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

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.

Download Full-text

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

BMC Nutrition ◽

10.1186/s40795-020-00384-1 ◽

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.

Download Full-text

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

10.21203/rs.3.rs-30692/v2 ◽

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.

Download Full-text

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

10.21203/rs.3.rs-30692/v1 ◽

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.

Download Full-text

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

10.21203/rs.3.rs-25229/v1 ◽

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.

Download Full-text

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

Clinical Nutrition ◽

10.1016/j.clnu.2018.09.002 ◽

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

Download Full-text

RESTING ENERGY EXPENDITURE IN WOMEN WITH CROHN’S DISEASE: A CROSS-SECTIONAL STUDY

10.26226/morressier.59a6b34bd462b80290b5597f ◽

2017 ◽

Author(s):

Nicola Imperatore

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Energy Expenditure ◽

Resting Energy Expenditure ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Resting Energy

Download Full-text

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

Nutrients ◽

10.3390/nu12051421 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1421 ◽

Cited By ~ 1

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.

Download Full-text