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Enregistrement W3158262077 · doi:10.1113/jp281562

Elucidation of a proposed cardiorespiratory circadian rhythm impacting survival in SUDEP

2021· letter· en· W3158262077 sur OpenAlexaff
Michael V. Tavolieri, Tharsan Kanagalingam

Notice bibliographique

RevueThe Journal of Physiology · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueEpilepsy research and treatment
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésEpilepsyCircadian rhythmCardiorespiratory fitnessMedicineCardiorespiratory arrestNon-rapid eye movement sleepPediatricsSudden deathPsychologyPsychiatryElectroencephalographyAnesthesiaInternal medicine

Résumé

récupéré en direct d'OpenAlex

A diagnosis of epilepsy indicates a strong, enduring predisposition to epileptic seizures. The classic clinical criteria for a diagnosis of epilepsy was two unprovoked instances of seizure occurring greater than 24 h apart, although the more modern definition now includes consideration for recurrent risk (Fisher et al. 2014). Individuals living with epilepsy face an increased mortality as a result of seizure-related causes, including accidental death, sudden unexpected death in epilepsy (SUDEP), complications of anti-epileptic therapy, and suicide, amongst others. Among these, SUDEP is the leading cause of epilepsy-related death (Thomson et al., 2008). SUDEP, as the name suggests, refers to the death of an individual living with epilepsy not as a result of an injury, drowning or other known cause. The incidence for SUDEP is estimated to be 1.16 cases per 1000 persons living with epilepsy, with the frequency being greatest in the third and fourth decade of life (Thurman et al. 2014). SUDEPs have a greater probability of occurring at night, although the reason for this remains unknown. Proposed explanations include the absence of an intervening witness, the position of the individual when lying prone in bed, reduced vigilance during sleep, sleep disorders or circadian rhythms (Purnell et al. 2018). SUDEPs tend to have a consistent pattern of cardiorespiratory dysfunction, suggesting that a cardiorespiratory circadian rhythm may play a role in nocturnal SUDEPs (Ryvlin et al. 2013). In their study published in The Journal of Physiology, Purnell et al. (2021) have investigated the role that circadian rhythms may play in SUDEP using two well-known mouse models of seizure. The researchers identified an increased susceptibility to SUDEPs during night-time hours using an audiogenic model, despite homogenous distribution of seizures induced across time points. Use of whole-body plethysmography in a maximal electroshock (MES) model showed a significant difference across time points in the respiratory response to seizure for several measured variables. Taken together, their data support the proposed impact of a cardiorespiratory circadian rhythm in response to SUDEP. Seizures were induced using audiogenic stimulus in DBA/1 mice housed under a normal 12:12 h light/dark photocycle. Unlike in previous uses of this model, the mice did not undergo the standard priming process, which involves repeated exposure to the stimulus until it consistently elicited seizure-induced respiratory failure. Instead, animals heard the stimulus only once, during the seizure trial. Seizure trials occurred at one of eight time points denoted using Zeitgeber time (ZT) nomenclature: 0, 2, 6, 10, 12, 14, 18 and 22. The response to the seizure trial was monitored using video recording. In a second set of trials, seizures were induced in C57BL/6J mice using MES. To control for the impact of light exposure on circadian rhythm, animals were housed in complete darkness for ≥14 days prior to the seizure trial. Activity levels of the animals were monitored by measuring wheel-running behaviour in home cage. The animals maintained a roughly 12:12 h active/inactive routine. Rather than ZT, circadian time (CT) was used, with CT 12 being denoting the beginning of active phase. Seizure trials occurred at one of six time points: CT 2, 6, 10, 14, 18 and 22. Whole-body plethysmography was monitored during seizure trials. For both sets of experiments, animals were transferred from home cage to an experimental apparatus during seizure trials. All animals were awake during seizure trials, thereby eliminating the direct impact of sleep on survival outcome. The results of audiogenic-induced seizures in DBA/1 mice showed no significant impact of time of day on distribution of seizure activity, although there was a significant impact on the distribution of seizure-induced death. The probability of a seizure occurring across all time points was 67.5%, the probability across daytime time points (ZT 0, 2, 6 and 10) was 63.3%, and the probability during night-time time points (ZT 12, 14, 18 and 22) was 71.7%. By contrast, the probability of seizure-induced death was 34.2% across all time points, 21.7% across only daytime time points and 46.7% across only night-time time points. Time of day did not significantly impact other measured variables. The results of the MES-induced