DataSheet_1_Effects of menstrual disorders and dysmenorrhea on cardiovascular disease: a Mendelian randomization study.docx

BackgroundObservational studies have demonstrated associations between menstrual disorders, dysmenorrhea, and cardiovascular disease (CVD). However, it remains unclear whether these associations are causal. This study is to investigate whether menstrual disorders and dysmenorrhea causally affect the risk of CVD.MethodsThe summary data for menstrual disorders (excessive menstruation and irregular menses) and dysmenorrhea were obtained from FinnGen study, summary data for CVD were obtained from UK Biobank and meta-analysis. The inverse-variance-weighted method was mainly used in the Mendelian randomization for causality analysis. Sensitivity analyses were performed by several methods under different model assumptions.ResultsGenetic liability to excessive menstruation was associated with higher risk of atrial fibrillation (odds ratio (OR), 1.078 [95% confidence interval (CI), 1.015-1.145]; P=0.014), but a lower risk of hypertension (OR, 0.994 [95% CI: 0.989-0.999]; P=0.016). Irregular menses was associated with higher risk of atrial fibrillation (OR, 1.095 [95% CI: 1.015-1.182]; P=0.02), hypertension (OR, 1.007 [95% CI: 1.000-1.013]; P=0.047), myocardial infarction (OR, 1.172 [95% CI: 1.060-1.295]; P=0.02), ischemic heart disease, (OR, 1.005 [95% CI: 1.000-1.010]; P=0.037) and coronary heart disease (OR, 1.004 [95% CI: 1.001-1.008]; P=0.026). Dysmenorrhea was associated with higher risk of atrial fibrillation (OR, 1.052 [95% CI: 1.014-1.092]; P=0.008) and Ischemic stroke (cardioembolic) (OR, 1.122 [95% CI: 1.002-1.257]; P=0.046). After Benjamini-Hochberg correction, irregular menses was associated with higher risk of myocardial infarction.ConclusionWe confirmed a causal relationship of excessive menstruation, irregular menses and dysmenorrhea on cardiovascular outcomes independent of sex hormone levels, with an emphasis on the link between irregular menses and myocardial infarction. These clinical features can be utilized as markers to identify women at higher risk of developing CVD in the future, recommending early clinical intervention of menstrual diseases.