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Data Mining Strategy May Spot Those At High Alzheimer’s Risk

Summary: Researchers use a multilayer clustering algorithm to help identify people at risk of developing Alzheimer’s disease.

Source: Duke.

Method could group similar Alzheimer’s patients for more precise drug trials.

The push to develop treatments for Alzheimer’s disease has been a promising and disappointing endeavor over the past two decades, yielding a greater understanding of the disease yet still failing to generate successful new drugs.

To blame are the many undefined subtypes of mild cognitive impairment, a precursor to Alzheimer’s disease.

“Everyone thinks Alzheimer’s is one disease, but it’s not,” said P. Murali Doraiswamy, M.D., professor of psychiatry and director of the neurocognitive disorders program at Duke Health. “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.”

But if scientists grouped people with similar types of cognitive impairment, they could more precisely test the impact of investigational drugs, according to findings in a July 28 article in the journal Scientific Reports, a publication of Nature Research.

The research was jointly led by Dragan Gamberger, Ph.D., an artificial intelligence expert at the Rudjer Boskovic Institute in Croatia and Doraiswamy.

To identify similar disease types, the team used a multilayer clustering algorithm to sort through dozens of data points from two large studies of the Alzheimer’s Disease Neuroimaging Initiative. Study data included cognition tests, brain scans and spinal fluid biomarkers from 562 people with mild cognitive impairment who were followed for up to five years.

Two groups of interest emerged — those whose cognition declined significantly and those who saw little or no decline in their symptoms. The brain scans of the 240 so-called ‘rapid decliners’ showed twice the rate of atrophy as the 184 people with a slow-moving disease. Rapid decliners also progressed from mild cognitive impairment to dementia at five times the rate of those with slow-moving disease, the analysis showed.

 Image shows a ven diagram.

Clustering of the total (N = 562) MCI sample into Rapid and Slow decliners. NeuroscienceNews.com image is credited to Doraiswamy et al./Scientific Reports.

The team went further to devise a simpler way scientists could identify these two distinct groups in future research using existing paper-and-pencil cognitive tests.

Researchers could use these findings to create more homogeneous trial groups, which would allow scientists to design shorter trials and gain better insights on the effectiveness of experimental therapies, Gamberger said.

The team will continue to delve more deeply into the rapid decliners to evaluate the impact of factors like sex, lifestyle and genetics to learn what could cause their cognition to spiral so quickly.

“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.”

About this neuroscience research article

In addition to Gamberger and Doraiswamy, study authors include Nada Lavrac, Ph.D., Shantanu Srivatsa and Rudolph E. Tanzi, Ph.D.

Funding: Doraiswamy was supported by the Cure Alzheimer’s Fund and the Karen L. Wrenn Trust. Gamberger received support from the European Commission, the Croatian Science Foundation and the Rudjer Boskovic Institute. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative and U.S. Department of Defense ADNI. Additional funding details and disclosures are detailed in the manuscript.

Source: Samiha Khanna – Duke
Image Source: NeuroscienceNews.com image is credited to Doraiswamy et al./Scientific Reports.
Original Research: Full open access research for “Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease” by Dragan Gamberger, Nada Lavrač, Shantanu Srivatsa, Rudolph E. Tanzi & P. Murali Doraiswamy in Scientific Reports. Published online July 28 2017 doi:10.1038/s41598-017-06624-y

Cite This NeuroscienceNews.com Article
Duke “Data Mining Strategy May Spot Those At High Alzheimer’s Risk.” NeuroscienceNews. NeuroscienceNews, 28 July 2017.
<http://neurosciencenews.com/data-mining-alzheimers-7195/>.
Duke (2017, July 28). Data Mining Strategy May Spot Those At High Alzheimer’s Risk. NeuroscienceNew. Retrieved July 28, 2017 from http://neurosciencenews.com/data-mining-alzheimers-7195/
Duke “Data Mining Strategy May Spot Those At High Alzheimer’s Risk.” http://neurosciencenews.com/data-mining-alzheimers-7195/ (accessed July 28, 2017).

Abstract

Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease

The heterogeneity of Alzheimer’s disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.

“Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease” by Dragan Gamberger, Nada Lavrač, Shantanu Srivatsa, Rudolph E. Tanzi & P. Murali Doraiswamy in Scientific Reports. Published online July 28 2017 doi:10.1038/s41598-017-06624-y

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