Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults.

Abstract

BACKGROUND:

Methods that can identify subgroups with different trajectories of cognitive decline are crucial for isolating the biologic mechanisms which underlie these groupings.

OBJECTIVE:

This study grouped older adults based on their baseline cognitive profiles using a latent variable approach and tested the hypothesis that these groups would differ in their subsequent trajectories of cognitive change.

METHODS:

In this study we applied time-varying effects models (TVEMs) to examine the longitudinal trajectories of cognitive decline across different subgroups of older adults in the Rush Memory and Aging Project.

RESULTS:

A total of 1,662 individuals (mean age = 79.6 years, SD = 7.4, 75.4%female) participated in the study; these were categorized into five previously identified classes of older adults differing in their baseline cognitive profiles: Superior Cognition (n = 328, 19.7%), Average Cognition (n = 767, 46.1%), Mixed-Domains Impairment (n = 71, 4.3%), Memory-Specific Impairment (n = 274, 16.5%), and Frontal Impairment (n = 222, 13.4%). Differences in the trajectories of cognition for these five classes persisted during 8 years of follow-up. Compared with the Average Cognition class, The Mixed-Domains and Memory-Specific Impairment classes showed steeper rates of decline, while other classes showed moderate declines.

CONCLUSION:

Baseline cognitive classes of older adults derived through the use of latent variable methods were associated with distinct longitudinal trajectories of cognitive decline that did not converge during an average of 8 years of follow-up.

• Authors: Andrea R Zammit, Jingyun Yang, Aron S Buchman, Sue E Leurgans, Graciela Muniz-Terrera, Richard B Lipton, Charles B Hall, Patricia Boyle, David A Bennett
• Journal: Journal of Alzheimer's disease : JAD (J Alzheimers Dis) Vol. 83 Issue 2 Pg. 641-652 ( 2021) ISSN: 1875-8908 [Electronic] Netherlands
• PMID: 34334404 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Topics

• Aged
• Aging (physiology)
• Algorithms
• Chicago
• Cognitive Dysfunction (classification)
• Female
• Humans
• Longitudinal Studies
• Male
• Neuropsychological Tests (statistics & numerical data)
• Residence Characteristics