Comorbidity patterns associated with severe COVID-19 outcomes: A cohort study based on the UK Biobank.

ABSTRACT: Pre-existing comorbidities are linked to increased risk of severe COVID-19, but comprehensive assessments of comorbidity patterns remain limited. We used network analysis to identify pre-existing comorbidity modules (i.e., groups of diseases more densely interconnected with each other than with other diseases in the comorbidity network) in a cohort of 420,920 individuals from the UK Biobank who were in England. We defined cases requiring hospitalization or who died of COVID-19 as "severe COVID-19". Logistic regression was used to examine associations between comorbidity modules and severe COVID-19, and a module-based comorbidity index was developed to predict severe COVID-19, compared with existing indices. Comorbidity network analysis identified 190 disease pairs with confirmed comorbidit

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12373198 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature