Role of proprotein convertase subtilisin/kexin type 9 (PCSK9) in diabetic complications
Bibliographic record
Abstract
Cardiovascular disease (CVD) complications have remained a major cause of death among patients with diabetes. Hence, there is a need for effective therapeutics against diabetes-induced CVD complications. Since its discovery, proprotein convertase subtilisin/kexin type 9 (PCSK9) has been reported to be involved in the pathology of various CVDs, with studies showing a positive association between plasma levels of PCSK9, hyperglycemia, and dyslipidemia. PCSK9 regulates lipid homeostasis by interacting with low-density lipoprotein receptors (LDLRs) present in hepatocytes and subsequently induces LDLR degradation via receptor-mediated endocytosis, thereby reducing LDL uptake from circulation. In addition, PCSK9 also induces pro-inflammatory cytokine expression and apoptotic cell death in diabetic-CVD. Furthermore, therapies designed to inhibit PCSK9 effectively reduces diabetic dyslipidemia with clinical studies reporting reduced cardiovascular events in patients with diabetes and no significant adverse effect on glycemic controls. In this review, we discuss the role of PCSK9 in the pathogenesis of diabetes-induced CVD and the potential mechanisms by which PCSK9 inhibition reduces cardiovascular events in diabetic patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".