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Characterizing the Effects of Sex, APOE ɛ4, and Literacy on Mid-life Cognitive Trajectories: Application of Information-Theoretic Model Averaging and Multi-model Inference Techniques to the Wisconsin Registry for Alzheimer’s Prevention Study

Published online by Cambridge University Press:  07 December 2018

Rebecca L. Koscik ,
Derek L. Norton ,
Samantha L. Allison ,
Erin M. Jonaitis ,
Lindsay R. Clark ,
Kimberly D. Mueller ,
Bruce P. Hermann ,
Corinne D. Engelman ,
Carey E. Gleason  and
Mark A. Sager
...Show all authors
Rebecca L. Koscik*
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Derek L. Norton
Affiliation:
Department of Biostatistics and Medical Informatics, 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
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
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
Corinne D. Engelman
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
Carey E. Gleason
Affiliation:
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
Mark A. Sager
Affiliation:
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
Richard J. Chappell
Affiliation:
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
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
*
Correspondence and reprint requests to: Rebecca Koscik, 610 Walnut Street (Room 944), Madison, WI, 53726. E-mail: rekoscik@wisc.edu
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Abstract

Objectives: Prior research has identified numerous genetic (including sex), education, health, and lifestyle factors that predict cognitive decline. Traditional model selection approaches (e.g., backward or stepwise selection) attempt to find one model that best fits the observed data, risking interpretations that only the selected predictors are important. In reality, several predictor combinations may fit similarly well but result in different conclusions (e.g., about size and significance of parameter estimates). In this study, we describe an alternative method, Information-Theoretic (IT) model averaging, and apply it to characterize a set of complex interactions in a longitudinal study on cognitive decline. Methods: Here, we used longitudinal cognitive data from 1256 late–middle aged adults from the Wisconsin Registry for Alzheimer’s Prevention study to examine the effects of sex, apolipoprotein E (APOE) ɛ4 allele (non-modifiable factors), and literacy achievement (modifiable) on cognitive decline. For each outcome, we applied IT model averaging to a set of models with different combinations of interactions among sex, APOE, literacy, and age. Results: For a list-learning test, model-averaged results showed better performance for women versus men, with faster decline among men; increased literacy was associated with better performance, particularly among men. APOE had less of an association with cognitive performance in this age range (∼40–70 years). Conclusions: These results illustrate the utility of the IT approach and point to literacy as a potential modifier of cognitive decline. Whether the protective effect of literacy is due to educational attainment or intrinsic verbal intellectual ability is the topic of ongoing work. (JINS, 2019, 25, 119–133)

Keywords

Kullback-Leibler divergenceModel averagingModel likelihoodsModel selectionCognitive declineAlzheimer’s disease
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 25 , Issue 2 , February 2019 , pp. 119 - 133
Copyright
Copyright © The International Neuropsychological Society 2018 

