Journal Highlight: 1H NMR based metabolic profiling in Crohn's disease by random forest methodology

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  • Published: Jun 30, 2014
  • Author: spectroscopyNOW
  • Channels: NMR Knowledge Base
thumbnail image: Journal Highlight: <sup>1</sup>H NMR based metabolic profiling in Crohn's disease by random forest methodology
1H NMR spectroscopy of the serum metabolites of patients with Crohn's disease, supported by random forest methodology, showed that valine and isoleucine are differentiating metabolites for diagnosis.

1H NMR based metabolic profiling in Crohn's disease by random forest methodology

Magnetic Resonance in Chemistry, 2014, 52, 370-376
Fariba Fathi, Laleh Majari-Kasmaee, Ahmad Mani-Varnosfaderani, Anahita Kyani, Mohammad Rostami-Nejad, Kaveh Sohrabzadeh, Nosratollah Naderi, Mohammad Reza Zali, Mostafa Rezaei-Tavirani, Mohsen Tafazzoli and Afsaneh Arefi-Oskouie

Abstract: The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment. Proton nuclear magnetic resonance spectroscopy (1H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for the external test set showed a 94% correct classification of CD and healthy subjects. The present study suggests Valine and Isoleucine as differentiating metabolites for CD diagnosis. These metabolites can be used for screening of risky samples at the early stages of CD diagnoses. Moreover, a robust random forest regression model with good prediction outcomes was developed for correlating serum zinc level and metabolite concentrations. The regression model showed the correlation (R2) and root mean square error values of 0.83 and 6.44, respectively. This model suggests valuable clues for understanding the mechanism of zinc deficiency in CD patients.

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