July 28 (UPI) -- Researchers at Duke University Medical Center have developed a new data mining strategy to more easily identify people at high risk for Alzheimer's disease.
"Everyone thinks Alzheimer's is one disease, but it's not," Dr. P. Murali Doraiswamy, a professor of psychiatry and director of the neurocognitive disorders program at Duke Health, said in a press release.
"There are many subgroups. If you enroll all different types of people in a trial, but your drug is targeting only one biological pathway, of course the people who don't have that abnormality are not going to respond to the drug, and the trial is going to fail."
The study, published July 28 in Scientific Reports, grouped individuals with similar types of cognitive impairment to more precisely test the impact of investigational drugs.
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Researchers used a multilayer clustering algorithm to sort through dozens of data points from two large studies of the Alzheimer's Disease Neuroimaging Initiative. They conducted cognition tests, brain scans and tested spinal fluid biomarkers on 562 people with mild cognitive impairment over a five-year period.
The study identified two groups -- those whose cognition declined significantly and others who had little or no decline in their symptoms.
Individuals who showed rapid decline had twice the rate of atrophy as the other group with slow decline, and the rapid decliners progressed from mild cognitive impairment to dementia five times faster than patients with slower decline.
"The findings have direct implications for the design of future trials," Doraiswamy said. "We have known bits and pieces of this information -- that there are dozens of genes that put people at risk, or that certain brain changes put people at risk -- but using unbiased tools, such as data mining with this novel algorithm, we can put all of these pieces of information together to identify those who are truly at the greatest risk."
