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Manfred J. Müller - One of the best experts on this subject based on the ideXlab platform.
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Normalizing Resting Energy Expenditure across the life course in humans: challenges and hopes
European Journal of Clinical Nutrition, 2018Co-Authors: Manfred J. Müller, Maryam Pourhassan, Wiebke Braun, Corinna Geisler, Mark Hübers, Anja Bosy-westphalAbstract:Whole-body daily Energy Expenditure is primarily due to Resting Energy Expenditure (REE). Since there is a high inter-individual variance in REE, a quantitative and predictive framework is needed to normalize the data. Complementing the assessment of REE with data normalization makes individuals of different sizes, age, and sex comparable. REE is closely correlated with body mass suggesting its near constancy for a given mass and, thus, a linearity of this association. Since body mass and its metabolic active components are the major determinants of REE, they have been implemented into allometric modeling to normalize REE for quantitative differences in body weight and/or body composition. Up to now, various size and allometric scale laws are used to adjust REE for body mass. In addition, the impact of the anatomical and physical properties of individual body components on REE has been quantified in large populations and for different age groups. More than 80% of the inter-individual variance in REE is explained by FFM and its composition. There is evidence that the impact of individual organs on REE varies between age groups with a higher contribution of brain and visceral organs in children/adolescents compared with adults where skeletal muscle mass contribution is greater than in children/adolescents. However, explaining REE variations by FFM and its composition has its own limitations (inter-correlations of organs/tissues). In future, this could be overcome by re-describing the organ-to-organ variation using principal components analysis and then using the scores on the components as predictors in a multiple regression analysis.
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issues in characterizing Resting Energy Expenditure in obesity and after weight loss
Frontiers in Physiology, 2013Co-Authors: B Schautz, Anja Bosywestphal, Wiebke Braun, Manfred J. MüllerAbstract:Normalization of Resting Energy Expenditure (REE) for body composition using the 2-compartment model (fat mass, FM and fat-free mass, FFM) has inherent limitations for the interpretation of REE and may lead to erroneous conclusions when comparing people with a wide range of adiposity as well as before and after substantial weight loss. We compared different methods of REE normalization: (i) for FFM and FM (ii) by the inclusion of %FM as a measure of adiposity and (iii) based on organ and tissue masses. Results were compared between healthy subjects with different degrees of adiposity as well as within subject before and after weight loss. Normalizing REE from an “REE vs. FFM and FM equation” that (i) was derived in obese participants and applied to lean people or (ii) was derived before weight loss and applied after weight loss leads to the erroneous conclusion of a lower metabolic rate (i) in lean persons and (ii) after weight loss. This is revealed by the normalization of REE for organ and tissue masses that was not significantly different between lean and obese or between baseline and after weight loss. There is evidence for an increasing specific metabolic rate of FFM with increasing %FM that could be explained by a higher contribution of liver, kidney and heart mass to FFM in obesity. Using “REE vs. FFM and FM equations” specific for different levels of adiposity (% fat mass) eliminated differences in REE before and after weight loss in women. In conclusion, the most established method for normalization of REE based on FFM and FM may lead to spurious conclusions about metabolic rate in obesity and the phenomenon of weight loss-associated adaptive thermogenesis. Using % fat mass-specific REE prediction from FFM and FM in kg may improve the normalization of REE when subjects with wide differences in % fat mass are investigated.
