The Unsolved Link of Genetic Markers and Crohn’s Disease Progression: A North American Cohort Experience
Bibliographic record
Abstract
BACKGROUND: While progress has been made in the identification of Crohn's disease (CD) susceptibility loci, efforts to identify a genetic basis for disease progression have been less fruitful. The specific aim of this study was to build upon the major genetic advances made in IBD by applying genome-wide technologies toward predicting disease progression in CD. METHODS: Crohn's disease cases (n = 1495) from 3 IBD centers were reviewed by experienced physicians. Clinical and demographic details were collected, focusing on the time to first disease progression. Genome-wide association (GWA) analysis was carried out on 3 clinical outcomes: 1) time to disease progression; 2) time to first abdominal surgery; and 3) a binary analysis of indolent vs progressive disease. Cox-proportional hazard and logistic regression models were used. RESULTS: A GWA analysis was carried out to determine any genetic variation associated with the time to disease progression; 662 cases were included after quality control (QC) and exclusion of any cases with B2/B3 behavior at baseline (n = 450). There were 1360 cases included after QC in the time to abdominal surgery analysis. No variant reached genome-wide significance in any of the 3 analyses performed. Eight known IBD susceptibility single nucleotide polymorphism (SNPs) were found to be associated with time-to-abdominal surgery SMAD3 (rs17293632), CCR6 (rs1819333), CNTF (rs11229555), TSPAN14 (rs7097656), CARD9 (rs10781499), IPMK (rs2790216), IL10 (rs3024505), and SMURF1 (rs9297145) (P < 0.05). CONCLUSION: Our GWA study failed to show any SNP-phenotype association reaching genome-wide significance. It is likely that multiple variables affect disease progression, with genetic factors potentially having only a small effect size.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".