Modifiable Comorbidities Associated with Cognitive Decline in Parkinson's Disease
Bibliographic record
Abstract
ABSTRACT Background Cognitive impairment (CI) is one of the most feared and debilitating complications of PD. No therapy has been shown to slow or prevent CI in PD. Objective To determine associations between modifiable comorbidities, including cardiovascular disease risk factors, mood disorders, and sleep characteristics, and rate of cognitive decline in Parkinson's disease (PD). Methods Data from the Parkinson's Progression Markers Initiative (PPMI) cohort was queried for baseline cardiovascular disease risk factors, mood disorders, and sleep characteristics. Linear mixed‐ effects models (LME) were used to examine the association between baseline factors and change in cognition, evaluated by the Montreal Cognitive Assessment (MoCA) over time. Baseline comorbidities found to affect MoCA decline were assessed for an association with focal cognitive domains using LME. Results Higher Body Mass Index (BMI) (β = −0.009, P = 0.039), State Trait Anxiety Inventory (STAI) (β = −0.005, P < 0.001), Geriatric Depression Scale (GDS) (β = −0.034, P < 0.001), Epworth Sleepiness Scale (ESS) (β = −0.017, P = 0.003), and REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) (β = −0.037, P < 0.001) were associated with faster rates of MoCA decline. Using established cut‐offs for clinically significant symptoms, being overweight, or the presence of depression, excessive day time sleepiness (EDS), and possible REM sleep behavior disorder (pRBD), were all associated with faster rate of cognitive decline. Conclusion Several modifiable baseline comorbidities are associated with faster rate of CI over time in patients with PD. These associations identify potential opportunities for early intervention that could influence CI in PD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".