Systematic review: Preventive and therapeutic applications of metformin in liver disease
Bibliographic record
Abstract
Metformin, a biguanide derivative, is the most commonly prescribed medication in the treatment of type 2 diabetes mellitus. More recently, the use of metformin has shown potential as a preventive and therapeutic agent for a broad spectrum of conditions, including liver disease and hepatic malignancies. In this systematic review, we critically analyze the literature behind the potential use of metformin across the spectrum of liver disease and malignancies. The PubMed and Ovid MEDLINE databases were searched from 2000 to March 2015, using a combination of relevant text words and MeSH terms: metformin and mammalian target of rapamycin, hepatitis B virus (HBV), hepatitis B virus (HCV), non-alcoholic fatty liver disease (NAFLD), hepatocellular carcinoma (HCC) or cholangiocarcinoma. The search results were evaluated for pertinence to the issue of metformin in liver disease as well as for quality of study design. Metformin has a number of biochemical effects that would suggest a benefit in treating chronic liver diseases, particularly in the context of insulin resistance and inflammation. However, the literature thus far does not support any independent therapeutic role in NAFLD or HCV. Nonetheless, there is Level III evidence for a chemopreventive role in patients with diabetes and chronic liver disease, with decreased incidence of HCC and cholangiocarcinoma. The use of metformin seems to be safe in patients with cirrhosis, and provides a survival benefit. Once hepatic malignancies are already established, metformin does not offer any therapeutic potential. In conclusion, there is insufficient evidence to recommend use of metformin in the adjunctive treatment of chronic liver diseases, including NAFLD and HCV. However, there is good evidence for a chemopreventive role against HCC among patients with diabetes and chronic liver disease, and metformin should be continued in patients even with cirrhosis to provide this benefit.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| 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) | 0.007 | 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".