2287 The impact of sleep on the progression of Parkinson’s disease: a mendelian randomization study
Bibliographic record
Abstract
Introduction Sleep disturbance is common in Parkinson’s disease (PD) and significantly impacts quality of life. Although often considered a sequelae of PD, there is emerging evidence that sleep disturbance may itself play a causal role in neurodegeneration via altered clearance of pathological proteins. Whether sleep disturbance affects the pathological progression of PD is unknown.Methods To elucidate the causality between sleep disorders and progression of PD, we performed two sample Mendelian randomization analysis using genetic variants identified from GWAS databases for sleep variables including insomnia, sleep duration, chronotype, napping and daytime sleepiness. Outcome measures were derived from a large collective GWAS of PD progression(N=4093 cases) including the Unified Parkinson’s disease rating scale (UPDRS total and UPDRS- III), motor fluctuations, Age of onset of PD, Mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MOCA). The robustness of results was examined using conventional Mendelian randomization sensitivity analyses.Results Genetic liability to increased sleep duration was associated with a lower rate of progression of motor symptoms in PD using UPDRS-III score. Meanwhile insomnia was associated with increased rate of motor progression of PD. Predisposition to daytime sleep was associated with lower rates of progression of cognitive decline in PD measured using MMSE. No robust relationships were determined between markers of progression and chronotype or dayime napping. Statistical measures showed significant pleiotropy for the relationships identified.Conclusion Sleep-related variables may have a deterministic role in the clinical progression in Parkinson’s disease and may represent a modifiable target for altering the trajectory of neurodegeneration.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".