Diet-Induced Models of Non-Alcoholic Fatty Liver Disease: Food for Thought on Sugar, Fat, and Cholesterol
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) affects approximately 1 in 4 people worldwide and is a major burden to health care systems. A major concern in NAFLD research is lack of confidence in pre-clinical animal models, raising questions regarding translation to humans. Recently, there has been renewed interest in creating dietary models of NAFLD with higher similarity to human diets in hopes to better recapitulate disease pathology. This review summarizes recent research comparing individual roles of major dietary components to NAFLD and addresses common misconceptions surrounding frequently used diet-based NAFLD models. We discuss the effects of glucose, fructose, and sucrose on the liver, and how solid vs. liquid sugar differ in promoting disease. We consider studies on dosages of fat and cholesterol needed to promote NAFLD versus NASH, and discuss important considerations when choosing control diets, mouse strains, and diet duration. Lastly, we provide our recommendations on amount and type of sugar, fat, and cholesterol to include when modelling diet-induced NAFLD/NASH in 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".