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Record W2922176834 · doi:10.1093/jcag/gwz006.004

A5 ASSOCIATION ANALYSIS BETWEEN BILE ACID-METABOLIZING MICROBIOTA ABUNDANCE AND ENDOSCOPIC INFLAMMATION IN INFLAMMATORY BOWEL DISEASE PATIENTS

2019· article· en· W2922176834 on OpenAlexaff
Cristian Hernández-Rocha, Krzysztof Borowski, Williams Turpin, Boyko Kabakchiev, Karen Boland, Larbi Bedrani, Joanne M. Stempak, Michelle I. Smith, Geoffrey C. Nguyen, Hillary Steinhart, Ken Croitoru, Mark S. Silverberg

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsGastroenterologyInflammatory bowel diseaseUlcerative colitisInternal medicinePrimary sclerosing cholangitisColonoscopyInflammationBile acidGut floraMedicineCrohn's diseaseDiseaseImmunologyColorectal cancerCancer

Abstract

fetched live from OpenAlex

Pathogenesis of inflammatory bowel disease (IBD) is influenced by differences in the function of gut microbiota. Bile acid (BA) metabolism is carried out in the gastrointestinal tract mainly by three bacterial enzymes. These include a bile acid hydrolase (BSH) that deconjugates BA, as well as two enzymes producing secondary BA: a hydroxysteroid dehydrogenase (HSDH) and an alpha-dehydroxylase (ADH). BA metabolizing-microbiota (BAMM) has been demonstrated to influence host homeostasis and inflammation of the gut, but there is scarce evidence of its role in IBD To evaluate if mucosal BAMM abundance is related to endoscopic inflammation in IBD patients Ulcerative colitis (UC), IBD unclassified (IBDU), Crohn’s disease (CD) patients and healthy controls (HC) undergoing colonoscopy were recruited. Microbial DNA from terminal ileum (TI) and sigmoid colon (SC) biopsies was extracted and the 16s rRNA gene was sequenced. BAMM gene function was inferred with PICRUSt from 9000 sequences/sample using the following Clusters of Orthologous Groups (COGs): COG3049 for BSH; COG1902 for HSDH and COG1062 for ADH. Abundance of these microbial genes was compared with endoscopic inflammation using the segmental simple endoscopic score (sSES-CD) and segmental Mayo endoscopic score (sMES) in CD and UC/IBDU patients, respectively. An sSES-CD and sMES of 0–2 and 0–1, respectively, were considered indicative of non-inflamed tissue. Clinical data were obtained and users of antibiotics before colonoscopy were excluded. Non-parametric tests and false discovery correction were used in this study A total of 436 samples (TI=183 and SC=253) from 260 subjects were analyzed. The mean age of all included subjects was 37.01 ± 14.3 years, 46.3% were female, 37% CD, 43% UC/IBDU and 20% HC. Non-inflamed tissue was observed in 66% of TI and 38% of SC from IBD patients, as well as all HC samples (TI=33 and SC=51). Overall, there was no difference in BSH, HSDH and ADH gene abundance when comparing non-inflamed TI and non-inflamed SC. Comparison of non-inflamed tissue (TI vs SC) within HC, CD and UC/IBDU patients did not show a significant difference. When we compared inflamed and non-inflamed tissues within different groups and sites, only one significant increase of ADH gene abundance (COG1062) was noted in TI from CD patients, after Benjamini-Hochberg correction (Q-value=0.049, figure) Differences in inferred mucosal BAMM might be associated with tissue inflammation in IBD patients. Specifically, BA-dehydroxylating bacterial genes are increased in inflamed TI from CD patients suggesting a potential role of this microbiota function in IBD Figure. Count of Clusters of Orthologous Groups (COG)1062 between healthy controls (HC), non-inflamed and inflamed terminal ileum in Crohn’s disease (CD) patients CCC

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)0.000
metaresearch head score (Gemma)0.001
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.210
Teacher spread0.205 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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