Sex differences in depression in Parkinson’s disease: cognitive dysfunction and female-specific associations
Bibliographic record
Abstract
Objective: This study used a linear mixed model to explore the relationship between cognitive function and depression in Parkinson's disease (PD) patients. Participants: The study data were collected from 450 Parkinson's disease patients who participated in the Parkinson's Disease Progress Marker Project (PPMI) from 2010 to 2024, including 176 women and 274 men. Measurements: Cognitive function was assessed using the Montreal Cognitive Assessment Scale (Moca), and depression was measured using the Geriatric Depression Scale (GDS). The correlation between cognitive function and depression was determined using a linear mixed model. Results: Anxiety, daily living ability, and autonomic nervous system function are significant factors affecting both men and women. Women experience a stronger impact of anxiety, daily activity limitations, and autonomic nervous system dysfunction on depression. The longer the duration of the illness, the more severe the depression in women. Moreover, cognitive abilities protect against depression only in women. Women exhibit higher individual heterogeneity in baseline depression and greater variability in the rate of change over time, with those who are more depressed showing a more gradual change. Conclusion: In female Parkinson's disease patients, there is a negative correlation between cognitive ability and depression, whereas this correlation is not observed in male patients. This study provides new evidence that sex differences influence the relationship between cognitive ability and depression in Parkinson's disease patients. Future research should consider the role of sex differences in the context of cognitive ability and depression.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".