Defining a long COVID ‘expotype’ within the P4O2 COVID-19 study

Abstract

Introduction: Long COVID is estimated to affect at least 10 % of COVID-19 patients, with fatigue being a common complaint. The combined contribution of environmental factors (i.e. exposome) has been associated with COVID-19 severity, however its association with long COVID remains underexplored. This study aims to identify possible exposome phenotypes (‘expotypes’) related to long COVID severity. Methods: We recruited 95 long COVID patients in the Netherlands and assessed a range of factors and symptoms at 3–6 months post-infection. Fatigue (FSS), Quality of Life (QoL) and fatigue over time were used as indicators of long COVID severity. We included air pollutants (n = 4), and neighborhood characteristics (n = 7). We performed frequentist and Bayesian analyses to determine factors associated with long COVID severity. Models were adjusted for age, BMI, education level, and sex. Results: We found population density (odds ratio (OR)[95 %Confidence interval(CI)] = 1.03[1.01–1.06]) and light at night (OR[95 %CI] = 0.95[0.90–1.00]) to be associated with fatigue. Decreased odds for having an optimal QoL score was found for increased distance to blue space (OR[95 %CI] = 0.41[0.15–0.93]) in the single exposure model. No significant associations were found for any exposure variables and fatigue over time. No exposure variables were selected in penalized regression models for any outcome. Discussion: The external exposome could be associated with fatigue severity and QoL in long COVID patients, however these associations were not found in the horseshoe model. Prevention strategies and urban planning could take these associations into account to optimize the living environment, however more research is needed to validate and investigate the impact of these results.