Evaluation of endogenous steroid sulfates and glucuronides in urine after oral and transdermal administration of testosterone. Part II: Female participants
Bibliographic record
Abstract
Detecting testosterone (T) doping remains a significant challenge, driving the search for novel biomarkers and advancements in the steroidal Athlete’s Biological Passport (ABP). Phase II metabolites of endogenous anabolic androgenic steroids (EAAS) have emerged as promising biomarkers, demonstrating prolonged detection times (DTs) and greater sensitivity compared to conventional biomarkers. Studies involving male participants investigated the effect of intramuscular, oral, and transdermal administration of T on these biomarkers and proposed their integration in future urinary steroid profiling. However, before the inclusion of phase II EAAS metabolites, it is crucial to address a range of possible doping scenarios and the influence of known confounding factors, like ethnicity or sex, on the steroid profile. This study addresses this gap by investigating the impact of oral and transdermal T administration on phase II EAAS metabolites in both men and women. This second part of the study presents the results for female participants, which have not been included in prior research on this topic. Results partially confirm the trends observed in men, with sulfate ratios exhibiting prolonged detection times and higher sensitivity compared to conventional steroid profile biomarkers following multiple oral and transdermal T administration. However, the evaluation in women showed greater variability due to lower steroid concentrations and greater fluctuations influenced by the menstrual cycle. This study provides additional evidence supporting the inclusion of phase II EAAS metabolites for enhanced detection of T doping. Further, it underscores the need for further research to address the unique challenges of female steroid profiling. • Oral and transdermal testosterone administration study including female participants • Analysis of phase II metabolites of EAAS enhances the testosterone doping monitoring system • Significantly prolonged detection times of 2-4 weeks of novel biomarkers compared to conventional steroid profile biomarkers • Ratios including EpiAS, AS, and EtioS in the numerator and TS, ES, and EG in the denominator are proposed for future steroid profiling • Individual APMU review of female athletes' biological passports is critical, due to the influence of the menstrual cycle and low concentrations
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".