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Enregistrement W2025039258 · doi:10.1113/jphysiol.2009.176925

The Frank–Starling mechanism and thermal stress: fundamentals applied!

2009· letter· en· W2025039258 sur OpenAlexaff
Daniel Gagnon

Notice bibliographique

RevueThe Journal of Physiology · 2009
Typeletter
Langueen
DomaineMedicine
ThématiqueThermoregulation and physiological responses
Établissements canadiensUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésFrank–Starling law of the heartMechanism (biology)StarlingStress (linguistics)CardiologyMedicineInternal medicinePhysicsPhilosophyHeart rateStroke volumeBlood pressureQuantum mechanics

Résumé

récupéré en direct d'OpenAlex

The Frank–Starling mechanism, by which changes in end-diastolic volume alter stroke volume, is one of the more fundamental concepts of human physiology. In humans, this concept is represented by a hyperbolic pulmonary capillary wedge pressure (index of left-ventricular filling pressure) to stroke volume curve. In a recent issue of the Journal of Physiology, Wilson et al. (2009) have provided an elegant example of how such fundamental concepts can be applied to elucidate underlying mechanisms of altered physiological responses, in this case, during combined orthostatic and thermal stresses. Thermal stress brings about significant cardiovascular adjustments that ultimately affect cardiac filling pressure and therefore the Frank–Starling mechanism. During passive heat stress, increases in core and skin temperatures require a redistribution of blood flow from non-cutaneous vascular beds and an increase in skin blood flow so that heat can be transferred from the body core to the skin. In such situations, central venous pressure and central blood volume decrease (Crandall et al. 2008). Despite these reductions in cardiac filling pressure, stoke volume is well maintained due to increased cardiac contractility (Brothers et al. 2009). As such, cardiac output can increase to upwards of ∼13 l min−1 which, along with splanchnic and renal vasoconstriction, allows skin blood flow to reach levels as high as ∼8 l min−1 (Rowell, 1974). Although mean arterial pressure is well maintained in the supine position, a competition develops between the maintenance of blood pressure and the need to maintain adequate skin perfusion for heat exchange when gravitational stress (e.g. lower body negative pressure, LBNP) is superimposed on whole-body heat stress. As such, many studies have shown that orthostatic tolerance is greatly reduced in the heat stressed human. On the other hand, during passive cold stress, peripheral vasoconstriction attenuates the rate of heat dissipation from the body by reducing the temperature gradient between the skin and the environment. Contrary to heat stress, decreases in skin blood flow parallel increases in total peripheral resistance and mean arterial pressure (Cui et al. 2005). As such, cold stress greatly improves orthostatic tolerance not only relative to hyperthermia but also to normothermia. While studies have established clear differences in orthostatic tolerance between heat and cold stresses, the underlying mechanisms for these differences have not been entirely elucidated. Cold stress has been shown to maintain greater levels of central blood volume, central venous pressure and consequently stroke volume for a given level of LBNP compared to both normothermia and hyperthermia (Cui et al. 2005). The maintained stroke volume during cold stress suggests a plasticity in the operating point of the Frank–Starling mechanism according to an individual's thermal status, thereby eliciting a different change in stroke volume for a given decrease in left-ventricular filling pressure. To test the hypothesis that such a mechanism might contribute to the observed differences in orthostatic tolerances between heat and cold stresses, Wilson et al. (2009) examined changes in stroke volume as a function of changes in pulmonary capillary wedge pressure in 11 healthy volunteers under the following conditions: (1) normothermia, (2) cold stress, and (3) heat stress. Capillary wedge pressure, an index of left-ventricular filling pressure, was determined by balloon inflation of a pulmonary artery catheter. Cardiac output was determined by thermodilution, and subsequently used to calculate stroke volume. Participants were fitted with a water-perfused suit and were placed in the supine position in a LBNP chamber. During the normothermic trial, water at 34°C perfused the suit. Subsequently, the water temperature was reduced to 20°C for a 20 min period of skin surface cooling (cold stress) while it was increased to 46°C until core temperature increased by 1°C for the heat stress trial. At each thermal condition, baseline measurements were followed by 15 mmHg of LBNP for 15 min which was immediately followed by 30 mmHg of LBNP for an additional 15 min. The two different levels of LBNP were used to induce changes in pulmonary capillary wedge pressure, to which changes in stroke volume were subsequently related. The results revealed that the thermal status of the individual clearly influenced the operating point of the Frank–Starling curve. Specifically, the pre-LBNP values for the operating point in normothermia were located midway between the flatter and steeper portion of the curve, whereas cold stress shifted the operating point to the right (i.e. further towards the flatter portion of the curve). In contrast, heat stress shifted the operating point to the left (i.e. closer to the steep portion of the curve) (see Fig. 1). As such, large reductions in stroke volume during heat stress were brought about by relatively small changes in pulmonary capillary wedge pressure, while stroke volume remained on or near the flat portion of the curve even at the highest level of LBNP during skin surface cooling. The plasticity in the operating point of the Frank–Starling mechanism as a function of an individuals’ thermal status represents a novel mechanism for the observed differences in orthostatic tolerance between cold and heat stress conditions. Although orthostatic tolerance was not directly assessed in the study of Wilson et al. (2009), three participants experienced syncopal symptoms while heat stressed at 30 mmHg LBNP. In contrast, all participants completed the 30 mmHg LBNP without any signs of syncope during the normothermic and cold stress conditions. Schematic illustration of the Frank–Starling relationship and how the operating point (i.e. prior to any perturbation in cardiac filling pressure) is shifted during cold (triangle) and heat (square) stress relative to normothermia (circle) Note how the operating point is shifted towards the flatter portion of the curve during cold stress while it is shifted towards the steeper portion of the curve during heat stress. Therefore, when cardiac filling decreases (e.g. LBNP), stroke volume will reach the steep portion of the curve relatively faster during heat stress compared to both normothermia and cold stress. While multi-mechanistic in nature, orthostatic intolerance during heat stress will ultimately develop due to substantial reductions in cardiac output which cannot be compensated by increases in systemic vascular resistance. As evidenced by the data of Wilson et al. (2009), the shift in the operating point of the Frank–Starling mechanism towards the flatter portion of the curve during skin surface cooling creates a ‘reserve’ such that cardiac filling pressure can decrease to a greater extent relative to heat stress before stroke volume, and therefore cardiac output, is significantly reduced. In fact, skin surface cooling in the study of Wilson et al. (2009) significantly attenuated the reductions in stroke volume, cardiac output and pulse pressure at both levels of LBNP compared to the heat stress condition. Thus, attenuated decreases in cardiac output due to skin surface cooling are a likely mechanism by which orthostatic tolerance is preserved. The data of Wilson et al. (2009) therefore provide a good example of how a fundamental concept of human physiology can be applied to determine the mechanisms of altered physiological responses. I would like to thank Dr Glen P. Kenny for his assistance in preparing this article.

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)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,025

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,007
Communication savante0,0030,007
Science ouverte0,0020,003
Intégrité de la recherche0,0040,008
Charge utile insuffisante (le modèle a refusé de juger)0,0080,005

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 Opus0,021
Tête enseignante GPT0,272
Écart entre enseignants0,251 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations1
Publié2009
Routes d'admission1
Résumé présentoui

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