Age, body mass index, race and other determinants of steroid hormone variability: the HERITAGE Family Study
Bibliographic record
Abstract
OBJECTIVE AND METHODS: To investigate from the HERITAGE Family Study database, 13 steroid hormones (androstane-3alpha, 17beta-diol glucuronide, androsterone glucuronide, cortisol, dehydroepiandrosterone (DHEA), DHEA ester (DHEAE), DHEA sulfate (DHEAS), dihydrotestosterone (DHT), estradiol, 17-hydroxyprogesterone, progesterone, pregnenolone ester, sex hormone binding globulin (SHBG) and testosterone in each sex for their relationships with age, body mass index (BMI), race and key lifestyle variables. Sample sizes varied from 676 to 750 per hormone. Incremental regression methods were used to examine the contributions of the variables to steroid hormone variability. RESULTS: Age was a major predictor for most steroid hormones. The greatest contribution of age was a negative relationship with DHEAS (R(2)=0.39). BMI was also associated with the variability of several steroid hormones, being the most important predictor of SHBG (R(2)=0.20) and of testosterone (R(2)=0.12) concentrations. When age and BMI were included, race still contributed significantly to the variations in cortisol (R(2)=0.02 for men and 0.04 for women), DHT (R(2)=0.02 for men and 0.03 for women), and progesterone (R(2)=0.03 for women). Nevertheless, race appeared to be less important than age and BMI. In addition, lifestyle indicators (food and nutrient intakes, smoking and physical activity) influenced steroid hormone variability. Their contributions, however, were minor in most cases once age, BMI and race had been taken into account. CONCLUSIONS: We conclude that age was the most important factor, followed by BMI, race and lifestyle factors in explaining steroid hormone variability.
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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 | 0.002 |
| 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 | 0.000 |
| 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".