Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients

The treatment of obesity requires creating an energy deficit through caloric restriction and physical activity. Energy needs are estimated assessing the resting energy expenditure (REE) that in the clinical practice is estimated using predictive equations. In the present cross sectional study, we co...

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Main Authors: Raffaella Cancello, Davide Soranna, Amelia Brunani, Massimo Scacchi, Antonella Tagliaferri, Stefania Mai, Paolo Marzullo, Antonella Zambon, Cecilia Invitti
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-07-01
Series:Frontiers in Endocrinology
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Online Access:https://www.frontiersin.org/article/10.3389/fendo.2018.00367/full
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author Raffaella Cancello
Davide Soranna
Amelia Brunani
Massimo Scacchi
Massimo Scacchi
Antonella Tagliaferri
Stefania Mai
Paolo Marzullo
Paolo Marzullo
Antonella Zambon
Cecilia Invitti
author_facet Raffaella Cancello
Davide Soranna
Amelia Brunani
Massimo Scacchi
Massimo Scacchi
Antonella Tagliaferri
Stefania Mai
Paolo Marzullo
Paolo Marzullo
Antonella Zambon
Cecilia Invitti
author_sort Raffaella Cancello
collection DOAJ
description The treatment of obesity requires creating an energy deficit through caloric restriction and physical activity. Energy needs are estimated assessing the resting energy expenditure (REE) that in the clinical practice is estimated using predictive equations. In the present cross sectional study, we compared, in a large cohort of morbidly obese patients, the accuracy of REE predictive equations recommended by current obesity guidelines [Harris-Benedict, WHO/FAO/ONU and Mifflin-St Jeor (MJ)] and/or developed for obese patients (Muller, Muller BC, Lazzer, Lazzer BC), focusing on the effect of comorbidities on the accuracy of the equations. Data on REE measured by indirect calorimetry and body composition were collected in 4,247 obese patients (69% women, mean age 48 ± 19 years, mean BMI 44 ± 7 Kg/m2) admitted to the Istituto Auxologico Italiano from 1999 to 2014. The performance of the equations was assessed in the whole cohort, in 4 groups with 0, 1, 2, or ≥ 3 comorbidities and in a subgroup of 1,598 patients with 1 comorbidity (47.1% hypertension, 16.7% psychiatric disorders, 13.3% binge eating disorders, 6.1% endocrine disorders, 6.4% type 2 diabetes, 3.5% sleep apnoea, 3.1% dyslipidemia, 2.5% coronary disease). In the whole cohort of obese patients, as well as in each stratum of comorbidity number, the MJ equation had the highest performance for agreement measures and bias. The MJ equation had the best performance in obese patients with ≥3 comorbidities (accuracy of 61.1%, bias of −89.87) and in patients with type 2 diabetes and sleep apnoea (accuracy/bias 69%/−19.17 and 66%/−21.67 respectively), who also have the highest levels of measured REE. In conclusion, MJ equation should be preferred to other equations to estimate the energy needs of Caucasian morbidly obese patients when measurement of the REE cannot be performed. As even MJ equation does not precisely predict REE, it should be better to plan the diet intervention by measuring rather than estimating REE. Future studies focusing on the clinical differences that determine the high inter-individual variability of the precision of the REE predictive equations (e.g., on the organ-tissue metabolic rate), could help to develop predictive equations with a better performance.
