Faecal Metabolomics in Paediatric Inflammatory Bowel Disease: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Paediatric inflammatory bowel disease [IBD] is characterized by altered immunological and metabolic pathways. Metabolomics may therefore increase pathophysiological understanding and could develop into characterization of biomarkers for diagnosis and IBD treatment response. However, no uniform metabolomic profiles have been identified to date. This systematic review aimed to identify faecal metabolomic signatures in paediatric IBD vs controls, and to describe metabolites associated with disease activity and treatment response. METHODS: A literature search was performed in Embase, Medline, Web of Science and Cochrane Library. Studies assessing faecal metabolomics in paediatric patients < 18 years with IBD [de novo, active, inactive] with comparative groups [IBD vs non-IBD; responders vs non-responders] were included. The quality of included studies was assessed according to the Newcastle-Ottawa Scale. RESULTS: Nineteen studies were included [540 patients with IBD, 386 controls], assessing faecal short-chain fatty acids [SCFA] [five studies], amino acids [AA] [ten studies], bile acids [BA] [eight studies] and other metabolites [nine studies] using various methodologies. Significantly increased levels of AA [particularly phenylalanine], primary BA and lower levels of secondary BA were described in paediatric IBD compared to controls. Faecal SCFA results varied across studies. Additionally, responders and non-responders to exclusive enteral nutrition and infliximab showed differences in baseline faecal metabolites [based on BA, AA]. CONCLUSIONS: This systematic review provides evidence for distinct faecal metabolomic profiles in paediatric IBD. However, results varied across studies, possibly due to differences in study design and applied analytical techniques. Faecal metabolomics could provide more insight into host-microbial interactions in IBD, but further studies with standardized methodologies and reporting are needed.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".