Effects of Ipragliflozin on Diabetic Nephropathy and Blood Pressure in Patients With Type 2 Diabetes: An Open-Label Study
Bibliographic record
Abstract
Background: Sodium-glucose cotransporter 2 (SGLT2) inhibitors are novel agents used to treat type 2 diabetic patients. We investigated the efficacy of the SGLT2 inhibitor ipragliflozin on diabetic nephropathy in Japanese patients with type 2 diabetes. Methods: A 50 mg dose of ipragliflozin was administered for 24 weeks to 50 patients with type 2 diabetes who were concomitantly managed with diet and exercise therapy alone or antidiabetic medications other than SGLT2 inhibitors. Results: At the end of the 24-week ipragliflozin treatment, significant decreases in mean glycated hemoglobin (HbA1c) (1.0±1.2%) and body weight (2.7 ± 2.5 kg) were observed; in addition, median urinary albumin-to-creatinine ratio (UACR) significantly decreased from 15.5 (8.0 - 85.7) to 12.9 (7.4 - 36.3) mg/gCr. Sub-analysis by renal function at baseline revealed that median UACR in patients with estimated glomerular filtration rate (eGFR) ? 90 mL/min/1.73 m 2 decreased significantly from 12.3 (7.5 - 89.6) to 10.6 (5.8 - 27.3) mg/gCr. Furthermore, mean eGFR decreased significantly from 102.4 ± 8.6 to 93.6 ± 10.5 mL/min/1.73 m 2 in these patients. In contrast, UACR and eGFR did not change significantly in patients with eGFR < 90. In addition, analysis of the relationship between the amount of change in UACR and blood pressure at 24 weeks revealed a significant positive correlation between UACR and SBP values, independently of the presence of diabetic nephropathy. Conclusions: Our results indicate that ipragliflozin may facilitate HbA1c control and body weight reduction. Furthermore, our results also raise the possibility that ipragliflozin significantly reduces urinary albumin levels and improves glomerular hyperfiltration in a subset of patients with type 2 diabetes. J Clin Med Res. 2017;9(2):154-162 doi: https://doi.org/10.14740/jocmr2875w
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".