Rac1 Polymorphisms and Thiopurine Efficacy in Children With Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVES: Thiopurines are effective for maintenance of remission in inflammatory bowel disease (IBD) in only about half of patients. Predictors of response may assist in selecting the most appropriate patients for thiopurine therapy. Thiopurines inhibit Rac1, a GTPase that exerts an antiapoptotic effect on T-lymphocytes. A genetic association was recently demonstrated between a Rac1 single nucleotide polymorphism (SNP) and poorer response to thiopurines in adult patients with Crohn disease. We aimed to determine whether Rac1 SNPs are associated with response to thiopurines in children with IBD. METHODS: Children with IBD treated with thiopurines were prospectively followed for 1 year and were genotyped for 3 Rac1 SNPs previously found to be relevant to IBD: rs10951982, rs4720672, and rs34932801. The rate of sustained steroid-free remission (SSFR) without treatment escalation by 12 months was compared between wild types (WTs) and heterozygotes. RESULTS: A total of 59 patients were studied (63% boys, 80% having Crohn disease, mean age 13 ± 4.1). Nineteen of the 41 WT (46%) and 9 of the 15 (60%) heterozygotes for rs10951982 were in SSFR (P = 0.55). Similarly, 21 of the 45 (47%) WT and 8 of the 12 (67%) heterozygotes for rs4720672 were in remission (P = 0.33). Finally, 21 of the 45 (47%) WT and 3 of the 5 (60%) heterozygotes for rs34932801 were in remission (P = 0.66). All of the 3 comparisons remained nonsignificant in a sensitivity analysis of only the patients with Crohn disease. CONCLUSIONS: We did not find an association between 3 Rac1 SNPs and thiopurine effectiveness by 12 months in a prospective study of children with IBD. Other predictors of response should be sought to optimize patient selection for thiopurine therapy.
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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 | 0.002 |
| 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".