Effect of physical activity on plasma metabonomics variation using 1H NMR, anthropometric and modeling methods
Abstract
The metabolic changes in serum during a sports program were explored using a metabonomic approach based on 1H NMR spectroscopy and anthropometry. The aim of this study was to classify two groups of female university students with body mass indexes greater than 25 kg/m² using multiple measured descriptors. The first group (n=16) underwent a complex, well-programmed, 18-week physical training course, and the second group (n=8) was the control group, which did not participate in a training course. Our descriptors consisted of anthropometric descriptors (including height, weight, circumferences of the arm, waist, hips and thighs, lean body mass and fat mass percentiles). Serum levels of growth hormone (GH), insulin, and insulin-like growth factor-1 were measured. 1H NMR spectra were obtained for all the individuals in both groups using a 500-MHz Bruker spectrometer and integrated for their chemical shifts using Chenomx software. The descriptors were measured both before and after the training program for the experimental group. To make a linear model between GH and the 1H NMR matrix using a multiple linear regression (MLR) stepwise as the variable selection method, the most important descriptors were selected. The results obtained for the square of correlation coefficients (R2) training and test show an agreement between experimental and theoretical GH values. By applying counter propagation artificial neural network (CP-ANN) classification methods, we significantly separated our experimental group from the control group based on GH and lipoprotein classes. Keywords: Physical activity, blood serum, nuclear magnetic resonance, multiple linear regressions, artificial neural network
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