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Hierarchical cognitive and psychosocial predictors of amnestic mild cognitive impairment

Published online by Cambridge University Press:  21 June 2010

S. DUKE HAN ,
HIDEO SUZUKI ,
AMY J. JAK ,
YU-LING CHANG ,
DAVID P. SALMON  and
MARK W. BONDI
S. DUKE HAN*
Affiliation:
Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois
HIDEO SUZUKI
Affiliation:
Department of Biology, Loyola University Chicago, Chicago, Illinois
AMY J. JAK
Affiliation:
Department of Psychiatry, University of California San Diego School of Medicine, San Diego, California Psychology Service, VA San Diego Healthcare System, San Diego, California
YU-LING CHANG
Affiliation:
Department of Psychiatry, University of California San Diego School of Medicine, San Diego, California
DAVID P. SALMON
Affiliation:
Department of Neuroscience, University of California San Diego School of Medicine, San Diego, California
MARK W. BONDI
Affiliation:
Department of Psychiatry, University of California San Diego School of Medicine, San Diego, California Psychology Service, VA San Diego Healthcare System, San Diego, California
*
*Correspondence and reprint requests to: S. Duke Han, Ph.D., Department of Behavioral Sciences, Rush University Medical Center, 1653 West Congress Parkway, Chicago, IL 60612-3833. E-mail: Duke_Han@rush.edu
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Abstract

To identify neuropsychological and psychosocial factors predictive of amnestic Mild Cognitive Impairment (aMCI) among a group of 94 nondemented older adults, we employed a novel nonlinear multivariate classification statistical method called Optimal Data Analysis (ODA) in a dataset collected annually for 3 years. Performance on measures of memory and visuomotor processing speed or symptoms of depression in year 1 predicted aMCI status by year 2. Performance on a measure of learning at year 1 predicted aMCI status at year 3. No other measures significantly predicted incidence of aMCI at years 2 and 3. Results support the utility of multiple neuropsychological and psychosocial measures in the diagnosis of aMCI, and the present model may serve as a testable hypothesis for prospective investigations of the development of aMCI. (JINS, 2010, 16, 721–729.)

Keywords

Amnestic mild cognitive impairmentaMCIMCINeuropsychologyMemoryVisuomotor processing speedD-KEFSDepressionOptimal data analysis
Type
Brief Communications
Information
Journal of the International Neuropsychological Society , Volume 16 , Issue 4 , July 2010 , pp. 721 - 729
Copyright
Copyright © The International Neuropsychological Society 2010

