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A blood-based prognostic biomarker to guide clinical decision-making at diagnosis of inflammatory bowel disease (IBD) would be immensely helpful. We investigated a composite serum N-glycomic biomarker to predict future disease course in 244 newly diagnosed IBD patients. Forty-seven individual glycan peaks were analysed using ultra-high performance liquid chromatography identifying 105 glycoforms from which 24 derived glycan traits were calculated. Multivariable logistic regression was performed to determine associations of derived glycan traits with disease. Cox proportional hazard models were used to predict treatment escalation from first-line treatment to biologics or surgery (hazard ratio (HR) 25.9, p = 1.1×10− 12; 95% confidence interval (CI), 8.52–78.78). Application to an independent replication cohort of 54 IBD patients yielded a HR of 5.1 (p = 1.1×10− 5; 95% CI, 2.54–10.1). These data demonstrate the predictive capacity of serum N-glycan biomarkers and represent a step towards personalized medicine in IBD.

Serum N-glycans have been identified as putative biomarkers for numerous diseases. The impact of different serum sample tubes and processing methods on N-glycan analysis has received relatively little attention. This study aimed to determine the effect of different sample tubes and processing methods on the whole serum N-glycan profile in both health and disease. A secondary objective was to describe a robot automated N-glycan release, labeling and cleanup process for use in a biomarker discovery system.25 patients with active and quiescent inflammatory bowel disease and controls had three different serum sample tubes taken at the same draw. Two different processing methods were used for three types of tube (with and without gel-separation medium). Samples were randomised and processed in a blinded fashion. Whole serum N-glycan release, 2-aminobenzamide labeling and cleanup was automated using a Hamilton Microlab STARlet Liquid Handling robot. Samples were analysed using a hydrophilic interaction liquid chromatography/ethylene bridged hybrid(BEH) column on an ultra-high performance liquid chromatography instrument. Data were analysed quantitatively by pairwise correlation and hierarchical clustering using the area under each chromatogram peak. Qualitatively, a blinded assessor attempted to match chromatograms to each individual.There was small intra-individual variation in serum N-glycan profiles from samples collected using different sample processing methods. Intra-individual correlation coefficients were between 0.99 and 1. Unsupervised hierarchical clustering and principal coordinate analyses accurately matched samples from the same individual. Qualitative analysis demonstrated good chromatogram overlay and a blinded assessor was able to accurately match individuals based on chromatogram profile, regardless of disease status.The three different serum sample tubes processed using the described methods cause minimal inter-individual variation in serum whole N-glycan profile when processed using an automated workstream. This has important implications for N-glycan biomarker discovery studies using different serum processing standard operating procedures.