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Multi-state Markov model in outcome of mild cognitive impairments among community elderly residents in Mainland China

Published online by Cambridge University Press:  03 January 2013

Hong-mei Yu ,
Shan-shan Yang ,
Jian-wei Gao ,
Li-ye Zhou ,
Rui-feng Liang  and
Cheng-yi Qu
Hong-mei Yu*
Affiliation:
Department of Health Statistics, School of Public Health, Shanxi Medical University, People's Republic of China
Shan-shan Yang
Affiliation:
Guangwai Community Health Service Center of Beijing, Beijing, People's Republic of China
Jian-wei Gao
Affiliation:
Department of Health Statistics, School of Public Health, Shanxi Medical University, People's Republic of China
Li-ye Zhou
Affiliation:
Department of Health Statistics, School of Public Health, Shanxi Medical University, People's Republic of China
Rui-feng Liang
Affiliation:
Department of Health Statistics, School of Public Health, Shanxi Medical University, People's Republic of China
Cheng-yi Qu
Affiliation:
Department of Health Statistics, School of Public Health, Shanxi Medical University, People's Republic of China
*
Correspondence should be addressed to: Hong-mei Yu, Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, People's Republic of China. Phone: +86-351-4135049; Fax: +86-351-2027943. Email: [email protected].
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Abstract

Background: Although knowledge of established risk factors for Alzheimer's disease (AD) can logically contribute to the search for predictors of the progression of cognitive impairment, it has not yet been firmly established where in the cognitive impairment process these risk factors exert their effects and how to predict quantitatively for the progression of mild cognitive impairments (MCI) to AD. This study aimed to determine whether known risk factors increased the risk of progression from MCI to AD and to make prediction based on transition probabilities.

Methods: Based on ten examinations of 600 community-dwelling MCI residents and cognitive assessments to classify individuals into MCI, global impairment, and AD, a multi-state Markov Cox's regression model was used and the hazard ratios with their confidence intervals and transition probabilities were estimated.

Results: Multivariate analysis showed that gender, age, and hypertension were statistically significant predictors of transition from MCI to global impairment; age, education, and reading statistically influenced transition from global impairment to MCI; gender, age, hypertension, diabetes, and apolipoprotein E geneε4 status were statistically associated with transition from global impairment to AD. Subjects at MCI were more likely (67%) to remain in that cognitive state at the next cognitive assessment than to transition to cognitive deterioration. For global impairment, probability of remaining in the same state was only 18% and that of forward transition was three times more likely than that of backward transition.

Conclusions: Known risk factors influenced differently for different transitions. Transition from global impairment was more likely to worsen to severe cognitive deterioration than transition from MCI.

Keywords

Alzheimer's diseasemild cognitive impairmentslongitudinal studyprogressionrisk factorsmulti-state Markov model
Type
Research Article
Information
International Psychogeriatrics , Volume 25 , Issue 5 , May 2013 , pp. 797 - 804
Copyright
Copyright © International Psychogeriatric Association 2013

