Effect of Diet on Expression of Genes Involved in Lipid Metabolism, Oxidative Stress, and Inflammation in Mouse Liver–Insights into Mechanisms of Hepatic Steatosis
Bibliographic record
Abstract
Nutritional intake is a fundamental determinant of health. Many studies have correlated excess caloric intake, as well as a high ratio of n-6:n-3 fatty acids, with detrimental health outcomes, such as the metabolic syndrome. In contrast, low-calorie diets have beneficial health effects. Despite these associations, our understanding of the causal relationship between diet and health remains largely elusive. The present study examined the molecular changes elicited by nine diets with varying fat, sugar, cholesterol, omega-3 fatty acids, omega-6 fatty acids, and calories in C57BL/6 male mice. Microarray analyses were conducted on liver samples from three mice per diet and detected 20,449 genes of which 3,734 were responsive to changes in dietary components. Principal component analysis showed that diet restriction correlated the least with the other diets and also affected more genes than any other diet. Interestingly, Gene Set Enrichment Analysis (GSEA) identified gene sets involved in glutathione metabolism, immune response, fatty acid metabolism, cholesterol metabolism, ABC transporters, and oxidative phosphorylation as being highly responsive to changes in diet composition. On the gene level, this study reveals novel findings such as the induction of the drug efflux pump Abcb1a (p-glycoprotein) by diet restriction and an atherogenic diet, as well as the suppression of the rate limiting step of bile acid synthesis, Cyp7a1, by a high fructose diet. This study provides considerable insight into the molecular changes incurred by a variety of diets and furthers our understanding of the causal relationships between diet and health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".