In the future, a simple retinal examination may be able to provide enough information to identify people at risk of heart attack.
The pattern of blood vessels in people’s retinas is providing scientists with new clues that enable an accurate prediction of an individual’s risk of cardiovascular disease according to new research presented this week at the European Society of Human Genetics.
The research team, led by Ana Villaplana-Velasco, a PhD student at the Usher and Roslin Institutes from the University of Edinburgh, hopes that the breakthrough will lead to the development of a simple screening process that calculates a person’s risk of heart attack during a routine eye test.
Ms Villaplana-Velasco explained that they developed the predictive model by studying the data from over 500,000 UK Biobank participants who had experienced a myocardial infarction (MI) after the collection of their retinal images.
“We already knew that variations in the vasculature of the retina might offer insights into our health, and given that retinal imaging is a non-invasive technique, we decided to investigate the health benefits we could obtain from these images,” Ms Villaplana-Velasco said.
“First, we studied the branching patterns of the retinal vasculature by calculating a measure named fractal dimension (Df) from data available from the UKB and found that lower Df, simplified vessel branching patterns, is related to CAD and hence MI.”
Fractal dimension is a term from geometry which describes a ratio of complexity that compares how the level of detail in a pattern changes depending on the scale at which it is measured – in this case, the complexity of an individual’s retinal vasculature.
The person’s Df was then combined with traditional demographic and clinical data, such as age, sex, systolic blood pressure, body mass index and smoking status to calculate their risk of MI.
“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,” Ms Villaplana-Velasco said.
The research was able to improve on past attempts at generating accurate predictive models using retinal vascular traits by developing a more robust clinical definition of MI, refining the diagnostic codes that describe such events in medical records.
“Once we validated our MI definition, we found that our model worked extremely well,” Ms Villaplana-Velasco said. “[And] the improvement of our model was even higher if we added a score related to the genetic propensity of developing MI.”
The researchers believe it is possible that every condition may have a unique retinal variation profile and their findings could lay the groundwork for similar risk assessment scans for other diseases.