Efficacy and Safety of Ipragliflozin in Japanese Patients With Type 2 Diabetes: Interim Outcome of the ASSIGN-K Study
Bibliographic record
Abstract
BACKGROUND: Ipragliflozin is a sodium-glucose co-transporter 2 inhibitor that can improve glycemic control and reduce body weight and blood pressure in patients with type 2 diabetes mellitus (T2DM). We evaluated the efficacy and safety of ipragliflozin in the real-world clinical setting, with a focus on the changes of body composition up to 3 months of treatment. METHODS: This was a prospective multicenter interventional trial. We investigated changes of the blood pressure, body composition, blood glucose, hemoglobin A1c (HbA1c), ketone bodies, lipids, and insulin after treatment with ipragliflozin (50 - 100 mg/day) for 12 weeks in Japanese patients with T2DM who showed poor glycemic control despite receiving diet and exercise therapy with or without oral antidiabetic drugs for more than 12 weeks. RESULTS: Two hundred and fifty-seven subjects were included in the efficacy analysis up to 12 weeks of treatment and 301 subjects were included in the safety analysis. From baseline to 12 weeks, HbA1c showed a change of -0.68% (95% confidence interval (CI): -0.83, -0.53) and fasting blood glucose showed a change of -23.9 mg/dL (95% CI: -30.5, -17.2), with both parameters displaying a significant reduction (P < 0.001). The difference of body weight from baseline was -1.82 kg (95% CI: -2.14, -1.50), and it also showed significant reduction (P < 0.001). Analysis of body composition revealed that body fat changed by -1.46 kg (95% CI: -1.79, -1.14, P < 0.001) and body water changed by -0.37 kg (95% CI: -0.60, -0.14, P < 0.01). Laboratory tests demonstrated improvement of liver function and the lipid profile. Adverse events (AEs) occurred in 22.6% of the subjects, with frequent events being vulvovaginal candidiasis in 2.7% and cystitis in 2.0%. Serious AEs occurred in three subjects. CONCLUSIONS: In patients with T2DM, ipragliflozin improved glycemic control after 1 month of treatment and caused weight loss by reducing body fat more than body water.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".