UDP‐glucuronosyltransferase 2B17 genotyping in Japanese athletes and evaluation of the current sports drug testing for detecting testosterone misuse
Bibliographic record
Abstract
Ethnicity has been found to influence urinary testosterone glucuronide to epitestosterone glucuronide (T/E) ratios among athletes. Uridine diphospho-glucuronosyltransferase 2B17 (UGT2B17) is the most active enzyme in testosterone glucuronidation. UGT2B17 polymorphism analysis is rarely performed in Japanese athletes, and the influence of testosterone administration on steroid profiles and carbon isotope ratios, according to gene polymorphisms, in Asians remains unknown. The prevalence of UGT2B17 genotypes and urinary androgenic steroid profiles, classified according to UGT2B17 genotypes, was investigated in Japanese athletes (255 male and 256 female). Testosterone enanthate (100 mg) was administered intramuscularly to Japanese female volunteers (del/del: n = 6, del/ins: n = 3, ins/ins: n = 1). The distribution rates of the UGT2B17 del/del genotype in Japanese male and female athletes were 74.5% and 60.2%, respectively. The ins/ins genotype was detected in only three male (1.2%) and seven female (2.7%) athletes. The prevalence of the UGT2B17 deletion genotype was extremely high in Japanese athletes. The T/E ratio in the del/del group was significantly lower than that in the other groups. After testosterone was administered to female volunteers, the T/E ratios for the del/del individuals failed to reach the positivity criterion of 4. By contrast, in all of the del/del subjects, the gas chromatography/combustion/isotope ratio mass spectrometry (GC-C-IRMS) analysis successfully fulfilled the positivity criterion. The overall result has demonstrated the limited effectiveness of population-based T/E ratios in screening tests for testosterone use. Subject-based steroid profiling with UGT2B17 genotyping will be an effective strategy for detecting testosterone 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 | 0.001 | 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 | 0.001 | 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".