The Gut Microbiome at the Onset of Inflammatory Bowel Disease: A Systematic Review and Unified Bioinformatic Synthesis
Bibliographic record
Abstract
BACKGROUND & AIMS: Few studies describe gut microbiome signatures in treatment-naïve new-onset inflammatory bowel disease (IBD). We present a novel secondary bioinformatic reanalysis of sequence outputs mapped to the latest microbial taxonomy. METHODS: MEDLINE and Embase searches were performed for microbiome studies in treatment-naïve IBD. Appraisal was completed with Risk Of Bias In Non-randomized Studies - of Exposures (ROBINS-E). Available 16S ribosomal RNA sequence data sets were downloaded and missing data sets requested. Integrated data were run through a unified QIIME2 bioinformatics pipeline. Multivariable models adjusting for methodologic differences were developed using MaAsLin2. RESULTS: There were 36 eligible studies; 18 contributed to bioinformatic reanalysis and 24 to supplementary meta-analysis. Samples from 1743 patients were included, comprising 678 from individuals with Crohn's disease (CD), 399 with ulcerative colitis (UC), 130 healthy controls (HCs), and 405 symptomatic controls (SCs); 990 of which were biopsy samples. Alpha diversity was reduced: feces-pediatric UC vs SCs, adult CD and UC vs HCs, and pediatric SCs vs HCs; pediatric biopsy samples-CD vs SCs, CD vs UC, and UC vs SCs. Beta diversity demonstrated clear distinctions between fecal and mucosal biopsy communities, least evident in UC, in addition to community separation by geography. Multivariate modeling revealed depletion of anaerobic and enrichment of aerobic and facultative anaerobic bacteria, alongside enrichment of oral genera across both CD and UC. CONCLUSIONS: Core microbial perturbations at onset of CD and UC are depletion of anaerobes and enrichment of oxygen-tolerant, orally associated bacteria. As we place greater emphasis on early diagnosis and prediction of IBD risk, this finding may support innovative diagnostic approaches. Microbiome-targeted intervention and alteration of luminal oxygen availability may offer novel therapeutic avenues for new-onset patients and identified high-risk groups.
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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 | 0.024 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.032 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".