A Randomized Controlled Trial of Vildagliptin Versus Alogliptin: Effective Switch From Sitagliptin in Patients With Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: We investigated the effects of vildagliptin or alogliptin on blood glucose and hemoglobin A1c (HbA1c) in patients with type 2 diabetes inadequately controlled by sitagliptin. METHODS: In a single-center open-label trial, 35 patients with inadequate glycemic control on sitagliptin therapy (50 mg once daily) were randomly switched to treatment with vildagliptin (50 mg twice daily) or alogliptin (25 mg once daily). After 12 weeks, patients who failed to achieve the target HbA1c level of < 7.0% with vildagliptin or alogliptin treatment were switched to high-dose sitagliptin (100 mg once daily) and the effect on glycemic control was assessed. RESULTS: Vildagliptin did not significantly alter the mean plasma glucose level (175.5 ± 54.4 mg/dL vs. 179.1 ± 73.4 mg/dL) or HbA1c (8.01% vs. 8.02%) after 12 weeks. With alogliptin, mean plasma glucose increased from 175.4 ± 50.9 mg/dL to 195.3 ± 55.0 mg/dL after 12 weeks and HbA1c increased significantly from 8.0% to 8.3% (P < 0.05). At 12 weeks after switching from vildagliptin to high-dose sitagliptin (100 mg daily), HbA1c was increased to 8.3%, but it was significantly (P < 0.05) reduced to the baseline level of 8.0% after switching from alogliptin. The reduction of HbA1c was significantly greater in the vildagliptin group than the alogliptin group (P = 0.008), but the response rate (achieving the target HbA1c < 7.0%) did not differ significantly between the two groups. CONCLUSION: The glucose-lowering effects of these three dipeptidyl peptidase-4 (DPP-4) inhibitors (vildagliptin, alogliptin, and sitagliptin) were different, and the effects of vildagliptin and sitagliptin were stronger than that of alogliptin.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".