Combination of genetic and quantitative serological immune markers are associated with complicated Crohn's disease behavior

Abstract

Background: Treatment of Crohn's disease (CD) with biologics may alter disease progression, leading to fewer disease-related complications, but cost and adverse event profiles often limit their effective use. Tools identifying patients at high risk of complications, who would benefit the most from biologics, would be valuable. Previous studies suggest that biomarkers may aid in determining the course of CD. We aimed to determine if combined serologic immune responses and NOD2 genetic markers are associated with CD complications.

Methods: In this cross-sectional study, banked blood from well-characterized CD patients (n = 593; mean follow-up: 12 years) from tertiary and community centers was analyzed for six serological biomarkers (ASCA-IgA, ASCA-IgG, anti-OmpC, anti-CBir1, anti-I2, pANCA). In a patient subset (n = 385), NOD2 (SNP8, SNP12, SNP13) genotyping was performed. Complications included stricturing and penetrating disease behaviors. A logistic regression model for the risk of complications over time was constructed and evaluated by cross-validation.

Results: For each serologic marker, complication rates were stratified by quartile. Complication frequency was significantly different across quartiles for each marker (P trend ≤ 0.001). Patients with SNP13 NOD2 risk alleles experienced increased complications versus patients without NOD2 mutations (P ≤ 0.001). A calibration plot of modeled versus observed complication rates demonstrated good agreement (R = 0.973). Performance of the model integrating serologic and genetic markers was demonstrated by area under the receiver operating characteristic curve (AUC = 0.801; 95% confidence interval: 0.757-0.846).

Conclusions: This model combining serologic and NOD2 genetic markers may provide physicians with a tool to assess the probability of patients developing a complication over the course of CD.

FIGURE 1

(A) QSS distributions by complication status: complicated and noncomplicated disease. (B) The curves were generated by logistic regression modeling in the absence of the SNP13 risk allele. (C) The curves were generated by logistic regression modeling with the single SNP13 risk allele. (D) Calibration plot for the comparison of predicted and observed rates of complication by category (decile). Predictions were grouped into categories and compared to observed rates of complications for each category. Number of patients in each category prediction group were: 13 in the 10%–20% category; 29 in the 21%–30% category; 32 in the 31%–40% category; 32 in the 41%–50% category; 59 in the 51%–60% category; 40 in the 61%–70% category; 53 in the 71%–80% category; 65 in the 81%–90% category; 62 in the 91%–100% category. Correlation: 0.973 (E) ROC curve for cross-validation predictions. Probabilities were generated using a leave-one-out cross-validation to repeatedly generate a sero-genetic logistic regression (AUC = 0.801).