The source of fat in a high fat diet affects systemic inflammatory markers and modulates gene expression in intestinal, hepatic and adipose tissues of growing pigs.
Bibliographic record
Abstract
Abstract While high fat diets have been associated with increased level of inflammation and metabolic health impairment, new evidences show that the source of fat plays an important role on these outcomes. In order to investigate the specific role of dairy fats on metabolic health, a model of growing pigs was used. A total of 40 pigs aged 6 weeks were fed a low fat diet, or high fat diets (17.5% fat) containing either lard only, a combination of lard and butter (butter: 4.5% fat; lard: 13% fat) or a combination of lard and cheddar cheese (cheddar: 4.5% fat; lard: 13% fat) for 10 weeks. After 5 and 10 weeks, blood samples were collected to determine hyperlipidemia and inflammatory markers level. After 10 weeks, pigs were euthanized to collect intestinal, hepatic and adipose tissues to measure the expression of a selection of genes involved in inflammation, oxidative stress and energy metabolism by qPCR. Blood levels of IL-1β and TNF-α were decreased in pigs fed either butter or cheddar compared to the lard-only high fat diet (P < 0.05), while no changes were observed in the level of blood lipids. To visualize the impact of dairy fats on gene expression, a Heatmap of gene expression was drawn and showed that both butter and cheddar affected gene expression in jejunum, while butter specifically modulated the gene expression in liver and fat tissues, and cheddar specifically changed gene expression in colon. In conclusion, replacement of lard for dairy fats in a high fat diet protects from systemic inflammation and changes gene expression in pig intestinal, hepatic and adipose tissues.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".