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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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References

Bacanu, S. A., Devlin, B., Chowdari, K. V., DeKosky, S. T., Nimgaonkar, V. L. and Sweet, R. A. (2005). Heritability of psychosis in Alzheimer disease. American Journal of Geriatric Psychiatry, 13, 624627.CrossRefGoogle ScholarPubMed
Barnes, L. L., Wilson, R. S., Schneider, J. A., Bienias, J. L., Evans, D. A. and Bennett, D. A. (2003). Gender, cognitive decline, and risk of AD in older persons. Neurology, 60, 17771781.CrossRefGoogle ScholarPubMed
Berg, L. et al. (1998). Clinicopathologic studies in cognitively healthy aging and Alzheimer's disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype. Archives of Neurology, 55, 326335.CrossRefGoogle ScholarPubMed
Bertram, K., McQueen, M. B., Mullin, K., Blacker, D. and Tanzi, R. E. (2007). Systematic Meta-Analysis of Alzheimer Disease Genetic Association Studies: The AlzGene Database. Nature Genetics, 39, 1723.Google Scholar
Bretsky, P., Guralnik, J. M., Launer, L., Albert, M. and Seeman, T. E. (2003). The role of APOE-epsilon4 in longitudinal cognitive decline: MacArthur Studies of Successful Aging. Neurology, 60, 10771081.CrossRefGoogle ScholarPubMed
Corder, E. H. et al. (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science, 261, 921923.Google Scholar
Craft, S. et al. (1998). Accelerated decline in apolipoprotein E-epsilon4 homozygotes with Alzheimer's disease. Neurology, 51, 149153.CrossRefGoogle ScholarPubMed
Curran, P. J. and Willoughby, M. T. (2003). Implications of latent trajectory models for the study of developmental psychopathology. Development and Psychopathology, 15, 581612.CrossRefGoogle Scholar
Dodge, H. H., Du, Y., Saxton, J. A. and Ganguli, M. (2006). Cognitive domains and trajectories of functional independence in nondemented elderly persons. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 61, 13301337.Google Scholar
Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975). Mini mental state: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189198.Google Scholar
Gruen, B. and Leisch, F. (2008). FlexMix Version 2: finite mixtures with concomitant variables and varying and constant parameters. Journal of Statistical Software, 28, 135.Google Scholar
Healy, D. G. (2006). Case-control studies in the genomic era: a clinician's guide. Lancet Neurology, 5, 701707.Google Scholar
Helgeson, V. S., Snyder, P. and Seltman, H. (2004). Psychological and physical adjustment to breast cancer over 4 years: identifying distinct trajectories of change. Health Psychology, 23, 315.CrossRefGoogle ScholarPubMed
Hollingworth, P. et al. (2007). Increased familial risk and genomewide significant linkage for Alzheimer's disease with psychosis. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 144B, 841848.CrossRefGoogle ScholarPubMed
Holmans, P. et al. (2005). Genome screen for loci influencing age at onset and rate of decline in late onset Alzheimer's disease. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 135B, 2432.Google Scholar
Hoyt, B. D., Massman, P. J., Schatschneider, C., Cooke, N. and Doody, R. S. (2005). Individual growth curve analysis of APOE epsilon 4-associated cognitive decline in Alzheimer disease. Archives of Neurology, 62, 454459.Google Scholar
Jones, B. L., Nagin, D. S. and Roeder, K. (2001). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods and Research, 29, 374393.Google Scholar
Kamboh, M. I., Aston, C. E. and Hamman, R. F. (1995). The relationship of APOE polymorphism and cholesteral levels in normoglycemic and diabetic subjects in biethnic population from the San Luis Valley, Colorado. Atherosclerosis, 112, 145159.CrossRefGoogle Scholar
Kleiman, T. et al. (2006). Apolipoprotein E ϵ4 allele is unrelated to cognitive or functional decline in Alzheimer's disease: retrospective and prospective analysis. Dementia and Geriatric Cognitive Disorders, 22, 7382.Google Scholar
Leisch, E. (2004). FlexMix: a general framework for finite mixture models and latent class regression in R. Journal of Statistical Software, 11, 118.Google Scholar
Lopez, O. L., Kuller, L. H., Fitzpatrick, A., Ives, D., Becker, J. T. and Beauchamp, N. (2003). Evaluation of dementia in the cardiovascular health cognition study. Neuroepidemiology, 22, 112.Google Scholar
Mann, U. M., Mohr, E., Gearing, M. and Chase, T. N. (1992). Heterogeneity in Alzheimer's disease: progression rate segregated by distinct neuropsychological and cerebral metabolic profiles. Journal of Neurology, Neurosurgery and Psychiatry, 55, 956959.CrossRefGoogle ScholarPubMed
Martins, C. A., Oulhaj, A., de Jager, C. A. and Williams, J. H. (2005). APOE alleles predict the rate of cognitive decline in Alzheimer disease: a nonlinear model. Neurology, 65, 18881893.CrossRefGoogle ScholarPubMed
McKeith, I. G. et al. (1996). Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology, 47, 11131124.CrossRefGoogle Scholar
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D. and Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer's disease. Neurology, 34, 939944.Google Scholar
O'Hara, R., Sommer, B., Way, N., Kraemer, H. C., Taylor, J. and Murphy, G. (2008). Slower speed-of-processing of cognitive tasks is associated with presence of the apolipoprotein ϵ4 allele. Journal of Psychiatric Research, 42, 199204.Google Scholar
R Development Core Team (2007). A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.Google Scholar
Rasmusson, D. X., Carson, K. A., Brookmeyer, R., Kawas, C. and Brandt, J. (1996). Predicting rate of cognitive decline in probable Alzheimer's disease. Brain and Cognition, 31, 133147.Google Scholar
Ropacki, S. A. and Jeste, D. V. (2005). Epidemiology of and risk factors for psychosis of Alzheimer's disease: a review of 55 studies published from 1990 to 2003. American Journal of Psychiatry, 162, 20222030.CrossRefGoogle ScholarPubMed
Royall, D. R., Palmer, R., Chiodo, L. K. and Polk, M. J. (2005). Normal rates of cognitive change in successful aging: the freedom house study. Journal of the International Neuropsychological Society, 11, 899909.Google Scholar
Scarmeas, N. et al. (2005). Delusions and hallucinations are associated with worse outcome in Alzheimer disease. Archives of Neurology, 62, 16011608.Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461464.CrossRefGoogle Scholar
Tariot, P. N. et al. (1995). The behavior rating scale for dementia of the Consortium to Establish a Registry for Alzheimer's Disease. American Journal of Psychiatry, 152, 13491357.Google Scholar
Tyas, S. L. et al. (2007). Transitions to mild cognitive impairments, dementia, and death: findings from the Nun Study. American Journal of Epidemiology, 165, 12311238.CrossRefGoogle ScholarPubMed