Reductions of Circulating Matrix Metalloproteinase 2 and Vascular Endothelial Growth Factor Levels after Treatment with Pegvisomant in Subjects with Acromegaly
Bibliographic record
Abstract
BACKGROUND: Vascular endothelial growth factor (VEGF) is involved in activation of the matrix metalloproteinase (MMP) system; the latter is implicated in atherosclerosis and cardiovascular disease. Patients with acromegaly have reduced life expectancy primarily due to cardiac disease. AIM: This study assessed plasma MMPs and VEGF levels in patients with active acromegaly (IGF-I > 130% upper limit of normal), and on treatment with pegvisomant. SUBJECTS AND METHODS: Twenty patients [nine female, mean age 56.1 +/- 13.8 yr (mean +/- sd)] were studied at baseline and on pegvisomant therapy and compared with data from 25 healthy volunteers (12 female; 56.6 +/- 14.2 yr). Plasma MMP-2, MMP-9, and VEGF levels were measured. RESULTS: Serum IGF-I fell from a baseline (mean +/- sd) level of 620.1 +/- 209.3 ng/ml to 237.5 +/- 118.5 ng/ml on pegvisomant (doses 10-60 mg; P < 0.001). MMP-2 levels at baseline were significantly higher in patients compared with healthy controls (380.7 +/- 204.8 vs. 207.4 +/- 62.6 ng/ml; P < 0.001), but with treatment a significant reduction in MMP-2 [380.7 +/- 204.8 vs. 203.0 +/- 77.4 ng/ml; P < 0.001] and VEGF (283.4 +/- 233.6 vs. 229.1 +/- 157.4 pg/ml; P = 0.008) was noted. There was no significant difference in MMP-9 levels between patients and controls at baseline (797.5 +/- 142.1 vs. 788.3 +/- 218.0 ng/ml; P = 0.87) or between baseline and posttreatment levels (797.5 +/- 142.1 vs. 780.0 +/- 214 ng/ml; P = 0.76). CONCLUSIONS: Our novel data demonstrate that treatment of acromegaly with pegvisomant leads to reductions in MMP-2 and VEGF concentrations. Further studies are required to determine the significance of these findings with relation to cardiac 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 | 0.000 | 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".