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A Machine Learning Approach to Understanding the Genetic Role in COVID-19 Prognosis: The Influence of Gene Polymorphisms Related to Inflammation, Vitamin D, and ACE2

  • 0Department of Pneumonology, University Clinical Hospital of Valladolid, 47003 Valladolid, Spain.
International Journal of Molecular Sciences +

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August 28, 2025

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August 28, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Genetic factors significantly impact COVID-19 outcomes. This study identified key gene polymorphisms related to inflammation and vitamin D metabolism that predict severe COVID-19, pneumonia, and mortality risks.

Area Of Science

  • Genetics
  • Immunology
  • Infectious Diseases

Background

  • COVID-19 severity varies significantly among individuals.
  • Genetic predisposition is a key factor influencing disease outcomes.
  • Understanding the genetic basis of COVID-19 is crucial for risk stratification.

Purpose Of The Study

  • To investigate the association between genetic polymorphisms in RAAS, cytokine, and vitamin D pathways and COVID-19 severity.
  • To identify genetic variants that significantly predict COVID-19 prognosis using machine learning.
  • To elucidate the genetic etiology of COVID-19 severity.

Main Methods

  • Analysis of 338 COVID-19 patients.
  • Utilized machine learning algorithms to identify significant genetic predictors.
  • Focused on polymorphisms in genes including IL6, IL6R, IL1α, IL1R, IFNγ, TNFα, CRP, VDR, VDBP, and ACE2.

Main Results

  • Polymorphisms in IL6, IL6R, IL1α, IL1R, IFNγ, TNFα, CRP, VDR, VDBP, and ACE2 genes were identified as significant predictors of COVID-19 severity.
  • Machine learning models achieved high AUC values (0.86 for pneumonia/mortality, 0.85 for rehospitalization).
  • These genetic factors influence risks of pneumonia, mortality, rehospitalization, and associated mortality.

Conclusions

  • Genetic background plays a critical role in COVID-19 prognosis.
  • Genetics-driven machine learning models can effectively identify high-risk patients.
  • Focusing on genetic variants in ACE2, inflammation, and vitamin D pathways aids in risk prediction.

Keywords:

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