The Cannabinoid 1 Receptor (CNR1) 1359 G/A Polymorphism Modulates Susceptibility to Ulcerative Colitis and the Phenotype in Crohn's Disease
Bibliographic record
Abstract
BACKGROUND: Recent evidence suggests a crucial role of the endocannabinoid system, including the cannabinoid 1 receptor (CNR1), in intestinal inflammation. We therefore investigated the influence of the CNR1 1359 G/A (p.Thr453Thr; rs1049353) single nucleotide polymorphism (SNP) on disease susceptibility and phenotype in patients with ulcerative colitis (UC) and Crohn's disease (CD). METHODS: Genomic DNA from 579 phenotypically well-characterized individuals was analyzed for the CNR1 1359 G/A SNP. Amongst these were 166 patients with UC, 216 patients with CD, and 197 healthy controls. RESULTS: Compared to healthy controls, subjects A/A homozygous for the CNR1 1359 G/A SNP had a reduced risk to develop UC (p = 0.01, OR 0.30, 95% CI 0.12-0.78). The polymorphism did not modulate CD susceptibility, but carriers of the minor A allele had a lower body mass index than G/G wildtype carriers (p = 0.0005). In addition, homozygous carriers of the G allele were more likely to develop CD before 40 years of age (p = 5.9x10(-7)) than carriers of the A allele. CONCLUSION: The CNR1 p.Thr453Thr polymorphism appears to modulate UC susceptibility and the CD phenotype. The endocannabinoid system may influence the manifestation of inflammatory bowel diseases, suggesting endocannabinoids as potential target for future therapies.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".