Mendelian randomization analysis establishes a causal relationship between COVID-19 and cardiometabolic diseases

Abstract

Objective

The causal impacts of COVID-19 on cardiometabolic diseases remained uncertain. This study utilized the two-sample Mendelian randomization (MR) method to evaluate causal relationships between COVID-19 (susceptibility and severity) and four primary cardiometabolic diseases (type 2 diabetes, coronary heart disease, ischemic stroke, and heart failure).

Methods

MR analysis was conducted using genome-wide association study (GWAS) results. Susceptibility and severity were defined as COVID-19-positive and COVID-19-hospitalization, respectively. Data from the COVID-19 Host Genetics Initiative were used for susceptibility and severity analysis. Consortium data from Spracklen CN, Nikpay, Malik R, and Neale lab were employed for type 2 diabetes, coronary heart disease, ischemic stroke, and heart failure, respectively.

Results

For COVID-19 susceptibility, the inverse variance weighted (IVW) method showed the odds ratio (OR) (95% confidence interval [CI], P-value) for type 2 diabetes was 1.719 (1.510–1.956, P ​= ​0.000). For COVID-19 severity, the IVW method estimate indicated that the OR (95% CI, P-value) for ischemic stroke was 1.051 (1.008–1.095, P ​= ​0.020). Moreover, the OR for heart failure was slightly higher in the hospitalized population than in the control population (1.001, 95% CI 1.000–1.002, P ​= ​0.010). The remaining results were negative.

Conclusion

This MR study establishes that genetically predicted COVID-19 susceptibility causally increases type 2 diabetes risk, while severe infection shows suggestive causal links with ischemic stroke and heart failure, redefining COVID-19 as an independent cardiometabolic risk factor.