scholarly journals New Predictive Equations for Resting Energy Expenditure in Normal to Overweight and Obese Population (P16-048-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Ali Almajwal ◽  
Mahmoud Abulmeaty
Keyword(s):  
Regression Analysis ◽  
Energy Expenditure ◽  
Linear Regression Analysis ◽  
Resting Energy Expenditure ◽  
Accurate Prediction ◽  
Future Research ◽  
Predictive Equations ◽  
Men And Women ◽  
New Equation ◽  
Resting Energy

Abstract Objectives The unique demographic and dietary characteristics of our population require the development of a new equation to estimate the resting energy expenditure (REE). This study presented new equations characteristic to our population. Methods A set of predictive equations for REE was derived for 427 healthy male and female subjects (aged 18–57 ± 14 years). Measurement of REE (REEm) was done by the indirect calorimetry (IC) and its prediction (REEp) by using nine equations. REEp was compared with REEm to determine the predictive accuracy of these equations. Using IC and anthropometrics for stepwise linear regression analysis, a new equation to predict REE of Saudi men and women was developed. Results Using a number of parameters (bias, underprediction, overprediction, % accurate prediction), our results suggested that almost all (9/9 in men and 7/9 in women) equations either underpredicted or overpredicted (2/9) REE. None of the already existing equations showed an acceptable REEp/REEm difference as low as 5%, and an accurate prediction (∼55%) at the individual level. Based on these findings, a new prediction equation (hereafter referred to as Almajwal–Abulmeaty [AA] equation) was developed using this study's data, after a rigorous stepwise regression analysis using the following formula: REE = 3832.955 + BW [Kg] × 48.037 − Ht [Cm] × 30.642 + gender × 141.268 − age [years] × 4.525. The regression model accounted for about 70% of the variance in REEm (R2 = 0.702). Conclusions Previous equations likely over- or underpredicted REE. Therefore, the new predictive “AA equation” developed in this study is recommended for the estimation of REE in young to middle-aged Saudi men and women with different body mass index. Future research is also required for further clinical and cross-validation of this new equation. Funding Sources This study was supported by the King Abdulaziz City for Science and Technology (grant number 11 – MED 1966 – 02). Supporting Tables, Images and/or Graphs

scholarly journals New Predictive Equations for Resting Energy Expenditure in Normal to Overweight and Obese Population

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Ali M. Almajwal ◽  
Mahmoud M. A. Abulmeaty
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Energy Expenditure ◽  
Linear Regression Analysis ◽  
Resting Energy Expenditure ◽  
Accurate Prediction ◽  
Future Research ◽  
Predictive Equations ◽  
Men And Women ◽  
Resting Energy

Background and Aims. The unique demographic and dietary characteristics of modern Arabic population require development of a new predictive equation for the estimation of resting energy expenditure (REE). This study presented new equations characteristic to Saudi population. Methods. A set of predictive equations for REE was derived for 427 healthy male and female subjects (aged 18–57 ± 14 years). REE was measured (REEm) by indirect calorimetry (IC) and predicted (REEp) using nine equations. REEp was compared with REEm to determine the predictive accuracy of these equations. Using IC and anthropometrics for stepwise linear regression analysis, a new set of equations to predict REE of men and women was developed. Accuracy of the new main equations was further tested in an external sample of 48 subjects (men = 50%). Results. Using a number of parameters (bias, underprediction, overprediction, and % accurate prediction), our results suggested that almost all (9/9 in men and 7/9 in women) equations either underpredicted or overpredicted (2/9) REE. None of the already existing equations showed an acceptable REEp/REEm difference as low as 5% and an accurate prediction (∼55%) at the individual level. Based on these findings, a new prediction equation (hereafter referred to as the Almajwal–Abulmeaty (AA) equation) was developed using this study’s data, after a rigorous stepwise regression analysis using the following formula: REE = 3832.955 + AdjWt (kg) × 48.037 − Ht (cm) × 30.642 + gender × 141.268 − age (years) × 4.525 [AdjWt is Adjusted body weight = (Wt − IBW)/4 + IBW. IBW is Ideal body weight; for men IBW = (Ht(cm) − 152.4) × 1.0714) + 45.36 and for women IBW = (Ht(cm)−152.4) × 0.8928) + 45.36]. The regression model accounted for approximately 70% of the variance in REEm (R2 = 0.702). Conclusion. Previous equations likely over- or underpredicted REE. Therefore, the new predictive AA equations developed in this study are recommended for the estimation of REE in young to middle-aged Saudi men and women with different body mass indexes. Future research is also required for further clinical and cross-validation of these new equations.

