Abstract
OBJECTIVE: Genetic and immunological data indicate that inflammatory bowel disease (IBD) are characterized by specific inflammatory protein profiles. However, the serum proteome of IBD is still to be defined. We aimed to characterize the inflammatory serum protein profiles of Crohn's disease (CD) and ulcerative colitis (UC), using the novel proximity extension assay. METHODS: A panel of 91 inflammatory proteins were quantified in a discovery cohort of CD (n = 54), UC patients (n = 54), and healthy controls (HCs; n = 54). We performed univariate analyses by t-test, with false discovery rate correction. A sparse partial least-squares (sPLS) approach was used to identify additional discriminative proteins. The results were validated in a replication cohort. RESULTS: By univariate analysis, 17 proteins were identified with significantly different abundances in CD and HCs, and 12 when comparing UC and HCs. Additionally, 64 and 45 discriminant candidate proteins, respectively, were identified with the multivariate approach. Correspondingly, significant cross-validation error rates of 0.12 and 0.19 were observed in the discovery cohort. Only FGF-19 was identified from univariate comparisons of CD and UC, but 37 additional discriminant candidates were identified using the multivariate approach. The observed cross-validation error rate for CD vs. UC remained significant when restricting the analyses to patients in clinical remission. Using univariate comparisons, 16 of 17 CD-associated proteins and 8 of 12 UC-associated proteins were validated in the replication cohort. The area under the curve for CD and UC was 0.96 and 0.92, respectively, when the sPLS model from the discovery cohort was applied to the replication cohort. CONCLUSIONS: By using the novel PEA method and a panel of inflammatory proteins, we identified proteins with significantly different quantities in CD patients and UC patients compared to HCs. Our data highlight the potential of the serum IBD proteome as a source for identification of future diagnostic biomarkers.