Elevated Cystatin C Levels Are Associated with Cognitive Impairment and Progression of Parkinson Disease
Bibliographic record
Abstract
OBJECTIVE: We investigated the relationship between serum cystatin C (CysC) levels and cognitive dysfunction and disease progression in patients with Parkinson disease. BACKGROUND: Previous studies have reported altered CysC levels in neurodegenerative disorders, but only a few studies have explored the role of CysC and its relationship to cognitive dysfunction in Parkinson disease. METHODS: We measured serum levels of CysC, creatinine, urea, and uric acid in 142 patients with Parkinson disease and 146 healthy controls. We assessed disease progression using the Hoehn and Yahr scale, and cognitive function using the Montreal Cognitive Assessment (Beijing version). RESULTS: The patients with Parkinson disease had significantly higher CysC levels than the controls (P<0.001). CysC level correlated significantly with age (r=0.494, P<0.001), sex (r=0.150, P=0.011), and serum creatinine level (r=0.377, P<0.001), but not with levels of urea or uric acid (P>0.05). CysC level was a significant independent predictor of Parkinson disease (odds ratio=23.143, 95% confidence interval: 5.485-97.648, P<0.001) in multivariate logistic regression analysis. In the Parkinson disease group, a higher CysC level was associated with a more advanced Hoehn and Yahr stage (r=0.098, P<0.05) and a lower Montreal Cognitive Assessment score (r=-0.381, P=0.003). CONCLUSIONS: Serum CysC levels can predict disease severity and cognitive dysfunction in patients with Parkinson disease. The exact role of CysC remains to be determined.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".