Genetic risk factors predict disease progression in Crohn’s disease patients of the Swiss inflammatory bowel disease cohort
Bibliographic record
Abstract
BACKGROUND: Crohn's disease (CD) may progress from an inflammatory to a stricturing or penetrating disease phenotype. The aim of our study was to identify single nucleotide polymorphisms (SNPs) that predict disease progression in patients of the Swiss IBD Cohort Study (SIBDCS). METHODS: We applied a multi-state Markov model for progression behavior of CD with three behavioral states according to the Montreal classification. The model considered transition from B1 to B2/B3 or from B2 to B3 stage. Model dynamics were summarized with transition intensities by including the effect of SNPs and calculating transition intensities for each SNP. RESULTS: We included 1276 CD patients [669 (52.4%) B1, 248 (19.4%) B2, 359 (28.1%) B3 patients] with a median follow-up of 6.8 (interquartile range = 3.6-9.1; range 0-11.6) years. Probability for a B1 patient to develop a stenosis (B1 to B2, q = 0.033) was twice as much as compared to developing a penetrating complication (B3) during the disease course. In contrast, the probability of entering B3 stage was similar regardless of whether antecedent stricture was present (B2 to B3, q = 0.016) or not (B1 to B3, q = 0.016). We identified SNPs within the gene loci encoding ZMIZ1, LOC105373831 and KSR1 as carrying the highest risk for progression to B3, while the presence of SNPs within gene loci TNFSF15 and CEBPB-PTPN1 protected from progression to B2 or B3. CONCLUSION: We identified new genetic risk factors that can predict disease course in CD patients. A closer understanding on the functional impact of these genetic variations might improve our treatment options finally to prevent disease progression in CD patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".