A Metabonomics Study on Celiac Disease by CART

Fariba Fathi¹, Fatemeh Ektefa², Kaveh Sohrabzadeh³, Afsaneh Arefi-Oskouie⁴, Mohsen Tafazzoli^1, , Kamran Rostami⁵, Mohammad-Reza Zali⁶ and Mohammad Rostami-Nejad^6,

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

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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References:

[1]	Rostami Nejad M, Rostami K, Pourhoseingholi M, Nazemalhosseini E, Dabiri H, Habibi M. Atypical presentation is dominant and typical for Celiac Disease. J Gastrointestin Liver Dis, 2009; 18: 285-91.
[2]	Nicholson J, Lindon J, Holmes. E. Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 1999; 29: 1181-9.
[3]	Hong Y-S, Hong KS, Park M-H, Ahn Y-T, Lee J-H, Huh C-S, et al. Metabonomic Understanding of Probiotic Effects in Humans With Irritable Bowel Syndrome. J Clin Gastroenterol, 2011(45): 415-25.
[4]	Bezabeh T, Somorjai RL, Smith ICP. MR metabolomics of fecal extracts:applications in the study of bowel diseases. Magn Reson Chem, 2009; 47: 54-61.
[5]	Gall GL, Noor SO, Ridgway K, Scovell L, Jamieson C, Johnson IT, et al. Metabolomics of Fecal Extracts Detects Altered Metabolic Activity of Gut Microbiota in Ulcerative Colitis and Irritable Bowel Syndrome. J Proteome Res, 2011; 10: 4208-18.
[6]	Breiman L, Friedman J, Olshen R, Stone C. Classification and Regression Trees. Chapman &Hall (Wadsworth I, editor. New York; 1984.
[7]	Fathi F, Tafazzoli M, Mehrpour M, Shahidi G. Study of metabolic profiling Parkinson's disease. HealthMED, 2013; 7: 204-10.
[8]	Fathi F, Kyani A, Rostami-Nejad M, Rezaye-Tavirani M, Naderi N, Zali MR, et al. A metabonomics study on Crohn’s Disease using Nuclear Magnetic Resonance spectroscopy. HealthMED, 2012; 6: 3577-84.
[9]	Viant MR. Improved methods for the acquisition and interpretation of NMR metabolomic data. Biochem Biophys Res Commun, 2003; 310: 943-8.
[10]	Zhang G, Hirasaki G. CPMG relaxation by diffusion with constant magnetic field gradient in a restricted geometry: numerical simulation and application. J Magn Reson, 2003; 163: 81-91.
[11]	Zhang MH, Xu QS, Daeyaert F, Lewi PJ, Massart DL. Application of boosting to classification problems in chemometrics. Anal Chim Acta, 2005; 544: 167-76.
[12]	Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Group WI, editor: Belmont; 1984.
[13]	Fathi F, Kyani A, Darvizeh F, Mehrpour M, Tafazzoli M, Shahidi G. Relationship Between Serum Level of Selenium and Metabolites Using 1HNMR-Based Metabonomics in Parkinson’s Disease. Appl Magn Reson, 2013; 44: 1-14.
[14]	Bertini I, Calabro A, Carli VD, Luchinat C, Nepi S, Porfirio B, et al. The Metabonomic Signature of Celiac Disease. J Proteome Res, 2009; 8: 170-7.