Dietary Macronutrient Composition Determines the Contribution of <scp>DGAT</scp>1 to Alcoholic Steatosis
Bibliographic record
Abstract
Background The first stage of alcoholic liver disease is hepatic steatosis. While alcohol is known to profoundly impact hepatic lipid metabolism, gaps in our knowledge remain regarding the mechanisms leading to alcohol‐induced hepatic triglyceride (TG) accumulation. As the sole enzymes catalyzing the final step in TG synthesis, diacylglycerol O‐acyltransferase ( DGAT ) 1 and 2 are potentially important contributors to alcoholic steatosis. Our goal was to study the effects of dietary fat content on alcohol‐induced hepatic TG accumulation, and the relative contribution of DGAT 1 and DGAT 2 to alcoholic steatosis. Methods These studies were carried out in wild‐type (WT) mice fed alcohol‐containing high‐fat or low‐fat formulations of Lieber‐DeCarli liquid diets, as well as follow‐up studies in Dgat1 −/− mice. Results A direct comparison of the low‐fat and high‐fat liquid diet in WT mice revealed surprisingly similar levels of alcoholic steatosis, although there were underlying differences in the pattern of hepatic lipid accumulation and expression of genes involved in hepatic lipid metabolism. Follow‐up studies in Dgat1 −/− mice revealed that these animals are protected from alcoholic steatosis when consumed as part of a high‐fat diet, but not a low‐fat diet. Conclusions Dietary macronutrient composition influences the relative contribution of DGAT 1 and DGAT 2 to alcoholic steatosis, such that in the context of alcohol and a high‐fat diet, DGAT 1 predominates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".