The Antidiabetic Hormone Glucagon-Like Peptide-1 Induces Formation of New Elastic Fibers in Human Cardiac Fibroblasts After Cross-Activation of IGF-IR
Bibliographic record
Abstract
Glucagon-like peptide 1 (GLP-1) is a metabolic hormone involved in the stimulation of insulin biosynthesis and secretion. It has been recently reported that GLP-1 also exerts cardioprotective effects and facilitates functional recovery after myocardial infarction through GLP-1 receptor-mediated signaling in cardiomyocytes. GLP-1 treatment has been also demonstrated to produce sustained improvement in cardiac function in long-term studies, suggesting the involvement of mechanisms beyond the acute metabolic and cytoprotective effects. For example, the possible interaction of GLP-1 with the cardiac fibroblasts, which are responsible for the postinfarct remodeling and extracellular matrix production, has not been previously explored. Here, we report that cultures of human cardiac fibroblasts treated with GLP-1 peptides display a selective up-regulation in elastin gene expression and a consequent increase in elastic fibers production, in the absence of the classic GLP-1 receptor. Importantly, we provide experimental evidence that this GLP-1-induced elastogenesis is triggered through the cross-activation of the IGF-I receptor. Because GLP-1 does not stimulate deposition of collagen I, nor promote the proliferation or apoptosis of cultured cardiac fibroblasts, we speculate that its elastogenic effect may also contribute to the beneficial remodeling of the human heart after myocardial infarction.
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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 | 0.001 |
| 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".