Background-Previous studies have demonstrated that serological markers can assist in diagnosing inflammatory bowel disease (IBD). In this study, we aim to build a diagnostic tool incorporating serological markers, genetic variants, and markers of inflammation into a computational algorithm to examine patterns of combinations of markers to (1) identify patients with IBD and (2) differentiate patients with Crohn's disease (CD) from ulcerative colitis (UC).

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.

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