Low Serum Testosterone and Estradiol Predict Mortality in Elderly Men
Bibliographic record
Abstract
CONTEXT: Age-related reduction of serum testosterone may contribute to the signs and symptoms of aging, but previous studies report conflicting evidence about testosterone levels and male mortality. No large prospective cohort study has determined a possible association between serum estradiol and mortality in men. OBJECTIVE: The main objective was to examine the association between serum testosterone and estradiol and all-cause mortality in elderly men. DESIGN, SETTING, AND PARTICIPANTS: We used specific gas chromatography-mass spectrometry to analyze serum sex steroids at baseline in older men who participated in the prospective population-based MrOS Sweden cohort (n = 3014; mean age, 75 yr; range, 69-80 yr). MAIN OUTCOME MEASURE: All-cause mortality by serum testosterone and estradiol levels. RESULTS: During a mean follow-up period of 4.5 yr, 383 deaths occurred. In multivariate hazards regression models, low levels (within quartile 1 vs. quartiles 2-4) of both testosterone [hazard ratio (HR), 1.65; 95% confidence interval (CI), 1.29-2.12] and estradiol (HR, 1.54; 95% CI, 1.22-1.95) associated with mortality. A model including both hormones showed that both low testosterone (HR, 1.46; 95% CI, 1.11-1.92) and estradiol (HR, 1.33; 95% CI, 1.02-1.73) predicted mortality. Risk of death nearly doubled (HR, 1.96; 95% CI, 1.46-2.62) in subjects with low levels of both testosterone and estradiol compared with subjects within quartiles 2-4 of both hormones. CONCLUSIONS: Elderly men with low serum testosterone and estradiol have increased risk of mortality, and subjects with low values of both testosterone and estradiol have the highest risk of mortality.
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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 | 0.000 |
| 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".