Cardiovascular disease, hypertension, diabetes and cystatin c jointly predict covid-19 mortality alongside age, male sex and black ethnicity

Elliott, J.; Bodinier, B.; Whitaker, M.; Tzoulaki, I.; Elliott, P.; Chadeau-Hyam, M.

Introduction: Studies of risk factors for severe/fatal COVID-19 to date may not have identified the optimal set of informative predictors. Hypothesis: Use of penalized regression with stability analysis may identify new, sparse sets of risk factors jointly associated with COVID-19 mortality. Methods: We investigated demographic, social, lifestyle, biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and air pollution data from UK Biobank (N=473,574) in relation to linked COVID-19 mortality, and compared with non-COVID-19 mortality. We used penalized regression models (LASSO) with stability analysis (80% selection threshold from 1,000 models with 80% subsampling) to identify a sparse set of variables associated with COVID-19 mortality. Results: Among 43 variables considered by LASSO stability selection, cardiovascular disease, hypertension, diabetes, cystatin C, age, male sex and Black ethnicity were jointly predictive of COVID-19 mortality risk at 80% selection threshold (Figure). Of these, Black ethnicity and hypertension contributed to COVID-19 but not non-COVID-19 mortality. Conclusions: Use of LASSO stability selection identified a sparse set of predictors for COVID-19mortality including cardiovascular disease, hypertension, diabetes and cystatin C, a marker of renalfunction that has also been implicated in atherogenesis and inflammation. These results indicate theimportance of cardiometabolic comorbidities as predisposing factors for COVID-19 mortality.Hypertension was differentially highly selected for risk of COVID-19 mortality, suggesting the need for continued vigilance with good blood pressure control during the pandemic.