Could an eye test predict your risk of heart disease?
A team of scientists believe a routine eye test, combined with genetic information, could accurately predict an individual’s risk of coronary artery disease and its potential outcome, heart attacks.
The researchers examined the patterns of blood vessels in the retina using data from the UK Biobank (UKB), which includes demographic, epidemiological, clinical and genetic data from 500,000 UK participants.
They found that simpler patterns were related to coronary heart disease (CAD) and myocardial infarctions (MI), also known as heart attacks, and developed a model that they say is able to predict an individual’s risk of MI based on multiple factors, including images of their eyes.
“Strikingly, we discovered that our model was able to better classify participants with low or high MI risk in UKB when compared with established models that only include demographic data. The improvement of our model was even higher if we added a score related to the genetic propensity of developing MI,” Ana Villaplana-Velasco, a PhD student at the University of UK, says.
The team also found that several regions of our genes drive branching of vessels in the eye - four of which are also involved in cardiovascular disease genetics.
“In particular, we found that these common genetic regions are involved in processes related to MI severity and recovery,” Ms Villaplana-Velasco says.
They also believe their findings could be useful for identifying risk of other diseases, with retinal vascular patterns potentially reflecting the development of diseases such as diabetic retinopathy and stroke.
“We would like to investigate this further, as well as undertaking a sex-specific analysis. We know that females with a higher MI or CAD risk tend to have pronounced retinal vascular deviations when compared to the male population. We would like to repeat our analysis separately in males and females to investigate if a sex-specific model for MI completes a better risk classification,” Ms Villaplana-Velasco says.
With the average age for an MI being 60, the team found that their model was most accurate at predicting risk more than five years before a person experiences an MI.
”So the calculation of an individualised MI risk from those over 50 years old would seem to be appropriate,” says Ms Villaplan-Velasco.
”This would enable doctors to suggest behaviours that could reduce risk, such as giving up smoking and maintaining normal cholesterol and blood pressure. Our work once more shows the importance of comprehensive analysis of data that is routinely collected and its value in the further development of personalised medicine.”
Their findings were presented at the annual conference of the European Society of Human Genetics.
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