Statins activate the NLRP3 inflammasome and impair insulin signaling via p38 and mTOR
Bibliographic record
Abstract
Statins lower cholesterol and risk of cardiovascular disease. Statins can increase blood glucose and risk of new-onset diabetes. It is unclear why statins can have opposing effects on lipids versus glucose. Statins have cholesterol-independent pleiotropic effects that influence both insulin and glucose control. Statin lowering of isoprenoids required for protein prenylation promotes pancreatic β-cell dysfunction and adipose tissue insulin resistance. Protein prenylation influences immune function and statin-mediated adipose tissue insulin resistance involves the NLR family pyrin domain-containing 3 (NLRP3) inflammasome and IL-1β. However, the intracellular cues that statins engage to activate the NLRP3 inflammasome and those responsible for IL-1β-mediated insulin resistance in adipose tissue have not been identified. We hypothesized that stress kinases or components of the insulin signaling pathway mediated statin-induced insulin resistance. We tested the associations of p38, ERK, JNK, phosphatase, and tensin homolog (PTEN), and mTOR in statin-exposed adipose tissue from WT and IL-1β −/− mice. We found that statins increased phosphorylation of p38 in WT and IL-1β −/− mice. Statin activation of p38 upstream of IL-1β led to priming of this NLRP3 inflammasome effector in macrophages. We found that mTORC1 inhibition with low doses of rapamycin (2 or 20 nM) lowered macrophage priming of IL-1β mRNA and secretion of IL-1β caused by multiple statins. Rapamycin (20 nM) or the rapalog everolimus (20 nM) prevented atorvastatin-induced lowering of insulin-mediated phosphorylation of Akt in mouse adipose tissue. These results position p38 and mTOR as mediators of statin-induced insulin resistance in adipose tissue and highlight rapalogs as candidates to mitigate the insulin resistance and glycemic side effects of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".