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Title:Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease
Authors:ID Gamberger, Dragan (Author)
ID Lavrač, Nada (Author)
ID Srivatsa, Shantanu (Author)
ID Tanzi, Rudolph E. (Author)
ID Doraiswamy, Murali (Author)
Files:.pdf publisheds41598-017-06624-y.pdf (1,78 MB)
MD5: 78A6009CC34D2A46C4FCEF1CAF7F3FF7
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract: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.
Keywords:Alzheimer's disease, Rapid decliners, Data clustering, Mild cognitive impairment
Article version:Publisher's version of article
Year of publishing:2017
Number of pages:12
PID:20.500.12556/RUNG-3207-b1067889-a6a1-e90e-c5bf-30ae123e7ad0 New window
COBISS.SI-ID:4875771 New window
DOI:10.1038/s41598-017-06624-y New window
Publication date in RUNG:17.08.2017
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Record is a part of a journal

Title:Scientific Reports
Year of publishing:2017

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Name:program "Knowledge Technologies" in projekt "Development and Applications of New Semantic Data Mining Methods in Life Sciences"


License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:14.08.2017