Combined Oral Administration of GABA and DPP-4 Inhibitor Prevents Beta Cell Damage and Promotes Beta Cell Regeneration in Mice
Bibliographic record
Abstract
γ-aminobutyric acid (GABA) or glucagon-like peptide-1 (GLP-1) based drugs, such as sitagliptin (a dipeptidyl peptidase-4 inhibitor), were shown to induce beta cell regenerative effects in various diabetic mouse models. We propose that their combined administration can bring forth an additive therapeutic effect. We tested this hypothesis in a multiple low-dose streptozotocin (STZ)-induced beta cell injury mouse model (MDSD). Male C57BL/6J mice were assigned randomly into four groups: non-treatment diabetic control, GABA, sitagliptin, or GABA plus sitagliptin. Oral drug administration was initiated one week before STZ injection and maintained for six weeks. GABA or sitagliptin administration decreased ambient blood glucose levels and improved the glucose excursion rate. This was associated with elevated plasma insulin and reduced plasma glucagon levels. Importantly, combined use of GABA and sitagliptin significantly enhanced these effects as compared with each of the monotherapies. An additive effect on reducing water consumption was also observed. Immunohistochemical analyses revealed that combined GABA and sitagliptin therapy was superior in increasing beta cell mass, associated with increased small-size islet numbers, Ki67+ and PDX-1+ beta cell counts; and reduced Tunel+ beta cell counts. Thus, beta cell proliferation was increased, whereas apoptosis was reduced. We also noticed a suppressive effect of GABA or sitagliptin on alpha cell mass, which was not significantly altered by combining the two agents. Although either GABA or sitagliptin administration delays the onset of MDSD, our study indicates that combined use of them produces superior therapeutic outcomes. This is likely due to an amelioration of beta cell proliferation and a decrease of beta cell apoptosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".