Unravelling Parkinson's progression using biomarkers
Source: https://adelaide.edu.au/about/news/2026/unravelling-parkinson-s-progression-using-biomarkers
Archived: 2026-04-23 17:18
Unravelling Parkinson's progression using biomarkers
Unravelling Parkinson's progression using biomarkers
Published on 22 April 2026
Researchers are exploring the potential of specific biomarkers to help improve the prediction of disease progression for people with Parkinson’s Disease (PD), opening the door to more personalised treatment strategies.
While primarily considered a motor disorder, up to 85 per cent of people with PD will also be affected by neuropsychiatric symptoms, like cognitive impairment and mood dysfunction, at some point. These non-motor symptoms are major predictors of quality of life and mortality for those with the disease.
Currently, characterisation of PD is based mainly on presentation of clinical symptoms, but how diverse these symptoms are between individuals can make prognosis difficult.
This means that it’s unclear what the disease will look like for the individual affected as it progresses. What specific symptoms will they experience, and when will they experience these? This can make it difficult for people to plan ahead and can make clinical management of the disease challenging.
A team from Adelaide University’s School of Pharmacy and Biomedical Sciences, Australian Institute for Machine Learning and School of Psychology have found that biomarkers, including those with established utility in other neurocognitive disorders like dementia can improve prediction of disease trajectory for people with PD.
The findings were published in a series of
papers
in the
Journal of Geriatric Psychiatry and Neurology
.
Biomarkers are measurable indicators of what's happening in the body. In the current studies, the researchers examined whether including biomarkers measured either in the cerebrospinal fluid (CSF) or using neuroimaging techniques, such as MRI, near time of diagnosis improved prediction of both motor and non-motor symptom presentation five years later using data from the Parkinson's Progressive Markers Initiative database.
The resulting data was sorted into clusters based on similarities in symptom presentation, allowing the research team to complete their analysis.
"Across both studies, biomarkers had utility for improving prediction of how an individual's symptoms would present at the five-year follow-up, beyond clinical symptom presentation alone,” said Adelaide University Associate Professor Lyndsey Collins-Praino, who is the senior author on the papers.
"For clusters based on cognitive and mood symptoms, membership in the more impaired cluster was related to lower baseline CSF levels of amyloid-beta and higher baseline CSF levels of phosphorylated tau, which are established biomarkers linked to neurocognitive function in other disorders, such as dementia.
"Conversely, for clusters based largely on motor function, higher CSF levels of phosphorylated tau and lower CSF levels of alpha synuclein at baseline were predictive of membership in the more impaired cluster."
Associate Professor Collins-Praino said this indicates that different combinations of biomarkers may have utility for predicting motor versus non-motor symptoms progression.
"Excitingly, using both statistical and machine learning techniques, we showed in both studies that utilising a multi-modal panel of prognostic markers, beyond clinical symptom presentation alone, significantly improved prediction of cluster membership at year five follow-up. This may have utility for informing prognosis of both motor and non-motor outcomes in PD," she said.
"This might allow for improved prediction of disease trajectory in PD, currently an unmet need, given that disease presentation can differ significantly between individuals.
"Such enhanced understanding has the potential to directly impact clinical management of PD, leading to enhanced monitoring, such as earlier specialist referral and more personalised management strategies."
The study team is now looking at whether they can use a more comprehensive set of both blood-based and neuroimaging-based biomarkers, coupled with life history, to further improve prediction of symptom presentation in PD.
They are actively recruiting volunteers who have been diagnosed with PD as part of this work, to find out more email
find_tbi@adelaide.edu.au
.
Media Contact
Rhiannon Koch
Tel:
+61 481 619 997
Email:
rhiannon.koch@adelaide.edu.au
Topics
Featured story
Research
Health
Other articles you may be interested in
Unravelling Parkinson's progression using biomarkers
Published on 22 April 2026
Researchers are exploring the potential of specific biomarkers to help improve the prediction of disease progression for people with Parkinson’s Disease (PD), opening the door to more personalised treatment strategies.
While primarily considered a motor disorder, up to 85 per cent of people with PD will also be affected by neuropsychiatric symptoms, like cognitive impairment and mood dysfunction, at some point. These non-motor symptoms are major predictors of quality of life and mortality for those with the disease.
Currently, characterisation of PD is based mainly on presentation of clinical symptoms, but how diverse these symptoms are between individuals can make prognosis difficult.
This means that it’s unclear what the disease will look like for the individual affected as it progresses. What specific symptoms will they experience, and when will they experience these? This can make it difficult for people to plan ahead and can make clinical management of the disease challenging.
A team from Adelaide University’s School of Pharmacy and Biomedical Sciences, Australian Institute for Machine Learning and School of Psychology have found that biomarkers, including those with established utility in other neurocognitive disorders like dementia can improve prediction of disease trajectory for people with PD.
The findings were published in a series of
papers
in the
Journal of Geriatric Psychiatry and Neurology
.
Biomarkers are measurable indicators of what's happening in the body. In the current studies, the researchers examined whether including biomarkers measured either in the cerebrospinal fluid (CSF) or using neuroimaging techniques, such as MRI, near time of diagnosis improved prediction of both motor and non-motor symptom presentation five years later using data from the Parkinson's Progressive Markers Initiative database.
The resulting data was sorted into clusters based on similarities in symptom presentation, allowing the research team to complete their analysis.
"Across both studies, biomarkers had utility for improving prediction of how an individual's symptoms would present at the five-year follow-up, beyond clinical symptom presentation alone,” said Adelaide University Associate Professor Lyndsey Collins-Praino, who is the senior author on the papers.
"For clusters based on cognitive and mood symptoms, membership in the more impaired cluster was related to lower baseline CSF levels of amyloid-beta and higher baseline CSF levels of phosphorylated tau, which are established biomarkers linked to neurocognitive function in other disorders, such as dementia.
"Conversely, for clusters based largely on motor function, higher CSF levels of phosphorylated tau and lower CSF levels of alpha synuclein at baseline were predictive of membership in the more impaired cluster."
Associate Professor Collins-Praino said this indicates that different combinations of biomarkers may have utility for predicting motor versus non-motor symptoms progression.
"Excitingly, using both statistical and machine learning techniques, we showed in both studies that utilising a multi-modal panel of prognostic markers, beyond clinical symptom presentation alone, significantly improved prediction of cluster membership at year five follow-up. This may have utility for informing prognosis of both motor and non-motor outcomes in PD," she said.
"This might allow for improved prediction of disease trajectory in PD, currently an unmet need, given that disease presentation can differ significantly between individuals.
"Such enhanced understanding has the potential to directly impact clinical management of PD, leading to enhanced monitoring, such as earlier specialist referral and more personalised management strategies."
The study team is now looking at whether they can use a more comprehensive set of both blood-based and neuroimaging-based biomarkers, coupled with life history, to further improve prediction of symptom presentation in PD.
They are actively recruiting volunteers who have been diagnosed with PD as part of this work, to find out more email
find_tbi@adelaide.edu.au
.
Media Contact
Rhiannon Koch
Tel:
+61 481 619 997
Email:
rhiannon.koch@adelaide.edu.au
Topics
Featured story
Research
Health
Other articles you may be interested in