Diets high in saturated fat increase intestinal inflammation associated with IBD via alterations in the microbiota
Bibliographic record
Abstract
Clinical evidence suggests the chronic inflammation seen in inflammatory bowel diseases (IBD) results from a combination of environmental/microbial factors on a background of genetic susceptibility. This study evaluates the environmental factor of dietary fat composition on the incidence of IBD via alterations in gut microbiota. Male C57bl/6j mice (6 w/o) were fed a high saturated fat (SF; 47% energy from carbohydrates, 16% energy from protein, 37% energy from fat), high polyunsaturated fat (PF; 47:16:37), or a low fat control diet (C; 70:16:14) for 3 wks (n=10/diet). Cecal contents were collected for microbial pyrosequencing of 16s rDNA and revealed a “bloom” of b.wadsworthia, a cytotoxic proteobacteria, only in the SF diet. Body weight was greater on SF diet than PF and LF despite no significant difference in kcals consumed, suggesting an increased microbial capacity for energy harvest on SF, a contributing factor to local inflammation. Next, male IL‐10−/− mice (4 w/o), a model of spontaneous colitis, were fed LF or SF (n=5/diet) for 12 wks. Only 20% of these mice developed colitis on LF vs. 100% on SF accompanied by a 4‐fold increase in pro‐inflammatory IL‐6 and IL‐12 and a microbial profile like that of the C57bl/6j mice fed SF. These data indicate that SF diets create an environment favorable for the growth of harmful bacteria contributing to incidence to IBD. Supported by the National Institutes of Health R01 and T32 Grant Funding Source : National Institutes of 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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".