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References

REFERENCES

Altmann, A., Tian, L., Henderson, V.W., & Greicius, M.D. (2014). Sex modifies the APOE-related risk of developing Alzheimer disease. Annals of Neurology, 75(4), 563–573.Google Scholar
Anderson, D.R., & Burnham, K.P. (2002). Avoiding pitfalls when using information-theoretic methods. The Journal of Wildlife Management, 66, 912–918.Google Scholar
Anderson, E.D., Wahoske, M., Huber, M., Norton, D., Li, Z., Koscik, R.L., … Asthana, S. (2016). Cognitive variability—A marker for incident MCI and AD: An analysis for the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 4, 47–55.Google Scholar
Ashendorf, L., Jefferson, A.L., Green, R.C., & Stern, R.A. (2009). Test–retest stability on the WRAT-3 reading subtest in geriatric cognitive evaluations. Journal of Clinical and Experimental Neuropsychology, 31(5), 605–610. https://doi.org/10.1080/13803390802375557 Google Scholar
Benton, A.L., Hamsher, K., & Sivan, A.B. (1994). Multilingual Aphasia Examination: Manual of instructions. Iowa City, IA: AJA Associates. Inc. Google Scholar
Berggren, R., Nilsson, J., & Lövdén, M. (2018). Education does not affect cognitive decline in aging: A Bayesian assessment of the association between education and change in cognitive performance. Frontiers in Psychology, 9, 1138.Google Scholar
Beydoun, M.A., Boueiz, A., Abougergi, M.S., Kitner-Triolo, M.H., Beydoun, H.A., Resnick, S.M., … Zonderman, A.B. (2012). Sex differences in the association of the apolipoprotein E epsilon 4 allele with incidence of dementia, cognitive impairment, and decline. Neurobiology of Aging, 33(4), 720–731.e4.Google Scholar
Burnham, K.P., & Anderson, D.R. (2002). Model selection and multimodel inference (2nd ed.). New York, NY: Springer.Google Scholar
Burnham, K.P., & Anderson, D.R. (2003). Model selection and multimodel inference: A practical information-theoretic approach. New York: Springer Science & Business Media.Google Scholar
Burnham, K.P., Anderson, D.R., & Huyvaert, K.P. (2011). AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecology and Sociobiology, 65(1), 23–35.Google Scholar
Cade, B.S. (2015). Model averaging and muddled multimodel inferences. Ecology, 96(9), 2370–2382. https://doi.org/10.1890/14-1639.1 Google Scholar
Caselli, R.J., Dueck, A.C., Osborne, D., Sabbagh, M.N., Connor, D.J., Ahern, G.L., … Woodruff, B.K. (2009). Longitudinal modeling of age-related memory decline and the APOE ε4 effect. New England Journal of Medicine, 361(3), 255–263.Google Scholar
Chang, Y.-L., Fennema-Notestine, C., Holland, D., McEvoy, L.K., Stricker, N.H., Salmon, D.P., … Alzheimer’s Disease Neuroimaging Initiative. (2014). APOE interacts with age to modify rate of decline in cognitive and brain changes in Alzheimer’s disease. Alzheimer’s & Dementia, 10(3), 336–348.Google Scholar
Chen, M.-H., Ibrahim, J.G., Shao, Q.-M., & Weiss, R.E. (2003). Prior elicitation for model selection and estimation in generalized linear mixed models. Journal of Statistical Planning and Inference, 111(1), 57–76. https://doi.org/10.1016/S0378-3758(02)00285-9 Google Scholar
Claeskens, G., & Hjort, N.L. (2008). Model selection and model averaging (Vol. 330). Cambridge: Cambridge University Press.Google Scholar
Darst, B.F., Koscik, R.L., Racine, A.M., Oh, J.M., Krause, R.A., Carlsson, C.M., … Bendlin, B.B. (2017). Pathway-specific polygenic risk scores as predictors of amyloid-β deposition and cognitive function in a sample at increased risk for Alzheimer’s disease. Journal of Alzheimer’s Disease, 55(2), 473–484.Google Scholar
Gleason, C.E., Norton, D., Anderson, E.D., Wahoske, M., Washington, D.T., Umucu, E., … Asthana, S. (2018). Cognitive variability predicts incident Alzheimer’s disease and mild cognitive impairment comparable to a cerebrospinal fluid biomarker. Journal of Alzheimer’s Disease, 61, 79–89. https://doi.org/10.3233/JAD-170498 Google Scholar
Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity: The lasso and generalizations. Boca Raton, FL: CRC press.Google Scholar
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York, NY: Springer.Google Scholar
Hegyi, G., & Garamszegi, L.Z. (2011). Using information theory as a substitute for stepwise regression in ecology and behavior. Behavioral Ecology and Sociobiology, 65(1), 69–76.Google Scholar
