Comparative Effects of Luteolin and Quercetin on Adipogenesis in 3T3-L1 Cells
Bibliographic record
Abstract
Purpose: Quercetin has been reported as a more potent inhibitor of fat accumulation than other flavonoids. However, little information is available regarding the strength and mechanism of the repressive action of luteolin on fat accumulation. Therefore, the aim of the present study was to evaluate the comparative effects of luteolin and quercetin on the differentiation of 3T3-L1 preadipocytes into mature adipocytes. Methods: 3T3-L1 preadipocytes were differentiated by treatment with insulin, dexamethasone, and 3-isobutyl-1-methylxanthine in the presence of luteolin or quercetin. Alterations in triacylglycerol (TG) levels, lipid-filled adipocyte quantity, and the mRNA and protein expression levels of CCAAT-enhancer-binding protein α (C/EBPα) and peroxisome proliferator–activated receptor γ (PPARγ) were measured. Results: Both luteolin and quercetin reduced TG levels, the number of lipid-filled adipocytes, and the mRNA expression levels of C/EBPα and PPARγ; however, these effects occurred with lower concentrations of luteolin than quercetin. Conclusions: These results suggest that luteolin may be more potent than quercetin in inhibiting adipocyte differentiation. These effects may be explained by differences in the inhibitory effects of the two compounds on C/EBPα and PPARγ expression. This study suggests that luteolin might be a beneficial dietary supplement for obesity and lifestyle-related diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".