Journal
JOURNAL OF ALZHEIMERS DISEASE
Volume 66, Issue 1, Pages 347-357Publisher
IOS PRESS
DOI: 10.3233/JAD-180604
Keywords
All-cause dementia; Alzheimer's disease; cognitive aging; cognitive subtypes; heterogeneity; individual differences; neuropsychology
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Funding
- National Institute On Aging of the National Institutes of Health [K01AG054700]
- Einstein Aging Study from National Institutes on Aging program [PO1 AG03949]
- National Institutes of Health CTSA from the National Center for Advancing Translational Sciences (NCATS) [1UL1TR001073]
- Sylvia and Leonard Marx Foundation
- Czap Foundation
- National Institute on Aging [R01AG17917]
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Identifying preclinical Alzheimer's disease (AD) is an important step toward developing approaches to early treatment and dementia prevention. We applied latent class analysis (LCA) to 10 baseline neuropsychological assessments for 1,345 participants from Einstein Aging Study. Time-to-event models for all-cause dementia and AD were run examining events in 4-year intervals. Five classes were identified: Mixed-Domain Impairment (n = 107), Memory-Specific Impairment (n = 457), Average (n = 539), Frontal Impairment (n = 118), and Superior Cognition (n = 124). Compared to the Average class, the Mixed-Domain Impairment and Memory-Specific Impairment classes were at higher risk of incident all-cause dementia and AD in the first 4 years from baseline, while the Frontal Impairment class was associated with higher risk between 4 and 8 years of follow-up. LCA identified classes which differ in cross-sectional cognitive patterns and in risk of dementia over specific follow-up intervals.
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