Effect of Constitution on Mass of Individual Organs and Their Association with Metabolic Rate in Humans—A Detailed View on Allometric Scaling

Müller, Manfred J.; Langemann, Dirk; Gehrke, Isabel; Later, Wiebke; Heller, Martin; Glüer, Claus C.; Heymsfield, Steven B.; Bosy‐Westphal, Anja

doi:10.1371/journal.pone.0022732

Cited by 69 publications

(60 citation statements)

References 29 publications

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“…This is an often quoted use of allometry (e.g., 149,200,235,246,247,256,267,268,287,289) and is particularly prevalent in the human literature where scaling principles have been used to predict rates of metabolism (e.g., 172, 173, 284) and drug clearance (e.g., 396,397,398). Such analyses of intraspecific scaling for humans now routinely include data for metabolic rate and organ sizes of hundreds of individuals, and have been particularly useful for establishing the influence of body composition and stature on the scaling of metabolic rate with size (171,172,173,284), and for estimating in vivo metabolic rates of organ-tissue compartments (409,410). Scaling relationships can also be applied to the prediction of physiological characteristics of extinct species (114,270,348,359,361,368), and, because of the allometric relationship between body mass and other morphological variables, can be used to predict morphological characteristics that are (440) shown ± SEM and compared with predicted BMR for a 33 g murid rodent (shown ± SEE) for the OLS and PIC regressions presented in (A).…”

Section: Using Scaling Relationships Predicting Traits From Body Massmentioning

confidence: 99%

Metabolic Scaling in Animals: Methods, Empirical Results, and Theoretical Explanations

White

Kearney

2014

Comprehensive Physiology

167

174

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Life on earth spans a size range of around 21 orders of magnitude across species and can span a range of more than 6 orders of magnitude within species of animal. The effect of size on physiology is, therefore, enormous and is typically expressed by how physiological phenomena scale with mass(b). When b ≠ 1 a trait does not vary in direct proportion to mass and is said to scale allometrically. The study of allometric scaling goes back to at least the time of Galileo Galilei, and published scaling relationships are now available for hundreds of traits. Here, the methods of scaling analysis are reviewed, using examples for a range of traits with an emphasis on those related to metabolism in animals. Where necessary, new relationships have been generated from published data using modern phylogenetically informed techniques. During recent decades one of the most controversial scaling relationships has been that between metabolic rate and body mass and a number of explanations have been proposed for the scaling of this trait. Examples of these mechanistic explanations for metabolic scaling are reviewed, and suggestions made for comparing between them. Finally, the conceptual links between metabolic scaling and ecological patterns are examined, emphasizing the distinction between (1) the hypothesis that size- and temperature-dependent variation among species and individuals in metabolic rate influences ecological processes at levels of organization from individuals to the biosphere and (2) mechanistic explanations for metabolic rate that may explain the size- and temperature-dependence of this trait.

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Section: Using Scaling Relationships Predicting Traits From Body Massmentioning

confidence: 99%

Metabolic Scaling in Animals: Methods, Empirical Results, and Theoretical Explanations

White

Kearney

2014

Comprehensive Physiology

167

174

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“…This research builds upon the quickly advancing development of imaging techniques, particularly computer tomography and magnetic resonance imaging (MRI), allowing for the quantification of the organ size in vivo and their contribution to variation in human BMR (e.g. Later et al 2008; Javed et al 2010; Müller et al 2002, 2011). For example, Elia (1992) estimated the mass-specific metabolic rates (in kcal/kg per day) of major human organs of young adults to be: 200 for liver, 240 for brain, 440 for heart and kidneys, 13 for skeletal muscle, 4.5 for adipose tissue and 12 for residual mass.…”

Section: Composition Of Bmr At Organ Levelmentioning

confidence: 99%

“…These estimates were recently validated with the use of imaging technologies, which also allowed for the fine-tuning of Elia’s estimates with respect to the effect of gender differences (Wang et al 2011a) and obesity (Wang et al 2011b). Likewise, a recent imaging-based comprehensive analysis of the scaling of human BMR and organ masses revealed that muscle, brain and liver explained up to 43 % of the inter-individual variance in human BMR (Müller et al 2011). …”

