Breathing during exercise: There is no such thing as a free lunch
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,008 |
| Communication savante | 0,005 | 0,010 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».