Faculty Opinions recommendation of Next generation diabetes scientists shape global research culture: A reflective proposal from postdoctoral researchers in diabetes research.

Improving research culture to value transparency and rigor is necessary to engage in a productive “Credibility Revolution.” The field of educational psychology is well positioned to act toward this goal. It will take specific actions by both grassroots groups plus leadership to set standards that will ensure that getting published, funded, or hired is determined by universally supported ideals. These improved standards must ensure that transparency, rigor, and credibility are valued above novelty, impact, and incredibility. Grassroots groups advocate for change and share experience so that the next generation of researchers have the experience needed to sustain these early moves. Each community can take inspiration from others that have made shifts toward better practices. These instances provide opportunities for emulating trail-blazers, training for new practices such as preregistration, and constructively evaluating or criticizing practice in ways that advances the reputation of all involved.

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Next-generation epidemiology: the role of high-resolution molecular phenotyping in diabetes research

Diabetologia ◽

10.1007/s00125-020-05246-w ◽

2020 ◽

Vol 63 (12) ◽

pp. 2521-2532

Author(s):

Paul W. Franks ◽

Hugo Pomares-Millan

Keyword(s):

Risk Factors ◽

Epidemiological Studies ◽

Molecular Data ◽

Diabetes Research ◽

Omics Data ◽

Data Generation ◽

Next Generation ◽

Public And Private ◽

Surrogate Outcomes ◽

Molecular Phenotyping

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.

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