Pankaj Kapahi, a professor at Buck Institute, has demonstrated how imaging the fundus in the retina can be used to monitor human aging. This non-invasive and cost-effective method is more precise than the current aging clocks.
Retinal imaging can track aging
To determine the genetic foundation of eyeAge, the researchers executed a genome-wide association study (GWAS). The analysis, eyeAge, was performed in partnership with Zuckerberg San Francisco General Hospital and Google Health.
Professor Kapahi, the study’s senior author, said that imaging the retina could be instrumental in tracking how efficient some interventions can be in slowing the aging process. According to the findings, it is possible to predict the aging trajectory with a 71% accuracy rate in under a year by observing noticeable alterations in the eyes of patients undergoing treatment.
According to Kapahi, retinal scans are considered more reliable than other biomarkers from blood as they are less influenced by day-to-day fluctuations, unlike blood biomarkers which can be affected by factors such as eating or infection.
As per recent studies, the microvasculature in the retina can be a reliable indication of the circulatory system and the brain’s overall health. Furthermore, it is observed that changes in the eye occur during the aging process and some age-related diseases such as Alzheimer’s disease, Parkinson’s, diabetic retinopathy, and age-related macular degeneration (AMD).
Early signs of disease can be visible from changes in the eye vascular system
Ophthalmologists can detect early signs of AIDS, tumors, and high blood pressure, in the eyes as any changes in the vascular system are initially visible in the smallest blood vessels, including the retina’s capillaries.
In the latest development, Google Research is using deep learning to identify diabetic retinopathy and other eye and non-eye diseases from retinal images due to subtle changes in small blood vessels that can go undetected by modern instruments. In addition, the researchers have developed models for diabetic prediction retinopathy and identified around 39 other diseases. Senior computational biologist at Alkahest and co-corresponding study author Sarah Ahadi said their study emphasized the significance of longitudinal data in determining aging trajectories.
