Effects of Canagliflozin on Fatty Liver Indexes in Patients with Type 2 Diabetes: A Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
PURPOSE: Non-alcoholic fatty liver disease (NAFLD) affects about 75% of patients with type 2 diabetes mellitus (T2DM). We conducted a meta-analysis to determine the effect of canagliflozin on fatty liver indexes in T2DM patients. METHODS: A literature search of PubMed, Embase and Cochrane was conducted up to March 30, 2017. The liver function test and lipid profile were extracted from randomized controlled trials (RCTs) to evaluate the effect of canagliflozin on fatty liver. Weighted mean differences (WMDs) or relative risks and 95% confidence intervals (CIs) were computed by using either fixed or random-effects models. Sensitivity analysis and publication bias were evaluated. RESULTS: Our results showed that canagliflozin decreased serum concentrations of alanine amino transferase (WMD: -11.68 [95% CI: -18.95, -10.95]; P<0.001), aspartate amino transferase (WMD: -7.50 [95% CI: -10.61, -4.38]; P<0.001), gamma-glutamyl transferase (WMD: -15.17 [95% CI: -17.73, -12.61]; P<0.001), triglycerides (WMD: -0.10 [95% CI: -0.15, -0.05]; P<0.001) but increased low-density lipoprotein cholesterol (WMD: 0.1 [95% CI: 0.06, 0.13]; P<0.001), high-density lipoprotein cholesterol (WMD: 0.06 [95% CI: 0.05, 0.07]; P<0.001) at week 26 or 52. CONCLUSIONS: Our results indicated that canagliflozin may have a protective effect on fatty liver in T2DM patients. The limitation was that the liver biopsy was hard to obtain in published studies. More RCTs specified on NAFLD are needed to get further information. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.056 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".