Applying a Big Data Approach to Biomarker Discovery: Running Before We Walk?

C irculating biomarkers play an increasingly important role in cardiovascular medicine. Clinicians routinely measure biomarkers of cardiac injury, neurohormonal activation, and renal function for diagnosis and risk assessment and for guiding therapeutic decision making. Several intersecting trends have heightened interest in the discovery and validation of additional cardiovascular biomarkers, including the highly anticipated transition to personalized or precision medicine and the pending availability of " big data " sets that promise a dizzying array of patient-level data, including unprecedented numbers of biomarkers. A number of strategies have been used to search for novel biomarkers of cardiovascular disease (CVD). Unbiased technologies , including genomics, proteomics, and metabolomics, all use a big data approach for novel biomarker discovery, 1 but to date, these technologies have failed to deliver on their initial promise, yielding no new clinically useful biomarkers in cardiac care. An alternative strategy is to focus on known proteins reflecting mediating pathways to ensure a higher probability of association with CVD, an approach that can now be implemented on a massive scale with the use of new multiplex immunoassay techniques that allow conservation of sample volume. In this issue of Circulation, Gerstein et al, 2 representing the Outcome Reduction With an Initial Glargine Intervention (ORIGIN) Trial investigators, used a commercially available Luminex multiplex immunoassay platform to screen hundreds of distinct biomarkers for their ability to predict CVD events. Although the marker selection process was described as an unbiased approach, each of the biomarkers tested was a known protein preselected by either the company or the investigators on the basis of some a priori rationale for inclusion in the test panel, a strategy that may increase the yield of discovery while limiting the full scope of what can be discovered. Within the field of circulating cardiovascular biomarkers, this effort is almost unprecedented in its scope and scale, given the large numbers of biomarkers measured (284), study subjects enrolled (8401), and primary composite clinical end points included (1405). The investigators created uniform algorithms to define which biomarkers were excluded on the basis of analytic concerns, how the biomarkers were modeled, and what criteria were used to select markers for inclusion in the final panels. This approach identified 8 to 15 biomarkers that were independently associated with various individual and composite end points. Adding these biomarkers to a multivariable model improved discrimination and risk classification compared with standard risk factors alone. Although N-terminal pro …