seizures in C57BL/6J mice showed similar results. The probability of seizure-induced death was 59.3% across all time points, 48.1% across only subjective daytime time points (CT 2, 6, and 10) and 70.4% across only subjective night-time time points (CT 14, 18, and 22). Additionally, cosinor analysis showed a significant oscillation in postictal changes in respiration as measured by respiratory frequency (fR), tidal volume (VT) and minute ventilation (VE). In all cases, the smallest reduction from baseline occurred at CT 10. The greatest postictal change occurred at CT 2 (VT) or CT 22 (fR, VE). The severity of seizure, as measured by extension/flexion (E/F) ratio, did not differ significantly across time points. The study by Purnell et al. (2021) presents a strong case for the impact of a proposed cardiorespiratory circadian rhythm on mortality outcomes in individuals living with epilepsy. Experiments conducted in normal 12:12 h photocycle housed DBA/1 mice show evidence that night-time poses an increased susceptibility to SUDEPs but not necessarily an increased risk of seizure. Because all experiments were conducted in wakeful mice, these findings exclude any effect that wakefulness may have on SUDEP, as has previously been proposed. Experiments in C57BL/6J mice housed in continuous darkness further recapitulate these findings and illustrate that the effect is independent of light exposure. Furthemore, the use of plethysmography demonstrates significant changes in respiratory responses following seizures over the measured time points. These changes may illustrate how differences in response following seizure affect outcome. Overall, we consider that the study by Purnell et al. (2021) has effectively accomplished an admittedly difficult task: resolving a nuanced physiological process at the same time as maintaining generalizability of findings. The circadian rhythm proposed by Ryvlin et al. (2013), which offers a reasonable explanation for increased frequency of nocturnal SUDEPs in humans, still lacks supporting physiological evidence. Here, Purnell et al. (2021) have offered supporting evidence, showing differences in the respiratory response to MES-induced seizures across time points. They also show that this circadian rhythm operates independently of the influences of light. The difficulty faced by the study by Purnell et al. (2021) is generalizing the results to a human model of SUDEPs. As they admit in their discussion, circadian rhythms differ between nocturnal and diurnal mammals. We feel that the generalizability of this paper would be greatly increased if the findings were supplemented with molecular markers of circadian rhythm illustrating the maintenance of previously described patterns. For example, a similar pattern of oscillation of serotonin concentrations has been shown in both nocturnal rodents and humans (Rao et al. 1994; Mateos et al. 2009; Agren et al. 1986). This would be particularly impactful for the animals housed in constant darkness because it would confirm the assertion that circadian rhythms are maintained in this artificial condition. The strength of study by Purnell et al. (2021) lies in its impact on future mice models of epilepsy. The finding that non-primed DBA/1 mice experience seizure-induced death at a rate of 71.1% during the night-time as a result of audiogenic stimulus has the potential to greatly impact the use of mice models in future studies of epilepsy. The priming process requires several days, in which mice are exposed to the stimulus, undergo respiratory arrest and are revived by mechanical ventilation. A shift away from priming can greatly reduce time and financial investment required to complete these studies. In summary, the study by Purnell et al. (2021) offers supporting evidence for a proposed cardiorespiratory circadian rhythm impacting survival outcomes during nocturnal seizure events. The results clearly illustrate a greater susceptibility to nocturnal seizures and plethysmography illustrates that respiratory response differs throughout the day. These findings will probably greatly impact the use of mouse models in future seizure studies. No competing interests declared. MT and TK were responsible for the conception or design of the work; the acquisition or analysis or interpretation of data for the work; drafting the work or revising it critically for important intellectual content; and final approval of the version submitted for publication. Both authors agree to be accountable for all aspects of the work. No funding received. Open access funding provided by IReL.

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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
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 savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
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 Opus0,037
Tête enseignante GPT0,313
Écart entre enseignants0,276 · 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

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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