Macrophage Cholesterol Efflux and Atherosclerosis in Psoriasis: A role for microRNA‐33
Bibliographic record
Abstract
Psoriatic patients have an increased risk in developing atherosclerosis. However, the precise mechanism by which the localized skin inflammation drives atherosclerosis is unclear. Macrophages have been shown to promote localized skin inflammation and also drive atherosclerosis by retaining modified excess cholesterol. Macrophage cholesterol efflux depends on 2 elements: (i) functional circulating HDL interacting with (ii) macrophage ABCA1 cholesterol transporter to release the excess cholesterol into HDL particles to be cleared. microRNA‐33 (miR‐33) levels are markedly elevated in CAD and it directly targets various genes regulating cholesterol transport, mitochondrial function and inflammation to inhibit macrophage cholesterol efflux, promoting atherosclerosis. Thus, novel therapeutics aims to inhibit miR‐33 to treat atherosclerosis. Nonetheless, the mechanism by which macrophage miR‐33 drives atherosclerosis in psoriasis and how psoriasiform‐inflammation accelerates atherosclerosis remains to be elucidated. Here, we aim to determine if psoriasiform macrophages have elevated miR‐33 levels and are defective in mediating cholesterol efflux and thus, accelerating atherosclerosis. Preliminary data indicate that human macrophages treated with key cytokines (e.g. IL‐17A or IL‐23 or TNFα) that drive psoriatic inflammation have increased miR‐33a expression and decreased macrophage cholesterol efflux. Ongoing studies are investigating whether human primary macrophages derived from psoriatic patients have decreased cholesterol efflux relative to control. These studies will provide mechanistic insight as to how miR‐33 is regulated in psoriasiform inflammation and whether anti‐miR33 therapy or other novel therapeutic targeting the macrophage efflux pathways can be used to treat cardiovascular disease in psoriatic patients.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".