A prospective observational cohort study to identify inflammatory biomarkers for the diagnosis and prognosis of patients with sepsis

D'Onofrio, V.; Heylen, D.; Pusparum, M.; Grondman, I.; Vanwalleghem, J.; Meersman, A.; Cartuyvels, R.; Messiaen, P.; Joosten, L. A. B.; Netea, M. G.; Valkenborg, D.; Ertaylan, G.; Gyssens, I. C.

Background: Sepsis is a life-threatening organ dysfunction. A fast diagnosis is crucial for patient management. Proteins that are synthesized during the inflammatory response can be used as biomarkers, helping in a rapid clinical assessment or an early diagnosis of infection. The aim of this study was to identify biomarkers of inflammation for the diagnosis and prognosis of infection in patients with suspected sepsis. Methods: In total 406 episodes were included in a prospective cohort study. Plasma was collected from all patients on the first day of a new episode. Samples were analysed using a 92-plex proteomic panel based on a proximity extension assay with oligonucleotide-labelled antibody probe pairs (OLink, Uppsala, Sweden). Supervised and unsupervised differential expression analyses and pathway enrichment analyses were performed. Results: Supervised differential expression analysis revealed 21 proteins that were significantly lower in circulation of patients with viral infections compared to patients with bacterial infections. More strongly, higher expression levels were observed for 38 proteins in patients with high SOFA scores (>4), and for 21 proteins in patients with worse outcome. These proteins are mostly involved in pathways known to be activated early in the inflammatory response. Unsupervised, hierarchical clustering confirmed that inflammatory response was more strongly related to disease severity than to aetiology. Conclusion: Several differentially expressed inflammatory proteins were identified that could be used as biomarkers for sepsis. These proteins are mostly related to disease severity. Within the setting of an emergency department, they could be used for outcome prediction, patient monitoring, and directing diagnostics.