A Mediterranean-like fat blend protects against the development of severe colitis in the mucin-2 deficient murine model
Bibliographic record
Abstract
There is a growing appreciation that the interaction between diet, the gut microbiota and the immune system contribute to the development and progression of inflammatory bowel disease (IBD). A mounting body of scientific evidence suggests that high-fat diets exacerbate IBD; however, there is a lack of information on how specific types of fat impact colitis. The Mediterranean diet (MD) is considered a health-promoting diet containing approximately 40% total fat. It is not known if the blend of fats found in the MD contributes to its beneficial protective effects. Mice deficient in the mucin 2 gene (Muc 2−/−) were weaned to 40% fat, isocaloric, isonitrogenous diets. We compared the MD fat blend (high monounsaturated, 2:1 n-6:n-3 polyunsaturated and moderate saturated fat) to diets composed of corn oil (CO, n-6 polyunsaturated-rich), olive oil (monounsaturated-rich) or milk fat (MF, saturated-rich) on spontaneous colitis development in Muc2−/− mice. The MD resulted in lower clinical and histopathological scores and induced tolerogenic CD103+ CD11b+ dendritic, Th22 and IL-17+ IL-22+ cells necessary for intestinal barrier repair. The MD was associated with beneficial microbes and associated with higher cecal acetic acid levels negatively correlated with colitogenic microbes like Akkermansia muciniphila. In contrast, CO showed a higher prevalence of mucin-degraders including A. muciniphila and Enterobacteriaceae, which have been associated with colitis. A dietary blend of fats mimicking the MD, reduces disease activity, inflammation-related biomarkers and improves metabolic parameters in the Muc2−/− mouse model. Our findings suggest that the MD fat blend could be incorporated into a maintenance diet for colitis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".