The NOD2 -Smoking Interaction in Crohn's Disease is likely Specific to the 1007 fs Mutation and may be Explained by Age at Diagnosis: A Meta-Analysis and Case-Only Study
Bibliographic record
Abstract
BACKGROUND: NOD2 and smoking are risk factors for Crohn's disease. We meta-analyzed NOD2-smoking interactions in Crohn's disease (Phase 1), then explored the effect of age at diagnosis on NOD2-smoking interactions (Phase 2). METHODS: Phase 1: MEDLINE and EMBASE were searched for studies (n=18) providing data on NOD2 and smoking in Crohn's disease. NOD2-smoking interactions were estimated using odds ratios (ORs) and 95% confidence intervals (CIs) calculated using random effects models. Phase 2: A case-only study compared the proportion of smokers and carriers of the 1007fs variant across ages at diagnosis (≤16, 17-40, >40years). FINDINGS: Phase 1: Having ever smoked was less common among carriers of the 1007fs variant of NOD2 (OR 0.74, 95%CI:0.66-0.83). There was no interaction between smoking and the G908R (OR 0.96, 95%CI:0.82-1.13) or the R702W variant (OR 0.89, 95%CI:0.76-1.05). Phase 2: The proportion of patients (n=627) carrying the 1007fs variant decreased with age at diagnosis (≤16years: 15%; 17-40: 12%; >40: 3%; p=0.003). Smoking was more common in older patients (≤16years: 4%; 17-40: 48%; >40: 71%; p<0.001). INTERPRETATION: The negative NOD2-smoking interaction in Crohn's disease is specific to the 1007fs variant. However, opposing rates of this variant and smoking across age at diagnosis may explain this negative interaction.
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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.052 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".