Abstract
Metabolites in urine can illustrate the physical condition of an individual as a whole. Ultra high performance liquid chromatography/time of flight mass spectrometry (UPLC/TOF-MS) is a relative new technique for the separation of complex samples. The aim of this study is to assess the feasibility of metabonomics in gender difference in unrestricted conditions, i.e. for healthy volunteers there are no strict controls such as food, life style and the collection of urine samples. In this work, 31 spontaneous urine samples were collected and analyzed by using UPLC/TOF-MS. Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models were tested and compared in samples classification. The gender discrimination was highly improved and some gender related biomarkers were found by PLS-DA. These preliminary results suggested that UPLC/MS-based approaches coupled with pattern recognition show promise for metabonomics.
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