The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Exercise-induced maximal metabolic rate scales with muscle aerobic capacity
Open accessAbstract
SUMMARY: The logarithmic nature of the allometric equation suggests that metabolic rate scaling is related to some fractal properties of the organism. Two universal models have been proposed, based on (1) the fractal design of the vasculature and (2) the fractal nature of the `total effective surface' of mitochondria and capillaries. According to these models, basal and maximal metabolic rates must scale as M3/4. This is not what we find. In 34 eutherian mammalian species (body mass Mbranging from 7 g to 500 kg) we found V̇O2max to scale with the 0.872 (±0.029) power of body mass, which is significantly different from 3/4 power scaling. Integrated structure-function studies on a subset of eleven species (Mb 20 g to 450 kg) show that the variation of V̇O2max with body size is tightly associated with the total volume of mitochondria and of the locomotor musculature capillaries. In athletic species the higher V̇O2max is linked to proportionally larger mitochondrial and capillary volumes. As a result, V̇O2max is linearly related to both total mitochondrial and capillary erythrocyte volumes, as well as to their surface areas. Consequently, the allometric variation of maximal metabolic rate is directly related to the scaling of the total effective surfaces of mitochondria and capillaries, thus confirming the basic conjecture of the second fractal models but refuting the arguments for 3/4 power scaling. We conclude that the scaling of maximal metabolic rate is determined by the energy needs of the cells active during maximal work. The vascular supply network is adapted to the needs of the cells at their working limit. We conjecture that the optimization of the arterial tree by fractal design is the result rather than the cause of the evolution of metabolic rate scaling. The remaining question is why the energy needs of locomotion scale with the 0.872 or 7/8 power of body mass.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A General Model for the Origin of Allometric Scaling Laws in Biology
West, G. B., Brown, J. H., & Enquist, B. J. (1997) · Science · doi:10.1126/science.276.5309.122 · Cited by 5,016
Kleiber, M. (1932) · Hilgardia · doi:10.3733/hilg.v06n11p315 · Cited by 2,222
Kleiber, M. (1947) · Physiological Reviews · doi:10.1152/physrev.1947.27.4.511 · Cited by 1,817
The Fourth Dimension of Life: Fractal Geometry and Allometric Scaling of Organisms
West, G. B., Brown, J. H., & Enquist, B. J. (1999) · Science · doi:10.1126/science.284.5420.1677 · Cited by 1,718 · cited 8×
“…observed scaling of the overall MMR. Interestingly, we found that the scaling of MMR could be explained by the fractal nature of the effective active surface according to the notions proposed by West et al. (1999), but only after modifying their fractal model to account for realistic conditions.”
BackgroundInfluentialENERGETICS OF FREE-RANGING MAMMALS, REPTILES, AND BIRDS
Nagy, K. A., Girard, I. A., & Brown, T. K. (1999) · Annual Review of Nutrition · doi:10.1146/annurev.nutr.19.1.247 · Cited by 806
Cited by
Fiber Types in Mammalian Skeletal Muscles
Schiaffino, S., & Reggiani, C. (2011) · Physiological Reviews · doi:10.1152/physrev.00031.2010 · Cited by 3,022
Energy limitation as a selective pressure on the evolution of sensory systems
Niven, J. E., & Laughlin, S. B. (2008) · Journal of Experimental Biology · doi:10.1242/jeb.017574 · Cited by 1,056
Mitochondrial reticulum for cellular energy distribution in muscle
Glancy, B., Hartnell, L. M., Malide, D., Yu, Z.-X., Combs, C. A., Connelly, P. S., Subramaniam, S., & Balaban, R. S. (2015) · Nature · doi:10.1038/nature14614 · Cited by 450
Oxygen, animals and oceanic ventilation: an alternative view
BUTTERFIELD, N. J. (2009) · Geobiology · doi:10.1111/j.1472-4669.2009.00188.x · Cited by 300
Jacobs, R. A., & Lundby, C. (2013) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01081.2012 · Cited by 222
Nielsen, J., Gejl, K. D., Hey‐Mogensen, M., Holmberg, H., Suetta, C., Krustrup, P., Elemans, C. P. H., & Ørtenblad, N. (2017) · The Journal of Physiology · doi:10.1113/jp273040 · Cited by 191
Global and regional brain metabolic scaling and its functional consequences.
