On June 18, 2024, researchers from UCL and the University Medical Center Goettingen in Germany announced a groundbreaking development in Parkinson’s disease prediction. They have created a straightforward blood test, enhanced by artificial intelligence (AI), capable of predicting Parkinson’s up to seven years before symptoms appear.
The Growing Challenge of Parkinson’s
Parkinson’s disease is the fastest-growing neurodegenerative disorder globally, affecting nearly 10 million people. This progressive condition results from the degeneration of nerve cells in the substantia nigra, a brain region responsible for movement control. These nerve cells either die or lose function due to the accumulation of a protein called alpha-synuclein, leading to a deficiency in dopamine, a crucial neurotransmitter. Current treatments focus on dopamine replacement after symptoms, such as tremors and movement difficulties, manifest. Researchers emphasize the importance of early detection for developing therapies that protect dopamine-producing cells, potentially slowing or halting disease progression.
Innovative Approach to Early Diagnosis
Professor Kevin Mills from the UCL Great Ormond Street Institute of Child Health, a senior author of the study, highlighted the need for pre-symptomatic diagnosis. He stated, “With emerging therapies for Parkinson’s, diagnosing patients before symptom onset is crucial. We must protect existing brain cells since we cannot regenerate them.”
The research team employed advanced technology to identify and develop new biomarkers for Parkinson’s disease. Their goal was to create a test that could be easily implemented in any large NHS laboratory, potentially within two years with adequate funding.
Breakthrough Findings
Published in Nature Communications, the study revealed that AI, specifically machine learning, could analyze eight blood-based biomarkers with altered concentrations in Parkinson’s patients, achieving 100% diagnostic accuracy. The researchers then tested the predictive power of this approach by analyzing blood samples from 72 individuals with Rapid Eye Movement Behavior Disorder (iRBD). This disorder, characterized by patients physically acting out their dreams, is a known precursor to synucleinopathies, including Parkinson’s.
The machine learning tool identified that 79% of iRBD patients exhibited biomarker profiles similar to those with Parkinson’s. Over a ten-year follow-up, the AI predictions aligned with clinical outcomes, correctly identifying 16 patients who later developed Parkinson’s, up to seven years before symptom onset. The team continues to monitor these individuals to further validate the test’s accuracy.
Implications for Early Treatment
Dr. Michael Bartl from the University Medical Center Goettingen, a co-first author of the study, emphasized the potential impact of early diagnosis. He explained, “By detecting eight proteins in the blood, we can identify potential Parkinson’s patients years in advance. Early intervention with drug therapies could slow disease progression or even prevent it.”
This novel test not only diagnoses the disease but also identifies markers linked to processes like inflammation and protein degradation, offering potential targets for new treatments.
Further Validation and Future Prospects
Professor Kailash Bhatia from the UCL Queen Square Institute of Neurology and the National Hospital for Neurology & Neurosurgery is leading efforts to validate the test’s accuracy by examining samples from individuals at high risk of developing Parkinson’s, such as those with specific genetic mutations like ‘LRRK2’ or ‘GBA’. The team is also seeking funding to develop a simpler blood spot test, where a drop of blood on a card could be mailed to a lab for analysis, potentially predicting Parkinson’s even earlier than the current seven-year window.
Funding and Support
The research received funding from an EU Horizon 2020 grant, Parkinson’s UK, the National Institute for Health and Care Research GOSH Biomedical Research Centre (NIHR GOSH BRC), and the Szeben-Peto Foundation.
Professor David Dexter, Director of Research at Parkinson’s UK, praised the study as a significant advancement towards a definitive and patient-friendly diagnostic test for Parkinson’s. He noted the benefits of identifying biological markers in the blood, which is less invasive than the increasingly common lumbar punctures in clinical research.
Dexter added, “With further development, this blood-based test could differentiate Parkinson’s from other conditions with similar early symptoms, like Multiple Systems Atrophy or Dementia with Lewy Bodies. The findings contribute to an exciting surge of recent progress towards a simple Parkinson’s diagnostic test.”
The development of an AI-enhanced blood test for early diagnosis represents a promising step forward in neurodegenerative disease research. Early detection could lead to timely therapeutic interventions, potentially altering the disease’s course and significantly improving patient outcomes. The ongoing research and future innovations will continue to build on this pivotal breakthrough.
