Metformin Preserves β-Cell Compensation in Insulin Secretion and Mass Expansion in Prediabetic Nile Rats
Bibliographic record
Abstract
Prediabetes is a high-risk condition for type 2 diabetes (T2D). Pancreatic β-cells adapt to impaired glucose regulation in prediabetes by increasing insulin secretion and β-cell mass expansion. In people with prediabetes, metformin has been shown to prevent prediabetes conversion to diabetes. However, emerging evidence indicates that metformin has negative effects on β-cell function and survival. Our previous study established the Nile rat (NR) as a model for prediabetes, recapitulating characteristics of human β-cell compensation in function and mass expansion. In this study, we investigated the action of metformin on β-cells in vivo and in vitro. A 7-week metformin treatment improved glucose tolerance by reducing hepatic glucose output and enhancing insulin secretion. Although high-dose metformin inhibited β-cell glucose-stimulated insulin secretion in vitro, stimulation of β-cell insulin secretion was preserved in metformin-treated NRs via an indirect mechanism. Moreover, β-cells in NRs receiving metformin exhibited increased endoplasmic reticulum (ER) chaperones and alleviated apoptotic unfold protein response (UPR) without changes in the expression of cell identity genes. Additionally, metformin did not suppress β-cell mass compensation or proliferation. Taken together, despite the conflicting role indicated by in vitro studies, administration of metformin does not exert a negative effect on β-cell function or cell mass and, instead, early metformin treatment may help protect β-cells from exhaustion and decompensation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".