Treatment of Active Crohn’s Disease With Exclusive Enteral Nutrition Diminishes the Immunostimulatory Potential of Fecal Microbial Products
Bibliographic record
Abstract
BACKGROUND: Exclusive enteral nutrition (EEN) is an effective treatment for active Crohn's disease (CD). This study explored the immunostimulatory potential of a cell-free fecal filtrate and related this with changes in the fecal microbiota and metabolites in children with active CD undertaking treatment with EEN. METHODS: Production of tumor necrosis factor α (TNFα) from peripheral blood mononuclear cells was measured following their stimulation with cell-free fecal slurries from children with CD, before, during, and at completion of EEN. The metabolomic profile of the feces used was quantified using proton nuclear magnetic resonance and their microbiota composition with 16S ribosomal RNA sequencing. RESULTS: Following treatment with EEN, 8 (72%) of 11 patients demonstrated a reduction in fecal calprotectin (FC) >50% and were subsequently labeled FC responders. In this subgroup, TNFα production from peripheral blood mononuclear cells was reduced during EEN (P = .008) and reached levels like healthy control subjects. In parallel to these changes, the fecal concentrations of acetate, butyrate, propionate, choline, and uracil significantly decreased in FC responders, and p-cresol significantly increased. At EEN completion, TNFα production from peripheral blood mononuclear cells was positively correlated with butyrate (rho = 0.70; P = .016). Microbiota structure (β diversity) was influenced by EEN treatment, and a total of 28 microbial taxa changed significantly in fecal calprotectin responders. At EEN completion, TNFα production positively correlated with the abundance of fiber fermenters from Lachnospiraceae_UCG-004 and Faecalibacterium prausnitzii and negatively with Hungatella and Eisenbergiella tayi. CONCLUSIONS: This study offers proof-of concept data to suggest that the efficacy of EEN may result from modulation of diet-dependent microbes and their products that cause inflammation in patients with CD.
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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 | 0.001 |
| 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 | 0.000 |
| 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".