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In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.

Purpose We aimed to examine the prospective association between manganese, iron, copper, zinc, iodine, selenium, selenoprotein P, free zinc, and their interplay, with incident type 2 diabetes (T2D), cardiovascular disease (CVD) and colorectal cancer (CRC). Methods Serum trace element (TE) concentrations were measured in a case-cohort study embedded within the EPIC-Potsdam cohort, consisting of a random sub-cohort (n = 2500) and incident cases of T2D (n = 705), CVD (n = 414), and CRC (n = 219). TE patterns were investigated using principal component analysis. Cox proportional hazard models were fitted to examine the association between TEs with T2D, CVD and CRC incidence. Results Higher manganese, zinc, iodine and selenium were associated with an increased risk of developing T2D (HR Q5 vs Q1: 1.56, 1.09–2.22; HR per SD, 95% CI 1.18, 1.05–1.33; 1.09, 1.01–1.17; 1.19, 1.06–1.34, respectively). Regarding CVD, manganese, copper and copper-to-zinc ratio were associated with an increased risk (HR per SD, 95% CI 1.13, 1.00–1.29; 1.22, 1.02–1.44; 1.18, 1.02–1.37, respectively). The opposite was observed for higher selenium-to-copper ratio (HR Q5 vs Q1, 95% CI 0.60, 0.39–0.93). Higher copper and zinc were associated with increasing risk of developing CRC (HR per SD, 95% CI 1.29, 1.05–1.59 and 1.14, 1.00–1.30, respectively). Selenium, selenoprotein P and selenium-to-copper-ratio were associated to decreased risk (HR per SD, 95% CI 0.82, 0.69–0.98; 0.81, 0.72–0.93; 0.77, 0.65–0.92, respectively). Two TE patterns were identified: manganese–iron–zinc and copper–iodine–selenium. Conclusion Different TEs were associated with the risk of developing T2D, CVD and CRC. The contrasting associations found for selenium with T2D and CRC point towards differential disease-related pathways.

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.