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Enregistrement W4296229799 · doi:10.1111/ene.15562

Sleep disorders and increased risk of dementia

2022· article· en· W4296229799 sur OpenAlexaboutno aff
Luigi Ferini‐Strambi

Notice bibliographique

RevueEuropean Journal of Neurology · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueSleep and related disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDementiaMedicineRisk factorSleep disorderCohortDepression (economics)Cohort studyPsychiatryInsomniaPediatricsDiseaseInternal medicine

Résumé

récupéré en direct d'OpenAlex

There is accumulating evidence linking sleep disturbances with the development of dementia. A 2018 meta-analysis of 18 prospective studies that considered 246,786 individuals in different countries (United States, Europe and Asia) showed that, over an average follow-up of 9.5 years, subjects with sleep disturbance at baseline had a higher risk of incident all-cause dementia compared with those not reporting disturbed sleep [1]. A complaint of insomnia was associated only with incident Alzheimer's disease (AD), while sleep disordered breathing (SDB) was a risk factor for all-cause dementia, including vascular dementia (VD). Could poor sleep be considered a modifiable risk factor for dementia? Several risk factors are nonmodifiable: older age, female gender, family history, severe traumatic brain injury, and predisposing genetic mutations. However, it has been reported that modifying 12 identified risk factors, including depression, physical inactivity, midlife untreated hypertension, obesity, tobacco use, and diabetes mellitus might prevent or delay up to 40% of dementias [2]. The study by Damsgaard et al. [3] could facilitate the inclusion of sleep disturbance among the modifiable risk factors for dementia. Their study was a nationwide cohort study that evaluated the association between sleep disorders diagnosed after age 50 years in the secondary healthcare sector and subsequent late-onset dementia. In the cohort of 1,491,276 people, subjects with any sleep disorder had a 17% higher risk of dementia compared to those with no sleep disorder. Interestingly, the risk of dementia was significantly increased in the first 5 years after sleep disorder diagnosis, while the association after 5 years was nonsignificant. Damsgaard et al. [3] distinguished different categories of sleep disorder diagnosis: any sleep disorders; narcolepsy; sleep apnea, other specific disorders; and unspecified disorders. The risk was statistically significant in analyses for all categories only for men: this is probably due to sleep apnea being the most prevalent sleep disorder in the cohort, with this being more frequently diagnosed in men than women. However, one challenge in this field is to explain the differences in the sex distribution of SDB (predominantly affecting men) and, for example, AD (more frequent in women). Women less frequently have moderate-to-severe SDB than men, but they might experience the adverse effects of sleep apnea more than men: they have more fragmented sleep and more frequently report insomnia [4]. Further studies on SDB as a risk factor for dementia should be planned, evaluating sex-based differences in terms of polysomnographic findings, clinical presentation, and consequences. In the study by Damsgaard et al.[3], out of a population of 1.5 million people, only 615 subjects had an insomnia diagnosis. However, insomnia is very rarely the main reason for a hospital contact because it is usually managed in the primary care setting. Insomnia has been reported to be related to a systemic inflammation process through the activation of microglial cells, thereby resulting in β-amyloid (Aβ) accumulation. Moreover, changes in cerebral blood perfusion and systemic inflammation related to insomnia can promote small vessel disease and ischemic phenomena, thus increasing the risk of VD. A recent study, which used the National Health Insurance Service database covering the entire population of the Republic of Korea from 2007 to 2014, evaluated 2,796,871 patients aged 40 years or older with insomnia [5]. The incidence rate ratios (IRRs) for AD and VD were 1.73 and 2.10, respectively, in patients with insomnia compared to those without. Higher IRRs for AD and VD were observed in men than in women. This finding suggests a need for further studies to assess gender influence in the relationship between insomnia and dementia risk. Moreover, in the evaluation of neurodegenerative risk, differentiating insomnia subtypes may also have a role. A recent Canadian population-based study showed that, compared to maintenance insomnia, subjects with sleep-onset insomnia have more objective cognitive dsyfunction [6]. Unfortunately, Damsgaard et al. [3] were unable to consider sleep duration and the sleep architecture data in their study. Recently, it has been reported that sleep duration of 6 h or less at age 50 and 60 years is associated with higher risk of dementia. Moreover, a cross-sectional study that evaluated amyloid positron emission tomography and sleep duration in 4425 cognitively unimpaired participants showed that the increased risk of Aβ deposition with reduced nighttime sleep duration occurred early, before cognitive impairment or significant Aβ deposition [7]. Concerning sleep architecture, a reduction in slow wave sleep (SWS) might induce glymphatic system damage. The glymphatic system is almost exclusively active during SWS, and this suggests that treatments that increase SWS should be encouraged. Interestingly, in patients with obstructive sleep apnea the use of positive airway pressure machines increases SWS, and SWS is significantly correlated with lower Aβ levels after treatment. Lastly, body position during sleep could also be crucial for protection against neurodegeneration: in the lateral decubitus position, decreased flow resistance is observed in the extracranial veins, with optimal cerebral venous outflow, which in turn optimizes glymphatic system function [8]. Further studies are needed to clarify the complex relationship between sleep and neurodegeneration, but the data already available indicate, as reported by Damsgaard et al. [3], that “there should be higher focus on sleep disorders as a red flag for possible early phase dementia disorders”. Open access funding provided by BIBLIOSAN. LF-S has received speaker fees from Jazz Pharma, Angelini, Valeas, Bioprojet, Fidia, Idorsia and has served on scientific advisory boards for Jazz Pharma, Italfarmaco, Bioprojet, Bayer, Idorsia. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,252
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
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)0,0020,000

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 Opus0,006
Tête enseignante GPT0,220
Écart entre enseignants0,214 · 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.

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

Citations14
Publié2022
Routes d'admission1
Résumé présentoui

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