engleski

Plasma protein N-glycans as emerging biomarkers of cardiometabolic risk

Cardiometabolic diseases represent a major clinical burden, rendering the recognition of at-risk individuals extremely important. In this regard, N-glycans offer a novel perspective. Herein, we evaluated the predictive value of plasma N-glycome for incident type 2 diabetes (T2D) and cardiovascular diseases (CVD), in baseline- healthy participants from EPIC-Potsdam cohort. Endpoint-specific case-cohorts within EPIC- Potsdam consisted of 2, 813 participants including 743 incident cases for T2D and 2, 558 participants including 418 incident cases for CVD. Cross-validated machine learning and non- linear modelling techniques were used to select robust disease predictors and construct weighted prediction scores from all age- adjusted N- glycans. Type 2 diabetes risk score (GST2D) was constructed using six N-glycan residuals, giving a hazard ratio (HR) of 3.73 (95 % CI 3.00-4.63) per standard deviation (SD). Moreover, GST2D successfully predicted incident T2D in internal validation cohort with C-Index of 0.83 (95 % CI 0.78-0.88). Higher GST2D was also significantly associated with increased CVD risk in men (HR per SD 1.23, 95% CI 1.09-1.40), but not in women. Therefore, sex-stratified CVD glycan scores (GSCVD) were built. For men, GSCVD represented a weighted combination of GST2D and two additional glycan residuals, showing a HR of 1.89 (95 % CI 1.57- 2.27) and moderate predictive performance for incident CVD (C-Index 0.66, 95% CI 0.60-0.72). For women, only one N-glycan peak (GP5) was independently associated with incident CVD (HR per SD 0.70, 95% CI 0.57-0.86), with moderate prediction value (C-index 0.64, 95% CI 0.55- 0.73). Moreover, inclusion of the aforementioned glycan predictors improved the accuracy of established risk prediction models for both T2D and CVD. Taken altogether, this research showed that plasma N-glycome profiling generates highly informative predictors of incident cardiometabolic diseases. Glycan-based risk scores have the potential to complement the toolbox of novel biomarkers that are considered for risk stratification in precision prevention approaches.

plasma N-glycome, type 2 diabetes, cardiovascular diseases

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