Effects of silymarin use on liver enzymes and metabolic factors in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Fatty liver disease comprises a wide range of related liver disorders affecting mainly people who drink no or minimal amounts of alcohol. Silymarin is a member of the Carduus marianum family that has been used for centuries to treat different diseases. There is little evidence supporting its efficacy in humans. Objectives: To evaluate the effects of Silymarin in patients with non alcoholic fatty liver disease (NAFLD) or recently renamed metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: We searched PubMed, SCOPUS, Web of Science, and Cochrane Library for relevant clinical trials assessing the use of silymarin in patients with NAFLD. A risk of bias assessment was performed using Cochrane's risk of bias tool. We included the following outcomes: alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transferase (GGT), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) (mg/dL), degree of fibrosis resolution, low-density lipoprotein (LDL), and HOMA-IR. We analyzed continuous data using mean difference (MD) and relative 95% confidence interval (CI). Results: We included nine clinical trials. We found that silymarin significantly reduced the levels of ALT (MD= -17.12 [-28.81, -4.43]), (P < 0.004), AST (MD= -12.56 [-19.02, -6.10]), (P < 0.0001) and TG (MD = −22.60 [−23.83, −21.38]) ( p < 0.00001). It also improved HDL (MD= 2.13 [1.60, 2.66]), (P < 0.01)). There was no significant difference regarding GGT (P=o.07), TC (P= 0.52), LDL (P= 0.06), HOMA-IR (P= 0.06) and BMI (p=0.1).One study reported significant improvement in the degree of fibrosis (P = 0.023). Conclusion: Silymarin treatment significantly reduces biochemical and transaminase levels in patients with MASLD.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".