Urinary phenylethylamine metabolites as potential markers for sports drug testing purposes
Bibliographic record
Abstract
The misuse of 2-phenylethylamine (PEA) in sporting competitions is prohibited by the World Anti-Doping Agency. As it is endogenously produced, a method is required to differentiate between naturally elevated levels of PEA and the illicit administration of the drug. In 2015, a sulfo-conjugated metabolite [2-(2-hydroxyphenyl)acetamide sulfate (M1)] was identified, and pilot study data suggested that the ratio M1/PEA could be used as a marker indicating the oral application of PEA. Within this project, the required reference material of M1 was synthesized, single and multiple dose elimination studies were conducted and 369 native urine samples of athletes were analyzed as a reference population. While the oral administration of only 100 mg PEA did not affect urinary PEA concentrations, an increase in urinary concentrations of M1 was observed for all volunteers. However, urinary concentrations of both PEA and M1 showed relatively large inter-individual differences and establishing a cut-off-level for M1/PEA proved difficult. Consequently, a second metabolite, phenylacetylglutamine, was considered. Binary logistic regression demonstrated a significant (P < 0.05) correlation of the urinary M1 and phenylacetylglutamine concentrations with an oral administration of PEA, suggesting that assessing both analytes can assist doping control laboratories in identifying PEA misuse.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".