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Trajectories of cognitive decline in Alzheimer's disease

Published online by Cambridge University Press:  28 September 2009

Patricia A. Wilkosz
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, U.S.A.
Howard J. Seltman
Affiliation:
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, U.S.A.
Bernie Devlin
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, U.S.A.
Elise A. Weamer
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, U.S.A. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, U.S.A.
Oscar L. Lopez
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, U.S.A. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, U.S.A.
Steven T. DeKosky
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, U.S.A. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, U.S.A.
Robert A. Sweet*
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, U.S.A. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, U.S.A. VISN 4 Mental Illness Research, Education and Clinical Center (MIRECC), VA Pittsburgh Healthcare System, Pittsburgh, PA, U.S.A.
*
Correspondence should be addressed to: Robert A. Sweet, M. D., Professor of Psychiatry and Neurology, University of Pittsburgh, Biomedical Science Tower, Rm W-1645, 3811 O'Hara Street, Pittsburgh, PA 15213–2593, U.S.A. Phone +1 412 383 8548; Fax +1 412 624 9910. Email: sweetra@upmc.edu.

Abstract

Background: Late-onset Alzheimer disease (LOAD) is a clinically heterogeneous complex disease defined by progressively disabling cognitive impairment. Psychotic symptoms which affect approximately one-half of LOAD subjects have been associated with more rapid cognitive decline. However, the variety of cognitive trajectories in LOAD, and their correlates, have not been well defined. We therefore used latent class modeling to characterize trajectories of cognitive and behavioral decline in a cohort of AD subjects.

Methods: 201 Caucasian subjects with possible or probable Alzheimer's disease (AD) were evaluated for cognitive and psychotic symptoms at regular intervals for up to 13.5 years. Cognitive symptoms were evaluated serially with the Mini-mental State Examination (MMSE), and psychotic symptoms were rated using the CERAD behavioral rating scale (CBRS). Analyses undertaken were latent class mixture models of quadratic trajectories including a random intercept with initial MMSE score, age, gender, education, and APOE ϵ4 count modeled as concomitant variables. In a secondary analysis, psychosis status was also included.

Results: AD subjects showed six trajectories with significantly different courses and rates of cognitive decline. The concomitant variables included in the best latent class trajectory model were initial MMSE and age. Greater burden of psychotic symptoms increased the probability of following a trajectory of more rapid cognitive decline in all age and initial MMSE groups. APOE ϵ4 was not associated with any trajectory.

Conclusion: Trajectory modeling of longitudinal cognitive and behavioral data may provide enhanced resolution of phenotypic variation in AD.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2009

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