Mendelian randomisation in type 2 diabetes and coronary artery disease

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Type 2 diabetes, coronary artery disease and hypertension are associated with anthropometric and biomarker traits, including waist-to-hip-ratio, body mass index and altered glucose and insulin levels. Clinical trials, for example of weight-loss interventions, show these factors are causal, but lifelong impact of subtle changes in body mass index and body fat distribution are less clear. The use of human genetics can quantify the causal effects of long-term exposure to subtle changes of modifiable risk factors. Mendelian randomisation (MR) uses human genetic variants associated with the risk factor to quantify the relationship between risk factor and disease outcome. The last two years have seen an increase in the number of MR studies investigating the relationship between anthropometric traits and metabolic diseases. This review provides an overview of these recent MR studies in relation to type 2 diabetes, coronary artery disease and hypertension. MR provides evidence for causal associations of waist-to-hip-ratio, body mass index and altered glucose levels with type 2 diabetes, coronary artery disease and hypertension.

Introduction

Metabolic diseases, such as type 2 diabetes (T2D), coronary artery disease (CAD) and hypertension affect large proportions of the world’s population. For example, diabetes was estimated to effect 4.25 million adults globally (population approximately 4.84 billion) in 2017, whilst hypertension and CAD affected 1.13 billion and 110 million people worldwide in 2015 respectively [1, 2, 3]. Many factors are associated with these metabolic diseases including altered lipid, glucose and metabolite levels, differential adiposity measures including waist-to-hip-ratio (WHR), fatty liver markers, glucose intolerance, insulin resistance, differential gene expression and epigenetic patterns [4, 5, 6]. Identifying which factors are causal and not a result of disease or its treatment is difficult. Many prospective studies imply causality by showing clear associations between these factors and disease by indicating that these changes occurred before disease onset. However, prospective associations do not prove causality because changes can occur long before disease and may be associated with unaccounted for confounding factors [7].

Genetic variation influences metabolism and can help us understand the true causal effects between traits and disease to deepen our understanding of the pathophysiology of metabolic diseases [8]. Genetic studies have resulted in an improved understanding of disease for several reasons. These reasons include the use of Mendelian randomisation (MR). The discovery and availability of many tens, sometimes hundreds, of genetic variants associated with a risk factor have facilitated more powerful MR studies. These variants, discovered by genome-wide association studies (GWAS), have provided important `instruments’ for MR studies that help define the role of these risk factors in disease and related traits.

Section snippets

Mendelian randomisation

MR can serve as a useful alternative or complement to randomised control trials (RCTs). Well-designed RCTs provide the most reliable evidence for causality. RCTs involve random allocation of interventions or placebos to participants eligible for study to determine efficacy of treatments. Randomisation reduces bias and confounding but is often costly and time-consuming and may be high-risk and ethically unjust [9]. MR can be used to test for causal associations between a risk factor and outcome

Methods

We performed an extensive literature search using Web of Science (https://apps.webofknowledge.com/) and PubMed databases [20]. The search terms sequentially used were “Mendelian Randomisation” OR “Mendelian Randomization” and the risk factor and disease name with all variations included (Table 1). We next filtered on year of publication; January 2015–present day inclusive. We then eliminated studies involving other traits. We scan-read the remaining papers and chose those using MR to find

Genetic variants associated with BMI to assess causality with cardiometabolic diseases

GWAS have identified 97 genetic variants associated with BMI. These 97 variants account for 2.7% of BMI variation [37••]. These variants, and subsets of them identified in earlier studies, have been used in MR tests to assess the causal relationship between BMI and disease.

Summary

Over the last two years, MR has provided evidence of causal genetic associations between anthropometric traits and cardiometabolic diseases. In most cases, MR has helped to support results obtained through observational studies and RCTs by providing genetic evidence for the causal relationships. The limitations of these MR studies include a lack of knowledge about the mechanistic actions of the genetic variants used as instruments. Careful consideration of the three assumptions (Figure 1) is

Conflict of interest statement

Charli Stoneman is an MRC CASE student partly funded by GSK. Tim Frayling has consulted for several pharmaceutical companies including Sanofi and Boehringer-Ingelheim.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as

  • • of special interest

  • •• of outstanding interest

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