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Predicting Cognitive Decline: Genetic, Environmental and Lifestyle Risk Factors

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Predicting Cognitive Decline: Genetic, Environmental and Lifestyle Risk Factors Predicting Cognitive Decline: Genetic, Environmental and Lifestyle Risk Factors Shea J. Andrews April 2017 A thesis by compilation submitted for the degree of Doctor of Philosophy of The Australian National University © Copyright by Shea John Frederick Andrews 2017 All Rights Reserved i Declaration This work was conducted from February 2013 to August 2016 at the Genome Diversity and Health Group, John Curtin School of Medical Research, The Australian National University, Canberra, ACT. This thesis by compilation consists of five original publications describing the analyses I have performed during my candidature investigating the role of genetic, environmental and lifestyle risk factors in normal cognitive aging. All five publications have been published in Q1 ranked journals according to the SCImago Journal & Country Rankings in the fields of neurology, genetics, psychiatry and mental health, and geriatric and gerontology. My specific contribution to each manuscript is detailed in the subsequent pages in the form of a statement signed by the senior author of each publication. This document has not been submitted for qualifications at any other academic institution. Shea Andrews Canberra, Australia April 2017 ii Published Papers Andrews, SJ, Das, D., Anstey, KJ., Easteal, S. (2015). Interactive effect of APOE genotype and blood pressure on cognitive decline: The PATH through life project. Journal of Alzheimer's Disease. 44(4): 1087-98. DOI: 10.3233/JAD- 140630 For this publication, I designed and performed all statistical analyses and wrote the manuscript. A slightly modified version of this paper is presented in Chapter 3. ____________________ Simon Easteal Senior Author April 2017 Andrews SJ, Das D, Cherbuin N, Anstey KJ, Easteal S. (2016). Association of genetic risk factors with cognitive decline: The PATH through life project. Neurobiology of Aging. 41: 150-158. DOI:10.1016/j.neurobiolaging.2016.02.016 For this publication, I designed and performed all statistical analyses and wrote the manuscript. A slightly modified version of this paper is presented in Chapter 4 ____________________ Simon Easteal Senior Author April 2017 iii Andrews SJ, Das D, Anstey KJ, Easteal S. (2017). Late Onset Alzheimer’s disease risk variants in cognitive decline: The PATH Though Life Study. Journal of Alzheimer’s Disease 57:423-436. DOI: 10.3233/JAD-160774 For this publication, I designed and performed all statistical analyses and wrote the manuscript. A slightly modified version of this paper is presented in Chapter 5. ____________________ Simon Easteal Senior Author April 2017 Andrews SJ, Das D, Anstey KJ, Easteal S. (2017) Association of AKAP6 and MIR2113 with cognitive performance in a population-based sample of older adults. Genes, Brain and Behavior. DOI: 10.1111/gbb.12368 For this publication, I designed and performed all statistical analyses and wrote the manuscript. A slightly modified version of this paper is presented in Chapter 6. ____________________ Simon Easteal Senior Author April 2017 iv Andrews SJ, Eramudugolla R, Velez JI, Cherbuin N, Easteal S, Anstey KJ. (2017) Validating the role of the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI) and a genetic risk score in progression to cognitive impairment in a population-based cohort of older adults followed for 12 years. Alzheimer's Research & Therapy. 6:1-16. DOI: 10.1101/070516 For this publication, I designed and performed all statistical analyses and wrote the manuscript. A slightly modified version of this paper is presented in Chapter 7. ____________________ Kaarin J. Anstey Senior Author April 2017 v Acknowledgments Throughout the course of my graduate work I received support, advice, encouragement and many thought provoking conversations from numerous individuals. I would like to specifically acknowledge and thank those people who have made my success possible. First, I would like to thank my primary supervisor Professor Simon Easteal for the opportunity to work on this project. Simon’s constructive feedback on the research I performed and manuscripts I have written has proved invaluable to my development as a researcher. I also thank my co-supervisors Professor Kaarin J. Anstey and Dr. Debjani Das for their mentorship, advice, and insightful feedback at every stage of my PhD. When I began my PhD. my knowledge of statistics was limited to say the least. It was with the support and guidance of Dr. Teresa Neeman and Dr. Jorge Velez that I was able to establish a firm understanding of the statistical procedures needed to complete the research presented in this thesis. In particular, thank you Jorge for introducing me to R. I would like to