Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis

Abstract

Background: Epidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses.

Methods and findings: We selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10-8 from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m2; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10-5; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m2, p = 2.1 × 10-5). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null.

Conclusions: In this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes.

Fig 1. Forest plot Mendelian randomization (MR) effect estimates and 95% confidence intervals for each exposure and the 2 main outcomes analyzed.

MR estimates are reported as odds ratios (ORs) per unit of the exposure: hemoglobin A1c, percent unit; fasting glucose, mg/dl; fasting insulin, natural log; body mass index (BMI), inverse normally transformed residuals; waist–hip-ratio, inverse normally transformed residuals; C-reactive protein, rank-based inverse normally transformed; low-density lipoprotein, standardized; high-density lipoprotein, standardized; triglycerides, standardized; systolic and diastolic blood pressure, mm Hg; estimated glomerular filtration rate (eGFR), ml/min/1.73 m²; type 1 diabetes, type 2 diabetes, coronary artery disease, chronic kidney disease, and any stroke, log-odds.

Fig 2. Sensitivity analyses using other Mendelian randomization (MR) methods and results using UK Biobank effect estimates.

MR estimates are reported as odds ratios (ORs) per unit increase in body mass index (BMI). Locke et al. [37]: inverse normally transformed residuals; UK Biobank: kg/m².