How Well Do Current Cardiovascular Risk Factors Predict Risk?

Is There a Need for New Cardiovascular Biomarkers? Discovery of novel biomarkers and improvement of risk prediction algorithms will be a key to fulfill the promise of personalized medicine. Although genuine personalized treatment is probably an unrealistic expectation within the budget constraints of current health systems, a stratified medicine approach to identify individuals at high risk may help to allocate available resources most efficiently in a setting with a rising epidemic of cardiovascular disease (CVD) fueled by obesity, hypertension, and diabetes mellitus and by an aging population. Besides, biomarkers may improve patient motivation; by facilitating risk communication and compliance to lifestyle changes and therapies. The search for cardiovascular biomarkers dates back to the mid 1960s when creatine kinase and its cardiospecific isoform creatine kinase-MB were established as indicators of acute myocardial damage. About a decade later, the Framingham Study pioneered the quest for long-term cardiovascular risk prediction. Framingham risk scores, as well as others, such as Prospective Cardiovascular Münster (PROCAM) study, Systemic Coronary Risk Evaluation (SCORE), and Reynolds, are widely accepted and implemented tools in clinical decision-making. These scores inform treatment decisions, but they leave ample room for improvement. Currently, the majority of cardiovascular risk is not explained by traditional risk factors. In fact, most events occur in patients with an average risk score who are erroneously deemed to be at intermediate or low risk because they have no or only 1 of the cardiovascular risk factors. In contrast, many high-risk individuals do not experience a cardiovascular event even in the long term. Thus, the renewed interest in biomarkers is warranted, and the recent advances in postgenomic technologies offer unprecedented opportunities for biomarker discovery.