Serum Lewis fucosylation reports on survivorship of patients with septic shock upon admission to intensive care unit

Septic shock, the excessive immune response to pathogen infection, poses a major health concern accounting globally for [~]20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific and insensitive, hindering timely and effective treatment. Aiming to identify easy-to-assay glyco-signatures that may identify and guide the clinical management of the most critically unwell patients, we applied quantitative glycomics and glycoproteomics to sera longitudinally collected from septic shock survivors (n=29) and non-survivors (n=8). Glycomics of all 134 serum samples (sampled daily until recovery/death) revealed significant N-glycome dynamics across both patient groups. Unsupervised clustering of the serum N-glycome upon ICU admission (day 1) indicated survivorship-specific glyco- signatures. We therefore employed machine learning to train a random forest model using the serum N-glycome data. The model accurately classified survivorship outcomes of 35 of 37 patients (specificity 94.6%) and correctly predicted 6 of 8 non-survivors (sensitivity 75%) based on ICU day 1 data. Further interrogation of the serum N-glycome data revealed elevated Lewis fucosylation in non-survivors relative to survivors at ICU admission, a finding recapitulated by glycoproteomics. Amongst 58 other serum proteins strongly linked to acute phase response and stress pathways, alpha-1-acid-glycoprotein was identified as a principal carrier of Lewis fucosylation with a potential to stratify septic shock survivors from non- survivors (AUC 0.935). This study lays a foundation for risk stratification of septic shock patients by uncovering easy-to-assay glyco-signatures that identify individuals with poor survival outcomes upon ICU admission, with the potential to translate to early individualised clinical care at the bedside.