Combination Therapy of Alpha-Glucosidase Inhibitor, Thiazolidinedione and Sodium Glucose Co-Transporter-2 Inhibitor in Japanese Type 2 Diabetes Patients
Bibliographic record
Abstract
Background: We additionally administered selective inhibitor of sodium glucose co-transporter-2 (SGLT2) inhibitor to type 2 diabetic patients on alpha-glucosidase inhibitor (alpha-GI) and/or thiazolidinedione (TZD), to determine whether the multi-drug combination therapy including SGLT2 inhibitor further improved the glycemic control in Japanese patients with diabetes mellitus. Methods: The subjects were divided into four groups. Group I was administered an SGLT2 inhibitor in addition to ongoing treatment using hypoglycemic agents other than alpha-GI and SGLT2 inhibitor. Group II was administered an SGLT2 inhibitor in addition to an alpha-GI. Group III was administered an SGLT2 inhibitor in addition to a TZD. Group IV was administered an SGLT2 inhibitor in addition to an alpha-GI and TZD. The body weight and biochemical parameters were measured before and 1, 2 and 3 years after the start of SGLT2 inhibitor treatment. Results: In patients receiving alpha-GI or TZD, additional administration of an SGLT2 inhibitor resulted in a reduction of the body weight, body mass index (BMI) and serum uric acid level, but had little effect on the glucose or lipid metabolism. On the other hand, additional administration of SGLT2 inhibitor in patients receiving both alpha-GI and TZD (three-drug combination therapy) resulted not only in a reduction of the body weight, BMI and serum uric acid, but also in improvement of the glucose and lipid metabolism, with a reduction of the hemoglobin A1c level and increase of the serum high-density lipoprotein cholesterol level. Conclusion: The results suggest that three-drug combination therapy with alpha-GI, TZD and SGLT2 inhibitor, each of which has different mechanism of action, is beneficial for the treatment of type 2 diabetes mellitus. J Endocrinol Metab. 2020;10(6):167-172 doi: https://doi.org/10.14740/jem708
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".