Genetic liability to atrial fibrillation, aortic valve disease, and mitral valve disease: a two-sample Mendelian randomization study

doi:10.5847/wjem.j.1920-8642.2025.086

Abstract

Abstract:

BACKGROUND: Research has revealed a relationship between atrial fibrillation (AF) and valvular heart disease; however, the causality remains largely unknown. This study explored whether a causal association between AF and non-rheumatic aortic valve disease (AVD) and mitral valve disease (MVD) could be found.

METHODS: A two-sample Mendelian randomization (TSMR) method was applied to determine the causal effect of AF on AVD, mitral regurgitation, and MVD. The inverse variance weighted (IVW) method was used as the primary analytical approach, and several complementary analyses were conducted. Outliers were detected using the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and radial Mendelian randomization (MR) methods.

RESULTS: Genetically predicted AF was found to be causally associated with the risk of MVD (odds ratio [OR]=1.001; 95% confidence interval [CI]: 1.000-1.001; P=1.33×10^-6) and mitral regurgitation (OR=1.001; 95% CI: 1.000-1.002; P=0.009). However, no significant causal associations between AF and AVD were detected (OR=1.000; 95% CI: 0.999-1.000; P=0.804). Causal effects were still detected, even after adjusting for potential risk factors or removing the identified outliers. Reverse MR analyses revealed no significant causal effect of valvular heart disease on AF.

CONCLUSION: Our findings demonstrate a positive causal association between AF, MVD, and mitral regurgitation, but not AVD. Further research and an aggressive AF management strategy should be explored as potential measures for preventing MVD.

Key words: Atrial fibrillation, Valvular heart disease, Mitral valve disease

Yun Zhang, Chengui Zhuo, Ting Chen, Xiaosheng Hu. Genetic liability to atrial fibrillation, aortic valve disease, and mitral valve disease: a two-sample Mendelian randomization study[J]. World Journal of Emergency Medicine, 2025, 16(5): 475-480.

Figures/Tables 4

Table 1.

Figure 1.

Table 2.

Table 3.

