Predicting the risk of Parkinson’s disease

The PREDIGT Score: Calculating Parkinson Disease Risk in Healthy Adults

Dr. Michael Schlossmacher
University of Ottawa/The Ottawa Hospital
Pilot Project Grant
$45,000 over 1 year

The idea of creating a mathematical model to predict who will develop Parkinson’s disease struck Dr. Michael Schlossmacher as he read Brilliant Blunders, a book about the significance of the mistakes five great scientists made.

“The book is about understanding how errors are made, in part by quantifying risks,” says Schlossmacher, a neurologist and professor at the University of Ottawa. “That made me think of other things we have already quantified and calculated in life … and I became intrigued by whether we could do that for Parkinson’s risk.”

Schlossmacher is convinced that by entering known risk factors for Parkinson’s into his model, it is indeed possible to predict who will get the disease.

Researchers already know that age, chronic constipation, a reduced sense of smell, family history, chronic inflammation such as hepatitis or certain types of gastritis, certain environmental exposures, chronic infections, and gender are all risk factors. Men, for example, are 1.5-2 times more likely than women to develop Parkinson’s.

Schlossmacher and his colleagues, including Dr. Tiago Mestre and Dr. Doug Manuel, are combing through databases that include case files and histories of people doctors have followed over time. By entering data points they collect from those files into the model, and then comparing that to a subsequent diagnosis, they’ll test the accuracy of their predictive scores.

If the predictive model works, doctors could then work with people who have high scores to modify some of the risk factors, and potentially delay or avoid developing Parkinson’s altogether.

“We could then tell people—you have to fix your constipation—or you have to treat your chronic sinusitis more aggressively,” Schlossmacher says.

In the future, if researchers develop medicines or other interventions that could slow down Parkinson’s progression or remove other risk factors, then it will be important to know who is most at risk in order to target them for the interventions, he adds.

Revising his model until he gets it right is one of the tasks Schlossmacher has set himself, just as the scientists portrayed in Brilliant Blunders did.

“That’s part of the scientific journey, to have an idea, to test it, and then to revise it,” he says.

Although Schlossmacher did his initial scientific training by studying Alzheimer’s disease, he moved to Parkinson’s research because he wanted to help speed up the progress into discovering causative genes and understanding the molecular mechanisms that produce it.

Later, he learned that his grandfather and his aunt died of Parkinson’s and dementia, and a cousin has the early-onset form of the illness. He’ll factor those different forms of Parkinson’s into his predictive modelling, convinced of its practical application.

“It’s not only a mind exercise, it’s a very doable project,” Schlossmacher says.