We celebrate our birthdays on the same date each year, but alongside our chronological age – the number of years we've been alive – we also have a biological age that reflects how quickly our bodies are succumbing to wear and tear.
Working out someone's biological age is more difficult than counting backward to a date of birth, though we are getting better at it.
Knowing this information can help in a variety of ways when it comes to health, including spotting serious problems earlier on.
Now researchers at Seoul National University have published a study in GeroScience showing that a scan of the back of the eye could offer a quick, noninvasive way to estimate biological aging – and one that predicts age more accurately than earlier retinal models.
The study builds on the retinal age gap (RAG) technology that scientists have been developing for several years.
It essentially uses a big training library of images to guess someone's age from their eye – a part of the body closely linked to the brain and heart – and to highlight potential differences in biological aging.
"Individuals of the same chronological age exhibit substantial variability in biological aging and health status, shaped by genetic, environmental, and lifestyle factors," write the researchers in their published paper.
"This underscores the growing need for accurate, accessible biomarkers of biological age that can capture this heterogeneity, improve risk prediction, and inform prevention strategies."
The researchers expanded on previous RAG research with new training data and some algorithm tweaks. Importantly, they used a mix of imagery from healthy eyes and those with retinal or optic nerve disease, which most previous studies hadn't done.
A total of 29,530 retinal scans from 7,535 participants were fed into the system, which was then put to the test on 14,832 different scans from another 7,416 participants.
The model was able to predict actual chronological age with an average error of 2.5–2.7 years, which stands up well against previous RAG setups.
In terms of biological aging, higher RAGs were associated with a variety of health and lifestyle factors: +2.52 years for diabetes, +0.5 years for current smokers, +0.46 years for former smokers, +0.6 years for macular degeneration, and +1.86 years for cataracts.
The researchers note that the cataract link may partly reflect cloudier images rather than faster retinal aging.
It's also worth noting that the researchers didn't test their approach against other ways of measuring biological aging. However, the links between larger RAG scores and conditions known to affect biological age suggest it could be used in this way.
"RAG, an imaging-derived age-prediction residual, is therefore associated with lifestyle, systemic, and ocular health," write the researchers.
The researchers emphasize the value in expanding training data libraries with eye scans from both healthy and diseased eyes, and in getting the underlying RAG model to account for sex at the same time as assessing age – what's known as "multi-task learning".
In simple terms, running two assessments at once potentially reduces inaccuracies that might be caused by differences between men and women.
And as well as assessing biological age, the technology could also be useful in spotting health problems that would otherwise get missed – although more research and development will be needed before that happens.
"In ophthalmology clinics where fundus imaging is routinely performed, RAG may help flag patients for further systemic evaluation," write the researchers.
"If validated in longitudinal studies, serial RAG measurements may provide a means to track within-person change and to assess responses to systemic interventions."
Potential next steps include tracking participants over time to see whether treatments or other changes affect retinal 'age', and comparing the accuracy of RAG against established biological aging assessments.
For now, the system will be most useful for tracking population-level trends rather than individuals, because many of the effects it picks up are small compared with the model's margin of error.
"Whether it reflects biological aging requires longitudinal validation against established aging biomarkers," write the researchers.
"At present, RAG suits population-level characterization better than individual-level risk stratification."
The research has been published in GeroScience.
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