scholarly journals Predictive equations for estimating resting energy expenditure in women with overweight and obesity at three postpartum stages

2020 ◽  
Vol 9 ◽  
Author(s):  
Siri Halland Nesse ◽  
Inger Ottestad ◽  
Anna Winkvist ◽  
Fredrik Bertz ◽  
Lars Ellegård ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Overweight And Obesity ◽  
Fat Free Mass ◽  
Postpartum Women ◽  
Predictive Equations ◽  
Lactating Women ◽  
X Ray ◽  
Overweight Or Obesity ◽  
Resting Energy

Abstract The objective was to investigate which predictive equations provide the best estimates of resting energy expenditure (REE) in postpartum women with overweight and obesity. Lactating women with overweight or obesity underwent REE measurement by indirect calorimetry, and fat-free mass (FFM) was assessed by dual-energy X-ray absorptiometry at three postpartum stages. Predictive equations based on body weight and FFM were obtained from the literature. Performance of the predictive equations were analysed as the percentage of women whose REE was accurately predicted, defined as a predicted REE within ±10 % of measured REE. REE data were available for women at 10 weeks (n 71), 24 weeks (n 64) and 15 months (n 57) postpartum. Thirty-six predictive equations (twenty-five weight-based and eleven FFM-based) were validated. REE was accurately predicted in ≥80 % of women at all postpartum visits by six predictive equations (two weight-based and four FFM-based). The weight-based equation with the highest performance was that of Henry (weight, height, age 30–60 years) (HenryWH30−60), with an overall mean of 83 % accurate predictions. The HenryWH30−60 equation was highly suitable for predicting REE at all postpartum visits (irrespective of the women's actual age), and the performance was sustained across changes in weight and lactation status. No FFM-based equation was remarkably superior to HenryWH30−60 for the total postpartum period.

scholarly journals Resting energy expenditure in obese women: comparison between measured and estimated values

2016 ◽  
Vol 116 (7) ◽  
pp. 1306-1313 ◽  
Author(s):  
Vanessa Fadanelli Schoenardie Poli ◽  
Ricardo Badan Sanches ◽  
Amanda dos Santos Moraes ◽  
João Pedro Novo Fidalgo ◽  
Maythe Amaral Nascimento ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Intraclass Correlation ◽  
Resting Energy Expenditure ◽  
Fat Free Mass ◽  
Interdisciplinary Therapy ◽  
Obese Women ◽  
Predictive Equations ◽  
Before And After ◽  
Resting Energy

AbstractAssessing energy requirements is a fundamental activity in clinical dietetic practice. The aim of this study was to investigate which resting energy expenditure (REE) predictive equations are the best alternatives to indirect calorimetry before and after an interdisciplinary therapy in Brazilian obese women. In all, twelve equations based on weight, height, sex, age, fat-free mass and fat mass were tested. REE was measured by indirect calorimetry. The interdisciplinary therapy consisted of nutritional, physical exercise, psychological and physiotherapy support during the course of 1 year. The average differences between measured and predicted REE, as well as the accuracy at the ±10 % level, were evaluated. Statistical analysis included paired t tests, intraclass correlation coefficients and Bland–Altman plots. Validation was based on forty obese women (BMI 30–39·9 kg/m2). Our major findings demonstrated a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid, obese women. The equations reported by Harris–Benedict and FAO/WHO/United Nations University (UNU) were the only ones that did not show significant differences compared with indirect calorimetry and presented a bias <5 %. The Harris–Benedict equation provided 40 and 47·5 % accurate predictions before and after therapy, respectively. The FAO equation provided 35 and 47·5 % accurate predictions. However, the Bland–Altman analysis did not show good agreement between these equations and indirect calorimetry. Therefore, the Harris–Benedict and FAO/WHO/UNU equations should be used with caution for obese women. The need to critically re-assess REE data and generate regional and more homogeneous REE databases for the target population is reinforced.

scholarly journals No consistent evidence of a disproportionately low resting energy expenditure in long-term successful weight-loss maintainers

2018 ◽  
Vol 108 (4) ◽  
pp. 658-666 ◽  
Author(s):  
Danielle M Ostendorf ◽  
Edward L Melanson ◽  
Ann E Caldwell ◽  
Seth A Creasy ◽  
Zhaoxing Pan ◽  
...  
Keyword(s):  
Weight Loss ◽  
Energy Expenditure ◽  
Body Mass ◽  
Resting Energy Expenditure ◽  
Weight Loss Maintenance ◽  
Fat Free Mass ◽  
Predictive Equations ◽  
Successful Weight Loss ◽  
Resting Energy

Abstract Background Evidence in humans is equivocal in regards to whether resting energy expenditure (REE) decreases to a greater extent than predicted for the loss of body mass with weight loss, and whether this disproportionate decrease in REE persists with weight-loss maintenance. Objectives We aimed to1) determine if a lower-than-predicted REE is present in a sample of successful weight-loss maintainers (WLMs) and 2) determine if amount of weight loss or duration of weight-loss maintenance are correlated with a lower-than-predicted REE in WLMs. Design Participants (18–65 y old) were recruited in 3 groups: WLMs (maintaining ≥13.6 kg weight loss for ≥1 y, n = 34), normal-weight controls [NCs, body mass index (BMI; in kg/m2) similar to current BMI of WLMs, n = 35], and controls with overweight/obesity (OCs, BMI similar to pre–weight-loss maximum BMI of WLMs, n = 33). REE was measured (REEm) with indirect calorimetry. Predicted REE (REEp) was determined via 1) a best-fit linear regression developed with the use of REEm, age, sex, fat-free mass, and fat mass from our control groups and 2) three standard predictive equations. Results REEm in WLMs was accurately predicted by equations developed from NCs and OCs (±1%) and by 3 standard predictive equations (±3%). In WLMs, individual differences between REEm and REEp ranged from −257 to +163 kcal/d. A lower REEm compared with REEp was correlated with amount of weight lost (r = 0.36, P < 0.05) but was not correlated with duration of weight-loss maintenance (r = 0.04, P = 0.81). Conclusions We found no consistent evidence of a significantly lower REE than predicted in a sample of long-term WLMs based on predictive equations developed from NCs and OCs as well as 3 standard predictive equations. Results suggest that sustained weight loss may not always result in a substantial, disproportionately low REE. This trial was registered at clinicaltrials.gov as NCT03422380.

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.

Reduction of Resting Energy Expenditure and Sarcopenic Obesity in Adults with Overweight and Obesity: A Brief Report

2020 ◽  
Vol 16 (4) ◽  
pp. 376-380 ◽  
Author(s):  
Hana Tannir ◽  
Dima Kreidieh ◽  
Leila Itani ◽  
Dana El Masri ◽  
Marwan El Ghoch
Keyword(s):  
Body Weight ◽  
Energy Expenditure ◽  
Linear Regression Analysis ◽  
Resting Energy Expenditure ◽  
Overweight And Obesity ◽  
Sarcopenic Obesity ◽  
Unit Body Weight ◽  
And Gender ◽  
Overweight Or Obesity ◽  
Resting Energy

Background and Aim: The last decade has seen the emergence of a new condition, describing the coexistence of obesity and sarcopenia, termed Sarcopenic Obesity (SO). The aim of this study was to assess the potential association between SO and reduced Resting Energy Expenditure (REE). Methods: Body composition and REE were measured using a bioimpedance analyser (Tanita BC-418) and Indirect Calorimeter (Vmax Encore 229), respectively in 89 adults with overweight or obesity of both genders, referred to the Outpatient Clinic of the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). Participants were then categorized on the basis of having SO or not. Results : Thirty-nine of the 89 participants met the criteria for SO (43.8%), and these participants displayed a significantly lower REE per unit body weight than those in the group without SO (19.02 ± 2.26 vs. 20.87 ± 2.77; p = 0.001). Linear regression analysis showed that the presence of SO decreases REE by 1.557 kcal/day for each kg of body weight (β = -1.557; CI = -0.261 – (-0.503); p = 0.004), after adjusting for age and gender. Conclusion : SO appears to be present in a high proportion of treatment-seeking adults with overweight or obesity of both genders, and it seems to be associated with a reduced REE, compared with those without SO. Future studies are needed to clarify whether this may influence clinical outcomes.

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.

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.

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