Impact of statins in the liver: A bane or a boon?
Bibliographic record
Abstract
Treatment of hypercholesterolemia with statins is considered one of the cornerstones in the management of atherosclerotic cardiovascular diseases. Statins exert their hypolipidemic effects by inhibiting HMG-CoA reductase, the key enzyme in cholesterol biosynthesis. Beyond cholesterol reduction, statins exhibit pleiotropic effects, including anti-inflammatory, antioxidant, and antiproliferative actions, making them valuable in mitigating atherosclerotic and non-atherosclerotic diseases. Though concerns of hepatotoxicity have been associated with the use of statins, extensive evidence suggests that the risk of statin-induced liver injury (SILI) is rare, with an incidence of <1%. Hepatic adverse effects include reversible asymptomatic transaminase elevation (most frequent), hepatitis, cholestasis, and rarely acute liver failure. While hepatotoxicity concerns should not be dismissed, the evidence overwhelmingly supports the safety of statins. Contrary to the myth of statin hepatotoxicity, real-world data and extensive research emphasize the safety and benefits of statins. They are therapeutic in various liver-related conditions, mainly non-alcoholic fatty liver disease. This scientific review aims to provide a comprehensive overview of statins, shedding light on their mechanism of action, hepatotoxicity concerns, and therapeutic potential in various liver-related conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".