About 55 million people worldwide live with dementia, according to the World Health Organization. The most common form is Alzheimer’s disease, an incurable condition that causes brain function to deteriorate.
In addition to its physical effects, Alzheimer’s causes psychological, social and economic effects not only on the people living with the disease, but also on those who love and care for them. Because its symptoms worsen over time, it is important for both patients and their caregivers to prepare for the eventual need to increase the amount of support as the disease progresses.
To that end, researchers at the University of Texas at Arlington have created a new learning-based framework to help Alzheimer’s patients pinpoint where they are on the spectrum of disease development. This will allow them to better predict the timing of the later stages, making it easier to plan future care as the disease progresses.
“For decades, a variety of prognostic approaches have been proposed and evaluated for their ability to predict Alzheimer’s disease and its precursor, mild cognitive impairment,” said Dajiang Zhu, associate professor of computer science and engineering at UTA. He is the lead author on a new peer-reviewed paper published in open access Pharmacological Research. “Many of these previous prediction tools overlooked the continuous nature of how Alzheimer’s disease develops and the transitional stages of the disease.”
In work supported by more than $2 million in grants from the National Institutes of Health and the National Institute on Aging, Zhu’s Medical Imaging and Neuroscience Discovery research lab and UTA associate professor of mathematics Li Wang developed a new integration framework based on in learning. encodes the various stages of Alzheimer’s disease development in a process they call the “disease integration tree,” or DETree. Using this framework, DETree can not only predict any of the five detailed clinical clusters of Alzheimer’s disease development efficiently and accurately, but can also provide more in-depth status information by projecting where the patient will be as the disease progresses.
To test their DETree framework, the researchers used data from 266 people with Alzheimer’s disease from the multicenter Alzheimer’s Disease Neuroimaging Initiative. The results of the DETree strategy were compared with other widely used methods for predicting the progression of Alzheimer’s disease, and the experiment was repeated several times using machine learning methods to validate the technique.
“We know that people living with Alzheimer’s often experience worsening symptoms at very different rates,” Zhu said. “We are excited that our new framework is more accurate than other available prediction models, which we hope will help patients and their families better plan for the uncertainties of this complex and devastating disease.”
He and his team believe that the DETree framework has the potential to help predict the progression of other diseases that have multiple clinical stages of development, such as Parkinson’s disease, Huntington’s disease, and Creutzfeldt-Jakob disease.