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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: [email protected]
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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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