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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| Vydané v: | Diabetes care Ročník 43; číslo 3; s. 661 |
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| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
01.03.2020
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| ISSN: | 1935-5548, 1935-5548 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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).OBJECTIVEPlasma 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).Based on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n = 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 = 820; median follow-up time 6.5 years) and CVD (n = 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.RESEARCH DESIGN AND METHODSBased on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n = 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 = 820; median follow-up time 6.5 years) and CVD (n = 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.The 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 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 N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD.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 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 N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD.Selected 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.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. |
| Author | Schulze, Matthias B Vučković, Frano Wittenbecher, Clemens Kuxhaus, Olga Schiborn, Catarina Gornik, Olga Štambuk, Tamara Rahelić, Dario Štambuk, Jerko Dietrich, Stefan Perola, Markus Boeing, Heiner Rudman, Najda Lauc, Gordan |
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-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational... Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational... |
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| SubjectTerms | 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 |
| Title | Plasma N -Glycans as Emerging Biomarkers of Cardiometabolic Risk: A Prospective Investigation in the EPIC-Potsdam Cohort Study |
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