Differences in metabolic and liver pathobiology induced by two dietary mouse models of nonalcoholic fatty liver disease
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a growing epidemic linked to metabolic disease. The first stage of NAFLD is characterized by lipid accumulation in hepatocytes, but this can progress into nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). Western diets, high in fats, sugars, and cholesterol, are linked to NAFLD development. Murine models are often used to study NAFLD; however, there remains debate on which diet-induced model best mimics both human disease progression and pathogenesis. In this study, we performed a side-by-side comparison of two popular diet models of murine NAFLD/NASH and associated HCC, a high-fat diet supplemented with 30% fructose water (HFHF) and a Western diet high in cholesterol (WDHC), and these were compared with a common grain-based chow diet (GBD). Mice on both experimental diets developed liver steatosis, and WDHC-fed mice had greater levels of hepatic inflammation and fibrosis than HFHF-fed mice. In contrast, HFHF-fed mice were more obese and developed more severe metabolic syndrome, with less pronounced liver disease. Despite these differences, WDHC-fed and HFHF-fed mice had similar tumor burdens in a model of diet-potentiated liver cancer. Response to diet and resulting phenotypes were generally similar between sexes, albeit delayed in females. This study shows that modest differences in diet can significantly uncouple glucose homeostasis and liver damage. In conclusion, long-term feeding of either HFHF or WDHC is a reliable method to induce NASH and diet-potentiated liver cancer in mice of both sexes; however, the choice of diet involves a trade-off between severity of metabolic syndrome and liver damage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".