Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-23T11:17:13.927Z Has data issue: false hasContentIssue false
  • English
  • Français

Longitudinal Standards for Mid-life Cognitive Performance: Identifying Abnormal Within-Person Changes in the Wisconsin Registry for Alzheimer’s Prevention

Published online by Cambridge University Press:  28 November 2018

Rebecca L. Koscik ,
Erin M. Jonaitis ,
Lindsay R. Clark ,
Kimberly D. Mueller ,
Samantha L. Allison ,
Carey E. Gleason ,
Richard J. Chappell ,
Bruce P. Hermann  and
Sterling C. Johnson
Rebecca L. Koscik*
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Erin M. Jonaitis
Affiliation:
Wisconsin Alzheimer’s Institute, 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 Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison 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
Samantha L. Allison
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 Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
Richard J. Chappell
Affiliation:
Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Department of Statistics, University of Wisconsin-Madison, 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
Sterling C. Johnson
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
*
The first two authors contributed equally to this article. Correspondence and reprint requests to: Rebecca Koscik, 610 Walnut Street (9th Floor), Madison, WI, 53726. E-mail: rekoscik@wisc.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: A major challenge in cognitive aging is differentiating preclinical disease-related cognitive decline from changes associated with normal aging. Neuropsychological test authors typically publish single time-point norms, referred to here as unconditional reference values. However, detecting significant change requires longitudinal, or conditional reference values, created by modeling cognition as a function of prior performance. Our objectives were to create, depict, and examine preliminary validity of unconditional and conditional reference values for ages 40–75 years on neuropsychological tests. Method: We used quantile regression to create growth-curve–like models of performance on tests of memory and executive function using participants from the Wisconsin Registry for Alzheimer’s Prevention. Unconditional and conditional models accounted for age, sex, education, and verbal ability/literacy; conditional models also included past performance on and number of prior exposures to the test. Models were then used to estimate individuals’ unconditional and conditional percentile ranks for each test. We examined how low performance on each test (operationalized as <7th percentile) related to consensus-conference–determined cognitive statuses and subjective impairment. Results: Participants with low performance were more likely to receive an abnormal cognitive diagnosis at the current visit (but not later visits). Low performance was also linked to subjective and informant reports of worsening memory function. Conclusions: The percentile-based methods and single-test results described here show potential for detecting troublesome within-person cognitive change. Development of reference values for additional cognitive measures, investigation of alternative thresholds for abnormality (including multi-test criteria), and validation in samples with more clinical endpoints are needed. (JINS, 2019, 25, 1–14)

Keywords

MemoryExecutive functionCognitive agingPreclinical cognitive declineConditional standardsQuantile regression
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 25 , Issue 1 , January 2019 , pp. 1 - 14
Copyright
Copyright © The International Neuropsychological Society 2018 

