Principal components analysis (PCA) can be a powerful, complementary technique for the overall longitudinal studies data evaluation and for its use in the endocrinological passport including steroid profiling, as proposed by the WADA itself. In controlled studies, the best predictive result for the Asian population were obtained, where some difficulties with the traditional approach remain. Instead of the Caucasians population preserve its complex behavior, due to the great intra-subject variability of some biomarkers (ex. T/E). The success of multivariate analysis (MVA) began with the right selection of experiments design: the appropriate identification of the most useful variables (numbers and types) is fundamental. If they are too few, the information may be insufficient, while, on the other side, if they are too many, they can increase the “noise”. The variables must (i) respond correctly to the target objective, and (ii) be independent of environmental factors. The aim is to expand the detection window in drug abuse (steroid abuse), as to reduce the number of false negatives. In this sense the concentration of hydroxy-steroids and the δ13C values of some biomarkers (T, And, Etio, 5aDiol, 5bDiol, DHT) could represent very remarkable variables. The first parameters (hydroxy-steroids) are indeed the end metabolites in the steroid cascade, and the δ13C values are relative stability and present low intra-subject variability.
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|Titolo:||Longitudinal studies in steroid profiling – a multivariate approach: IRMS extension.|
|Data di pubblicazione:||2012|
|Appartiene alla tipologia:||04b Atto di convegno in volume|