Resting energy expenditure from DXA: A Cross-Sectional Study

2020 ◽  
Author(s):  
Ana Claudia Rossini Venturini ◽  
Analiza Mónica Silva ◽  
Pedro Pugliesi Abdalla ◽  
André Pereira Santos ◽  
Franciane Goes Borges ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
Fat Free Mass ◽  
Whole Body ◽  
Sectional Study ◽  
Bias Analysis ◽  
Anthropometric Measures ◽  
Cross Sectional ◽  
Resting Energy

Abstract Background: The traditional methods used to estimate Resting Energy Expenditure (REE) is bases of the indirect calorimetry (IC) is a global approach, and does not consider the different metabolic activities of organs (heart, kidney and liver), bone tissue (BT), adipose tissue (AT) and skeletal muscle tissue (SMT). Then, our objective is to validate a 3C approach model of REE measured by dual-energy x-ray absorptiometry (DXA) based on IC. Methods: Participated in this cross-sectional study 155 college students, both sexes (18 to 30 years old). Anthropometric measures, REEs estimated via IC and whole-body DXA-scan (3C approach) were determined. The REE of each component was determined after transforming the DXA components at the molecular level to the organ tissue level. Bland-Altman and proportional bias analysis were used to verify agreement between methods (IC and DXA). Results: The results show significant ( p<0.05 ) higher fat-free mass index (FFMI), cranial area and residual tissue (RT) in men. And they have smaller amounts of fat mass (FM) and AT in comparison to women. Also higher REEs ( p <0.001) were found among men for all components in comparison to women, except for AT expenditure ( p <0.001). Conclusions: This approach has important implications for the interpretation of multicomponent energy metabolism, considering interpersonal differences in terms of heat production. It is a strategy applicable in the health and sports contexts to prescribe exercises and diets because it shows the REE magnitude of each body component.

scholarly journals External Validation of Equations to Estimate Resting Energy Expenditure in 2037 Children and Adolescents with and 389 without Obesity: A Cross-Sectional Study

Nutrients ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1421 ◽  
Author(s):  
Giorgio Bedogni ◽  
Simona Bertoli ◽  
Ramona De Amicis ◽  
Andrea Foppiani ◽  
Alessandra De Col ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Cross Validation ◽  
External Validation ◽  
Resting Energy Expenditure ◽  
Prediction Equation ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Inclusion Criteria ◽  
Cross Sectional ◽  
Resting Energy

We performed an external cross-validation study of 10 equations to estimate resting energy expenditure (REE) in 2037 children with and 389 without obesity. Inclusion criteria were Caucasian ethnicity, age ≤ 18 years, and availability of REE. REE was measured using indirect calorimetry. The correct classification fraction (CCF) of an equation was defined as the fraction of subjects whose estimated REE was within 10% of measured REE. The Molnár equation was the most accurate REE prediction equation with CCFs of 0.70 (95% CI 0.65 to 0.76) in girls without obesity, 0.64 (95% CI 0.61 to 0.66) in girls with obesity, 0.76 (95% CI 0.67 to 0.83) in boys without obesity, and 0.66 (95% CI 0.63 to 0.69) in boys with obesity. The Mifflin equation was the second most accurate equation with CCFs of 0.67 (95% CI 0.61 to 0.73) in girls without obesity, 0.61 (95% CI 0.58 to 0.64) in girls with obesity, 0.75 (95% CI 0.66 to 0.82) in boys without obesity, and 0.66 (95% CI 0.63 to 0.69) in boys with obesity.

Effects of HIV and Antiretroviral Therapy on Resting Energy Expenditure in Adult HIV-Infected Women—A Matched, Prospective, Cross-Sectional Study

2013 ◽  
Vol 113 (8) ◽  
pp. 1037-1043 ◽  
Author(s):  
Alison L. Mittelsteadt ◽  
Corrilynn O. Hileman ◽  
Stephanie R. Harris ◽  
Kelly M. Payne ◽  
Barbara M. Gripshover ◽  
...  
Keyword(s):  
Antiretroviral Therapy ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Resting Energy

Accuracy of predictive equations for resting energy expenditure in oncology patients; a cross-sectional study

2018 ◽  
Vol 37 ◽  
pp. S336
Author(s):  
S. Verschure-Dorsman ◽  
E. Heijkoop ◽  
J. Vonk ◽  
L. Kemperman ◽  
S. Tjon A Joe
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Oncology Patients ◽  
Cross Sectional ◽  
Predictive Equations ◽  
Resting Energy

scholarly journals P294 Resting energy expenditure in women with Crohn’s disease: A cross-sectional study

2018 ◽  
Vol 12 (supplement_1) ◽  
pp. S249-S249
Author(s):  
N Imperatore ◽  
I Cioffi ◽  
R Sammarco ◽  
A Testa ◽  
A Rispo ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Resting Energy

Estimating resting energy expenditure from dual‐energy X‐ray absorptiometry: A cross‐sectional study in healthy young adults

2020 ◽  
Author(s):  
Ana Cláudia Rossini Venturini ◽  
Analiza Mónica Silva ◽  
Pedro Pugliesi Abdalla ◽  
André Pereira Santos ◽  
Franciane Goes Borges ◽  
...  
Keyword(s):  
Young Adults ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
Dual Energy ◽  
Sectional Study ◽  
Cross Sectional ◽  
X Ray ◽  
Resting Energy

scholarly journals Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: What can we use?

