Genetic variation in the farnesoid X-receptor predicts Crohn’s disease severity in female patients
Bibliographic record
Abstract
The farnesoid X receptor (FXR) is implicated in Crohn's disease (CD) pathogenesis. It is unclear how genetic variation in FXR impacts CD severity versus genetic variation in nuclear receptors such as pregnane X receptor (PXR) and the multi-drug resistance protein 1 (MDR1, ABCB1). To evaluate FXR-1G > T as a genomic biomarker of severity in CD and propose a plausible molecular mechanism. A retrospective study (n = 542) was conducted in a Canadian cohort of CD patients. Genotypic analysis (FXR-1G > T, MDR1 3435C > T and PXR -25385C > T) as well as determination of the FXR downstream product, fibroblast growth factor (FGF) 19 was performed. Primary outcomes included risk and time to first CD-related surgery. The effect of estrogen on wild type and variant FXR activity was assessed in HepG2 cells. The FXR-1GT genotype was associated with the risk of (odds ratio, OR = 3.34, 95% CI = 1.58-7.05, p = 0.002) and earlier progression to surgery (hazard ratio, HR = 3.00, 95% CI = 1.86-4.83, p < 0.0001) in CD. Female carriers of the FXR-1GT genotype had the greatest risk of surgery (OR = 14.87 95% CI = 4.22-52.38, p < 0.0001) and early progression to surgery (HR = 6.28, 95% CI = 3.62-10.90, p < 0.0001). Women carriers of FXR-1GT polymorphism had a three-fold lower FGF19 plasma concentration versus women with FXR-1GG genotype (p < 0.0001). In HepG2 cells cotransfected with estrogen receptor (ER) and FXR, presence of estradiol further attenuated variant FXR activity. MDR1 and PXR genotypes were not associated with surgical risk. Unlike MDR1 and PXR, FXR-1GT genetic variation is associated with earlier and more frequent surgery in women with CD. This may be through ER-mediated attenuation of FXR activation.
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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".