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.