Effects of Sodium-Glucose Cotransporter 2 Inhibitors on Metabolic Parameters in Patients With Type 2 Diabetes: A Chart-Based Analysis
Bibliographic record
Abstract
BACKGROUND: Effects of the new class of anti-diabetic drugs, sodium-glucose cotransporter 2 (SGLT2) inhibitors, on metabolic parameters in patients with type 2 diabetes remain largely unknown. METHODS: We retrospectively picked up patients who had been continuously prescribed SGLT2 inhibitors for 1 month or more between April 2014 and November 2015 by a chart-based analysis, and compared the data before the SGLT2 inhibitor treatment with the data at 1, 2, 3 and 6 months after the SGLLT2 inhibitor treatment started. RESULTS: Fifty patients were eligible for the analyses in our study. The HbA1c levels as well as body weight significantly decreased at 1, 2, 3 and 6 months after the start of SGLT2 inhibitors. Systolic blood pressure tended to decrease only at 1 and 2 months, but there was no change at 3 and 6 months. No significant change was observed in serum high-density lipoprotein-cholesterol (HDL-C), triglyceride (TG), low-density lipoprotein-cholesterol (LDL-C) and non-HDL-C levels. Serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels significantly decreased at 3 and 6 months after the prescription. The hematocrit levels significantly increased at 1, 2, 3 and 6 months, and the estimated glomerular filtration rate (eGFR) levels significantly decreased at 1 month after the start of SGLT2 inhibitors. A significant correlation between reductions in HbA1c levels and HbA1c levels at baseline was observed at 1, 3 and 6 months. The decreases in serum ALT levels were also significantly correlated with the baseline ALT levels at 3 and 6 months. CONCLUSION: Present study demonstrated that SGLT2 inhibitors significantly reduced HbA1c and body weight and improved liver functions, whereas no significant change was observed in serum lipid profiles.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".