<scp>CSF</scp> Biomarker‐Based Cognitive Trajectories in Parkinson's Disease‐Subjective Cognitive Decline
Bibliographic record
Abstract
ABSTRACT Objective Cognitive complaints without objective cognitive impairment in Parkinson's Disease, termed Parkinson's Disease‐Subjective Cognitive Decline (PD‐SCD), have been associated with cognitive decline. However, its progression is heterogeneous, highlighting the need for improved identification of patients at greater risk for deterioration. This cohort study aims to investigate associations between CSF biomarkers and cognitive decline in PD‐SCD. Methods We included patients from the PPMI cohort with PD‐SCD, available baseline CSF beta‐amyloid1‐42 (Aβ42) and phosphorylated‐tau181 (p‐tau181), and longitudinal Montreal Cognitive Assessment (MoCA). Results A total of 80 patients were included, with a mean age of 62.1 years and a median disease duration of 5.6 months at baseline. Five patients were identified with biological AD, who showed a more pronounced decline in MoCA (year 7: β = −4.15, p = 0.009). After excluding AD patients, linear mixed‐effects models (LMEM) demonstrated an association between baseline log‐transformed Aβ42 and MoCA progression (β = 2, p = 0.004). Based on these data, we created three CSF‐based subgroups: Normal Aβ42 (n = 50), Low Aβ42 (n = 25), and Biological AD (n = 5). LMEM predicted greater cognitive decline for Low Aβ42 versus Normal Aβ42 (β = −0.161 points‐per‐year, p = 0.007), and for Biological AD versus Normal Aβ42 (β = −0.390 points‐per‐year, p < 0.001). The risk of dementia was increased for Low Aβ42 (HR = 5.2, p = 0.029) and Biological AD subgroups (HR = 7.7, p = 0.013). Interpretation In PD‐SCD, baseline CSF Aβ42 is associated with cognitive progression. Furthermore, we identified three CSF biomarker‐based cognitive trajectories (Normal Aβ42, Low Aβ42, and Biological AD), each characterized by progressively worse cognitive outcomes. These findings could be useful for implementing enrichment strategies in future clinical trials targeting PD‐associated cognitive decline.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".