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  1. NTU Theses and Dissertations Repository
  2. 公共衛生學院
  3. 流行病學與預防醫學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80274
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dc.contributor.advisor盧子彬(Tzu-Pin Lu)
dc.contributor.authorKuan-Chen Luen
dc.contributor.author呂冠臻zh_TW
dc.date.accessioned2022-11-24T03:03:40Z-
dc.date.available2021-07-20
dc.date.available2022-11-24T03:03:40Z-
dc.date.copyright2021-07-20
dc.date.issued2021
dc.date.submitted2021-07-01
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Go, A.S., et al., Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. Jama, 2001. 285(18): p. 2370-5. 5. Krijthe, B.P., et al., Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. Eur Heart J, 2013. 34(35): p. 2746-51. 6. Patel, N.J., et al., Contemporary trends of hospitalization for atrial fibrillation in the United States, 2000 through 2010: implications for healthcare planning. Circulation, 2014. 129(23): p. 2371-9. 7. Miyasaka, Y., et al., Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation, 2006. 114(2): p. 119-25. 8. Rahman, F., G.F. Kwan, and E.J. Benjamin, Global epidemiology of atrial fibrillation. Nat Rev Cardiol, 2014. 11(11): p. 639-54. 9. Aune, D., et al., Body mass index, abdominal fatness, fat mass and the risk of atrial fibrillation: a systematic review and dose-response meta-analysis of prospective studies. Eur J Epidemiol, 2017. 32(3): p. 181-192. 10. Frost, L., et al., Body fat, body fat distribution, lean body mass and atrial fibrillation and flutter. A Danish cohort study. Obesity (Silver Spring), 2014. 22(6): p. 1546-52. 11. Karas, M.G., et al., Measures of Body Size and Composition and Risk of Incident Atrial Fibrillation in Older People: The Cardiovascular Health Study. Am J Epidemiol, 2016. 183(11): p. 998-1007. 12. Nattel, S., Atrial Fibrillation and Body Composition: Is it Fat or Lean That Ultimately Determines the Risk? J Am Coll Cardiol, 2017. 69(20): p. 2498-2501. 13. Fenger-Grøn, M., et al., Lean Body Mass Is the Predominant Anthropometric Risk Factor for Atrial Fibrillation. J Am Coll Cardiol, 2017. 69(20): p. 2488-2497. 14. Tikkanen, E., et al., Body composition and atrial fibrillation: a Mendelian randomization study. Eur Heart J, 2019. 40(16): p. 1277-1282. 15. Chatterjee, N.A., et al., Genetic Obesity and the Risk of Atrial Fibrillation: Causal Estimates from Mendelian Randomization. Circulation, 2017. 135(8): p. 741-754. 16. Larsson, S.C., et al., Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: a Mendelian randomization study. European Heart Journal, 2020. 41(2): p. 221-226. 17. Siahkouhian, M. and M. Hedayatneja, Correlations of Anthropometric and Body Composition Variables with the Performance of Young Elite Weightlifters. Journal of Human Kinetics, 2010. 25(2010): p. 125-131. 18. Fewell, Z., G. Davey Smith, and J.A. Sterne, The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol, 2007. 166(6): p. 646-55. 19. Davey Smith, G. and S. Ebrahim, Epidemiology--is it time to call it a day? Int J Epidemiol, 2001. 30(1): p. 1-11. 20. Kovesdy, C.P. and K. Kalantar-Zadeh, Observational studies versus randomized controlled trials: avenues to causal inference in nephrology. Adv Chronic Kidney Dis, 2012. 19(1): p. 11-8. 21. Sedgwick, P., Randomised controlled trials: balance in baseline characteristics. BMJ : British Medical Journal, 2014. 349: p. g5721. 