Abstract 67: Intervention With Citrus Flavonoids Reverses Existing Metabolic Disorders and Attenuates the Progression of Advanced Atherosclerosis in High Fat--Fed LDLr-/- Mice
Bibliographic record
Abstract
Previous studies demonstrated that addition of the citrus flavonoids naringenin or nobiletin to a high-fat diet prevented the development of many disorders linked to the metabolic syndrome. In the present study, we assessed the ability of intervention with nobiletin or naringenin to reverse pre-established obesity, insulin resistance, hepatic steatosis, dyslipidemia and attenuate atherogenesis. Ldlr-/- mice were fed chow or a high-fat, cholesterol-containing (HFHC) diet for 12 weeks. For an additional 12 weeks, the HFHC-fed mice: (1) continued on the HFHC diet or were transferred to (2) chow, (3) HFHC + 3% naringenin, or (4) HFHC + 0.3% nobiletin. Following rapid weight gain during HFHC-induction, intervention with naringenin or nobiletin stimulated weight loss, while maintaining caloric intake. Micro-CT imaging revealed flavonoid intervention reversed adipose tissue accumulation by 40-60% in both subcutaneous and visceral depots. At 12 weeks, the HFHC-fed mice were hyperinsulinemic (6-fold), which was accompanied by increased fasting plasma glucose. Intervention with either flavonoid normalized plasma insulin and glucose and corrected impaired insulin and glucose tolerance. The HFHC diet increased cholesterol within VLDL (10-fold) and LDL (6-fold), which was reduced (~50%) by either naringenin or nobiletin intervention. HFHC-induction significantly increased hepatic steatosis. Flavonoid intervention reduced hepatic cholesterol (>50%) and triglyceride (~20%) via increased expression of Pgc1a and Cpt1a and reduced expression of Srebp1c. HFHC-induction increased atherosclerotic lesion area (13-fold), which was increased a further 2.5-fold at 24 weeks. Flavonoid intervention modestly retarded lesion size progression (16-20%). As well, intervention with naringenin or nobiletin slowed the accumulation of aortic cholesterol (~30-45%) and reduced lesional necrotic area (~25%), suggesting improved lesion morphology. These studies demonstrate in mice with pre-existing metabolic dysregulation and atherosclerosis that intervention with naringenin or nobiletin reverses obesity, dyslipidemia, hepatic steatosis and insulin resistance, and modestly attenuates the progression of advanced atherosclerosis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".