Literature DB >> 19853980

Validity of predictive equations for resting energy expenditure in Belgian normal weight to morbid obese women.

Peter J M Weijs1, Greet A A M Vansant.   

Abstract

BACKGROUND & AIMS: Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry, mainly due to the time-consuming procedure and the high costs. To analyze which resting energy expenditure (REE) predictive equation is the best alternative for indirect calorimetry in Belgian normal weight to morbid obese women.
METHODS: Predictive equations were included when based on weight, height, gender, age, fat free mass and fat mass. REE was measured with indirect calorimetry. Accuracy of equations was evaluated by the percentage of subjects predicted within 10% of REE measured, the root mean squared prediction error (RMSE) and the mean percentage difference (bias) between predicted and measured REE.
RESULTS: Twenty-seven predictive equations (of which 9 based on FFM) were included. Validation was based on 536 F (18-71 year). Most accurate and precise for the Belgian women were the Huang, Siervo, Muller (FFM), Harris-Benedict (HB), and the Mifflin equation with 71%, 71%, 70%, 69%, and 68% accurate predictions, respectively; bias -1.7, -0.5, +1.1, +2.2, and -1.8%, RMSE 168, 170, 163, 167, and 173kcal/d. The equations of HB and Mifflin are most widely used in clinical practice and both provide accurate predictions across a wide range of BMI groups. In an already overweight group the underpredicting Mifflin equation might be preferred. Above BMI 45kg/m(2), the Siervo equation performed best, while the FAO/WHO/UNU or Schofield equation should not be used in this extremely obese group.
CONCLUSIONS: In Belgian women, the original Harris-Benedict or the Mifflin equation is a reliable tool to predict REE across a wide variety of body weight (BMI 18.5-50). Estimations for the BMI range between 30 and 40kg/m(2), however, should be improved. Copyright 2009 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

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Year:  2009        PMID: 19853980     DOI: 10.1016/j.clnu.2009.09.009

Source DB:  PubMed          Journal:  Clin Nutr        ISSN: 0261-5614            Impact factor:   7.324


  25 in total

1.  Predicted vs. Actual Resting Energy Expenditure and Activity Coefficients: Post-Gastric Bypass, Lean and Obese Women.

Authors:  Farah A Ramirez-Marrero; Kim L Edens; Michael J Joyner; Timothy B Curry
Journal:  Obes Control Ther       Date:  2014

Review 2.  Measurement of body composition as a surrogate evaluation of energy balance in obese patients.

Authors:  Carlo Maria Rotella; Ilaria Dicembrini
Journal:  World J Methodol       Date:  2015-03-26

3.  Accuracy of prediction equations for calculating resting energy expenditure in morbidly obese patients.

Authors:  S Ullah; R Arsalani-Zadeh; J MacFie
Journal:  Ann R Coll Surg Engl       Date:  2012-03       Impact factor: 1.891

Review 4.  Is Obesity Associated with Altered Energy Expenditure?

Authors:  Isabella P Carneiro; Sarah A Elliott; Mario Siervo; Raj Padwal; Simona Bertoli; Alberto Battezzati; Carla M Prado
Journal:  Adv Nutr       Date:  2016-05-16       Impact factor: 8.701

5.  UCP2 and PLIN1 Expression Affects the Resting Metabolic Rate and Weight Loss on Obese Patients.

Authors:  Bruno Affonso Parenti de Oliveira; Marcela Augusta de Souza Pinhel; Carolina Ferreira Nicoletti; Cristiana Cortes de Oliveira; Driele Cristina Gomes Quinhoneiro; Natália Yumi Noronha; Priscila Giácomo Fassini; Wilson Araújo da Silva Júnior; Wilson Salgado Junior; Carla Barbosa Nonino
Journal:  Obes Surg       Date:  2017-02       Impact factor: 4.129

6.  Comparison of five equations for estimating resting energy expenditure in Chinese young, normal weight healthy adults.

Authors:  Zhi-yong Rao; Xiao-ting Wu; Bin-miao Liang; Mao-yun Wang; Wen Hu
Journal:  Eur J Med Res       Date:  2012-09-01       Impact factor: 2.175

7.  Validity of resting energy expenditure predictive equations before and after an energy-restricted diet intervention in obese women.

Authors:  Jonatan R Ruiz; Francisco B Ortega; Gerardo Rodríguez; Pilar Alkorta; Idoia Labayen
Journal:  PLoS One       Date:  2011-09-06       Impact factor: 3.240

8.  Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients.

Authors:  Peter J M Weijs; Wilhelmus G P M Looijaard; Ingeborg M Dekker; Sandra N Stapel; Armand R Girbes; H M Oudemans-van Straaten; Albertus Beishuizen
Journal:  Crit Care       Date:  2014-01-13       Impact factor: 9.097

9.  Resting energy expenditure prediction in recreational athletes of 18-35 years: confirmation of Cunningham equation and an improved weight-based alternative.

Authors:  Twan ten Haaf; Peter J M Weijs
Journal:  PLoS One       Date:  2014-10-02       Impact factor: 3.240

10.  Comparison of Indirect Calorimetry and Predictive Equations in Estimating Resting Metabolic Rate in Underweight Females.

Authors:  Soghra Aliasgharzadeh; Reza Mahdavi; Mohammad Asghari Jafarabadi; Nazli Namazi
Journal:  Iran J Public Health       Date:  2015-06       Impact factor: 1.429

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