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evolving concepts on adjusting human Resting Energy Expenditure measurements for body size
Obesity Reviews, 2012Co-Authors: Steven B. Heymsfield, Wei Shen, Anja Bosywestphal, Diana M Thomas, Courtney M Peterson, Manfred J. MüllerAbstract:Establishing if an adult’s Resting Energy Expenditure (REE) is high or low for their body size is a pervasive question in nutrition research. Early workers applied body mass and height as size measures and formulated the Surface Law and Kleiber’s Law, although each has limitations when adjusting REE. Body composition methods introduced during the mid-twentieth century provided a new opportunity to identify metabolically homogeneous “active” compartments. These compartments all show improved correlations with REE estimates over body mass-height approaches, but collectively share a common limitation: REE-body composition ratios are not “constant” but vary across men and women and with race, age, and body size. The now-accepted alternative to ratio-based norms is to adjust for predictors by applying regression models to calculate “residuals” that establish if a REE is relatively high or low. The distinguishing feature of statistical REE-body composition models is a “non-zero” intercept of unknown origin. The recent introduction of imaging methods has allowed development of physiological tissue-organ based REE prediction models. Herein we apply these imaging methods to provide a mechanistic explanation, supported by experimental data, for the non-zero intercept phenomenon and in that context propose future research directions for establishing between subject differences in relative Energy metabolism.
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intra and interindividual variability of Resting Energy Expenditure in healthy male subjects biological and methodological variability of Resting Energy Expenditure
British Journal of Nutrition, 2005Co-Authors: Nicolle Bader, Anja Bosywestphal, Britta Dilba, Manfred J. MüllerAbstract: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.
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l tri iodothyronine is a major determinant of Resting Energy Expenditure in underweight patients with anorexia nervosa and during weight gain
European Journal of Endocrinology, 2005Co-Authors: Simone Onur, Anja Bosywestphal, Verena Haas, Maren Hauer, Thomas Paul, Detlev O Nutzinger, Harald Klein, Manfred J. MüllerAbstract:Objective: We aimed to define the effect of L-3,5,3 0 -tri-iodothyronine (T3) on metabolic adaptation in underweight patients with anorexia nervosa (AN) as well as during weight gain. Methods: This involved clinical investigation of 28 underweight patients with AN, who were compared with 49 normal-weight controls. A subgroup of 17 patients was followed during weight gain. Resting Energy Expenditure was measured by indirect calorimetry. Body composition was measured by anthropometry as well as bioelectrical impedance analysis. Energy intake (EI) was assessed by a 3-day dietary record. Plasma concentrations of thyroid hormones (thyroxine (T4), T3 and thyrotropin (TSH)) were analyzed by enzyme immunoassays. Results: When compared with normal-weight women, underweight patients with AN had reduced fat mass (FM) (2 71.3%), fat-free mass (FFM) (2 13.1%), Resting Energy Expenditure (REE) (2 21.8%), T3 -( 2 33.4%) and T4-concentrations (2 19.8%) at unchanged TSH. REE remained reduced after adjustment for FFM (2 24.6%). T3 showed a close association with REE. This association remained after adjustment of REE for FFM. Treatment of underweight AN patients resulted in a mean weight gain of 8.3 kg. This was mainly explained by an increase in FM with small or no changes in FFM. REE and T3 also increased (þ 9.3% and þ 33.3% respectively) at unchanged TSH and T4. There was a highly significant association between weight gain-induced changes in T3 and changes in adjusted REE (r ¼ 0.78, P , 0.001, based on Pearson’s correlation). An increase in plasma T3 concentrations of 1.8 pmol/l could explain an increase in REE of 0.6 MJ/day (that is, a 32% increase in T3 was associated with a 13% increase in REE). Conclusions: Our data provide evidence that the low T3 concentrations add to metabolic adaptation in underweight patients with AN. During weight gain, increases in T3 are associated with increases in REE, which is independent of FFM. Both results are evidence for a physiologic role of T3 in modulation of Energy Expenditure in humans.
Steven B. Heymsfield - One of the best experts on this subject based on the ideXlab platform.
