, SWA, IOM, and WHO in relation to IC were 0.635, 0.640, 0.657, and 0.683, respectively. No significant differences (p-values < 0.05) were observed between the measured REE, FF, SWA, IOM, and WHO in REE estimates. CONCLUSION: The estimates of REE from the FF, SWA, IOM, and WHO equation were similar to measured REE. The relatively high accuracy of the FF and SWA in estimating REE suggests that they have great potential to be utilized in intervention and surveillance studies aimed at precisely estimating total daily energy expenditure.

Longitudinal change in energy expenditure and effects on energy requirements of the elderly

BackgroundVery little is known about the longitudinal changes in energy requirements in late life. The purposes of this study were to: (1) determine the energy requirements in late life and how they changed during a 7 year time-span, (2) determine whether changes in fat free mass (FFM) were related to changes in resting metabolic rate (RMR), and (3) determine the accuracy of predicted total energy expenditure (TEE) to measured TEE.MethodsTEE was assessed via doubly labeled water (DLW) technique in older adults in both 1999 (n = 302; age: 74 ± 2.9 yrs) and again in 2006 (n = 87 age: 82 ± 3.1 yrs). RMR was measured with indirect calorimetry, and body composition was assessed with dual-energy x-ray absorptiometry.ResultsThe energy requirements in the 9th decade of life were 2208 ± 376 kcal/d for men and 1814 ± 337 kcal/d for women. This was a significant decrease from the energy requirements in the 8th decade of life in men (2482 ± 476 kcal/d vs. 2208 ± 376 kcal/d) but not in women (1892 ± 271 kcal/d vs. 1814 ± 337 kcal/d). In addition to TEE, RMR, and activity EE (AEE) also decreased in men, but not women, while FFM decreased in both men and women. The changes in FFM were correlated with changes in RMR for men (r = 0.49, p < 0.05) but not for women (r = −0.08, ns). Measured TEE was similar to Dietary Reference Intake (DRI) predicted TEE for men (2208 ± 56 vs. 2305 ± 35 kcal/d) and women (1814 ± 42 vs. 1781 ± 20 kcal/d). However, measured TEE was different than the World Health Organization (WHO) predicted TEE in men (2208 ± 56 vs. 2915 ± 31 kcal/d (p < 0.05)) and women (1814 ± 42 vs. 2315 ± 21 kcal/d (p < 0.05)).ConclusionsTEE, RMR and AEE decreased in men, but not women, from the 8th to 9th decade of life. The DRI equation to predict TEE was comparable to measured TEE, while the WHO equation over-predicted TEE in our elderly population.

Resting Energy Expenditure After Fontan Surgery in Children With Single‐Ventricle Heart Defects

Data on resting energy expenditure (REE) and oxygen consumption (VO(2)) after pediatric cardiopulmonary bypass (CPB) will facilitate optimal nutrient prescription. The authors measured continuous REE and VO(2), using an in-line indirect calorimetery (IC) in 30 consecutive children with single-ventricle physiology immediately after Fontan surgery. REE during steady state at 8 hours after surgery was compared with standard equation-estimated energy expenditure (EEE). Patients were classified into 3 groups: hypermetabolic (measured REE [MREE]/EEE ratio >1.2), hypometabolic (MREE/EEE ratio <0.8), and normometabolic (MREE/EEE ratio 0.8-1.2). Demographic, anthropometric, and perioperative clinical characteristics were examined for their correlation with metabolic status. In 26 of 30 patients with completed IC, mean REE at 8 hours after surgery was 57 ± 20 kcal/kg/d, and mean VO(2) was 110 ± 35 mL/min. Mean values of VO(2) and REE did not change within the first 24 hours after surgery. There was poor correlation between MREE at 8 hours and the EEE using the World Health Organization equation (r = 0.32, P = .11). Most patients (n = 19, 73%) were either normometabolic or hypometabolic. Lack of hypermetabolism was significantly associated with higher intraoperative serum lactate level and positive fluid balance compared with the rest of the group. The authors report a low prevalence of hypermetabolism in children with single-ventricle defects after Fontan surgery. Measured REE had poor correlation with equation-estimated energy expenditure in a majority of the cohort. The absence of increased energy expenditure after CPB will influence energy prescription in this group.

Twenty-four hour metabolic rate measurements utilized as a reference to evaluate several prediction equations for calculating energy requirements in healthy infants

BackgroundTo date, only short-duration metabolic rate measurements of less than four hours have been used to evaluate prediction equations for calculating energy requirements in healthy infants. Therefore, the objective of this analysis was to utilize direct 24-hour metabolic rate measurements from a prior study to evaluate the accuracy of several currently used prediction equations for calculating energy expenditure (EE) in healthy infants.MethodsData from 24-hour EE, resting (RMR) and sleeping (SMR) metabolic rates obtained from 10 healthy infants, served as a reference to evaluate 11 length-weight (LWT) and weight (WT) based prediction equations. Six prediction equations have been previously derived from 50 short-term EE measurements in the Enhanced Metabolic Testing Activity Chamber (EMTAC) for assessing 24-hour EE, (EMTACEE-LWT and EMTACEE-WT), RMR (EMTACRMR-LWT and EMTACRMR-WT) and SMR (EMTACSMR-LWT and EMTACSMR-WT). The last five additional prediction equations for calculating RMR consisted of the World Health Organization (WHO), the Schofield (SCH-LWT and SCH-WT) and the Oxford (OXFORD-LWT and OXFORD-WT). Paired t-tests and the Bland & Altman limit analysis were both applied to evaluate the performance of each equation in comparison to the reference data.Results24-hour EE, RMR and SMR calculated with the EMTACEE-WT, EMTACRMR-WT and both the EMTACSMR-LWT and EMTACSMR-WT prediction equations were similar, p = NS, to that obtained from the reference measurements. However, RMR calculated using the WHO, SCH-LWT, SCH-WT, OXFORD-LWT and OXFORD-WT prediction equations were not comparable to the direct 24-hour metabolic measurements (p < 0.05) obtained in the 10 reference infants. Moreover, the EMTACEE-LWT and EMTACRMR-LWT were also not similar (p < 0.05) to direct 24-hour metabolic measurements.ConclusionsWeight based prediction equations, derived from short-duration EE measurements in the EMTAC, were accurate for calculating EE, RMR and SMR in healthy infants.

