Prebiotic Enriched Exclusive Enteral Nutrition Suppresses Colitis via Gut Microbiome Modulation and Expansion of Anti-inflammatory T Cells in a Mouse Model of Colitis
Bibliographic record
Abstract
Background & Aims Exclusive enteral nutrition (EEN) is used to treat pediatric Crohn's disease (CD), but therapeutic benefits are variable, and EEN can lead to microbial dysbiosis. Because of reported lower efficacy EEN is not routinely used to treat pediatric ulcerative colitis (UC). Inulin-type fructans (IN) beneficially modulate the gut microbiome and promote expansion of anti-inflammatory immune cells. We hypothesized that enriching EEN with IN (EEN IN) would enhance treatment efficacy. To test this, we examined the effects of EEN IN on colitis development, the gut microbiome, and CD4 + T cells using an adoptive T-cell transfer model of colitis. Methods TCR-β deficient ( -/- ) mice were randomized to 1 of 4 groups: (1) Control, (2) Chow, (3) EEN, and (4) EEN IN, and naive CD4 + T cells were adoptively transferred into groups 2–4, after which mice were monitored for 5 weeks before experimental endpoint. Results Mice fed EEN IN showed greater colitis protection, with colonic shortening, goblet cell, and crypt density loss reduced compared with EEN fed mice and reduced disease activity and immune cell infiltration compared with chow fed mice, and less crypt hyperplasia and higher survival compared with both groups. EEN IN mice had less deterioration in the colonic mucus layer and had increased levels of Foxp3 + IL-10 + and Rorγt + IL-22 + and reduced levels of Tbet + IFNγ + and Tbet + TNF + CD4 + T cells. EEN IN also led to higher butyrate concentrations, Bifidobacterium spp. and Anaerostipes caccae relative abundance, and lower [ Clostridium ] innocuum group spp. and Escherichia-Shigella spp. relative abundance. Conclusions The EEN IN group showed reduced colitis development as compared with the chow and EEN groups. This highlights the potential benefits of EEN IN as a novel induction therapy for pediatric CD and UC patients.
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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) | 0.000 | 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 | 0.001 | 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".