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evolving concepts on adjusting human Resting Energy Expenditure measurements for body size
Obesity Reviews, 2012Co-Authors: Steven B. Heymsfield, Wei Shen, Anja Bosywestphal, Diana M Thomas, Courtney M Peterson, Manfred J. MüllerAbstract:Establishing if an adult’s Resting Energy Expenditure (REE) is high or low for their body size is a pervasive question in nutrition research. Early workers applied body mass and height as size measures and formulated the Surface Law and Kleiber’s Law, although each has limitations when adjusting REE. Body composition methods introduced during the mid-twentieth century provided a new opportunity to identify metabolically homogeneous “active” compartments. These compartments all show improved correlations with REE estimates over body mass-height approaches, but collectively share a common limitation: REE-body composition ratios are not “constant” but vary across men and women and with race, age, and body size. The now-accepted alternative to ratio-based norms is to adjust for predictors by applying regression models to calculate “residuals” that establish if a REE is relatively high or low. The distinguishing feature of statistical REE-body composition models is a “non-zero” intercept of unknown origin. The recent introduction of imaging methods has allowed development of physiological tissue-organ based REE prediction models. Herein we apply these imaging methods to provide a mechanistic explanation, supported by experimental data, for the non-zero intercept phenomenon and in that context propose future research directions for establishing between subject differences in relative Energy metabolism.
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differences between brain mass and body weight scaling to height potential mechanism of reduced mass specific Resting Energy Expenditure of taller adults
Journal of Applied Physiology, 2009Co-Authors: Steven B. Heymsfield, Thamrong Chirachariyavej, Im Joo Rhyu, Chulaporn Roongpisuthipong, Moonseong Heo, Angelo PietrobelliAbstract:Adult Resting Energy Expenditure (REE) scales as height∼1.5, whereas body weight (BW) scales as height∼2. Mass-specific REE (i.e., REE/BW) is thus lower in tall subjects compared with their shorter...
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A cellular-level approach to predicting Resting Energy Expenditure across the adult years
The American Journal of Clinical Nutrition, 2005Co-Authors: Zimian Wang, Stanley Heshka, Steven B. Heymsfield, Wei Shen, Dympna GallagherAbstract:BACKGROUND We previously derived a whole-body Resting Energy Expenditure (REE) prediction model by using organ and tissue mass measured by magnetic resonance imaging combined with assumed stable, specific Resting metabolic rates of individual organs and tissues. Although the model predicted REE well in young persons, it overpredicted REE by approximately 11% in elderly adults. This overprediction may occur because of a decline in the fraction of organs and tissues as cell mass with aging. OBJECTIVE The aim of the present study was to develop a cellular-level REE prediction model that would be applicable across the adult age span. Specifically, we tested the hypothesis that REE can be predicted from a combination of organ and tissue mass, the specific Resting metabolic rates of individual organs and tissues, and the cellular fraction of fat-free mass. DESIGN Fifty-four healthy subjects aged 23-88 y had REE, organ and tissue mass, body cell mass, and fat-free mass measured by indirect calorimetry, magnetic resonance imaging, whole-body (40)K counting, and dual-Energy X-ray absoptiometry, respectively. RESULTS REE predicted by the cellular-level model was highly correlated with measured REE (r = 0.92, P < 0.001). The mean difference between measured REE (x+/- SD: 1487 +/- 294 kcal/d) and predicted REE (1501 +/- 300 kcal/d) for the whole group was not significant, and the difference between predicted and measured REE was not associated with age (r = 0.009, NS). CONCLUSION The present approach establishes an REE-body composition link with the use of a model at the cellular level. The combination of 2 aging-related factors (ie, decline in both the mass and the cellular fraction of organs and tissues) may account for the lower REE observed in elderly adults.
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Body-size dependence of Resting Energy Expenditure can be attributed to nonenergetic homogeneity of fat-free mass.
American Journal of Physiology-Endocrinology and Metabolism, 2002Co-Authors: Steven B. Heymsfield, Dympna Gallagher, Zimian Wang, Donald P. Kotler, David B. Allison, Stanley HeshkaAbstract: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...
