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Record W2962793054 · doi:10.1113/ep087922

Breathing during exercise: There is no such thing as a free lunch

2019· letter· en· W2962793054 on OpenAlexaff
A. William Sheel, Paolo B. Dominelli

Bibliographic record

VenueExperimental Physiology · 2019
Typeletter
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of WaterlooUniversity of British Columbia
Fundersnot available
KeywordsBlood flowRespiratory systemWork of breathingVentilation (architecture)Skeletal muscleMedicineCardiorespiratory fitnessExercise physiologyPhysical exerciseAnatomyPhysical medicine and rehabilitationCardiologyInternal medicinePhysics

Abstract

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When humans perform whole-body exercise, such as running or cycling, blood flow to active muscles increases; this is well known. Specifically, skeletal muscle blood flow and metabolism are closely matched during dynamic exercise. The coupling of blood flow and metabolism occurs across a range of intensities from rest to heavy exercise and during both small and large muscle mass exercise. But can all active muscles obtain their fair share of blood flow during heavy exercise? Probably not. Several research groups have shown that the arms and legs cannot both be perfused maximally during heavy exercise. For example, Calbet et al. (2004) showed that maximal blood flow conductance to the arms and legs of well-trained cross-country skiers must be constrained in order to maintain appropriate blood pressure. It is important to recognize that the act of breathing during exercise requires repeated forceful contractions of the respiratory musculature, which impose significant metabolic and blood flow demands. During heavy exercise, when both the respiratory muscles and locomotor muscles are contracting near maximally, how does this influence blood flow distribution? Harms et al. (1998) addressed this question by manipulating respiratory muscle work during heavy cycle exercise and showed that locomotor muscle blood flow was inversely related to respiratory muscle work. In other words, when the respiratory muscles have to work hard to generate high levels of ventilation during exercise, active limb blood flow decreased, despite constant limb work. To paraphrase the conclusion of Harms et al. (1998), the respiratory muscles get their own ‘piece of the pie’ and will do so at the expense of active locomotor muscles. Owing to methodological and technical limitations, Harms et al. (1998) were unable to determine where the ‘extra’ leg blood flow came from during unloaded breathing. Likewise, during loading breathing, where did the diminished leg blood flow ‘go’? To explore the relationship between blood flow and the metabolic demands of limb and respiratory muscle during heavy exercise, we (Dominelli et al., 2017) asked whether the high levels of respiratory muscle work alter blood flow distribution, with simultaneous measures of quadriceps and respiratory muscle blood flow. In essence, we sought to provide the missing link from the study by Harms et al. (1998) by measuring respiratory muscle blood flow during conditions of experimental manipulation of the work of breathing. To this end, we calculated an index of blood flow, based on the Fick principle, by using near-infrared spectroscopy and the light-absorbing tracer Indocyanine Green dye. We then developed a proportional assist ventilator in order to lower the mechanical work of breathing and used inspiratory resistance to increase the work of breathing. To summarize our findings, we demonstrated bi-directional changes in both respiratory and quadriceps blood flow when the work of breathing was either increased or decreased experimentally. To put another way, blood flow increased to the respiratory muscles and decreased to the quadriceps during loaded breathing (increased work of breathing), whereas unloaded breathing (decreased work of breathing) resulted in increased quadriceps blood flow and decreased respiratory muscle blood flow. Our results confirmed the linkage between blood flow and metabolism and extended this to the respiratory musculature. Our findings add support to idea of the respiratory muscle metaboreflex. Our findings suggest that the respiratory muscles are able to ‘steal’ blood flow from other exercising muscle groups. The above-mentioned studies used healthy, young research participants to demonstrate, in our view, physiological principles. A relevant question is: do our findings in healthy humans translate to other human models that are health related? For example, people with obesity are at increased risk for many serious diseases and health conditions. The effects of obesity also have an impact on the response to exercise, including an increased O2 cost and diminished exercise tolerance. It is also known that obesity is associated with altered respiratory mechanics and a higher O2 cost of breathing, presumably related to fat mass on the chest wall. We read with interest the recent work of Alemayehu et al. (2018), who had obese male teenagers perform specific training of the respiratory muscles for 3 weeks during a weight-loss programme. Respiratory muscle training reduced the O2 cost of breathing during walking and improved exercise tolerance. It is possible to speculate that specific training of the respiratory muscles lowered the O2 cost of breathing and blood flow demands of exercise hyperpnoea in obesity. The observations by Alemayehu et al. (2018) are important, because improvements in exercise tolerance in obesity have the potential to be a relevant adjunct intervention in the control of obesity. Additional research should be targeted to gain an understanding of the underlying physiology of their observations and the efficacy of the intervention. Although the specific mechanistic bases for the findings of Alemayehu et al. (2018) are not necessarily known, they fit with our working hypothesis; namely, that the muscles of respiration have a preferentially important role in the response to exercise. Overall, it should be appreciated that breathing, although normally performed without conscious effort, should not be taken for granted. The respiratory muscles will command their own share of the ‘cardiac output pie’, and this impacts the integrative response to exercise. None declared.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.259
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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