22. Kabisch, M., et al., Randomized controlled trials: part 17 of a series on evaluation of scientific publications. Dtsch Arztebl Int, 2011. 108(39): p. 663-8. 23. Black, N., Why we need observational studies to evaluate the effectiveness of health care. Bmj, 1996. 312(7040): p. 1215-8. 24. Burgess, S., D.S. Small, and S.G. Thompson, A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res, 2017. 26(5): p. 2333-2355. 25. Swerdlow, D.I., et al., Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. Int J Epidemiol, 2016. 45(5): p. 1600-1616. 26. Davey Smith, G. and S. Ebrahim, ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*. International Journal of Epidemiology, 2003. 32(1): p. 1-22. 27. VanderWeele, T.J., et al., Methodological challenges in mendelian randomization. Epidemiology, 2014. 25(3): p. 427-35. 28. Lawlor, D.A., et al., Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 2008. 27(8): p. 1133-63. 29. Teumer, A., Common Methods for Performing Mendelian Randomization. Frontiers in Cardiovascular Medicine, 2018. 5(51). 30. Sheehan, N.A., S. Meng, and V. Didelez, Mendelian randomisation: a tool for assessing causality in observational epidemiology. Methods Mol Biol, 2011. 713: p. 153-66. 31. Austin, P.C., An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res, 2011. 46(3): p. 399-424. 32. ROSENBAUM, P.R. and D.B. RUBIN, The central role of the propensity score in observational studies for causal effects. Biometrika, 1983. 70(1): p. 41-55. 33. Shadish, W.R. and P.M. Steiner, A Primer on Propensity Score Analysis. Newborn and Infant Nursing Reviews, 2010. 10(1): p. 19-26. 34. Anderson, C.A., et al., Data quality control in genetic case-control association studies. Nat Protoc, 2010. 5(9): p. 1564-73. 35. Marees, A.T., et al., A tutorial on conducting genome-wide association studies: Quality control and statistical analysis. Int J Methods Psychiatr Res, 2018. 27(2): p. e1608. 36. Purcell, S., et al., PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007. 81(3): p. 559-75. 37. Ho, D., et al., MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. 2011, 2011. 42(8): p. 28. 38. Palmer, T.M., et al., Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res, 2012. 21(3): p. 223-42. 39. Burgess, S. and S.G. Thompson, Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol, 2011. 40(3): p. 755-64. 40. Palmer, T.M., et al., Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. Am J Epidemiol, 2011. 173(12): p. 1392-403. 41. Bowden, J., G. Davey Smith, and S. Burgess, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 2015. 44(2): p. 512-525. 42. Zheutlin, A.B. and D.A. Ross, Polygenic Risk Scores: What Are They Good For? Biol Psychiatry, 2018. 83(11): p. e51-e53. 43. Khera, A.V., et al., Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet, 2018. 50(9): p. 1219-1224. 44. Ge, T., et al., Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature Communications, 2019. 10(1): p. 1776. 45. Hemani, G., J. Bowden, and G. Davey Smith, Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet, 2018. 27(R2): p. R195-r208. 46. Bowden, J., et al., Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol, 2016. 40(4): p. 304-14. 47. Burgess, S. and S.G. Thompson, Interpreting findings from Mendelian randomization using the MR-Egger method. European Journal of Epidemiology, 2017. 32(5): p. 377-389. 48. Verbanck, M., et al., Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet, 2018. 