Scientific Contributions of Population-Based Studies to Cardiovascular Epidemiology in the GWAS Era

Population-Based Biobanking

Population-based biobanking is an essential element of medical research that has grown substantially over the last two decades, and many countries are currently pursuing large national biobanking initiatives. The rise of individual biobanks is paralleled by various networking activities in the field at both the national and international level, such as BBMRI-ERIC in the EU. A significant contribution to population-based biobanking comes from large cohort studies and national repositories, including the United Kingdom Biobank (UKBB), the CONSTANCES project in France, the German National Cohort (NAKO), LifeLines in the Netherlands, FinnGen in Finland, and the All of Us project in the U.S. At the same time, hospital-based biobanking has also gained importance in medical research. We describe some of the scientific questions that can be addressed particularly well by the use of population-based biobanks, including the discovery and calibration of biomarkers and the identification of molecular correlates of health parameters and disease states. Despite the tremendous progress made so far, some major challenges to population-based biobanking still remain, including the need to develop strategies for the long-term sustainability of biobanks, the handling of incidental findings, and the linkage of sample-related and sample-derived data to other relevant resources.

Invited Commentary: Standards, Inputs, and Outputs-Strategies for Improving Data-Sharing and Consortia-Based Epidemiologic Research

Data-sharing improves epidemiologic research, but the sharing of data frustrates epidemiologic researchers. The inefficiencies of current methods and options for data-sharing are increasingly documented and easily understood by any study group that has shared its data and any researcher who has received shared data. In this issue of the Journal, Temprosa et al. (Am J Epidemiol. 2021;191(1):147-158) describe how the Consortium of Metabolomics Studies (COMETS) developed and deployed a flexible analytical platform to eliminate key pain points in large-scale metabolomics research. COMETS Analytics includes an online tool, but its cloud computing and technology are the supporting rather than the leading actors in this script. The COMETS team identified the need to standardize diverse and inconsistent metabolomics and covariate data and models across its many participating cohort studies, and then developed a flexible tool that gave its member studies choices about how they wanted to meet the consortium's analytical requirements. Different specialties will have different specific research needs and will probably continue to use and develop an array of diverse analytical and technical solutions for their projects. COMETS Analytics shows how important-and enabling-the upstream attention to data standards and data consistency is to producing high-quality metabolomics, consortia-based, and large-scale epidemiology research.