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Enregistrement W2566798620 · doi:10.1113/jp273529

Pushing it to the limit: enhanced diffusing membrane capacity facilitates greater pulmonary diffusing capacity in athletes during exercise

2016· letter· en· W2566798620 sur OpenAlexaff
Leah Groves, S. Brade, Stephen P. Wright

Notice bibliographique

RevueThe Journal of Physiology · 2016
Typeletter
Langueen
DomaineMedicine
ThématiqueChronic Obstructive Pulmonary Disease (COPD) Research
Établissements canadiensUniversity of TorontoQueen's UniversityUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésDLCOPulmonary Diffusing CapacityDiffusing capacityCardiologyMedicineVO2 maxInternal medicineOxygen transportAthletesStroke volumePulmonary function testingCardiac outputLung volumesAnaerobic exerciseEndurance trainingAerobic capacityPhysical therapyChemistryLungHemodynamicsHeart rateOxygenLung functionBlood pressure

Résumé

récupéré en direct d'OpenAlex

The capacity to perform aerobic exercise is commonly considered in terms of , the maximum rate at which the body can uptake and utilize oxygen during high intensity exercise. This capacity is in large part determined by oxygen delivery (), in turn dependent on both cardiac output (CO) and arterial oxygen content (), as described by the Fick equation. It is generally accepted that CO represents the primary central factor limiting aerobic performance in healthy individuals. While stroke volume and maximal CO increase markedly with endurance training, the effect of training on pulmonary function is less clear. That remains high in athletes near maximal exercise despite reduced pulmonary capillary transit time as pulmonary flow increases is a result of exercise-related increases in pulmonary diffusing capacity, as assessed by the diffusing capacity of the lung for carbon monoxide (DLCO). Augmentation of DLCO is secondary to increased pulmonary capillary blood volume (VC) and diffusing membrane capacity (DM), and one or both of these factors may be enhanced in athletes to facilitate blood oxygenation at maximal exercise; however, the challenges associated with data acquisition at near-maximal exercise have left several important questions regarding pulmonary exercise physiology unanswered. In a recent issue of The Journal of Physiology, Tedjasaputra et al. (2016) endeavoured to address these knowledge gaps, and investigated the changes in DLCO, VC and DM during submaximal exercise up to 90% in a group of endurance trained athletes and a group of healthy non-athletes. The authors hypothesized that DLCO, VC, and DM would be higher in athletes compared to non-athletes during incremental cycling exercise. To test this hypothesis, 15 male endurance-trained athletes ( = 64.6 ± 1.8 ml kg–1 min–1) and 14 non-athletes ( = 45.0 ± 1.2 ml kg–1 min–1) were matched for age and height. Subjects performed a graded exercise test to determine , as well as three study visits each consisting of five single-breath determinations of DLCO at exercise intensities corresponding to 30%, 50%, 70%, 80% and 90% of . Study visits were separated by at least 48 h, and the Roughton and Forster method was used to calculate DM and VC. The authors noted that DLCO and DM were higher in the endurance-trained group compared to the untrained group during high-intensity exercise, although VC was not different. From these findings, the authors concluded that differences in the pulmonary alveolar–capillary membrane of endurance-trained athletes allow for increased oxygen diffusion during high-intensity exercise. The study design utilized by Tedjasaputra et al. (2016) has several strengths and limitations which merit discussion. Volunteers were appropriately selected with no history of pulmonary or cardiovascular disease. It is notable that the study did not include female volunteers. Although there are specific considerations associated with the inclusion of pre-menopausal female participants, the necessity of understanding sex differences in cardiorespiratory physiology is increasingly recognized, and the inclusion of such groups would have been a significant strength. Although it appears that exercise assessments may have been performed in a semi-upright position, which may limit , both graded exercise testing and submaximal exercise studies were performed on the same ergometer, limiting the potential for variation in mechanical efficiency. The study consisted of four exercise sessions performed within approximately 7 days. In untrained individuals, the initiation of exercise training results in acute plasma volume expansion that is associated with gains in (Goodman et al. 2005), and which may be observed after even a single training session. However, that the study protocol involved randomization of exercise conditions would mitigate the influence of a training effect on the results. Moreover, randomization of experimental conditions is an important though occasionally overlooked aspect of small human studies, particularly in the context of exercise studies in which there may be time-varying responses (i.e. warm-up effects), and the authors are to be acknowledged for this aspect of their experimental design. The main finding of the current study is that diffusing membrane capacity, DM, appears to be greater in athletes compared to non-athletes during exercise, with group separation occurring at moderate intensities. Interestingly, this