Taxa metabólica basal em universitários: comparação entre valores medidos e preditos

OBJETIVO: Comparar a taxa metabólica basal medida por recursos de calorimetria indireta com a taxa metabólica basal estimada por equações de predição em universitários. MÉTODOS: A amostra foi constituída por 48 voluntários (23 moças e 25 rapazes) na faixa etária entre 20 e 25 anos. A taxa metabólica basal medida por calorimetria indireta foi comparada à taxa metabólica basal estimada pelas equações de predição propostas pela World Health Organization e por Henry &amp; Rees. As informações foram analisadas mediante o teste t pareado, coeficiente de correlação intraclasse, desvio-padrão das diferenças e técnica de Bland-Altman. RESULTADOS: Não ocorreram diferenças significativas entre os valores da taxa metabólica basal medidos e estimados por ambas as equações de predição. Os coeficientes de correlação intraclasse entre os valores medidos e estimados foram significativos em linguagem estatística nos dois gêneros e mais elevados nos rapazes, e a magnitude do desvio-padrão das diferenças foi similar em ambos os gêneros. A técnica de Bland-Altman sugere comportamento de variabilidade similar entre a taxa metabólica basal medida e predita por ambas as equações, porém a taxa metabólica basal predita pela equação de Henry &amp; Rees apresentou coincidências mais elevadas, sobretudo entre as moças. CONCLUSÃO: Mediante indicadores estatísticos mais conservadores, conclui-se que a taxa metabólica basal medida por recursos de calorimetria indireta e predita por ambas as equações não apresentam diferenças significativas. No entanto, ao considerar procedimentos estatísticos aparentemente de maior sensibilidade na detecção de eventuais diferenças entre os valores medidos e preditos, verificou-se algum comprometimento quanto à potencialidade das equações para estimar a taxa metabólica basal, apresentando viés entre 12,5% e 15,6% em comparação com a taxa metabólica basal medida por recursos de calorimetria indireta.

Energy expenditure in obesity associated with craniopharyngioma.

Obesity is a common yet incompletely understood complication of childhood craniopharyngioma. We hypothesized that craniopharyngioma is associated with specific defects in energy balance compared to obese control children. Eleven craniopharyngioma patients were recruited for a study on body composition and energy balance. Eight subjects were obese. The obese craniopharyngioma patients had a mean age (+/-SD) of 11.2 +/- 1.7 years. The average body mass index z score was 2.33 (+/-0.32). A previously studied group of obese children (BMI z score 2.46 +/- 0.46) served as controls. Resting energy expenditure (REE) was determined by indirect calorimetry and body composition by dual energy X-ray absorptiometry in all children. Obese craniopharyngioma patient subjects had increased mean (+/-standard error) fat-free mass compared to obese controls (57% +/- 0.88 % vs 50.0% +/- 0.87%, p = 0.02). The obese craniopharyngioma patients had a 17% lower REE compared to values expected from the World Health Organization equation (1,541 +/- 112.6 vs 1,809 +/- 151.8 kcal; p = 0.01). In contrast, the obese control children had measured REE within 1% of predicted (1,647 +/- 33.2 vs. 1,652 +/- 40.2; p = 0.8). In a linear regression model, REE remained significantly lower than predicted after controlling for FFM. Lower REE may be a factor contributing to obesity in children with craniopharyngioma. Further study is needed into the mechanisms for reduced energy expenditure in patients with craniopharyngioma.

Validation of a New Method Developed to Measure the Height of Adult Patients in Bed

We present the performance of a new instrument developed to measure the height of patients in bed (Luft Ruler). Standing height was the gold standard measured in 116 hospitalized adults with a stadiometer, compared with measurements from the Luft Ruler and calculated estimates. Using the Luft Ruler in women, men, <60 years old, and >60 years old, the mean difference from the gold standard was, respectively, 1.5 cm (P= .02), 1.3 cm (P= .06), 1.2 cm (P= .07), and 1.6 cm (P= .02); total arm span: 6.7 cm, 7.7 cm, 5.4 cm, and 9.0 cm (P< .01 for all strata); 2x half arm span: 7.8 cm, 10.2 cm, 7.7 cm, and 10.3 cm (P< .01 for all); World Health Organization (WHO) equation: 5.5 cm, 6.2 cm, 4.7 cm, and 6.9 cm (P< .01 for all); and the Hospital de Clínicas de Porto Alegre equation: 1.9 cm (P< .01), 2.5 cm (P< .01), 1.1 cm (P= .12), and 3.3 cm (P< .01). The proportion of differences >5 cm was 0.9% (n=1) using the Luft Ruler, 62.9% using total arm span, 71.6% using 2x half arm span, 64.7% using the WHO equation, and 27.6% using the Hospital de Clínicas de Porto Alegre equation. The Luft Ruler was the only method that presented minor mean differences in relation to the gold standard in both gender and age strata. The other estimates are not recommended because of high frequency of relevant errors.