Next-generation epidemiology: the role of high-resolution molecular phenotyping in diabetes research
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REVIEW
Next-generation epidemiology: the role of high-resolution molecular phenotyping in diabetes research Paul W. Franks 1,2 & Hugo Pomares-Millan 1 Received: 2 October 2019 / Accepted: 1 June 2020 # The Author(s) 2020
Abstract Epidemiologists have for many decades reported on the patterns and distributions of diabetes within and between populations and have helped to elucidate the aetiology of the disease. This has helped raise awareness of the tremendous burden the disease places on individuals and societies; it has also identified key risk factors that have become the focus of diabetes prevention trials and helped shape public health recommendations. Recent developments in affordable high-throughput genetic and molecular phenotyping technologies have driven the emergence of a new type of epidemiology with a more mechanistic focus than ever before. Studies employing these technologies have identified gene variants or causal loci, and linked these to other omics data that help define the molecular processes mediating the effects of genetic variation in the expression of clinical phenotypes. The scale of these epidemiological studies is rapidly growing; a trend that is set to continue as the public and private sectors invest heavily in omics data generation. Many are banking on this massive volume of diverse molecular data for breakthroughs in drug discovery and predicting sensitivity to risk factors, response to therapies and susceptibility to diabetes complications, as well as the development of disease-monitoring tools and surrogate outcomes. To realise these possibilities, it is essential that omics technologies are applied to well-designed epidemiological studies and that the emerging data are carefully analysed and interpreted. One might view this as next-generation epidemiology, where complex high-dimensionality data analysis approaches will need to be blended with many of the core principles of epidemiological research. In this article, we review the literature on omics in diabetes epidemiology and discuss how this field is evolving.
Keywords Bioinformatics . Biomarkers . Diabetes . Epidemiology . Genetics . Omics . Review Abbreviations EPIC European Prospective Investigation into Cancer and Nutrition FDA Food and Drug Administration GWAS Genome-wide association studies IMI Innovative Medicines Initiative MR Mendelian randomisation Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00125-020-05246-w) contains a slide of the figure for download, which is available to authorised users. * Paul W. Franks [email protected] 1
Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Jan Waldenströmsgata 35, Skåne University Hospital, SE-20502 Malmö, Sweden
2
Harvard T.H. Chan School of Public Health, Boston, MA, USA
Introduction The aetiology and clinical presentation of diabetes often differ greatly from one patient to the next, as do patients’ responses to therapies and the rates at which they dev
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