Serum androgen profile and physical performance in women Olympic athletes
Bibliographic record
Abstract
BACKGROUND: The role of endogenous androgens for body composition and physical performance in women athletes is still not elucidated. AIM: To examine the serum androgen profile in relation to body composition and physical performance in women Olympic athletes and to compare endocrine variables and body composition to controls. STUDY DESIGN: Cross-sectional study, conducted between 2011 and 2015 at the Women's Health Research Unit, Karolinska University Hospital, Stockholm. METHODS: Swedish women Olympic athletes (n=106) and age-matched and body mass index-matched sedentary controls (n=117) were included in the study. Blood sampling was performed in a rested, fasting state for the measurement of serum androgens and their metabolites by liquid chromatography-tandem mass spectrometry. Body composition was determined by dual-energy X-ray absorptiometry (controls n=100, athletes n=65). The athletes performed standardised performance tests (n=59) (squat jump (SJ) and countermovement jump (CMJ). RESULTS: The athletes demonstrated significantly higher levels of the precursor androgens dehydroepiandrosterone (DHEA) and 5-androstene-3β, 17β-diol (5-DIOL) and the metabolite etiocholanolone glucuronide (Etio-G), significantly lower levels of estrone (p<0.05, respectively), higher bone mineral density (p<0.001) and more lean mass (p<0.001) compared with controls. Serum levels of DHEA, 5-DIOL and Etio-G correlated positively to lean mass variables and physical performance in the athletes. DHEA and lean mass legs explained 66% of the variance in SJ, whereas lean mass explained 52% of the variance in CMJ. CONCLUSIONS: The present data suggest that endogenous androgens are associated with a more anabolic body composition and enhanced performance in women athletes. These results are of importance for the current discussion regarding hyperandrogenism in women athletes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".