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Project: Alternative Steroid Profiling part II

project duration
01/01/11 – 31/12/11
abstract
The project aims to contribute to the development of the steroidal subunit of the Athlete’s Biological Passport (ABP) which is hitherto solely validated on a few markers, such as the T/E ratio. Recently, novel biomarkers were found that increase significantly the time detection window after administration of small doses of T, DHT and DHEA using the Adaptive Model of the ABP. These biomarkers consist of steroid ratios including minor metabolites sensitive to steroid administration. More data on intra-individual variation of the involved minor metabolites are nevertheless necessary to validate the proposed biomarkers. Therefore a large-scale investigation of long-term within-subject behaviour of an extended steroid profile will be conducted. Data obtained on a larger cohort with comprehensive steroid profiling methods will allow the development of a multi-parametric marker of steroid doping that comprises the whole steroid profile. This model statistically classifies abnormal steroid profiles by outputting a single score. Longitudinal evaluation of this ‘Abnormal Steroid Profile Score’ (cfr. Abnormal Blood Profile Score in the Blood Passport) monitors any alteration in the steroid profile regardless to its cause. The goals are twofold. Firstly, when applied at the individual level, this model will allow the general screening of doping with endogenous steroids, food supplements and substances manipulating the steroid profile, such as after ethanol consumption. Secondly, and in contrary to blood doping and doping with growth hormone wherein markers having a detection time long enough to estimate the prevalence of doping already exist, this score might provide accurate estimates of the prevalence of steroid doping in elite sports when applied at the population level. Moreover, the influence of genetic polymorphism on the new steroid profile parameters and an Abnormal Steroid Profile Score will be studied in order to increase the sensitivity of the model.