Section: Composition Of Bmr At Organ Levelmentioning

confidence: 99%

Determinants of intra-specific variation in basal metabolic rate

2012

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Basal metabolic rate (BMR) provides a widely accepted benchmark of metabolic expenditure for endotherms under laboratory and natural conditions. While most studies examining BMR have concentrated on inter-specific variation, relatively less attention has been paid to the determinants of within-species variation. Even fewer studies have analysed the determinants of within-species BMR variation corrected for the strong influence of body mass by appropriate means (e.g. ANCOVA). Here, we review recent advancements in studies on the quantitative genetics of BMR and organ mass variation, along with their molecular genetics. Next, we decompose BMR variation at the organ, tissue and molecular level. We conclude that within-species variation in BMR and its components have a clear genetic signature, and are functionally linked to key metabolic process at all levels of biological organization. We highlight the need to integrate molecular genetics with conventional metabolic field studies to reveal the adaptive significance of metabolic variation. Since comparing gene expressions inter-specifically is problematic, within-species studies are more likely to inform us about the genetic underpinnings of BMR. We also urge for better integration of animal and medical research on BMR; the latter is quickly advancing thanks to the application of imaging technologies and ‘omics’ studies. We also suggest that much insight on the biochemical and molecular underpinnings of BMR variation can be gained from integrating studies on the mammalian target of rapamycin (mTOR), which appears to be the major regulatory pathway influencing the key molecular components of BMR.

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“…In fed subjects the estimated glycogen concentrations of the liver ( C liv ) (Nilsson, 1973; Rothman et al, 1991; Taylor et al, 1996; Petersen et al, 2004; Awad et al, 2010) approximate 500 mmol/kg dry weight and in muscle ( C sk ) 300 mmol/kg dry weight. Müller et al (Müller et al, 2011) published scaling relationships for total muscle mass by DXA and liver mass from MRI scanning in 262 adult humans. These were muscle mass (kg) =0.93× BW 0.76 and liver mass (g) =0.1× BW 0.62 .…”

Section: Methods and Resultsmentioning

confidence: 99%

A mathematical model of weight loss under total starvation: evidence against the thrifty-gene hypothesis

Speakman

2012

Disease Models &Amp; Mechanisms

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SUMMARYThe thrifty-gene hypothesis (TGH) posits that the modern genetic predisposition to obesity stems from a historical past where famine selected for genes that promote efficient fat deposition. It has been previously argued that such a scenario is unfeasible because under such strong selection any gene favouring fat deposition would rapidly move to fixation. Hence, we should all be predisposed to obesity: which we are not. The genetic architecture of obesity that has been revealed by genome-wide association studies (GWAS), however, calls into question such an argument. Obesity is caused by mutations in many hundreds (maybe thousands) of genes, each with a very minor, independent and additive impact. Selection on such genes would probably be very weak because the individual advantages they would confer would be very small. Hence, the genetic architecture of the epidemic may indeed be compatible with, and hence support, the TGH. To evaluate whether this is correct, it is necessary to know the likely effects of the identified GWAS alleles on survival during starvation. This would allow definition of their advantage in famine conditions, and hence the likely selection pressure for such alleles to have spread over the time course of human evolution. We constructed a mathematical model of weight loss under total starvation using the established principles of energy balance. Using the model, we found that fatter individuals would indeed survive longer and, at a given body weight, females would survive longer than males, when totally starved. An allele causing deposition of an extra 80 g of fat would result in an extension of life under total starvation by about 1.1–1.6% in an individual with 10 kg of fat and by 0.25–0.27% in an individual carrying 32 kg of fat. A mutation causing a per allele effect of 0.25% would become completely fixed in a population with an effective size of 5 million individuals in 6000 selection events. Because there have probably been about 24,000 famine events since the evolution of hominins 4 million years ago, there has been ample time even for genes with only very minor impacts on adiposity to move to fixation. The observed polymorphic variation in the genes causing the predisposition to obesity is incompatible with the TGH, unless all these single nucleotide polymorphisms (SNPs) arose in the last 900,000 years, a requirement we know is incorrect. The TGH is further weakened by the observation of no link between the effect size of these SNPs and their prevalence, which would be anticipated under the TGH model of selection if all the SNPs had arisen in the last 900,000 years.

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Effect of Constitution on Mass of Individual Organs and Their Association with Metabolic Rate in Humans—A Detailed View on Allometric Scaling

Cited by 69 publications

References 29 publications

Metabolic Scaling in Animals: Methods, Empirical Results, and Theoretical Explanations

Metabolic Scaling in Animals: Methods, Empirical Results, and Theoretical Explanations

Determinants of intra-specific variation in basal metabolic rate

A mathematical model of weight loss under total starvation: evidence against the thrifty-gene hypothesis

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