Dipeptidyl peptidase‐4 inhibition improves cardiac function in experimental myocardial infarction: Role of stromal cell‐derived factor‐1<i>α</i>
Bibliographic record
Abstract
BACKGROUND: In addition to degrading glucagon-like peptide-1 (GLP-1), dipeptidyl peptidase-4 (DPP-4) inactivates several chemokines, including stromal cell-derived factor-1α (SDF-1α), a pro-angiogenic and cardiomyocyte protective protein. We hypothesized that DPP-4 inhibition may confer benefit following myocardial infarction (MI) in the diabetic setting as a consequence of enhanced SDF-1α availability rather than potentiating GLP-1. To test this we compared the effects of saxagliptin with those of liraglutide and used the SDF-1α receptor (CXCR4) antagonist plerixafor. METHODS: Studies were conducted in streptozotocin-diabetic rats. Rats were randomized to receive saxagliptin (10 mg/kg per day), liraglutide (0.2 mg/kg, s.c., b.i.d.), plerixafor (1 mg/kg per day, s.c.), saxagliptin plus plerixafor or vehicle (1% phosphate-buffered saline). Two weeks later, rats underwent experimental MI, with cardiac function examined 4 weeks after MI. RESULTS: Glycemic control and MI size were similar in all groups. Four weeks after MI, mortality was reduced in saxagliptin-treated rats compared with vehicle treatment (P < 0.05). Furthermore, rats receiving saxagliptin had improved cardiac function compared with vehicle-treated rats (P < 0.05). Antagonism of CXCR4 prevented the improvement in cardiac function in saxagliptin-treated rats and was associated with increased mortality (P < 0.05). CONCLUSION: Saxagliptin-mediated DPP-4 inhibition, but not liraglutide-mediated GLP-1R agonism, improved cardiac function after MI independent of glucose lowering. These findings suggest that non-GLP-1 actions of DPP-4 inhibition, such as SDF-1α potentiation, mediate biological effects.
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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) | 0.001 | 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) | 0.002 | 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".