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Progress Toward Predicting Parkinson’s Disease

Progress Toward Predicting Parkinson’s Disease

Who will develop Parkinson’s disease?

Our inability to answer this question slows research and vexes people with family history of Parkinson’s disease (PD).

With new findings from the MJFF-sponsored Parkinson’s Progression Markers Initiative, though, we may be a step closer to an answer.

Researchers from the National Institute on Aging (NIA), part of the National Institutes of Health, recently published a paper in The Lancet Neurology that outlines five criteria they used to differentiate people with Parkinson’s disease from control volunteers: olfactory function (smell loss), genetic risk score, family history of PD, age and gender.

“The idea is that we could start to get at things that might be able to predict disease. These are things that we thought might be early markers of Parkinson’s disease,” said Andrew Singleton, PhD, chief of the NIA Laboratory of Neurogenetics and lead author of the paper.

Identifying the Five Criteria
Last year this same research team identified 28 genetic risk factors for Parkinson’s disease. Looking at PPMI, they gave each individual a genetic risk composite score based on the presence of those mutations.

They then turned to “easy-to-access” data points that could be easily assembled for any individual as opposed to, for example, a time-consuming and costly imaging scan. They ran every qualified factor from PPMI data through a model with the genetic risk score. Only those four other criteria were informative (smell loss, family history of PD, age and gender).

“Those in combination in this model allow us to predict who has disease and who doesn’t,” Dr. Singleton said.

After refining their model with PPMI data, NIA researchers used data from five other studies to validate their findings.

The NIA team also applied its formula to PPMI subjects without evidence of dopaminergic deficit (SWEDD). This participant population showed clinical symptoms of PD but not the trademark dopamine activity loss in brain scans upon enrollment in the study. Five PPMI SWEDD subjects showed dopamine loss in follow-up scans one to two years later. The NIA criteria classified four of the five as PD (versus control) with the final one on the cusp of PD classification. These findings give evidence for the predictive use of their five-point formula.

Accelerating Intervention for Greatest Impact
Researchers are interested in predicting who will develop Parkinson’s disease because earlier detection can mean earlier intervention. Many researchers believe that PD clinical trials have failed because they were not intervening in the disease process early enough to show substantial benefit.

“We know that when people begin showing Parkinson’s cardinal symptoms, there is already significant dopamine loss,” said Mark Frasier, PhD, MJFF senior vice president of research programs. “If we could predict who may develop Parkinson’s and start them on safe preventive therapies, we may be able to save those dopamine cells and stop PD.”

As for how they’ll find those people, Dr. Singleton thinks technology will help.

“We’re collecting all this data in electronic medical records (EMR), and we’ll start to use that data to predict disease. Your smell status — if you’ve had it evaluated — genetics, age, gender will all be in your EMR. I think we’ll use this to highlight individuals who we should be keeping an eye on,” he said.

Opening Data Access for Discovery and Validation
“This study would not have been possible without PPMI and more importantly would not have been possible without open-source data. The value of PPMI is two-fold: it’s the fact that the data is there and that it’s been an example for others,” said Dr. Singleton.

His group is now working with a health care system to identify individuals without PD who may develop Parkinson’s in the future. While, today, there is no preventive intervention, this population could enroll in clinical trials and help develop those next-generation therapies to stop the disease.

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