Bibliographic record
Abstract
Glucagon-like peptide-1 receptor (GLP-1R) agonists and dipeptidyl peptidase-4 (DPP-4) inhibitors represent 2 distinct classes of incretin-based therapies used for the treatment of type 2 diabetes mellitus. Activation of GLP-1R signaling or inhibition of DPP-4 activity produces a broad range of overlapping and unique cardiovascular actions. Native GLP-1 regulates cardiovascular biology via activation of the classical GLP-1R, or through GLP-1(9-36), a cardioactive metabolite generated by DPP-4-mediated cleavage. In contrast, clinically approved GLP-1R agonists are not cleaved to GLP-1(9-36) and produce the majority of their actions through the classical GLP-1R. The cardiovascular mechanisms engaged by DPP-4 inhibition are more complex, encompassing increased levels of intact GLP-1, reduced levels of GLP-1(9-36), and changes in levels of numerous cardioactive peptides. Herein we review recent experimental and clinical advances that reveal how GLP-1R agonists and DPP-4 inhibitors affect the normal and diabetic heart and coronary vasculature, often independent of changes in blood glucose. Improved understanding of the complex science of incretin-based therapies is required to optimize the selection of these therapeutic agents for the treatment of diabetic patients with cardiovascular disease.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".