Interplay between demographic, clinical and polygenic risk factors for severe COVID-19

Samantha S R Crossfield1, Natalie J M Chaddock1, Mark M Iles1

  • 1School of Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.

Insights

Clinical, socio-demographic, and genetic factors predict severe COVID-19. A polygenic risk score (PRS) identified individuals at higher risk, highlighting immune pathways involved in severe disease outcomes.

Area of Science:

  • Genetics
  • Epidemiology
  • Immunology

Background:

  • Severe COVID-19 risk factors are complex, involving clinical, socio-demographic, and genetic elements.
  • Understanding these factors is crucial for predicting disease severity and outcomes in the general population.

Purpose of the Study:

  • To identify clinical, socio-demographic, and genetic risk factors associated with severe COVID-19 outcomes.
  • To develop and validate a polygenic risk score (PRS) for severe COVID-19.
  • To enhance biological understanding of severe COVID-19 through pathway analysis.

Main Methods:

  • Observational study of 9560 UK Biobank participants diagnosed with COVID-19 in 2020.
  • Development and optimization of a PRS for severe COVID-19 using genome-wide summary statistics.
  • Assessment of associations between severe COVID-19 and socio-demographic factors, comorbidities, immunosuppressant use, and PRS.

Main Results:

  • Severe COVID-19 risk increased with age, obesity, male sex, smoking, socio-economic deprivation, prior immunosuppressant use, and comorbidities.
  • An optimized PRS, enriched for immune-related pathways (e.g., 'oligoadenylate synthetase antiviral response', 'interleukin-10 signalling'), was significantly associated with severe COVID-19.
  • Individuals in the highest PRS quintile had a 1.32-fold increased odds of severe COVID-19.

Conclusions:

  • Genetic data, particularly PRS, offers novel insights into severe COVID-19 beyond traditional risk factors.
  • Integrating genetic information with clinical and socio-demographic data improves biological understanding of severe COVID-19.
  • Findings emphasize the potential of polygenic risk scores for personalized risk assessment in infectious diseases.
Abstract

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