Translational Epidemiology: Entering a Brave New World of Team Science.

Abstract

> The measure of greatness in a scientific idea is the extent to which it stimulates thought and opens up new lines of research. > > —Paul A.M. Dirac Translational epidemiology is the study of risk factors and diseases in large populations, harnessing the power of modern phenotyping, including large-scale omics approaches. In this review, we comment on the power and limitations of modern molecular translational epidemiology and suggest collaborative team science as a specific avenue to secure its existence in the next era of genomic research. In the past 15 years, the emergence of faster, cheaper high-throughput methods to quantify circulating biomolecules and genetic signatures has transformed the landscape of cardiovascular investigation. At the heart of this, Big Data revolution resides the longitudinal cohort study, a collection of individuals exquisitely phenotyped across various axes of cardiovascular health and prospectively followed for prognosis and disease development. Since the 1960s, cohort-based epidemiology has provided landmarks in cardiovascular medicine (eg, smoking as a risk factor for coronary heart disease1). More recently, a marriage between human cardiovascular epidemiology and high-throughput omics research has birthed translational epidemiology, based on the premise that the comprehensive integration of genetic, epigenetic, transcriptional, proteomic, and metabolic signatures with phenotypes informs disease and uncovers risk and prognostic factors, personalizing cardiovascular care. Because the initiative to grow prospective cohort studies from the thousands (Framingham, Jackson Heart Study, and Multi-Ethnic Study of Atherosclerosis) to the hundreds of thousands (eg, UK Biobank and Million Veterans Project) expands, it is imperative to define the benefits and limitations of translational epidemiology to best use the ongoing and newer cohort studies to the benefit of our patients. Early work establishing a link between genetic variation and phenotype relied on defined, extreme phenotypes (eg, familial hypercholesterolemia and the low-density lipoprotein receptor; hypertension and the renin–angiotensin system; and Jervell …