Clemens Wittenbecher1,2,3, Tamara Štambuk4, Olga Kuxhaus1,3, Najda Rudman4, Frano Vučković5, Jerko Štambuk5, Catarina Schiborn1,3, Dario Rahelić6, Stefan Dietrich1, Olga Gornik4,5, Markus Perola7,8, Heiner Boeing1, Matthias B Schulze9,3,10, Gordan Lauc4,5. 1. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. 2. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA. 3. German Center for Diabetes Research (DZD), München-Neuherberg, Germany. 4. Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia. 5. Genos Glycoscience Research Laboratory, Zagreb, Croatia. 6. University Clinics for Diabetes, Endocrinology and Metabolism, School of Medicine, University of Zagreb, Zagreb, Croatia. 7. National Institute for Health and Welfare, Helsinki, Finland. 8. Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 9. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany mschulze@dife.de. 10. Institute of Nutritional Sciences, University of Potsdam, Nuthetal, Germany.
Abstract
OBJECTIVE: 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 METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: 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 METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Eric K Broni; Chiadi E Ndumele; Justin B Echouffo-Tcheugui; Rita R Kalyani; Wendy L Bennett; Erin D Michos Journal: Curr Diab Rep Date: 2022-02-14 Impact factor: 4.810
Authors: Anika Mijakovac; Julija Jurić; Wendy M Kohrt; Jasminka Krištić; Domagoj Kifer; Kathleen M Gavin; Karlo Miškec; Azra Frkatović; Frano Vučković; Marija Pezer; Aleksandar Vojta; Peter A Nigrović; Vlatka Zoldoš; Gordan Lauc Journal: Front Immunol Date: 2021-05-25 Impact factor: 8.786
Authors: Domagoj Kifer; Panayiotis Louca; Ana Cvetko; Helena Deriš; Ana Cindrić; Harald Grallert; Annette Peters; Ozren Polašek; Olga Gornik; Massimo Mangino; Tim D Spector; Ana M Valdes; Sandosh Padmanabhan; Christian Gieger; Gordan Lauc; Cristina Menni Journal: J Hypertens Date: 2021-12-01 Impact factor: 4.776