Intervention with citrus flavonoids reverses obesity and improves metabolic syndrome and atherosclerosis in obese Ldlr−/− mice
Bibliographic record
Abstract
Obesity and its associated metabolic dysfunction and cardiovascular disease risk represent a leading cause of adult morbidity worldwide. Currently available pharmacological therapies for obesity have had limited success in reversing existing obesity and metabolic dysregulation. Previous prevention studies demonstrated that the citrus flavonoids, naringenin and nobiletin, protect against obesity and metabolic dysfunction in Ldlr −/− mice fed a high-fat cholesterol-containing (HFHC) diet. However, their effects in an intervention model are unknown. In this report, we show that, in Ldlr −/− mice with diet-induced obesity, citrus flavonoid supplementation to a HFHC diet reversed existing obesity and adipocyte size and number through enhanced energy expenditure and increased hepatic fatty acid oxidation. Caloric intake was unaffected and no evidence of white adipose tissue browning was observed. Reversal of adiposity was accompanied by improvements in hyperlipidemia, insulin sensitivity, hepatic steatosis, and a modest reduction in blood monocytes. Together, this resulted in atherosclerotic lesions that were unchanged in size, but characterized by reduced macrophage content, consistent with a more stable plaque phenotype. These studies further suggest potential therapeutic utility of citrus flavonoids, especially in the context of existing obesity, metabolic dysfunction, and cardiovascular disease.
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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 | 0.001 |
| 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".