Matrix metalloproteinase-2 and -9 exacerbate arterial stiffening and angiogenesis in diabetes and chronic kidney disease
Bibliographic record
Abstract
AIMS: Chronic kidney disease (CKD) and diabetes are the prominent risk factors of cardiovascular disease (CVD). Matrix metalloproteinase (MMP)-2 and -9 regulate vascular structure by degrading elastic fibre and inhibit angiogenesis by generating angiostatin. We hypothesized that MMP-2 and -9 were up-regulated in the arterial vasculature from CKD patients with diabetes, compared with those without diabetes. METHODS AND RESULTS: During living donor transplantation procedures, arteries from donors (n = 8) and recipients (non-diabetic, n = 8; diabetic, n = 8; matched in age, gender, and dialysis treatments) were harvested. Diabetic arteries had increased MMP-2 and -9 activities by 42 and 116% compared with non-diabetic ones. Diabetic arteries were the stiffest, and the stiffness measurement was highly correlated with the summation of MMP-2 + MMP-9 activities (r = 0.738, P = 0.0002). Pulse wave velocity measurements correlated with MMP activity (r = 0.683, P = 0.005). Elastic fibre degradation and calcification were worst in diabetic vessels. The phosphate level, which was 25% higher in diabetic patients, correlated with MMP activity (r = 0.513, P = 0.04) and in vitro stiffness (r = 0.545, P = 0.03), respectively. Angiostatin expression was doubled, whereas vascular endothelial growth factor was 50% reduced in diabetic compared with non-diabetic vessels. Microvascular density in diabetic vessels was 48% of that in non-diabetic ones, and it was strongly associated with MMP activity (r = -0.792, P < 0.0001) and vasorelaxation (r = 0.685, P = 0.0009). CONCLUSION: Using a matched case-control design, we report up-regulation of MMP-2 and -9 in diabetic CKD arteries and correlate those with stiffening, impaired angiogenesis, and endothelial dysfunction. These findings may help to explain the high susceptibility of CVD in diabetic and non-diabetic CKD 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 | 0.000 |
| 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 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".