Abstract
Over the past three decades, the number of people globally with diabetes mellitus has more than doubled. It is estimated that by 2030, 439 million people will be suffering from the disease, 90-95% of whom will have type 2 diabetes (T2D). In 2017, 5 million deaths globally were attributable to T2D, placing it in the top 10 global causes of death. Because T2D is a result of both genetic and environmental factors, identification of individuals with high genetic risk can help direct early interventions to prevent progression to more serious complications. Genome-wide association studies have identified ~400 variants associated with T2D that can be used to calculate polygenic risk scores (PRS). Although PRSs are not currently more accurate than clinical predictors and do not yet predict risk with equal accuracy across all ethnic populations, they have several potential clinical uses. Here, we discuss potential usages of PRS for predicting T2D and for informing and optimising interventions. We also touch on possible health inequality risks of PRS and the feasibility of large-scale implementation of PRS in clinical practice. Before PRSs can be used as a therapeutic tool, it is important that further polygenic risk models are derived using non-European genome-wide association studies to ensure that risk prediction is accurate for all ethnic groups. Furthermore, it is essential that the ethical, social and legal implications of PRS are considered before their implementation in any context.
Highlights
Type 2 diabetes mellitus (T2D) is a major global health issue
More research should be conducted on Partitioned polygenic scores (pPS), genetic clusters that could predict T2D complications based on relationships between genetics and electronic health record (EHR), to inform better monitoring or choice of therapeutic interventions
polygenic risk scores (PRS) could potentially be deployed to assign those with highest genetic risk to effective disease management programmes (DMPs), and direct-to-consumer testing can steer high-risk individuals to effective DMPs, incorporating digital interventions, which are cheap and effective especially if supported by compliance-enhancing tools and incentive schemes
Summary
Type 2 diabetes mellitus (T2D) is a major global health issue. Since the 1990s, the number of people with diabetes has more than doubled globally, with 439 million people estimated to be suffering from the disease by 2030, 90–95% of whom will have T2D1. Early efforts to group variants in this way identified a pathophysiological process that contributes to T2D risk through co-causing insulin resistance characterised phenotypically by lower levels of adiposity[28,29] This represents a case where pPS may be more effective than clinical factors in accurately predicting likelihood of T2D and facilitate earlier diagnosis. This means that the effect size of approximately a quarter of SNPs will be overestimated in non-European ancestries[57] This clearly illustrates the predictive inaccuracy of PRS in their current state; incorrectly defining an entire population as having an extremely high risk of disease would likely lead to stigma and exacerbation of existing health inequalities. Within the group reporting stigma, incidence was strongly linked to BMI60, suggesting that negative psychological effects associated with T2D prediction could be linked to a fear of being/becoming overweight rather than of T2D itself
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