difference was independent of either alveolar volume or pulmonary flow, and peak DM was correlated with peak . The authors speculate that augmented DM may represent enhanced capillary recruitment in athletes during exercise. The transfer of oxygen from the alveoli to capillary blood is typically perfusion-limited; however, during strenuous exercise, pulmonary capillary transit time decreases from 0.75 s and can approach 0.25 s, where diffusion may become limited. DM is directly related to the surface area available for gas exchange, which must be both ventilated and perfused, and inversely related to membrane thickness. In humans in an upright or semi-upright position, pulmonary intravascular pressure decreases moving upward from the heart due to the hydrostatic pressure gradient, and blood flow becomes pulsatile when alveolar pressure exceeds intravascular pressure (so-called West Lung Zone 2). While it is possible that increasing pulmonary intravascular pressure may recruit vessels unperfused at rest, capillary recruitment is likely to complete with even mild exercise (Reeves & Taylor, 1996). The current study observed that the greater DM in athletes relative to non-athletes began at moderate intensities and became pronounced at the 90% effort condition, when athletes appeared to augment DM from 70% effort, while conversely, non-athletes trended slightly downward; put differently, DM/ remained stable in athletes while DM/ declined in non-athletes. Interestingly, this pattern is similar to that of the stroke volume response to exercise observed by Stickland et al. (2006). Despite the routine consideration of mean blood flow, it is important to consider that blood flow is pulsatile, delivered into (and offloaded from) the pulmonary circulation in boluses. Although the present study did not detect a difference between groups, greater stroke volume and the ability to augment stroke volume to maximal effort is a well-documented adaptation to chronic endurance training, while the stroke volume of non-athletes typically plateaus above moderate exercise (Gledhill et al. 1994). Moreover, athletes generate higher pulmonary artery systolic pressures (and presumably higher pulse pressures) during exercise than non-athletes (La Gerche et al. 2010), and augment the amount of blood stored in the pulmonary circulation within each beat as exercise intensity progresses. Although speculative, larger within-beat fluctuations in intravascular volume (due to greater pulse pressure and/or a more distensible vasculature) may distend the pulmonary capillaries to a greater extent in athletes, increasing surface area for gas exchange during cardiac systole when pulmonary flow peaks and capillary transit time is shortest. Indeed, mechanical stress of the pulmonary circulation during exercise in high-performance athletes has been associated with changes in blood–gas barrier integrity (Hopkins et al. 1997). It may thus be interesting to examine the relationship between DM and stroke volume during exercise in these groups. Tedjasaputra et al. (2016) conjectured that endurance-trained athletes may have beneficial adaptations within the pulmonary vasculature, allowing for greater and . The authors note that a greater resting VC at similar in athletes, coupled with observations in other studies of modestly lower pressures in the pulmonary circulation, suggests that athletes have a more compliant pulmonary vasculature than non-athletes. Estimates of pulmonary vascular distensibility are remarkably consistent in healthy subjects at approximately 2% mmHg–1. Although pulmonary vascular distensibility decreases with age and disease (Reeves et al. 2005), and it is tempting to hypothesize that endurance training may enhance pulmonary vascular distensibility, it remains unclear if this is true. While the authors note the inverse relationship between pulmonary vascular resistance and compliance, the primary benefit of enhanced pulmonary vascular compliance lies in the attenuation of increased pulsatile right ventricular afterload with exercise, promoting more favourable right ventricular–vascular coupling, and enhanced right ventricular output at a given performance of stroke work. Such adaptations, if confirmed, may contribute to the ability of endurance athletes to recruit enhanced stroke volume while maintaining physiological levels of pulmonary capillary pressure. In conclusion, Tedjasaputra et al. (2016) demonstrate that pulmonary diffusion capacity at near-maximal exercise is greater in endurance-trained male athletes compared to non-athletes, and that this is primarily a result of enhanced diffusing membrane capacity, independent of mean pulmonary flow or alveolar volume. This adaptation permits the transfer of oxygen from the alveoli to the blood without limitation during the performance of high intensity exercise. Future work may consider whether this is a result of intrinsic remodelling of the blood–gas barrier, or secondary to divergent pulmonary haemodynamic responses. None declared. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. We thank Dr Susanna Mak for her helpful insights and review of this manuscript.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,041
Tête enseignante GPT0,269
Écart entre enseignants0,227 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2016
Routes d'admission1
Résumé présentoui

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