Study population and setting
This study used Mendelian randomization to examine associations between 17 cardiometabolic traits (type 1 diabetes, type 2 diabetes, hemoglobin A1c, fasting glucose, fasting insulin, body mass index [BMI], waist–hip ratio, LDL cholesterol, HDL cholesterol, triglycerides, systolic blood pressure, diastolic blood pressure, creatinine-based estimated glomerular filtration rate [eGFR], chronic kidney disease, coronary artery disease, any stroke, and C-reactive protein) and two outcomes: susceptibility to COVID-19 and hospitalization for COVID-19. Mendelian randomization is an analytic technique designed to circumvent potential confounding, in which a genetic variant associated with an exposure is tested for association with an outcome. Genetic variants associated with the 17 cardiometabolic traits were taken from meta-analyses of genome-wide association studies (GWAS). Tests for association between genetic variants and outcomes were performed using data from the COVID-19 Host Genetics Initiative, to which 22 cohorts had contributed. Analyses were restricted to individuals of European ancestry. For COVID-19 susceptibility, those with a positive test for SARS-CoV-2 infection via PCR, a clinical diagnosis of COVID-19, or serological evidence for prior SARS-CoV-2 infection (n=14,134) were compared to population controls (n=1,284,876). For hospitalization, 6,406 hospitalized patients with laboratory-confirmed or clinically diagnosed COVID-19 were compared to 902,088 population controls. Controls were required to have no history of COVID-19 diagnosis or laboratory-confirmed SARS-CoV-2 infection. Two-sample Mendelian randomization was performed using inverse variance weighting with random effects, and odds ratios were estimated for each exposure-outcome relationship. To explore the degree to which the exclusion restriction may have been met (that the genetic variant can only affect the outcome through the exposure of interest), the authors performed sensitivity analyses using different methods (weighted median estimator, MR-Egger regression, mode-based estimation). To test for mediation and to account for possible pleiotropy (when one gene influences two or more unrelated phenotypic traits), the authors performed multivariable (pairwise) Mendelian randomization analyses of the BMI-outcome relationships by including genetic effects of coronary artery disease, stroke, chronic kidney disease, and type – diabetes one exposure variable at a time.
Summary of Main Findings
In Mendelian randomization analyses of the 17 cardiometabolic exposures and 2 outcomes, only the BMI-hospitalization association was statistically significant after adjustment for multiple tests. Authors repeated the analysis in UK Biobank data to obtain an interpretable effect estimate, finding that each unit increase of BMI (kg/m^2) was associated with 1.14 times the odds of COVID-19 hospitalization (95% CI: 1.07 to 1.21). Estimates using the weighted median estimator, MR-Egger regression, and mode-based estimation yielded results that were heterogeneous but in the same direction. In the pairwise multivariable Mendelian randomization analyses, BMI had no statistically significant associations with either outcome after adjustment for the effects of (any one of) coronary artery disease, stroke, chronic kidney disease, or type 2 diabetes, implying that any effect of BMI on the outcome may be mediated by these conditions.
The authors performed analyses using several methods to test the robustness of their findings to alternate assumptions. The study was able to draw from a large amount of genotype and phenotype data linked to COVID-19 outcomes.
Control groups were population-based, and thus may have differed from the source population giving rise to COVID-19 cases; this may have biased effect estimates. Population-based controls were required to have no prior positive SARS-CoV-2 test, and presumably included individuals who would have been hospitalized with COVID-19 had they become infected; a more appropriate counterfactual proxy for hospitalized COVID-19 cases would be individuals who were infected but not hospitalized. The study population included only those of European ancestry, limiting generalizability. Other characteristics of the cases and controls (e.g., age, sex, clinical characteristics) were not described in detail. Causal hypotheses underlying the multivariable analyses and their interpretation were unclear (e.g., postulated causal relationships among genetic variants, BMI, and other cardiometabolic risk factors). The genetic instruments were weak (they explained a small proportion of variation in the exposures), and pleiotropy (pathways from variant to outcome that do not involve the exposure) was likely. In general, results from Mendelian randomization are valid causal effects only when largely untestable assumptions about causal dynamics are met. Finally, the effect of BMI on the log odds of hospitalization was modeled as linear, though associations between BMI and other outcomes (e.g., mortality) are known to be nonlinear.
This study adds to the evidence base implicating obesity (as measured by BMI) as a causal risk factor for severe COVID-19.
This review was posted on: 23 March 2021