Impaired glymphatic clearance independently contributes to poor outcomes in Parkinson’s disease
Bibliographic record
Abstract
Abstract Background Impaired glymphatic clearance may contribute to pathological accumulations in Parkinson’s (PD), but how it interacts with other processes causing dementia and poor outcomes remains unclear. Objectives Clarify how glymphatic clearance impacts cognition in PD and its interaction with established imaging markers. Methods We used diffusion tensor image analysis along the perivascular space (DTI-ALPS) as an indirect marker of glymphatic clearance in 98 PD patients (31 PD-poor outcomes: dementia, mild cognitive impairment, frailty or death within 3-year follow-up; 67 PD-good outcomes) and 28 controls. We assessed DTI-ALPS relationship to cognition, white matter (fibre cross-section), cortical thickness, iron accumulation (quantitative susceptibility mapping (QSM)), and plasma markers (phosphorylated tau-181 (p-tau181 and neurofilament light (NFL)) cross-sectionally and longitudinally. Results DTI-ALPS was lower in PD-poor outcomes compared to PD-good outcomes and controls (p=0.005) with further longitudinal reductions only in PD-poor outcomes (group*time interaction: β=-0.013, p=0.021). Lower DTI-ALPS was associated with lower fibre cross-section in P, at baseline and longitudinally but with different spatial distribution from white matter changes relating to PD cognition. There was no correlation between baseline DTI-ALPS and plasma ptau-181 (p=0.642), NFL (p=0.448) or baseline cortical thickness. Lower DTI-ALPS was associated with accelerated cortical thinning within left precentral gyrus and changes in brain iron distribution. Conclusions PD patients who develop poor outcomes show impaired glymphatic clearance at baseline that worsened longitudinally. DTI-ALPS correlated with white matter integrity and brain iron accumulation. However, both showed different spatial distribution than that seen in PD dementia; suggesting impaired glymphatic clearance contributes to cognitive decline in a distinct manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".