Naringenin Prevents Obesity, Hepatic Steatosis, and Glucose Intolerance in Male Mice Independent of Fibroblast Growth Factor 21
Bibliographic record
Abstract
The molecular mechanisms and metabolic pathways whereby the citrus flavonoid, naringenin, reduces dyslipidemia and improves glucose tolerance were investigated in C57BL6/J wild-type mice and fibroblast growth factor 21 (FGF21) null (Fgf21(-/-)) mice. FGF21 regulates energy homeostasis and the metabolic adaptation to fasting. One avenue of this regulation is through induction of peroxisome proliferator-activated receptor-γ coactivator-1α (Pgc1a), a regulator of hepatic fatty acid oxidation and ketogenesis. Because naringenin is a potent activator of hepatic FA oxidation, we hypothesized that induction of FGF21 might be an integral part of naringenin's mechanism of action. Furthermore, we predicted that FGF21 deficiency would potentiate high-fat diet (HFD)-induced metabolic dysregulation and compromise metabolic protection by naringenin. The absence of FGF21 exacerbated the response to a HFD. Interestingly, naringenin supplementation to the HFD robustly prevented obesity in both genotypes. Gene expression analysis suggested that naringenin was not primarily targeting fatty acid metabolism in white adipose tissue. Naringenin corrected hepatic triglyceride concentrations and normalized hepatic expression of Pgc1a, Cpt1a, and Srebf1c in both wild-type and Fgf21(-/-) mice. HFD-fed Fgf21(-/-) mice displayed greater muscle triglyceride deposition, hyperinsulinemia, and impaired glucose tolerance as compared with wild-type mice, confirming the role of FGF21 in insulin sensitivity; however, naringenin supplementation improved these metabolic parameters in both genotypes. We conclude that FGF21 deficiency exacerbates HFD-induced obesity, hepatic steatosis, and insulin resistance. Furthermore, FGF21 is not required for naringenin to protect mice from HFD-induced metabolic dysregulation. Collectively these studies support the concept that naringenin has potent lipid-lowering effects and may act as an insulin sensitizer in vivo.
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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 | 0.001 |
| 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 | 0.001 |
| 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".