Atorvastatin increases intestinal expression of NPC1L1 in hyperlipidemic men
Bibliographic record
Abstract
Inhibition of cholesterol synthesis by 3-hydroxy-3-methylglutaryl-CoA reductase (HMG-CoAR) inhibitors has been associated with an increase in intestinal cholesterol absorption. This study examined how HMG-CoAR inhibition by atorvastatin modulates expression of key genes involved in intestinal cholesterol metabolism. A crossover study was conducted in which 22 hyperlipidemic men received atorvastatin, 40 mg/day, or placebo, each for 12 weeks. Gene expression was assessed by real-time PCR using duodenal biopsy samples obtained at the end of each phase of treatment. Treatment with atorvastatin was associated with a 76% reduction in lathosterol and significant increases in sitosterol (70%). Atorvastatin significantly increased intestinal mRNA levels of HMG-CoAR (59%), LDL receptor (LDLR) (52%), PCSK9 (187%), SREBP-2 (44%), and HNF-4α (13%). Furthermore, atorvastatin significantly increased intestinal mRNA levels of NPC1L1 by 19% and decreased mRNA levels of both ABCG5 and ABCG8 by 14%. Positive correlations were observed between changes in SREBP-2 and HNF-4α expression and concurrent changes in the intestinal mRNA levels of HMG-CoAR, LDLR, and NPC1L1. These results indicate that HMG-CoAR inhibition with atorvastatin stimulates the intestinal expression of NPC1L1, LDLR, and PCSK9; increases cholesterol absorption; and reduces expression of ABCG5/8; these effects are most likely mediated by upregulation of the transcription factors SREBP-2 and HNF-4α.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".