Approved Research

Investigating causal associations and gene-environment interactions in health and age-related disease

Lay summary

The food we eat and the way we live have a significant impact on our health and our risk of disease throughout our lives. These effects are often influenced by the interplay between our genes and the environment we are exposed to. This interaction can contribute to the development of common complex diseases like cardiovascular disease, hormonal imbalances, and conditions related to obesity, as well as influencing how we age.

To better understand these relationships, we plan to use a scientific technique known as Mendelian Randomization. This method involves examining specific genes in DNA that act like markers for your exposure to different foods, lifestyles, and environments. By studying these genetic markers, we hope to determine if they are directly linked to health. Our research also suggests that we might be able to find certain genetic variations in DNA that are linked to living a healthy life and being free from major diseases like heart disease and strokes. By combining these genetic markers with a thorough assessment of factors such as blood pressure and exercise, we may gain insights into the underlying mechanisms that control our health and the development of diseases.

One age-related disease of interest is hypertension, or high blood pressure. There are many medications available for patients with hypertension. However, many patients will not benefit from some or all of these medications and continue to suffer from hypertension. It is not understood why some patients are not able to benefit from these medications. As such, clinicians are unable to know which patients will benefit or not. It would be advantageous if we could identify which patients would benefit from these medications to better deliver clinical care for hypertension and reduce adverse health outcomes as a result.

This three-year project aims to uncover specific biomarkers in the body related to complex diseases. This could help us better understand the detailed molecular processes involved in these conditions and possibly lead to new healthcare strategies to promote healthy ageing and prevent diseases. Additionally, data models developed in this project will be able to predict which patients respond to treatment. This will have the potential to better control diseases and reduce significant side-effects and cost to healthcare institutions associated with poor treatment management.