Impact of serum cystatin C level on long-term cognitive impairment after acute ischemic stroke and transient ischemic attack
Bibliographic record
Abstract
Abstract Objective:Cognitive impairment after stroke/transient ischemic attack (TIA) has a high prevalence. Cystatin C (CysC) has been found as a novel biomarker of neurodegenerative diseases, such as dementia and Alzheimer’s disease. We aimed to explore the possible correlations of serum cystatin C level with cognitive impairment in patients who had mild stroke and TIA after 1 year.Methods:We measured serum CysC levels in 1025 participants with a minor ischemic stroke/TIA from enrolled from the Impairment of Cognition and Sleep (ICONS) study of the China National Stroke Registry-3 (CNSR-3). They were divided into four groups according to quartiles of baseline CysC levels. Patients’ cognitive functions were assessed by MoCA-Beijing at day 14 and at 1 year. Multiple logistic regression models were performed to evaluate the relationship between CysC and PSCI at 1 year follow-up.Results: Cognitive impairment was defined as MoCA-Beijing ≤22. Most patients were in 60s (61.52±10.97 years old) with a median (interquartile range) National Institute of Health Stroke Scale score of 3.00(4.00) and greater than primary school level of education, and 743 participants (72.49%) were male. Among the 1025 participants, 331 participants (32.29%) patients suffered PSCI at 1 year follow-up. A U-shaped association was observed between CysC and 1-year PSCI [quartile (Q)1 vs. Q3: adjusted odds ratio (aOR) 2.64, 95% CI 1.65-4.20, p＜0.0001; Q2 vs. Q3: aOR 1.83, 95% CI 1.17-2.84, p = 0.0078; Q4 vs. Q3: aOR 1.86, 95% CI 1.20-2.87, p = 0.0055]. Moreover, the U-shaped trends were also found between CysC level and the subscores of attention, recall, abstraction and language in MoCA.Conclusions: CysC showed a U-shaped correlation with 1-year overall cognitive function. It is probable that measurement of the serum cystatin C level would aid in the early diagnosis of PSCI.
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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 | 0.004 |
| 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 | 0.001 | 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".