Matrix metalloproteinase-2 and -9 exacerbate arterial stiffening and angiogenesis in diabetes and chronic kidney disease
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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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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 | 0.000 |
| 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 it