What’s Testosterone Got to Do with It? A Critical Assessment of the Contribution of Testosterone to Gender Disparities in COVID-19 Infections and Deaths
Bibliographic record
Abstract
In the short time since severe acute respiratory syndrome coronavirus 2 (henceforth referred to as coronavirus disease 2019 [COVID-19]) appeared, numerous articles have suggested that testosterone (T) may be a major contributor to infection and death since more men than women die, and two proteins involved in viral host entry are thought to be upregulated by androgens. We investigated whether the available data supported this supposition. A MEDLINE search was performed with keywords of COVID-19 variations and androgens or T. Data regarding COVID-19 infections and deaths were obtained from the literature, the World Health Organization, and the U.S. Center for Disease Control, GLOBAL HEALTH5050 and the Harvard School of Public Health Gender Science Laboratory. Studies with T measurements in COVID-19 patients were reviewed. Studies investigating the relationship between T and angiotensin converting enzyme 2 (ACE2) and the transmembrane protease serine 2 (TMPRSS2) expression were reviewed. Global and U.S. data reveal that infection rates in men and women are similar. Men accounted for 58% and women 42% of global deaths. U.S. data revealed a ratio of 54% male deaths to 46% female deaths. However, this finding was inconsistent, as several countries reported greater numbers of female deaths, for example, Canada, Portugal, Finland, and Vietnam. In the United States, 23.5% of states and territories reported more deaths among females. Highest death rates for men and women occurred among the elderly, when serum T is at its lifetime nadir, and low death rates were observed in young adults when serum T is at its peak. All four studies reporting T measurements in COVID-19 patients indicated that low T levels were associated with adverse outcomes, that is, transfer to intensive care unit or death. Although several studies did show androgenic upregulation of TMPRSS2 and ACE2 in prostate, and cancer cell lines of prostate and lung, human and murine lung tissue fails to show a difference in expression between males and females. Observed data fail to support the popular notion that androgens contribute meaningfully to COVID-19 infection and severity of illness. On the contrary, these data raise the possibility that low T may be responsible for disease severity. There is no evidence that androgens upregulate key proteins involved with COVID-19 infection in lung. The possibility that T therapy may aid management of hospitalized COVID-19 patients merits investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".