Clinical benefits and adverse effects of genetically-elevated free testosterone levels: a Mendelian randomization analysis
Bibliographic record
Abstract
ABSTRACT BACKGROUND Testosterone products are increasingly being prescribed to males for a variety of possible health benefits but the causal relationship between testosterone and health-related outcomes is unclear. Evidence from well-powered randomized controlled trials are difficult to obtain, particularly regarding effects on long-term or adverse outcomes. We sought to determine the effects of genetically-predicted calculated free testosterone (CFT) on 23 health outcomes. METHODS Genetic variants associated with CFT were determined from 136,531 white British males in the UK Biobank. One-sample and two-sample Mendelian randomization (MR) analyses were performed to infer the effects of genetically-predicted CFT on 23 health outcomes selected based on relevance with known or suspected effects of testosterone therapy. FINDINGS In males from the UK Biobank, 81 independent genetic variants were associated with CFT levels at genome-wide significance (p<5×10 −8 ). Each 0.1 nmol/L increase in genetically-predicted CFT was associated with clinical benefits on increased heel bone mineral density (0.053 SD; 95% CI = 0.038 to 0.068; p=8.77×10 −12 ) and decreased body fat percentage (−1.86%; 95% CI = −2.35 to −1.37; p=1.56×10 −13 ), and adverse effects on increased risk of prostate cancer (OR=1.28; 95% CI=1.11 to 1.49; p=1.0×10 −3 ), risk of androgenic alopecia (OR=1.82; 95% CI = 1.55 to 2.14; p=3.52×10 −13 ), risk of benign prostate hyperplasia (BPH) (OR=1.81; 95% CI = 1.34 to 2.44; p=1.05×10 −4 ) and hematocrit percentage (1.49%; 95% CI = 1.24 to 1.74; p=3.49×10 −32 ). CONCLUSIONS Long-term elevated free testosterone levels cause prostate cancer, BPH, and hair loss while reducing body fat percentage and increasing bone density. It also has a neutral effect on type 2 diabetes, cardiovascular and cognitive outcomes. Well powered randomized trials are needed to address the effects of shorter term use of exogenous testosterone on these outcomes.
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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 | 0.038 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".