Combined effect of GABA and glucagon‐like peptide‐1 receptor agonist on cytokine‐induced apoptosis in pancreatic β‐cell line and isolated human islets
Bibliographic record
Abstract
BACKGROUND: Treatment with GABA or glucagon-like peptide-1 (GLP-1) can preserve pancreatic β-cell mass and prevent diabetes. Recently, we reported that the combination of GABA and sitagliptin (a dipeptidyl peptidase-4 inhibitor that increases endogenous GLP-1) was more effective than either agent alone in reducing drug-induced β-cell damage and promoting β-cell regeneration in mice. However, in human islets, it remains unclear whether GABA and GLP-1 exert similar effects. METHODS: To investigate GABA and GLP-1 interactions, human islets or INS-1 cells were treated with GABA and/or exendin-4, a GLP-1 receptor agonist (GLP-1RA) in clinical use, and incubated with a cytokine mixture for 24 hours. Cleaved caspase-3 and annexin V binding were measured by western blot and flow cytometry analysis, respectively, to investigate effects on cytokine-induced apoptosis. RESULTS: Cytokine-induced apoptosis was reduced by either GABA or exendin-4 alone. This was markedly improved by combining GABA and exendin-4, resulting in a reversal of apoptosis. The combination notably increased Akt pathway signaling. Furthermore, sirtuin-1 (SIRT1) and α-Klotho, both reported to have protective effects on β-cells, were increased. Importantly, the combination ameliorated insulin secretion by human β-cells. CONCLUSIONS: The combination of GABA and a GLP-1RA exerted additive effects on β-cell survival and function, suggesting that this combination may be superior to either drug alone in the treatment of diabetes.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".