Hexanoic, Octanoic and Decanoic Acids Promote Basal and Insulin-Induced Phosphorylation of the Akt-mTOR Axis and a Balanced Lipid Metabolism in the HepG2 Hepatoma Cell Line
Bibliographic record
Abstract
Metabolic illnesses such as non-alcoholic fatty liver disease (NAFLD) are in constant increase worldwide. Highly consumed long chain fatty acids (LCFA) are among the most obesogenic and steatogenic nutrients. Hepatic steatosis is associated with several complications such as insulin resistance. Growing evidence points to medium chain fatty acids (MCFA), more efficiently oxidized than LCFA, as a promising dietary alternative against NAFLD. However, reports on the hepatic effects of MCFA are sometimes conflicting. In this study we exposed HepG2 cells, a human hepatocellular model, to 0.25 mM of hexanoic (C6), or octanoic (C8), and decanoic (C10) acids separately or in a C8 + C10 equimolar mix reflecting commercially available MCFA-rich oils. We found that C6, a poorly studied MCFA, as well as C8 and C10 did not provoke the deleterious lipid anabolism runaway typically induced by LCFA palmitate. MCFA tended, instead, to promote a balanced metabolic profile and were generally non-cytotoxic. Accordingly, mitochondrial integrity was mostly preserved following MCFA treatment. However, treatments with C8 induced a mitochondrial membrane potential decrease, suggesting prolonged exposure to this lipid could be problematic. Finally, MCFA treatments maintained optimal insulin sensitivity and even fostered basal and insulin-dependent phosphorylation of the Akt-mTOR pathway. Overall, MCFA could constitute an effective nutritional tool to manage liver steatosis and hepatic insulin resistance.
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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) | 0.002 | 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".