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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".