Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-23T20:47:53.250Z Has data issue: false hasContentIssue false
  • English
  • Français

Intraindividual Cognitive Variability in Middle Age Predicts Cognitive Impairment 8–10 Years Later: Results from the Wisconsin Registry for Alzheimer’s Prevention

Published online by Cambridge University Press:  01 December 2016

Rebecca L. Koscik ,
Sara E. Berman ,
Lindsay R. Clark ,
Kimberly D. Mueller ,
Ozioma C. Okonkwo ,
Carey E. Gleason ,
Bruce P. Hermann ,
Mark A. Sager  and
Sterling C. Johnson
Rebecca L. Koscik*
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Sara E. Berman
Affiliation:
Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Lindsay R. Clark
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Kimberly D. Mueller
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Ozioma C. Okonkwo
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Carey E. Gleason
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison Wisconsin
Bruce P. Hermann
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Mark A. Sager
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Sterling C. Johnson
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison Wisconsin
*
Correspondence and reprint requests to: Rebecca Koscik, Wisconsin Alzheimer’s Institute, 7818 Big Sky Drive, Suite 215, University of Wisconsin School of Medicine and Public Health, Madison, WI 53719. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Intraindividual cognitive variability (IICV) has been shown to differentiate between groups with normal cognition, mild cognitive impairment (MCI), and dementia. This study examined whether baseline IICV predicted subsequent mild to moderate cognitive impairment in a cognitively normal baseline sample. Methods: Participants with 4 waves of cognitive assessment were drawn from the Wisconsin Registry for Alzheimer’s Prevention (WRAP; n=684; 53.6(6.6) baseline age; 9.1(1.0) years follow-up; 70% female; 74.6% parental history of Alzheimer’s disease). The primary outcome was Wave 4 cognitive status (“cognitively normal” vs. “impaired”) determined by consensus conference; “impaired” included early MCI (n=109), clinical MCI (n=11), or dementia (n=1). Primary predictors included two IICV variables, each based on the standard deviation of a set of scores: “6 Factor IICV” and “4 Test IICV”. Each IICV variable was tested in a series of logistic regression models to determine whether IICV predicted cognitive status. In exploratory analyses, distribution-based cutoffs incorporating memory, executive function, and IICV patterns were used to create and test an MCI risk variable. Results: Results were similar for the IICV variables: higher IICV was associated with greater risk of subsequent impairment after covariate adjustment. After adjusting for memory and executive functioning scores contributing to IICV, IICV was not significant. The MCI risk variable also predicted risk of impairment. Conclusions: While IICV in middle-age predicts subsequent impairment, it is a weaker risk indicator than the memory and executive function scores contributing to its calculation. Exploratory analyses suggest potential to incorporate IICV patterns into risk assessment in clinical settings. (JINS, 2016, 22, 1016–1025)

