13 Aug, 2019

Genomics and Type 2 Diabetes

Learn about the genetic causes of Type 2 Diabetes, the disease progression, complications and patient response to treatment.

Genetic research has led to numerous advances in our understanding of heterogeneous conditions such as Depression (as covered in detail in our previous article). In this article we will focus on Type 2 Diabetes (T2D) - a metabolic disorder with a rising global prevalence. In the 2018 study by the International Diabetes Federation, it was estimated that in 2017 there were 451 million people with diabetes worldwide and projected that 693 million will be affected by 2045. These unnerving statistics reinforce the increasingly urgent need to understand how to decrease the risk factors for T2D.

T2D is influenced by environmental and lifestyle factors (excess body weight and physical inactivity) as well as a significant but heterogeneous genetic component. In this article, we discuss some of the most recent genome-wide association studies (GWAS) that have identified more than 400 genetic variants associated with increased risk of T2D. We also explore how risk prediction models are being used to predict future risk of diabetes. Finally, we discuss ongoing research to discover the processes that may contribute to the rate of T2D progression and risk of developing further health complications.

Understanding the genetic causes of diabetes

Large-scale genome-wide association studies provide a way to find sites of genetic variation that alter an individual's risk of type 2 diabetes. In a recent study on 900,000 European participants, researchers identified over 400 distinct genetic signals for type 2 diabetes, including a set of 18 validated targets that are causally related to type 2 diabetes pathogenesis. Mahajan et al. states that they “highlight 18 genes as human validated targets based on causal coding variant effects and provide novel insights into the biological mechanisms operating at several fine-mapped regulatory signals. These findings represent mechanistic hypotheses that can now be targeted for large-scale empirical validation at both the level of the variants (e.g. through massively parallel reporter assays) and the candidate effector genes (through CRISPR screens in appropriate cellular models, and manipulation in in vivo models).”

While these findings move the field of genetic research forward, they are limited by the fact that most genome-wide association studies have been conducted on European populations, and therefore the replication and transferability of findings on other ethnicities must be investigated before they can be clinically useful. Promising results have been reported from the largest non-European GWAS, conducted on 430,000 East Asian participants. The meta-GWAS identified 298 distinct genetic signals for type 2 diabetes at 178 loci, including 56 new loci. Spracklen et al. states that the results “emphasize substantial shared T2D susceptibility with European individuals, as shown by the strong correlation of effect sizes among T2D-associated genetic variants with common allele frequencies in both East Asian and European ancestry populations. The results also detect novel associations in East Asian individuals, several of which are identified because they have higher allele frequencies in East Asian populations, exhibit larger effect sizes, and/or are influenced by other environmental risk factors or lifestyle behaviors such as alcohol consumption.”

Understanding disease progression, diabetic complications and patient response to treatment

Interest has also been high in developing Polygenic Risk Scores (PRS) to identify patients at high risk of T2D based on genetic predisposition. As discussed in our article ‘ The Utility of Polygenic Risk Scores’, PRS combine hundreds of variants to calculate an overall T2D predisposition for each individual. While each individual genetic variation may only have a modest effect on risk, combined, they offer the potential to stratify individuals from “low-T2D-risk” to “highT2D-risk”, and potentially even to classify individuals into sub-types. Researchers are using this approach to develop more granular risk stratification to enable improved intervention strategies and better understand the shared genetic risk profiles between T2D and other comorbidities, such as obesity.

In a 2018 paper exploring subtypes of T2D, researchers identified five “robust clusters” across 17,365 European individuals, each with distinct trait associations based on analysis of epigenomic data from 28 cell types. 30% of individuals fell within the top decile of T2D-risk in one cluster and “reproducibly exhibited the predicted cluster-associated phenotypes”. When summarising the findings of their clustering analysis, Udler et al. states that “it will be exciting to explore whether such individuals respond differentially to medications based on the pathway predominantly disrupted or whether they have a differential rate of disease progression and diabetic complications. Classification of patients using data from designated genetic pathways may offer a step toward genetically informed patient management of T2D in order to individualize and improve care.”

Stratification within diabetes is entering in number of trials on the route to clinical application. One ongoing initiative is the IMI DIRECT (Innovative Medicines Initiative Diabetes Research on Patient Stratification). The IMI DIRECT Consortium have combined genomics with the assessment of incretin and glucagon secretion, diet and lifestyle measures to identify subtypes across 3000 people. The Consortium states that “the ultimate aim of DIRECT is patient stratification, biomarkers arising from the discovery work will be used to design one or more prospective clinical trials. These will validate the biomarker(s) of interest, and establish utility in clinical practice and/or trial design and drug development. As a result, this consortium offers considerable potential to achieve major progress towards a personalized medicines approach to the treatment of type 2 diabetes.”

As genetic research and advancements continue, we will be able to better understand the underlying biological processes contributing to diabetes risk. Genetic variants, alongside environmental and lifestyle factors, will not only be used to inform individuals about their diabetes risk, but they will also be able to provide a more granular understanding of diabetes subtypes, that help determine the rate of progression, the risk of other health complications and the most effective therapeutic options. In turn, this information will help us to decrease the risk factors for T2D and improve clinical outcomes for individuals with diabetes.

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