Cystatin C predicts cognitive decline in multiple system atrophy: A 1-year prospective cohort study
Bibliographic record
Abstract
Background Accumulating evidence has suggested that cystatin C is associated with cognitive impairment in patients with neurodegenerative diseases. However, the association between cystatin C and cognitive decline in patients with multiple system atrophy (MSA) remains largely unknown. Objectives The objective was to determine whether cystatin C was independently associated with cognitive decline in patients with early-stage MSA. Methods Patients with MSA underwent evaluation at baseline and the 1-year follow-up. Cognitive function was evaluated with Montreal cognitive assessment (MoCA). Changes in the MoCA score and the absolute MoCA score at the 1-year assessment were considered the main cognitive outcome. The cystatin C concentrations in patients with MSA and age, sex, and body mass index matched-healthy controls (HCs) were measured. A multiple linear regression model was used to test the association between cystatin C and cognitive decline. Results A total of 117 patients with MSA and 416 HCs were enrolled in the study. The cystatin C levels were significantly higher in patients with MSA than in HCs (p < 0.001). Cystatin C levels were negatively correlated with MoCA score at baseline and at 1-year follow-up. Multiple linear regression model adjusted for potential confounders showed that baseline cystatin C levels were significantly associated with the MoCA score (p = 0.004) or change in the MoCA score (p = 0.008) at 1-year follow-up. Conclusion Our results suggested that cystatin C may serve as a potential biomarker of cognitive decline in patients with early-stage MSA.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".