The impact of sleep disorders on nonmotor symptoms in patients with Parkinson's disease; a systematic review
Bibliographic record
Abstract
Sleep disorders are highly prevalent in Parkinson's disease (PD), affecting up to 90% of patients and significantly impairing quality of life. Emerging evidence suggested that sleep disturbances, particularly REM sleep behavior disorder (RBD), might reflect underlying neurodegenerative processes and predict disease progression. However, the precise impact of sleep disorders on non-motor symptoms (NMSs) in PD remains incompletely understood. This systematic review synthesized current evidence on the association between sleep disorders and NMS in PD, focusing on clinical correlates, pathophysiology, and therapeutic implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive search of PubMed, Web of Science, SCOPUS, and Science Direct was conducted for studies examining sleep disorders and NMS in PD. Thirteen studies meeting the inclusion criteria were analyzed, including cross-sectional, longitudinal, and interventional designs. Risk of bias was assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool. RBD was consistently associated with worse motor and non-motor outcomes, including cognitive decline, autonomic dysfunction, and cortical atrophy. Insomnia and excessive daytime sleepiness correlated with increased neuropsychiatric symptoms and reduced quality of life. Mechanistic studies implicated dopaminergic and non-dopaminergic pathways, neuroinflammation, and circadian dysregulation. Interventions such as melatonin and Qigong improved sleep quality, while deep brain stimulation modulated sleep architecture. Sleep disorders, particularly RBD, are significant predictors of NMS severity and disease progression in PD. Early identification and targeted interventions might improve clinical outcomes. Future research should integrate polysomnography, biomarkers, and standardized assessments to refine therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".