Scientists have developed a new blood test that uses artificial intelligence (AI) to identify individuals who might develop Parkinson’s disease up to seven years before symptoms appear. This innovation has the potential to significantly improve research into treatments aimed at slowing or preventing the disease.
The test leverages AI to detect a specific pattern of eight proteins in the blood that are associated with Parkinson’s. Researchers from University College London (UCL) and the University of Göttingen have successfully trained a machine learning algorithm to recognize this protein signature in patients with Parkinson’s. The algorithm was then able to predict future cases of Parkinson’s in other individuals based on their blood samples. In one notable instance, the test correctly predicted the onset of Parkinson’s more than seven years before symptoms appeared.
Professor Kevin Mills, a senior author of the study from the UCL Great Ormond Street Institute of Child Health, highlighted the importance of early detection. “At the moment, we’re shutting the stable door after the horse has bolted,” he said. “We need to get to people before they develop symptoms. It’s always better to do prevention rather than cure.”
Parkinson’s disease is the fastest-growing neurodegenerative condition globally, mainly due to the aging population. It affects over 150,000 people in the UK and around 10 million worldwide. The disease is characterized by the accumulation of a protein called alpha-synuclein, which damages or destroys nerve cells that produce dopamine in a part of the brain known as the substantia nigra. This results in symptoms such as tremors, difficulty with movement, muscle stiffness, balance issues, memory problems, dizziness, and nerve pain. While dopamine replacement therapy is a common treatment, researchers are actively seeking ways to slow or stop the progression of the disease.
Professor Roger Barker, a neurologist at the University of Cambridge, emphasized the potential impact of this test. If validated by further studies, it could enable the earliest possible diagnosis of Parkinson’s, allowing patients to participate in clinical trials for disease-modifying therapies before significant brain cell loss occurs. “We could treat people with Parkinson’s with disease-modifying therapies before they have lost many cells in their brain,” he said. “This study is a step in the right direction.”
However, the development and implementation of such a predictive test come with challenges. Professor Ray Chaudhuri, the medical director of the Parkinson Foundation International Centre of Excellence, pointed out that Parkinson’s is a syndrome with varied presentations, which complicates management. He also noted ethical concerns and the potential impact on patients’ insurance policies if early diagnosis becomes routine without effective treatments available.
Despite these challenges, Chaudhuri acknowledged the value of having a group of individuals identified as “at risk” for Parkinson’s. These individuals could be suitable candidates for future trials of neuroprotective treatments. Additionally, there is some preliminary evidence that physical activity and structured exercise may benefit those at risk, potentially slowing the progression of the disease.
This research represents a significant advance in the fight against Parkinson’s and highlights the critical role of early detection in managing neurodegenerative diseases. The study’s findings were published in Nature Communications.
This article is based on information from an article by Ian Sample, Science Editor at The Guardian, originally published on June 18, 2024. You can check out the full article here.
Hi, I'm Voss Xolani, and I'm passionate about all things AI. With many years of experience in the tech industry, I specialize in explaining the functionality and benefits of AI-powered software for both businesses and individual users. My content explores the latest AI tools, offering practical insights on how they can streamline workflows, boost productivity, and drive innovation. I also review new software solutions to help readers understand their features and applications. Beyond that, I stay up-to-date with AI trends and experiment with emerging technologies to provide the most relevant information.