Researchers have developed an AI-enhanced blood test that can predict the onset of Parkinson’s disease up to seven years before symptoms manifest.
The study, led by scientists from University College London (UCL) and the University Medical Center Goettingen in Germany and published in Nature Communications, could unlock early, targeted treatments to slow the progression of this debilitating neurodegenerative disorder.
Parkinson’s disease is a rising global health concern, affecting nearly 10 million people worldwide, including over 1 million individuals in the US and 150,000 people in the UK.
The disease is characterized by symptoms such as tremors, movement difficulties, muscle stiffness, balance issues, memory problems, dizziness, and nerve pain. Symptoms arise due to the death of nerve cells in the “substantia nigra,” a part of the brain responsible for controlling movement.
Currently, there are no treatments available to halt or reverse the disease progression, and most patients are diagnosed only after symptoms have already developed. As Dr Jenny Hällqvist from UCL, a study co-author, explained, “People are diagnosed when neurons are already lost. We must protect those neurons, not wait until they are gone.”
This breakthrough AI-enhanced blood test can predict Parkinson’s disease with up to 79% accuracy as long as seven years before symptoms surface.
Research News 📣
Parkinson’s UK funded research shows promise for a blood
test that could identify Parkinson’s before movement symptoms occur.Read the full story and what this means for people with Parkinson’s 👉🏽 https://t.co/2LwhkHRXbf pic.twitter.com/yxTZHRSgJQ
— Parkinson’s UK (@ParkinsonsUK) June 18, 2024
More about the study
Here’s how the study worked:
- Identifying potential biomarkers: The study began by analyzing blood samples from recently diagnosed Parkinson’s patients and healthy controls using advanced mass spectrometry techniques. This allowed the researchers to identify 47 proteins that were expressed differently between the two groups.
- Developing a targeted blood test: From their initial analysis, the team developed a targeted blood test to measure the levels of 121 specific proteins.
- Validating the test: The researchers then applied the targeted test to blood samples from an independent group of Parkinson’s patients, healthy controls, individuals with other neurological disorders, and patients with isolated REM sleep behavior disorder (iRBD), a known risk factor for Parkinson’s. This confirmed that 23 of the measured proteins significantly differed between Parkinson’s patients and healthy controls.
- Applying machine learning: The data from the validation step was used to train machine learning models to distinguish between Parkinson’s disease and healthy controls based on protein levels. A model using just eight of the proteins was able to correctly classify Parkinson’s and healthy samples with 100% accuracy.
- Results: To further confirm the findings, the researchers refined the test and applied it to a separate group of 54 iRBD patients who had provided 146 blood samples over time. The machine learning models predicted that 70-79% of these samples were Parkinson’s-like, with some of these predictions being made up to seven years before the individuals developed Parkinson’s symptoms.
The team now plans to simplify the test further, allowing patients to simply mail a drop of blood on a card to the lab for analysis.
Professor David Dexter, research director at Parkinson’s UK, a charity that helped fund the study, lauded the findings, stating, “The findings add to an exciting flurry of recent activity towards finding a simple way to test for and measure Parkinson’s.” He also suggested that the test may be able to differentiate between Parkinson’s and other similar conditions.
While larger trials are necessary to validate the accuracy and reliability of this AI-enhanced blood test, it represents a massive step forward in the quest for early diagnosis of Parkinson’s disease.
It’s not the first time AI has been deployed to identify risk factors or signs of Parkinson’s.
Not long ago, researchers developed an eye test that could similarly identify the disease before symptoms develop.
Google’s longstanding AlphaFold project shows promise in discovering precisely how diseases like Parkinson’s develop, and the University of Cambridge researchers developed a model for discovering Parkinson’s drugs that were 1000 times faster than conventional methods.
AI is having a blockbuster year for combating disease and accelerating cutting-edge treatments, with OpenAI and Color Health collaborating on a cancer copilot earlier this week.
