The role of exercise in limiting progression from liver inflammation and fibrosis to cirrhosis and carcinoma: a systematic review with meta-analysis of human and animal studies
Bibliographic record
Abstract
Abstract Background Exercise may prevent the progression of liver disease and protect against liver cancer. This review with meta-analysis synthesised the evidence from both human and animal studies to better understand whether exercise has the capacity to (i) promote regression of early fibrosis; (ii) decrease and/or delay progression to cirrhosis; and (iii) progression to carcinoma. Methods A systematic search was performed to identify studies comprising of humans and animals with liver disease that compared exercise to an inactive or less active control. Outcomes included liver disease regression and progression, and markers of liver function and damage. Results We found 18 human and 29 animal studies. A single study provided direct evidence that exercise can reverse NAFLD and decrease progression to cirrhosis. Meta-analysis of human studies identified decreases in liver enzymes; ALT (SMD = -0.28, 95%CI = -0.53, -0.03), AST (SMD = -0.12, 95%CI = -0.32, 0.07), GGT (SMD = -0.23, 95%CI = -0.36, -0.10), as well as a small increase in ALP (SMD = 0.23, 95%CI = -0.13, 0.59), and liver triglycerides (SMD = -0.24, 95%CI = -0.66, 0.18). Meta-analysis of animal studies identified decreases in liver enzymes; ALT (SMD = -2.85, 95%CI = -4.55, -1.14), AST (SMD = -2.85, 95%CI = -4.55, -1.14), and liver triglycerides (SMD = -1.36, 95%CI = -2.08, -0.65), liver weight (SMD = -1.94, 95%CI = -2.78, - 1.10), and the NAFLD activity score (SMD = -1.36, 95%CI = -2.08, -0.65). Conclusion Only one study provided direct evidence that exercise has the capacity to regress early fibrosis, as well as delay the progression to cirrhosis. Several studies, however, indicate that exercise intervention reduce markers of liver function and damage.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| 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) | 0.003 | 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".