Affiliation:
1. Department of Mathematics, University of California Irvine, Irvine, CA 92697, USA
Abstract
The variability in the progression of Alzheimer's disease (AD) across patients has made identification of disease-delaying treatments difficult. Quantitative analysis of this variability has important implications in understanding the pathophysiology of AD and identifying disease-delaying treatments. The functional assessment staging (FAST) procedure characterizes seven stages in the course of AD from normal ageing to severe dementia. The present study applied statistical methods to analyse FAST stage durations from a dataset of 648 AD patients. These methods uncovered two distinct types of disease progression, characterized by different mean progression rates. We identified two separate distributions of FAST stage progression times differing by up to 2 years in mean duration within each stage. These results further indicate that if a patient progresses rapidly through a given FAST stage, then their further progression is also likely to be rapid. These findings support the hypothesis that progression of AD can occur via two different pathophysiological mechanisms that lead to distinct average rates of decline.
Subject
Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology
Cited by
37 articles.
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