Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) is an incretin hormone that enhances glucose-stimulated insulin secretion and exerts direct and indirect actions on the cardiovascular system. GLP-1 and its related incretin hormone, glucose-dependent insulinotropic polypeptide, are rapidly inactivated by the enzyme dipeptidyl peptidase 4 (DPP-4), a key determinant of incretin bioactivity. Two classes of medications that enhance incretin action, GLP-1 receptor (GLP-1R) agonists and DPP-4 inhibitors, are used for the treatment of type 2 diabetes mellitus. We review herein the cardiovascular biology of GLP-1R agonists and DPP-4 inhibitors, including direct and indirect effects on cardiomyocytes, blood vessels, adipocytes, the control of blood pressure, and postprandial lipoprotein secretion. Both GLP-1R activation and DPP-4 inhibition exert multiple cardioprotective actions in preclinical models of cardiovascular dysfunction, and short-term studies in human subjects appear to demonstrate modest yet beneficial actions on cardiac function in subjects with ischemic heart disease. Incretin-based agents control body weight, improve glycemic control with a low risk of hypoglycemia, decrease blood pressure, inhibit the secretion of intestinal chylomicrons, and reduce inflammation in preclinical studies. Nevertheless, there is limited information on the cardiovascular actions of these agents in patients with diabetes and established cardiovascular disease. Hence, a more complete understanding of the cardiovascular risk to benefit ratio of incretin-based therapies will require completion of long-term cardiovascular outcome studies currently underway in patients with type 2 diabetes mellitus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".