Lifetime cumulative risk factors predict cardiovascular disease mortality in a 50-year follow-up study in Finland

Abstract

Summary. Background. Systolic blood pressure, total cholesterol and smoking are known predictors of cardiovascular disease (CVD) mortality. Less is known about the effect of lifetime accumulation and changes of risk factors over time as predictors of CVD mortality, especially in very long follow-up studies. Methods. Data from the Finnish cohorts of the Seven Countries Study were used. The baseline examination was in 1959 and seven re-examinations were carried out approximately in five-year intervals. Cohorts were followed up for mortality until the end of 2011. Time-dependent Cox models with regular time-updated risk factors, time-dependent averages of risk factors and latest changes in risk factors, using smoothing splines to discover nonlinear effects were used to analyse the predictive effect of risk factors for CVD mortality. Results. A model using cumulative risk factors, modelled as the individual-level verages of several risk factor measurements over time, predicted CVD mortality better than a model using the most recent measurement information. This difference seemed to be most prominent for systolic blood pressure. U-shaped effects of the original predictors can be explained by partitioning a risk factor effect between the recent level and the change trajectory. The change in body mass index predicted the risk although body mass index itself did not. Conclusions. The lifetime accumulation of risk factors and the observed changes in risk factor levels over time are strong predictors of CVD mortality. It is important to investigate different ways of using the longitudinal risk factor measurements to take full advantage of them.