Decline in Presumptively Protective Gut Bacterial Species and Metabolites Are Paradoxically Associated with Disease Improvement in Pediatric Crohn’s Disease During Enteral Nutrition
Bibliographic record
Abstract
BACKGROUND: The gut microbiota is implicated in the pathogenesis of Crohn's disease (CD). Exclusive enteral nutrition (EEN) is a successful treatment, but its mode of action remains unknown. This study assessed serial changes in the fecal microbiota milieu during EEN. METHODS: Five fecal samples were collected from CD children: 4 during EEN (start, 15, 30, end EEN approximately 60 days) and the fifth on habitual diet. Two samples were collected from healthy control subjects. Fecal pH, bacterial metabolites, global microbial diversity abundance, composition stability, and quantitative changes of total and 7 major bacterial groups previously implicated in CD were measured. RESULTS: Overall, 68 samples were from 15 CD children and 40 from 21 control subjects. Fecal pH and total sulfide increased and butyric acid decreased during EEN (all P < 0.05). Global bacterial diversity abundance decreased (P < 0.05); a higher degree of microbiota composition stability was seen in control subjects than in CD children during EEN (at P ≤ 0.008). Faecalibacterium prausnitzii spp concentration significantly decreased after 30 days on EEN (P < 0.05). In patients who responded to EEN, the magnitude of the observed changes was greater and the concentration of Bacteroides/Prevotella group decreased (P < 0.05). All these changes reverted to pretreatment levels on free diet, and EEN microbiota diversity increased when the children returned to their free diet. CONCLUSIONS: EEN impacts on gut microbiota composition and changes fecal metabolic activity. It is difficult to infer a causative association between such changes and disease improvement, but the results do challenge the current perception of a protective role for F. prausnitzii in 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".