Affiliations: [a]
Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| [b]
Einstein Aging Study, Albert Einstein College of Medicine, Bronx, NY, USA
| [c]
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| [d]
The University of Edinburgh, Scotland
| [e]
Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
Correspondence:
[*]
Correspondence to: Andrea R. Zammit, Saul B. Korey, Department of Neurology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Van Etten Building, Rm 3C9A, Bronx, NY 10461, USA. Tel.: +1 718 430 3831; E-mail: andrea.zammit@einstein.yu.edu.
Abstract: 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.
