Dietary Polyphenols Protect Against Oleic Acid-Induced Steatosis in an in Vitro Model of NAFLD by Modulating Lipid Metabolism and Improving Mitochondrial Function
Bibliographic record
Abstract
In this study, we aimed to determine the relative effectiveness of common dietary polyphenols or the isoquinoline alkaloid berberine in protecting against molecular mechanisms underlying non-alcoholic fatty liver disease (NAFLD) involving changes to cellular lipid metabolism and bioenergetics. In a model of steatosis using HepG2 hepatocytes, exposure of the cells to 1.5 mM oleic acid (OA) for 24 h caused steatosis and distorted cell morphology, induced the expression of mRNA for enzymes that are involved in lipogenesis and fatty acid oxidation (FAS and CPT1A), and impaired indices of aerobic energy metabolism (PPARγ mRNA expression, mitochondrial membrane potential (MMP), and galactose-supported ATP production). Co-treatment with 10 µM of selected polyphenols all strongly protected against the steatosis and changes in cell morphology. All polyphenols, except cyanidin, inhibited the effects on FAS and PPARγ and further increased CPT1A1 expression, suggesting a shift toward increased β-oxidation. Resveratrol, quercetin, catechin, and cyanidin, however not kuromanin or berberine, ameliorated the decreases in MMP and galactose-derived ATP. Berberine was unique in worsening the decrease in galactose-derived ATP. In further investigations of the mechanisms involved, resveratrol, catechin, and berberine increased SIRT1 enzyme activity and p-AMPKαThr172 protein, which are involved in mitochondrial biogenesis. In conclusion, selected polyphenols all protected against steatosis with similar effectiveness, however through different mechanisms that increased aerobic lipid metabolism and mitochondrial function.
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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".