Physiology and role of PCSK9 in vascular disease: Potential impact of localized PCSK9 in vascular wall
Bibliographic record
Abstract
Abstract Proprotein convertase subtilisin/kexin type‐9 (PCSK9), a member of the proprotein convertase family, is an important drug target because of its crucial role in lipid metabolism. Emerging evidence suggests a direct role of localized PCSK9 in the pathogenesis of vascular diseases. With this in our consideration, we reviewed PCSK9 physiology with respect to recent development and major studies (clinical and experimental) on PCSK9 functionality in vascular disease. PCSK9 upregulates low‐density lipoprotein (LDL)‐cholesterol levels by binding to the LDL‐receptor (LDLR) and facilitating its lysosomal degradation. PCSK9 gain‐of‐function mutations have been confirmed as a novel genetic mechanism for familial hypercholesterolemia. Elevated serum PCSK9 levels in patients with vascular diseases may contribute to coronary artery disease, atherosclerosis, cerebrovascular diseases, vasculitis, aortic diseases, and arterial aging pathogenesis. Experimental models of atherosclerosis, arterial aneurysm, and coronary or carotid artery ligation also support PCSK9 contribution to inflammatory response and disease progression, through LDLR‐dependent or ‐independent mechanisms. More recently, several clinical trials have confirmed that anti‐PCSK9 monoclonal antibodies can reduce systemic LDL levels, total nonfatal cardiovascular events, and all‐cause mortality. Interaction of PCSK9 with other receptor proteins (LDLR‐related proteins, cluster of differentiation family members, epithelial Na + channels, and sortilin) may underlie its roles in vascular disease. Improved understanding of PCSK9 roles and molecular mechanisms in various vascular diseases will facilitate advances in lipid‐lowering therapy and disease prevention.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".