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Human energy expenditure: advances in organ‐tissue prediction models
Author(s) -
Heymsfield S. B.,
Peterson C. M.,
Bourgeois B.,
Thomas D. M.,
Gallagher D.,
Strauss B.,
Müller M. J.,
BosyWestphal A.
Publication year - 2018
Publication title -
obesity reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.845
H-Index - 162
eISSN - 1467-789X
pISSN - 1467-7881
DOI - 10.1111/obr.12718
Subject(s) - energy expenditure , function (biology) , production (economics) , energy (signal processing) , organ system , energy metabolism , econometrics , computer science , biochemical engineering , statistics , biology , medicine , pathology , mathematics , economics , endocrinology , disease , evolutionary biology , engineering , macroeconomics
Summary Humans expend energy at rest ( REE ), and this major energy exchange component is now usually estimated using statistical equations that include weight and other predictor variables. While these formulas are useful in evaluating an individual's or group's REE , an important gap remains: available statistical models are inadequate for explaining underlying organ‐specific and tissue‐specific mechanisms accounting for resting heat production. The lack of such systems level REE prediction models leaves many research questions unanswered. A potential approach that can fill this gap began with investigators who first showed in animals and later in humans that REE reflects the summated heat production rates of individual organs and tissues. Today, using advanced imaging technologies, REE can be accurately estimated from the measured in vivo mass of 10 organ‐tissue mass components combined with their respective mass‐specific metabolic rates. This review examines the next frontier of energy expenditure models and discusses how organ‐tissue models have the potential not only to better predict REE but also to provide insights into how perturbations in organ mass lead to structure–function changes across other interacting organ systems. The introductory ideas advanced in this review provide a framework for future human energy expenditure modelling research.

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