A New Approach to an Old Problem: One Brave Idea

Despite sophisticated and increasingly effective assaults on traditional atherosclerotic risk factors, in particular LDL (low-density lipoprotein cholesterol) and most recently inflammation, there remains a large, unmet coronary heart disease (CHD) burden.1 Modern experimental and therapeutic programs have focused on a small number of biological pathways, so it is possible that we have reached the point where to identify additional causal mechanisms and create novel therapies, fundamentally new approaches are required. The recently developed One Brave Idea program, generously sponsored by the American Heart Association and Verily with AstraZeneca, was designed to identify alternative strategies to address CHD. In this perspective, we examine the existing frameworks in atherosclerosis biology, highlighting their successes and focusing on areas where there may be missing information. We will outline general principles which would enable biomedical researchers to identify and capture relevant missing information in CHD. Central to the program are efforts to radically improve the phenotypic repertoire of human biology and to define environmental drivers of disease, including nutrition, at a resolution and scale only feasible in the current connected era. This approach also implies the potential for a universal objective framework for the definition of personal health or disease, harmonized across the entire biomedical community to accelerate the integration of discovery more fully with prevention and clinical care. To find disease causation, one must first detect exposures (environmental or genetic) of large enough effect size that they can be clearly associated with specific outcomes. Many associations may be observed, but only a small number of such correlations are truly causal or represent targets for therapeutic intervention. Defining causality or therapeutic efficacy is demanding, particularly when observations are sparse and temporally distant from the outcomes of relevance. By measuring both exposures and outcomes at a more granular level and across time, causality is more …