The effect of tea consumption on the steroid profile
Bibliographic record
Abstract
Green tea (GT), along with its flavonol epigallocatechin-3-gallate (EGCG), has shown to inhibit the UGT2B17 isoenzyme, which is highly involved in the glucuronidation of testosterone (T) and its metabolites. Since the steroid profile (SP) is composed of urinary concentrations of T and related metabolites excreted in both the free and the glucuronide fractions, GT consumption could alter the SP, leading to misunderstanding in doping controls. The aim of the present work was to study the effect of GT consumption on the SP. This study was performed with 29 male volunteers, which could be classified in 2 arms depending on their T/E values (0.12 ± 0.02, n = 12; 1.64 ± 0.90, n = 17). The clinical protocol was designed to evaluate the effect of GT administration on the SP biomarkers. Participants were asked to consume GT with a high content of EGCG for 7 days (5 GT beverages along the whole day for days 1-6 and 9 GT beverages on day 7, corresponding to 520 and 936 mg/day of EGCG, respectively). Urine samples were collected before and during GT consumption at different time periods. The SP was measured using gas chromatography-mass spectrometry. The excretion rates of the SP metabolites did not change after GT consumption. Moreover, the individual evaluation of the subject's steroidal biological passport resulted in normal sequences. The results obtained show that GT consumption does not distort the establishment of normal ranges of SP parameters. Therefore, GT consumption does not need to be considered a confounding factor in the SP evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".