| Title: | Comorbidity patterns associated with severe COVID-19 outcomes: A cohort study based on the UK Biobank |
| Journal: | PLOS ONE |
| Published: | 22 Aug 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40845057/ |
| DOI: | https://doi.org/10.1371/journal.pone.0329701 |
| URL: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0329701&type=printable |
Publication 19691
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Abstract
BACKGROUND: Pre-existing comorbidities are linked to increased risk of severe COVID-19, but comprehensive assessments of comorbidity patterns remain limited.</p>
METHODS: 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.</p>
RESULTS: Comorbidity network analysis identified 190 disease pairs with confirmed comorbidity associations, which were further divided into seven comorbidity modules. Among the 30,914 individuals diagnosed with COVID-19, 3,970 were identified as severe cases (median age of 73.6 years, 58.77% being male). Six of seven identified modules showed statistically significant associations with severe COVID-19, especially modules related to circulatory and respiratory diseases (odds ratio = 1.67 [95% confidence interval 1.54-1.81]) and age-related eye diseases (1.39 [1.27-1.52]). Associations did not differ by sex, age or vaccination status but were generally stronger during the first wave of COVID-19 pandemic (i.e., 31st January-1st October, 2020). Our newly developed module-based comorbidity index showed better performance in predicting severe COVID-19 (AUC = 0.779) compared to the existing Charlson Comorbidity Index (0.714) and the 16-comorbidity index (0.714).</p>
CONCLUSIONS: Our study demonstrated that pre-existing comorbidity modules, particularly modules related to circulatory and respiratory diseases and age-related eye diseases, were associated with severe COVID-19. Moreover, the module-based comorbidity index provides better prediction of severe COVID-19 than existing prediction indices.</p>
16 Keywords
- Aged
- Aged, 80 and over
- Biological Specimen Banks
- COVID-19
- Cohort Studies
- Comorbidity
- Female
- Hospitalization
- Humans
- Male
- Middle Aged
- Risk Factors
- SARS-CoV-2
- Severity of Illness Index
- UK Biobank
- United Kingdom
10 Authors
- Jian Zhang
- Can Hou
- Wenwen Chen
- Yao Hu
- Shishi Xu
- Haowen Liu
- Yao Yang
- Unnur A. Valdimarsdóttir
- Fang Fang
- Huan Song
1 Application
| Application ID | Title |
|---|---|
| 54803 | Deciphering complex traits - phenotypic and genetic associations between traits in the UK Biobank Cohort |
Enabling scientific discoveries that improve human health