Relationships Between Plasma Levels of Matrix Metalloproteinases and Neurohormonal Profile in Patients with Heart Failure
Bibliographic record
Abstract
BACKGROUND: Both neurohormonal derangements and alterations in the myocardial extracellular matrix are thought to contribute to adverse ventricular remodelling that results in worsening heart failure (HF). There is also emerging preclinical information to suggest that these signalling pathways mutually regulate in HF. AIM: To assess the relationships between plasma levels of matrix metalloproteinases (MMPs), tissue inhibitor of metalloproteinase (TIMP), and neurohormonal profiles in chronic HF. METHODS AND RESULTS: In this substudy of 184 HF patients enrolled in the Randomized Evaluation of Strategies for Left Ventricular Dysfunction (RESOLVD) trial, plasma norepinephrine and epinephrine were measured with HPLC; atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), angiotensin II, aldosterone, and endothelin-1 were measured with immunoassays; MMP-2, MMP-9, and TIMP-1 were measured with 2-site sandwich ELISA assays. We used Spearman's rank correlation to examine the relationships between plasma MMP and neurohormone levels. Circulating ANP, BNP, and endothelin-1 levels were positively correlated with MMP-2 and TIMP-1 levels. Plasma level of aldosterone showed a weak positive correlation with MMP-9, but there was no significant correlation between angiotensin II, epinephrine or norepinephrine and MMP-2, MMP-9, or TIMP-1. CONCLUSIONS: These findings suggest that specific neurohormones and extracellular matrix modulators may play a coordinated role in the pathogenesis of HF.
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How this classification was reachedexpand
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) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".