Sitagliptin Versus Ipragliflozin for Type 2 Diabetes in Clinical Practice
Bibliographic record
Abstract
Background: Dipeptidyl peptidase-4 inhibitors and sodium-glucose cotransporter-2 inhibitors are frequently used to treat type 2 diabetes. However, there have been no direct comparisons of these antidiabetic drugs in Japanese patients with type 2 diabetes. Methods: We retrospectively assessed the effects of treatment with sitagliptin (a dipeptidyl peptidase-4 inhibitor) for 24 weeks in the ASSET-K study or treatment with ipragliflozin (a sodium-glucose cotransporter-2 inhibitor) for 24 weeks in the ASSIGN-K study. In both studies, patients with poor glycemic control received the study drug in addition to standard care with/without other antidiabetic medications or were switched to the study drug. The effects of each drug on metabolic risk factors (body weight, blood glucose, and lipids), blood pressure, and renal function were compared. Results: After 4 weeks of treatment, hemoglobin A1c was significantly lower in patients receiving ipragliflozin than in those receiving sitagliptin, but the difference was not significant at 12 or 24 weeks. Body mass index showed a significantly larger decrease with ipragliflozin treatment than sitagliptin treatment throughout most of the study period (P < 0.001 at 24 weeks). The mean blood pressure also showed a significantly larger decrease with ipragliflozin treatment than sitagliptin treatment throughout most of the study period (P = 0.007 at 24 weeks). In contrast, the decrease of the estimated glomerular filtration rate after 24 weeks was significantly larger in patients treated with sitagliptin than those receiving ipragliflozin (P = 0.012). Conclusions: Ipragliflozin may be more effective than sitagliptin for Japanese patients with type 2 diabetes who have hypertension, obesity, and/or renal dysfunction. J Endocrinol Metab. 2019;9(5):151-158 doi: https://doi.org/10.14740/jem604
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".