scholarly journals Comparison of Predictive Equations for Resting Energy Expenditure in Overweight and Obese Adults

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Erick Prado de Oliveira ◽  
Fábio Lera Orsatti ◽  
Okesley Teixeira ◽  
Nailza Maestá ◽  
Roberto Carlos Burini
Keyword(s):  
Body Mass Index ◽  
Energy Expenditure ◽  
Systematic Error ◽  
Indirect Calorimetry ◽  
Intraclass Correlation ◽  
Resting Energy Expenditure ◽  
Obese Adults ◽  
Predictive Equations ◽  
New Equation ◽  
Resting Energy

Objective. To compare values from predictive equations of resting energy expenditure (REE) with indirect calorimetry (IC) in overweight and obese adults.Methods. Eighty-two participants aged 30 to 60 years old were retrospectively analyzed. The persons had a body mass index ≥25 kg/m2. REE was estimated by IC and other five equations of the literature (Harris and Benedict, WHO1, WHO2, Owen, Mifflin).Results. All equations had different values when compared to those of IC. The best values were found by Harris and Benedict, WHO1, and WHO2, with high values of intraclass correlation coefficient and low values of mean difference. Furthermore, WHO1 and WHO2 showed lower systematic error and random.Conclusion. No predictive equations had the same values of REE as compared to those of indirect calorimetry, and those which least underestimated REE were the equations of WHO1, WHO2, and Harris and Benedict. The next step would be to validate the new equation proposed.

scholarly journals A Prediction Equation to Assess Resting Energy Expenditure in Japanese Patients with COPD

2020 ◽  
Vol 9 (11) ◽  
pp. 3455
Author(s):  
Keisuke Morikawa ◽  
Kazuyuki Tabira ◽  
Hiroyuki Takemura ◽  
Shogo Inaba ◽  
Haruka Kusuki ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Energy Expenditure ◽  
Linear Regression Analysis ◽  
Resting Energy Expenditure ◽  
Fat Free Mass ◽  
Stepwise Linear Regression Analysis ◽  
Stepwise Linear Regression ◽  
Copd Patients ◽  
Resting Energy

Background: Medical nutrition therapy is important in the management of chronic obstructive pulmonary disease (COPD) patients. Determination of resting energy expenditure is essential to define therapeutic goals for medical nutrition. Previous studies proposed the use of equations to predict resting energy expenditure. No prediction equation is currently available for the Japanese population. The objective of this study was to develop an equation to predict resting energy expenditure in Japanese chronic obstructive pulmonary disease patients. To this end, we investigated clinical variables that correlate with the resting energy expenditure. Methods: This study included 102 COPD patients admitted at the Matsusaka Municipal Hospital Respiratory Center. We measured resting energy expenditure by indirect calorimetry and explored the relationship of resting energy expenditure with clinical variables by univariate and stepwise linear regression analysis. Results: The resting energy expenditure by indirect calorimetry was significantly correlated with fat-free mass, body weight, body mass index, height, gender, and pulmonary function test by univariate analysis. In the stepwise linear regression analysis, the fat-free mass, body weight, and age remained significantly correlated with indirect calorimetry’s resting energy expenditure. The fat-free mass, body weight, and age explained 50.5% of the resting energy expenditure variation. Conclusion: Fat-free mass, body weight, and age were significantly correlated with resting energy expenditure by stepwise linear regression analysis, and they were used to define a predictive equation for Japanese COPD patients.

