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Pattern recognition to objectively differentiate the etiology of cognitive decay in longitudinal cognitive data: Analysis of stroke, Alzheimer’s disease, and normal aging
Author(s) -
Clouston Sean,
Richmond Lauren,
Scott Stacey,
Luhmann Christian,
Natale Ginny,
Hanes Douglas,
Zhang Yun,
Smith Dylan
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.041098
Subject(s) - cognition , episodic memory , stroke (engine) , disease , alzheimer's disease , psychology , cognitive decline , neuroimaging , longitudinal study , audiology , medicine , dementia , neuroscience , pathology , mechanical engineering , engineering
Background Estimates suggest that Alzheimer’s disease and stroke account for a majority of age‐related cognitive decay. This study utilized novel pattern‐recognition protocols to estimate the extent to which cognitive decay might be attributable to Alzheimer’s disease and stroke. Method Secondary analyses of data collected for the Health & Retirement Study (N=17,579) were used to objectively type Alzheimer’s disease and stroke‐related cognitive decay. Patterns of decay in episodic memory were the measure, while rate of linear decay in episodic memory and, separately, in orientation were the outcome. Result After adjusting for demographics, Alzheimer’s disease and stroke accounted for approximately half of age‐associated decay in cognition (51.07‐55.6% for orientation and episodic memory respectively) and explained variance attributed to random slopes in longitudinal multilevel models. Conclusion Results support prior histopathological and neuroimaging efforts suggesting that approximately half of cognitive decay, commonly attributed directly to aging, may be due to stroke and other cerebrovascular disease, and Alzheimer’s disease.