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References

REFERENCES

Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Albert, M.S., Moss, M.B., Tanzi, R., & Jones, K. (2001). Preclinical prediction of AD using neuropsychological tests. Journal of the International Neuropsychological Society, 7, 631–639.CrossRefGoogle ScholarPubMed
Arnaiz, E., & Almkvist, O. (2003). Neuropsychological features of mild cognitive impairment and preclinical Alzheimer’s disease. Acta Neurologica Scandinavica, 179(Suppl.), 34–41.CrossRefGoogle ScholarPubMed
Bäckman, L., Jones, S., Berger, A.K., Laukka, E.J., Small, B.J. (2005). Cognitive impairment in preclinical Alzheimer’s disease: A meta-analysis. Neuropsychology, 19, 520–531.CrossRefGoogle ScholarPubMed
Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification and regression trees. Belmont, CA: Wadsworth.Google Scholar
Bremner, A.P., & Taplin, R.H. (2002). Modified classification and regression tree splitting criteria for data with interactions. Australian and New Zealand Journal of Statistics, 44, 169–176.CrossRefGoogle Scholar
Chen, P., Ratcliff, G., Belle, S.H., Cauley, J.A., DeKoskey, S.T., & Ganguli, M. (2001). Patterns of cognitive decline in presymptomatic Alzheimer disease: A prospective community study. Archives of General Psychiatry, 58, 853–858.CrossRefGoogle ScholarPubMed
Clark, L.A., & Pregibon, D. (1992). Tree-based models. In Charmbers, J.M. & Hastie, T.J. (Eds.), Statistical models in S (pp. 377–419). Pacific Grove, CA: Wadsworth & Brooks/Cole.Google Scholar
Corder, E.H., Saunders, A.M., Strittmatter, W.J., Schmechel, D.E., Gaskell, P.C., Small, G.W, (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science, 261, 921–923.CrossRefGoogle Scholar
Das, S.K., Bose, P., Biswas, A., Dutt, A., Banerjee, T.K., Hazra, A.M., et al. . (2007). An epidemiologic study of mild cognitive impairment in Kolkata, India. Neurology, 68, 2019–2026.CrossRefGoogle ScholarPubMed
Dubois, B., Feldman, H.H., Jacova, C., DeKoskey, S.T., Barberger-Gateau, P., Cummings, J., et al. . (2007). Research criteria for the diagnosis of Alzheimer’s disease: Revising the NINCDS–ADRDA criteria. Lancet, 6, 734–746.CrossRefGoogle ScholarPubMed
Fox, J. (2000). Multiple and generalized nonparametric regression. Quantitative Applications in the Social Sciences Series, No.131. Thousand Oaks, CA: Sage Publications.CrossRefGoogle Scholar
Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R.C., Ritchie, K., Broich, K., et al. . (2006). Mild cognitive impairment. Lancet, 367, 1262–1270.CrossRefGoogle ScholarPubMed
Grober, E., & Kawas, C. (1997). Learning and retention in preclinical and early Alzheimer’s disease. Psychology and aging, 12(1), 183–8.CrossRefGoogle Scholar
Jaccard, J. (2001). Interaction effects in logistic regression. Quantitative Applications in the Social Sciences Series, No. 135. Thousand Oaks, CA: Sage Publications.CrossRefGoogle Scholar
Jak, A.J., Bondi, M.W., Delano-Wood, L., Wierenga, C., Corey-Bloom, J., Salmon, D.P., & Delis, D.C. (2009). Quantification of five neuropsychological approaches to defining mild cognitive impairment. American Journal of Geriatric Psychiatry, 17, 368–375.CrossRefGoogle ScholarPubMed
Menard, S. (1995). Applied logistic regression analysis. Thousand Oaks, CA: Sage Publications.Google Scholar
Morris, J.C. (2005). Mild cognitive impairment and preclinical Alzheimer’s disease. Geriatrics, (Suppl.), 9–14Google ScholarPubMed
Panza, F., D’Introno, A., Colacicco, A.M., Capurso, C., Del Parigi, A., Caselli, R.J., et al. . (2005). Current epidemiology of mild cognitive impairment and other predementia syndromes. American Journal of Geriatric Psychiatry, 13, 633–644.CrossRefGoogle ScholarPubMed
Peduzzi, P., Concato, J., Kemper, E., Holford, T.R., & Feinstein, A.R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49, 1373–1379.CrossRefGoogle Scholar
Petersen, R.C., & Morris, J.C. (2005). Mild cognitive impairment as a clinical entity and treatment target. Archives of Neurology, 62, 1160–1163.CrossRefGoogle ScholarPubMed
Petersen, R.C., Doody, R., Kurz, A.Mohs, R.C., Morris, J.C., Rabins, P.V., et al. . (2001). Current concepts in mild cognitive impairment. Archives of Neurology, 58, 1985–1992.CrossRefGoogle ScholarPubMed
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., & Kokmen, E. (1999). Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 56, 303–308.CrossRefGoogle ScholarPubMed
Rabin, L.A., Pare, N., Saykin, A.J., Brown, M.J., Wishart, H.A., Flashman, L.A., & Santulli, R.B. (2009). Differential memory test sensitivity for diagnosing amnestic mild cognitive impairment and predicting conversion to Alzheimer’s disease. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn, 16, 357–376.CrossRefGoogle Scholar
Ramakers, I.H.G.B., Visser, P.J., Aalten, P., Bekers, O., Sleegers, K., van Broeckhoven, C.L., et al. . (2008). The association between APOE genotype and memory dysfunction in subjects with mild cognitive impairment is related to age and Alzheimer pathology. Dementia and Geriatric Cognitive Disorders, 26, 101–108.CrossRefGoogle ScholarPubMed
Saunders, A.M., Strittmatter, W.J., & Schmechel, D.E. (1993). Association of apolipoprotein E allele e4 with late-onset familial and sporadic Alzheimer’s disease. Neurology, 43, 1467–1472.CrossRefGoogle Scholar
Saxton, J., Snitz, B.E., Lopez, O.L., Ives, D.G., Dunn, L.O., Fitzpatrick, A., et al. , for the GEM Study Investigators. (2009). Functional and cognitive criteria produce different rates of mild cognitive impairment and conversion to dementia. Journal of Neurology, Neurosurgery, & Psychiatry, 80, 737–743.CrossRefGoogle ScholarPubMed
Soltysik, R.C., & Yarnold, P.R. (1993). ODA 1.0: Optimal data analysis for DOS. Chicago: Optimal Data Analysis.Google Scholar
Sonquist, J.A., & Morgan, J.N. (1964). The detection of interaction effects. Monograph No. 35. Institute of Social Research, University of Michigan, Ann Arbor.Google Scholar
Stevens, J.P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Erlbaum.Google Scholar
Tabachnick, B.G., & Fidell, L.S. (1989). Using multivariate statistics (2nd ed.). New York: Harper & Row.Google Scholar
Teng, E., Lu, P.H., & Cummings, J.L. (2007). Neuropsychiatric symptoms are associated with progression from mild cognitive impairment to Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders, 24, 253–259.CrossRefGoogle ScholarPubMed
Twamley, E., Ropacki, S., Bondi, M. (2006). Neuropsychological and neuroimaging changes in preclinical Alzheimer’s disease. J Int Neuropsychol Soc, 12, 707–735.CrossRefGoogle Scholar
Yarnold, P.R., & Soltysik, R.C. (2005). Optimal Data Analysis: A guidebook with software for Windows. Washington, DC: American Psychological Association.Google Scholar
Yarnold, P.R., Soltysik, R.C., & Bennett, C.L. (1997). Predicting in-hospital mortality of patients with AIDS-related pneumocystis carinii pneumonia: An example of hierarchically optimal classification tree analysis. Statistics in Medicine, 16, 1451–1463.3.0.CO;2-F>CrossRefGoogle ScholarPubMed
Yarnold, P.R., Soltysik, R.C., Lefevre, F., & Martin, G.J. (1998). Predicting in-hospital mortality of patients receiving cardiopulmonary resuscitation: Unit-weighted multi-ODA for binary data. Statistics in Medicine, 17, 2405–2414.3.0.CO;2-F>CrossRefGoogle ScholarPubMed
Yarnold, P.R., Soltysik, R.C., & Martin, G.J. (1994). Heart rate variability and inducibility for sudden cardiac death: An example of optimal discriminant analysis. Statistics in Medicine, 13, 1015–1021.CrossRefGoogle Scholar