Dataset Information

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

ABSTRACT: 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).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.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).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.

SUBMITTER: Plevy S

PROVIDER: S-EPMC3792797 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Plevy Scott S Silverberg Mark S MS Lockton Steve S Stockfisch Tom T Croner Lisa L Stachelski Jordan J Brown Michelle M Triggs Cheryl C Chuang Emil E Princen Fred F Singh Sharat S

Inflammatory bowel diseases 20130501 6

<h4>Background</h4>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).<h4>Methods</h4>In this cross-sect ...[more]

PMID: 23518807

Similar Datasets

Project description:BackgroundCrohn's disease and ulcerative colitis are the two major forms of inflammatory bowel disease; treatment strategies have historically been determined by this binary categorisation. Genetic studies have identified 163 susceptibility loci for inflammatory bowel disease, mostly shared between Crohn's disease and ulcerative colitis. We undertook the largest genotype association study, to date, in widely used clinical subphenotypes of inflammatory bowel disease with the goal of further understanding the biological relations between diseases.MethodsThis study included patients from 49 centres in 16 countries in Europe, North America, and Australasia. We applied the Montreal classification system of inflammatory bowel disease subphenotypes to 34,819 patients (19,713 with Crohn's disease, 14,683 with ulcerative colitis) genotyped on the Immunochip array. We tested for genotype-phenotype associations across 156,154 genetic variants. We generated genetic risk scores by combining information from all known inflammatory bowel disease associations to summarise the total load of genetic risk for a particular phenotype. We used these risk scores to test the hypothesis that colonic Crohn's disease, ileal Crohn's disease, and ulcerative colitis are all genetically distinct from each other, and to attempt to identify patients with a mismatch between clinical diagnosis and genetic risk profile.FindingsAfter quality control, the primary analysis included 29,838 patients (16,902 with Crohn's disease, 12,597 with ulcerative colitis). Three loci (NOD2, MHC, and MST1 3p21) were associated with subphenotypes of inflammatory bowel disease, mainly disease location (essentially fixed over time; median follow-up of 10·5 years). Little or no genetic association with disease behaviour (which changed dramatically over time) remained after conditioning on disease location and age at onset. The genetic risk score representing all known risk alleles for inflammatory bowel disease showed strong association with disease subphenotype (p=1·65 × 10(-78)), even after exclusion of NOD2, MHC, and 3p21 (p=9·23 × 10(-18)). Predictive models based on the genetic risk score strongly distinguished colonic from ileal Crohn's disease. Our genetic risk score could also identify a small number of patients with discrepant genetic risk profiles who were significantly more likely to have a revised diagnosis after follow-up (p=6·8 × 10(-4)).InterpretationOur data support a continuum of disorders within inflammatory bowel disease, much better explained by three groups (ileal Crohn's disease, colonic Crohn's disease, and ulcerative colitis) than by Crohn's disease and ulcerative colitis as currently defined. Disease location is an intrinsic aspect of a patient's disease, in part genetically determined, and the major driver to changes in disease behaviour over time.FundingInternational Inflammatory Bowel Disease Genetics Consortium members funding sources (see Acknowledgments for full list).

Project description:BackgroundWe established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly.MethodsMSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model.ResultsAmong all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly. Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077).ConclusionsThe nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets