Roosmarijn F H Lemmers1, Marija Vilaj2, Daniel Urda3, Felix Agakov3, Mirna Šimurina4, Lucija Klaric5, Igor Rudan6, Harry Campbell6, Caroline Hayward7, Jim F Wilson8, Aloysius G Lieverse9, Olga Gornik4, Eric J G Sijbrands10, Gordan Lauc11, Mandy van Hoek12. 1. Department of Internal Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Internal Medicine, Maxima Medical Center, Eindhoven, The Netherlands. 2. Genos Glycoscience Research Laboratory, Zagreb, Croatia. 3. Pharmatics Limited, Edinburgh, United Kingdom. 4. Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia. 5. Genos Glycoscience Research Laboratory, Zagreb, Croatia; Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia; MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom. 6. Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom. 7. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom. 8. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom; Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom. 9. Department of Internal Medicine, Maxima Medical Center, Eindhoven, The Netherlands. 10. Department of Internal Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands. 11. Genos Glycoscience Research Laboratory, Zagreb, Croatia; Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia. 12. Department of Internal Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands. Electronic address: m.vanhoek@erasmusmc.nl.
Abstract
BACKGROUND: Type 2 diabetes results from interplay between genetic and acquired factors. Glycans on proteins reflect genetic, metabolic and environmental factors. However, associations of IgG glycans with type 2 diabetes have not been described. We compared IgG N-glycan patterns in type 2 diabetes with healthy subjects. METHODS: In the DiaGene study, a population-based case-control study, (1886 cases and 854 controls) 58 IgG glycan traits were analyzed. Findings were replicated in the population-based CROATIA-Korcula-CROATIA-Vis-ORCADES studies (162 cases and 3162 controls), and meta-analyzed. AUCs of ROC-curves were calculated using 10-fold cross-validation for clinical characteristics, IgG glycans and their combination. RESULTS: After correction for extensive clinical covariates, 5 IgG glycans and 13 derived traits significantly associated with type 2 diabetes in meta-analysis (after Bonferroni correction). Adding IgG glycans to age and sex increased the AUC from 0.542 to 0.734. Adding them to the extensive model did not substantially improve the AUC. The AUC for IgG glycans alone was 0.729. CONCLUSIONS: Several IgG glycans and traits firmly associate with type 2 diabetes, reflecting a pro-inflammatory and biologically-aged state. IgG glycans showed limited improvement of AUCs. However, IgG glycans showed good prediction alone, indicating they may capture information of combined covariates. The associations found may yield insights in type 2 diabetes pathophysiology. GENERAL SIGNIFICANCE: This work shows that IgG glycomic changes have biomarker potential and may yield important insights into pathophysiology of complex public health diseases, illustrated here for the first time in type 2 diabetes.
BACKGROUND: Type 2 diabetes results from interplay between genetic and acquired factors. Glycans on proteins reflect genetic, metabolic and environmental factors. However, associations of IgG glycans with type 2 diabetes have not been described. We compared IgG N-glycan patterns in type 2 diabetes with healthy subjects. METHODS: In the DiaGene study, a population-based case-control study, (1886 cases and 854 controls) 58 IgG glycan traits were analyzed. Findings were replicated in the population-based CROATIA-Korcula-CROATIA-Vis-ORCADES studies (162 cases and 3162 controls), and meta-analyzed. AUCs of ROC-curves were calculated using 10-fold cross-validation for clinical characteristics, IgG glycans and their combination. RESULTS: After correction for extensive clinical covariates, 5 IgG glycans and 13 derived traits significantly associated with type 2 diabetes in meta-analysis (after Bonferroni correction). Adding IgG glycans to age and sex increased the AUC from 0.542 to 0.734. Adding them to the extensive model did not substantially improve the AUC. The AUC for IgG glycans alone was 0.729. CONCLUSIONS: Several IgG glycans and traits firmly associate with type 2 diabetes, reflecting a pro-inflammatory and biologically-aged state. IgG glycans showed limited improvement of AUCs. However, IgG glycans showed good prediction alone, indicating they may capture information of combined covariates. The associations found may yield insights in type 2 diabetes pathophysiology. GENERAL SIGNIFICANCE: This work shows that IgG glycomic changes have biomarker potential and may yield important insights into pathophysiology of complex public health diseases, illustrated here for the first time in type 2 diabetes.
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