Mice Fed a High‐Cholesterol Diet Supplemented with Quercetin‐3‐Glucoside Show Attenuated Hyperlipidemia and Hyperinsulinemia Associated with Differential Regulation of PCSK9 and LDLR in their Liver and Pancreas
Bibliographic record
Abstract
SCOPE: Hepatic LDL receptor (LDLR) and proprotein convertase subtilisin/kexin type 9 (PCSK9) regulate the clearance of plasma LDL-cholesterol (LDL-C): LDLR promotes it, and PCSK9 opposes it. These proteins also express in pancreatic β cells. Using cultured hepatocytes, we previously showed that the plant flavonoid quercetin-3-glucoside (Q3G) inhibits PCSK9 secretion, stimulated LDLR expression, and enhanced LDL-C uptake. Here, we examine whether Q3G supplementation could reverse the hyperlipidemia and hyperinsulinemia of mice fed a high-cholesterol diet, and how it affects hepatic and pancreatic LDLR and PCSK9 expression. METHODS AND RESULTS: For 12 weeks, mice are fed a low- (0%) or high- (1%) cholesterol diet (LCD or HCD), supplemented or not with Q3G at 0.05 or 0.1% (w/w). Tissue LDLR and PCSK9 is analyzed by immunoblotting, plasma PCSK9 and insulin by ELISA, and plasma cholesterol and glucose by colorimetry. In LCD-fed mice, Q3G has no effect. In HCD-fed mice, it attenuates the increase in plasma cholesterol and insulin, accentuates the decrease in plasma PCSK9, and increases hepatic and pancreatic LDLR and PCSK9. In cultured pancreatic β cells, however, it stimulates PCSK9 secretion. CONCLUSION: In mice, dietary Q3G could counter HCD-induced hyperlipidemia and hyperinsulinemia, in part by oppositely modulating hepatic and pancreatic PCSK9 secretion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".