Naringenin prevents cholesterol-induced systemic inflammation, metabolic dysregulation, and atherosclerosis in Ldlr mice
Bibliographic record
Abstract
Obesity-associated chronic inflammation contributes to metabolic dysfunction and propagates atherosclerosis. Recent evidence suggests that increased dietary cholesterol exacerbates inflammation in adipose tissue and liver, contributing to the proatherogenic milieu. The ability of the citrus flavonoid naringenin to prevent these cholesterol-induced perturbations is unknown. To assess the ability of naringenin to prevent the amplified inflammatory response and atherosclerosis induced by dietary cholesterol, male Ldlr⁻/⁻ mice were fed either a cholesterol-enriched high-fat or low-fat diet supplemented with 3% naringenin for 12 weeks. Naringenin, through induction of hepatic fatty acid (FA) oxidation and attenuation of FA synthesis, prevented hepatic steatosis, hepatic VLDL overproduction, and hyperlipidemia induced by both cholesterol-rich diets. Naringenin attenuated hepatic macrophage infiltration and inflammation stimulated by dietary cholesterol. Insulin resistance, adipose tissue expansion, and inflammation were alleviated by naringenin. Naringenin attenuated the cholesterol-induced formation of both foam cells and expression of inflammatory markers in peritoneal macrophages. Naringenin significantly decreased atherosclerosis and inhibited the formation of complex lesions, which was associated with normalized aortic lipids and a reversal of aortic inflammation. We demonstrate that in mice fed cholesterol-enriched diets, naringenin attenuates peripheral and systemic inflammation, leading to protection from atherosclerosis. These studies offer a therapeutically relevant alternative for the prevention of cholesterol-induced metabolic dysregulation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".