Literature DB >> 31177226

Disentangling Heterogeneity in Alzheimer's Disease: Two Empirically-Derived Subtypes.

Anna E Blanken1, Shubir Dutt1, Yanrong Li1, Daniel A Nation1.   

Abstract

BACKGROUND: Clinical-pathological Alzheimer's disease (AD) subtypes may help distill heterogeneity in patient presentation. To date, no studies have utilized neuropsychological and biological markers to identify preclinical subtypes with longitudinal stability.
OBJECTIVE: The objective of this study was to empirically derive AD endophenotypes using a combination of cognitive and biological markers.
METHODS: Hierarchical cluster analysis grouped dementia-free older adults using memory, executive and language abilities, and cerebrospinal fluid amyloid-β and phosphorylated tau. Brain volume differences, neuropsychological trajectory, and progression to dementia were compared, controlling for age, gender, education, and apolipoprotein E4 (ApoE4).
RESULTS: Subgroups included asymptomatic-normal (n = 653) with unimpaired cognition and subthreshold biomarkers, typical AD (TAD; n = 191) showing marked memory decline, high ApoE4 rates and abnormal biomarkers, and atypical AD (AAD; n = 132) with widespread cognitive decline, intermediate biomarker levels, older age, less education and more white matter lesions. Cognitive profiles showed longitudinal stability with corresponding patterns of cortical atrophy, despite nearly identical rates of progression to AD dementia.
CONCLUSION: Two clinical-pathological AD subtypes are identified with potential implications for preventative efforts.

Entities:  

Keywords:  Alzheimer’s disease; atypical AD; cluster analysis; heterogeneity; neuroimaging

Mesh:

Substances:

Year:  2019        PMID: 31177226      PMCID: PMC8820245          DOI: 10.3233/JAD-190230

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  53 in total

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4.  Distilling Heterogeneity of Mild Cognitive Impairment in the National Alzheimer Coordinating Center Database Using Latent Profile Analysis.

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