A statistical focus on doping using a metabolomics approach

Abstract

Identify potential biomarkers that are indicative of doping in equine plasma through statistical analysis of metabolomics data. The purpose of metabolomics is to detect endogenous changes in molecular entities, such as metabolites and adducts, that are indicative of challenges to the system (Fiehn et al., Metabolomics, 2015, 11, 1036–1040). These challenges to the system may include things such as doping, disease and environmental changes. Doping is an ever-growing field due to the changing nature of new and popular agents. For the racing industry, any performance altering agents are prohibited to maintain the welfare of athletes involved and the integrity of the sport and breeding industry (Cawley et al., Drug Testing and Analysis, 2017, 9, 1441–1447). Therefore, an alternative approach to conventional detection methods is essential for the decreasing the bottleneck that is the introduction of new compounds into routine screening efforts. Using only 100 μL of equine plasma, a rapid protein precipitation method was developed for the analysis of endogenous compounds. The IMTAKT Intrada Amino Acid column (100 mm × 2 mm, 3 μm) was able to separate dopamine-related compounds. The method used positive and negative ionisation mode with an 11-minute gradient method on the Agilent 1290 Infinity II LC system coupled to an Agilent 6545 QTOF mass spectrometer. A substantial reference population study was completed to assess basal concentrations of endogenous compounds of interest. A 12-horse administration study of Stalevo® (800 mg levodopa, 200 mg carbidopa, 1600 mg entacapone) was analysed. Agilent Technologies’ Profinder was used to complete a batch recursive feature extraction of the data. The statistical analysis included longitudinal profiling and identification of significant entities through volcano plot analysis, principal component analysis and heatmap visualisation. Biomarker quality was determined using a ‘best biomarker quality (BBQ)’ assessment. The LC-QTOF-MS method was successfully with respect to the quantification of 3-methoxytyrosine. The reference population study revealed variable levels of 3-methoxytyrosine in the population thus monitoring through individual reference limits was proposed. The longitudinal profiling approach, using 3-methoxytyrosine as an up-regulated biomarker, was proven to be more sensitive than a threshold proposed from a population. The untargeted approach using Profinder extracted a wealth of data across the administration study. The statistical analysis was able to determine significant biomarkers. The most significant biomarkers were investigated and attempted to be identified through database searching and confirmation with reference standards. An individualised reference limit approach using longitudinal profiling allowed for raceday control of levodopa misuse within the equine athlete. The untargeted metabolomics method provided an interesting alternative to conventional detection methods thus enabling the identification of biomarkers that are indicative of doping. Biomarker ratios were investigated to provide more statistical power than a single biomarker. Further work investigating an orthogonal approach using a reverse phase analytical method would allow for wider coverage of the metabolome. Doping continues to be a threat to the integrity of horse racing and thus new detection methods, such as metabolomics and statistical profiling, must be implemented to combat this challenge. 3-methoxytyrosine was identified to be a viable up-regulated biomarker for doping of levodopa. An untargeted metabolomics approach enabled the detection of further biomarkers thus allowing for a more statistically powerful biomarker ratio to be investigated.