MMP-2, MMP-9, and TIMP-4 and Response to Aspirin in Diabetic and Nondiabetic Patients with Stable Coronary Artery Disease: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: High on-aspirin treatment platelets reactivity (HPR) is a significant problem in long-term secondary prevention of cardiovascular events. We hypothesize that imbalance between platelets MMPs/TIMPs results in cardiovascular disorders. We also explored whether chronically elevated blood glucose affects MMP-2/TIMP-4 release from platelets. MATERIALS AND METHODS: Seventy patients with stable coronary artery disease, supplemented with aspirin, participated in this pilot study. The presence of HPR and/or diabetes mellitus was considered as the differentiating factor. Light aggregometry, impedance aggregometry, and ELISA tests for TXB2, MMP-2, MMP-9, and TIMP-4 were performed in serum, plasma, platelet-rich plasma, and platelets-poor plasma, as appropriate. RESULTS: Aspirin-HPR did not affect plasma MMP-2, MMP-9, and TIMP-4. Arachidonic acid-induced aggregation of platelets from aspirin-HPR patients did not lead to increased release of MMP-2, MMP-9, and TIMP-4. Studying patients at the lowest TXB2 serum concentration quartile revealed that high concentration of plasma TIMP-4 and TIMP-4 negatively correlated with TXB2 and platelet aggregation. Diabetics showed an increased plasma MMP-2 as well as an increased MMP-2 in supernatants after platelet aggregation. However, diabetes mellitus did not affect MMP-9 and TIMP-4. CONCLUSION: Aspirin-HPR did not affect the translocation and release of MMPs and TIMP-4 from platelets. TIMP-4 may serve as a marker of TXA2-mediated platelet aggregation. Chronically elevated plasma glucose increases plasma MMP-2, and HPR potentiates this phenomenon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".