Effect of Atorvastatin on High-Density Lipoprotein Apolipoprotein A-I Production and Clearance in the New Zealand White Rabbit
Bibliographic record
Abstract
BACKGROUND: HMG-CoA reductase inhibitors reduce the incidence of cardiovascular disease predominantly by their LDL-lowering effect. Recently, there has been great interest in the pleiotropic effects of statins, which appear to differ among the various agents in this class. Unlike other statins, atorvastatin exhibits a decline in its HDL-raising effect at higher doses in humans. Whether atorvastatin-mediated alterations in HDL turnover in vivo contribute to this effect has not previously been investigated. We therefore studied the effect of atorvastatin on HDL apolipoprotein (apo) A-I production and clearance in normolipidemic male New Zealand White rabbits. METHODS AND RESULTS: Kinetic studies of HDL-apoA-I radiolabeled with 131I were performed in chow-fed rabbits after 3 weeks of atorvastatin treatment of 5 mg x kg(-1) x d(-1) (n=7) versus placebo-treated rabbits (n=7). Our results showed a significantly (P<0.001) more rapid clearance ( approximately 2-fold) of HDL apoA-I in atorvastatin-treated animals compared with the control group (0.121+/-0.012 versus 0.061+/-0.004 pools/h, respectively), accompanied by a lesser 48% increase in the apoA-I production rate (3.84+/-0.38 versus 2.59+/-0.41 mg x kg(-1) x h(-1), P=0.06). Accordingly, plasma apoA-I levels in atorvastatin-treated animals declined significantly (P<0.05, n=8 animals) after 3 weeks of treatment (173.5+/-1.8 mg/dL) from baseline values. CONCLUSIONS: These data suggest that the effect on apoA-I levels observed with atorvastatin at higher drug doses in humans may be caused at least in part by enhanced HDL apoA-I catabolism, which is not entirely offset by a concomitant increase in apoA-I production. Whether this finding results from an effect of atorvastatin on HDL particle composition or on receptors involved in circulating HDL holoparticle clearance will require further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".