Association between Plasma Levels of PAI-1, tPA/PAI-1 Molar Ratio, and Mild Cognitive Impairment in Chinese Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Plasminogen activator inhibitor 1 (PAI-1) and tissue plasminogen activator (tPA) are involved in the complications of type 2 diabetes mellitus (T2DM) and early pathology of Alzheimer's disease. OBJECTIVE: This study aimed to investigate the association between plasma PAI-1, tPA/PAI-1 molar ratio, and mild cognitive impairment (MCI) in Chinese T2DM patients. METHODS: A total of 162 Chinese T2DM patients were recruited and divided into two groups according to the Montreal Cognitive Assessment score. Demographic data were collected, plasma PAI-1 and tPA levels were measured through enzyme-linked immunosorbent assay, tPA/PAI-1 molar ratio was calculated, and neuropsychological test results were examined. The association between PAI-1, tPA/PAI-1 molar ratio, and cognition was analyzed. RESULTS: There were 66 diabetic MCI patients and 96 healthy cognition participants (controls). T2DM patients with MCI displayed significantly increased plasma PAI-1 levels (p = 0.016) and decreased tPA/PAI-1 molar ratio (p = 0.021) compared with the controls. High PAI-1 levels and low tPA/PAI-1 molar ratio were associated with MCI in T2DM patients, e.g., plasma level of PAI-1 were negatively correlated (r = -0.343, p = 0.007) with logic memory in T2DM patients with MCI. Linear regression analysis further revealed that PAI-1 concentration was an independent factor of diabetic MCI (p = 0.001). CONCLUSIONS: High PAI-1 levels and low tPA/PAI-1 molar ratio were significantly correlated with T2DM-associated cognitive impairment, especially memory function, in Chinese patients.
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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 | 0.001 | 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 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".