Statins Induce Plasma Levels of Proprotein Convertase Subtilisin/Kexin Type 9
Bibliographic record
Abstract
LDL receptor (LDLR) number is regulated transcriptionally by sterol regulatory element‐binding protein (SREBP)‐2. Studies have shown that inhibitors of HMG‐CoA reductase (statins) used to treat hypercholesterolemia trigger a regulatory response in cells leading to elevated levels of active SREBP‐2, thereby increasing LDLR expression. Proprotein convertase subtilisin/kexin type 9 (PCSK9), a SREBP‐2 regulated protein secreted by the liver, binds to the LDLR and mediates its degradation. Thus, induction of PCSK9 may counter‐balance the effect of statins on liver LDLR protein levels. We demonstrate that simvastatin‐fed rats exhibit elevated levels of nuclear SREBP‐2 in liver and increased transcription of SREBP‐2 target genes, including PCSK9 . This was associated with a >3‐fold increase in circulating PCSK9 levels compared to control rats. In human studies, 13 subjects with moderate to slightly elevated plasma LDL‐cholesterol levels (127 – 238 mg/dl) were administered a statin for 7 days (Lipitor ‐ 40 mg/day), which resulted in a 25% decrease in plasma cholesterol levels. ELISA measurements of circulating PCSK9 concentrations showed a mean increase of 35% in these subjects. These studies demonstrate that circulating PCSK9 levels are reflective of liver SREBP‐2 activity and the induction of PCSK9 by statins may partially offset their efficacy in lowering plasma LDL‐cholesterol levels. Supported by CIHR.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".