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Enregistrement W2992029164 · doi:10.1113/ep088315

Through thick and thin: The interdependence of blood viscosity, shear stress and endothelial function

2019· letter· en· W2992029164 sur OpenAlexaff
Joshua C. Tremblay

Notice bibliographique

RevueExperimental Physiology · 2019
Typeletter
Langueen
DomaineMedicine
ThématiqueBlood properties and coagulation
Établissements canadiensOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésBlood viscosityShear rateShear stressViscosityRheologyViscometerMaterials scienceApparent viscosityChemistryNewtonian fluidHemorheologyReduced viscosityShear (geology)Blood flowComposite materialMechanicsThermodynamicsMedicineCardiologyPhysics

Résumé

récupéré en direct d'OpenAlex

The intrinsic resistance of a fluid to flow is termed viscosity. Thick, sludge-like fluids have a high viscosity, whereas thin fluids like have a low viscosity; molasses has a greater resistance to flow than water. Monitoring the viscosity of fluids in commercial settings is integral to quality control and processing. The viscosity of the fluid determines the pumping systems required to maintain a desired flow. This draws a clear parallel to the cardiovascular system. The cellular components of blood (haematocrit and plasma viscosity) and their properties (erythrocyte deformability and aggregation) complicated early attempts to determine blood viscosity. Viscometers used to measure properties of tar, toothpaste, molten chocolate and paints were adapted for the assessment of blood viscosity across a range of shear rates. Applying these measurements to blood led to the assertion that blood is a non-Newtonian fluid with shear-thinning behaviour, meaning that blood viscosity is lower at higher shear rates (Leo, Simmonds, & Sabapathy, 2020: fig. 1; Wells, Denton, & Merrill, 1961); a property that is largely attributed to erythrocyte rheological properties. Shear rate is often estimated from ultrasound-derived arterial diameter and blood velocity (shear rate = blood velocity/diameter). The product of shear rate and blood viscosity is shear stress, the frictional force exerted by flowing blood on the endothelial surface. Hence, viscosity ‘communicates’ to the endothelium via shear stress. In a rigid tube, heightened viscosity increases resistance to flow; however, human arteries are not rigid tubes. Increasing blood viscosity increases shear stress at a given blood flow. The increase in shear stress is sensed by complex mechanotransduction signalling in endothelial cells that stimulates the production of molecules (i.e. nitric oxide, prostaglandins, epoxyeicosatrienoic acids) that cause vasodilatation. In turn, vasodilatation decreases vascular resistance. Therefore, increased blood viscosity provokes simultaneous increases and decreases in vascular resistance. Additionally, endothelium-derived factors that are released during increases in shear stress can influence erythrocyte deformability and aggregation, thereby impacting viscosity. The nuanced interplay between blood viscosity, shear stress and endothelial function determines the net outcome on vascular resistance (Forconi & Gori, 2009). Endothelial function may be central to the communication between heightened viscosity and the net effect on vascular resistance; a healthy endothelium may promote vasodilatation, whereas a dysfunctional endothelium may result in a net increase in resistance. This integrated interplay is complicated further by the vasodilator synthesis and scavenging properties of erythrocytes (Salazar Vázquez et al., 2010). Shear rate has been implemented as a surrogate for shear stress when examining shear stress-mediated vasodilatation [widely referred to as flow-mediated dilatation (FMD)], with blood viscosity often being ignored or deemed unnecessary to take into account (Parkhurst et al., 2012). However, studies that have measured blood viscosity to calculate shear stress have done so only at a single shear rate, typically 225 s−1, thereby neglecting the shear-thinning properties of blood described above. In a recent publication in Experimental Physiology, Leo et al. (2020) challenged this dogma by measuring viscosity across a large range of shear rates (75–1500 s−1) to model the relationship between shear rate and blood viscosity and, subsequently, calculate the shear rate-specific shear stress during progressive handgrip exercise. Venous blood was sampled at baseline and in the final 30 s of 3.5 min stages of handgrip exercise at 20, 40, 60 and 80% of maximal work rate. Although handgrip exercise elicited haemoconcentration, increasing blood viscosity at a given shear rate during the 60 and 80% stages, shear rate-specific blood viscosity decreased at all stages (by 6–11%) owing to an increase in erythrocyte deformability at the higher shear rates provoked during the exercise. Furthermore, using shear rate or shear stress calculated from viscosity measured at a single shear rate overestimated the shear stress stimulus for FMD and underestimated the shear stress–FMD relationship. These finding have critical implications for how we: (i) accurately measure shear stress; (ii) characterize the shear stress stimulus for FMD; and (iii) interpret conduit artery endothelial function in humans. Collectively, these findings rebut the general assertion that shear rate is a suitable surrogate for shear stress. Like arteries are not rigid tubes, blood viscosity is not a constant. Blood viscosity is dynamic, changing with temperature, haemoconcentration and shear rate, and the viscosity can vary considerably between populations. Table 1 provides a brief list of factors that have been shown to impact blood viscosity. Importantly, many of these factors are also associated with alterations in FMD. Blood viscosity is elevated in many pathologies and is associated with cardiovascular risk factors, implicated in the pathophysiology of atherosclerosis and has been identified as a predictor of cardiovascular events (Lowe, Lee, Rumley, Price, & Fowkes, 1997). The magnitude of difference is not equivocal; in an extreme example, individuals with high-altitude excessive erythrocytosis presented a blood viscosity more than twofold higher than sea-level residents (8.70 ± 1.15 versus 3.86 ± 0.14 cP measured at a shear rate of 225 s−1; unpublished observations by J.C. Tremblay). Conversely, increases in blood viscosity have been shown to reduce blood pressure and augment FMD via elevations in shear stress (Salazar Vázquez et al., 2010). Given the intra- and interindividual differences in blood viscosity, forthcoming studies should examine whether calculating shear stress from shear rate-specific viscosity influences the interpretation of FMD. The clear methodology presented by Leo et al. (2020) encourages researchers to consider shear rate-specific blood viscosity and prompts several logical follow-up questions. How may disruptions in rheological properties of the blood, in particular erythrocyte deformability, impact exercise hyperaemia and shear stress? Are we under- or overestimating differences in shear stress between groups or after interventions? What impact does this have on the interpretation of endothelial function? In addition to the progressive handgrip exercise model for examining FMD, future investigations examining the viscosity–shear stress–FMD relationship in reactive hyperaemia and heating techniques will have to consider further complex factors (turbulent flow and increased temperature, respectively). Preclinical human studies of endothelial function have long dismissed blood viscosity; however, it is now prime time and highly appropriate to embrace and integrate haemorheology. None declared.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,082
Score d'incertitude au seuil0,531

Scores Codex et Gemma par catégorie

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

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 Opus0,019
Tête enseignante GPT0,261
Écart entre enseignants0,242 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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

Citations6
Publié2019
Routes d'admission1
Résumé présentoui

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