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Record W4403482839 · doi:10.1093/ibd/izae199

Gut Microbial Signatures in Pediatric Crohn’s Disease Vary According to Disease Activity Measures and Are Influenced by Proxies of Gastrointestinal Transit Time: An ImageKids Study

2024· article· en· W4403482839 on OpenAlexaff
Ben Nichols, Richard K. Russell, Bryn Short, Rodanthi Papadopoulou, Gili Focht, Umer Zeeshan Ijaz, Thomas D. Walters, Małgorzata Sładek, Richard M. Hansen, David Mack, Eytan Wine, Anne M. Griffiths, Dan Turner, Konstantinos Gerasimidis

Bibliographic record

VenueInflammatory Bowel Diseases · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of AlbertaChildren's Hospital of Eastern OntarioUniversity of TorontoSickKids FoundationUniversity of OttawaHospital for Sick Children
FundersGlasgow Children's Hospital CharityGlasgow Children’s Hospital Charity
KeywordsDiseaseMedicineTransit timeCrohn's diseaseGastrointestinal diseaseInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: We investigated relationships between disease activity measures and the gut microbiome in children with Crohn's disease (CD) and how these were confounded by gastrointestinal transit time. METHODS: Microbiome was profiled (16S rRNA sequencing) in feces from 196 children with CD. Sixty participants also provided samples after 18 months. Mural inflammation (Pediatric Inflammatory Crohn's Magnetic Resonance Enterography Index, PICMI), the simple endoscopic score for CD, and the weighted pediatric Crohn's disease activity index (wPCDAI) were assessed. Fecal calprotectin, plasma C-reactive protein (CRP), and fecal water content (FWC), a proxy of gastrointestinal transit time, were measured too. RESULTS: Microbiome α diversity, clustering, and differential taxa were related to disease status, but varied remarkably by disease activity measure used. The strongest relationships between microbiome and disease activity status were observed using wPCDAI; fewer or no relationships were seen using more objective measures like PICMI. Taxa predictive of disease activity status were dependent on the disease activity measure used with negligible overlap. Active disease was associated with more pathobionts (eg, Viellonella, Enterobacterales) and fewer fiber-fermenting organisms. The effect FWC had on microbiome superseded the effect of active disease for all disease activity measures, particularly with wPCDAI. Accounting for FWC, the differences in microbial signatures explained by disease activity status were attenuated or lost. CONCLUSIONS: In CD, microbiome signatures fluctuate depending on the measure used to assess disease severity; several of these signals might be secondary disease effects linked with changes in gut motility in active disease. PICMI appears to be less influenced when studying relationships between microbiome and mural inflammation in CD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.251
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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