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

Jian Zhang1,2, Can Hou2,3, Wenwen Chen2,3

  • 1Mental Health Center and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.

Plos One
|August 22, 2025
PubMed

Insights

Pre-existing comorbidity modules, especially those involving circulatory, respiratory, and age-related eye diseases, are linked to severe COVID-19. A new module-based index better predicts severe COVID-19 outcomes than existing tools.

Area of Science:

  • Network analysis in epidemiology
  • Biostatistics
  • Public health

Background:

  • Pre-existing health conditions increase COVID-19 severity risk.
  • Comprehensive understanding of comorbidity patterns is limited.

Purpose of the Study:

  • To identify patterns of pre-existing comorbidities associated with severe COVID-19.
  • To develop and validate a novel comorbidity index for predicting severe COVID-19.

Main Methods:

  • Network analysis of UK Biobank data (n=420,920) to identify comorbidity modules.
  • Logistic regression to assess associations between modules and severe COVID-19.
  • Development and comparison of a module-based comorbidity index against existing indices.

Main Results:

  • Seven distinct comorbidity modules were identified.
  • Six modules were significantly associated with severe COVID-19, notably circulatory/respiratory and age-related eye disease modules.
  • The novel module-based index achieved higher predictive accuracy (AUC=0.779) than Charlson (0.714) and 16-comorbidity (0.714) indices.

Conclusions:

  • Specific comorbidity modules, particularly those affecting circulation, respiration, and eyes, are key predictors of severe COVID-19.
  • The developed module-based comorbidity index offers superior prediction of severe COVID-19 compared to current standards.
Abstract

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