Reliability of resting energy expenditure in major burns: Comparison between measured and predictive equations

2019 ◽  
Vol 38 (6) ◽  
pp. 2763-2769 ◽  
Author(s):  
Jinwoo Jeon ◽  
Dohern Kym ◽  
Yong Suk Cho ◽  
Youngmin Kim ◽  
Jaechul Yoon ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Predictive Equations ◽  
Resting Energy

scholarly journals Resting energy expenditure in elite athletes: development of new predictive equations based on anthropometric variables and bioelectrical impedance analysis derived phase angle

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Maurizio Marra ◽  
Olivia Di Vincenzo ◽  
Iolanda Cioffi ◽  
Rosa Sammarco ◽  
Delia Morlino ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Phase Angle ◽  
Elite Athletes ◽  
Resting Energy Expenditure ◽  
Sport Performance ◽  
Accurate Estimation ◽  
Anthropometric Parameters ◽  
Predictive Equations ◽  
Good Prediction Accuracy ◽  
Resting Energy

Abstract Background An accurate estimation of athletes’ energy needs is crucial in diet planning to improve sport performance and to maintain an appropriate body composition. This study aimed to develop and validate in elite athletes new equations for estimating resting energy expenditure (REE) based on anthropometric parameters as well as bioimpedance analysis (BIA)-derived raw variables and to validate the accuracy of selected predictive equations. Methods Adult elite athletes aged 18–40 yrs were studied. Anthropometry, indirect calorimetry and BIA were performed in all subjects. The new predictive equations were generated using different regression models. The accuracy of the new equations was assessed at the group level (bias) and at the individual level (precision accuracy), and then compared with the one of five equations used in the general population or three athletes-specific formulas. Results One-hundred and twenty-six male athletes (age 26.9 ± 9.1 yrs; weight 71.3 ± 10.9 kg; BMI 22.8 ± 2.7 kg/m2) from different sport specialties were randomly assigned to the calibration (n = 75) or validation group (n = 51). REE was directly correlated with individual characteristics, except for age, and raw BIA variables. Most of the equations from the literature were reasonably accurate at the population level (bias within ±5%). The new equations showed a mean bias −0.3% (Eq. A based on anthropometric parameters) and −0.6% (Eq. B based on BIA-derived raw variables). Precision accuracy (individual predicted-measured differences within ±5%) was ~75% in six out of eight of the selected equations and even higher for Eq. A (82.4%) and Eq. B (92.2%). Conclusion In elite athletes, BIA-derived phase angle is a significant predictor of REE. The new equations have a very good prediction accuracy at both group and individual levels. The use of phase angle as predictor of REE requires further research with respect to different sport specialties, training programs and training level.

scholarly journals Accuracy and Validity of Resting Energy Expenditure Predictive Equations in Middle-Aged Adults

Nutrients ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1635 ◽  
Author(s):  
Francisco Amaro-Gahete ◽  
Lucas Jurado-Fasoli ◽  
Alejandro De-la-O ◽  
Ángel Gutierrez ◽  
Manuel Castillo ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Prediction Accuracy ◽  
Weight Status ◽  
Resting Energy Expenditure ◽  
Normal Weight ◽  
Middle Aged ◽  
Predictive Equations ◽  
Middle Aged Adults ◽  
Resting Energy

Indirect calorimetry (IC) is considered the reference method to determine the resting energy expenditure (REE), but its use in a clinical context is limited. Alternatively, there is a number of REE predictive equations to estimate the REE. However, it has been shown that the available REE predictive equations could either overestimate or underestimate the REE as measured by IC. Moreover, the role of the weight status in the accuracy and validity of the REE predictive equations requires further attention. Therefore, this study aimed to determine the accuracy and validity of REE predictive equations in normal-weight, overweight, and obese sedentary middle-aged adults. A total of 73 sedentary middle-aged adults (53% women, 40–65 years old) participated in the study. We measured REE by indirect calorimetry, strictly following the standard procedures, and we compared it with the values obtained from 33 predictive equations. The most accurate predictive equations in middle-aged sedentary adults were: (i) the equation of FAO/WHO/UNU in normal-weight individuals (50.0% of prediction accuracy), (ii) the equation of Livingston in overweight individuals (46.9% of prediction accuracy), and (iii) the equation of Owen in individuals with obesity (52.9% of prediction accuracy). Our study shows that the weight status plays an important role in the accuracy and validity of different REE predictive equations in middle-aged adults.

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