Cargando…

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

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 in...

Descripción completa

Detalles Bibliográficos
Autores principales: Grassi, Thaiciane, Boeno, Francesco Pinto, de Freitas, Mauren Minuzzo, de Paula, Tatiana Pedroso, Viana, Luciana Vercoza, de Oliveira, Alvaro Reischak, Steemburgo, Thais
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525981/
https://www.ncbi.nlm.nih.gov/pubmed/33005431
http://dx.doi.org/10.1186/s40795-020-00384-1
_version_ 1783588785673469952
author Grassi, Thaiciane
Boeno, Francesco Pinto
de Freitas, Mauren Minuzzo
de Paula, Tatiana Pedroso
Viana, Luciana Vercoza
de Oliveira, Alvaro Reischak
Steemburgo, Thais
author_facet Grassi, Thaiciane
Boeno, Francesco Pinto
de Freitas, Mauren Minuzzo
de Paula, Tatiana Pedroso
Viana, Luciana Vercoza
de Oliveira, Alvaro Reischak
Steemburgo, Thais
author_sort Grassi, Thaiciane
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7525981
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-75259812020-09-30 Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use? Grassi, Thaiciane Boeno, Francesco Pinto de Freitas, Mauren Minuzzo de Paula, Tatiana Pedroso Viana, Luciana Vercoza de Oliveira, Alvaro Reischak Steemburgo, Thais BMC Nutr Research Article 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. BioMed Central 2020-09-30 /pmc/articles/PMC7525981/ /pubmed/33005431 http://dx.doi.org/10.1186/s40795-020-00384-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Grassi, Thaiciane
Boeno, Francesco Pinto
de Freitas, Mauren Minuzzo
de Paula, Tatiana Pedroso
Viana, Luciana Vercoza
de Oliveira, Alvaro Reischak
Steemburgo, Thais
Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?
title Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?
title_full Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?
title_fullStr Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?
title_full_unstemmed Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?
title_short Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?
title_sort predictive equations for evaluation for resting energy expenditure in brazilian patients with type 2 diabetes: what can we use?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525981/
https://www.ncbi.nlm.nih.gov/pubmed/33005431
http://dx.doi.org/10.1186/s40795-020-00384-1
work_keys_str_mv AT grassithaiciane predictiveequationsforevaluationforrestingenergyexpenditureinbrazilianpatientswithtype2diabeteswhatcanweuse
AT boenofrancescopinto predictiveequationsforevaluationforrestingenergyexpenditureinbrazilianpatientswithtype2diabeteswhatcanweuse
AT defreitasmaurenminuzzo predictiveequationsforevaluationforrestingenergyexpenditureinbrazilianpatientswithtype2diabeteswhatcanweuse
AT depaulatatianapedroso predictiveequationsforevaluationforrestingenergyexpenditureinbrazilianpatientswithtype2diabeteswhatcanweuse
AT vianalucianavercoza predictiveequationsforevaluationforrestingenergyexpenditureinbrazilianpatientswithtype2diabeteswhatcanweuse
AT deoliveiraalvaroreischak predictiveequationsforevaluationforrestingenergyexpenditureinbrazilianpatientswithtype2diabeteswhatcanweuse
AT steemburgothais predictiveequationsforevaluationforrestingenergyexpenditureinbrazilianpatientswithtype2diabeteswhatcanweuse