Statins, PCSK9 inhibitors and cholesterol homeostasis: a view from within the hepatocyte
Bibliographic record
Abstract
Statins and PCSK9 inhibitors dramatically lower plasma LDL levels and dramatically increase LDL receptor number within hepatocyte cell membranes. It seems self-evident that total clearance of LDL particles from plasma and total delivery of cholesterol to the liver must increase in consequence. However, based on the results of stable isotope tracer studies, this analysis demonstrates the contrary to be the case. Statins do not change the production rate of LDL particles. Accordingly, at steady state, the clearance rate cannot change. Because LDL particles contain less cholesterol on statin therapy, the delivery of cholesterol to the liver must, therefore, be reduced. PCSK9 inhibitors reduce the production of LDL particles and this further reduces cholesterol delivery to the liver. With both agents, a larger fraction of a smaller pool is removed per unit time. These findings are inconsistent with the conventional model of cholesterol homeostasis within the liver, but are consistent with a new model of regulation, the multi-channel model, which postulates that different lipoprotein particles enter the hepatocyte by different routes and have different metabolic fates within the hepatocyte. The multi-channel model, but not the conventional model, may explain how statins and PCSK9 inhibitors can produce sustained increases in LDL receptor number.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".