Activation of Peroxisome Proliferator-Activated Receptor δ Inhibits Human Macrophage Foam Cell Formation and the Inflammatory Response Induced by Very Low-Density Lipoprotein
Bibliographic record
Abstract
OBJECTIVE: Hypertriglyceridemia is an important risk factor for cardiovascular disease. Elevated plasma very low-density lipoprotein (VLDL) puts insulin-resistant patients at risk for atherosclerosis. VLDL readily induces macrophage lipid accumulation and inflammatory responses, for which targeted therapeutic strategies remain elusive. We examined the ability of VLDL to induce macrophage foam cells and the inflammatory response and sought to define the cell signaling cascades involved. We further examined the potential of peroxisome proliferator-activated receptor (PPAR) δ activation to attenuate both VLDL-stimulated lipid accumulation and cytokine expression. METHODS AND RESULTS: THP-1 macrophages exposed to VLDL displayed significant triglyceride accumulation, which was attenuated by PPARδ activation. PPARδ agonists stimulated a transcriptional program resulting in inhibition of lipoprotein lipase activity, activation of fatty acid uptake, and enhanced β-oxidation. VLDL-treated macrophages significantly increased the expression of activator protein 1 associated cytokines interleukin-1β, macrophage inflammatory protein 1α, and intercellular adhesion molecule-1. VLDL treatment significantly increased the phosphorylation of both extracellular signal-related kinase 1 and 2 and p38. VLDL reduced AKT phosphorylation as well as its downstream effector forkhead box protein O1, concomitant with increased nuclear forkhead box protein O1. Cells treated with PPARδ agonists were completely resistant to VLDL-induced expression of inflammatory cytokines, mediated by normalization of mitogen-activated protein kinase (MAPK)(erk) and AKT/forkhead box protein O1 signaling. CONCLUSIONS: The combined PPARδ-mediated reductions of lipid accumulation and inflammatory cytokine expression suggest a novel macrophage-targeted therapeutic option in treating atherosclerosis.
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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 | 0.000 |
| 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 | 0.001 | 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".