Fecal Microbial Community Profiling Allows Discrimination of Phenotype and Treatment Response in Pediatric Crohn’s Disease and Ulcerative Colitis—An International Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The pathophysiology of pediatric inflammatory bowel disease (PIBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), is not entirely understood. Dysregulation of the intestinal microbiome is recognized as both a disease-driving and a potential therapeutic target. This study aimed to systematically analyze gut microbiome compositions and its applicability as a biomarker for disease progress and treatment response. METHODS: Bibliographic and nucleotide databases were searched. Raw 16S-rRNA sequencing reads were subjected to a uniform downstream dada2/phyloseq pipeline to extract taxonomy, community structure, and abundance information. Patient metadata were extracted from publications, and study authors were contacted for further details if required. RESULTS: Twenty-six studies comprising 3956 stool samples (CD 41%, UC 36%, 23% healthy) were included in the analyses. Median age of individuals was 12 (interquartile range 4). Sex distribution was comparable. Alpha diversity was reduced between the healthy and both UC and CD treatment-naïve groups (P < .001) and further reduced with increasing clinical disease activity. Beta diversity revealed altered community structure in treatment-naïve children with PIBD (P < .001). This alteration remained in patients in clinical remission (P < .001). Machine learning models discriminated between treatment-naïve patients with CD or UC with an area under the receiver operating characteristics curve (AUROC) of 98%. Microbial communities differed between patient responders versus nonresponders to treatment (P < .001). Further, microbial community profiling distinguished treatment response (eg, steroid, nutrition, or TNFα) with AUROCs of 82%-90%. CONCLUSIONS: Gut microbial community structure is substantially altered in active and inactive PIBD and may be utilized as a biomarker for differentiating PIBD subtype and predicting treatment response.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".