The role of atorvastatin in regulating the immune response leading to vascular damage in a model of Kawasaki disease
Bibliographic record
Abstract
Superantigens have been implicated in a number of diseases including Kawasaki disease (KD), a multi-system vasculitis resulting in coronary artery aneurysms. We have characterized a murine disease model in which coronary arteritis is induced by a novel superantigen found in Lactobacillus casei cell wall extract (LCWE). Using this animal model of KD, we have identified three pathogenic steps leading to coronary artery aneurysm formation. These steps include T cell activation and proliferation, production of the proinflammatory cytokine tumour necrosis factor (TNF)-α and up-regulation of matrix metalloproteinase 9 (MMP-9), an elastolytic protease. In addition to their cholesterol-lowering effects, 3-hydroxy-3-methylglutaryl (HMG) coenzyme A (CoA) reductase inhibitors (statins) have pleotropic immunomodulatory properties. Thus, we examined the effect of atorvastatin in modulating each of these three critical pathogenic processes leading to aneurysm formation in the disease model. Atorvastatin inhibited lymphocyte proliferation in response to superantigen stimulation in a dose-dependent manner. This inhibition was also observed for production of soluble mediators of inflammation including interleukin (IL)-2 and TNF-α. The inhibitory effect on proliferation was rescued completely by mevalonic acid, confirming that the mechanism responsible for this inhibitory activity on immune activation was inhibition of HMG-CoA reductase. Similarly, TNF-α-induced MMP-9 production was reduced in a dose-dependent manner in response to atorvastatin. Inhibition of extracellular-regulated kinase (ERK) phosphorylation appears to be the mechanism responsible for inhibition of MMP-9 production. In conclusion, atorvastatin is able to inhibit critical steps known to be important in the development of coronary aneurysms, suggesting that statins may have therapeutic benefit in patients with KD.
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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 | 0.001 | 0.001 |
| 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".