N‐glycosylation is a ubiquitous protein modification, and N‐glycosylation profiles are emerging as both biomarkers and functional effectors in various types of diabetes. Genome‐wide association studies identified glycosyltransferase genes as candidate causal genes for type 1 and type 2 diabetes. Studies focused on N‐glycosylation changes in type 2 diabetes demonstrated that patients can be distinguished from healthy controls based on N‐glycome composition. In addition, individuals at an increased risk of future disease development could be identified based on N‐glycome profiles. Moreover, accumulating evidence indicates that N‐glycans have a major role in preventing the impairment of glucose‐stimulated insulin secretion by maintaining the glucose transporter in proper orientation, indicating that interindividual variation in protein N‐glycosylation might be a novel risk factor contributing to diabetes development. Defective N‐glycosylation of T cells has been implicated in type 1 diabetes pathogenesis. Furthermore, studies of N‐glycan alterations have successfully been used to identify individuals with rare types of diabetes (such as the HNF1A‐MODY), and also to evaluate functional significance of novel diabetes‐associated mutations. In conclusion, both N‐glycans and glycosyltransferases emerge as potential therapeutic targets in diabetes.

Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma N-glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke). RESEARCH DESIGN AND METHODSBased on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n 5 27,548), we constructed case-cohorts including a random subsample of 2,500 participants and all physician-verified incident cases of type 2 diabetes (n 5 820; median follow-up time 6.5 years) and CVD (n 5 508; median follow-up time 8.2 years). Information on the relative abundance of 39 N-glycan groups in baseline plasma samples was generated by chromatographic profiling. We selected predictive N-glycans for type 2 diabetes and CVD separately, based on cross-validated machine learning, nonlinear model building, and construction of weighted prediction scores. This workflow for CVD was applied separately in men and women. RESULTSThe N-glycan-based type 2 diabetes score was strongly predictive for diabetes risk in an internal validation cohort (weighted C-index 0.83, 95% CI 0.78-0.88), and this finding was externally validated in the Finland Cardiovascular Risk Study (FINRISK) cohort. N-glycans were moderately predictive for CVD incidence (weighted Cindices 0.66, 95% CI 0.60-0.72, for men; 0.64, 95% CI 0.55-0.73, for women). Information on the selected N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD. CONCLUSIONSSelected N-glycans improve type 2 diabetes and CVD prediction beyond established risk markers. Plasma protein N-glycan profiling may thus be useful for risk stratification in the context of precisely targeted primary prevention of cardiometabolic diseases.

OBJECTIVE N-glycosylation is a functional posttranslational modification of immunoglobulins (Igs). We hypothesized that specific IgG N-glycans are associated with incident type 2 diabetes and cardiovascular disease (CVD). RESEARCH DESIGN AND METHODS We performed case-cohort studies within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam cohort (2,127 in the type 2 diabetes subcohort [741 incident cases]; 2,175 in the CVD subcohort [417 myocardial infarction and stroke cases]). Relative abundances of 24 IgG N-glycan peaks (IgG-GPs) were measured by ultraperformance liquid chromatography, and eight glycosylation traits were derived based on structural similarity. End point–associated IgG-GPs were preselected with fractional polynomials, and prospective associations were estimated in confounder-adjusted Cox models. Diabetes risk associations were validated in three independent studies. RESULTS After adjustment for confounders and multiple testing correction, IgG-GP7, IgG-GP8, IgG-GP9, IgG-GP11, and IgG-GP19 were associated with type 2 diabetes risk. A score based on these IgG-GPs was associated with a higher diabetes risk in EPIC-Potsdam and independent validation studies (843 total cases, 3,149 total non-cases, pooled estimate per SD increase 1.50 [95% CI 1.37–1.64]). Associations of IgG-GPs with CVD risk differed between men and women. In women, IgG-GP9 was inversely associated with CVD risk (hazard ratio [HR] per SD 0.80 [95% CI 0.65–0.98]). In men, a weighted score based on IgG-GP19 and IgG-GP23 was associated with higher CVD risk (HR per SD 1.47 [95% CI 1.20–1.80]). In addition, several derived traits were associated with cardiometabolic disease incidence. CONCLUSIONS Selected IgG N-glycans are associated with cardiometabolic risk beyond classic risk factors, including clinical biomarkers.

Aims/hypothesis Individual variation in plasma N-glycosylation has mainly been studied in the context of diabetes complications, and its role in type 1 diabetes onset is largely unknown. Our aims were to undertake a detailed characterisation of the plasma and IgG N-glycomes in patients with recent onset type 1 diabetes, and to evaluate their discriminative potential in risk assessment. Methods In the first part of the study, plasma and IgG N-glycans were chromatographically analysed in a study population from the DanDiabKids registry, comprising 1917 children and adolescents (0.6–19.1 years) who were newly diagnosed with type 1 diabetes. A follow-up study compared the results for 188 of these participants with those for their 244 unaffected siblings. Correlation of N-glycan abundance with the levels and number of various autoantibodies (against IA-2, GAD, ZnT8R, ZnT8W), as well as with sex and age at diagnosis, were estimated by using general linear modelling. A disease predictive model was built using logistic mixed-model elastic net regression, and evaluated using a 10-fold cross-validation. Results Our study showed that onset of type 1 diabetes was associated with an increase in the proportion of plasma and IgG high-mannose and bisecting GlcNAc structures, a decrease in monogalactosylation, and an increase in IgG disialylation. ZnT8R autoantibody levels were associated with higher IgG digalactosylated glycan with bisecting GlcNAc. Finally, an increase in the number of autoantibodies (which is a better predictor of progression to overt diabetes than the level of any individual antibody) was accompanied by a decrease in the proportions of some of the highly branched plasma N-glycans. Models including age, sex and N-glycans yielded notable discriminative power between children with type 1 diabetes and their healthy siblings, with AUCs of 0.915 and 0.869 for addition of plasma and IgG N-glycans, respectively. Conclusions/interpretation We defined N-glycan changes accompanying onset of type 1 diabetes, and developed a predictive model based on N-glycan profiles that could have valuable potential in risk assessment. Increasing the power of tests to identify individuals at risk of disease development would be a considerable asset for type 1 diabetes prevention trials. Graphical abstract

Human protein glycosylation is a complex process, and its in vivo regulation is poorly understood. Changes in glycosylation patterns are associated with many human diseases and conditions. Understanding the biological determinants of protein glycome provides a basis for future diagnostic and therapeutic applications. Genome-wide association studies (GWAS) allow to study biology via a hypothesis-free search of loci and genetic variants associated with a trait of interest. Sixteen loci were identified by three previous GWAS of human plasma proteome N-glycosylation. However, the possibility that some of these loci are false positives needs to be eliminated by replication studies, which have been limited so far. Here, we use the largest set of samples so far (4802 individuals) to replicate the previously identified loci. For all but one locus, the expected replication power exceeded 95%. Of the 16 loci reported previously, 15 were replicated in our study. For the remaining locus (near the KREMEN1 gene), the replication power was low, and hence, replication results were inconclusive. The very high replication rate highlights the general robustness of the GWAS findings as well as the high standards adopted by the community that studies genetic regulation of protein glycosylation. The 15 replicated loci present a good target for further functional studies. Among these, eight loci contain genes encoding glycosyltransferases: MGAT5, B3GAT1, FUT8, FUT6, ST6GAL1, B4GALT1, ST3GAL4 and MGAT3. The remaining seven loci offer starting points for further functional follow-up investigation into molecules and mechanisms that regulate human protein N-glycosylation in vivo.