Omics, Big Data, and Precision Medicine in Cardiovascular Sciences.

How do our individual genomes and life histories influence our well-being, risk for diseases, and responses to medical treatments? This is the fundamental question precision medicine seeks to address. Understand how the confluence of genes and environment defines pathophysiological traits, and we can, in theory, prescribe the most suitable treatments to each individual, better predict population health to improve policy-making, and perhaps even unlock some of the mysteries behind the circuitry of life itself. Although we have not yet achieved this goal, for the first time we possess the investigative tools that suggest how it can be accomplished. It is with recent technological advances in mind that for this Circulation Research Omics Compendium, we invited leaders in our field to discuss essential aspects of omics technologies from genomics and transcriptomics to proteomics, metabolomics, phenomics, and beyond, and to explore what the integration of large-scale digital data means for precision health and medicine. We begin with 2 essays on how evolving technologies are changing the ways health status can be assessed. Kellogg et al1 describe the emergence of mobile health (m-health) devices and sensors that have revolutionized the measurement of human dynamic physiology, a concept which encompasses not only genetic information, but also continuous measurements of high-dimensional phenotypes. Small devices and smartphones can now be used to collect quasi-continuous data on blood pressure, heart rhythm, oxygen saturation, brain waves, air quality, radiation, and an ever-expanding list of metrics. The resulting physiological and environmental information can be connected to other omics layers such as genomes, metabolomes, and microbiomes to discover subclinical imbalances or elevated disease risk in otherwise healthy individuals. Cranley and MacRae2 further expand on the theme of deriving a phenotypic repertoire at scale. Using atherosclerosis as an example, the authors argue that the slow progress on disease mechanisms …