The scaling of human basal and resting metabolic rates
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ORIGINAL ARTICLE
The scaling of human basal and resting metabolic rates Heather M. Bowes1 · Catriona A. Burdon1 · Nigel A. S. Taylor1 Received: 21 May 2020 / Accepted: 23 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose In tachymetabolic species, metabolic rate increases disproportionately with body mass, and that inter-specific relationship is typically modelled allometrically. However, intra-specific analyses are less common, particularly for healthy humans, so the possibility that human metabolism would also scale allometrically was investigated. Methods Basal metabolic rate was determined (respirometry) for 68 males (18–40 years; 56.0–117.1 kg), recruited across five body-mass classes. Data were collected during supine, normothermic rest from well-rested, well-hydrated and postabsorptive participants. Linear and allometric regressions were applied, and three scaling methods were assessed. Data from an historical database were also analysed (2.7–108.9 kg, 4811 males; 2.0–96.4 kg, 2364 females). Results Both linear and allometric functions satisfied the statistical requirements, but not the biological pre-requisite of an origin intercept. Mass-independent basal metabolic data beyond the experimental mass range were not achieved using linear regression, which yielded biologically impossible predictions as body mass approached zero. Conversely, allometric regression provided a biologically valid, powerful and statistically significant model: metabolic rate = 0.739 * body mass0.547 (P 0.05), so their data were retained. Historical participants Resting metabolic rates for two additional (independent) population samples were extracted from the literature (one male and one female dataset). Arguably the most extensive, and perhaps also the most carefully filtered compilations of such data are contained within Schofield (1985; Appendix 3). Those data were extracted from literature published over the preceding 60 years, although the level of experimental control would vary across time and laboratories. The reasons for using those datasets were two-fold. In the first instance, a cross-validation of the scaling model derived using the experimental participants would be undertaken by applying that model to data from an independent and very large (male) population sample. That sample included 4811 males, aged between 0.02 and 52.3 years, and covered a body-mass range from 2.7–108.9 kg. A second historical sample was also used, which contained 2364 females (body mass, 2.0–96.4 kg; age, 0.14–64.0 years). Now the objective was to evaluate the gender-independence of scaling models obtained using the male data. For both datasets, morphological and resting metabolic means (with standard deviations and sample sizes) were provided in increments of 1 kg (bins), with 101 and 93 body-mass classifications (class intervals), respectively. In all such data-retrieval exercises, just as there is with meta-analyses, there is a reliance upon the skill and precision of the original investigato
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