This study aims at evaluating Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current coronavirus disease 2019 (COVID-19) pandemic. Many patients with severe COVID-19 develop sepsis. Cardiometabolic traits have been associated with increased risk of severe COVID-19 and sepsis; however, it is difficult to infer causal effects from observational studies because of the possibility that any identified associations may be attributable to confounding. Here, we leverage data from large-scale genetic association studies to identify genetic proxies for body mass index (BMI), lifetime smoking score, low-density lipoprotein cholesterol, systolic blood pressure, and type 2 diabetes (T2DM) liability, and apply these in Mendelian randomization (MR) analyses investigating their associations with risk of sepsis and severe COVID-19. Through leverage of randomly allocated genetic variants, this approach can better overcome the confounding that hinders causal inference in observational studies. The methods and data sources relating to this work are described in detail elsewhere.1 Briefly, genetic variants selected as instrumental variables were uncorrelated (r2<0.001) single-nucleotide polymorphisms associated with the corresponding exposure trait at genome-wide significance (P<5×10−8) in previously published genome-wide association study analyses.1 Summary genetic association estimates for sepsis were obtained from the UK Biobank.