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Early inflammatory profiles predict maximal disease severity in COVID-19: An unsupervised cluster analysis
Grace Kenny1,2, Gurvin Saini1, Colette Marie Gaillard1
1Centre for Experimental Pathogen Host Research, University College Dublin, Dublin, Ireland.
Heliyon
|August 15, 2024
Summary
Early inflammatory profiles in COVID-19 patients predict disease severity. A specific dysfunctional inflammatory pattern, marked by alveolar epithelial injury, indicates the highest risk for severe illness.
Area of Science:
- Immunology
- Infectious Diseases
- Biomarker Discovery
Background:
- COVID-19 presentations are heterogeneous, with underlying inflammatory changes not fully understood.
- Identifying early inflammatory profiles can guide immunomodulatory therapy and new treatment development.
Purpose of the Study:
- To identify distinct inflammatory profiles preceding severe COVID-19.
- To understand how these profiles correlate with disease progression and severity.
Main Methods:
- Analyzed 61 plasma biomarkers in 312 individuals within 10 days of symptom onset.
- Utilized principal component analysis and hierarchical clustering to define biomarker clusters.
- Employed ordinal logistic regression to assess the association between clusters and maximal disease severity.
Main Results:
- Four inflammatory clusters were identified, ranging from low inflammation to high innate immune activation and coagulation markers.
- Cluster 3 showed high alveolar epithelial injury markers with downregulated growth factors, indicating a dysfunctional inflammatory pattern.
- Cluster 3 demonstrated the highest odds of progressing to severe COVID-19, independent of known risk factors.
Conclusions:
- Early inflammatory profiles effectively predict subsequent COVID-19 severity.
- A specific profile characterized by alveolar epithelial injury and dysfunctional inflammation carries the highest risk for severe disease.
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