Combined serological, genetic, and inflammatory markers differentiate non-IBD, Crohn's disease, and ulcerative colitis patients

Abstract

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).

Methods: In this cross-sectional study, patient blood samples from 572 CD, 328 UC, 437 non-IBD controls, and 183 healthy controls from academic and community centers were analyzed for 17 markers: 8 serological markers (ASCA-IgA, ASCA-IgG, ANCA, pANCA, OmpC, CBir1, A4-Fla2, and FlaX), 4 genetic markers (ATG16L1, NKX2-3, ECM1, and STAT3), and 5 inflammatory markers (CRP, SAA, ICAM-1, VCAM-1, and VEGF). A diagnostic Random Forest algorithm was constructed to classify IBD, CD, and UC.

Results: Receiver operating characteristic analysis compared the diagnostic accuracy of using a panel of serological markers only (ASCA-IgA, ASCA-IgG, ANCA, pANCA, OmpC, and CBir1) versus using a marker panel that in addition to the serological markers mentioned above also included gene variants, inflammatory markers, and 2 additional serological markers (A4-Fla2 and FlaX). The extended marker panel increased the IBD versus non-IBD discrimination area under the curve from 0.80 (95% confidence interval [CI], ±0.05) to 0.87 (95% CI, ±0.04; P < 0.001). The CD versus UC discrimination increased from 0.78 (95% CI, ±0.06) to 0.93 (95% CI, ±0.04; P < 0.001).

Conclusions: Incorporating a combination of serological, genetic, and inflammation markers into a diagnostic algorithm improved the accuracy of identifying IBD and differentiating CD from UC versus using serological markers alone.

FIGURE 1

Venn diagram describing the relationships between serological markers in the CD cohort (n = 572). The percentage of positive patients for each marker or combination of markers is shown. A, The relationship between anti-CBirl, anti-A4-Fla2, and anti-FlaX. B, The relationship between ASCA-lgA/ASCA-lgG, OmpC, and the combination of anti-CBirl/anti-A4-Fla2/anti-FlaX.

FIGURE 2

Venn diagram describing the relationships between the inflammatory markers. The percentage of positive patients for each marker or combination is shown. A, The relationship between CRP and SAA in the IBD cohort (n = 900). B, The relationship between the combination CRP/SAA, VEGF, and the combination of ICAM-1/VCAM-1 in the IBD cohort (n = 900).

FIGURE 3

Differentiation between IBD and non-IBD patients was enhanced with biomarker combinations. A, The relationship between the number of positive serological markers (ASCA-lgA, ASCA-IgG, ANCA, anti-OmpC, anti-CBirl, anti-A4-Fla2, and anti-FlaX) and the percentage of IBD and non-IBD patients. B, The relationship between the number of positive inflammatory markers (CRP, SAA, VEGF, ICAM-1, and VCAM-1) and the percentage of IBD and non-IBD patients. A patient was considered positive if their biomarker measurement was equal to or above the third quartile for that marker’s measures.

FIGURE 4

Receiver operating characteristic curves for discriminating IBD and non-IBD patients. AUC is shown for the combination of serological, genetic, and inflammatory markers and for the following continuous markers: ASCA-lgA, ASCA-IgG, ANCA, anti-OmpC, anti-CBirl, anti-A4-Fla2, anti-FlaX, CRP, SAA, ICAM-1, VCAM-1, and VEGF.

FIGURE 5

Receiver operating characteristic curves for discriminating patients with CD and UC in a direct performance comparison of the biomarker panel combination of serology, genetics, and inflammation markers against a panel built from the serological biomarkers alone (ASCA-IgA, ASCA-IgG, OmpC, CBirl, ANCA, and pANCA) using 373 patient samples.