Keywords

Prodromal periodMild cognitive impairmentAlzheimer’s diseaseNeuropsychological testsRisk screeningPsychometrics
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 22 , Special Issue 10: Special Issue: Preclinical Prediction , November 2016 , pp. 1016 - 1025
Copyright
Copyright © The International Neuropsychological Society 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aisen, P.S., Petersen, R.C., Donohue, M.C., Gamst, A., Raman, R., Thomas, R.G., & Weiner, M.W. (2010). Clinical core of the Alzheimer’s disease neuroimaging initiative: Progress and plans. Alzheimer’s & Dementia, 6(3), 239–246. http://doi.org/10.1016/j.jalz.2010.03.006.CrossRefGoogle Scholar
Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., & Petersen, R.C. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270–279.CrossRefGoogle ScholarPubMed
Andersson, C., Lindau, M., Almkvist, O., Engfeldt, P., Johansson, S.-E., & Eriksdotter Jönhagen, M. (2006). Identifying patients at high and low risk of cognitive decline using Rey Auditory Verbal Learning Test among middle-aged memory clinic outpatients. Dementia and Geriatric Cognitive Disorders, 21(4), 251–259.Google Scholar
Bondi, M.W., Edmonds, E.C., Jak, A.J., Clark, L.R., Delano-Wood, L., McDonald, C.R., & Galasko, D. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer’s Disease, 42(1), 275–289.CrossRefGoogle ScholarPubMed
Clark, L.R., Koscik, R.L., Nicholas, C.R., Okonkwo, O.C., Engelman, C.D., Bratzke, L.C., & Johnson, S.C. (2016). Mild cognitive impairment in late middle age in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) study: Prevalence and characteristics using robust and standard neuropsychological normative data. [Epub ahead of print].Google Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155.Google Scholar
Dowling, N.M., Hermann, B., La Rue, A., & Sager, M.A. (2010). Latent structure and factorial invariance of a neuropsychological test battery for the study of preclinical Alzheimer’s disease. Neuropsychology, 24(6), 742–756. http://doi.org/10.1037/a0020176.CrossRefGoogle Scholar
Drebing, C.E., Van Gorp, W.G., Stuck, A.E., Mitrushina, M., & Beck, J. (1994). Early detection of cognitive decline in higher cognitively functioning older adults: Sensitivity and specificity of a neuropsychological screening battery. Neuropsychology, 8(1), 31.Google Scholar
Duara, R., Loewenstein, D.A., Greig, M.T., Potter, E., Barker, W., Raj, A., & Potter, H. (2011). Pre-MCI and MCI: Neuropsychological, clinical, and imaging features and progression rates. The American Journal of Geriatric Psychiatry, 19(11), 951–960. http://doi.org/10.1097/JGP.0b013e3182107c69 CrossRefGoogle ScholarPubMed
Grice, J.W. (2001). Computing and evaluating factor scores. Psychological Methods, 6(4), 430.Google Scholar
Heaton, R., Miller, S., Taylor, J.R., & Grant, I. (2004). Comprehensive norms for an expanded Halstead-Reitan battery: Demographically adjusted neuropsychological norms for African American and Caucasian adults (HRB). Professional Manual. Lutz, FL: Psychological Assessment Resources Inc..Google Scholar
Hilborn, J.V., Strauss, E., Hultsch, D.F., & Hunter, M.A. (2009). Intraindividual variability across cognitive domains: Investigation of dispersion levels and performance profiles in older adults. Journal of Clinical and Experimental Neuropsychology, 31(4), 412–424.CrossRefGoogle ScholarPubMed
Holtzer, R., Verghese, J., Wang, C., Hall, CB., & Lipton, RB. (2008). WIthin-person across-neuropsychological test variability and incident dementia. JAMA, 300(7), 823–830. http://doi.org/10.1001/jama.300.7.823.Google Scholar
Imtiaz, B., Tolppanen, A.-M., Kivipelto, M., & Soininen, H. (2014). Future directions in Alzheimer’s disease from risk factors to prevention. Biochemical Pharmacology, 88(4), 661–670.Google Scholar
Jessen, F., Wolfsgruber, S., Wiese, B., Bickel, H., Mösch, E., Kaduszkiewicz, H., & Wagner, M. (2014). AD dementia risk in late MCI, in early MCI, and in subjective memory impairment. Alzheimer’s & Dementia, 10(1), 76–83. http://doi.org/10.1016/j.jalz.2012.09.017.Google Scholar
Kälin, A.M., Pflüger, M., Gietl, A.F., Riese, F., Jäncke, L., Nitsch, R.M., && Hock, C. (2014). Intraindividual variability across cognitive tasks as a potential marker for prodromal Alzheimer’s disease. Frontiers in Aging Neuroscience, 6, 147.CrossRefGoogle ScholarPubMed
Koepsell, T.D., & Monsell, S.E. (2012). Reversion from mild cognitive impairment to normal or near-normal cognition Risk factors and prognosis. Neurology, 79(15), 1591–1598.CrossRefGoogle ScholarPubMed
Koscik, R.L., La Rue, A., Jonaitis, E.M., Okonkwo, O.C., Johnson, S.C., Bendlin, B.B., & Sager, M.A. (2014). Emergence of mild cognitive impairment in late middle-aged adults in the Wisconsin registry for Alzheimer’s prevention. Dementia and Geriatric Cognitive Disorders, 38(1–2), 16–30. http://doi.org/10.1159/000355682.CrossRefGoogle ScholarPubMed
Lewis, F., Butler, A., & Gilbert, L. (2011). A unified approach to model selection using the likelihood ratio test. Methods in Ecology and Evolution, 2(2), 155–162. http://doi.org/10.1111/j.2041-210X.2010.00063.x CrossRefGoogle Scholar
MacDonald, S.W., Hultsch, D.F., & Dixon, R.A. (2003). Performance variability is related to change in cognition: Evidence from the Victoria Longitudinal Study. Psychology and Aging, 18(3), 510.Google Scholar
Petersen, R.C., Aisen, P.S., Beckett, L.A., Donohue, M.C., Gamst, A.C., Harvey, D.J., & Toga, A.W. (2010). Alzheimer’s disease Neuroimaging Initiative (ADNI) clinical characterization. Neurology, 74(3), 201–209.Google Scholar
Sager, M.A., Hermann, B., & La Rue, A. (2005). Middle-aged children of persons with Alzheimer’s disease: APOE genotypes and cognitive function in the Wisconsin Registry for Alzheimer’s Prevention. Journal of Geriatric Psychiatry and Neurology, 18(4), 245–249. http://doi.org/10.1177/0891988705281882 Google Scholar
Salthouse, T.A., & Soubelet, A. (2014). Heterogeneous ability profiles may be a unique indicator of impending cognitive decline. Neuropsychology, 28(5), 812.CrossRefGoogle ScholarPubMed
Schmidt, M. (1996). Rey auditory verbal learning test: A handbook. Los Angeles: Western Psychological Services.Google Scholar
Sperling, R.A., Karlawish, J., & Johnson, K.A. (2013). Preclinical Alzheimer disease —the challenges ahead. Nature Reviews. Neurology, 9(1), 54–58. http://doi.org/10.1038/nrneurol.2012.241 Google Scholar
Vaughan, L., Leng, I., Dagenbach, D., Resnick, S.M., Rapp, S.R., Jennings, J.M., & Coker, L.H. (2013). Intraindividual variability in domain-specific cognition and risk of mild cognitive impairment and dementia. Current Gerontology and Geriatrics Research, 2013, 495793.Google Scholar
Wechsler, D. (1987). WMS-R: Wechsler memory scale-revised. San Antonio, TX: Psychological Corporation.Google Scholar
Wilkinson, G.S. (1993). WRAT-3: Wide range achievement test. San Antonio, TX: Pearson.Google Scholar
Supplementary material: File

Koscik supplementary material

Figures S1-S4

Download Koscik supplementary material(File)
File 80.3 KB
Supplementary material: File

Koscik supplementary material

Description of Tables S1-S3 and Figures S1-S4

Download Koscik supplementary material(File)
File 19.9 KB
Supplementary material: File

Koscik supplementary material

Table S1

Download Koscik supplementary material(File)
File 21.7 KB
Supplementary material: File

Koscik supplementary material

Table S2

Download Koscik supplementary material(File)
File 19.8 KB
Supplementary material: File

Koscik supplementary material

Table S3

Download Koscik supplementary material(File)
File 19.8 KB