Nitrite an anion that is ubiquitous found in in the environment and is a natural component of our diet. The highest concentration in food is found in some green leafy vegetables and root vegetables, including spinach, lettuce and beetroots. Nitrate is also present in drinking water, and elevated concentrations can be found in agricultural areas. Nitrate exposure has been proposed to be associated reduced risk of cardiovascular disease, acting via blood pressure regulation. Nevertheless, nitrate may also have negative health effects as it has been suggested to increase the risk of some cancers. Currently nitrate is classified by IARC as a probable human carcinogen (2A), with the rationale that increasing nitrate exposure increases endogenous formation of N-nitroso compounds. The overarching aim of this project is to assess the association of exogenous exposure to nitrate from diet and drinking water in relation to incidence of cardiovascular disease and cancer in middle-aged to elderly men and women in SIMPLER. We will use data from the Swedish Mammography Cohort (SMC) and the Cohort of Swedish Men (COSM), using a prospective cohort design and starting follow-up in 1997. We will build a database on waterborne exposures that includes nitrate, and link this to the study participants residential addresses (obtained from Tätortsregistret and Registret över totalbefolkningen at Statistics Sweden). Dietary nitrate exposure will be assessed by building a database on nitrate content in common foods and beverages, and combine this with the intake frequencies from the FFQ. The nitrate content of meals and beverages prepared with tap water will be weighted according to the concentration in tap water at the participants homes. First incident cases of cardiovascular disease and cancer will be obtained using the computerized linkage of the cohorts to the Swedish National Patient Register, the Swedish Cancer register and the Cause of Death Register (National Board of Health and Welfare). We will use Cox proportional hazards regression to estimate disease outcome associations, generating hazard ratios (HR) and 95% confidence intervals (CI). The models will be adjusted for potential confounding variables carefully selected using directed acyclic graphs (DAGs).