References 33

1	Chao TF, Chan NY, Chan YH, Cho MS, Krittayaphong R, Lee KN, et al. Direct oral anticoagulant dosing in patients with Atrial fibrillation: an asian perspective. JACC Asia. 2023; 3(5):707-23.
2	Joglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM, et al. 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines. Circulation. 2024; 149(1): e1-e156.
3	Banerjee A, Allan V, Denaxas S, Shah A, Kotecha D, Lambiase PD, et al. Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis. Europace. 2019; 21(12): 1776-84. doi: 10.1093/europace/euz220 pmid: 31408153
4	Hamid N, Bursi F, Benfari G, Vanoverschelde JL, Tribouilloy C, Biagini E, et al. Degenerative mitral regurgitation outcomes in Asian compared with European-American institutions. JACC Asia. 2024; 4(6):468-80. doi: 10.1016/j.jacasi.2024.03.003 pmid: 39100700
5	Gertz ZM, Raina A, Saghy L, Zado ES, Callans DJ, Marchlinski FE, et al. Evidence of atrial functional mitral regurgitation due to atrial fibrillation: reversal with arrhythmia control. J Am Coll Cardiol. 2011; 58(14): 1474-81. doi: 10.1016/j.jacc.2011.06.032 pmid: 21939832
6	Otsuji Y, Kumanohoso T, Yoshifuku S, Matsukida K, Koriyama C, Kisanuki A, et al. Isolated annular dilation does not usually cause important functional mitral regurgitation: comparison between patients with lone atrial fibrillation and those with idiopathic or ischemic cardiomyopathy. J Am Coll Cardiol. 2002; 39(10): 1651-6. pmid: 12020493
7	Zhou XY, Otsuji Y, Yoshifuku S, Yuasa T, Zhang H, Takasaki K, et al. Impact of atrial fibrillation on tricuspid and mitral annular dilatation and valvular regurgitation. Circ J. 2002; 66(10): 913-6. pmid: 12381084
8	Hartwig FP, Borges MC, Horta BL, Bowden J, Davey Smith G. Inflammatory biomarkers and risk of schizophrenia: a 2-sample Mendelian randomization study. JAMA Psychiatry. 2017; 74(12): 1226-33. doi: 10.1001/jamapsychiatry.2017.3191 pmid: 29094161
9	Larsson SC, Butterworth AS, Burgess S. Mendelian randomization for cardiovascular diseases: principles and applications. Eur Heart J. 2023; 44(47): 4913-24. doi: 10.1093/eurheartj/ehad736 pmid: 37935836
10	Gao J, Bai YL, Ji HW. Genetically predicted atrial fibrillation and valvular heart disease: a two-sample Mendelian randomization study. Front Cardiovasc Med. 2022; 9: 845734.
11	Nielsen JB, Thorolfsdottir RB, Fritsche LG, Zhou W, Skov MW, Graham SE, et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet. 2018; 50(9): 1234-9. doi: 10.1038/s41588-018-0171-3 pmid: 30061737
12	No authors listed. GWAS results. Available at: http://www.nealelab.is/uk-biobank/
13	Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018; 7: e34408.
14	Lv Y, Xia X, Lei L, Xiang W, Wu X, Xie SB, et al. Health outcomes of age at menarche in European women: a two-sample Mendelian randomization study. Postgrad Med J. 2023; 99(1175): 993-9. doi: 10.1093/postmj/qgad023 pmid: 37302123
15	Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted Median estimator. Genet Epidemiol. 2016; 40(4): 304-14. doi: 10.1002/gepi.21965 pmid: 27061298
16	Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through egger regression. Int J Epidemiol. 2015; 44(2): 512-25. doi: 10.1093/ije/dyv080 pmid: 26050253
17	Fabiola Greco M, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015; 34(21): 2926-40. doi: 10.1002/sim.6522 pmid: 25950993
18	Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018; 50(5): 693-8. doi: 10.1038/s41588-018-0099-7 pmid: 29686387
19	Bowden J, Spiller W, Del Greco M F, Sheehan N, Thompson J, Minelli C, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018; 47(4): 1264-78. doi: 10.1093/ije/dyy101 pmid: 29961852
20	Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015; 181(4): 251-60. doi: 10.1093/aje/kwu283 pmid: 25632051
21	Yengo L, Sidorenko J, Kemper KE, Zheng ZL, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in -700000 individuals of European ancestry. Hum Mol Genet. 2018; 27(20): 3641-9. doi: 10.1093/hmg/ddy271 pmid: 30124842
22	Liu MZ, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019; 51(2): 237-44. doi: 10.1038/s41588-018-0307-5 pmid: 30643251
23	Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012; 44(9): 981-90. doi: 10.1038/ng.2383 pmid: 22885922
24	Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013; 45(11): 1274-83. doi: 10.1038/ng.2797 pmid: 24097068
25	Dönertaş HM, Fabian DK, Valenzuela MF, Partridge L, Thornton JM. Common genetic associations between age-related diseases. Nat Aging. 2021; 1(4): 400-12. doi: 10.1038/s43587-021-00051-5 pmid: 33959723
26	Yadgir S, Johnson CO, Aboyans V, Adebayo OM, Adedoyin RA, Afarideh M, et al. Global, regional, and national burden of calcific aortic valve and degenerative mitral valve diseases, 1990-2017. Circulation. 2020; 141(21): 1670-80. doi: 10.1161/CIRCULATIONAHA.119.043391 pmid: 32223336
27	Kim DH, Heo R, Handschumacher MD, Lee S, Choi YS, Kim KR, et al. Mitral valve adaptation to isolated annular dilation: insights into the mechanism of atrial functional mitral regurgitation. JACC Cardiovasc Imaging. 2019; 12(4): 665-77.
28	Pinto G, Fragasso G. Aortic valve stenosis: drivers of disease progression and drug targets for therapeutic opportunities. Expert Opin Ther Targets. 2022; 26(7): 633-44.
29	Okuno T, Hagemeyer D, Brugger N, Ryffel C, Heg D, Lanz J, et al. Valvular and nonvalvular atrial fibrillation in patients undergoing transcatheter aortic valve replacement. JACC Cardiovasc Interv. 2020; 13(18): 2124-33.
30	Urena M, Hayek S, Cheema AN, Serra V, Amat-Santos IJ, Nombela-Franco L, et al. Arrhythmia burden in elderly patients with severe aortic stenosis as determined by continuous electrocardiographic recording: toward a better understanding of arrhythmic events after transcatheter aortic valve replacement. Circulation. 2015; 131(5): 469-77. doi: 10.1161/CIRCULATIONAHA.114.011929 pmid: 25466975
31	Widgren V, Dencker M, Juhlin T, Platonov P, Willenheimer R. Aortic stenosis and mitral regurgitation as predictors of atrial fibrillation during 11 years of follow-up. BMC Cardiovasc Disord. 2012; 12: 92.
32	Kubala M, Bohbot Y, Rusinaru D, Maréchaux S, Diouf M, Tribouilloy C. Atrial fibrillation in severe aortic stenosis: Prognostic value and results of aortic valve replacement. J Thorac Cardiovasc Surg. 2023; 166(3): 771-9.
33	Minelli C, Del Greco MF, van der Plaat DA, Bowden J, Sheehan NA, Thompson J. The use of two-sample methods for Mendelian randomization analyses on single large datasets. Int J Epidemiol. 2021; 50(5): 1651-9. doi: 10.1093/ije/dyab084 pmid: 33899104

