Lysosomal Polygenic Burden Drives Cognitive Decline in Parkinson's Disease with Low Alzheimer Risk
Bibliographic record
Abstract
BACKGROUND: Genetics influence cognitive progression in Parkinson's disease, possibly through mechanisms related to Lewy and Alzheimer's disease pathology. Lysosomal polygenic burden has recently been linked to more severe Lewy pathology post mortem. OBJECTIVES: To assess the influence of lysosomal polygenic burden on cognitive progression in Parkinson's disease patients with low Alzheimer's disease risk. METHODS: Using Cox regression we assessed association between lysosomal polygenic scores and time to Montreal Cognitive Assessment score ≤ 21 in the Parkinson's Progression Markers Initiative cohort (n = 374), with replication in data from the Parkinson's Disease Biomarker Program (n = 777). Patients were stratified by Alzheimer's disease polygenic risk. RESULTS: The lysosomal polygenic score was associated with faster progression of cognitive decline in patients with low Alzheimer's disease risk in both datasets (P = 0.0032 and P = 0.0054, respectively). CONCLUSION: Our study supports complex interplay between genetics and neuropathology in Parkinson's disease-related cognitive impairment, emphasizing the role of lysosomal polygenic burden. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".