Thiopurine Effectiveness in Patients with Crohnʼs Disease
Bibliographic record
Abstract
BACKGROUND: Thiopurines are efficacious in the treatment of Crohn's disease and were recently shown to induce T-cell apoptosis by modulation of Rac1 activation. To assess whether polymorphisms in Rac1 and other apoptosis-related genes, combined with clinical parameters, can predict response to thiopurines. METHODS: A retrospective cohort of 156 thiopurine-treated patients with Crohn's disease was genotyped for 11 single-nucleotide polymorphisms (SNPs): 9 SNPs in Rac1, 1 SNP in the Fas ligand -843 T>C, and 1 SNP in the Caspase-9 93 C>T. Clinical data were extracted from the medical charts. Odds ratios (ORs) and 95% confidence intervals (CIs) of the association between demographic, clinical, and genetic variables and thiopurine response rates were calculated. RESULTS: The overall response rate to thiopurines was 74% (115/156). The Rac1 SNP rs34932801 heterozygote genotype GC was associated with a lower response rate compared with the wild-type GG genotype (46% versus 76%; OR = 0.26; 95% CI, 0.08-0.91; P = 0.036). Only wild-type homozygotes were found for 5 Rac1 SNPs. None of the other 3 Rac1 SNPs were associated with response to thiopurines. Patients with Montreal B3 behavior pattern responded worse than those with a B1 behavior pattern (59%, versus 80%; OR = 0.37; 95% CI, 0.17-0.83; P = 0.016). Sephardic Jews had a lower response rate to thiopurines compared with Jews of Ashkenazi or mixed ancestry (60% versus 82%; OR = 0.32; 95% CI, 0.15-0.69, P = 0.003). CONCLUSIONS: Rac1 SNP rs34932801carriage, Montreal B3 disease behavior, and a Sephardic Jewish origin were associated with unfavorable response to thiopurines. Corroboration of these associations in larger cohorts is warranted.
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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 | 0.001 | 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) | 0.001 | 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".