A novel gas chromatography mass spectrometry-based serum diagnostic and assessment approach to ulcerative colitis

To improve the clinical course of ulcerative colitis (UC), more accurate serum diagnostic and assessment methods are required. We used serum metabolomics to develop diagnostic and assessment methods for UC. Sera from UC patients, Crohn's disease (CD) patients, and healthy volunteers (HV) were c...

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Bibliographic Details
Published in:Journal of Crohn's and colitis 2014-09, Vol.8 (9), p.1010-1021
Main Authors: Kohashi, Michitaka, Nishiumi, Shin, Ooi, Makoto, Yoshie, Tomoo, Matsubara, Atsuki, Suzuki, Makoto, Hoshi, Namiko, Kamikozuru, Koji, Yokoyama, Yoko, Fukunaga, Ken, Nakamura, Shiro, Azuma, Takeshi, Yoshida, Masaru
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Biomarker
Biomarkers - blood
Child
Colitis, Ulcerative - blood
Colitis, Ulcerative - diagnosis
Female
Follow-Up Studies
Gas Chromatography-Mass Spectrometry - methods
GC/MS
Humans
Male
Metabolomics
Metabolomics - methods
Middle Aged
Multiple logistic regression analysis
Prognosis
Reproducibility of Results
Retrospective Studies
ROC Curve
Ulcerative colitis
Young Adult
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Summary:To improve the clinical course of ulcerative colitis (UC), more accurate serum diagnostic and assessment methods are required. We used serum metabolomics to develop diagnostic and assessment methods for UC. Sera from UC patients, Crohn's disease (CD) patients, and healthy volunteers (HV) were collected at multiple institutions. The UC and HV were randomly allocated to the training or validation set, and their serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS). Using the training set, diagnostic and assessment models for UC were established by multiple logistic regression analysis. Then, the models were assessed using the validation set. Additionally, to establish a diagnostic model for discriminating UC from CD, the CD patients' data were used. The diagnostic model for discriminating UC from HV demonstrated an AUC of 0.988, 93.33% sensitivity, and 95.00% specificity in the training set and 95.00% sensitivity and 98.33% specificity in the validation set. Another model for discriminating UC from CD exhibited an AUC of 0.965, 85.00% sensitivity, and 97.44% specificity in the training set and 83.33% sensitivity in the validation set. The model for assessing UC showed an AUC of 0.967, 84.62% sensitivity, and 88.23% specificity in the training set and 84.62% sensitivity, 91.18% specificity, and a significant correlation with the clinical activity index (rs=0.7371, P
ISSN:1873-9946
1876-4479
DOI:10.1016/j.crohns.2014.01.024