Single and Combined Impact of Semaglutide, Tirzepatide, and Metformin on β-Cell Maintenance and Function Under High-Glucose–High-Lipid Conditions: A Comparative Study
Bibliographic record
Abstract
Type 2 diabetes (T2D), the most common form, is marked by insulin resistance and β-cell failure. β-cell dysfunction under high-glucose-high-lipid (HG-HL) conditions is a key contributor to the progression of T2D. This study evaluates the comparative effects of 10 nM semaglutide, 10 nM tirzepatide, and 1 mM metformin, both alone and in combination, on INS-1 β-cell maintenance and function under HG-HL conditions. INS-1 cells were pretreated for 2 h with single doses of metformin (1 mM), semaglutide (10 nM), tirzepatide (10 nM), or combinations of 1 mM metformin with either 10 nM semaglutide or 10 nM tirzepatide, followed by 48 h of HG-HL stimulation. The results indicate that combining 1 mM metformin with either 10 nM semaglutide or 10 nM tirzepatide significantly enhances the effects of 10 nM semaglutide and 10 nM tirzepatide on HG-HL-induced apoptosis and dysregulated cell cycle. Specifically, the combination treatments demonstrated superior restoration of glucose-stimulated insulin secretion (GSIS) functionality compared to 1 mM metformin, 10 nM semaglutide, and 10 nM tirzepatide.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 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".