A Systematic Review of Monogenic Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND & AIMS: Advances in genomic technologies have led to increasing reports of monogenic inflammatory bowel disease (IBD). Here, we systematically review the literature to determine the clinical features, genetic profile, and previously used treatment strategies in monogenic IBD. METHODS: A systematic review of MEDLINE articles published between January 2000 and December 2020 was conducted. A total of 750 individual monogenic IBD cases were identified from 303 eligible articles. RESULTS: The most frequently reported monogenic IBD genes were IL10RA/B, XIAP, CYBB, LRBA, and TTC7A. In total, 63.4% of patients developed IBD before 6 years of age, 17.4% developed IBD between ages 10 and 17.9 years, and 10.9% developed IBD after age 18. There was a substantial difference between these age groups and the underlying monogenic disorders. Only 31.7% had any history of extraintestinal comorbidity (EIC) before IBD onset, but 76.0% developed at least 1 EIC during their clinical course. The most common EICs were atypical infection (44.7%), dermatologic abnormality (38.4%), and autoimmunity (21.9%). Bowel surgery, biologic therapy, and hematopoietic stem cell transplantation were performed in 27.1%, 32.9%, and 23.1% of patients, respectively. CONCLUSIONS: Monogenic IBD cases, although rare, have varied extraintestinal comorbidities and limited treatment options including surgery and transplant. Early identification and improved understanding of the characteristics of the genes and underlying disease processes in monogenic IBD is important for effective management.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".