Correlation between serum cystatin-C levels and cognitive impairment
Bibliographic record
Abstract
Objective To investigate the correlation between serum cystatin-C (Cys-C) levels and cognitive impairment. Methods A perspective study involving 273 patients in our hospital was performed from 2013 to 2015.They were divided into the cognitive impairment group and the control group.Cys-C measurement results and cognitive impairment assessment scores were collected.Possible influence factors were adjusted, and the correlation between Cys-C levels and mild cognitive impairment was analyzed. Results The distribution of age, hypertension, diabetes mellitus, smoking, drinking, dyslipidemia, and creatinine showed significant differences between the groups at different Cys-C levels (all P<0.05). There was a significant difference in the Cys-C level between the cognitive impairment group and the control group 〔(0.727±0.082) mg/L vs. (0.514±0.045)mg/L, t=23.68, P<0.01〕. The Cys-C level was negatively correlated with the scores of mini-mental state examination (MMSE) and Montreal cognitive assessment (MOCA) in the cognitive impairment group (r=-0.318 and -0.572, P<0.05). The incidence of cognitive impairment was elevated with increasing Cys-C levels (χ2=13.12, P<0.01). Logistic regression analysis showed that high levels of Cys-C (OR=3.298, 95% CI: 1.417-7.675, P=0.001), history of diabetes mellitus (OR=7.971, 95% CI: 3.036-31.562, P=0.03), education level (OR=2.237, 95% CI: 1.022-4.896, ), smoking (OR=5.692, 95% CI: 1.060-2.614), drinking (OR=1.227, 95% CI: 0.083-1.228), and dyslipidemia (OR=2.267, 95% CI: 1.177-4.366) are independent risk factors for cognitive impairment. Conclusions High serum cystatin C levels are closely correlated with the risk of cognitive impairment occurrence. Key words: Cysteine proteinase inhibitors; Cognitive impairment
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".