scholarly journals Resting Energy Expenditure during Breastfeeding: Body Composition Analysis vs. Predictive Equations Based on Anthropometric Parameters

Nutrients ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1274
Author(s):  
Agnieszka Bzikowska-Jura ◽  
Adriana Szulińska ◽  
Dorota Szostak-Węgierek
Keyword(s):  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Accurate Estimation ◽  
Composition Analysis ◽  
Anthropometric Parameters ◽  
Predictive Equations ◽  
Body Composition Analysis ◽  
Mathematical Equations ◽  
Resting Energy

Accurate estimation of energy expenditure in a breastfeeding woman is crucial for maintaining the proper nutritional status of the woman and healthy development of the infant. The current literature does not contain data regarding resting energy expenditure (REE) in breastfeeding women. Using mathematical equations is the most common method of REE assessment. However, due to changes in metabolism and body composition during pregnancy and lactation, the mathematical equations used among the general population may not apply. The aim of this study was to evaluate the resting energy expenditure of exclusively breastfeeding women by using body composition analysis–estimated REE (eREE) and to provide the most appropriate predictive equations–predicted REE (pREE) based on anthropometric parameters to estimate it. This was a pilot study with 40 exclusively breastfeeding women. Height and weight were measured and body composition analysis was performed. We predicted REE using fourteen self-selected equations, based on anthropometric parameters and/or age, and/or sex. The median eREE was 1515.0 ± 68.4 kcal (95% Cl, 1477–1582 kcal) and the pREE ranged from 1149.7 kcal (95% Cl, 1088.7–1215.0) by Bernstein et al., to 1576.8 kcal (95% Cl, 1479.9–1683.4), by Müller et al. Significant differences between eREE and all pREE were observed (p < 0.001, except Korth et al. equations). The Müller et al. equation was the most accurate with the smallest individual variation. All predictive equations showed low agreement, and in most cases, the results were underestimated. These findings indicate the need for further studies to propose more suitable methods to determine the energy requirements for breastfeeding women.

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 Age-Dependent Changes in Resting Energy Expenditure (REE): Insights from Detailed Body Composition Analysis in Normal and Overweight Healthy Caucasians

Nutrients ◽  
2016 ◽  
Vol 8 (6) ◽  
pp. 322 ◽  
Author(s):  
Corinna Geisler ◽  
Wiebke Braun ◽  
Maryam Pourhassan ◽  
Lisa Schweitzer ◽  
Claus-Christian Glüer ◽  
...  
Keyword(s):  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Age Dependent ◽  
Resting Energy

scholarly journals Accuracy of predictive equations for evaluation of resting energy expenditure in Brazilian patients with type 2 diabetes.

2020 ◽  
Author(s):  
Thaiciane Grassi ◽  
Francesco Pinto Boeno ◽  
Mauren Minuzzo de Freitas ◽  
Tatiana Pedroso de Paula ◽  
Luciana Vercoza Viana ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Fat Free Mass ◽  
Composition Analysis ◽  
Cross Sectional ◽  
Predictive Equations ◽  
Resting Energy

Abstract Background Evaluation of the resting energy expenditure (REE) is essential to ensure an appropriate dietary prescription for patients with type 2 diabetes. The aim of this study was to evaluate the accuracy of predictive equations for REE estimation in patients with type 2 diabetes, considering indirect calorimetry (IC) as the reference method.Methods A cross-sectional study was conducted in 62 patients (31 men and 31 women) with type 2 diabetes. Clinical and laboratory variables were evaluated, as well as body composition by electrical bioimpedance. The REE was measured by IC (QUARK RMR, Cosmed, Rome, Italy) and estimated by predictive equations. Data were analyzed using Bland–Altman plots, paired t-tests, and Pearson’s correlation coefficients.Results Patients in the sample had a mean age of 63.1 ± 5.2 years, median diabetes duration of 11 (1–36) years, and mean A1C of 7.6 ± 1.2%. Body composition analysis revealed a mean fat free mass of 35.2 ± 11.8 kg and fat mass of 29.1 ± 8.8 kg. There was wide variation in the accuracy of REE values predicted by equations when compared to those measured by IC. For women, the FAO/WHO/UNO equation provided the best REE prediction in comparison to measured REE (-1.8% difference). For men, the Oxford equation yielded values closest to those measured by IC (-1.3% difference).Conclusions In this sample of the patients with type 2 diabetes, the best predictive equations to estimate REE were FAO/WHO/UNO and Oxford for women and men, respectively.

