Effects of Sodium-Glucose Cotransporter 2 Inhibitors on Hepatic Fibrosis in Patients With Type 2 Diabetes: A Chart-Based Analysis
Bibliographic record
Abstract
Background: The presence of nonalcoholic fatty liver diseases (NAFLDs) and type 2 diabetes was associated with elevated risks of cardiovascular events as well as the progression of NAFLD to fibrosis/cirrhosis and hepatocellular carcinoma. Sodium-glucose cotransporter 2 inhibitor (SGLT2i) is a widely used antidiabetic drug, which promotes urinary excretion of glucose. Recent animal and human studies demonstrated the beneficial effects of SGLT2is on lipid accumulation and fibrosis in the liver. The purpose of the current study was to elucidate the effects of SGLT2is on hepatic fibrosis in the real-world setting. Methods: We selected patients with type 2 diabetes who had been prescribed SGLT2is continuously for 12 months between April 1, 2014 and March 31, 2018 by a chart-based analysis. We compared the data before the SGLT2is treatment with the data at 6 and 12 months after the SGLT2is treatment started. Fibrosis in the liver was evaluated by fibrosis-4 (FIB4) index. Results: We enrolled 315 patients in this study. The body weight, body mass index (BMI), serum levels of aspartate aminotransferase, alanine aminotransferase and gamma-glutamyl transferase were significantly decreased at 6 months and maintained at 12 months, whereas there was no significant change in FIB4 index. We divided the studied patients into three groups according to the baseline FIB4 index. Only in the group of high value of the baseline FIB4 index, FIB4 index was significantly decreased at 12 months. The correlations between the change of FIB4 index during 12-month SGLT2i treatment was correlated inversely with the baseline FIB4 index. Conclusion: Present study demonstrated that SGLT2i could ameliorate fibrosis in the liver in high-risk patients for hepatic fibrosis. J Endocrinol Metab. 2020;10(1):1-7 doi: https://doi.org/10.14740/jem632
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".