COVID-19 and the risk of acute cardiovascular diseases: a two-sample Mendelian randomization study

Abstract

Abstract Background Evidence suggests that coronavirus disease 2019 (COVID-19) is associated with the risk of cardiovascular diseases (CVDs). However, the results are inconsistent, and the causality remains to be established. We aimed to investigate the potential causal relationship between COVID-19 and CVDs by using two-sample Mendelian randomization (MR) analysis. Methods Summary‐level data for COVID-19 and CVDs including myocarditis, heart failure (HF), acute myocardial infarction (AMI), arrhythmia and venous thromboembolism (VTE) were obtained from the IEU OpenGWAS project, a public genome-wide association study (GWAS). Single nucleotide polymorphisms (SNPs) were used as instrumental variables. Five complementary MR methods were performed, including inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode and simple mode methods. IVW method was considered as the primary approach. Besides, sensitivity analyses, including Cochran’s Q test, MR-Egger intercept test, and leave-one-out analysis, were performed to evaluate the robustness of the results. Results According to the IVW results, our MR study indicated that genetically predicted COVID-19 was not causally connected with the risk of CVDs [myocarditis: odds ratio (OR) = 1.407, 95% confidence interval (CI) = 0.761-2.602, p-value = 0.277; HF: OR = 1.180, 95% CI = 0.980-1.420, p-value = 0.080; AMI: OR = 1.002, 95% CI = 0.998-1.005, p-value = 0.241; arrhythmia: OR = 0.865, 95% CI = 0.717-1.044, p-value = 0.132; VTE: OR = 1.013, 95% CI = 0.997-1.028, p-value = 0.115]. The supplementary MR methods showed similar results. Sensitivity analyses suggested that the causal estimates were robust. Conclusion This two-sample MR analysis did not provide sufficient evidence for a causal relationship between COVID-19 and the risk of acute CVDs, which may provide new insights into the prevention of acute CVDs in COVID-19 patients.

Keywords

• COVID-19
• Genome-Wide Association Study
• Mendelian Randomization
• Cardiovascular disease
• Causal association