thank the other members of the Genome Diversity & Health group and my colleagues at the John Curtin School of Medical Research. To Susan Tan and all her help in the wet lab; to Shaun Lehmann and Dr. Saul Newman for all the thought provoking conversions; to Dr. Hardip Patel for all the stimulating (and sometimes heated) discussions at both lunch and happy hour; to Dr. Mauricio Arcos-Burgos thank you for your advice and the opportunity to work with you and the Paisa Cohort, it provided a new perspective on the research I was undertaking. To Cameron Jack and his wife Mary-Ann, thank you for providing me with a place to live during the final months of my PhD. I said it would only be a few weeks. It turned out to be little longer than that, which you were cool with. Mary-Anne, thank you for proofreading my thesis. I thank my family, my brother, sister and in particular my parents Sue and Fred. It was with your support and encouragement throughout the entirety of my education that I even managed to arrive at this moment. Finally, I would like to thank my wife, Gaby. While we may have been separated by distance for the duration of my PhD., your love, encouragement, support and advice have made this journey immeasurably easier. Thank you for your understanding and patience as this stage of our lives draws to a close. vi Abstract With advancing age individuals experience a deterioration in cognitive abilities that is characterized by substantial inter-individual variation in the observed trajectories of cognitive decline. Late onset Alzheimer’s disease (LOAD) susceptibility genes and environmental risk factors are good candidates for association with cognitive decline, as the pathological features of LOAD progress to varying degrees in individuals without dementia or cognitive impairment and are associated with nonclinical cognitive decline. This thesis investigates whether Alzheimer’s disease risk factors and genetic variants previously associated with cognitive function are also associated with cognitive decline. Data collected from the 60+ cohort of the Personality and Total Health (PATH) through life project was used, in which 2,551 participants were assessed at 4-year intervals for a total of 12 years on a comprehensive battery of cognitive tests. The publications in this thesis investigate the following. First, whether APOE*e4 moderates the association between high blood pressure and cognitive function in late life. It was observed that a APOE–hypertension interaction was associated with a small but statistically significant increase in the rate of decline of episodic memory, verbal ability and global cognition. In contrast, the interaction between APOE and mean arterial pressure interaction had no effect on rate of decline. Second, the role of 25 LOAD risk loci in non-linear cognitive change was examined, both individually and collectively as a genetic risk score (GRS). Twelve LOAD risk loci were associated with baseline cognitive performance (ABCA7, MS4A4E, SORL1), linear rate of change (APOE, ABCA7, EPHA1, INPP5D, ZCWPW1, CELF1) or quadratic rate of change (APOE, CLU, FERMT2). In addition, a weighted GRS was associated with linear rate of change in episodic memory and information processing speed. Third, the role of 9 single nucleotide polymorphisms that have been previously associated with cognitive performance was further examined, with 6 SNPs observed to be associated with baseline cognitive performance (BDNF, PDE7A, AKAP6), linear rate of change (COMT, CTNNBL1, PDE7A) or quadratic rate of change (MIR2113). vii Finally, it was examined whether a risk score comprised of lifestyle, medical and demographic factors (the Australian National University Alzheimer’s disease Risk Index; ANU-ADRI) and a LOAD GRS were predictors of progression to Mild Cognitive Impairment (MCI). A higher ANU-ADRI score was associated with a higher probability of transitioning from normal cognition to cognitive impairment, while the GRS was associated with an increased risk of transitioning from normal cognition to dementia. These results suggest that a subset of LOAD related SNPs may be associated with cognitive decline. However, the effect size of each locus is small and when demographic and lifestyle factors are taken into account, neither individual SNPs nor GRS explain a significant proportion of the variance in cognitive decline in our sample. Further research is required to verify these results and to examine the effect of preclinical LOAD in genetic association studies of cognitive decline. The identification of LOAD risk loci associated with cognitive performance may help in screening for individuals at greater risk of cognitive decline. viii Contents Declaration ii Published Papers iii Acknowledgments vi Abstract vii Contents ix List of Figures xii List of Tables xiii Abbreviations xiv Chapter 1: Introduction 1 1.1 The Continuum
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