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Effects of general medical health on Alzheimer's progression: the Cache County Dementia Progression Study

Published online by Cambridge University Press:  12 June 2012

Jeannie-Marie S. Leoutsakos ,
Dingfen Han ,
Michelle M. Mielke ,
Sarah N. Forrester ,
JoAnn T. Tschanz ,
Chris D. Corcoran ,
Robert C. Green ,
Maria C. Norton ,
Kathleen A. Welsh-Bohmer  and
Constantine G. Lyketsos
Jeannie-Marie S. Leoutsakos*
Affiliation:
Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
Dingfen Han
Affiliation:
Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
Michelle M. Mielke
Affiliation:
Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
Sarah N. Forrester
Affiliation:
Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
JoAnn T. Tschanz
Affiliation:
Center for Epidemiologic Studies, Consumer and Human Development, Utah State University, Logan, Utah, USA Department of Psychology, Consumer and Human Development, Utah State University, Logan, Utah, USA
Chris D. Corcoran
Affiliation:
Center for Epidemiologic Studies, Consumer and Human Development, Utah State University, Logan, Utah, USA Department of Mathematics and Statistics, Consumer and Human Development, Utah State University, Logan, Utah, USA
Robert C. Green
Affiliation:
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
Maria C. Norton
Affiliation:
Center for Epidemiologic Studies, Consumer and Human Development, Utah State University, Logan, Utah, USA Department of Psychology, Consumer and Human Development, Utah State University, Logan, Utah, USA Department of Family, Consumer, and Human Development, Utah State University, Logan, Utah, USA
Kathleen A. Welsh-Bohmer
Affiliation:
Department of Psychiatry and Behavioral Sciences and the Joseph and Kathleen Bryan Alzheimer's Disease Research Center, Duke University, Durham, North Carolina, USA
Constantine G. Lyketsos
Affiliation:
Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
*
Correspondence should be addressed to: Dr. Jeannie-Marie S. Leoutsakos, PhD, MHS, Assistant Professor, Department of Psychiatry, Division of Geriatric Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Bayview – Alpha Commons Building 4th Floor, Baltimore, MD 21224, USA. Phone: +1 410-550-9884; Fax: +1 410-550-1407. Email: jeannie-marie@jhu.edu.
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Abstract

Background: Several observational studies have suggested a link between health status and rate of decline among individuals with Alzheimer's disease (AD). We sought to quantify the relationship in a population-based study of incident AD, and to compare global comorbidity ratings to counts of comorbid conditions and medications as predictors of AD progression.

Methods: This was a case-only cohort study arising from a population-based longitudinal study of memory and aging, in Cache County, Utah. Participants comprised 335 individuals with incident AD followed for up to 11 years. Patient descriptors included sex, age, education, dementia duration at baseline, and APOE genotype. Measures of health status made at each visit included the General Medical Health Rating (GMHR), number of comorbid medical conditions, and number of non-psychiatric medications. Dementia outcomes included the Mini-Mental State Examination (MMSE), Clinical Dementia Rating – sum of boxes (CDR-sb), and the Neuropsychiatric Inventory (NPI).

Results: Health status tended to fluctuate over time within individuals. None of the baseline medical variables (GMHR, comorbidities, and non-psychiatric medications) was associated with differences in rates of decline in longitudinal linear mixed effects models. Over time, low GMHR ratings, but not comorbidities or medications, were associated with poorer outcomes (MMSE: β = –1.07 p = 0.01; CDR-sb: β = 1.79 p < 0.001; NPI: β = 4.57 p = 0.01).

Conclusions: Given that time-varying GMHR, but not baseline GMHR, was associated with the outcomes, it seems likely that there is a dynamic relationship between medical and cognitive health. GMHR is a more sensitive measure of health than simple counts of comorbidities or medications. Since health status is a potentially modifiable risk factor, further study is warranted.

Keywords

Alzheimer's diseasecomorbidityGMHRdisease progressionrate of declinemedical carecohort study
Type
Research Article
Information
International Psychogeriatrics , Volume 24 , Issue 10 , October 2012 , pp. 1561 - 1570
Copyright
Copyright © International Psychogeriatric Association 2012

