Abstract
The detection in urine of recombinant human erythropoietin (rHuEPO), a hormone misused by
endurance athletes as a doping agent, is based on the differentiation of its isoelectric pattern from
that of the corresponding natural hormone. Different empirical criteria have been proposed for
discriminating the images of the patterns, but none of them have been elaborated from a rational
statistical approach.
Discriminant analysis was applied to a data set of profiles defined as positive (n = 116) (presence
of rHuEPO and possibly residual natural endogenous hormone) and negative (n = 131) (presence of
natural endogenous hormone only). The different bands were numbered according to a template of
16 possible positions and their relative intensities constituted the 16 variables of the statistical
analysis. This method was then tested with data from an administration trial of low doses (6.7 to 10
IU/kg) following high-dose (265 IU/kg) injections.
The analysis of the data set clearly separated the negative and positive profiles. A cross validation
procedure confirmed that the analysis was extremely stable : with ten-fold cross-validation, no false
positives were observed even with 100,000 simulations. Furthermore, the detection of rHuEPO in
the profiles from the low-dose trial was greatly improved in comparison with a previously validated
empirical criterion