Correlation between Autonomic Dysfunction and Cognitive Impairment in Patients with Parkinson's Disease
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".