Women with endometriosis have a 20 per cent greater risk of significant cardiac outcomes compared with women without endometriosis, according to new research.
The study revealed that women with endometriosis had around a 20 per cent increased risk of the of acute myocardial infarction and ischaemic stroke compared with those without endometriosis.
lead study author, Dr. Eva Havers-Borgersen from Rigshospitalet Copenhagen University Hospital, Copenhagen, Denmark, stated: “For decades, cardiovascular disease (CVD) has been thought of as a man’s disease and risk factors have been considered from the male perspective, for example, including erectile dysfunction in guidelines on CVD risk assessment.
“Yet, one in three women die from CVD and 1 in 10 women suffer from endometriosis. Our results suggest that it may be time to routinely consider the risk of CVD in women with endometriosis.”
When this was broken down to the individual components, women with endometriosis had around a 20 per cent increased risk of ischaemic stroke and around 35 per cent increased risk of acute myocardial infarction compared with those without endometriosis. Moreover, women with endometriosis also had increased risk of arrhythmias and heart failure compared with those without endometriosis.
Growing evidence suggests there is a close relationship between endometriosis and the cardiovascular system and that they may share common disease pathways.
Dr. Havers-Borgersen concluded: “Although the absolute differences were small, the relative differences were 20%, and with the high prevalence of endometriosis, these results provide more evidence that female-specific risk factors and CVD in women need greater attention.
“We suggest that women with endometriosis undergo CVD risk assessment, and it is now time for female-specific risk factors – such as endometriosis, but also gestational diabetes and pre-eclampsia – to be considered in cardiovascular risk prediction models.
“Further research is needed to confirm our findings and integrate these factors into effective risk prediction models.”
