Journal of
Bioinformatics and Sequence Analysis

  • Abbreviation: J. Bioinform. Seq. Anal.
  • Language: English
  • ISSN: 2141-2464
  • DOI: 10.5897/JBSA
  • Start Year: 2009
  • Published Articles: 49

Full Length Research Paper

Effect of physical activity on plasma metabonomics variation using 1H NMR, anthropometric and modeling methods

Mohammad Arjmand1, Fatemeh Darvizeh2, Ziba Akbari1, Reyhaneh Mohabati1and Zahra Zamani1*
1Department of Biochemistry, Pasteur Institute of Iran, Pasteur Ave, Tehran 13164, Iran. 2Department of Chemistry, Sharif University of Technology, Tehran 11155-9516, Iran.  
Email: [email protected]

  •  Accepted: 18 September 2012
  •  Published: 31 October 2012


The metabolic changes in serum during a sport program were explored using a metabonomic approach, based on proton nuclear magnetic resonance (1H-NMR) spectroscopy and anthropometry. The aim of this study was to classify two groups of female university students with body mass index over 25 kg/m², using multiple measured descriptors. The first group (n=16) underwent a complex and well programmed 18-week physical training courses, and the second group (n=8), which was our control group, did not participate in any training course. Our descriptors consist of anthropometric descriptors (including height, weight, circumferences of arm, waist, hip and thigh, lean body mass and fat mass percentiles). Serum levels of growth hormone, insulin, and insulin like growth factor-1 were measured. 1H-NMR spectra was obtained using a 500-MHz Bruker spectrometer and was calculated for certain chemical shift integrals using Chenomx software for all the individuals in both groups. These descriptors were measured both before and after the training program for the experimental group. In order to make a linear model between growth hormone (GH) and 1H-NMR matrix as a set of variables, initially by multiple linear regression (MLR) stepwise as the variable selection method, the most important descriptors were selected by MLR modeling approaches. The results obtained for R2 training and test show an agreement between experimental and theoretical GH values. By applying counter-propagation Artificial Neural Networks (CP-ANN) classification methods, we significantly separated our 1st group from the other one. 

Key words: Physical activity, blood serum, nuclear magnetic resonance, multiple linear regressions, artificial neural network.