Health and Medicine, Australia (Commonwealth Union) – Parkinson’s disease (PD) is a complex neurodegenerative disorder that primarily affects movement. While genetic factors play a role in certain cases, researchers have identified a variety of non-genetic causes that contribute to the development of Parkinson’s disease. Understanding these non-genetic factors is crucial for improving prevention strategies, developing targeted therapies, and enhancing the overall management of this debilitating condition.
Exposure to certain environmental toxins has been linked to an increased risk of Parkinson’s disease. One of the most well-known toxins is MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine), which was accidentally discovered to cause Parkinson’s-like symptoms in drug users in the 1980s. Other toxins, such as pesticides, heavy metals (e.g., lead and manganese), and industrial chemicals, have also been associated with an elevated risk of developing PD.
Oxidative stress, a condition characterized by an imbalance between the production of reactive oxygen species (ROS) and the body’s antioxidant defense mechanisms, has been implicated in the development of Parkinson’s disease. ROS can cause damage to cells, including neurons, leading to inflammation and the gradual degeneration of brain tissue. Additionally, mitochondrial dysfunction, which disrupts the energy production process within cells, is closely linked to oxidative stress and has been observed in Parkinson’s patients.
The absence of a test to diagnose non-genetic PD may soon be resolved by researchers from The University of New South Wales (UNSW) chemists.
Researchers from UNSW joined hands with Boston University for the production of a tool demonstrating early promise in identifying PD years prior to the emergence of the 1st symptoms.
In a study that appeared recently in the journal ACS Central Science, the researchers outlined the way they applied neural networks to analyze biomarkers in the bodily fluids of patients.
Scientists from the UNSW School of Chemistry evaluated blood samples obtained from healthy individuals that were collected by the Spanish European Prospective Investigation into Cancer and Nutrition (EPIC). Attention had been drawn to 39 patients who formed Parkinson’s up to 15 years later, researchers ran their machine learning over datasets that had extensive details regarding metabolites, which are the chemical compounds that the body forms in the process of breaking down food, drugs, or chemicals.
Following the contrasting of these metabolites to those of 39 matched control patients, the individuals in the same study that did not develop PD, the researchers identified unique combinations of metabolites that were capable of blocking or possibly being early warning signs of PD.
Diana Zhang a UNSW scientist indicated that together with Associate Professor W. Alexander Donald produced a machine learning tool known as CRANK-MS (Classification and Ranking Analysis using Neural network generates Knowledge from Mass Spectrometry).
Ms. Zhang indicated that the most frequent process for analyzing metabolomics data is via statistical approaches.
“So to figure out which metabolites are more significant for the disease versus control groups, researchers usually look at correlations involving specific molecules.”
“But here we take into account that metabolites can have associations with other metabolites – which is where machine learning comes in. With hundreds to thousands of metabolites, we’ve used computational power to understand what’s going on.”
The diagnosis of PD is conducted right now by noting physical symptoms like a resting hand tremor. There is an absence of blood or laboratory tests to diagnose the non-genetic cases. However atypical symptoms like sleep disorder and apathy may emerge for individuals with PD decades prior to the motor symptoms appearing. Hence CRANK-MS may be utilized at the 1st sign of these atypical symptoms for ruling in or out, the risk of PD in the future.
Associate Professor Donald indicated however that further studies across the world, are required to validate these findings.
