A26 A SYSTEMTIC REVIEW OF MONOGENIC INFLAMMATORY BOWEL DISEASE: CLINICAL PHENOTYPE AND GENOTYPE
Bibliographic record
Abstract
Abstract Background Advances in genomic technologies have led to an increase in reports of monogenic inflammatory bowel disease (IBD). The majority of the studies on monogenic IBD have focused only on young children aged <6 years. There are no detailed reports containing a comprehensive picture of monogenic IBD with specific numbers on the clinical features, genetic profiles and disease course. Since each gene-specific cause of monogenic IBD is rare, it is difficult to collect cases in a single study even with international cohort studies. Aims To elucidate a comprehensive picture of monogenic IBD, we conducted a systematic review of all reported cases of monogenic IBD. Methods A systematic review of MEDLINE articles published between January 2000 and December 2019 was conducted. 662 monogenic IBD cases were identified from 273 eligible articles. Data on clinical manifestation, genotype, and management were collected. Results The most frequently reported genes causative of monogenic IBD patients were IL10RA, CYBB, IL10RB, and TTC7A (Figure). In total, 64.8% of patients developed IBD before six years old, 15.6% between ages 10 and 17.9 years, and 11.6% at age 18 years or older. Only 32.7% had any history of extra-intestinal manifestation (EIM) before IBD onset. There was substantial difference in the onset age groups and the underlying monogenic disorders. 74.4% developed at least one EIM during their clinical course. The most common EIMs were atypical infection (44.1%), dermatologic abnormality (39.2%), autoimmunity (22.7%) and lymphoid organ abnormality (11.5%). Autosomal recessive (62.8%) was the most common inheritance pattern, and missense-variants (44.3%) were the most identified type of genetic variants. Deletions including CNVs, intronic, synonymous and inversion, which have the possibility to be overlooked by whole exome sequencing were shown in some cases. Bowel surgery, biologics, and hematopoietic stem cell transplantation were performed in 28.3%, 32.8%, and 24.4% of patients, respectively. Conclusions Monogenic IBD diagnosis and management is a challenging clinical problem across age groups; the EIMs are more diverse and the management is evidently more difficult compared to non-monogenic IBD. An improved understanding of the characteristics of the genes and underlying disease processes in monogenic IBD is necessary for effective management. Funding Agencies CAG, CIHRUehara Memorial Foundation Fellowship
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".