Hoeting, J.A., Madigan, D., Raftery, A.E., & Volinsky, C.T. (1999). Bayesian Model averaging: A tutorial. Statistical Science, 14(4), 382–401.Google Scholar
Holtzer, R., Verghese, J., Wang, C., Hall, C.B., & Lipton, R.B. (2008). Within-person across-neuropsychological test variability and incident dementia. JAMA, 300(7), 823–830.Google Scholar
Hurvich, C.M., & Tsai, C.-L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297–307. https://doi.org/10.1093/biomet/76.2.297 Google Scholar
Jack, C.R. Jr., Wiste, H.J., Weigand, S.D., Knopman, D.S., Vemuri, P., Mielke, M.M., … Petersen, R.C. (2015). Age, sex, and apoe ε4 effects on memory, brain structure, and β-amyloid across the adult life span. JAMA Neurology, 72(5), 511–519. https://doi.org/10.1001/jamaneurol.2014.4821 Google Scholar
Johnson, S.C., Koscik, R.L., Jonaitis, E.M., Clark, L.R., Mueller, K.D., Berman, S.E., … Sager, M.A. (2017). The Wisconsin Registry for Alzheimer’s Prevention: A review of findings and current directions. Alzheimers & Dementia, 10, 130–142.Google Scholar
Kaplan, E., Goodglass, H., & Weintraub, S. (2001). Boston naming test. Austin, TX: Pro-ed.Google Scholar
Kaup, A.R., Nettiksimmons, J., Harris, T.B., Sink, K.M., Satterfield, S., Metti, A.L., … Yaffe, K. (2015). Cognitive resilience to apolipoprotein E ε4: Contributing factors in black and white older adults. JAMA Neurology, 72(3), 340–348.Google Scholar
Koran, M.E.I., Wagener, M., & Hohman, T.J. (2017). Sex differences in the association between AD biomarkers and cognitive decline. Brain Imaging and Behavior, 11(1), 205–213.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 8–10 years later: Results from the Wisconsin Registry for Alzheimer’s Prevention. Journal of the International Neuropsychological Society, 22(10), 1016–1025.Google Scholar
Lee, J.D., Sun, D.L., Sun, Y., & Taylor, J.E. (2016). Exact post-selection inference, with application to the lasso. The Annals of Statistics, 44(3), 907–927.Google Scholar
Lenehan, M.E., Summers, M.J., Saunders, N.L., Summers, J.J., & Vickers, J.C. (2015). Relationship between education and age-related cognitive decline: A review of recent research. Psychogeriatrics, 15(2), 154–162.Google Scholar
Lezak, M.D., Howieson, D.B., Bigler, E.D., & Tranel, D. (2012). Neuropsychological assessment (5th ed.). New York, NY: Oxford University Press.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
McCarrey, A.C., An, Y., Kitner-Triolo, M.H., Ferrucci, L., & Resnick, S.M. (2016). Sex differences in cognitive trajectories in clinically normal older adults. Psychology and Aging, 31(2), 166–175. https://doi.org/10.1037/pag0000070 Google Scholar
Mielke, M.M., Vemuri, P., & Rocca, W.A. (2014). Clinical epidemiology of Alzheimer’s disease: Assessing sex and gender differences. Clinical Epidemiology, 6, 37.Google Scholar
Mortensen, E.L., & Høgh, P. (2001). A gender difference in the association between APOE genotype and age-related cognitive decline. Neurology, 57(1), 89–95.Google Scholar
Neu, S.C., Pa, J., Kukull, W., Beekly, D., Kuzma, A., Gangadharan, P., … Redolfi, A. (2017). Apolipoprotein E genotype and sex risk factors for Alzheimer disease: A meta-analysis. JAMA Neurology, 74(10), 1178–1189.Google Scholar
Olsen, J.P., Fellows, R.P., Rivera-Mindt, M., Morgello, S., & Byrd, D.A. (2015). Reading ability as an estimator of premorbid intelligence: Does it remain stable among ethnically diverse HIV+ adults? The Clinical Neuropsychologist, 29(7), 1034–1052. https://doi.org/10.1080/13854046.2015.1122085 Google Scholar
Payami, H., Zareparsi, S., Montee, K.R., Sexton, G.J., Kaye, J.A., Bird, T.D., … Litt, M. (1996). Gender difference in apolipoprotein E-associated risk for familial Alzheimer disease: A possible clue to the higher incidence of Alzheimer disease in women. American Journal of Human Genetics, 58(4), 803.Google Scholar
Price, J.L., McKeel, D.W., Buckles, V.D., Roe, C.M., Xiong, C., Grundman, M., … Dickson, D.W. (2009). Neuropathology of nondemented aging: Presumptive evidence for preclinical Alzheimer disease. Neurobiology of Aging, 30(7), 1026–1036.Google Scholar
Price, J.L., & Morris, J.C. (1999). Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Annals of Neurology, 45(3), 358–368.Google Scholar
Radloff, L.S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401.Google Scholar
