Parkinson’s disease, the fastest-growing neurological disorder worldwide, might soon be detectable long before symptoms emerge. Thanks to groundbreaking research from University College London and University Medical Center Goettingen, a new artificial intelligence system can identify the disease early by analyzing specific proteins in blood samples.
Early Detection via AI
In an interview with Interesting Engineering, Professor Kevin Mills, the senior author of the study, explained that the AI focuses on patients with Rapid Eye Movement Sleep Behavior Disorder (iRBD). Approximately 75% to 80% of iRBD patients develop synucleinopathy, including Parkinson’s disease. The AI tool compared blood profiles of iRBD patients with those of known Parkinson’s patients, identifying a 79% match.
Long-Term Study and Results
Over a decade, researchers followed the iRBD patients, continuously refining the AI tool. They successfully identified 16 individuals who developed Parkinson’s years before any symptoms appeared. The AI’s accuracy has now reached 100%, a significant milestone in early disease detection.
Potential for Early Treatment
Dr. Michael Bartl, co-first author of the study, highlighted the implications of this early detection method: “By determining eight proteins in the blood, we can identify potential Parkinson’s patients years in advance. This means that drug therapies could potentially be given at an earlier stage, which could possibly slow down disease progression or even prevent it from occurring.”
Future Prospects
The research team aims to simplify the blood test further, potentially allowing for detection even earlier than seven years before symptoms arise. This advancement not only holds promise for early treatment but also for the development of new drugs targeting Parkinson’s disease.