Body composition modeling. Application to exploration of the resting energy expenditure fat-free mass relationship.
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.
Heymsfield
S B
SB
Obesity Research Center, St. Luke's/Roosevelt Hospital, Columbia University, College of Physicians and Surgeons, New York, New York 10025, USA. SBH2@Columbia.edu
Gallagher
D
D
Wang
Z
Z
D000273:Adipose Tissue; D000818:Animals; D001481:Basal Metabolism; D001823:Body Composition; D001835:Body Weight; D002151:Calorimetry; D006801:Humans; D008322:Mammals; D008954:Models, Biological; D012044:Regression Analysis