Early inflammatory profiles predict maximal disease severity in COVID-19: An unsupervised cluster analysis
Grace Kenny, Gurvin Saini, Colette Marie Gaillard, Riya Negi, Dana Alalwan, Alejandro Garcia Leon, Kathleen McCann, Willard Tinago, Christine Kelly, Aoife G. Cotter, Eoghan de Barra, Mary Horgan, Obada Yousif, Virginie Gautier, Alan Landay, Danny McAuley, Eoin R. Feeney

TL;DR
The study finds that early immune system patterns in COVID-19 patients can predict how severe their disease will become, offering insights for better treatment strategies.
Contribution
The novel contribution is identifying specific early inflammatory profiles that strongly predict severe disease outcomes in COVID-19 patients.
Findings
Four distinct inflammatory profiles were identified in early-stage COVID-19 patients.
Cluster 3 showed the highest odds of progressing to severe disease despite lower growth factor and endothelial activation markers.
Early alveolar epithelial injury markers were strongly associated with severe disease progression.
Abstract
The inflammatory changes that underlie the heterogeneous presentations of COVID-19 remain incompletely understood. In this study we aimed to identify inflammatory profiles that precede the development of severe COVID-19, that could serve as targets for optimised delivery of immunomodulatory therapies and provide insights for the development of new therapies. We included individuals sampled <10 days from COVID-19 symptom onset, recruited from both inpatient and outpatient settings. We measured 61 biomarkers in plasma, including markers of innate immune and T cell activation, coagulation, tissue repair and lung injury. We used principal component analysis and hierarchical clustering to derive biomarker clusters, and ordinal logistic regression to explore associations between cluster membership and maximal disease severity, adjusting for known risk factors for severe COVID-19. In 312…
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Taxonomy
TopicsCOVID-19 Clinical Research Studies · Long-Term Effects of COVID-19 · SARS-CoV-2 and COVID-19 Research
