International Journal of Celiac Disease
ISSN (Print): 2334-3427 ISSN (Online): 2334-3486 Website: http://www.sciepub.com/journal/ijcd Editor-in-chief: Samasca Gabriel
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International Journal of Celiac Disease. 2014, 2(2), 44-46
DOI: 10.12691/ijcd-2-2-3
Open AccessArticle

A Metabonomics Study on Celiac Disease by CART

Fariba Fathi1, Fatemeh Ektefa2, Kaveh Sohrabzadeh3, Afsaneh Arefi-Oskouie4, Mohsen Tafazzoli1, , Kamran Rostami5, Mohammad-Reza Zali6 and Mohammad Rostami-Nejad6,

1Department of Chemistry, Sharif University of Technology, Tehran, Iran

2Department of Chemistry, Tarbiat Modares University, Tehran, Iran

3Department of Electrical Engineer, Payam Nonprofit Higher Education Institution, Golpayegan, Iran

4Department of Basic Science Faculty of Paramedical, Shahid Beheshti University of Medical Sciences, Tehran, Iran

5Gastroenterology Department, Worcestershire Royal Hospital Worcester, UK

6Gastroenterology and Liver Disease Research center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Pub. Date: April 25, 2014

Cite this paper:
Fariba Fathi, Fatemeh Ektefa, Kaveh Sohrabzadeh, Afsaneh Arefi-Oskouie, Mohsen Tafazzoli, Kamran Rostami, Mohammad-Reza Zali and Mohammad Rostami-Nejad. A Metabonomics Study on Celiac Disease by CART. International Journal of Celiac Disease. 2014; 2(2):44-46. doi: 10.12691/ijcd-2-2-3

Abstract

Celiac disease (CD) is an immune reaction as a consequence of ingestion of gluten. Diagnosis of CD is not easily using the clinical tests. Then, the discovery of appropriate methods for CD diagnosis is necessary. This study was concentrated to seek the metabolic biomarkers causes of CD compare to healthy subjects.In the present study, we classify CD and healthy subjects using classification and regression tree (CART). To find metabolites in serum which are helpful for the diagnosis of CD, the metabolic profiling was employed using the proton nuclear magnetic resonance spectroscopy (1HNMR). Based on CART results, it was concluded that just using one descriptor, CD and control groups could be classified separately. The 89 % of data in the test set was predicted correctly by the obtained classification model. Our study indicates that quantitative metabolite analysis of serum can be employed to distinguish healthy from CD subjects.

Keywords:
nuclear magnetic resonance spectroscopy celiac disease classification and regression tree metabonomics

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