Outcomes	Before correction for outliers			After correction for outliers
Outcomes	Method	OR (95% CI)	P-value	Method	OR (95% CI)	P-value
Non-rheumatic aortic valve disease	IVW	1.000 (0.999-1.000)	0.804	IVW	1.000 (0.999-1.000)	0.505
	MR-Egger	1.000 (0.999-1.001)	0.922	MR‒Egger	1.000 (0.999-1.001)	0.842
	Weighted median	1.000 (0.999-1.001)	0.275	Weighted median	1.000 (0.999-1.001)	0.265
Non-rheumatic mitral valve disease	IVW	1.001 (1.000-1.001)	1.330×10^-6	IVW	1.001 (1.000-1.001)	1.750×10^-5
	MR‒Egger	1.000 (0.999-1.001)	0.567	MR-Egger	1.000 (0.999-1.001)	0.400
	Weighted median	1.000 (0.999-1.001)	0.266	Weighted median	1.000 (0.999-1.001)	0.270
Mitral regurgitation	IVW	1.001 (1.000-1.002)	0.009	IVW	1.001 (1.000-1.001)	0.008
	MR-Egger	1.000 (0.998-1.002)	0.706	MR-Egger	1.000 (0.999-1.002)	0.850
	Weighted median	1.001 (1.000-1.002)	0.035	Weighted median	1.001 (1.000-1.002)	0.038

Model	Effect estimate	SE	P-value
Unadjusted model	0.00068	0.00014	1.33×10^-6
Adjusted for BMI	0.00060	0.00018	8.76×10^-4
Adjusted for smoking	0.00067	0.00014	9.96×10^-7
Adjusted for alcohol consumption	0.00071	0.00014	3.33×10^-7
Adjusted for T2D	0.00064	0.00016	7.07×10^-5
Adjusted for LDL-C	0.00063	0.00016	9.77×10^-5
Adjusted for hypertension	0.00072	0.00017	1.57×10^-5

Model	Effect estimate	SE	P-value
Unadjusted model	0.00095	0.00036	0.009
Adjusted for BMI	0.00095	0.00040	0.018
Adjusted for smoking	0.00093	0.00038	0.014
Adjusted for alcohol consumption	0.00096	0.00037	0.010
Adjusted for T2D	0.00085	0.00039	0.027
Adjusted for LDL-C	0.00082	0.00037	0.029
Adjusted for hypertension	0.00089	0.00041	0.031