Richards, S.A. (2005). Testing ecological theory using the information-theoretic approach: Examples and cautionary results. Ecology, 86(10), 2805–2814.Google Scholar
Richards, S.A., Whittingham, M.J., & Stephens, P.A. (2011). Model selection and model averaging in behavioural ecology: The utility of the IT-AIC framework. Behavioral Ecology and Sociobiology, 65(1), 77–89.Google Scholar
Riedel, B.C., Thompson, P.M., & Brinton, R.D. (2016). Age, APOE and sex: Triad of risk of Alzheimer’s disease. The Journal of Steroid Biochemistry and Molecular Biology, 160, 134–147.Google Scholar
Roberts, R.O., Geda, Y.E., Knopman, D.S., Cha, R.H., Pankratz, V.S., Boeve, B.F., … Petersen, R.C. (2012). The incidence of MCI differs by subtype and is higher in men: The Mayo Clinic Study of Aging. Neurology, 78(5), 342–351. https://doi.org/10.1212/WNL.0b013e3182452862 Google Scholar
Schelldorfer, J., Bühlmann, P., & Van de Geer, S. (2011). Estimation for high-dimensional linear mixed-effects models using ℓ1-penalization. Scandinavian Journal of Statistics, 38(2), 197–214.Google Scholar
Schmidt, M. (1996). Rey auditory verbal learning test: A handbook. Los Angeles, CA: Western Psychological Services.Google Scholar
Strittmatter, W.J., & Roses, A.D. (1996). Apolipoprotein E and Alzheimer’s disease. Annual Review of Neuroscience, 19(1), 53–77.Google Scholar
Sundermann, E.E., Biegon, A., Rubin, L.H., Lipton, R.B., Landau, S., & Maki, P.M. (2017). Does the Female advantage in verbal memory contribute to underestimating Alzheimer’s disease pathology in women versus men? Journal of Alzheimer’s Disease, 56(3), 947–957.Google Scholar
Sundermann, E.E., Maki, P.M., Rubin, L.H., Lipton, R.B., Landau, S., Biegon, A., & For the Alzheimer’s Disease Neuroimaging Initiative. (2016). Female advantage in verbal memory: Evidence of sex-specific cognitive reserve. Neurology, 87(18), 1916–1924. https://doi.org/10.1212/WNL.0000000000003288 Google Scholar
Symonds, M.R., & Moussalli, A. (2011). A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology, 65(1), 13–21.Google Scholar
Tang, M.-X., Maestre, G., Tsai, W.-Y., Liu, X.-H., Feng, L., Chung, W.-Y., … Tycko, B. (1996). Relative risk of Alzheimer disease and age-at-onset distributions, based on APOE genotypes among elderly African Americans, Caucasians, and Hispanics in New York City. American Journal of Human Genetics, 58(3), 574.Google Scholar
Tang, M.-X., Stern, Y., Marder, K., Bell, K., Gurland, B., Lantigua, R., … Mayeux, R. (1998). The APOE-∊ 4 allele and the risk of Alzheimer disease among African Americans, whites, and Hispanics. JAMA, 279(10), 751–755.Google Scholar
Taylor, J., & Tibshirani, R.J. (2015). Statistical learning and selective inference. Proceedings of the National Academy of Sciences of the United States of America, 112(25), 7629. https://doi.org/10.1073/pnas.1507583112 Google Scholar
Trenerry, M.R., Crosson, B., DeBoe, J., & Leber, W.R. (1989). Stroop neuropsychological screening test. Odessa, FL: Psychological Assessment Resources.Google Scholar
Tucker-Drob, E.M., Johnson, K.E., & Jones, R.N. (2009). The Cognitive Reserve Hypothesis: A longitudinal examination of age-associated declines in reasoning and processing speed. Developmental Psychology, 45(2), 431–446. https://doi.org/10.1037/a0014012 Google Scholar
Vemuri, P., Lesnick, T.G., Przybelski, S.A., Machulda, M., Knopman, D.S., Mielke, M.M., … Jack, C.R. Jr. (2014). Association of lifetime intellectual enrichment with cognitive decline in the older population. JAMA Neurology, 71(8), 1017–1024. https://doi.org/10.1001/jamaneurol.2014.963 Google Scholar
Wechsler, D. (1997). WAIS-III: Wechsler adult intelligence scale. Lutz, FL: Psychological Corporation.Google Scholar
Wilkinson, G.S. (1993). WRAT-3: Wide range achievement test administration manual. Wide Range, Incorporated. Retrieved from https://www.librarything.com/work/17422599 Google Scholar
Wisdom, N.M., Callahan, J.L., & Hawkins, K.A. (2011). The effects of apolipoprotein E on non-impaired cognitive functioning: A meta-analysis. Neurobiology of Aging, 32(1), 63–74.Google Scholar
Yu, L., Boyle, P.A., Leurgans, S., Schneider, J.A., & Bennett, D.A. (2014). Disentangling the effects of age and APOE on neuropathology and late life cognitive decline. Neurobiology of Aging, 35(4), 819–826.Google Scholar
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