This study uses advanced statistical methods to identify important non-genetic risk factors for COVID-19 hospitalization from a large dataset of UK Biobank participants. Traditional methods often miss key variables, but our approach, called Doublethink, helps find significant risk factors while controlling for errors. We analyzed data from nearly 202,000 participants and discovered nine important individual risk factors and seven groups of related factors. Some well-known risks like age and obesity were confirmed, while others like cardiovascular disease were not found to be significant. Our findings suggest overlooked factors such as dementia and prior infections also play a role. This research highlights the benefits of using a broad approach to uncover important health risks.
AI simplified
Big data approaches to discovering nongenetic risk factors have lagged behind genome-wide association studies that routinely uncover novel genetic risk factors for diverse diseases. Instead, epidemiology typically focuses on candidate risk factors. Since modern biobanks contain thousands of potential risk factors, candidate approaches may introduce bias, inadequately control for multiple testing, and overlook important signals. Doublethink, a model-averaged hypothesis testing approach, offers a solution that simultaneously controls the Bayesian false discovery rate (FDR) and frequentist familywise error rate (FWER) while accounting for uncertainty in variable selection. Here, we investigate direct risk factors for COVID-19 hospitalization from among 1,912 variables in 201,917 UK Biobank participants by implementing a Doublethink-based exposome-wide association study using Markov Chain Monte Carlo. Focusing on the 2020 outbreak, we find nine individual variables and seven groups of variables exposome-wide significant at 9% FDR and 0.05% FWER. We identify significant direct effects among relatively overlooked risk factors including aging, dementia, and prior infection, which we evaluate in relation to studies of other populations. We detect significant direct effects among some commonly reported risk factors like age, sex, and obesity, but not others like cardiovascular disease. The effects of hypertension, depression, and diabetes appeared to be mediated via general comorbidity. Doublethink produces interchangeable posterior odds and P-values for individual variables and arbitrary groups, facilitating flexible and powerful post hoc hypothesis testing. We discuss the potential for impact and limitations of joint Bayesian-frequentist hypothesis testing, including the benefits of an agnostic exposome-wide approach to discovery.
