Evidence for Geographical and Racial Variation in Serum Sex Steroid Levels in Older Men
Bibliographic record
Abstract
BACKGROUND: Despite considerable racial and geographical differences in human phenotypes and in the incidence of diseases that may be associated with sex steroid action, there are few data concerning variation in sex steroid levels among populations. We designed an international study to determine the degree to which geography and race influence sex steroid levels in older men. METHODS: Using mass spectrometry, concentrations of serum androgens, estrogens, and sex steroid precursors/metabolites were measured in 5003 older men from five countries. SHBG levels were assessed using radioimmunoassay. RESULTS: There was substantial geographical variation in the levels of sex steroids, precursors, and metabolites, as well as SHBG. For instance, Asian men in Hong Kong and Japan, but not in the United States, had levels of total testosterone approximately 20% higher than in other groups. Even greater variation was present in levels of estradiol, SHBG, and dihydrotestosterone. Group differences in body mass index did not explain most geographical differences. In addition, body mass index-independent racial differences were present; Black men had higher levels of estrogens (estradiol, estrone), and Asian men had lower levels of glucuronidated androgen metabolites. CONCLUSIONS: On a global scale, there are important geographical and racial differences in the concentrations of serum sex steroids and SHBG in older men.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".