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Body Composition Modeling: Application to Exploration of the Resting Energy Expenditure Fat‐free Mass Relationship
Author(s) -
HEYMSFIELD STEVEN B.,
GALLAGHER DYMPNA,
WANG ZIMIAN
Publication year - 2000
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2000.tb06470.x
Subject(s) - resting energy expenditure , fat free mass , composition (language) , linear regression , function (biology) , fat mass , regression analysis , energy (signal processing) , field (mathematics) , regression , basis (linear algebra) , component (thermodynamics) , computer science , econometrics , statistics , mathematics , energy expenditure , body weight , biology , pure mathematics , endocrinology , evolutionary biology , physics , thermodynamics , linguistics , philosophy , geometry
A bstract : There are now many published methods for predicting resting energy expenditure (REE) from measured body mass and 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 between these many 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 ultimate aim of clarifying prevailing ambiguities in the field. Our classification scheme is founded on the mathematical function type (descriptive and mechanistic) and body composition level (whole body → molecular) used in REE prediction model development. The model is applied in an exploration of the well‐established empirical relationship between REE and fat‐free body mass (FFM). The developed relationships indicate that REE vs. FFM is a curvilinear relationship in mammals as a whole, that the relationship can be described as a linear function in humans, and that the simple linear regression line coefficients can be reconstructed from established tissue‐system level component relationships. Our classification system, the first founded on a conceptual basis, highlights similarities and differences between the many diverse REE body composition prediction methods, provides a framework for teaching REE‐body composition relationships to students, and suggests important future research opportunities.