Australian researchers have established that a vascular ‘fingerprint’ at the back of the eye can accurately predict stroke risk.
The study, which involved researchers from the University of Melbourne, Monash University and Hong Kong Polytechnic University, and which was published by the Centre for Eye Research Australia (CERA), showed that the ‘fingerprint’ was as accurate as traditional risk factors and could be used without the need for invasive tests.
Stroke affects around 100 million people around the globe and kills 6.7m every year. Most cases are caused by modifiable risk factors, such as high blood pressure, high cholesterol, poor diet, and smoking.
A media release supporting the research said the fingerprint, comprising 29 indicators of vascular health, was a practical and readily implementable approach “that is particularly well suited for primary healthcare and low-resource settings”.
“The retina’s intricate vascular network is known to share common anatomical and physiological features with the vasculature of the brain, making it an ideal candidate for assessing damage from systemic ill health, such as diabetes,” said the researchers.
“Its potential for stroke risk prediction hasn’t been fully explored, due to variable study findings and inconsistent use of the specialised imaging technique for the back of the eye— fundus photography.”
They said machine learning (AI), such as the Retina-based Microvascular Health Assessment System, had opened up the possibilities for the identification of biological markers that could accurately predict stroke risk without the need for invasive lab tests.
To explore this further, they measured 30 indicators across five categories of retinal vascular architecture in fundus images from 68,753 UK Biobank study participants.
The five categories included calibre (length, diameter, ratio) density, twistedness, branching angle and complexity of the veins and arteries. And they accounted for potentially influential risk factors: background demographic and socioeconomic factors; lifestyle; and health parameters, including blood pressure, cholesterol, HbA1c (blood glucose indicator), and weight (BMI).
The final analysis included 45,161 participants (average age 55), said the release. During an average monitoring period of 12.5 years, 749 participants had a stroke.
In all, 118 retinal vascular measurable indicators were included, of which 29 were significantly associated with first-time stroke risk after adjusting for traditional risk factors. Over half (17) were density indicators; eight fell into the complexity category; three were calibre indicators; and one came under the twistedness category.
Each change in density indicators was associated with an increased stroke risk of 10-19%, while similar changes in calibre indicators was associated with an increased risk of 10-14%.
Each decrease in the complexity and twistedness indicators was associated with an increased risk of 10.5-19.5%. This retinal ‘vascular fingerprint’, even when combined with just age and sex, was as good as the use of traditional risk factors alone for predicting future stroke risk, the findings showed.
“Given that age and sex are readily available, and retinal parameters can be obtained through routine fundus photography, this model presents a practical and easily implementable approach for incident stroke risk assessment, particularly for primary healthcare and low-resource settings.”
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