Older Men With Low Serum Estradiol and High Serum SHBG Have an Increased Risk of Fractures
Bibliographic record
Abstract
Osteoporosis-related fractures constitute a major health concern not only in women but also in men. To study the predictive role of serum sex steroids for fracture risk in men, serum sex steroids were analyzed by the specific gas chromatography-mass spectrometry technique at baseline in older men (n = 2639; mean, 75 yr of age) of the prospective population-based MrOS Sweden cohort. Fractures occurring after baseline were validated (average follow-up of 3.3 yr). The incidence for having at least one validated fracture after baseline was 20.9/1000 person-years. Estradiol (E2; hazard ratio [HR] per SD decrease, 1.34; 95% CI, 1.22-1.49), free estradiol (fE2; HR per SD decrease, 1.41; 95% CI, 1.28-1.55), testosterone (T; HR per SD decrease, 1.27; 95% CI, 1.16-1.39), and free testosterone (fT; HR per SD decrease, 1.32; 95% CI, 1.21-1.44) were all inversely, whereas sex hormone-binding globulin (SHBG; HR per SD increase, 1.41; 95% CI, 1.22-1.63) was directly related to fracture risk. Multivariable proportional hazards regression models, adjusted for age, suggested that fE2 and SHBG (p < 0.001), but not fT, were independently associated with fracture risk. Further subanalyses of fracture type showed that fE2 was inversely associated with clinical vertebral fractures (HR per SD decrease, 1.57; 95% CI, 1.36-1.80), nonvertebral osteoporosis fractures (HR per SD decrease, 1.42; 95% CI, 1.23-1.65), and hip fractures (HR per SD decrease, 1.44; 95% CI, 1.18-1.76). The inverse relation between serum E2 and fracture risk was nonlinear with a strong relation <16 pg/ml for E2 and 0.3 pg/ml for fE2. In conclusion, older Swedish men with low serum E2 and high SHBG levels have an increased risk of fractures.
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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 | 0.001 |
| 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".