Literature DB >> 33406965

Serological markers facilitate the diagnosis of Crohn's disease.

Xin Gao1, Yan Zhang1.   

Abstract

Background and aim: The diagnosis of Crohn's disease (CD) is challenging. Ongoing search for biomarkers to facilitate the diagnosis is a worthwhile endeavor. The aim of this study was to explore the role of serological markers in the diagnosis of CD at an inflammatory bowel disease (IBD) referral center.
Methods: This was a retrospective study including 196 suspected CD patients. The expression of ASCA-IgG, ASCA-IgA, AYMA-IgG, AYCA-IgA, FI2Y-IgG, and pANCA in the patient's serum was determined by enzyme-linked immunosorbent assay (ELISA) and indirect immunofluorescence (IF).
Results: ASCA was a relatively specific marker for CD (p = 0.0005), but not AYMA-IgG, AYCA-IgA, F12Y-IgG (p = 0.5936, 0.7974, 0.1085, respectively). However, a high sensitivity of 96.77% (95% CI 90.19%-99.83%) was noted for ASCA+/FI2Y+ to identify CD patients among the suspected cases, albeit with low PPV. The more combinations of serological markers, the higher sensitivity, and NPV. No correlation was found between the age of onset or disease location and the expression of ASCA, AYMA, AYCA, FI2Y, or pANCA. There was no significant difference between the expression of ASCA and the disease behavior at diagnosis (p = 0.3307). However, a decreased proportion of AYMA+ CD patients was found in those who received surgery compared with their non-surgical counterparts (p = 0.0488).Conclusions: ASCA was found to be the most accurate serological marker for the differential diagnosis of CD. Combinations of ASCA, AYMA, AYCA, and FI2Y improved diagnostic accuracy of CD.

Entities:  

Keywords:  ASCA; AYCA; AYMA; CD; FI2Y; UC; differentiation; serological markers

Year:  2021        PMID: 33406965     DOI: 10.1080/00325481.2021.1873649

Source DB:  PubMed          Journal:  Postgrad Med        ISSN: 0032-5481            Impact factor:   3.840


  1 in total

1.  Differentiation of intestinal tuberculosis and Crohn's disease through an explainable machine learning method.

Authors:  Futian Weng; Yu Meng; Fanggen Lu; Yuying Wang; Weiwei Wang; Long Xu; Dongsheng Cheng; Jianping Zhu
Journal:  Sci Rep       Date:  2022-02-02       Impact factor: 4.379

  1 in total

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