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

Changes in Resting Energy Expenditure and Body Composition after Weight Loss following Roux-en-Y Gastric Bypass

2007 ◽  
Vol 17 (5) ◽  
pp. 608-616 ◽  
Author(s):  
Fernando Carrasco ◽  
Karin Papapietro ◽  
Attila Csendes ◽  
Gabriela Salazar ◽  
Constanza Echenique ◽  
...  
Keyword(s):  
Weight Loss ◽  
Gastric Bypass ◽  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Resting Energy

Resting energy expenditure, weight loss, and altered body composition in HIV infection

Nutrition ◽  
1996 ◽  
Vol 12 (9) ◽  
pp. 595-601 ◽  
Author(s):  
Achim Schwenk ◽  
Elmar Höffer-Belitz ◽  
Barthel Jung ◽  
Gisela Kremer ◽  
Babette Bürger ◽  
...  
Keyword(s):  
Weight Loss ◽  
Body Composition ◽  
Energy Expenditure ◽  
Hiv Infection ◽  
Resting Energy Expenditure ◽  
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 Relationship of insulin dynamics to body composition and resting energy expenditure following weight loss

Obesity ◽  
2015 ◽  
Vol 23 (11) ◽  
pp. 2216-2222 ◽  
Author(s):  
Bridget M. Hron ◽  
Cara B. Ebbeling ◽  
Henry A. Feldman ◽  
David S. Ludwig
Keyword(s):  
Weight Loss ◽  
Body Composition ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Relationship Of ◽  
Resting Energy

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.

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.

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