Plasma PCSK9 levels are significantly modified by statins and fibrates in humans
Bibliographic record
Abstract
BACKGROUND: Proprotein convertase subtilisin kexin-like 9 (PCSK9) is a secreted glycoprotein that is transcriptionally regulated by cholesterol status. It modulates levels of circulating low density lipoprotein cholesterol (LDLC) by negatively regulating low density lipoprotein receptor (LDLR) levels. PCSK9 variants that result in 'gain of function' have been linked to autosomal dominant hypercholesterolemia, while significant protection from coronary artery disease has been documented in individuals who carry 'loss of function' PCSK9 variants. PCSK9 circulates in human plasma, and we previously reported that plasma PCSK9 is positively correlated with total cholesterol and LDLC in men. RESULTS: Herein, we report the effects of two lipid-modulating therapies, namely statins and fibrates, on PCSK9 plasma levels in human subjects. We also document their effects on endogenous PCSK9 and LDLR expression in a human hepatocyte cell line, HepG2, using immunoprecipitation and immunoblot analyses. Changes in plasma PCSK9 following fenofibrate or gemfibrozil treatments (fibric acid derivatives) were inversely correlated with changes in LDLC levels (r = -0.558, p = 0.013). Atorvastatin administration (HMGCoA reductase inhibitor) significantly increased plasma PCSK9 (7.40%, p = 0.033) and these changes were inversely correlated with changes in LDLC levels (r = -0.393, p = 0.012). Immunoblot analyses of endogenous PCSK9 and LDLR expression by HepG2 cells in response to statins and fibrates showed that LDLR is more upregulated than PCSK9 by simvastatin (2.6x vs 1.5x, respectively at 10 muM), while fenofibrate did not induce changes in either. CONCLUSION: These results suggest that in vivo (1) statins directly increase PCSK9 expression while (2) fibrates affect PCSK9 expression indirectly through its modulation of cholesterol levels and (3) that these therapies could be improved by combination with a PCSK9 inhibitor, constituting a novel hypercholesterolemic therapy, since PCSK9 was significantly upregulated by both treatments.
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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 | 0.000 | 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 | 0.000 |
| 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".