The Effect of Testosterone on Cardiovascular Disease and Cardiovascular Risk Factors in Men: A Review of Clinical and Preclinical Data
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide. The effects of testosterone, the primary male sex hormone, on cardiovascular risk have been of special interest due to the increased risk of CVD in men. Although it is well established that testosterone levels decline and cardiovascular mortality increases with age, the association between testosterone and CVD remains unclear. Observational and randomized studies on the effects of endogenous and exogenous testosterone have produced conflicting data, and meta-analyses have been inconclusive, suggesting significant study heterogeneity. Despite a lack of adequately powered randomized controlled trials, large observational studies in the early 2010s led to advisories on the use of testosterone replacement therapy. Similar advisories have been mandated for certain types of androgen deprivation therapy. Additional research suggests that testosterone shortens the heart-rate-corrected QT interval, improves glycemic control, induces vasodilation, is prothrombotic, and has anti-obesity effects, whereas associations with atherosclerosis and inflammation are less clear. Despite inconclusive evidence on cardiovascular risk and inconsistencies among clinical practice guidelines, millions of men continue to use testosterone replacement and androgen deprivation therapy. In addition to summarizing clinical and preclinical data, this review provides insight on potential mechanisms of action of testosterone on CVD, applications of this knowledge to clinical settings, and avenues for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".