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Background <p>The coronavirus disease 2019 (COVID-19) ranges from asymptomatic to very severe infection and death, largely depending on host factors, including genetics. We have investigated clinical and genetic data from 200 COVID-19 patients to search for factors predisposing to increased disease severity.</p> Methods <p>Patients were divided into non-hospitalized mild/pauci-symptomatic and hospitalized severe. An interpretable Machine Learning approach was applied to blood biomarkers while genome-wide associations were performed for COVID-19 severity. Finally, a possible causal role of chronic low-grade inflammation on COVID-19 severity was searched by Mendelian Randomization.</p> Results <p>A high severity predictive role was observed in our sample by Machine Learning for the C-Reactive Protein measured in the course of SARS-CoV-2 infection (iCRP). This was also suggested by evidence of association with variants known to be involved in the CRP levels in the general population (pCRP). Finally, a possible causal role of chronic low-grade inflammation on COVID-19 severity could be shown by Mendelian Randomization using publicly available summary statistics of two COVID-19 Genome-Wide Association Studies.</p> Conclusions <p>Consistent with previous results, a predictive role of CRP levels on COVID-19 severity was detected in our sample. Furthermore, Mendelian Randomization supported a causal role of genetically predicted chronic CRP levels.</p>

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Machine learning, whole genome sequencing, and Mendelian randomization support a role of CRP on COVID-19 severity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Francesca Lantieri,
  • Stefania Croci,
  • Sergio Decherchi,
  • Marta Rusmini,
  • Giada Recchi,
  • Martina Bonacini,
  • Ilaria Ferrigno,
  • Alessandro Rossi,
  • Yeraldin Chiquinquira Castillo De Spelorzi,
  • Edoardo Henzen,
  • Francesca Rosamilia,
  • Davide Cangelosi,
  • Fabio Landuzzi,
  • Andrea Angius,
  • Vincenzo Rallo,
  • Pamela Mancuso,
  • Annamaria Pezzarossi,
  • Paolo Giorgi Rossi,
  • Mariagrazia Catanoso,
  • Marco Gattorno,
  • Andrea Cavalli,
  • Pier Luigi Meroni,
  • Diego Vozzi,
  • Paolo Uva,
  • Isabella Ceccherini,
  • Carlo Salvarani

摘要

Background

The coronavirus disease 2019 (COVID-19) ranges from asymptomatic to very severe infection and death, largely depending on host factors, including genetics. We have investigated clinical and genetic data from 200 COVID-19 patients to search for factors predisposing to increased disease severity.

Methods

Patients were divided into non-hospitalized mild/pauci-symptomatic and hospitalized severe. An interpretable Machine Learning approach was applied to blood biomarkers while genome-wide associations were performed for COVID-19 severity. Finally, a possible causal role of chronic low-grade inflammation on COVID-19 severity was searched by Mendelian Randomization.

Results

A high severity predictive role was observed in our sample by Machine Learning for the C-Reactive Protein measured in the course of SARS-CoV-2 infection (iCRP). This was also suggested by evidence of association with variants known to be involved in the CRP levels in the general population (pCRP). Finally, a possible causal role of chronic low-grade inflammation on COVID-19 severity could be shown by Mendelian Randomization using publicly available summary statistics of two COVID-19 Genome-Wide Association Studies.

Conclusions

Consistent with previous results, a predictive role of CRP levels on COVID-19 severity was detected in our sample. Furthermore, Mendelian Randomization supported a causal role of genetically predicted chronic CRP levels.