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Synergistic Associations of Genetic, Demographic, Health, and Lifestyle Risk Factors On Synergistic Associations of Genetic, Demographic, Health, and Lifestyle Risk Factors on Neurocognitive Performance and Change in Aging by Shraddha Sapkota A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Centre for Neuroscience University of Alberta © Shraddha Sapkota, 2016 ii ABSTRACT Objective: Neurocognitive phenotypes observed in aging have been linked to select combinations of candidate genetic polymorphisms and modifiable risk factors. In this dissertation, I test multiple methods and approaches to examine three modifiable risk domains (i.e., demographic, health, lifestyle) and six single nucleotide polymorphisms (SNPs) (i.e., Apolipoprotein E [APOE] Catechol-O-methyltransferase [COMT; rs4680], Brain-derived neurotrophic factor [BDNF; rs6265], Complement receptor 1 [CR1; rs6656401], Clusterin [CLU; rs11136000], and Phosphatidylinositol-binding clathrin assembly protein [PICALM; rs3851179]) on concurrent and longitudinal neurocognitive performance in non-demented aging and Mild Cognitive Impairment (MCI). This dissertation includes three studies. Study 1 tested SNPs, demographic, health, and lifestyle risk factors to build, compare, and validate a multi- domain risk score to predict episodic memory (EM) performance and 9-year change. Study 2 tested independent, interactive, and additive associations of two normal aging SNPs (COMT, BDNF), and as stratified by AD-related SNP (APOE) on EF performance in normal aging. Study 3 examined independent and additive associations of (a) COMT, BDNF, and APOE, (b) COMT and BDNF as separated by APOE risk, and (c) as moderated by age and lifestyle activities groups on EF performance and 9-year change. Method: This dissertation uses data from normal aging older adults and adults classified as MCI from the Victoria Longitudinal Study (VLS): Study 1 (normal aging: n = 568, mean age at baseline = 68.32 years; MCI: n = 69, mean age at baseline = 73.36 years), Study 2 and Study 3 (normal aging: n = 634, mean age = 70.58 years). Study 1 was longitudinal, Study 2 was cross-sectional, and Study 3 followed an accelerated longitudinal design. I used appropriate combinations of confirmatory factor analysis, longitudinal invariance testing, parallel process latent growth models, and receiver operating characteristic curves to test iii research questions in all three studies. Results: In Study 1, first, I observed that higher risk scores on demographic, health, and lifestyle risk factors predicted worse EM performance at age 75 years and steeper 9-year decline. Second, higher risk scores on independent and additive risk for demographic, health, lifestyle, and genetic factors predicted worse EM performance at baseline and time point 3. Third, independent risk score for demographic and health risk domains distinguished non-demented older adults from those with MCI. In Study 2, I observed that older adults with a high-risk allelic (COMT allelic risk + BDNF allelic risk) combination performed differentially worse on EF compared to their non-risk counterparts (COMT no allelic risk + BDNF no allelic risk). In Study 3, I observed that APOE risk carriers showed a magnified COMT + BDNF risk panel effect on EF performance at age 75 years but this effect was not present in the high lifestyle activities group. Discussion: I used methods and approaches to building a pre- clinical risk score with multiple domains (genetic, demographic, health, and lifestyle risk factors) that were selected to detect cognitive decline in normal aging at a point prior to dementia onset. In addition, select additive versus interactive mechanisms for cognitive aging genes may provide insight into the complex underlying mechanisms and pathways that influence neurocognitive performance in non-demented older adults. Future studies can investigate and address the applicability of our synergistic methods using select risk factors to develop theoretical concepts and identify genetic and modifiable risk factors to inform dementia prevention strategies. Such approaches also have the potential to help identify complex neurobiological and neurogenetic underpinnings of polygenic phenotypes observed in normal aging. iv PREFACE The present research is supported by grants from the (a) National Institutes of Health (National Institute on Aging; R01 AG008235) to Roger A. Dixon and (b) Alberta Health Services (University Hospital Foundation) to David Westaway, Jack Jhamandas, and Roger A. Dixon. The data were collected by the Victoria Longitudinal Study (VLS) staff at the University of Victoria. The VLS and all data collection procedures were in full and certified compliance with prevailing human and/or institutional research ethics guidelines. Written informed consent was obtained from all participants. University of Alberta Centre for Prions and Protein Folding Diseases provided laboratory and technical support for the VLS genetics initiative (2009-2011). Chapter 5 (Study 2) of this dissertation has been published as Sapkota, S., Vergote, D., Westaway, D., Jhamandas, J., & Dixon, R.A. (2015). Synergistic associations of Catechol-O- methyltransferase and Brain-derived neurotrophic factor with executive function in aging are selective and modified by Apolipoprotein E. Neurobiology of Aging, 36, 249-256. doi:10.1016/j.neurobiolaging.2014.06.020. For this published article, S. Sapkota was responsible for the concept, design, data analysis, writing, and editing the manuscript. D. Vergote was responsible for DNA extraction and genotyping. D. Westaway and J Jhamandas were involved in overall plan, concept, and editing of the manuscript. R.A. Dixon was the supervisory author and was involved with all aspects of the research, including concept, design, methods, writing, and editing the manuscript.? This study is reprinted in this dissertation with written permission from the publisher (Elsevier). v To my parents vi ACKNOWLEDGMENTS First and foremost, I would like to express my deepest gratitude to my supervisor, Dr. Roger A. Dixon for his unwavering guidance, support, and patience throughout my graduate program. I would like to thank my examination committee members, Dr. Richard Camicioli, Dr. Sandra Wiebe, Dr. Esther Fujiwara, and Dr. Michelle Carlson for carefully reading my thesis and providing comments and suggestions for improvement. I would like to thank my colleagues and members of Victoria Longitudinal Study (VLS) lab for their help throughout my program. I would also like to thank the volunteer participants in the VLS. Last, but not least, I would like to thank my husband and my parents for their love and support, and also my friends and all of those special people that have helped me along the way. vii TABLE OF CONTENTS CHAPTER 1: GENERAL INTRODUCTION ............................................................................... 1 Overview of Current Studies .............................................................................................. 3 Organization of the Dissertation ......................................................................................... 4 CHAPTER 2: GENERAL LITERATURE REVIEW .................................................................... 6 Healthy Cognitive Aging. ................................................................................................... 6 Dementia Risk Indices. ....................................................................................................... 8 Genetic Polymorphisms and Cognition in Aging and Dementia. ..................................... 13 Summary. .......................................................................................................................... 18 CHAPTER 3: GENERAL METHODS ........................................................................................ 21 Participants ........................................................................................................................ 21 DNA Extraction and Genotyping. .................................................................................... 21 Neurocognitive Measures and Risk Factors ..................................................................... 23 References for Chapters 1-3 ............................................................................................. 29 CHAPTER 4: STUDY 1 ............................................................................................................... 44 Multi-domain risk index for cognitive aging: Testing demographic, health, lifestyle, and genetic risk effects on episodic memory performance and change in non-demented aging and mild cognitive impairment .................................................................................................................... 44 Introduction ....................................................................................................................... 45 Research Goals ..................................................................................................... 49 Method .............................................................................................................................. 53 Study 1a ............................................................................................................................ 59 Statistical Analysis ................................................................................................ 59 viii Results ................................................................................................................... 61 Discussion
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