Dipeptidyl Peptidase-4 Inhibitors Administered in Combination With Metformin Result in an Additive Increase in the Plasma Concentration of Active GLP-1
Bibliographic record
Abstract
The aim of the study was to investigate the effects of a dipeptidyl peptidase-4 (DPP-4) inhibitor, of metformin, and of the combination of the two agents, on incretin hormone concentrations. Active and inactive (or total) incretin plasma concentrations, plasma DPP-4 activity, and preproglucagon (GCG) gene expression were determined after administration of each agent alone or in combination to mice with diet-induced obesity (DIO) and to healthy human subjects. In mice, metformin increased Gcg expression in the large intestine and elevated the plasma concentrations of inactive glucagon-like peptide 1 (GLP-1) (9-36) and glucagon. In healthy subjects, a DPP-4 inhibitor elevated both active GLP-1 and glucose dependent insulinotropic polypeptide (GIP), metformin increased total GLP-1 (but not GIP), and the combination resulted in additive increases in active GLP-1 plasma concentrations. Metformin did not inhibit plasma DPP-4 activity either in vitro or in vivo. The study results show that metformin is not a DPP-4 inhibitor but rather enhances precursor GCG expression in the large intestine, resulting in increased total GLP-1 concentrations. DPP-4 inhibitors and metformin have complementary mechanisms of action and additive effects with respect to increasing the concentrations of active GLP-1 in plasma.
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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 | 0.000 |
| 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) | 0.002 | 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".