Using a technique they developed for studying eye fluid, Stanford Medicine researchers and their collaborators have found a way to measure ocular aging, opening avenues for treatment of numerous eye diseases.

The scientists looked at nearly 6,000 proteins in the fluid and found that they can use 26 of them to predict aging. Using artificial intelligence, they developed an eye-aging “clock,” indicating which proteins accelerate aging in each disease and revealing new potential targets for therapies.

The culprit cells behind aging eyes

Using eye fluid from 46 healthy patients, Mahajan and his team trained an AI algorithm to predict the age of the patient. They then fed the algorithm the nearly 6,000 proteins present in the fluid to see if a subset of these proteins could predict the patient’s age. They found 26 that could do so when used as a group.

Comparing the diseased eye fluid with the healthy fluid, they found that patients with diseased eyes had proteins that indicated a higher age: 12 years older in patients with early-stage diabetic retinopathy, 31 years in those with late-stage diabetic retinopathy, 16 years in retinitis pigmentosa patients and 29 years in uveitis patients.

The model also found that the cells responsible for indicating increased age were different with each disease: vascular cells in late-stage diabetic retinopathy, retinal cells in retinitis pigmentosa and immune cells in uveitis.

They also found that some cells commonly targeted in treatment are not the ones most involved in disease, encouraging a reevaluation of therapies. For example, diabetes drugs commonly target blood vessel cells because they become leaky with disease, but they found a big increase in proteins from healthy to late-stage diabetic retinopathy is in macrophages, an immune cell that removes dead cells.

The researchers found that some cells had showed accelerated aging before symptoms appeared. Treating the molecular pathway early, Mahajan said, could prevent disease damage before it becomes irreparable.

Informing clinical trials

Targeting both aging and disease cells could make treatment more effective, Mahajan said, because the two appear to act separately but simultaneously to damage the eye.

Mahajan anticipates that researchers will apply the TEMPO technique and aging clock to other organ fluids such as liver bile and joint fluid.

Mahajan hopes that by knowing these biomarkers, researchers will run more successful clinical trials because they will have a more refined look into the cellular processes driving disease. Currently, 90% of drug candidates tested in mice models or human cells fail in clinical trials. Knowing the cells driving disease and aging may increase chances of success, Mahajan said.

Source: https://med.stanford.edu/news/all-news/2023/10/eye-aging-clock.html