Separating the Contenders From the Pretenders

Despite great enthusiasm for biomarkers as tools to enhance risk prediction and to lead the way in a transformation towards personalized cardiovascular medicine, progress in the biomarker field has been painstakingly slow, particularly in the area of population screening. Some individual biomarkers such as C-reactive protein (CRP) have demonstrated consistent associations with incident cardiovascular events across multiple studies, but the magnitude of these associations is modest,1 and only small improvements in discrimination and reclassification are seen.2,3 One attractive solution to the limitations of individual biomarkers is to combine nonredundant biomarkers into panels to enhance risk assessment. However, results of studies testing multiple biomarkers for risk prediction in primary prevention populations have not provided a clear picture, with some studies showing qualified promise4–6 and others suggesting limited value.2,7,8Article see p 2388 In this issue of Circulation , Blankenberg et al9 report findings from an impressive large-scale effort to screen multiple biomarkers for risk assessment in the general population. The investigators evaluated 30 biomarkers reflecting different components of the pathophysiology of atherosclerotic heart disease in 2 cohorts totaling almost 10 000 individuals. Unfortunately, most of the novel biomarkers tested either did not associate with incident cardiovascular events at all or showed promising results in 1 cohort that did not replicate. Although the novel biomarkers largely failed, consistent, albeit modest, associations with incident events were seen across the 2 cohorts for CRP, N-terminal pro B-type natriuretic peptide (NT-proBNP), and cardiac troponin (cTn) I, 3 well-established cardiovascular biomarkers. A weighted biomarker score was developed in men from one of the cohorts (FINRISK 97) and then validated in a second cohort of men (PRIME Belfast). Adding the biomarker score to a standard risk factor model improved multiple metrics of risk prediction, including measures of association, discrimination, and most important …