Clinical Phenotypes and Outcomes in Monogenic Versus Non-monogenic Very Early Onset Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Over 80 monogenic causes of very early onset inflammatory bowel disease [VEOIBD] have been identified. Prior reports of the natural history of VEOIBD have not considered monogenic disease status. The objective of this study is to describe clinical phenotypes and outcomes in a large single-centre cohort of patients with VEOIBD and universal access to whole exome sequencing [WES]. METHODS: Patients receiving IBD care at a single centre were prospectively enrolled in a longitudinal data repository starting in 2012. WES was offered with enrollment. Enrolled patients were filtered by age of diagnosis <6 years to comprise a VEOIBD cohort. Monogenic disease was identified by filtering proband variants for rare, loss-of-function, or missense variants in known VEOIBD genes inherited according to standard Mendelian inheritance patterns. RESULTS: This analysis included 216 VEOIBD patients, followed for a median of 5.8 years. Seventeen patients [7.9%] had monogenic disease. Patients with monogenic IBD were younger at diagnosis and were more likely to have Crohn's disease phenotype with higher rates of stricturing and penetrating disease and extraintestinal manifestations. Patients with monogenic disease were also more likely to experience outcomes of intensive care unit [ICU] hospitalisation, gastrostomy tube, total parenteral nutrition use, stunting at 3-year follow-up, haematopoietic stem cell transplant, and death. A total of 41 patients [19.0%] had infantile-onset disease. After controlling for monogenic disease, patients with infantile-onset IBD did not have increased risk for most severity outcomes. CONCLUSIONS: Monogenic disease is an important driver of disease severity in VEOIBD. WES is a valuable tool in prognostication and management of VEOIBD.
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 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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".