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

Dragan Gamberger (Author), Nada Lavrač (Author)

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;

Data

Language:	English
Year of publishing:	2017
Typology:	1.01 - Original Scientific Article
Organization:	UNG - University of Nova Gorica
UDC:	577
COBISS:	4875771
ISSN:	2045-2322
Views:	3537
Downloads:	343
Average score:	0 (0 votes)
Metadata:

Other data

URN:	URN:SI:UNG
Type (COBISS):	Not categorized
Pages:	str. 1-12
Issue:	ǂVol. ǂ7
Chronology:	2017
DOI:	10.1038/s41598-017-06624-y
ID:	10859184