Non-Steroidal Anti-Inflammatory Drugs Do Not Influence the Urinary Testosterone/Epitestosterone Glucuronide Ratio
Bibliographic record
Abstract
The UDP Glucuronosyl Transferase (UGT) enzymes are important in the pharmacokinetics, and conjugation, of a variety of drugs including non-steroidal anti-inflammatory drugs (NSAIDs) as well as anabolic androgenic steroids (AAS). Testosterone glucuronidation capacity is strongly associated with a deletion polymorphism in the UGT2B17 gene. As the use of high doses of NSAIDs has been observed in athletes there is a risk for a drug-drug interaction that may influence the doping tests for AAS. In vitro studies show inhibitory potential on UGT2B7, 2B15, and 2B17 enzymes by NSAIDs. The aim of this study was to investigate if concomitant use of NSAIDs and a single dose of testosterone enanthate would affect the excretion rate of testosterone and epitestosterone glucuronide (TG and EG) as well as the T/E ratio, thereby affecting the outcome of the testosterone doping test. The study was designed as an open, randomized, cross-over study with subjects being their own control. The 23 male healthy volunteers, with either two, one or no allele (ins/ins, ins/del, or del/del) of the UGT2B17 gene, received the maximum recommended dose of NSAID (Ibuprofen or Diclofenac) for 6 days. On day three, 500 mg of testosterone enanthate was administered. Spot urine samples were collected for 17 days. After a wash-out period of 4 months the volunteers received 500 mg testosterone enanthate only, with subsequent spot urine collection for 14 days. The glucuronides of testosterone and epitestosterone were quantified. NSAIDs did not affect the excretion of TG or EG before the administration of testosterone. The concomitant use of NSAIDs and testosterone slightly increased the TG excretion while the EG excretion was less suppressed compared to testosterone use only. The effects of the NSAIDs on the TG and EG excretion did not differ between the UGT2B17 genotype groups. In conclusion, the outcome of testosterone doping tests does not seem to be affected by the use of NSAIDs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".