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References

Aggarwal, N. T., Wilson, R. S., Beck, T. L., Berry-Kravis, E. and Bennett, D. A. (2005). The apolipoprotein E ε4 allele and incident Alzheimer's disease in persons with mild cognitive impairment. Neurocase, 11, 3–7.CrossRefGoogle ScholarPubMed
Aguirre-Hernandez, R. and Farewell, V. T. (2002). A Pearson-type goodness-of-fit test for stationary and time-continuous Markov regression models. Statistics in Medicine, 21, 1899–1911.CrossRefGoogle ScholarPubMed
Amieva, H.et al. (2004). Annual rate and predictors of conversion to dementia in subjects presenting mild cognitive impairment criteria defined according to a population-based study. Dementia and Geriatric Cognitive Disorders, 18, 87–93.CrossRefGoogle ScholarPubMed
Andersen, P. K. and Perme, M. P. (2008). Inference for outcome probabilities in multi-state models. Lifetime Data Analysis, 14, 405–431.CrossRefGoogle ScholarPubMed
Arvanitakis, Z.et al. (2006). Diabetes is related to cerebral infarction but not to AD pathology in older persons. Neurology, 67, 1960–1965.CrossRefGoogle Scholar
Dickstein, D. L., Walsh, J., Brautigam, H., Stockton, S. D., Gandy, S. and Hof, P. R. (2010). Role of vascular risk factors and vascular dysfunction in Alzheimer's disease. Mount Sinai Journal of Medicine, 77, 82–102.CrossRefGoogle ScholarPubMed
Gauthier, S.et al. (2006). Mild cognitive impairment. Lancet, 367, 1262–1270.CrossRefGoogle ScholarPubMed
Hsiung, G. Y. R., Sadovnick, A. D. and Feldman, H. (2004). Apolipoprotein E-ε4 genotype as a risk factor for cognitive decline and dementia: data from the Canadian Study of Health and Aging. Canadian Medical Association Journal, 171, 863–867.CrossRefGoogle ScholarPubMed
Jackson, C. (2009). msm: multi-state Markov and hidden Markov models in continuous time (version 0.9.3). Available at: http://CRAN.R-project.org/package=msm; last accessed 10 November 2009.Google Scholar
Jackson, C. (2011). Multi-state models for panel data: the msm.package for R. Journal of Statistical Software, 38, 1–28.CrossRefGoogle Scholar
Kalaria, R. N.et al. (2008). Alzheimer's disease and vascular dementia in developing countries: prevalence, management, and risk factors. Lancet Neurology, 7, 812–826.CrossRefGoogle ScholarPubMed
Lawton, M. P. and Brody, E. M. (1970). Assessment of older people: self-maintaining and instrumental activities of daily living. Nursing Research, 3, 278.CrossRefGoogle Scholar
Ma, F., Qu, C. Y., Wang, T., Yin, J., Bai, J. X. and Ding, Q. H. (2009). Prevalence and distribution of cognitive impairment no dementia (CIND) among the aged population and the analysis of socio-demographic characteristics: the community-based cross-sectional study. Alzheimer Disease and Associated Disorders, 23, 130–138.Google Scholar
Meira-Machado, L., de Uña-Álvarez, J., Cadarso-Suárez, C. and Andersen, P. K. (2009). Multi-state models for the analysis of time-to-event data. Statistical Methods in Medical Research, 18, 195–222.CrossRefGoogle ScholarPubMed
Morris, J. C.et al. (2001). Mild cognitive impairment represents early-stage Alzheimer disease. Archives of Neurology, 58, 397–405.CrossRefGoogle ScholarPubMed
Ocana-Riola, R. (2005). Non-homogeneous Markov processes for biomedical data analysis. Biometrical Journal, 47, 369–376.CrossRefGoogle ScholarPubMed
Palmer, K., Fratiglioni, L. and Winblad, B. (2003). What is mild cognitive impairment? Variations in definitions and evolution of nondemented persons with cognitive impairment. Acta Neurologica Scandinavica, 107, 14–20.CrossRefGoogle Scholar
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Kokmen, E. and Tangelos, E. G. (1997). Aging memory and mild cognitive impairment. International Psychogeriatrics, 9, 65–69.CrossRefGoogle ScholarPubMed
Petersen, R. C., Stevens, J. C., Ganguli, M., Tangalos, E. G., Cummings, J. L. and DeKosky, S. T. (2001). Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology, 56, 1133–1142.CrossRefGoogle Scholar
Posner, H. B., Tang, M. X., Luchsinger, J., Stern, Y. and Mayeux, R. (2000). The relationship of hypertension in the elderly to AD, vascular dementia, and cognitive function. Neurology, 58, 1175–1181.CrossRefGoogle Scholar
Profenno, L. A., Porsteinsson, A. P. and Faraone, S. V. (2010). Meta-analysis of Alzheimer's disease risk with obesity, diabetes, and related disorders. Biological Psychiatry, 67, 505–512.CrossRefGoogle ScholarPubMed
Roselli, F., Tartaglione, B., Federico, F., Lepore, V., Defazio, G. and Livrea, P. (2009). Rate of MMSE score change in Alzheimer's disease: influence of education and vascular risk factors. Clinical Neurology and Neurosurgery, 111, 327–330.CrossRefGoogle ScholarPubMed
Steenland, K., MacNeil, J., Vega, I. and Levey, A. (2009). Recent trends in Alzheimer's disease mortality in the United States, 1999–2004. Alzheimer Disease and Associated Disorders, 23, 165–170.CrossRefGoogle Scholar
Teipel, S. J., Ewers, M., Reisig, V., Schweikert, B., Hampel, H. and Happich, M. (2007). Long-term cost-effectiveness of donepezil for the treatment of Alzheimer's disease. European Archives of Psychiatry and Clinical Neuroscience, 257, 330–336.CrossRefGoogle ScholarPubMed
Tervo, S.et al. (2004). Incidence and risk factors for mild cognitive impairment: a population-based three-year follow-up study of cognitively healthy elderly subjects. Dementia and Geriatric Cognitive Disorders, 17, 196–203.CrossRefGoogle ScholarPubMed
Tian, J., Bucks, R. S., Haworth, J. and Wilcock, G. (2003). Neuropsychological prediction of conversion to dementia from questionable dementia: statistically significant but not yet clinically useful. Journal of Neurology, Neurosurgery & Psychiatry, 74, 433–438.CrossRefGoogle 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, 1231–1238.CrossRefGoogle ScholarPubMed
Wilson, R. S.et al. (2010). Cognitive activity and the cognitive morbidity of Alzheimer disease. Neurology, 75, 990–996.CrossRefGoogle ScholarPubMed
Yaffe, K., Petersen, R. C., Lindquist, K., Kramer, J. and Miller, B. (2006). Subtype of mild cognitive impairment and progression to dementia and death. Dementia and Geriatric Cognitive Disorders, 22, 312–319.CrossRefGoogle ScholarPubMed
Zhou, D. F.et al. (2006). Prevalence of dementia in rural China: impact of age, gender and education. Acta Neurologica Scandinavica, 114, 273–280.CrossRefGoogle ScholarPubMed