Retinal age gap linked to heightened death risk - research suggests
(British Medical Journal Newsroom) - The difference between the biological age of the retina, the light sensitive layers of nerve tissue at the back of the eye, and a person’s real (chronological) age, is linked to their risk of death, finds research published online in the British Journal of Ophthalmology.
This ‘retinal age gap’ could be used as a screening tool, suggest the researchers.
A growing body of evidence suggests that the network of small vessels (microvasculature) in the retina might be a reliable indicator of the overall health of the body’s circulatory system and the brain.
While the risks of illness and death increase with age, it’s clear that these risks vary considerably among people of the same age, implying that ‘biological ageing’ is unique to the individual and may be a better indicator of current and future health, say the researchers.
Several tissue, cell, chemical, and imaging-based indicators have been developed to pick up biological ageing that is out of step with chronological ageing. But these techniques are fraught with ethical/privacy issues as well as often being invasive, expensive, and time consuming, say the researchers.
They therefore turned to deep learning to see if it might accurately predict a person’s retinal age from images of the fundus, the internal back surface of the eye, and to see whether any difference between this and a person’s real age, referred to as the ‘retinal age gap’, might be linked to a heightened risk of death.
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way people acquire certain types of knowledge. But unlike classic machine learning algorithms that are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity.
The researchers drew on 80,169 fundus images taken
from 46,969 adults aged 40 to 69, all of whom were part of the UK Biobank, a large, population-based study of more than half a million middle aged and older UK residents.
Some 19,200 fundus images from the right eyes of 11,052 participants in relatively good health at the initial Biobank health check were used to validate the accuracy of the deep learning model for retinal age prediction.
This showed a strong association between predicted retinal age and real age, with an overall accuracy to within 3.5 years.
The retinal age gap was then assessed in the remaining 35,917 participants during an average monitoring period of 11 years.
During this time, 1871(5%) participants died: 321(17%) of cardiovascular disease; 1018 (54.5%) of cancer; and 532 (28.5%) of other causes including dementia.