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Do we need race-specific resting metabolic rate prediction equations?

BACKGROUND: Resting metabolic rate (RMR) is a key determinant of daily caloric needs. Respirometry, a form of indirect calorimetry (IC), is considered one of the most accurate methods to measure RMR in clinical and research settings. It is impractical to measure RMR by IC in routine clinical practic...

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Autores principales: Reneau, James, Obi, Brittaney, Moosreiner, Andrea, Kidambi, Srividya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662665/
https://www.ncbi.nlm.nih.gov/pubmed/31358726
http://dx.doi.org/10.1038/s41387-019-0087-8
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author Reneau, James
Obi, Brittaney
Moosreiner, Andrea
Kidambi, Srividya
author_facet Reneau, James
Obi, Brittaney
Moosreiner, Andrea
Kidambi, Srividya
author_sort Reneau, James
collection PubMed
description BACKGROUND: Resting metabolic rate (RMR) is a key determinant of daily caloric needs. Respirometry, a form of indirect calorimetry (IC), is considered one of the most accurate methods to measure RMR in clinical and research settings. It is impractical to measure RMR by IC in routine clinical practice; therefore, several formulas are used to predict RMR. In this study, we sought to determine the accuracy of these formulas in determining RMR and assess additional factors that may determine RMR. METHODS: We measured RMR in 114 subjects (67% female, 30% African American [AA]) using IC. Along with standard anthropometrics, dual-energy X-ray absorptiometry was used to obtain fat-free mass(FFM) and total fat mass. Measured RMR (mRMR) by respirometry was compared with predicted RMR (pRMR) generated by Mifflin–St.Joer, Cunningham, and Harris–Benedict (HB) equations. Linear regression models were used to determine factors affecting mRMR. RESULTS: Mean age, BMI, and mRMR of subjects were 46 ± 16 years (mean ± SD), 35 ± 10 kg/m(2), and 1658 ± 391 kcal/day, respectively. After adjusting for age, gender, and anthropometrics, the two largest predictors of mRMR were race (p < 0.0001) and FFM (p < 0.0001). For every kg increase in FFM, RMR increased by 28 kcal/day (p < 0.0001). AA race was associated with 144 kcal/day (p < 0.0001) decrease in mRMR. The impact of race on mRMR was mitigated by adding in truncal FFM to the model. When using only clinically measured variables to predict mRMR, we found race, hip circumference, age, gender, and weight to be significant predictors of mRMR (p < 0.005). Mifflin–St.Joer and HB equations that use just age, gender, height, and weight overestimated kcal expenditure in AA by 138 ± 148 and 242 ± 164 (p < 0.0001), respectively. CONCLUSION: We found that formulas utilizing height, weight, gender, and age systematically overestimate mRMR and hence predict higher calorie needs among AA. The lower mRMR in AA could be related to truncal fat-free mass representing the activity of metabolically active intraabdominal organs.
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spelling pubmed-66626652019-08-01 Do we need race-specific resting metabolic rate prediction equations? Reneau, James Obi, Brittaney Moosreiner, Andrea Kidambi, Srividya Nutr Diabetes Article BACKGROUND: Resting metabolic rate (RMR) is a key determinant of daily caloric needs. Respirometry, a form of indirect calorimetry (IC), is considered one of the most accurate methods to measure RMR in clinical and research settings. It is impractical to measure RMR by IC in routine clinical practice; therefore, several formulas are used to predict RMR. In this study, we sought to determine the accuracy of these formulas in determining RMR and assess additional factors that may determine RMR. METHODS: We measured RMR in 114 subjects (67% female, 30% African American [AA]) using IC. Along with standard anthropometrics, dual-energy X-ray absorptiometry was used to obtain fat-free mass(FFM) and total fat mass. Measured RMR (mRMR) by respirometry was compared with predicted RMR (pRMR) generated by Mifflin–St.Joer, Cunningham, and Harris–Benedict (HB) equations. Linear regression models were used to determine factors affecting mRMR. RESULTS: Mean age, BMI, and mRMR of subjects were 46 ± 16 years (mean ± SD), 35 ± 10 kg/m(2), and 1658 ± 391 kcal/day, respectively. After adjusting for age, gender, and anthropometrics, the two largest predictors of mRMR were race (p < 0.0001) and FFM (p < 0.0001). For every kg increase in FFM, RMR increased by 28 kcal/day (p < 0.0001). AA race was associated with 144 kcal/day (p < 0.0001) decrease in mRMR. The impact of race on mRMR was mitigated by adding in truncal FFM to the model. When using only clinically measured variables to predict mRMR, we found race, hip circumference, age, gender, and weight to be significant predictors of mRMR (p < 0.005). Mifflin–St.Joer and HB equations that use just age, gender, height, and weight overestimated kcal expenditure in AA by 138 ± 148 and 242 ± 164 (p < 0.0001), respectively. CONCLUSION: We found that formulas utilizing height, weight, gender, and age systematically overestimate mRMR and hence predict higher calorie needs among AA. The lower mRMR in AA could be related to truncal fat-free mass representing the activity of metabolically active intraabdominal organs. Nature Publishing Group UK 2019-07-29 /pmc/articles/PMC6662665/ /pubmed/31358726 http://dx.doi.org/10.1038/s41387-019-0087-8 Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Reneau, James
Obi, Brittaney
Moosreiner, Andrea
Kidambi, Srividya
Do we need race-specific resting metabolic rate prediction equations?
title Do we need race-specific resting metabolic rate prediction equations?
title_full Do we need race-specific resting metabolic rate prediction equations?
title_fullStr Do we need race-specific resting metabolic rate prediction equations?
title_full_unstemmed Do we need race-specific resting metabolic rate prediction equations?
title_short Do we need race-specific resting metabolic rate prediction equations?
title_sort do we need race-specific resting metabolic rate prediction equations?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662665/
https://www.ncbi.nlm.nih.gov/pubmed/31358726
http://dx.doi.org/10.1038/s41387-019-0087-8
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