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References

Agresti, A. (1999). Modelling ordered categorical data: recent advances and future challenges. Statistics in Medicine, 18, 2191–2207.3.0.CO;2-M>CrossRefGoogle ScholarPubMed
Aguero-Torres, H., Fratiglioni, L. and Winblad, B. (1998). Natural history of Alzheimer's disease and other dementias: review of the literature in the light of the findings from the Kungsholmen Project. International Journal of Geriatric Psychiatry, 13, 755–766.3.0.CO;2-Y>CrossRefGoogle ScholarPubMed
Alzheimer's Association (2010). 2010 Alzheimer's disease facts and figures. Alzheimer's & Dementia, 6, 158–194. doi: 10.1016/j.jalz.2010.01.009.CrossRefGoogle ScholarPubMed
American Psychiatric Association (1987). Diagnostic and Statistical Manual of Mental Disorders, DSM-III-R. Washington, DC: American Psychiatric Association.Google ScholarPubMed
Andel, R., Vigen, C., Mack, W. J., Clark, L. J. and Gatz, M. (2006). The effect of education and occupational complexity on rate of cognitive decline in Alzheimer's patients. Journal of the International Neuropsychological Society, 12, 147–152.CrossRefGoogle ScholarPubMed
Bartels, S. J. (2003). Improving system of care for older adults with mental illness in the United States. Findings and recommendations for the President's New Freedom Commission on Mental Health. American Journal of Geriatric Psychiatry, 11, 486–497.CrossRefGoogle ScholarPubMed
Boksay, I., Boksay, E., Reisberg, B., Torossian, C. and Krishnamurthy, M. (2005). Alzheimer's disease and medical disease conditions: a prospective cohort study. Journal of the American Geriatrics Society, 53, 2235–2236.CrossRefGoogle Scholar
Bracco, L. et al. (2007). Pattern and progression of cognitive decline in Alzheimer's disease: role of premorbid intelligence and ApoE genotype. Dementia and Geriatric Cognitive Disorders, 24, 483–491. doi: 10.1159/000111081.CrossRefGoogle ScholarPubMed
Breitner, J. C. et al. (1999). APOE-epsilon4 count predicts age when prevalence of AD increases, then declines: the Cache County Study. Neurology, 53, 321–331.CrossRefGoogle ScholarPubMed
Brickman, A. M. et al. (2008). Measuring cerebral atrophy and white matter hyperintensity burden to predict the rate of cognitive decline in Alzheimer disease. Archives of Neurology, 65, 1202–1208. doi: 10.1001/archneur.65.9.1202.CrossRefGoogle ScholarPubMed
Buerger, K. et al. (2005). Phosphorylated tau predicts rate of cognitive decline in MCI subjects: a comparative CSF study. Neurology, 65, 1502–1503. doi: 10.1212/01.wnl.0000183284.92920.f2.CrossRefGoogle Scholar
Callahan, C. M. et al. (2011). Implementing dementia care models in primary care settings: the Aging Brain Care Medical Home. Aging & Mental Health, 15, 5–12. doi: 10.1080/13607861003801052.CrossRefGoogle ScholarPubMed
Casella, G. and Berger, R. L. (2002). Statistical Inference, Second Edition. Pacific Grove, CA: Duxbury Press.Google Scholar
Colantuoni, E., Surplus, G., Hackman, A., Arrighi, H. M. and Brookmeyer, R. (2010). Web-based application to project the burden of Alzheimer's disease. Alzheimer's & Dementia, 6, 425–428. doi: 10.1016/j.jalz.2010.01.014.CrossRefGoogle Scholar
Cortes, F. et al. (2008). Prognosis of Alzheimer's disease today: a two-year prospective study in 686 patients from the REAL-FR Study. Alzheimers & Dementia, 4, 22–29.CrossRefGoogle Scholar
Counsell, S. R. et al. (2007). Geriatric care management for low-income seniors: a randomized controlled trial. JAMA, 298, 2623–2633. doi: 10.1001/jama.298.22.2623.CrossRefGoogle Scholar
Counsell, S. R., Callahan, C. M., Tu, W., Stump, T. E. and Arling, G. W. (2009). Cost analysis of the geriatric resources for assessment and care of elders care management intervention. Journal of the American Geriatrics Society, 57, 1420–1426.CrossRefGoogle ScholarPubMed
Cummings, J. L. (1997). The Neuropsychiatric Inventory: assessing psychopathology in dementia patients. Neurology, 48, S10–S16.CrossRefGoogle ScholarPubMed
Dooneief, G., Marder, K., Tang, M. X. and Stern, Y. (1996). The Clinical Dementia Rating scale: community-based validation of “profound” and “terminal” stages. Neurology, 46, 1746–1749.CrossRefGoogle ScholarPubMed
Doraiswamy, P. M., Leon, J., Cummings, J. L., Marin, D. and Neumann, P. J. (2002). Prevalence and impact of medical comorbidity in Alzheimer's disease. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 57, M173–M177.CrossRefGoogle 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 Psychiatry Research, 12, 189–198.CrossRefGoogle ScholarPubMed
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, 454–459. doi: 10.1001/archneur.62.3.454.CrossRefGoogle ScholarPubMed
Hughes, C. P., Berg, L., Danziger, W. L., Coben, L. A. and Martin, R. L. (1982). A new clinical scale for the staging of dementia. British Journal of Psychiatry, 140, 566–572.CrossRefGoogle Scholar
