Untargeted Metabolomics in Doping Control: Detection of New Markers of Testosterone Misuse by Ultrahigh Performance Liquid Chromatography Coupled to High-Resolution Mass Spectrometry
Bibliographic record
Abstract
The use of untargeted metabolomics for the discovery of markers is a promising and virtually unexplored tool in the doping control field. Hybrid quadrupole time-of-flight (QTOF) and hybrid quadrupole Orbitrap (Q Exactive) mass spectrometers, coupled to ultrahigh pressure liquid chromatography, are excellent tools for this purpose. In the present work, QTOF and Q Exactive have been used to look for markers for testosterone cypionate misuse by means of untargeted metabolomics. Two different groups of urine samples were analyzed, collected before and after the intramuscular administration of testosterone cypionate. In order to avoid analyte losses in the sample treatment, samples were just 2-fold diluted with water and directly injected into the chromatographic system. Samples were analyzed in both positive and negative ionization modes. Data from both systems were treated under untargeted metabolomic strategies using XCMS application and multivariate analysis. Results from the two mass spectrometers differed in the number of detected features, but both led to the same potential marker for the particular testosterone ester misuse. The in-depth study of the MS and MS/MS behavior of this marker allowed for the establishment of 1-cyclopentenoylglycine as a feasible structure. The putative structure was confirmed by comparison with synthesized material. This potential marker seems to come from the metabolism of the cypionic acid release after hydrolysis of the administered ester. Its suitability for doping control has been evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".