Female sex-specific considerations to improve rigor and reproducibility in cardiovascular research

Authors:
Publisher website
Google Scholar
Abstract
Cardiovascular disease is the leading cause of death in women. Despite recognition of sex-specific differences in cardiovascular health, females are underrepresented across all aspects of cardiovascular research, playing a key role in reducing rigor and reproducibility in cardiovascular research and contributing to these poorer health outcomes. Therefore, we propose a framework to capture factors associated with the female sex at the preclinical, recruitment, data collection, and data analysis stages. In preclinical cardiovascular research, female experimental models are commonly excluded despite similar variability in findings compared with males. To reduce this sex bias, the inclusion of female models and the incorporation of sex as a biological variable are critical to improve reproducibility and inform clinical research and care. Although funding agencies have mandated the inclusion of women in clinical trials, greater efforts are needed to achieve optimal participation-to-prevalence ratio to increase the generalizability of results to real-world settings. Female participants face more stringent exclusion criteria in research compared with males owing to sex-specific factors. However, their routine exclusion from cardiovascular research is not only unethical but limits generalizability and applicability to clinical practice. Identifying sex assigned at birth, collecting information on female sex-specific and -predominant factors associated with cardiovascular health and risk, and stratifying data by sex, including adverse events, are essential to ensure reproducibility and relevance of findings to target populations. Increasing female representation and the incorporation of female sex-specific cardiovascular risk factors in cardiovascular research will not only lead to enhanced rigor and reproducibility but improved cardiovascular health for all.

This publication has 96 references indexed in Scilit: