Heterogeneous regulation of cerebral blood flow in hypoxia; implications for dynamic cerebral autoregulation and susceptibility to acute mountain sickness
Notice bibliographique
Résumé
The recent publication by Horiuchi et al. (2016) has highlighted a linear reduction in dynamic cerebral autoregulation (dCA) during acute exposure to progressive poikilocapnic normobaric hypoxia (fractional inspired O2 reduced from 21 to 12%) that was associated with increased susceptibility to the neurological syndrome, acute mountain sickness (AMS). Although it is an interesting study justified by the ongoing controversy underlying AMS pathophysiology, it is nonetheless important to acknowledge the experimental limitations when using common carotid artery (CCA) blood flow to interpret changes in dCA metrics given the potential confounding factors associated with the differential vasoregulation of downstream feed arteries, notably the internal (ICA) and external (ECA) carotid arteries. Originating from the bifurcation of the CCA, both arteries feed into distinct vascular territories, with the face, anterior neck and cranium wall supplied by the ECA and cerebral cortex furnished via the ICA through the middle, anterior and posterior cerebral arteries. Indeed, the regional heterogeneity of blood flow is evident during reperfusion-induced hypotension following thigh-cuff occlusion, the experimental manoeuvre selected in the present study to provide the autoregulatory stimulus. We have previously shown this manoeuvre to be characterized by a selective redistribution of blood flow from the ECA to facilitate rapid recovery of ICA flow, which we took to reflect a neuroprotective response given the need to optimize dCA through maintenance of adequate intracranial blood flow, oxygenation and substrate delivery throughout the physiological challenge of hypotension (Ogoh et al. 2014). Furthermore, related research has identified lower metrics for dCA (Ogoh et al. 2014; Smirl et al. 2014) and CO2 vasoreactivity (Sato et al. 2012) in the ECA compared with the ICA, highlighting ‘prioritized’ defense of the intracranial circulation given the need to couple cerebral metabolism with flow at the expense of the ECA vasculature that subserves thermoregulation. The mechanisms underlying these regional differences are likely to be complex, attributable in part to anatomical heterogeneity in microvascular density, tone, autonomic innervation and nitric oxide/K+ channel activity (Ogoh et al. 2014). Regardless, such differences make it difficult to infer reliable changes in dCA during hypocapnic hypoxia without accounting for the differential vasoregulation exhibited by these downstream arteries. This complication may well account for the authors’ previous findings (Subudhi et al. 2015) which, in stark contrast to their present conclusions, failed to support a functional link between impaired dCA and AMS symptomatology despite the use of comparable methodology (thigh-cuff occlusion–reperfusion). On that occasion, dCA metrics were confined more distally to the middle cerebral artery, which is characterized by higher CO2 reactivity compared with the ICA and ECA (Sato et al. 2012). Thus, given the complexities of the underlying physiology, we encourage future investigators to consider these observations to help resolve to what extent, if indeed any at all, impaired dCA can be considered as a sensitive and clinically relevant haemodynamic risk factor for AMS.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».