337-LB: Combined Oral Administration of GABA and GLP-1 Promotes Human ß-Cell Proliferation and Reduces Apoptosis
Bibliographic record
Abstract
Gamma-aminobutyric acid (GABA) and glucagon-like peptide-1 (GLP-1) improve rodent β cell survival and function. In human β cells, GABA exerts stimulatory effects on proliferation and anti-apoptotic effects, whereas GLP-1-based drugs have only limited effects on proliferation. We previously demonstrated that GABA and sitagliptin, a dipeptidyl peptidase-4 inhibitor which increases endogenous GLP-1 levels, mediated a synergistic β cell protective effect in mice islets. However, it remains unclear whether this combination has similar effects on human β cells. To address this question, we transplanted a suboptimal mass of human islets into immunodeficient NOD-scid-gamma mice with streptozotocin-induced diabetes, and then treated them with GABA, sitagliptin, or both. The oral administration of either GABA or sitagliptin ameliorated blood glucose levels, increased transplanted human β cell mass and plasma human insulin levels. However, combined administration of the drugs generated significantly superior results in all these responses, as compared to monotherapy. Proliferation and/or regeneration, improved by the combination, were demonstrated by increased Ki67+, pancreatic and duodenal homeobox-1 (PDX-1+), or Nkx6.1+ β cell numbers. Protection against apoptosis was also significantly improved by the combined therapy. GABA and GLP-1 in combination notably increased Akt phosphorylation and insulin secretion, as well as the expression of sirtuin-1 and α-Klotho, both of which have been reported to have protective effects on β-cells. Our study indicates that combined use of GABA and sitagliptin produced greater therapeutic benefits, which was likely due to an enhancement of β cell proliferation and a decrease of apoptosis. Disclosure W. Liu: None. T. Jin: None. Q. Wang: None. Funding National Natural Science Foundation of China (81570518, 81630020); JDRF (2-SRA-2017-64-G-R to Q.W., T.J.), (2-SRA-2015-64 to Q.W., T.J.); Canadian Diabetes Association (OG-3-13-4066 to Q.W.)
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".