Affiliations: [a] Albert Einstein College of Medicine, Bronx, NY, USA
| [b] Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| [c] University of Edinburgh, Edinburgh, UK
Correspondence:
[*]
Correspondence to: Andrea R. Zammit, PhD, Saul B. Korey, Department of Neurology, Albert Einstein College of
Medicine, 1300 Morris Park Avenue, Van Etten Building, Rm 3C9A, Bronx, NY, 10461, USA.
E-mail: andrea.zammit@einstein.yu.edu.
Abstract: Background:Conceptualizing cognitive aging as a step-sequential process is useful in identifying particular stages of cognitive function and impairment. Objective:We applied latent transition analysis (LTA) to determine 1) whether the underlying structure of cognitive profiles found at every measurement occasion are uniform across three waves of assessment, 2) whether class-instability is predictive of distal outcomes, and 3) whether class-reversions from impaired to non-impaired using latent modelling is lower than when using clinical criteria of mild cognitive impairment (MCI). Methods:A mover-stayer LTA model with dementia as a distal outcome was specified to model transitions of ten neuropsychological measures over three annual waves in the Rush Memory and Aging Project (n = 1,661). The predictive validity of the mover-stayer status for incident Alzheimer’s disease (AD) was then assessed. Results:We identified a five-class model across the three time-points: Mixed-Domain Impairment, Memory-Specific Impairment, Frontal Impairment, Average, and Superior Cognition. None of the individuals in the Impairment classes reverted to the Average or Superior classes. Conventional MCI classification identified 26.4% and 14.1% at Times 1 and 2 as false-positive cases. “Movers” had 87% increased risk of developing dementia compared to those classified as “Stayers”. Conclusion:Our findings support the use of latent variable modelling that incorporates comprehensive neuropsychological assessment to identify and classify cognitive impairment.
