The Dipeptidyl Peptidase-4 Inhibitor Linagliptin Directly Enhances the Contractile Recovery of Mouse Hearts at a Concentration Equivalent to that Achieved with Standard Dosing in Humans
Bibliographic record
Abstract
Despite a similar mechanism of action underlying their glucose-lowering effects in type 2 diabetes, dipeptidyl peptidase-4 (DPP-4) inhibitors have diverse molecular structures, raising the prospect of agent-specific, glucose-independent actions. To explore the issue of possible DPP-4 inhibitor cardiac heterogeneity, we perfused different DPP-4 inhibitors to beating mouse hearts ex vivo, at concentrations equivalent to peak plasma levels achieved in humans with standard dosing. We studied male and female mice, young non-diabetic mice, and aged diabetic high fat diet-fed mice and observed that linagliptin enhanced recovery after ischemia-reperfusion, whereas sitagliptin, alogliptin, and saxagliptin did not. DPP-4 transcripts were not detected in adult mouse cardiomyocytes by RNA sequencing and the addition of linagliptin caused ≤0.2% of cardiomyocyte genes to be differentially expressed. In contrast, incubation of C166 endothelial cells with linagliptin induced cell signaling characterized by phosphorylation of Akt and endothelial nitric oxide synthase, whereas the nitric oxide (NO) donor, S-nitroso-N-acetylpenicillamine increased serine 16 phosphorylation of the calcium regulatory protein, phospholamban in cardiomyocytes. Furthermore, linagliptin increased cardiomyocyte cGMP when cells were co-cultured with C166 endothelial cells, but not when cardiomyocytes were cultured alone. Thus, at a concentration comparable to that achieved in patients, linagliptin has direct effects on mouse hearts. The effects of linagliptin on cardiomyocytes are likely to be either off-target or indirect, mediated through NO generation by the adjacent cardiac endothelium.
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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) | 0.000 | 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) | 0.002 | 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".