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Resting Energy Expenditure systematic organization and critique of prediction methods
Obesity Research, 2001Co-Authors: Zimian Wang, Stanley Heshka, Kuan Zhang, Carol N Boozer, Steven B. HeymsfieldAbstract:There are many published methods for predicting Resting Energy Expenditure (REE) from measured body composition. Although these published reports extend back almost a century, new related studies appear on a regular basis. It remains unclear what the similarities and differences are among these various methods and what, if any, advantages the newly introduced REE prediction models offer. These issues led us to develop an organizational system for REE prediction methods with the goal of clarifying prevailing ambiguities in the field. Our classification scheme is founded on body composition level (whole-body, tissue-organ, cellular, and molecular) and related components as the REE predictor variables. Each existing REE prediction method by body composition must belong to one body composition level. The suggested classification system, founded on a conceptual basis, highlights similarities and differences among the diverse REE-body composition prediction methods, provides a framework for teaching REE-body composition relationships, and identifies important future research opportunities.
Anja Bosywestphal - One of the best experts on this subject based on the ideXlab platform.
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effect of weight loss and regain on adipose tissue distribution composition of lean mass and Resting Energy Expenditure in young overweight and obese adults
International Journal of Obesity, 2013Co-Authors: Anja Bosywestphal, C C Gluer, M Heller, B Schautz, M Lagerpusch, Maryam Pourhassan, Wiebke Braun, Kristin Goele, M J MullerAbstract:Effect of weight loss and regain on adipose tissue distribution, composition of lean mass and Resting Energy Expenditure in young overweight and obese adults
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issues in characterizing Resting Energy Expenditure in obesity and after weight loss
Frontiers in Physiology, 2013Co-Authors: B Schautz, Anja Bosywestphal, Wiebke Braun, Manfred J. MüllerAbstract:Normalization of Resting Energy Expenditure (REE) for body composition using the 2-compartment model (fat mass, FM and fat-free mass, FFM) has inherent limitations for the interpretation of REE and may lead to erroneous conclusions when comparing people with a wide range of adiposity as well as before and after substantial weight loss. We compared different methods of REE normalization: (i) for FFM and FM (ii) by the inclusion of %FM as a measure of adiposity and (iii) based on organ and tissue masses. Results were compared between healthy subjects with different degrees of adiposity as well as within subject before and after weight loss. Normalizing REE from an “REE vs. FFM and FM equation” that (i) was derived in obese participants and applied to lean people or (ii) was derived before weight loss and applied after weight loss leads to the erroneous conclusion of a lower metabolic rate (i) in lean persons and (ii) after weight loss. This is revealed by the normalization of REE for organ and tissue masses that was not significantly different between lean and obese or between baseline and after weight loss. There is evidence for an increasing specific metabolic rate of FFM with increasing %FM that could be explained by a higher contribution of liver, kidney and heart mass to FFM in obesity. Using “REE vs. FFM and FM equations” specific for different levels of adiposity (% fat mass) eliminated differences in REE before and after weight loss in women. In conclusion, the most established method for normalization of REE based on FFM and FM may lead to spurious conclusions about metabolic rate in obesity and the phenomenon of weight loss-associated adaptive thermogenesis. Using % fat mass-specific REE prediction from FFM and FM in kg may improve the normalization of REE when subjects with wide differences in % fat mass are investigated.
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evolving concepts on adjusting human Resting Energy Expenditure measurements for body size
Obesity Reviews, 2012Co-Authors: Steven B. Heymsfield, Wei Shen, Anja Bosywestphal, Diana M Thomas, Courtney M Peterson, Manfred J. MüllerAbstract:Establishing if an adult’s Resting Energy Expenditure (REE) is high or low for their body size is a pervasive question in nutrition research. Early workers applied body mass and height as size measures and formulated the Surface Law and Kleiber’s Law, although each has limitations when adjusting REE. Body composition methods introduced during the mid-twentieth century provided a new opportunity to identify metabolically homogeneous “active” compartments. These compartments all show improved correlations with REE estimates over body mass-height approaches, but collectively share a common limitation: REE-body composition ratios are not “constant” but vary across men and women and with race, age, and body size. The now-accepted alternative to ratio-based norms is to adjust for predictors by applying regression models to calculate “residuals” that establish if a REE is relatively high or low. The distinguishing feature of statistical REE-body composition models is a “non-zero” intercept of unknown origin. The recent introduction of imaging methods has allowed development of physiological tissue-organ based REE prediction models. Herein we apply these imaging methods to provide a mechanistic explanation, supported by experimental data, for the non-zero intercept phenomenon and in that context propose future research directions for establishing between subject differences in relative Energy metabolism.
