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Final Copy 2021 01 21 Carter This electronic thesis or dissertation has been downloaded from Explore Bristol Research, http://research-information.bristol.ac.uk Author: Carter, Alice R Title: Strengthening causal inference in educational inequalities in cardiovascular disease General rights Access to the thesis is subject to the Creative Commons Attribution - NonCommercial-No Derivatives 4.0 International Public License. A copy of this may be found at https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode This license sets out your rights and the restrictions that apply to your access to the thesis so it is important you read this before proceeding. Take down policy Some pages of this thesis may have been removed for copyright restrictions prior to having it been deposited in Explore Bristol Research. However, if you have discovered material within the thesis that you consider to be unlawful e.g. breaches of copyright (either yours or that of a third party) or any other law, including but not limited to those relating to patent, trademark, confidentiality, data protection, obscenity, defamation, libel, then please contact [email protected] and include the following information in your message: •Your contact details •Bibliographic details for the item, including a URL •An outline nature of the complaint Your claim will be investigated and, where appropriate, the item in question will be removed from public view as soon as possible. Strengthening causal inference in educational inequalities in cardiovascular disease Alice R Carter A dissertation submitted to the University of Bristol in accordance with the requirements for award of the degree of PhD in the Faculty Health Sciences MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, United Kingdom September 2020 Word count: 56 764 Abstract Despite reducing rates of cardiovascular disease in high income countries, individuals who are the most socioeconomically deprived remain at the highest risk of disease. The mechanisms by which the inequalities arise are still unknown. In this thesis I use causal inference methods, including Mendelian randomisation (MR), mediation analysis and polygenic scores, to understand the aetiology of educational inequalities in cardiovascular disease, using UK Biobank. Establishing causality in epidemiology can be challenging, due to unmeasured (or mis- measured) confounding, measurement error and reverse causality. One method to overcome these sources of bias is MR. In this thesis I demonstrate using simulations and applied examples how MR can be applied to mediation analysis, identifying sources of bias and methodological limitations. Using MR mediation methods and non-genetic (phenotypic) mediation methods I demonstrate that body mass index, systolic blood pressure and lifetime smoking behaviour mediate up to 40% of the association between education and cardiovascular disease. Intervening on these intermediate risk factors would likely reduce cases of cardiovascular disease attributable to low educational attainment. I then investigate inequalities in prescribing of statins as a primary cardiovascular preventative medication. I identified clear inequalities, where for a given level of underlying cardiovascular risk (assessed via QRISK3 score) individuals with lower educational attainment were less likely to receive statins. Finally, explore the role of education as an effect modifier of genetic suscepbility to cardiovascular disease.I demonstrate that on the additive scale, higher education protects against genetic susceptibility to body mass index and smoking but accentuates genetic susceptibility to low-density lipoprotein cholesterol and systolic blood pressure. On the multiplicative scale, higher education accentuates genetic susceptibility to atrial fibrillation and coronary heart disease. This thesis demonstrates that body mass index, systolic blood pressure, smoking and statin use all likely contribute to educational inequalities in cardiovascular disease, whilst contributing to the development of methods to improve causal inference in social epidemiology. 