Cardiovascular diseases (CVDs) and neoplasms, especially breast cancer, mainly contribute to morbidity and mortality among women [1,2]. CVDs prevention is important through the early identification of high-risk groups based on CVD risk prediction and using targeted interventions (lifestyle modification and lipid-lowering drugs) [3]. Several countries have implemented population-based mammography screening to reduce breast cancer mortality [4]. Recently, some mammographic features have been associated with breast cancer risk as well as extra-breast morbidities, such as CVD [5,6]. Breast density is one of the most important breast cancer risk factors, with a 4- to 6-fold elevated risk in women with dense breasts than in those with fatty breasts [[7], [8], [9], [10], [11], [12]]. However, previous studies have shown an inverse association between breast density and CVDs, in contrast to a positive association between breast density and breast cancer risk; women with dense breasts have a lower CVDs risk than women with fatty breasts [5,6]. Although the association between mammographic breast features and CVDs has been evaluated [5,[13], [14], [15], [16]], the role of breast density in predicting CVD risk has not been addressed.

We hypothesized that adding breast density to the conventional CVD risk factors would improve the CVD prediction in womaren. Thus, we aimed to determine whether adding mammographic breast density to the Framingham Risk Score (FRS), one of the most validated CVD prediction models, improves the prediction of CVD events in women who participate in population-based breast cancer screening.