The Cardiovascular Implications of Gender-Affirming Estrogen Therapy Use in Transgender Women
Bibliographic record
Abstract
Cardiovascular disease is a leading cause of death globally, with transgender and gender diverse (TGD) individuals, and particularly transgender women (sex assigned male at birth who identify as women) facing significant rates of cardiovascular-related morbidity and mortality. While uncertainties exist regarding the potential contribution of gender-affirming estrogen therapy in the increased cardiovascular risk noted in this population, there remains a critical evidence gap in understanding how factors such as serum estradiol levels, sex assigned at birth, and gender identity impact cardiovascular risk. Therefore it was the objective of this thesis to determine the cardiovascular implications of gender-affirming hormone therapy in transgender women, which was explored from various angles through conducting three independent studies. Our first study provides an understanding of the association between serum estradiol and cardiovascular-related mortality, adverse cardiovascular events and cardiovascular related risk factors using a systematic review and meta-analysis approach. We determined that the rate of serum estradiol change across the use of gender-affirming estrogen therapy may influence cardiovascular risk factors, which may have implications for clinical cardiovascular outcomes. Using a cross-sectional approach, our second study evaluated the impact of gender identity and sex assigned at birth on measures of vascular hemodynamics and arterial stiffness in transgender women as compared to cisgender women and men. Our findings underscore the importance of considering gender identity in addition sex assigned at birth when evaluating measures of cardiovascular health and risk. Our final study addressed the major methodological limitation with retrospective studies within the TGD population by generating and validating numerous case-definitions to identify both transgender women and men in routinely-collected healthcare data in Alberta, Canada. By linking numerous administrative data sources and testing against a reference standard of self-identified gender identity, a combination of using sex assigned in provincial registry and exogenous hormone dispensations or TGD-related billing codes were the most sensitive at identifying TGD individuals, and were employed to approximate both prevalence and incidence per 100,000 person-years, providing a first-ever assessment of the TGD population within Alberta. Despite recent advances and calls for the incorporation of both sex and gender into health research and clinical care, gaps in the understanding of cardiovascular health in transgender women remain. Our findings highlight numerous factors that may be implicated in cardiovascular health and risk in the transgender women population. This thesis may inform areas for further interventions aimed at improving cardiovascular health, mitigating cardiovascular disease risk, and exploring clinical cardiovascular outcomes in transgender women.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".