Plasma Concentrations and Genetic Variation of Matrix Metalloproteinase 9 and Prognosis of Patients With Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinase (MMP)-9 secretion by macrophages and other inflammatory cells accelerates atherosclerotic progression and destabilizes vulnerable plaque in animal models. However, epidemiological data evaluating the prognostic impact of circulating concentrations and functional genetic variations of MMP-9 are lacking. METHODS AND RESULTS: In a prospective study of 1127 patients with documented coronary artery disease, we measured baseline plasma MMP-9 levels and determined the MMP-9/C-1562T and MMP-9/R279Q genotypes. During the follow-up period (mean of 4.1 years), 97 patients died from cardiovascular (CV) causes. Median concentrations of MMP-9 were significantly higher among patients who experienced a fatal CV event than among those who did not (62.2 versus 47.8 ng/mL; P<0.0001). The crude hazard risk ratio of CV death associated with increasing quartiles of MMP-9 was 1.4 (95% CI, 1.2 to 1.8; P<0.0001), and after adjustment for clinical and therapeutic confounders, it was 1.3 (95% CI, 1.1 to 1.6; P=0.005). Additional adjustment for highly sensitive CRP, interleukin-6, fibrinogen, and interleukin-18 revealed a hazard risk ratio to 1.2 (95% CI, 0.9 to 1.6; P=0.15). The T allele of the C-1562T polymorphism was associated with increased MMP-9 levels in a fairly codominant fashion (P=0.004). Although none of the polymorphisms was significantly related with future CV death, there was a significant association (P=0.02) between the R279Q polymorphism and CV events in patients with stable angina. CONCLUSIONS: Plasma MMP-9 concentration was identified as a novel predictor of CV mortality in patients with coronary artery disease. Whether it provides independent prognostic information compared with other inflammatory markers will have to be additionally assessed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".