A metabonomics investigation of multiple sclerosis by nuclear magnetic resonance. (18th December 2012)

• Record Type: Journal Article
• Authors: Mehrpour, Masoud; Kyani, Anahita; Tafazzoli, Mohsen; Fathi, Fariba; Joghataie, Mohammad‐Taghi
• Is Part Of: Magnetic resonance in chemistry. Volume 51:Number 2(2013:Feb.)
• Page Start: 102
• Page End: 109
• Publication Date: 2012-12-18
• Subjects: Nuclear magnetic resonance spectroscopy -- Periodicals; Chemistry, Organic -- Periodicals; Magnetic resonance -- Periodicals; 538.36
• DOI: 10.1002/mrc.3915
• Languages: English
• ISSNs: 0749-1581

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Multiple sclerosis (MS) is a nervous system disease that affects the fatty myelin sheaths around the axons of the brain and spinal cord, leading to demyelination and a broad range of signs and symptoms. MS can be difficult to diagnose because its signs and symptoms may be similar to other medical problems. To find out which metabolites in serum are effective for the diagnosis of MS, we utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy (<sup>1</sup>H‐NMR). Random forest (RF) was used to classify the MS patients and healthy subjects. Atomic absorption spectroscopy was used to measure the serum levels of selenium. The results showed that the levels of selenium were lower in the MS group, when compared with the control group. RF was used to identify the metabolites that caused selenium changes in people with MS by building a correlation model between these metabolites and serum levels of selenium. For the external test set, the obtained classification model showed a 93% correct classification of MS and healthy subjects. The regression model of levels of selenium and metabolites showed the correlation (<italic>R</italic><sup>2</sup>) value of 0.88 for the external test set. The results indicate the suitability of NMR as a screen for identifying MS patients and healthy subjects. A novel model with good prediction outcomes was constructed between serum levels of selenium and NMR data. Copyright © 2012 John Wiley & Sons, Ltd.</p> </abstract>