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

Attix, D.K., Story, T.J., Chelune, G.J., Ball, J.D., Stutts, M.L., Hart, R.P., & Barth, J.T. (2009). The prediction of change: Normative neuropsychological trajectories. The Clinical Neuropsychologist, 23(1), 21–38. https://doi.org/10.1080/13854040801945078 Google Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate – A practical and powerful approach. Journal of the Royal Statistical Society Series B-Methodological, 57(1), 289–300.Google Scholar
Berkey, C.S., Reed, R.B., & Valadian, I. (1983). Longitudinal growth standards for preschool children. Annals of Human Biology, 10(1), 57–67.Google Scholar
Berman, S.E., Koscik, R.L., Clark, L.R., Mueller, K.D., Bluder, L., Galvin, J.E., & Johnson, S.C. (2017). Use of the Quick Dementia Rating System (QDRS) as an initial screening measure in a longitudinal cohort at risk for Alzheimer’s disease. JAD Reports, 1(1), 9–13. https://doi.org/10.3233/ADR-170004 Google Scholar
Bowditch, H. (1891). The growth of children studied by Galton’s percentile grades. In 22nd annual report of the State Board of Health of Massachusetts (pp. 479–525). Boston, MA: Wright and Potter.Google Scholar
Brown, L.D., Cai, T.T., & DasGupta, A. (2001). Interval estimation for a binomial proportion. Statistical Science, 16(2), 101–133. https://doi.org/10.1214/ss/1009213286 Google Scholar
Cameron, N. (1980). Conditional standards for growth in height of British children from 5.0 to 15.99 years of age. Annals of Human Biology, 7(4), 331–337.Google Scholar
Cheung, Y.B., Xu, Y., Feng, L., Feng, L., Nyunt, M.S.Z., Chong, M.S., … Ng, T.P. (2015). Unconditional and conditional standards using cognitive function curves for the Modified Mini-Mental State Exam: Cross-sectional and longitudinal analyses in older Chinese adults in Singapore. The American Journal of Geriatric Psychiatry, 23(9), 915–924. https://doi.org/10.1016/j.jagp.2014.08.008 Google Scholar
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 Study: Prevalence and characteristics using robust and standard neuropsychological normative data. Archives of Clinical Neuropsychology. [Epub ahead of print]. https://doi.org/10.1093/arclin/acw024 Google Scholar
Cole, T.J. (1995). Conditional reference charts to assess weight gain in British infants. Archives of Disease in Childhood, 73(1), 8–16.Google Scholar
Cole, T.J. (2012). The development of growth references and growth charts. Annals of Human Biology, 39(5), 382–394. https://doi.org/10.3109/03014460.2012.694475 Google Scholar
Cook, S., & Marsiske, M. (2006). Subjective memory beliefs and cognitive performance in normal and mildly impaired older adults. Aging & Mental Health, 10(4), 413–423. https://doi.org/10.1080/13607860600638487 Google Scholar
Corvalan, C. (2014). Unconditional or conditional change: Does it matter? Growth charts for monitoring weight gain during pregnancy. The American Journal of Clinical Nutrition, 99(2), 245–246. https://doi.org/10.3945/ajcn.113.079483 Google Scholar
Crawford, J.R., Garthwaite, P.H., Denham, A.K., & Chelune, G.J. (2012). Using regression equations built from summary data in the psychological assessment of the individual case: Extension to multiple regression. Psychological Assessment, 24(4), 801–814. https://doi.org/10.1037/a0027699 Google Scholar
Duff, K., Schoenberg, M.R., Patton, D., Paulsen, J.S., Bayless, J.D., Mold, J., … Adams, R.L. (2005). Regression-based formulas for predicting change in RBANS subtests with older adults. Archives of Clinical Neuropsychology, 20(3), 281–290. https://doi.org/10.1016/j.acn.2004.07.007 Google Scholar
Galvin, J.E. (2015). The Quick Dementia Rating System (QDRS): A rapid dementia staging tool. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 1(2), 249–259. https://doi.org/10.1016/j.dadm.2015.03.003 Google Scholar
Gilewski, M.J., Zelinski, E.M., & Schaie, K.W. (1990). The Memory Functioning Questionnaire for assessment of memory complaints in adulthood and old age. Psychology and Aging, 5(4), 482–490.Google Scholar
Hagemann, A. (2017). Cluster-Robust Bootstrap Inference in Quantile Regression Models. Journal of the American Statistical Association, 112(517), 446–456. https://doi.org/10.1080/01621459.2016.1148610 Google Scholar
Hawkins, K.A., Dean, D., & Pearlson, G.D. (2004). Alternative forms of the Rey Auditory Verbal Learning Test: A review. Behavioural Neurology, 15(3-4), 99–107.Google Scholar
Healy, M.J. (1974). Notes on the statistics of growth standards. Annals of Human Biology, 1(1), 41–46. https://doi.org/10.1080/03014467400000041 Google Scholar
Hedden, T., Oh, H., Younger, A.P., & Patel, T.A. (2013). Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology, 80(14), 1341–1348. https://doi.org/10.1212/WNL.0b013e31828ab35d Google Scholar
Hermann, B.P., Seidenberg, M., Schoenfeld, J., Peterson, J., Leveroni, C., & Wyler, A.R. (1996). Empirical techniques for determining the reliability, magnitude, and pattern of neuropsychological change after epilepsy surgery. Epilepsia, 37(10), 942–950.Google Scholar
Højsgaard, S., Halekoh, U., & Yan, J. (2006). The R Package geepack for generalized estimating equations. Journal of Statistical Software, 15/2, 1–11.Google Scholar
Jack, C.R., Bennett, D.A., Blennow, K., Carrillo, M.C., Dunn, B., Haeberlein, S.B., …Silverberg, N. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14(4), 535–562. https://doi.org/10.1016/j.jalz.2018.02.018 Google Scholar
