Machine learning, whole genome sequencing, and Mendelian randomization support a role of CRP on COVID-19 severity
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.
Authors
- Isabella Ceccherini (ORCID: https://orcid.org/0000-0001-8732-1955)
- Sergio Decherchi (ORCID: https://orcid.org/0000-0001-8371-2270)
- Marta Rusmini (ORCID: https://orcid.org/0000-0002-3109-413X)
- Fabio Landuzzi (ORCID: https://orcid.org/0000-0002-7608-3514)
- Ilaria Ferrigno (ORCID: https://orcid.org/0000-0002-8224-5997)
- Pier Luigi Meroni (ORCID: https://orcid.org/0000-0002-3394-1451)
- Andrea Angius (ORCID: https://orcid.org/0000-0003-2596-6461)
- Francesca Rosamilia (ORCID: https://orcid.org/0000-0003-4979-9397)
- Paolo Uva (ORCID: https://orcid.org/0000-0002-9524-8492)
- Stefania Croci (ORCID: https://orcid.org/0000-0002-8622-0439)
- Annamaria Pezzarossi (ORCID: https://orcid.org/0000-0003-3042-5563)
- Davide Cangelosi (ORCID: https://orcid.org/0000-0002-6010-5619)
- Martina Bonacini (ORCID: https://orcid.org/0000-0002-6830-6781)
- Francesca Lantieri (ORCID: https://orcid.org/0000-0002-8923-0165)
- Vincenzo Rallo (ORCID: https://orcid.org/0000-0002-8938-5650)
- Diego Vozzi (ORCID: https://orcid.org/0000-0002-2902-8184)
- Mariagrazia Catanoso
- Paolo Giorgi Rossi (ORCID: https://orcid.org/0000-0001-9703-2460)
- Pamela Mancuso
- Yeraldin Chiquinquira Castillo De Spelorzi
- Alessandro Rossi (ORCID: https://orcid.org/0000-0002-2031-2955)
- Carlo Salvarani
- Marco Gattorno
- Andrea Cavalli
- Giada Recchi (ORCID: https://orcid.org/0009-0009-1772-9504)
- Edoardo Henzen
Institutions
- University of Modena and Reggio Emilia (IT)
- University of Cagliari (IT)
- Istituto Giannina Gaslini (IT)
- Italian Institute of Technology (IT)
- Azienda Sanitaria Unità Locale di Reggio Emilia (IT)
- IRCCS Istituto Auxologico Italiano (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- École Polytechnique Fédérale de Lausanne (CH)
- University of Genoa (IT)
Publication Details
- Journal
- Molecular Medicine
- Published
- 2026-05-27
- DOI
- https://doi.org/10.1186/s10020-026-01512-6
- Primary Topic
- COVID-19 Clinical Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministero della Salute
- Foundation for Research in Rheumatology