Understanding the Etiology of Inflammatory Complications Following Ileal Pouch-Anal Anastomosis
Bibliographic record
Abstract
Introduction: Inflammatory pouch complications, including pouchitis, chronic pouchitis (CP) and a Crohn’s disease-like phenotype (CDL) of the pouch following ileal pouch-anal anastomosis (IPAA), are relatively common, and arise via unknown mechanisms. The phenotypic similarities between pouch inflammation and inflammatory bowel disease (IBD) suggest there may be common pathways involved in both disorders. The aim of this thesis is to investigate the serological, genetic and microbial factors contributing to the development of pouch inflammation in a large, well characterized patient cohort. Methods: Subjects with IPAA were recruited, and clinical and demographic information was obtained through medical chart review and patient questionnaire, allowing patients to be grouped based on post-surgical phenotype. Blood and tissue was collected for genetic, serological and microbial analyses. Anti-microbial antibodies were detected using enzyme-linked immunosorbent assay (ELISA), genotyping was carried out using the Illumina Goldengate custom SNP assay and Sequenome iPLEX platform, and tissue-associated microbial communities were assessed using 454 pyrosequencing. Results: Among our cohort, smoking was associated with CDL (P=0.003) and Ashkenazi Jewish heritage with CP (P<0.008). NOD2insC (rs2066847) (P=7.4x10-5), anti-CBir1 (P<0.0001) and ASCA (IgG) (P=0.03) were significantly associated with inflammatory pouch outcomes. Additional SNPs in NOX3, DAGLB, and NCF4 were also marginally associated with pouch outcome. A multi-variable risk model combining clinical, serologic and genetic markers was constructed and could differentiate between chronic pouch inflammatory phenotypes and no pouchitis. Genus level microbial analysis demonstrated that several organisms (Bacteroides, Parabacteroides, Blautia and Moryella) were detected less frequently among the inflammatory outcome groups (P<0.05). These associations remained significant even following adjustment for antibiotic use, smoking, country of birth and gender. Conclusions: CD-associated anti-microbial antibodies and genetic markers are associated with chronic inflammatory pouch phenotypes. Additionally, changes in the composition of the pouch associated microbiome are associated with inflammation. These observations suggest that similar mechanisms may be involved in non-surgical IBD and pouchitis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".