Combining information about the pattern of blood vessels in the retina with genetic data can accurately predict the risk of coronary artery disease (CAD) and its potentially fatal outcome, myocardial infarction (MI), commonly known as a heart attack. The discovery could lead to a simple screening process where an MI risk can be calculated when a person undergoes a routine eye test, researchers will tell today (Monday) at the annual conference of the European Society of Human Genetics.
We already knew that variations in the retinal vascular system can provide insight into our health. Since retinal imaging is a non-invasive technique, we decided to investigate the health benefits we can derive from these images. First, we studied the branching patterns of the retinal vasculature by calculating a measure called fractal dimension (Df) from data available from the UK Biobank (UKB). UKB includes demographic, epidemiological, clinical, imaging and genotyping data from over 500,000 participants in the UK. We found that lower Df, simplified branching patterns of blood vessels, is related to CAD and thus MI.”
Mrs. Ana Villaplana-Velasco, PhD student at the Usher and Roslin Institutes, University of Edinburgh, Edinburgh, UK
The researchers then developed a model capable of predicting MI risk prediction by studying UKB participants who had experienced an MI event after collecting their retinal images. The model included Df as well as traditional clinical factors, such as age, gender, systolic blood pressure, body mass index, and smoking status to calculate personalized MI risk. “Remarkably, we found that our model was able to better classify participants at low or high MI risk in UKB compared to established models that only included demographics. The improvement of our model was even greater if we added a score related to with a genetic tendency to develop MI,” said Ms. Villaplana-Velasco.
“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 the genetics of cardiovascular disease. In particular, we found that these common genetic regions are involved in processes related to the severity and recovery of MI.”
These findings may also be helpful in identifying predisposition to other diseases. Variations in the retinal vascular pattern also reflect the development of other ocular and systemic diseases, such as diabetic retinopathy and stroke. The researchers think it’s possible that each condition has a unique retinal variation profile. “We want to explore this further and conduct a gender-specific analysis. We know that women at higher MI or CAD risk often have pronounced retinal vascular abnormalities compared to the male population. We want to conduct our analysis separately in males and females to examine whether a gender-specific model for MI completes a better risk classification,” said Ms. Villaplana-Velasco.
Although the researchers knew that variations in the retinal vascular system were related to a person’s health, their convincing results came as a surprise. “There have been multiple attempts to improve CAD and MI risk predictive models by taking into account retinal vascular features, but these showed no significant improvement compared to established models. In our case, we found that the clinical MI definition – the diagnostic codes describing Myocardial infarction events in medical records – is central to the successful development of predictive models, supporting the need for developing robust disease definitions in large studies such as UKB.Once we validated our MI definition, we found that our model worked extremely well,” said Villaplana-Velasco.
In the future, a simple retinal exam may provide enough information to identify at-risk individuals. The mean age for an MI is 60, and the researchers found that their model reached its best predictive performance more than five years before the MI event. “So the calculation of an individualized MI risk of people older than 50 years seems appropriate,” said Ms. Villaplan-Velasco. “This would allow physicians to suggest behaviors that may reduce risk, such as smoking cessation and maintaining normal cholesterol and blood pressure. Our work demonstrates once again the importance of a comprehensive analysis of data routinely collected and its value in the further development of personalized medicine.”
Professor Alexandre Reymond, chair of the conference, said: “This study demonstrates the importance of prevention now, and how personalized health gives us the tools to do so.”
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