Differential Effect of Genetic Burden on Disease Phenotypes in Crohn’s Disease and Ulcerative Colitis in a Canadian Cohort
Bibliographic record
Abstract
Abstract Background and Aims Crohn’s disease (CD) and ulcerative colitis (UC) demonstrate considerable phenotypic heterogeneity and course. Accurate predictors of disease behaviour are lacking. The contribution of genetics and specific polymorphisms is widely appreciated; however, their cumulative effect(s) upon disease behaviour remains poorly understood. Here, we investigate the relationship between genetic burden and disease phenotype in a Canadian inflammatory bowel disease (IBD) Cohort. Methods We retrospectively examined a cohort of CD and UC patients recruited from a single tertiary referral center genotyped using a Goldengate Illumina platform. A genetic risk score (GRS) incorporating strength of association (log odds ratio) and allele dose for 151 IBD-risk loci was calculated and evaluated for phenotypic associations. Results Among CD patients, higher GRS was associated with earlier onset of disease (regression coefficient −2.19, 95% confidence interval [CI] −3.77 to −0.61, P = 0.007), ileal disease (odds ratio [OR] 1.45), stricturing/penetrating disease (OR 1.72), perianal disease (OR 1.57) and bowel resection (OR 1.66). Higher GRS was associated with use of anti-tumor necrosis factor (TNF) (P < 0.05) but not immunomodulators. Interestingly, we could not demonstrate an association between higher GRS and family history of IBD (OR 1.27, P = 0.07). Onset of disease remained statistically significant for never smokers (P = 0.03) but not ever smokers (P = 0.13). For UC, having a higher GRS did not predict the age of diagnosis nor was it predictive of UC disease extent (P = 0.18), the need for surgery (P = 0.74), nor medication use (immunomodulators P = 0.53, anti-TNF P = 0.49). We could not demonstrate an association between increased GRS and having a family history of IBD in the UC group. Conclusions Increasing genetic burden is associated with early age of diagnosis in CD and may be useful in predicting disease behaviour in CD but not UC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".