Literature DB >> 11346676

Resting energy expenditure: systematic organization and critique of prediction methods.

Z Wang1, S Heshka, K Zhang, C N Boozer, S B Heymsfield.   

Abstract

There are many published methods for predicting resting energy expenditure (REE) from measured body composition. Although these published reports extend back almost a century, new related studies appear on a regular basis. It remains unclear what the similarities and differences are among these various methods and what, if any, advantages the newly introduced REE prediction models offer. These issues led us to develop an organizational system for REE prediction methods with the goal of clarifying prevailing ambiguities in the field. Our classification scheme is founded on body composition level (whole-body, tissue-organ, cellular, and molecular) and related components as the REE predictor variables. Each existing REE prediction method by body composition must belong to one body composition level. The suggested classification system, founded on a conceptual basis, highlights similarities and differences among the diverse REE-body composition prediction methods, provides a framework for teaching REE-body composition relationships, and identifies important future research opportunities.

Mesh:

Year:  2001        PMID: 11346676     DOI: 10.1038/oby.2001.42

Source DB:  PubMed          Journal:  Obes Res        ISSN: 1071-7323


  20 in total

1.  Specific metabolic rates of major organs and tissues across adulthood: evaluation by mechanistic model of resting energy expenditure.

Authors:  Zimian Wang; Zhiliang Ying; Anja Bosy-Westphal; Junyi Zhang; Britta Schautz; Wiebke Later; Steven B Heymsfield; Manfred J Müller
Journal:  Am J Clin Nutr       Date:  2010-10-20       Impact factor: 7.045

2.  Body composition analysis: Cellular level modeling of body component ratios.

Authors:  Z Wang; S B Heymsfield; F X Pi-Sunyer; D Gallagher; R N Pierson
Journal:  Int J Body Compos Res       Date:  2008

3.  Resting energy expenditure in male athletes with a spinal cord injury.

Authors:  Fiona E Pelly; Elizabeth M Broad; Natalie Stuart; Mark A Holmes
Journal:  J Spinal Cord Med       Date:  2017-05-04       Impact factor: 1.985

4.  Methodological differences cannot explain associations between health, anthropometrics, and excess resting metabolic rate.

Authors:  Michael Gurven; Benjamin Trumble; Jonathan Stieglitz; Dan Cummings; Hillard Kaplan; Aaron D Blackwell; Gandhi Yetish; Herman Pontzer
Journal:  Am J Phys Anthropol       Date:  2019-03-18       Impact factor: 2.868

5.  Postoperative effects of anesthesia and surgery on resting energy expenditure in horses as measured by indirect calorimetry.

Authors:  Antonio M Cruz; Nathalie Coté; Wayne N McDonell; Raymond J Geor; Brian A Wilson; Gabrielle Monteith; Ronald Li
Journal:  Can J Vet Res       Date:  2006-10       Impact factor: 1.310

6.  Evidence for energy conservation during pubertal growth. A 10-year longitudinal study (EarlyBird 71).

Authors:  M Mostazir; A Jeffery; J Hosking; B Metcalf; L Voss; T Wilkin
Journal:  Int J Obes (Lond)       Date:  2016-09-08       Impact factor: 5.095

7.  Metabolically active portion of fat-free mass: a cellular body composition level modeling analysis.

Authors:  ZiMian Wang; Stanley Heshka; Jack Wang; Dympna Gallagher; Paul Deurenberg; Zhao Chen; Steven B Heymsfield
Journal:  Am J Physiol Endocrinol Metab       Date:  2006-08-01       Impact factor: 4.310

8.  Four-compartment cellular level body composition model: comparison of two approaches.

Authors:  Wei Shen; Marie-Pierre St-Onge; Angelo Pietrobelli; Jack Wang; ZiMian Wang; Stanley Heshka; Steven B Heymsfield
Journal:  Obes Res       Date:  2005-01

9.  Body composition and resting energy expenditure in patients aged 11 to 21 years with spinal cord dysfunction compared to controls: comparisons and relationships among the groups.

Authors:  Rungsinee Amanda Liusuwan; Lana M Widman; Richard Ted Abresch; Dennis M Styne; Craig M McDonald
Journal:  J Spinal Cord Med       Date:  2007       Impact factor: 1.985

10.  A cellular level approach to predicting resting energy expenditure: Evaluation of applicability in adolescents.

Authors:  Zimian Wang; Steven B Heymsfield; Zhiliang Ying; Richard N Pierson; Dympna Gallagher; Sonia Gidwani
Journal:  Am J Hum Biol       Date:  2010 Jul-Aug       Impact factor: 1.937

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