Abstract
We have detected differences in metabolite levels between doped athletes, clean athletes, and volunteers (non athletes). This outcome is obtained by comparing results of measurements from two analytical platforms: UHPLC-QTOF/MS and FT-ICR/MS. Twenty-seven urine samples tested positive for glucocorticoids or beta-2-agonists and twenty samples coming from volunteers and clean athletes were analyzed with the two different mass spectrometry approaches using both positive and negative electrospray ionization modes. Urine is a highly complex matrix containing thousands of metabolites having different chemical properties and a high dynamic range. We used multivariate analysis techniques to unravel this huge data set. Thus, the several groups we created were studied by Principal Components Analysis (PCA) and Partial Least Square regression (PLS-DA and OPLS) in the search of discriminating m/z values. The selected variables were annotated and placed on pathway by using MassTRIX.
Highlights
The molecular diversity of the human urinary metabolome is very well reflected by the existing databases [1,2] displaying thousands of metabolites classified in as much as 70 different structural classes [3,4,5]
Two are believed to have played a key role: the introduction of high (Time of Flight mass spectrometry, TOF) and ultra-high resolution techniques (Fourier Transform Ion Cyclotron Resonance mass spectrometry, FT-ICR/MS) and the development of algorithms capable of handling the thousands of signals generated by such analytical platforms
The LC-quadrupole time-of-flight (QTOF) data set was compared with the FT-ICR/MS data set by means of PCA-CCA (Principal Components Analysis-Canonical Correlation Analysis)
Summary
The molecular diversity of the human urinary metabolome is very well reflected by the existing databases [1,2] displaying thousands of metabolites classified in as much as 70 different structural classes [3,4,5]. The recent advances in the pre-processing [9], mathematical modeling [10,11] and the statistical analysis [10] lead to more comprehensive biological interpretation of the metabolomics data [12] Up to present, this strategy has been applied in a variety of fields, including drug discovery [13,14], nutrition [15,16], toxicology [17], clinical trials [18] and more recently chemical submission [19].
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