Meta‐analysis of the effect of sodium–glucose cotransporter 2 inhibitors on hepatic fibrosis in patients with type 2 diabetes mellitus complicated with non‐alcoholic fatty liver disease
Bibliographic record
Abstract
AIM: This study aimed to analyze the effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors on the indexes of liver fibrosis in patients with type 2 diabetes mellitus complicated with non-alcoholic fatty liver disease, and also to observe the effects on liver enzymes and liver fat. METHODS: This meta-analysis was performed using RevMan 5.3 statistical software. RESULTS: SGLT2 inhibitors could significantly reduce the level of hepatic fibrosis index: fibrosis-4 (mean difference [MD] 0.25, 95% CI -0.39 to -0.11, p = 0.0007); serum type Ⅳ collagen 7s (MD 0.32, 95% CI -0.59 to -0.04, p = 0.02); and ferritin (MD 26.7, 95% CI 50.64, 2.76, p = 0.03). SGLT2 inhibitors could significantly reduce the level of liver enzymes: alanine aminotransferase (MD 3.49, 95% CI -5.1 to 1.58, p < 0.0001); aspartate aminotransferase (MD 3.64, 95% CI -5.10 to -2.18, p < 0.00001); and glutamate aminotransferase (MD 7.13, 95% CI -12.95 to -1.32, p = 0.02). SGLT2 inhibitors could significantly reduce the level of liver fat: liver-to-spleen attenuation ratio (MD 0.16, 95% CI 0.10-0.22, p < 0.00001); magnetic resonance imaging proton density fat fraction (MD 1.97, 95% CI -3.49 to -0.45, p = 0.01); liver controlled attenuation parameter (MD 0.29, 95% CI -26.95 to -13.64, p < 0.00001); liver fat score (MD 0.55, 95% CI 1.04 to -0.05, p = 0.03); and liver fat index (MD 11.21, 95% CI -16.53 to -5.89, p < 0.0001). CONCLUSION: SGLT2 inhibitors could improve liver fibrosis, liver enzymes, liver fat, and metabolic indexes in patients with type 2 diabetes mellitus complicated with non-alcoholic fatty liver disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.000 | 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".