Abstract 95: Naringenin Supplementation of a Low-Fat Diet Enhances the Reversal of Metabolic Dysfunction, Promotes Atherosclerosis Regression and Improves Lesion Pathology in <i>Ldlr</i> <sup>-/-</sup> Mice
Bibliographic record
Abstract
Diet-induced metabolic dysregulation (MetS) and atherosclerosis in mouse models can be reversed by intervention with a low-fat diet. Supplementation of a high-fat diet (HFHC) with naringenin prevents development of abnormal lipid and glucose metabolism and attenuates atherosclerosis. In the present study, we hypothesized that intervention by the addition of naringenin to a low-fat diet would enhance the reversal of MetS and atherosclerosis. Ldlr -/- mice were fed a HFHC diet for 12 weeks to induce MetS and intermediate atherosclerosis (baseline). Intervention for an additional 12 weeks consisted of transfer to: 1) a low-fat, isoflavone-free chow diet (IFF), 2) IFF with 3% naringenin (IFF+Nar) or 3) continuation on HFHC. HFHC-feeding induced rapid weight gain and adiposity, which were reversed to a greater extent by intervention with IFF+Nar (-93% and -76%, P <0.05) compared to IFF (-60% and -46%, P <0.05), independent of caloric intake. The hypercholesterolemia and hypertriglyceridemia induced by HFHC were further decreased by intervention with IFF+Nar (-105% and -124%, P <0.05) compared to IFF (-96% vs -103%, P <0.05). Hepatic lipids were normalized by both IFF+Nar and IFF, although IFF+Nar induced a greater decrease in liver triglyceride (-110% vs -86%, P =0.05). Hepatic expression of Pgc1a , Cpt1a and Pnpla2 were significantly higher with IFF+Nar compared to IFF. Fasting plasma glucose, plasma insulin and insulin sensitivity were further improved by IFF+Nar, compared to IFF. Relative to baseline, aortic cholesteryl ester (CE) increased with intervention by IFF alone (+40%), whereas IFF+Nar reversed aortic CE content (-19%, P <0.04). Compared to baseline, aortic sinus lesion size continued to increase with IFF (+47%), whereas with IFF+Nar lesion size increased only 14% ( P <0.05), indicating almost complete attenuation of lesion growth. Both intervention diets decreased lesion apoptotic cells similarly, although fewer lesion macrophages (35% vs 43%, P <0.05) and reduced necrotic area (6.5% vs 7.5%, trend) resulted from intervention with IFF+Nar, compared to IFF. In conclusion, intervention with naringenin enhances improvements in metabolic function, halts progression of atherosclerosis and improves lesion pathology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".