Plasma N -Glycans as Emerging Biomarkers of Cardiometabolic Risk: A Prospective Investigation in the EPIC-Potsdam Cohort Study

Plasma protein -glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma -glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e.,...

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Published in:Diabetes care Vol. 43; no. 3; p. 661
Main Authors: Wittenbecher, Clemens, Štambuk, Tamara, Kuxhaus, Olga, Rudman, Najda, Vučković, Frano, Štambuk, Jerko, Schiborn, Catarina, Rahelić, Dario, Dietrich, Stefan, Gornik, Olga, Perola, Markus, Boeing, Heiner, Schulze, Matthias B, Lauc, Gordan
Format: Journal Article
Language:English
Published: United States 01.03.2020
Subjects:
Adult
Aged
Biomarkers - blood
Cardiovascular Diseases - blood
Cardiovascular Diseases - diagnosis
Cardiovascular Diseases - epidemiology
Cardiovascular Diseases - etiology
Cohort Studies
Diabetes Mellitus, Type 2 - blood
Diabetes Mellitus, Type 2 - diagnosis
Diabetes Mellitus, Type 2 - epidemiology
Diabetes Mellitus, Type 2 - etiology
Female
Finland - epidemiology
Glycosylation
Humans
Incidence
Male
Middle Aged
Myocardial Infarction - blood
Myocardial Infarction - epidemiology
Myocardial Infarction - etiology
Polysaccharides - blood
Prognosis
Prospective Studies
Risk Factors
Stroke - blood
Stroke - epidemiology
Stroke - etiology
Finland
ISSN:1935-5548, 1935-5548
Online Access:Get more information
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Summary:Plasma protein -glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma -glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke). Based on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort ( = 27,548), we constructed case-cohorts including a random subsample of 2,500 participants and all physician-verified incident cases of type 2 diabetes ( = 820; median follow-up time 6.5 years) and CVD ( = 508; median follow-up time 8.2 years). Information on the relative abundance of 39 -glycan groups in baseline plasma samples was generated by chromatographic profiling. We selected predictive -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. The -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. -glycans were moderately predictive for CVD incidence (weighted C-indices 0.66, 95% CI 0.60-0.72, for men; 0.64, 95% CI 0.55-0.73, for women). Information on the selected -glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD. Selected -glycans improve type 2 diabetes and CVD prediction beyond established risk markers. Plasma protein -glycan profiling may thus be useful for risk stratification in the context of precisely targeted primary prevention of cardiometabolic diseases.
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ISSN:1935-5548
1935-5548
DOI:10.2337/dc19-1507