Ezetimibe increases intestinal expression of the <scp>LDL</scp> receptor gene in dyslipidaemic men with insulin resistance
Bibliographic record
Abstract
AIM: To gain further insight into intestinal cholesterol homeostasis in dyslipidaemic men with insulin resistance (IR) by examining the impact of treatment with ezetimibe on the expression of key genes involved in cholesterol synthesis and LDL receptor (R)-mediated uptake of lipoproteins. METHODS: A total of 25 men with dyslipidaemia and IR were recruited to participate in this double-blind, randomized, crossover, placebo-controlled trial. Participants received 10 mg/day ezetimibe or placebo for periods of 12 weeks each. Intestinal gene expression was measured by quantitative PCR in duodenal biopsy samples collected by gastroduodenoscopy at the end of each treatment. RESULTS: A total of 20 participants completed the protocol. Treatment with ezetimibe significantly increased intestinal LDLR (+16.2%; P = .01), 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMG-CoAR; +14.0%; P = .04) and acetyl-Coenzyme A acetyltransferase 2 (ACAT-2) mRNA expression (+12.5%; P = .03). Changes in sterol regulatory element-binding transcription factor 2 (SREBP-2) expression were significantly correlated with changes in HMG-CoAR (r = 0.55; P < .05), ACAT-2 (r = 0.69; P < .001) and proprotein convertase substilisin/kexin type 9 (PCSK9) expression (r = 0.45; P < .05). CONCLUSIONS: These results show that inhibition of intestinal cholesterol absorption by ezetimibe increases expression of the LDLR gene, supporting the concept that increased LDL clearance with ezetimibe treatment occurs not only in the liver but also in the small intestine.
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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) | 0.001 | 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".