Correlation between Autonomic Dysfunction and Cognitive Impairment in Patients with Parkinson's Disease
Notice bibliographique
Résumé
Background Parkinson's disease (PD) is frequently accompanied with anxiety, depression, sleep disorders, autonomic dysfunction, cognitive impairment, and other non-motor symptoms, among which autonomic dysfunction and cognitive impairment severely impair the quality of life. The relationship between autonomic dysfunction and cognitive impairment in PD is still controversial. Objective To analyze the characteristic of autonomic dysfunction and its correlation with cognitive impairment in PD patients. Methods A total of 156 patients with idiopathic PD admitted to Henan Provincial People's Hospital between September 2018 and November 2020 were enrolled. General data of patients, including age, sex, duration of disease, age of onset and years of education, were collected. According to the prevalence and duration of autonomic dysfunction, they were divided into autonomic dysfunction group (AutD group, n=107) and non-autonomic dysfunction group (NAutD group, n=49) . The part Ⅲ of the Unified Parkinson's Disease Rating Scale (UPDRS-Ⅲ) was used to assess motor symptoms. The Hoehn-Yahr (H-Y) scale was used to assess the severity of PD. The non-motor symptoms were assessed by the Non-motor Symptoms Scale (NMSS) . The Hamilton Anxiety Scale (HAMA) and Hamilton Rating Scale for Depression 24-item (HAMD-24) were used to evaluate anxiety and depression symptoms, respectively. The 39-item Parkinson's Disease Questionnaire (PDQ-39) was used to evaluate PD-specific health related quality of life. Autonomic function was evaluated by Scales for Outcomes in Parkinson's disease-Autonomic (SCOPA-AUT) . Cognitive function was evaluated by Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) . Spearman correlation analysis was used to explore the correlation between autonomic function (assessed using the SCOPA-AUT score) and cognitive function (assessed using the MMSE score and MoCA score) . Results Among the 156 patients, 83 were male and 73 were female, with a mean age of (63.9±0.7) years. AutD group had greater mean age (t=-4.391, P<0.001) and longer mean duration of PD (Z=-6.947, P<0.001) than NAutD group. AutD group also had higher mean UPDRS-Ⅲscore (t=-8.967) , higher prevalence of moderate-to-severe PD graded by the H-Y scale (χ2=44.350) , higher mean NMSS score (t=-13.707) , HAMA score (t=-3.665) , HAMD-24 score (t=-4.808) , and PDQ-39 score (t=-11.893) (P<0.001) . Digestive symptoms were the most prevalent symptoms (91.7%) , followed by urinary symptoms (91.0%) , dysregulation of body temperature (59.6%) and cardiovascular symptoms (50.0%) , while pupillary dysregulation (12.2%) and sexual dysfunction (5.1%) were less common. Compared to NAutD group, AutD group had lower mean total score of MMSE (Z=-3.826, P<0.001) , total score of MoCA (Z=-2.921, P=0.003) and the mean score of the naming item (Z=-2.868, P=0.004) , mean domain scores of attention (Z=-2.968, P=0.003) , language (Z=-3.546, P<0.001) , and delayed recall (Z=-2.804, P=0.005) . Spearman correlation analysis showed that, the score of SCOPA-AUT (rs=-0.214, -0.181) , the score of urinary domain (rs=-0.245, -0.191) and the score of thermoregulatory domain (rs=-0.215, -0.225) were negatively correlated with the scores of MMSE and MoCA (P<0.05) . Conclusion PD patients with autonomic dysfunction have more severe non-motor symptoms such as anxiety, depression, and cognitive impairment, and lower quality of life. The incidence of autonomic dysfunction in PD is high, involving multiple systems. And the increase in the severity of autonomic dysfunction is associated with increased severity of cognitive impairment.
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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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».