The NLRP3 Inflammasome Contributes to Statin Myopathy
Bibliographic record
Abstract
As a front-line treatment for cardiovascular disease, statins are among some of the most widely prescribed drugs worldwide. Statins are effective at lowering cholesterol, but approximately 7-29% of patients report some form of adverse muscle effect during the course of treatment. The severity of these side effects ranges from low-level to life-threatening myopathy. The mechanism of statin myopathy remains ill-defined, but muscle-specific E3 ubiquitin ligases have been implicated. In addition, statins have been shown to activate caspase-1 (and increase IL-1β) in immune cells, which is a key effector of the NLRP3 inflammasome. The relevance of this inflammatory response in statin myopathy remains unknown. Using C2C12 myotubes, an in vitro model of statin-induced myopathy was developed to test the impact of NLRP3 inflammasome activation on markers of statin myopathy. Gene expression of the muscle-specific E3 ubiquitin ligases atrogin-1 and MuRF-1 (atrogenes) were used as markers of statin-induced myopathy. Lipopolysaccharide priming of the NLRP3 inflammasome was found to lower the effective dose of fluvastatin required to augment atrogene expression. This effect correlated with reduced phosphorylation of Akt and FOXO3a, a transcription factor regulating atrogene expression. Statin-induced atrogene expression was also found to be dependent on an isoprenoid that is required for protein prenylation rather than cholesterol biosynthesis pathways. Fluvastatin increased caspase-1 activity in a prenylation-dependent manner and selective inhibitors of NLRP3 and caspase-1 were able to prevent increased atrogene expression with fluvastatin treatment. Therefore, the NLRP3 inflammasome contributes to markers of statin-induced myopathy through a prenylation-dependant pathway in muscle cells. This work presents a novel mechanism involved in statin myopathy, and has shown that the inflammasome may represent a new drug target to mitigate muscle symptoms in patients taking statins.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".