Johnson, S.C., Koscik, R.L., Jonaitis, E.M., Clark, L.R., Mueller, K.D., Berman, S.E., …Sager, M.A. (2018). The Wisconsin Registry for Alzheimer’s prevention: A review of findings and current directions. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10, 130–142. https://doi.org/10.1016/j.dadm.2017.11.007 Google Scholar
Jorm, A., & Jacomb, P. (1989). The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): Socio-demographic correlates, reliability, validity and some norms. Psychological Medicine, 19(4), 1015–1022.Google Scholar
Kenny, R.A., Coen, R.F., Frewen, J., Donoghue, O.A., Cronin, H., & Savva, G.M. (2013). Normative values of cognitive and physical function in older adults: Findings from the Irish Longitudinal Study on Ageing. Journal of the American Geriatrics Society, 61(Suppl. 2), S279–S290. https://doi.org/10.1111/jgs.12195 Google Scholar
Koenker, R. (2017). Quantreg: Quantile Regression. Version 5.36. https://cran.r-project.org/web/packages/quantreg/index.html Google Scholar
Koscik, R.L., Berman, S.E., Clark, L.R., Mueller, K.D., Okonkwo, O.C., Gleason, C.E., … Johnson, S.C. (2016). Intraindividual cognitive variability in middle age predicts cognitive impairment. Journal of the International Neuropsychological Society, 22(10), 1016–1025. https://doi.org/10.1017/S135561771600093X Google Scholar
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.Google Scholar
Maassen, G.H., Bossema, E., & Brand, N. (2009). Reliable change and practice effects: Outcomes of various indices compared. Journal of Clinical and Experimental Neuropsychology, 31(3), 339–352. https://doi.org/10.1080/13803390802169059 Google Scholar
Manly, J.J., Touradji, P., Tang, M.-X., & Stern, Y. (2003). Literacy and memory decline among ethnically diverse elders. Journal of Clinical and Experimental Neuropsychology, 25(5), 680–690.Google Scholar
Morris, J.C. (1997). Clinical dementia rating: A reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. International Psychogeriatrics, 9(Suppl. 1), 173–176; discussion 177–178.Google Scholar
R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Reitan, R.M. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills, 8(3), 271–276.Google Scholar
Roberts, J.L., Clare, L., & Woods, R.T. (2009). Subjective memory complaints and awareness of memory functioning in mild cognitive impairment: A systematic review. Dementia and Geriatric Cognitive Disorders, 28(2), 95–109. https://doi.org/10.1159/000234911 Google Scholar
RStudio Team. (2016). RStudio: Integrated development environment for R. Boston, MA: RStudio, Inc.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. https://doi.org/10.1177/0891988705281882 Google Scholar
Schmidt, M. (1996). Rey Auditory Verbal Learning Test: A handbook. Los Angeles, CA: Western Psychological Services.Google Scholar
Steinberg, B.A., Bieliauskas, L.A., Smith, G.E., Ivnik, R.J., & Malec, J.F. (2005). Mayo’s older americans normative studies: Age- and IQ-adjusted norms for the Auditory Verbal Learning Test and the Visual Spatial Learning Test. The Clinical Neuropsychologist, 19(3–4), 464–523. https://doi.org/10.1080/13854040590945193 Google Scholar
Tanner, J., Whitehouse, R., & Takaishi, M. (1966). Standards from birth to maturity for height, weight, height velocity, and weight velocity: British children, 1965. Parts I and II. Archives of Disease in Childhood, 41, 454–471; 613–635.Google Scholar
Temkin, N.R., Heaton, R.K., Grant, I., & Dikmen, S.S. (1999). Detecting significant change in neuropsychological test performance: A comparison of four models. Journal of the International Neuropsychological Society, 5(4), 357–369.Google Scholar
Touloumis, A. (2015). R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses. Journal of Statistical Software, 64(8), 1–14.Google Scholar
Trenerry, B. (1989). The Stroop Neuropsychological Screening Test. Odessa, FL: Psychological Assessment Resources, Inc.Google Scholar
Vannini, P., Amariglio, R., Hanseeuw, B., Johnson, K.A., McLaren, D.G., Chhatwal, J., … Sperling, R.A. (2017). Memory self-awareness in the preclinical and prodromal stages of Alzheimer’s disease. Neuropsychologia, 99, 343–349. https://doi.org/10.1016/j.neuropsychologia.2017.04.002 Google Scholar
Venables, W.N., & Ripley, B.D. (2002). Modern applied statistics with S (Fourth). New York: Springer.Google Scholar
Wechsler, D. (1997). Wechsler Adult Intelligence Scale - III. San Antonio, TX: The Psychological Corporation.Google Scholar
Wei, Y., Pere, A., Koenker, R., & He, X. (2006). Quantile regression methods for reference growth charts. Statistics in Medicine, 25(8), 1369–1382. https://doi.org/10.1002/sim.2271 Google Scholar
WHO Multicentre Growth Reference Study Group. (2006). WHO Child Growth Standards based on length/height, weight and age. Acta Paediatrica (Oslo, Norway: 1992). Supplement, 450, 76–85.Google Scholar
Wickham, H. (2009). Ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag.Google Scholar
Wilkinson, G.S. (1993). The Wide Range Achievement Test: Manual (3rd ed.). Wilmington, DE: Jastak Association.Google Scholar
Xie, Y. (2017). Knitr: A general-purpose package for dynamic report generation in R. Version 1.20. https://cran.r-project.org/web/packages/knitr/index.html Google Scholar
Xu, J., Luntamo, M., Kulmala, T., Ashorn, P., & Cheung, Y.B. (2014). A longitudinal study of weight gain in pregnancy in Malawi: Unconditional and conditional standards. The American Journal of Clinical Nutrition, 99(2), 296–301. https://doi.org/10.3945/ajcn.113.074120 Google Scholar
Supplementary material: File

Koscik et al. supplementary material

Table S1

Download Koscik et al. supplementary material(File)
File 34.3 KB