The role of matrix metalloproteinases in patients with pulmonary hypertension: data from a prospective study
Bibliographic record
Abstract
BACKGROUND: Despite several therapies, pulmonary hypertension (PH) is still a severe disease which can lead to right heart failure. Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are involved in cardiac and vascular remodeling in PH. Therefore, these biomarkers play an important role in PH patients. This study investigated whether TIMP-4, MMP-2, and N-terminal Pro-B-Type Natriuretic Peptide (NT-proBNP) plasma levels are useful in assessing the severity of PH and other clinical or echocardiographic parameters. METHODS: The concentrations of MMP-2, TIMP-4, and NT-proBNP in 68 PH patients were compared with those of 12 controls without PH. All patients underwent a physical examination, echocardiography, and were checked for the presence of cardiovascular risk factors; also, plasma concentrations of MMP-2, TIMP-4, NT-proBNP, total cholesterol, and triglycerides were determined. RESULTS: In PH patients, significantly elevated plasma levels of TIMP-4 (PH: 2877.99 ± 1363.78 pg/ml, control: 2028.38 ± 762.67 pg/ml, p = 0.0068) and NT-proBNP ( PH: 2405.00 pg/ml-5423.47 ± 6703.38 pg/ml, control: 411.0000 pg/ml-421.75 ± 315.37 pg/ml, p = 0.01) were detected. We also observed that MMP-2 and NT-proBNP were significantly increased in patients with higher WHO functional class (p = 0.001 for MMP-2, p = 0.008 for NT-proBNP), higher pressure in the pulmonary artery (p = 0.002 for MMP-2, p = 0.001 for NT-proBNP), and more severe tricuspid regurgitation (p = 0.001 for MMP-2, p = 0.009 for NT-proBNP). TIMP-4 was elevated in patients with more severe pressure in the pulmonary artery (p = 0.006). CONCLUSIONS: The plasma levels of TIMP-4 and NT-proBNP are higher in PH patients. MMP-2 and NT-proBNP correlates with different PH parameters severity (WHO functional class, sPAP severity, TV regurgitation severity). Therefore, plasmatic levels of MMP-2 and NT-proBNP at this kind of patients reflect disease severity and may have a prognostic role. MMP-2 can help assess the beneficial effects of PH pharmacotherapy on tissue remodeling. These remodeling biomarkers may not have a diagnostic value but they have the potential to predict survival. Nevertheless, a greater understanding of the involvement of MMPs in PH is mandatory to further explore the prognostic role and the possibilities of therapeutic MMP inhibition in PH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".