Increased Matrix Metalloproteinase 2 Activity in the Human Internal Mammary Artery Is Associated with Ageing, Hypertension, Diabetes and Kidney Dysfunction
Bibliographic record
Abstract
Dysregulation of matrix metalloproteinase (MMP)-2 in the vasculature has been suggested to be associated with increased prevalence of cardiovascular disease and renal injury. In this descriptive study, we hypothesized that arterial MMP-2 activity is elevated in the presence of cardiovascular risk factors such as diabetes, hypertension, smoking and ageing, and that it correlates with the degree of kidney function. MMP-2 activity in internal mammary arteries (n = 37) was measured using gelatinolytic zymography, and cutoffs were determined using sample-derived medians. Patient demographics and clinical data were analyzed, and the estimated glomerular filtration rate (eGFR) was calculated. High MMP-2 activity (>60,000 units) was associated with age, hypertension and diabetes (p = 0.0034, 0.06 and 0.0034, respectively). Multivariate analysis showed that age and diabetes were independent predictors of high MMP-2 activity. There is a trend towards increased MMP-2 activity and reduced eGFR (p = 0.010). The current exploratory work describes that the activity of MMP-2 in the internal mammary artery is correlated with age, hypertension, diabetes and eGFR. It is the first report suggesting that MMP-2 in the arterial vasculature could be the possible mediator crucial in linking the progression of kidney function to cardiovascular disease.
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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 | 0.000 | 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 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".