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Alternative steroid profiling: advances in detection of misuse with endogenous steroids in sports

(2010)
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Abstract
Naturally occurring anabolic androgenic steroids, such as testosterone, are widely misused in sports and can be harmful to the health of the athlete. Their popularity can be explained by the difficulty to unequivocally establish the difference between synthetic or endogenous origin of measured urinary androgens and metabolites constituting the steroid profile. As these compounds are always present in urine, only when an unnaturally high steroid value exceeds a certain threshold, misuse can be established. Although steroid profiling is the most versatile method to determine the misuse of endogenous steroids, the currently used steroid profile parameters suffer from large inter-individual variances resulting in limited efficiency of the detection methods using population-based threshold values. Moreover, a wide diversity of endogenous steroids on the market can cause various alterations of the steroid profile complicating its interpretation and the use of micro doses still remains unnoticed in doping control laboratories. Therefore, there is compelling need for novel detection methods and better biomarkers to detect the misuse of endogenous steroids in a more efficient way. The focus of this work was put on specific hydroxylated steroid metabolites usually occurring in low urinary concentrations as an alternative approach on current steroid profiling. An analytical method was developed that monitors a broad range of steroid metabolites and their corresponding reference ranges were calculated. Application of these reference criteria on the evaluation of excretion urines after administration of testosterone, dihydrotestosterone and dehydroepiandrosterone indicated the relevance of minor steroid metabolites for doping control. In combination with a Bayesian model, the potential of the alternative steroid profile was investigated for implementation in the Biological Passport. Interesting steroid ratios were proposed as biomarkers to support the probability of guilt. These markers showed good detection accuracy and were suitable for longitudinal following. Finally, a support vector machine model was developed that optimally discriminated between normal and altered steroid profiles. With this model, a boost in detection accuracy was demonstrated, longer detection times were obtained, steroid gel formulations could be detected and various influences on the steroid profile could be differentiated from the normal state.
Keywords
Biological Passport, support vector machine, Bayesian adaptive model, longitudinal following, Steroid profiling, minor steroid metabolites, Abnormal Steroid Profile Score

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Please use this url to cite or link to this publication:

Chicago
Van Renterghem, Pieter. 2010. “Alternative Steroid Profiling: Advances in Detection of Misuse with Endogenous Steroids in Sports”. Ghent, Belgium: Ghent University. Faculty of Medicine and Health Sciences.
APA
Van Renterghem, P. (2010). Alternative steroid profiling: advances in detection of misuse with endogenous steroids in sports. Ghent University. Faculty of Medicine and Health Sciences, Ghent, Belgium.
Vancouver
1.
Van Renterghem P. Alternative steroid profiling: advances in detection of misuse with endogenous steroids in sports. [Ghent, Belgium]: Ghent University. Faculty of Medicine and Health Sciences; 2010.
MLA
Van Renterghem, Pieter. “Alternative Steroid Profiling: Advances in Detection of Misuse with Endogenous Steroids in Sports.” 2010 : n. pag. Print.
@phdthesis{1078457,
  abstract     = {Naturally occurring anabolic androgenic steroids, such as testosterone, are widely misused in sports and can be harmful to the health of the athlete. Their popularity can be explained by the difficulty to unequivocally establish the difference between synthetic or endogenous origin of measured urinary androgens and metabolites constituting the steroid profile. As these compounds are always present in urine, only when an unnaturally high steroid value exceeds a certain threshold, misuse can be established. Although steroid profiling is the most versatile method to determine the misuse of endogenous steroids, the currently used steroid profile parameters suffer from large inter-individual variances resulting in limited efficiency of the detection methods using population-based threshold values. Moreover, a wide diversity of endogenous steroids on the market can cause various alterations of the steroid profile complicating its interpretation and the use of micro doses still remains unnoticed in doping control laboratories.  Therefore, there is compelling need for novel detection methods and better biomarkers to detect the misuse of endogenous steroids in a more efficient way. 
The focus of this work was put on specific hydroxylated steroid metabolites usually occurring in low urinary concentrations as an alternative approach on current steroid profiling. An analytical method was developed that monitors a broad range of steroid metabolites and their corresponding reference ranges were calculated. Application of these reference criteria on the evaluation of excretion urines after administration of testosterone, dihydrotestosterone and dehydroepiandrosterone indicated the relevance of minor steroid metabolites for doping control.  
In combination with a Bayesian model, the potential of the alternative steroid profile was investigated for implementation in the Biological Passport. Interesting steroid ratios were proposed as biomarkers to support the probability of guilt. These markers showed good detection accuracy and were suitable for longitudinal following. 
Finally, a support vector machine model was developed that optimally discriminated between normal and altered steroid profiles. With this model, a boost in detection accuracy was demonstrated, longer detection times were obtained, steroid gel formulations could be detected and various influences on the steroid profile could be differentiated from the normal state.},
  author       = {Van Renterghem, Pieter},
  isbn         = {9789490695422},
  keyword      = {Biological Passport,support vector machine,Bayesian adaptive model,longitudinal following,Steroid profiling,minor steroid metabolites,Abnormal Steroid Profile Score},
  language     = {eng},
  pages        = {XXII, 274},
  publisher    = {Ghent University. Faculty of Medicine and Health Sciences},
  school       = {Ghent University},
  title        = {Alternative steroid profiling: advances in detection of misuse with endogenous steroids in sports},
  url          = {http://lib.ugent.be/fulltxt/RUG01/001/438/072/RUG01-001438072\_2010\_0001\_AC.pdf},
  year         = {2010},
}