Predictive Equations Based on Body Composition for Resting Energy Expenditure Estimation in Adults with Obesity

2020 ◽  
Vol 16 (4) ◽  
pp. 381-386
Author(s):  
Dana El Masri ◽  
Leila Itani ◽  
Dima Kreidieh ◽  
Hana Tannir ◽  
Marwan El Ghoch
Keyword(s):  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Fat Free Mass ◽  
Accurate Estimation ◽  
Predictive Equations ◽  
Bland Altman Method ◽  
The Mean ◽  
Mean Differences ◽  
Resting Energy

Background and Aim: An accurate estimation of Resting Energy Expenditure (REE) in patients with obesity is crucial. Therefore, our aim was to assess the validity of REE predictive equations based on body composition variables in treatment-seeking Arab adults with obesity. Methods: Body composition and REE were measured by Tanita BC-418 bioimpedance and Vmax Encore 229 IC, respectively, and predictive equations based on fat mass and fat-free mass were used in REE estimations among 87 adults of both genders, in the Outpatient Clinic in the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). The mean differences between the measured and estimated REE values were calculated to assess the accuracy, and the Bland-Altman method was used to assess the level of agreement. Results: Ten predictive equations were included. In males, all the predictive equations gave significantly different estimates of REE when compared to that measured by IC. On the other hand, in females, the mean difference between the REE value estimated by Huang and Horie-Waitzberg equations and that measured using IC was not significant, and the agreement was confirmed using Bland-Altman plots. Conclusion: Huang and Horie-Waitzberg equations are suggested for accurate REE estimation in females; however, new validated REE estimation equations for males in this population are still needed.

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

Abstract BackgroundAn 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.MethodsAdult elite athletes aged 18-40 years were studied. Anthropometry, indirect calorimetry and BIA were performed in all subjects. The new predictive equations were generated using different regression models. Prediction accuracy of the new equations was assessed and compared with the one of five equations for estimating REE in normal-weight subjects and three athletes-specific predictive formulas as suggested in the literature.ResultsOne-hundred and twenty-six male athletes 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 ±5%). The new equations showed a mean bias -0.3% (Equation A based on anthropometric parameters) and -0.6% (Equation B based on (BIA) derived raw variables). Individual accuracy was ~75% in six out of eight of the selected equations and was even higher for Equation A (82.4%) and equation B (92.2%).ConclusionIn 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 PhA as predictor of REE requires further research with respect to different sport specialties, training programs and training level.

Weight and body composition changes affect resting energy expenditure predictive equations during a 12‐month weight‐loss intervention

Obesity ◽  
2021 ◽  
Vol 29 (10) ◽  
pp. 1596-1605
Author(s):  
Jared H. Dahle ◽  
Danielle M. Ostendorf ◽  
Zhaoxing Pan ◽  
Paul S. MacLean ◽  
Daniel H. Bessesen ◽  
...  
Keyword(s):  
Weight Loss ◽  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Weight Loss Intervention ◽  
Predictive Equations ◽  
Composition Changes ◽  
Resting Energy

scholarly journals Validity of Predictive Equations for Resting Energy Expenditure Developed for Obese Patients: Impact of Body Composition Method

Nutrients ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 63 ◽  
Author(s):  
Najate Achamrah ◽  
Pierre Jésus ◽  
Sébastien Grigioni ◽  
Agnès Rimbert ◽  
André Petit ◽  
...  
Keyword(s):  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Obese Patients ◽  
Predictive Equations ◽  
Composition Method ◽  
Resting Energy

scholarly journals The Diagnostic-Measurement Method—Resting Energy Expenditure Assessment of Polish Children Practicing Football

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Edyta Łuszczki ◽  
Anna Bartosiewicz ◽  
Katarzyna Dereń ◽  
Maciej Kuchciak ◽  
Łukasz Oleksy ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Composition ◽  
Energy Expenditure ◽  
Muscle Mass ◽  
Indirect Calorimetry ◽  
Energy Demand ◽  
Body Height ◽  
Resting Energy Expenditure ◽  
Blood Pressure Monitor ◽  
Resting Energy

Establishing the amount of energy needed to cover the energy demand of children doing sport training and thus ensuring they achieve an even energy balance requires the resting energy expenditure (REE) to be estimated. One of the methods that measures REE is the indirect calorimetry method, which may be influenced by many factors, including body composition, gender, age, height or blood pressure. The aim of the study was to assess the correlation between the resting energy expenditure of children regularly playing football and selected factors that influence the REE in this group. The study was conducted among 219 children aged 9 to 17 using a calorimeter, a device used to assess body composition by the electrical bioimpedance method by means of segment analyzer and a blood pressure monitor. The results of REE obtained by indirect calorimetry were compared with the results calculated using the ready-to-use formula, the Harris Benedict formula. The results showed a significant correlation of girls’ resting energy expenditure with muscle mass and body height, while boys’ resting energy expenditure was correlated with muscle mass and body water content. The value of the REE was significantly higher (p ≤ 0.001) than the value of the basal metabolic rate calculated by means of Harris Benedict formula. The obtained results can be a worthwhile suggestion for specialists dealing with energy demand planning in children, especially among those who are physically active to achieve optimal sporting successes ensuring proper functioning of their body.

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