Statins Promote Interleukin-1β–Dependent Adipocyte Insulin Resistance Through Lower Prenylation, Not Cholesterol
Bibliographic record
Abstract
Statins lower cholesterol and adverse cardiovascular outcomes, but this drug class increases diabetes risk. Statins are generally anti-inflammatory. However, statins can promote inflammasome-mediated adipose tissue inflammation and insulin resistance through an unidentified immune effector. Statins lower mevalonate pathway intermediates beyond cholesterol, but it is unknown whether lower cholesterol underpins statin-mediated insulin resistance. We sought to define the mevalonate pathway metabolites and immune effectors that propagate statin-induced adipose insulin resistance. We found that LDL cholesterol lowering was dispensable, but statin-induced lowering of isoprenoids required for protein prenylation triggered NLRP3/caspase-1 inflammasome activation and interleukin-1β (IL-1β)–dependent insulin resistance in adipose tissue. Multiple statins impaired insulin action at the level of Akt/protein kinase B signaling in mouse adipose tissue. Providing geranylgeranyl isoprenoids or inhibiting caspase-1 prevented statin-induced defects in insulin signaling. Atorvastatin (Lipitor) impaired insulin signaling in adipose tissue from wild-type and IL-18−/− mice, but not IL-1β−/− mice. Atorvastatin decreased cell-autonomous insulin-stimulated lipogenesis but did not alter lipolysis or glucose uptake in 3T3-L1 adipocytes. Our results show that statin lowering of prenylation isoprenoids activates caspase-1/IL-1β inflammasome responses that impair endocrine control of adipocyte lipogenesis. This may allow the targeting of cholesterol-independent statin side effects on adipose lipid handling without compromising the blood lipid/cholesterol-lowering effects of statins.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".