2020 ◽  
Author(s):  
Thaiciane Grassi ◽  
Francesco Pinto Boeno ◽  
Mauren Minuzzo de Freitas ◽  
Tatiana Pedroso de Paula ◽  
Luciana Vercoza Viana ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Energy Expenditure ◽  
Correlation Coefficients ◽  
Reference Method ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
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 record 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 performed in outpatients with type 2 diabetes. Clinical, body composition by electrical bioimpedance and laboratory variables were evaluated. The REE was measured by IC (QUARK RMR, Cosmed, Rome, Italy) and estimated by eleven predictive equations. Data were analyzed using Bland–Altman plots, paired t-tests, and Pearson’s correlation coefficients.Results: 62 patients were evaluated [50% female; mean age 63.1 ± 5.2 years; diabetes duration of 11 (1-36) years, and mean A1C of 7.6 ± 1.2%]. There was a wide variation in the accuracy of REE values predicted by equations when compared to IC REE measurement. In all patients, Ikeda and Mifflin St-Jeor equations were that most underestimated REE. And, the equations that overestimated the REE were proposed by Dietary Reference Intakes and Huang. The most accurate equations were FAO/WHO/UNO in women (-1.8% difference) and Oxford in men (-1.3% difference).Conclusion: In patients with type 2 diabetes, in the absence of IC, FAO/WHO/UNO and Oxford equations provide the best REE prediction in comparison to measured REE for women and men, respectively.

scholarly journals Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?

BMC Nutrition ◽  
2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Thaiciane Grassi ◽  
Francesco Pinto Boeno ◽  
Mauren Minuzzo de Freitas ◽  
Tatiana Pedroso de Paula ◽  
Luciana Vercoza Viana ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Energy Expenditure ◽  
Correlation Coefficients ◽  
Reference Method ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
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 record 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 performed in outpatients with type 2 diabetes. Clinical, body composition by electrical bioimpedance and laboratory variables were evaluated. The REE was measured by IC (QUARK RMR, Cosmed, Rome, Italy) and estimated by eleven predictive equations. Data were analyzed using Bland–Altman plots, paired t-tests, and Pearson’s correlation coefficients. Results Sixty-two patients were evaluated [50% female; mean age 63.1 ± 5.2 years; diabetes duration of 11 (1–36) years, and mean A1C of 7.6 ± 1.2%]. There was a wide variation in the accuracy of REE values predicted by equations when compared to IC REE measurement. In all patients, Ikeda and Mifflin St-Jeor equations were that most underestimated REE. And, the equations that overestimated the REE were proposed by Dietary Reference Intakes and Huang. The most accurate equations were FAO/WHO/UNO in women (− 1.8% difference) and Oxford in men (− 1.3% difference). Conclusion In patients with type 2 diabetes, in the absence of IC, FAO/WHO/UNO and Oxford equations provide the best REE prediction in comparison to measured REE for women and men, respectively.

scholarly journals Resting Energy Expenditure and Related Factors in 6- to 9-Year-Old Southern African Children of Diverse Population Groups

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1983
Author(s):  
Adeline Pretorius ◽  
Paola Wood ◽  
Piet Becker ◽  
Friedeburg Wenhold
Keyword(s):  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Bioelectrical Impedance ◽  
Population Group ◽  
Fat Free Mass ◽  
African Women ◽  
Healthy Children ◽  
Related Factors ◽  
Cross Sectional ◽  
Resting Energy

Lower resting energy expenditure (REE) may partially explain the disproportionate prevalence of overweight/obesity among black African women. As no previous studies have investigated the REE of Southern African (South. Afr.) children, we aimed to determine, by sex and population group, the REE of 6- to 9-year-old urban school children. In a cross-sectional study with quota sampling, REE was measured with indirect calorimetry (IC). Confounders considered were: body composition (BC) (fat-free mass (FFM), FFM index, fat mass (FM), FM index), assessed using multifrequency bioelectrical impedance analysis, and physical activity (PA) measured with a pedometer. Multivariate regression was used to calculate REE adjusted for phenotypes (BC, z-scores of weight-for-age, height-for-age, body mass index-for-age) and PA. Sex and population differences in REE were determined with two-way ANOVA. Ninety-four healthy children (59.6% girls; 52.1% black) with similar socioeconomic status and PA opportunities participated. Despite BC variations, sex differences in REE were not significant (41 kcal/day; P = 0.375). The REE of black participants was lower than of white (146 kcal/day; P = 0.002). When adjusted for FFM and HFA z-score, the differences in REE declined but remained clinically meaningful at 91 kcal/day (P = 0.039) and 82 kcal/day (P = 0.108), respectively. We recommend the development of population-specific REE prediction equations for South. Afr. children.

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