A 12-year polysomnographic study in Huntington’s: sleep problems predict disease onset and severity
Notice bibliographique
Résumé
Abstract Increasing evidence suggests that the sleep pathology associated with neurodegenerative diseases can in turn exacerbate both the cognitive deficits and underlying pathobiology of these conditions. Treating sleep may therefore bear significant, even disease-modifying, potential for these conditions, but how best and when to do so remains undetermined. Huntington’s Disease (HD), by virtue of being an autosomal-dominant neurodegenerative disease presenting in mid-life, presents a key ‘model’ condition through which to advance this field. To date, however, there has been no clinical longitudinal study of sleep abnormalities in HD, and no robust interrogation of their association with disease onset, cognitive deficits and markers of disease activity. Here we present the first such study. HD gene carriers ( n =28) and age- and sex-matched controls ( n =21) were studied at baseline and 10- and 12-year follow up. All HD gene carriers were premanifest at baseline, and were stratified at follow up into prodromal/manifest and premanifest groups. Sleep abnormalities were assessed through two-night inpatient polysomnography (PSG) and two-week domiciliary actigraphy, and their association was explored against i)validated cognitive and affective outcomes (Montreal Cognitive Assessment, Trail A/B task, Symbol Digit Modalities Task [SDMT], Hopkins Verbal Learning Task [HVLT], Montgomery-Asberg Depression Rating Scale [MADRS]) and ii)serum neurofilament-light (NfL) levels. Statistical analysis incorporated cross-sectional ANCOVA, longitudinal repeated measures linear models and regressions adjusted for multiple confounders including disease stage. 15 HD gene carriers phenoconverted to prodromal/early manifest HD by study completion. At follow-up, these gene carriers showed more frequent sleep stage changes ( p =<0.001,ƞ p 2 =0.62) and higher levels of sleep maintenance insomnia ( p =0.002,ƞ p 2 =0.52). The latter finding was corroborated by nocturnal motor activity patterns on follow-up actigraphy ( p =0.004,ƞ p 2 =0.32). Greater sleep maintenance insomnia was associated with greater cognitive deficits (Trail A p =<0.001,R²=0.78;SDMT p =0.008,R²=0.63;Trail B p =0.013,R²=0.60) and higher levels of NfL (p=0.015,R²=0.39). Longitudinal modelling suggested that sleep stage instability accrues from the early premanifest phase, whereas sleep maintenance insomnia emerges closer to phenoconversion. Baseline sleep stage instability was able to discriminate those who phenoconverted within the study period from those who remained premanifest (area under curve=0.81, p =0.024). These results demonstrate that the key sleep abnormalities of premanifest/early HD are sleep stage instability and sleep maintenance insomnia, and suggest that the former bears value in predicting disease onset, while the latter is associated with greater disease activity and cognitive deficits. Intervention studies to interrogate causation within this association could not only benefit patients with HD, but also help provide fundamental proof-of-concept findings for the wider sleep-neurodegeneration field.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».