Association Of Serum Cystatin C Levels On The Progression And Cognition In Parkinson’s Disease (P4.045)
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association of serum cystatin C levels on the progression and cognition in Parkinson’s Disease. BACKGROUND: Cystatin C is a small protein molecule produced by nearly all eukaryotic cells. Many studies showed alteration of cystatin C levels in various neurological disorders. However, few studies have explored the role of serum cystatin C in Parkinson’s disease (PD). We evaluated cystatin C as an important risk factor for PD. DESIGN/METHODS:A total of 142 PD patients and 146 healthy controls were included in this study. The patients were further divided into subgroups according to Hoehn and Yahr (H&Y) stage and Montreal Cognitive Assessment (MoCA) scores. We compared the cystatin C levels in PD patients with healthy controls and did a correlation study about cystatin C level with serum urea, creatinine, triglyceride, cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, age, and male sex in patients with PD. We also did subgroup analysis of serum cystatin C levels according to H&Y stages or MoCA scores and tested the correlation between cystatin C levels and severity of the disease as well as cognitive dysfunction. RESULTS: Our results indicated that serum levels of cystatin C were significantly elevated in PD patients than healthy controls (p<0.05). Cystatin C levels were correlated only with age, and creatinine level (p<0.05). There were no correlations between cystatin C levels and triglyceride, cholesterol, low density lipoprotein(LDL), high density lipoprotein(HDL), urea and uric acid (p>0.05). Patients with higher H&Y stages and lower MoCA scores had elevated cystatin C levels (p<0.05). CONCLUSIONS: Cystatin C levels can be used to predict the severity of PD and evaluate the extent of cognitive dysfunction in PD patients. Study Supported by:
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".