Cardiovascular Benefits of Native GLP-1 and its Metabolites: An Indicator for GLP-1-Therapy Strategies
Bibliographic record
Abstract
Cardiovascular disease is a common co-morbidity and leading cause of death in patients with type 2 diabetes mellitus (T2DM). Glucagon-like peptide 1 (GLP-1) is a peptide hormone produced by intestinal L cells in response to feeding. Native GLP-1 (7-36) amide is rapidly degraded by diaminopeptidyl peptidase-4 (DPP4) to GLP-1 (9-36) amide, making 9-36a the major circulating form. While it is 7-36a, and not its metabolites, which exerts trophic effects on islet β-cells, recent studies suggest that both 7-36a and its metabolites have direct cardiovascular effects, including preserving cardiomyocyte viability, ameliorating cardiac function, and vasodilation. In particular, the difference in cardiovascular effects between 7-36a and 9-36a is attracting attention. Growing evidence has strengthened the presumption that their cardiovascular effects are overlapping, but distinct and complementary to each other; 7-36a exerts cardiovascular effects in a GLP-1 receptor (GLP-1R) dependent pathway, whereas 9-36a does so in a GLP-1R independent pathway. GLP-1 therapies have been developed using two main strategies: DPP4-resistant GLP-1 analogs/GLP-1R agonists and DPP4 inhibitors, which both aim to prolong the life-time of circulating 7-36a. One prominent concern that should be addressed is that the cardiovascular benefits of 9-36a are lacking in these strategies. This review attempts to differentiate the cardiovascular effects between 7-36a and 9-36a in order to provide new insights into GLP-1 physiology, and facilitate our efforts to develop a superior GLP-1-therapy strategy for T2DM and cardiovascular diseases.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".