Evaluation of sulfate metabolites as markers of intramuscular testosterone administration in Caucasian and Asian populations
Bibliographic record
Abstract
The introduction of alternative markers to the steroid profile can be an effective approach to improving the screening capabilities for the detection of testosterone (T) misuse. In this work, endogenous steroid sulfates were evaluated as potential markers to detect intramuscular (IM) T administration. Fourteen sulfate metabolites were quantified using mixed-mode solid-phase extraction and analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Urine samples after a single IM injection (100 mg) of T cypionate to six Caucasian and six Asian healthy male volunteers were analyzed. Principal component analysis (PCA) was used to characterize the sample cohort and to obtain the most useful markers for discrimination between pre- and post-administration samples. For Caucasian volunteers, a separation between pre- and post-administration samples was observed in PCA, whereas for Asian volunteers no separation was obtained. Seventeen ratios between sulfate metabolites were selected and further considered. Detection times (DTs) of each marker were evaluated using individual thresholds for each volunteer. The best results were obtained using ratios involving T and epitestosterone (E) sulfates in the denominator. The best marker was the ratio androsterone sulfate/testosterone sulfate (A-S/T-S) which prolonged the DT 1.2-2.1 times in respect to those obtained using T/E ratio in all Caucasian volunteers and 1.3-1.5 times in two Asian volunteers. Other ratios between A-S or etiocholanolone sulfate and E-S, and sulfates of etiocholanolone, dehydroandrosterone or epiandrosterone, and T-S were also found adequate. These ratios improve the DT after IM T administration and their incorporation to complement the current steroid profile is recommended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".