Obstructive Sleep Apnea and Other Vascular Risk factors' Impact on Non‐Motor Symptoms in Parkinson's Disease
Bibliographic record
Abstract
Background: Previous studies revealed an association between vascular comorbidities and obstructive sleep apnea (OSA) and the severity of motor and cognitive symptoms in Parkinson's disease (PD). However, there is a lack of studies assessing the entire spectrum of non-motor symptoms (NMS). Objective: To investigate the relationship between vascular comorbidities and NMS in PD patients. Methods: Patients were assessed at baseline and 4 years later with the Non-Motor Symptom Assessment Scale, Parkinson's Psychosis Questionnaire, Unified Parkinson's Disease Rating Scale (UPDRS), Montreal Cognitive Assessment, and Apathy scale. After tetrachoric correlation matrix, we conducted linear regression models (adjusted for age, gender, disease duration, and UPDRS-III) to investigate the relationship between vascular comorbidities and NMS. Results: In 73 PD patients, (mean disease duration 7.1 [5.3]), 57% had hypertension, 44% body mass index >25, 44% elevated cholesterol, 15% diabetes mellitus, 15% OSA, 14% cigarette-smoking history, 8% prior stroke, and 8% coronary disease. Cognition, psychotic symptoms, apathy, urinary function, and miscellaneous domains significantly worsened at the 4-year follow-up. OSA was significantly associated with higher severity of hallucinations/illusions at baseline and with a more severe deterioration of attention/memory, psychotic symptoms, and apathetic mood at the 4-year follow-up. At baseline, but not at follow-up, hypertension was negatively associated with miscellaneous domain scores and coronary disease with autonomic function scores (gastrointestinal tract and urinary function domains). Conclusion: Among PD-associated comorbidities, OSA was the main factor of decline. In addition to cognitive impairment, OSA might also potentially worsen psychotic symptoms and apathy. Treatment of OSA could be a strategy to improve these important NMS.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".