Effects of Six Kinds of Sodium-Glucose Cotransporter 2 Inhibitors on Metabolic Parameters, and Summarized Effect and Its Correlations With Baseline Data
Bibliographic record
Abstract
BACKGROUND: Sodium-glucose cotransporter 2 inhibitor (SGLT2i) blocks reabsorption of glucose by inhibiting SGLT2 in kidney, promotes the renal excretion of glucose and improves blood glucose control without requiring insulin secretion. Anti-atherosclerotic effects of SGLT2is have not been fully elucidated until today. METHODS: We retrospectively picked up patients with type 2 diabetes who had been continuously prescribed SGLT2i for 3 months or more between April 2014 and December 2016 by a chart-based analysis, and compared metabolic parameters including coronary risk factors before the SGLT2i treatment with the data at 3 and 6 months after the SGLT2i treatment started. RESULTS: We found 26 patients treated with tofogliflozin, 34 patients with canagliflozin, 27 patients with empagliflozin, 23 patients with ipragliflozin, 68 patients with dapagliflozin and 71 patients with luseogliflozin. Each SGLT2i ameliorated metabolic parameters, in different patterns. SGLT2is reduced body weight, systolic and diastolic blood pressures, plasma glucose, hemoglobin A1c, aspartate aminotransferase, alanine aminotransferase, γ-glutamyltransferase, uric acid, triglyceride and non-high-density lipoprotein-cholesterol (HDL-C), and elevated HDL-C; however, they did not affect LDL-cholesterol levels. Change in each metabolic parameter was significantly correlated with each metabolic parameter at baseline. CONCLUSION: The present study demonstrated that SGLT2i ameliorated body weight, blood pressure, liver function, serum lipids and uric acid, in addition to improvement of glucose metabolism in patients with type 2 diabetes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".