50(5): p. 693-698. 49. Egger, M., et al., Bias in meta-analysis detected by a simple, graphical test. BMJ, 1997. 315(7109): p. 629-634. 50. Yavorska, O.O. and S. Burgess, MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol, 2017. 46(6): p. 1734-1739. 51. Tikkanen, E., S. Gustafsson, and E. Ingelsson, Associations of Fitness, Physical Activity, Strength, and Genetic Risk With Cardiovascular Disease: Longitudinal Analyses in the UK Biobank Study. Circulation, 2018. 137(24): p. 2583-2591.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80274-
dc.description.abstract"研究背景:流行病學研究表明,肥胖與心房顫動有所相關,並且已證實具有因果關係。從臨床觀察,體型的增加亦可能會增加心房顫動的風險,且與肥胖無關。而身體質量(body mass)及身體質量指數(body mass index, BMI)並非合適的人體測量指標,因其無法區分非脂肪質量和脂肪質量。本研究採用和軀幹非脂肪體重(trunk fat-free mass)與非脂肪體重指標(fat-free mass index)相關的單核苷酸多態性(SNP)為工具變項,執行孟德爾隨機化,藉此推論身形和心房顫動的因果關係。 材料與方法:使用英國生物資料庫(UK Biobank),共納入了7,350名受試者,包括1,225(16.67%)名心房顫動病例以及6,125名(83.33%)非心房顫動對照組。為了揭示軀幹非脂肪體重及非脂肪體重指標與心房顫動之間的因果關係,執行孟德爾隨機化。並使用逆方差加權法於英國生物資料庫(UK Biobank)進行因果估計。輔以加權中位數方法、MR-Egger迴歸和孟德爾隨機多效性殘差和離群值(MR-PRESSO)全局檢驗來檢測水平多效性效應。 結果:孟德爾隨機分配分析揭示非脂肪體重指標(β_IVW=0.202, p=8.42Ε-04)與軀幹非脂肪體重(β_IVW=0.169, p=1.53Ε-04)對心房顫動具有因果關係。顯示隨著非脂肪體重增加,對於罹患心房顫動之風險有正向且統計上顯著的因果關係。 結論:非脂肪體重指標及軀幹非脂肪體重與心房顫動之間存在強而一致的正相關,以孟德爾隨機分配分析表明非脂肪體重與心房顫動有因果關係。本研究亦使用多基因風險評分(polygenic risk score)作為工具變量,亦表明非脂肪體重指標和軀幹非脂肪體重對心房顫動的因果作用。與傳統的孟德爾隨機分配分析一致。 "zh_TW
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Previous issue date: 2021
en
dc.description.tableofcontents論文口試委員審定書 i 謝辭 ii 摘要 iii Abstract v Chapter 1 Introduction 1 1.1 The epidemiology of arrhythmia 1 1.2 The diagnosis of atrial fibrillation 3 1.3 Observational study and Randomized controlled trial (RCT) 3 1.4 Mendelian randomization study (MR) 5 1.5 The aim of this study 6 Chapter 2 Material and Methods 7 2.1 Data source and study population 7 2.2 Propensity score matching (PSM) 8 2.3 Quality control 9 2.4 Genetic components selection 10 2.5 Definition of study exposure 11 2.6 Definition of study outcome 11 2.7 Mendelian randomization analyses 12 2.8 Polygenic risk score 14 2.9 Sensitivity analysis 16 Chapter 3 Results 19 3.1 Descriptive statistics 19 3.2 Association analysis 20 3.3 Mendelian randomization of causality inference 20 3.4 Sensitivity analyses 21 Chapter 4 Discussion 23 4.1 Principal findings 23 4.2 Possible interpretations 23 4.3 Strengths 24 4.4 Limitations 25 Chapter 5 References 27 Chapter 6 Appendix 31
dc.language.isoen
dc.subject多基因風險評分zh_TW
dc.subject孟德爾隨機化zh_TW
dc.subject心房顫動zh_TW
dc.subject英國生物庫zh_TW
dc.subject因果推論zh_TW
dc.subject非脂肪體重zh_TW
dc.subjectcausal inferenceen
dc.subjectMendelian randomizationen
dc.subjectpolygenic risk scoreen
dc.subjectfat-free massen
dc.subjectatrial fibrillationen
dc.subjectUK Biobanken
dc.title孟德爾隨機化探非脂肪體重指標與心房顫動之因果關係zh_TW
dc.titleAssociation of Fat-Free Mass Indices and Atrial Fibrillation: Mendelian Randomization Analysisen
dc.date.schoolyear109-2
dc.description.degree碩士
dc.contributor.oralexamcommittee郭柏秀(Hsin-Tsai Liu),蕭朱杏(Chih-Yang Tseng),蕭自宏
dc.subject.keyword孟德爾隨機化,多基因風險評分,非脂肪體重,心房顫動,英國生物庫,因果推論,zh_TW
dc.subject.keywordMendelian randomization,polygenic risk score,fat-free mass,atrial fibrillation,UK Biobank,causal inference,en
dc.relation.page51
dc.identifier.doi10.6342/NTU202101222
dc.rights.note同意授權(限校園內公開)
dc.date.accepted2021-07-02
dc.contributor.author-college公共衛生學院zh_TW
dc.contributor.author-dept流行病學與預防醫學研究所zh_TW
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