Jung, S. H. and Ahn, C. (2003). Sample size estimation for GEE method for comparing slopes in repeated measurements data. Statistics in Medicine, 22, 1305–1315.CrossRefGoogle ScholarPubMed
Kawas, C., Segal, J., Stewart, W. F., Corrada, M. and Thal, L. J. (1994). A validation study of the dementia questionnaire. Archives of Neurology, 51, 901–906.CrossRefGoogle ScholarPubMed
Kester, M. I. et al. (2009). CSF biomarkers predict rate of cognitive decline in Alzheimer disease. Neurology, 73, 1353–1358. doi: 10.1212/WNL.0b013e3181bd8271.CrossRefGoogle ScholarPubMed
Kuo, T. C., Zhao, Y., Weir, S., Kramer, M. S. and Ash, A. S. (2008). Implications of comorbidity on costs for patients with Alzheimer disease. Medical Care, 46, 839–846.CrossRefGoogle ScholarPubMed
Laird, N. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963–974.CrossRefGoogle ScholarPubMed
Lyketsos, C. G. (2012). Prevention of unnecessary hospitalization for patients with dementia: the role of ambulatory care. JAMA, 307, 197–198. doi: 10.1001/jama.2011.2005.CrossRefGoogle ScholarPubMed
Lyketsos, C. G. et al. (1999). The General Medical Health Rating: a bedside global rating of medical comorbidity in patients with dementia. Journal of the American Geriatrics Society, 47, 487–491.CrossRefGoogle Scholar
Lyketsos, C. G., Steinberg, M., Tschanz, J. T., Norton, M. C., Steffens, D. C. and Breitner, J. C. (2000). Mental and behavioral disturbances in dementia: findings from the Cache County Study on Memory in Aging. American Journal of Psychiatry, 157, 708–714.CrossRefGoogle Scholar
Lyketsos, C. G. et al. (2005). Population-based study of medical comorbidity in early dementia and “cognitive impairment, no dementia (CIND)”: association with functional and cognitive impairment: the Cache County Study. American Journal of Psychiatry, 13, 656–664.Google ScholarPubMed
Lyketsos, C. G. et al. (2006). Position statement of the American Association for Geriatric Psychiatry regarding principles of care for patients with dementia resulting from Alzheimer disease. American Journal of Geriatric Psychiatry, 14, 561–572. doi:10.1097/01.JGP.0000221334.65330.55.CrossRefGoogle Scholar
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, 1888–1893.CrossRefGoogle ScholarPubMed
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, 939–944.CrossRefGoogle ScholarPubMed
Mielke, M. M. et al. (2007). Vascular factors predict rate of progression in Alzheimer disease. Neurology, 69, 1850–1858. doi: 10.1212/01.wnl.0000279520.59792.fe.CrossRefGoogle ScholarPubMed
Mungas, D. et al. (2002). Volumetric MRI predicts rate of cognitive decline related to AD and cerebrovascular disease. Neurology, 59, 867–873.CrossRefGoogle ScholarPubMed
Rabins, P. V., Lyketsos, C. G. and Steele, C. (2006). Practical Dementia Care. New York: Oxford University Press.CrossRefGoogle Scholar
StataCorp LP (2009). STATA Version 11.1. Texas: StataCorp.Google Scholar
Storandt, M., Grant, E. A., Miller, J. P. and Morris, J. C. (2002). Rates of progression in mild cognitive impairment and early Alzheimer's disease. Neurology, 59, 1034–1041.CrossRefGoogle Scholar
Teng, E. L. and Chui, H. C. (1987). The Modified Mini-Mental State (3MS) Examination. Journal of Clinical Psychiatry, 48, 314–318.Google ScholarPubMed
Tschanz, J. T. et al. (2000). Dementia diagnoses from clinical and neuropsychological data compared: the Cache County study. Neurology, 54, 1290–1296.CrossRefGoogle ScholarPubMed
Tschanz, J.T., Welsh-Bohmer, K.A., Plassman, B.L., Norton, M.C., Wyse, B.W. and Breitner, J.C. (2002). An adaptation of the modified Mini-Mental State Examination: analysis of demographic influences and normative data: the Cache County study. Neuropsychology, Neuropsychiatry, and Behavioral Neurology, 15, 28–38.Google ScholarPubMed
Tschanz, J. T. et al. (2011). Progression of cognitive, functional, and neuropsychiatric symptom domains in a population cohort with Alzheimer dementia: the Cache County Dementia Progression Study. American Journal of Geriatric Psychiatry, 19, 532–542. doi: 10.1097/JGP.0b013e3181faec23.CrossRefGoogle Scholar
Vickrey, B. G. et al. (2006). The effect of a disease management intervention on quality and outcomes of dementia care: a randomized, controlled trial. Annals of Internal Medicine, 145, 713–726.CrossRefGoogle ScholarPubMed
Wilkosz, P. A. et al. (2010). Trajectories of cognitive decline in Alzheimer's disease. International Psychogeriatrics, 22, 281–290.CrossRefGoogle Scholar
Wilson, R. S. et al. (2004). Education and the course of cognitive decline in Alzheimer disease. Neurology, 63, 1198–1202.CrossRefGoogle Scholar
Xie, S. X., Ewbank, D. C., Chittams, J., Karlawish, J. H., Arnold, S. E. and Clark, C. M. (2009). Rate of decline in Alzheimer disease measured by a dementia severity rating scale. Alzheimer Disease and Associated Disorders, 23, 268–274. doi: 10.1097/WAD.0b013e318194a324.CrossRefGoogle ScholarPubMed