A high‐fat diet supplemented with medium‐chain triglycerides ameliorates hepatic steatosis by reducing ceramide and diacylglycerol accumulation in mice
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is projected to be the most common chronic liver disease worldwide and is closely linked to obesity, insulin resistance and type 2 diabetes. Currently, no pharmacological treatments are available to treat NAFLD, and lifestyle modification, including dietary interventions, is the only remedy. Therefore, we conducted a study to determine whether supplementation with medium-chain triglycerides (MCTs), containing a mixture of C8 and C10 (60/40), attenuates NAFLD in obese and insulin-resistant mice. To achieve that, we fed C57BL/6 male mice a high-fat diet (HFD) for 12 weeks to induce obesity and hepatic steatosis, after which obese mice were assigned randomly either to remain on the HFD or to transition to an HFD supplemented with MCTs (HFD + MCTs) or a low-fat diet (LFD) for 6 weeks as another dietary intervention model. Another group of mice was kept on an LFD throughout the study and used as a lean control group. Obese mice that transitioned to HFD + MCTs exhibited improvement in glucose and insulin tolerance tests, and the latter improvement was independent of changes in adiposity when compared with HFD-fed mice. Additionally, supplementation with MCTs significantly reduced hepatic steatosis, improved liver enzymes and decreased hepatic expression of inflammation-related genes to levels similar to those observed in obese mice transitioned to an LFD. Importantly, HFD + MCTs markedly lowered hepatic ceramide and diacylglycerol content and prevented protein kinase C-ε translocation to the plasma membrane. Our study demonstrated that supplementation with MCTs formulated mainly from C8 and C10 effectively ameliorated NAFLD in obese mice.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".