Atorvastatin decreases lipoprotein lipase and endothelial lipase expression in human THP-1 macrophages
Bibliographic record
Abstract
Macrophage-derived lipases are associated with atherosclerosis in human and animal studies. Despite numerous non-lipid-lowering effects of statins, their effect on macrophage LPL and endothelial lipase (EL) expression has not been investigated. In the present study, atorvastatin and simvastatin dose-dependently decreased LPL and EL expression as well as Rho, liver X receptor alpha (LXRalpha), and nuclear factor kappaB (NF-kappaB) activation in THP-1 macrophages. Atorvastatin-reduced LPL and EL expression was only partially recovered by mevalonate cotreatment, indicating that mechanisms independent of reductase inhibition may be present. By contrast, Rho activation by lysophosphatidyl acid further decreased LPL and EL expression in the presence or absence of atorvastatin. Another Rho activator, farnysyl pyrophosphate, decreased EL expression only in the absence of atorvastatin. LXRalpha activation by T0901317 and 22(R)-hydroxycholesterol not only rescued but also significantly increased LPL expression in the presence and absence of atorvastatin, respectively, whereas LXRalpha inhibition by 22(S)-hydroxycholesterol decreased LPL expression. By contrast, EL expression was suppressed by LXRalpha activation in the presence or absence of atorvastatin. NF-kappaB inhibition by SN50 was associated with an approximately 30% reduction of EL expression. Furthermore, atorvastatin treatment significantly attenuated the lipid accumulation in macrophages treated with oxidized LDL. We conclude that atorvastatin reduces LPL and EL expression by reducing the activation of LXRalpha and NF-kappaB, respectively.
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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) | 0.000 | 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) | 0.002 | 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".