A promising new AI tool
Parkinson’s disease is a neurodegenerative disease that affects movement.
It currently has no cure and is typically diagnosed when physical symptoms such as hand tremors are felt.
However, scientists from UNSW Sydney and Boston University have recently made a significant step towards early detection of Parkinson’s disease, potentially years before these symptoms appear.
Using AI to uncover disease biomarkers
In a study published in ACS Central Science, the researchers describe how they used machine learning – a form of artificial intelligence – to examine biomarkers, or biological indicators of disease, in blood samples from patients.
The team examined blood samples from healthy people, focusing in particular on 39 people who later developed Parkinson’s disease.
They ran a machine learning program on datasets containing extensive information about metabolites, which are the chemical compounds our bodies produce when we break down food, drugs, or other substances.
CRANK-MS: A new machine learning tool
The team developed a machine learning tool called CRANK-MS (Classification and Ranking Analysis using Neural Network Geneses Knowledge from Mass Spectrometry) to analyze the metabolites.
Unlike traditional statistical approaches that focus on specific molecules, CRANK-MS takes into account the relationships between different metabolites, allowing for a more comprehensive understanding of the biochemical processes in the body.
Researchers fed all available data into CRANK-MS without first reducing the number of chemical features, as is common in other machine learning applications.
This approach allowed them to identify critical metabolites and determine their role in predicting the onset of Parkinson’s disease.
Implications for the diagnosis of Parkinson’s disease
Current methods for diagnosing Parkinson’s disease are limited to observing physical symptoms.
However, atypical symptoms such as sleep disturbances and apathy can appear decades before the onset of the motor symptoms of Parkinson’s disease.
This is where CRANK-MS could be groundbreaking. The tool could be used early on at the first sign of these atypical symptoms to detect the risk of future development of Parkinson’s disease.
In the limited cohort examined for this study, the results were promising. CRANK-MS was able to analyze chemicals in blood samples to detect Parkinson’s disease with an impressive accuracy of up to 96 percent.
Dietary habits and Parkinson’s disease
The study also revealed interesting insights into the diets of people who later developed Parkinson’s disease.
For example, lower levels of triterpenoids, a well-known neuroprotectant found in foods like apples, olives and tomatoes, were found in the blood of those who later developed Parkinson’s disease.
Future studies could explore whether a diet rich in these foods might provide natural protection against the disease.
Another intriguing discovery was the presence of polyfluorinated alkyl substances (PFAS), which are industrial chemicals, in people who later developed Parkinson’s disease.
This finding suggests a possible link between exposure to certain chemicals and onset of the disease.
A tool available to everyone
CRANK-MS is publicly available to any researcher interested in using machine learning to diagnose disease using metabolomics data.
The tool’s design makes it suitable for various applications and it can provide results in less than 10 minutes on a regular laptop.
This is just one example of how AI can improve disease diagnosis and monitoring, potentially paving the way for early detection and better treatment of various diseases.
If you are interested in Parkinson’s disease, please read studies about it Vitamin E can help prevent Parkinson’sAnd Vitamin D could benefit people with Parkinson’s disease.
For more information on brain health, see the latest studies on new ways to treat Parkinson’s disease and their results Foods rich in flavonoids could improve survival in Parkinson’s disease.
The study was published In ACS Central Science.
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