The multi-variable and continues nature of atherosclerotic disease development as a novel paradigm in cardiovascular disease risk assessment

Abstract

Cardiovascular disease is the number one cause of death world wide. Although the death rates of cardiovascular disease (CVD) have declined, the burden of the disease remains high. Furthermore, a growing population has an increased risk for CVD. Therefore, there is a strong possibility that the decline of the CVD death rate will come to a halt and the medical burden soon will start to increase again. Identification of patients at risk for a cardiovascular event is, therefore, still the highest concern among healthcare workers. Currently, CVD risk is estimated through models that predict the 10-year risk of cardiovascular disease related events or death. This is only a long-term risk estimation and monitoring disease progression is not possible. The individuals at the highest levels of risk gain the most from risk factor management recommended by these models; however the most deaths in a community come from those patients at lower levels of risk. This emphasizes the important need for individual based short-term risk assessment and CVD disease monitoring. The common risk factors are mostly static variables or have low dynamics; therefore they are better suited to predict long-term risk than near-term risk. Novel risk factors that reflect acute processes influencing atherosclerotic plaque progression and rupture are needed. New CVD risk assessment models should include continuous multi-marker profiles that take biomarker kinetics into consideration. Multi-marker dynamics could predict trends toward a clinical manifestation and thereby enable disease monitoring and short-term risk assessment. This overview will summarize potential targets for cardiovascular disease biomarkers. Furthermore, a potential platform to analyze these targets will be discussed. Finally, the shortcomings of current CVD risk prediction models and the potential to develop new multi-biomarker dynamical models which can be used for individual based short-term cardiovascular risk assessment will be discussed.