An innovative eye test, developed by scientists at UCL in collaboration with London’s Western Eye Hospital, may predict wet AMD, the leading cause of severe vision loss, three years before forward symptoms.
Researchers hope their test could be used to identify the disease early enough so that treatment can prevent any vision loss.
The findings of the study, funded by Wellcome, are published today in Expert Review of Molecular Diagnostics.
Age-related macular degeneration (AMD), also known as macular disease, is the most common cause of permanent and severe vision loss in the UK.
Currently a diagnosis of wet AMD depends on a person developing symptoms, which will then require them to seek advice from a clinician. Initially, someone with wet AMD would notice a distortion in their vision, usually impeding their reading. Very quickly, this can proceed to complete moderate vision loss, which can be extremely difficult for elderly patients who experience blindness and loss of independence.
Wet AMD involves excessive growth of blood vessels, which allows water to enter the retina. The introduction of new treatments has led to much better outcomes for patients, for a disease that 20 years ago was considered untreated. However, patient outcomes may be even better if treatment is initiated in the earliest stages of the disease.
The test, called DARC (Detection of Apoptosing Retinal Cells), involves injecting into the bloodstream (through the arm) a fluorescent dye that binds to retinal cells , and clarifies those under stress or in the process of apoptosis, a type of programming. cell death. The damaged cells appear bright white when observed in eye tests – the more damaged cells are detected, the higher the DARC count.
One challenge with evaluating eye diseases is that experts often disagree when looking at the same scans, so the researchers have introduced an AI algorithm into their approach. .
Using the same technology (experiment) the researchers previously found that they can detect the earliest signs of glaucoma progression. This new study, which is part of the same follow-up clinical trial of DARC, evaluated 19 study participants who had already shown signs of AMD, but who may not have been in both eyes. The AI was retrained to detect leaks and new blood vessels, which were in line with the spots raised by DARC.
The new analysis found that DARC can stress endothelial cells (which stretch our blood vessels) under stress in the retina. These pressure cells then predict the future activity of wet AMD with the formation of leaks and new blood vessels seen in patients three years later, using routine eye scans with Optical Coherence Tomography ( OCT).
The researchers say their test could be valuable in finding new lesions in someone affected by AMD, often on the other hand, without side effects, and may it would ultimately be useful for screening people over a certain age or with known risk factors.
Our results are very promising because they show that DARC could be used as a biomarker for wet AMD when combined with the AI-supported algorithm.
Our new test was able to predict new AMD wet lesions up to 36 months before they occur and that’s huge – it means DARC activity can lead a clinician to more intensive treatment than usual. -those patients at high risk from AMD wet new lesions and also used as a screening tool. “
Francesca Cordeiro, Principal Investigator, Professor, UCL Institute of Ophthalmology, Imperial College London, and Imperial College Western Health Care NHS Trust
The study team hopes to continue their research with a clinical trial with more participants, and hope to study the trial in other eye diseases as well.
Head of the charity Fight for Sight eye study, Sherine Krause, said: “Our Time to Focus report on the social and economic impact of sight loss emphasized the importance of early detection for sight loss prevention, so this is an encouraging development in addressing the root cause of blindness. “
DARC is commercialized by Novai, a new company of which Professor Cordeiro is Chief Scientific Officer.
Source:
University College London