Sodium-glucose cotransporter-2 inhibitors improve liver enzymes in patients with co-existing non-alcoholic fatty liver disease: a systematic review and metanalysis
Bibliographic record
Abstract
Introduction: Non-alcoholic fatty liver disease (NAFLD) is characterized by hepatic steatosis, inflammation, and fibrosis. While sodium-glucose cotransporter-2 (SGLT-2) inhibitors have been established to improve glycaemic control in type-2 diabetes mellitus (T2DM), evidence of the beneficial effects in diabetics with coexisting NAFLD has yet to be quantitatively summarized. Material and methods: We searched the PubMed, Medline, CINAHL, and Cochrane databases and ClinicalTrial.gov from database inception to July 2020. We included randomized controlled trials assessing the impact of SGLT2 inhibitors on liver enzymes among patients with NAFLD. Our primary outcome included liver inflammation as measured using liver transaminase. Secondary outcomes included drug efficacy on hepatic steatosis and body mass index. Risk differences were calculated using a random model. Results: = 3430). The treatment duration ranged from 8 to 52 weeks. Patients with T2DM, who were treated with SGLT2 inhibitor had decrease in ALT (SMD = -0.22, 95% CI: -0.27 to -0.20) and AST levels (SMD = -0.20, 95% CI: -0.31 to -0.08). The SGLT-2 inhibitor did not cause statistically significant weight loss (SMD = -0.21, 95% CI: -0.47 to 0.06), fibrosis regression utilizing FIB-4 score (SMD = -0.12, 95% CI: -0.41 to 0.18), and hepatic steatosis by using MRI-PDFF (SMD = -0.31, 95% CI: -0.68 to 0.07), as compared to controls. Conclusions: The SGLT2 inhibitor treatment may improve liver function, as demonstrated in the statistically significant reduction in transaminase levels. There were also notable trends in improved liver fibrosis and steatosis across the study periods.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".