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Prediction of resting energy expenditure from fat-free mass and fat mass
Abstract
On the basis of literature values, the relationship between fat-free mass (FFM), fat mass (FM), and resting energy expenditure [REE (kJ/24 h)] was determined for 213 adults (86 males, 127 females). The objectives were to develop a mathematical model to predict REE based on body composition and to evaluate the contribution of FFM and FM to REE. The following regression equations were derived: 1) REE = 1265 + (93.3 x FFM) (r2 = 0.727, P < 0.001); 2) REE = 1114 + (90.4 x FFM) + (13.2 x FM) (R2 = 0.743, P < 0.001); and 3) REE = (108 x FFM) + (16.9 x FM) (R2 = 0.986, P < 0.001). FM explained only a small part of the variation remaining after FFM was accounted for. The models that include both FFM and FM are useful in examination of the changes in REE that occur with a change in both the FFM and FM. To account for more of the variability in REE, FFM will have to be divided into organ mass and skeletal muscle mass in future analyses.
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“lean mass strongly determines heat production (metabolic rate) of the body: the greater the muscle mass, the more heat is produced by the body, even in a resting state [75,76].”
From Drivers of diversity in human thermal perception – A review for holistic comfort models (2018) · cites this paper as [75]
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“2 kcal/ kg/day are the regression coefficients relating resting metabolic rate versus fat-free mass (FFM) and fat mass (F), respectively [26].”
BackgroundPersistent metabolic adaptation 6 years after “The Biggest Loser” competition
Fothergill, E., Guo, J., Howard, L., Kerns, J. C., Knuth, N. D., Brychta, R., Chen, K. Y., Skarulis, M. C., Walter, M., Walter, P. J., et al. (2016) · Obesity · doi:10.1002/oby.21538 · Cited by 672
“Despite all our subjects having class III obesity at baseline, the coefficients of the best-fit RMR regression equation above were similar to those previously published using data from subjects with less severe obesity (11-13).”
ResultAssessment of Physical Activity and Energy Expenditure: An Overview of Objective Measures
Hills, A. P., Mokhtar, N., & Byrne, N. M. (2014) · Frontiers in Nutrition · doi:10.3389/fnut.2014.00005 · Cited by 649
“Along with age and gender, physical fitness has a major influence on the amount and proportion of FFM.”
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“The additional role played by FM as an independent factor influencing BMR has been a matter of previous debate, with some studies finding a significant independent effect (4, 5, 43) and others failing to replicate this result (6, 7).”
BackgroundResult
Metadata
- Authors
- Nelson, K., Weinsier, R., Long, C., & Schutz, Y.
- Affiliations
- University of Alabama at Birmingham (US)
- Corresponding author
- Nelson, KM
- Venue
- The American Journal of Clinical Nutrition
- Publisher
- Elsevier BV
- Published
- 1992-11
- Volume
- 56
- Issue
- 5
- Pages
- 848-856
- ISSN
- 0002-9165 (ISSN-L)
- OpenAlex
- W1954962848
- Cited by
- 371
- References
- 44
- Citations by year
- 2026: 11 · 2025: 3 · 2024: 10 · 2023: 13 · 2022: 16 · 2021: 23 · 2020: 19 · 2019: 9 · 2018: 17 · 2017: 15 · 2016: 15 · 2015: 19 · 2014: 16 · 2013: 16 · 2012: 13
- FWCI
- 1.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Clinical Nutrition (publishedVersion)
- PubMed (publishedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology (medical) › Body Composition Measurement Techniques
- Health Sciences › Nursing › Nutrition and Dietetics › Clinical Nutrition and Gastroenterology
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Regulation of Appetite and Obesity
- Keywords
- resting energy expenditure, fat-free mass, fat mass, body composition, energy expenditure estimation, regression models, skeletal muscle mass, organ mass, adults
- MeSH terms
- Adipose Tissue, Adult, Basal Metabolism, Body Composition, Energy Metabolism, Female, Humans, Male, Mathematics, Models, Biological, Obesity, Regression Analysis
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