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Enregistrement W3014521097 · doi:10.1113/jp279727

New insights into the complexity of arterial baroreflex control of muscle sympathetic outflow in humans

2020· letter· en· W3014521097 sur OpenAlexafffundabout
Anthony V. Incognito, Philip J. Millar

Notice bibliographique

RevueThe Journal of Physiology · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueHeart Rate Variability and Autonomic Control
Établissements canadiensToronto General HospitalUniversity of Guelph
Organismes subventionnairesOntario Ministry of Economic Development, Job Creation and TradeNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceCanada Foundation for Innovation
Mots-clésMicroneurographyBaroreflexNeuroscienceSympathetic nervous systemMedicineCardiologyHeart rateInternal medicinePsychologyBlood pressure

Résumé

récupéré en direct d'OpenAlex

Our understanding of the functional organization governing sympathetic control in humans has expanded greatly since the development of microneurography over 50 years ago (for review of major milestones, see Carter, 2019). Sympathetic microneurography permits direct recording of sympathetic outflow to skin and muscle vasculature. A hallmark feature of integrated (multi-unit) muscle sympathetic nerve activity (MSNA) is synchronicity of bursts to cardiac cycles, mediated by the arterial baroreflex (Hagbarth & Vallbo, 1968). Unattainable from multi-unit assessments is information on how individual muscle sympathetic neurons are regulated. Thus, the current frontier of human sympathetic control has involved examination of the raw MSNA neurogram to assess discharge characteristics and recruitment patterns. The first glimpse into human sympathetic nerve discharge characteristics arose from the development of single-unit microneurography (Macefield et al. 1994), which revealed that muscle sympathetic neurons possess both a low firing rate and a low incidence of multiple spike firing per cardiac cycle – characteristics that can be altered in several cardiovascular conditions (Macefield & Wallin, 2018). One limitation of this method is that only a small number of neurons can be captured within a subject. Thus, whether this data is generalizable to the larger neuronal pool is unclear. More recently, Shoemaker and colleagues have employed a wavelet denoising approach to sort a larger proportion of APs into amplitude ‘clusters’ (Shoemaker et al. 2018). This method has proved invaluable to elucidating the ordered, size-dependent regulation of sympathetic nerve discharge probability and recruitment during stress. Does neuron size influence arterial baroreflex gain? Increasing AP size is associated with reduced latency (i.e. faster conduction velocity), thus reflecting neuron size (Shoemaker et al. 2018). In the present study, baroreflex gain appeared inversely related to cluster size in medium to large APs but the small APs were dissociated from this relationship. Alternatively, discharge probability appeared to be a more crucial determinant of cluster gain, likely reflective of neuron excitability. Comparison across most clusters showed a direct relationship between discharge probability and the strength of the baroreflex gain. Furthermore, baroreflex gain was increased during LBNP only in clusters that had increases in discharge probability (i.e. medium to large APs), and the magnitude of which appeared proportional to the magnitude increase in gain. Thus, whether neuron size per se or the underlying excitability of the AP cluster influences baroreflex control remains unclear. Is sympathetic recruitment mediated by alterations in baroreflex thresholds? The parallel increases in discharge probability and baroreflex gain in medium and large APs during LBNP suggest an orientation to a steeper portion of a sigmoid-shaped baroreflex curve. This ordered pattern would support a role of the arterial baroreflex in regulating larger latent AP clusters, which may be fully inhibited by the baroreflex at rest. Open-loop baroreflex assessments of AP cluster gains and operating ranges are warranted to better answer this question. LBNP evoked an increase in diastolic pressure, considered to be the primary input stimulus of the sympathetic baroreflex (Sundlöf & Wallin, 1978), due to the reflex-mediated sympathetic activation. Thus, diastolic pressure reductions were only small (and rhythmic), preventing a baroreflex assessment across a large diastolic pressure range. The potential contribution of changes in stroke volume, systolic pressure or pulse pressure to sympathetic regulation were not computed. We recently used sequential bolus injections of nitroprusside and phenylephrine to elicit large fluctuations in diastolic pressure and found that MSNA single units possess heterogenous baroreflex gains, as well as operating ranges (Incognito et al. 2020). Using these methods, it may be possible to assess whether threshold levels of arterial baroreflex inhibition exist in the latent and larger APs. If so, it may be that sympathetic recruitment, seen in the authors’ previous work with apnoea and static handgrip (Shoemaker et al. 2018), may be due to alterations or ‘resetting’ of arterial baroreflex inhibition thresholds. Less clear is the baroreflex control of the small APs. Given that these small-sized AP clusters also appear to paradoxically reduce firing during larger multi-unit MSNA bursts, coincident with rate coding and recruitment of larger APs at rest (Salmanpour & Shoemaker, 2012) and during stress (Shoemaker et al. 2018), their importance to maintaining tonic sympathetic control represents an important target of future work. Does arterial baroreflex control of AP occurrence vs. content per cardiac cycle differ? The present study quantified baroreflex control of AP probability, which is the concurrent assessment of AP cluster occurrence and AP content per occurrence. These discharge modes are likely controlled differently, as is control over multi-unit burst occurrence vs. height, shown and discussed by the authors (Klassen et al. 2020). Our work showed the arterial baroreflex to possess weaker control over multiple spike firing per cardiac cycle compared with spike firing probability (Incognito et al. 2020). Measuring both modes of sympathetic discharge will be crucial, as single-unit assessments demonstrate the capacity to increase multiple spike firing per cardiac cycle without altering firing probability (Macefield & Wallin, 2018). What is the physiological significance of differences in arterial baroreflex control of individual AP clusters? A notable observation of the present data was that the baroreflex gain of multi-unit MSNA burst frequency was unchanged during LBNP despite alterations in the gain of individual AP clusters. Which one of these measures best aligns with end-organ neurotransmitter release or neurovascular responses is unclear and remains a key missing link in understanding the functional importance of distinct regulation of individual sympathetic APs. In conclusion, the capacity for heterogenous baroreflex control over AP clusters uncovers a novel functional organization in the sympathetic defence against orthostatic stress. The findings by Klassen and colleagues provide a framework for expanding our assessments of sympathetic baroreflex control, which may uncover unrecognized autonomic alterations in healthy ageing and clinical populations. No competing interests declared. AVI and PJM were both involved in all aspects of the drafting and finalization of the manuscript. PJM is supported by a Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant (#06019), the Canada Foundation for Innovation (#34379), the Ontario Ministry of Research, Innovation and Science (#34379), and an Early Researcher Award from the Ontario Ministry of Economic Development, Job Creation and Trade (18-14-288). AVI is supported by a NSERC Alexander Graham Bell Canada Graduate Scholarship.

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)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,001
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,035
Tête enseignante GPT0,270
Écart entre enseignants0,235 · 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
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

Citations0
Publié2020
Routes d'admission3
Résumé présentoui

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