This study will use one of the largest UK population-based studies, the UK Biobank, and apply genetic and statistical techniques to investigate the causal relationship between more than 40 factors and type 2 diabetes.
The size of UK Biobank will provide a powerful resource for these studies and enable us to draw robust conclusions about which factors or biological chemicals (known as biomarkers) are causally linked to diabetes.
This project will investigate causality using a Mendelian randomisation approach; a technique that has already helped further our understanding the disease.
It will be organized into three sequential stages:
In Stage 1 we will investigate the association between biomarkers and incident (n=3,500) and prevalent type 2 diabetes cases (n~14,500) using regression models.
Stage 2 will identify genetic variants associated with our selected biomarkers using regression models.
Finally, stage 3, we will use instrumental variable analysis to assess the causal relationship between circulating biomarkers and type 2 diabetes.
Epidemiological studies have demonstrated that type 2 diabetes is associated with a broad range of factors and a myriad of biomarkers, often as a consequence, rather than cause of the disease process. Understanding which factors and biomarkers play a causal role in type 2 diabetes is of critical importance for understanding the disease, improving diagnosis, and successfully developing treatments.
Type 2 diabetes represents a huge health burden worldwide – in the UK, it costs the NHS £420 per second – and research is underway to determine how this burden can be reduced.
It is a complex disease, with many factors contributing to disease development including obesity, poor diet, lower socioeconomic status, physical inactivity, poor sleep and a range of biomarkers in the body.
This project will be of critical importance to our understanding of the causes of diabetes. Identifying the number of factors and biomarkers that truly influence the onset of the disease will highlight the pathways and systems that researchers and pharmaceutical companies should focus on for disease prevention and improvement of treatments.