Metabonomic Assessment of Physiological Disruptions Using 1H−13C HMBC-NMR Spectroscopy Combined with Pattern Recognition Procedures Performed on Filtered Variables

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Abstract
Metabonomic characterization of long-lasting although weak physiological events such as anabolic disruptions remains poorly investigated. We have validated 1H−13C HMBC-NMR as a suitable generator of instrumental variables that are strongly linked to the concentration of endogenous metabolites in biological fluids. This method is interfaced to multivariate pattern recognition procedures. Fingerprints established from urine sample collected on cattle treated with anabolic steroids were used to validate this method. Four main results arise from this study. (i) 2D NMR is as informative as 1D NMR. (ii) 2D NMR variable clustering highlights successfully a contingent redundancy of variables, although a relevant hierarchical model of statistical correlations covering from structural relationships to physiologic links can also be evidenced. (iii) To enhance pattern recognition performances, we have validated a variable selection algorithm for accurate prediction of unknown individuals belonging to predetermined groups achieved by linear discriminant analysis (LDA). This algorithm synthesizes the whole information contained in the data set by selecting preferentially nonredundant variables. Parameters generating variable subsets are validated by predicted variance efficiency obtained when minimizing error rates calculated by cross-validation methods. (iv) Provided variables are correctly filtered, LDA fairly competes with partial least-squares methods for both classification of individuals and statistical interpretation of metabolic responses obtained in such a physiological disruption context.