Naringenin Supplementation to a Chow Diet Enhances Energy Expenditure and Fatty Acid Oxidation, and Reduces Adiposity in Lean, Pair‐Fed <i>Ldlr<sup>−/−</sup></i> Mice
Bibliographic record
Abstract
Scope Naringenin is a citrus‐derived flavonoid that has potent lipid‐lowering and insulin‐sensitizing effects in obese mouse models of metabolic dysfunction. However, in these models, a significant effect of naringenin supplementation is the prevention of weight gain, which in itself can confer metabolic protection. Therefore, in the present study, the effect of naringenin supplementation in lean, chow‐fed Ldlr−/− mice is investigated. Methods and results In Ldlr−/− mice with isocaloric food consumption, treatment with naringenin for 8 weeks reduces body weight and adiposity compared to littermate controls pair‐fed the chow diet alone. Furthermore, naringenin treatment reduces plasma lipids and enhances insulin sensitivity compared to chow‐fed controls. Metabolic cage studies reveal that naringenin‐treated mice have elevated energy expenditure with no change in ambulatory activity. Additionally, naringenin‐treated mice have an increased respiratory exchange ratio and food consumption during the dark cycle. Treatment increases the expression of fatty acid oxidation genes in liver, and increased β‐hydroxybutyrate concentrations in plasma, indicating that one mechanism through which naringenin mediates metabolic improvement is enhanced hepatic fatty acid oxidation. Conclusions These studies highlight the potential therapeutic utility of naringenin and suggest that this flavonoid maintains potent metabolic properties in the absence of obesity or a high‐fat diet.
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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) | 0.002 | 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".