Fenofibrate, but not ezetimibe, prevents fatty liver disease in mice lacking phosphatidylethanolamine N-methyltransferase
Bibliographic record
Abstract
Mice lacking phosphatidylethanolamine N -methyltransferase (PEMT) are protected from high-fat diet (HFD)-induced obesity and insulin resistance. However, these mice develop severe nonalcoholic fatty liver disease (NAFLD) when fed the HFD, which is mainly due to inadequate secretion of VLDL particles. Our aim was to prevent NAFLD development in mice lacking PEMT. We treated Pemt −/− mice with either ezetimibe or fenofibrate to see if either could ameliorate liver disease in these mice. Ezetimibe treatment did not reduce fat accumulation in Pemt −/− livers, nor did it reduce markers for hepatic inflammation or fibrosis. Fenofibrate, conversely, completely prevented the development of NAFLD in Pemt −/− mice: hepatic lipid levels, as well as markers of endoplasmic reticulum stress, inflammation, and fibrosis, in fenofibrate-treated Pemt −/− mice were similar to those in Pemt +/+ mice. Importantly, Pemt −/− mice were still protected against HFD-induced obesity and insulin resistance. Moreover, fenofibrate partially reversed hepatic steatosis and fibrosis in Pemt −/− mice when treatment was initiated after NAFLD had already been established. Increasing hepatic fatty acid oxidation can compensate for the lower VLDL-triacylglycerol secretion rate and prevent/reverse fatty liver disease in mice lacking PEMT.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".