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influence of methods used in body composition analysis on the prediction of Resting Energy Expenditure
European Journal of Clinical Nutrition, 2007Co-Authors: O Korth, Anja Bosywestphal, P Zschoche, C C Gluer, M Heller, M J MullerAbstract:Influence of methods used in body composition analysis on the prediction of Resting Energy Expenditure
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intra and interindividual variability of Resting Energy Expenditure in healthy male subjects biological and methodological variability of Resting Energy Expenditure
British Journal of Nutrition, 2005Co-Authors: Nicolle Bader, Anja Bosywestphal, Britta Dilba, Manfred J. MüllerAbstract: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.
Sonja Strangkarlsson - One of the best experts on this subject based on the ideXlab platform.
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Resting Energy Expenditure in young adults born preterm the helsinki study of very low birth weight adults
PLOS ONE, 2011Co-Authors: Marika Sipolaleppanen, Petteri Hovi, Sture Andersson, Karoliina Wehkalampi, Marja Vaarasmaki, Sonja StrangkarlssonAbstract: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.
Marialena Mouzaki - One of the best experts on this subject based on the ideXlab platform.
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can vco2 based estimates of Resting Energy Expenditure replace the need for indirect calorimetry in critically ill children
Journal of Parenteral and Enteral Nutrition, 2017Co-Authors: Marialena Mouzaki, Steven M Schwartz, Haifa Mtaweh, Gustavo La Rotta, Kandice Mah, Joann Herridge, Glen S Van Arsdell, Christopher S Parshuram, Alejandro A FlohAbstract:Background: Optimal Energy provision, guided by measured Resting Energy Expenditure (REE), is fundamental in the care of critically ill children. REE should be determined by indirect calorimetry (IC), which has limited availability. Recently, a novel equation was developed for estimating REE derived from carbon dioxide production (Vco2). The aim of this study was to validate the accuracy of this equation in a population of critically ill children following cardiopulmonary bypass (CPB). Methods: This is an ancillary study to a larger trial of children undergoing CPB. Respiratory mass spectrometry was used measure oxygen consumption (Vo2) and Vco2. REE was then calculated according to the established Weir equation (REEW) and the modified, Vco2-based equation (REECO2). The agreement between the 2 measurements was assessed using Bland-Altman plots and mixed-model regressions accounting for repeated measures. Results: Data from 104 patients, which included 575 paired measurements, were included. The agreement ...
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predictive equations are inaccurate in the estimation of the Resting Energy Expenditure of children with end stage liver disease
Journal of Parenteral and Enteral Nutrition, 2017Co-Authors: Andrea Carpenter, Karen Chapman, Simon C Ling, Marialena MouzakiAbstract:Background and Objectives: Malnutrition is common in children with end-stage liver disease (ESLD) and is associated with increased morbidity and mortality. The inability to accurately estimate Energy needs of these patients may contribute to their poor nutrition status. In clinical practice, predictive equations are used to calculate Resting Energy Expenditure (cREE). The objective of this study is to assess the accuracy of commonly used equations in pediatric patients with ESLD. Methods: Retrospective study performed at the Hospital for Sick Children. Clinical, laboratory, and indirect calorimetry data from children listed for liver transplant between February 2013 and December 2014 were reviewed. Calorimetry results were compared with cREE estimated using the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), Schofield [weight], and Schofield [weight and height] equations. Results: Forty-five patients were included in this study. The median age was 9 mon...