Karbowski, J. (2007) · BMC Biology · doi:10.1186/1741-7007-5-18 · Cited by 161
Exercise and mitochondrial dynamics: keeping in shape with ROS and AMPK
Trewin, A., Berry, B., & Wojtovich, A. (2018) · Antioxidants · doi:10.3390/antiox7010007 · Cited by 129
Phylogenetic Analyses: Comparing Species to Infer Adaptations and Physiological Mechanisms
Rezende, E. L., & Diniz‐Filho, J. A. F. (2012) · Comprehensive Physiology · doi:10.1002/cphy.c100079 · Cited by 120
Menshikova, E. V., Ritov, V. B., Ferrell, R. E., Azuma, K., Goodpaster, B. H., & Kelley, D. E. (2007) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01228.2006 · Cited by 113
Mitochondrial Dysfunction in Skeletal Muscle Pathologies
Abrigo, J., Simon, F., Cabrera, D., Vilos, C., & Cabello-Verrugio, C. (2019) · Current Protein & Peptide Science · doi:10.2174/1389203720666190402100902 · Cited by 88
Pawlak-Chaouch, M., Boissière, J., Gamelin, F. X., Cuvelier, G., Berthoin, S., & Aucouturier, J. (2016) · Nitric Oxide · doi:10.1016/j.niox.2016.01.001 · Cited by 81
HR Index-A Simple Method for the Prediction of Oxygen Uptake
WICKS, J. R., OLDRIDGE, N. B., NIELSEN, L. K., & VICKERS, C. E. (2011) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e318217276e · Cited by 76
Mitochondrial health, the epigenome and healthspan
Aon, M. A., Cortassa, S., Juhaszova, M., & Sollott, S. J. (2016) · Clinical Science · doi:10.1042/cs20160002 · Cited by 66
Low‐intensity training increases peak arm VO2 by enhancing both convective and diffusive O2 delivery
Boushel, R., Ara, I., Gnaiger, E., Helge, J. W., González‐Alonso, J., Munck‐Andersen, T., Sondergaard, H., Damsgaard, R., van Hall, G., Saltin, B., et al. (2014) · Acta Physiologica · doi:10.1111/apha.12258 · Cited by 64
Andersson Hall, U., Edin, F., Pedersen, A., & Madsen, K. (2016) · Applied Physiology Nutrition and Metabolism · doi:10.1139/apnm-2015-0452 · Cited by 41
Misconceptions and Issues Regarding Allometric Scaling during the Drug Development Process
Mahmood, I. (2018) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2018.1499725 · Cited by 35
Phenotypic differences between people varying in muscularity
Heymsfield, S. B., Smith, B., Chung, E. A., Watts, K. L., Gonzalez, M. C., Yang, S., Heo, M., Thomas, D. M., Turner, D., Bosy‐Westphal, A., et al. (2022) · Journal of Cachexia, Sarcopenia and Muscle · doi:10.1002/jcsm.12959 · Cited by 35
Persson, H. L., Sioutas, A., Kentson, M., Jacobson, P., Lundberg, P., Dahlqvist Leinhard, O., & Forsgren, M. F. (2022) · Journal of Inflammation Research · doi:10.2147/jir.s366204 · Cited by 25
Metadata
- Authors
- Weibel, E. R., & Hoppeler, H.
- Venue
- Journal of Experimental Biology
- Publisher
- The Company of Biologists
- Published
- 2005-05-01
- Volume
- 208
- Issue
- 9
- Pages
- 1635-1644
- ISSN
- 0022-0949 (ISSN-L)
- 1477-9145
- OpenAlex
- W2161570633
- Cited by
- 324
- References
- 5
- FWCI
- 9.80
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.