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spelling doaj.art-88e42a7820a84f24a12f4825808212d92022-12-21T19:40:21ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922018-07-01910.3389/fendo.2018.00367336296Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese PatientsRaffaella Cancello0Davide Soranna1Amelia Brunani2Massimo Scacchi3Massimo Scacchi4Antonella Tagliaferri5Stefania Mai6Paolo Marzullo7Paolo Marzullo8Antonella Zambon9Cecilia Invitti10Obesity Research Laboratory, IRCCS Istituto Auxologico Italiano, Milan, ItalyIRCCS Istituto Auxologico Italiano, Milan, ItalyDivision of Rehabilitation Medicine, IRCCS Istituto Auxologico Italiano, Piancavallo-Oggebbio, ItalyDivision of Endocrinology and Metabolic Diseases, IRCCS Istituto Auxologico Italiano, Piancavallo-Oggebbio, ItalyDepartment of Clinical Sciences and Community Health, University of Milan, Milan, ItalyDivision of Endocrinology and Metabolic Diseases, IRCCS Istituto Auxologico Italiano, Piancavallo-Oggebbio, ItalyLaboratory of Metabolic Research, IRCCS Istituto Auxologico Italiano, Piancavallo-Oggebbio, ItalyDivision of Endocrinology and Metabolic Diseases, IRCCS Istituto Auxologico Italiano, Piancavallo-Oggebbio, ItalyDepartment of Translational Medicine, University of Piemonte Orientale, Novara, ItalyDepartment of Statistics and Quantitative Methods, Biostatistics, Epidemiology and Public Health, Milano-Bicocca University, Milan, ItalyObesity Research Laboratory, IRCCS Istituto Auxologico Italiano, Milan, ItalyThe treatment of obesity requires creating an energy deficit through caloric restriction and physical activity. Energy needs are estimated assessing the resting energy expenditure (REE) that in the clinical practice is estimated using predictive equations. In the present cross sectional study, we compared, in a large cohort of morbidly obese patients, the accuracy of REE predictive equations recommended by current obesity guidelines [Harris-Benedict, WHO/FAO/ONU and Mifflin-St Jeor (MJ)] and/or developed for obese patients (Muller, Muller BC, Lazzer, Lazzer BC), focusing on the effect of comorbidities on the accuracy of the equations. Data on REE measured by indirect calorimetry and body composition were collected in 4,247 obese patients (69% women, mean age 48 ± 19 years, mean BMI 44 ± 7 Kg/m2) admitted to the Istituto Auxologico Italiano from 1999 to 2014. The performance of the equations was assessed in the whole cohort, in 4 groups with 0, 1, 2, or ≥ 3 comorbidities and in a subgroup of 1,598 patients with 1 comorbidity (47.1% hypertension, 16.7% psychiatric disorders, 13.3% binge eating disorders, 6.1% endocrine disorders, 6.4% type 2 diabetes, 3.5% sleep apnoea, 3.1% dyslipidemia, 2.5% coronary disease). In the whole cohort of obese patients, as well as in each stratum of comorbidity number, the MJ equation had the highest performance for agreement measures and bias. The MJ equation had the best performance in obese patients with ≥3 comorbidities (accuracy of 61.1%, bias of −89.87) and in patients with type 2 diabetes and sleep apnoea (accuracy/bias 69%/−19.17 and 66%/−21.67 respectively), who also have the highest levels of measured REE. In conclusion, MJ equation should be preferred to other equations to estimate the energy needs of Caucasian morbidly obese patients when measurement of the REE cannot be performed. As even MJ equation does not precisely predict REE, it should be better to plan the diet intervention by measuring rather than estimating REE. Future studies focusing on the clinical differences that determine the high inter-individual variability of the precision of the REE predictive equations (e.g., on the organ-tissue metabolic rate), could help to develop predictive equations with a better performance.https://www.frontiersin.org/article/10.3389/fendo.2018.00367/fullresting energy expenditureindirect calorimetrycomorbiditiesREE predictive equationsobesity
spellingShingle Raffaella Cancello
Davide Soranna
Amelia Brunani
Massimo Scacchi
Massimo Scacchi
Antonella Tagliaferri
Stefania Mai
Paolo Marzullo
Paolo Marzullo
Antonella Zambon
Cecilia Invitti
Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients
Frontiers in Endocrinology
resting energy expenditure
indirect calorimetry
comorbidities
REE predictive equations
obesity
title Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients
title_full Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients
title_fullStr Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients
title_full_unstemmed Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients
title_short Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients
title_sort analysis of predictive equations for estimating resting energy expenditure in a large cohort of morbidly obese patients
topic resting energy expenditure
indirect calorimetry
comorbidities
REE predictive equations
obesity
url https://www.frontiersin.org/article/10.3389/fendo.2018.00367/full
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