We all know the eyes are the windows to our soul, but according to new research, they could also be the windows to our health.
New findings to be presented at the annual European Society of Human Genetics Conference in Vienna, Austria on Monday (local time) has revealed that genetic data - plus an eye exam - could predict a person's risk of coronary artery disease and heart attacks.
According to the team of researchers, combining information about the pattern of blood vessels in the retina with genetic data can lead to an accurate prediction of an individual's risk of coronary artery disease (CAD) and its potentially fatal outcome, myocardial infarction (MI) - commonly known as a heart attack.
Knowing that the pattern of blood vessels in the retina can offer insights into a person's health, researchers from the University of Edinburgh decided to use UK Biobank (UKB) data to compare retinal scans with heart attacks, using the database of more than 500,000 participants. The Biobank is a large, long-term biobank study in the United Kingdom which is investigating the respective contributions of genetic predisposition and environmental exposure to the development of disease. It began in 2006.
The researchers said the model they have created is capable of predicting the risk of a heart attack with better accuracy than current models based on demographics.
The discovery could lead to a simple screening process where a person's risk of suffering a heart attack is calculated when they undergo a routine eye test.
"We already knew that variations in the vasculature of the retina might offer insights into our health. Given that retinal imaging is a non-invasive technique, we decided to investigate the health benefits we could obtain from these images," said Ana Villaplana-Velasco, a PhD student at the University of Edinburgh's Usher and Roslin Institutes.
"First, we studied the branching patterns of the retinal vasculature by calculating a measure named fractal dimension (Df) from data available from the UKB. The UKB includes demographic, epidemiological, clinical, imaging and genotyping data from over 500,000 participants across the UK.
"We found that lower Df - simplified vessel branching patterns - is related to CAD and hence MI."
Following their findings, the researchers developed a model that is able to predict a person's MI risk, based on their research into UKB participants who had experienced an MI event after the collection of their retinal images.
The model included Df as well as traditional clinical factors, such as age, sex, systolic blood pressure, body mass index and smoking status to calculate their personalised MI risk.
"Strikingly, we discovered 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," Villaplana-Velasco said.
"We wondered if the Df-MI association was influenced by shared biology, so we looked at the genetics of Df and found nine genetic regions driving retinal vascular branching patterns. Four of these regions are known to be involved in cardiovascular disease genetics. In particular, we found these common genetic regions are involved in processes related to MI severity and recovery."
The findings may also be useful in identifying a person's risk of developing other diseases, the researchers noted, as variations in the retinal vascular pattern also reflect the development of other ocular and systemic conditions - such as diabetic retinopathy and stroke.
The researchers said it's possible that every condition may have a unique retinal variation profile.
"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."
Although the researchers knew that variations in retinal vasculature are associated with the person's state of health, their convincing results still came as a surprise.
"There have been multiple attempts to improve CAD and MI risk predictive models by accounting for retinal vascular traits, but these showed no significant improvement when compared with established models. In our case… we found our model worked extremely well," Villaplana-Velasco said.
In the future, a simple retinal examination may be able to provide enough information to identify people at risk. The average age for an MI is 60, and the researchers found their model achieved its best predictive performance more than five years before the heart attack strikes.
"So the calculation of an individualised MI risk from those over 50 years old would seem to be appropriate," said 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."
Professor Alexandre Reymond, chair of the conference, added: "This study demonstrates the importance of implementing prevention now, and how personalised health is providing us with the tools to do so."
As the findings are to be presented at a conference, there is currently no research paper and the work has yet to be peer-reviewed.