2 Acknowledgements First and foremost, my biggest thanks and gratitude goes to my supervisors, Laura Howe, Neil Davies, Amy Taylor and George Davey Smith. The support, encouragement, teaching, generosity, friendship, kindness, patience and passion they have shown me knows no bounds. They have encouraged me to follow my research interests, to break out of my comfort zone and to put myself and my work out there (as well as having a good cry when it all gets too much – extra thanks to the tissue supply in Laura’s office). I feel exceptionally lucky that I have had the most enjoyable four years during my PhD, and I know this has been down to having the best group of supervisors I could have wished for, so thank you. Secondly, I would like to thank the Medical Research Council Integrative Epidemiology Unit, not only for funding my PhD, but for providing such a welcoming, friendly and accommodating environment to complete my PhD. I have been lucky enough to work with a number of wonderful colleagues from whom I have learnt so much. I would especially like to thank all of the PALS (Postdocs and PhDs of Abi and Laura) from over the years, who have taught me so much. I would like to thank Marcus Munafò and Nic Timpson for their feedback during my annual reviews. This was invaluable and helped shape so much of this work. Special thanks also go to all of the A-team for helping arrange my supervision meetings, fixing my messed-up travel bookings and helping to organise my MR mediation workshop. I have been lucky enough to publish frequently throughout my PhD and all co-authors have provided invaluable support, so thank you to all of you. In particular, I would like to thank Deborah Lawlor, Carolina Borges, Eleanor Sanderson, Sean Harrison, Teri-Louise North and Dipender Gill. These co-authors have gone above and beyond in their support and assistance in our publications and I look forward to continuing with these collaborations. Thank you also to the peer reviewers of these publications, which helped develop both my research and me as a researcher. This work would not have been possible without the UK Biobank resource. Thank you to all participants who have so generously given up their time and data to make this possible. I wouldn’t have made it through these years without a wonderful set of friends, so thank you to the Epi Queens, Anastasia, Elise and Isha. I’m so glad we’ve been on this journey together and can’t wait to see what epidemiological breakthroughs we all achieve in the years to come. To Sophie, for the friendship and numerous PhD fuelled rants over the years. Here’s to many more years of cocktails and Christmas films with a little less complaining. To Mary and 3 George, for the lovely PhD free activities. And to Katie, you might not be here to celebrate, but I know the years of friendship shaped so much of who I am. Finally, thank you to my family. This PhD would not have been possible without so many sacrifices from so many of my people. To Alex (and Nellie) for the being the best PhD distractions. To Nan and Grandad for always supporting me, even if you weren’t quite sure why I still wanted to be a student in my late twenties. To Grace and Matt, and more recently Darcey. If it wasn’t for your generosity, I would never have made it through my MSc, let alone PhD. And last but not least, thank you to my Mum for being my biggest supporter and role model over the years. You taught me to never, ever, ever, give up. This achievement is as much yours as it is mine. 4 Author declaration I declare that the work in this dissertation was carried out in accordance with the requirements of the University's Regulations and Code of Practice for Research Degree Programmes and that it has not been submitted for any other academic award. Except where indicated by specific reference in the text, the work is the candidate's own work. Work done in collaboration with, or with the assistance of, others, is indicated as such. Any views expressed in the dissertation are those of the author. SIGNED: DATE: 8th September 2020 5 Abbreviations used in this thesis Abbreviation Term CVD Cardiovascular disease BMI Body mass index C Measured confounder CDE Controlled direct effect CHD Coronary heart disease CI Confidence interval DIAGRAM DIAbetes Genetic Replication and Meta-analysis Consortium DNA Deoxyribonucleic acid GIANT Genetic Investigation of ANthropometric traits GBD Global Burden of Disease GSCAN GWAS and Sequencing Consortium of Alcohol and Nicotine use GWAS Genome-wide association study HDL High density lipoprotein cholesterol HES Hospital episode statistics HSE-SHS Health Surveys for England and Scottish Health Surveys ICD International classification of disease ISCED International Standard Classification for Education IV Instrumental variable IVW Inverse variance weighted LD Linkage disequilibrium LDL-C Low-density lipoprotein cholesterol M Mediator MI Myocardial infarction MR Mendelian randomisation MRC Medical Research Council MVMR Multivariable Mendelian randomisation 6 NDE Natural direct effect NHS National Health Service NICE National Institute for Health and Care Excellence NIE Natural indirect effect OR Odds ratio PC Principal Components PGS Polygenic score PHE Public Health England RCT Randomised controlled trial RoSLA Raising of School Leaving Age SBP Systolic blood pressure SD Standard deviation SEP Socioeconomic position SMR Scottish morbidity records SNP Single
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