Objective: Relatives of individuals with Crohn's disease (CD) carry an increased number of CD-associated genetic variants and are at increased risk of developing the disease. Multiple environmental and genetic factors contribute to this increased risk. We aimed to estimate the utility of genotype, smoking, family history, and a panel of biomarkers to predict risk in asymptomatic first-degree relatives (FDRs) of CD patients. Design: We calculated a combined genotype (72 CD-associated genetic markers) and smoking relative risk score in 454 FDRs, and performed capsule endoscopy and collected 22 biomarkers in individuals from the highest and lowest risk quartiles. We then predicted small intestinal inflammation using genetic risk score, smoking status, number of relatives with CD, capsule transit time, and the panel of biomarkers in 124 individuals with complete data. Our principal analysis was to calculate the predictive utility from two machine learning classifiers: an elastic net and a random forest. Results: Both classifiers successfully predicted FDRs with intestinal inflammation: elastic net (AUC=0.80, 95% CI: 0.62-0.98), random forest (AUC=0.87, 95% CI: 0.75-1.00). The elastic net selected a 3-predictor solution: CD family history (OR=1.31), genetic risk score (OR=1.14), and faecal calprotectin (OR=1.04). The same 3 variables were among the top 5 most important predictors as ranked by the random forest. Conclusion: A readily collectable panel of genetic risk variants, added to family history and faecal calprotectin, predicts those at greatest risk for developing CD with a good degree of accuracy.

Predictors of intestinal inflammation in asymptomatic first-degree relatives of patients with Crohn's disease