Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
Publ. Version/Full Text
Research data
DOI
PMC
 |
Open Access Gold |
|
as soon as is submitted to ZB.
Longitudinal study reveals plasma glycans associations with prediabetes/type 2 diabetes in KORA study.
Cardiovasc. Diabetol. 24:321 (2025)
BACKGROUND: Altered plasma N-glycosylation is increasingly recognized as a contributor to metabolic dysregulation. This study aimed to investigate the role of plasma N-glycans in glucose metabolism and the progression from normoglycemia to prediabetes and type 2 diabetes (T2D). METHODS: We analyzed longitudinal data from 473 participants in the Cooperative Health Research in the Region of Augsburg (KORA) cohort over 7 years. N-glycan profiles were measured using hydrophilic interaction ultrahigh-performance liquid chromatography with fluorescence detection (HILIC-UHPLC-FLR). Glycan associations with incident prediabetes/T2D and related traits, such as body mass index (BMI), fasting glucose, homeostasis model assessment of insulin resistance (HOMA-IR) were evaluated using longitudinal models based on N-glycan measurements obtained at F4 and FF4. Classification performance at FF4 was assessed using machine learning models interpreted with SHapley Additive exPlanation (SHAP) values. Mendelian randomization (MR) and glycan quantitative trait loci (glycan-QTL) analyses were conducted to explore causality and genetic determinants. RESULTS: During follow-up, 231 individuals progressed to prediabetes/T2D, while 242 remained normoglycemic. Nineteen glycans were associated with diabetes progression in the basic model; 12 remained significant after full adjustment. Glycans such as GP18 and GP32 were also linked to metabolic traits. A glycan-clinical model achieved high classification accuracy (AUC = 0.895). MR supported causal roles for GP18, GP19, and S1. Glycan-QTL analysis revealed SNPs and genes (FUT8, ST3GAL4) are associated with key glycans. CONCLUSIONS: Plasma N-glycans are diagnostic of early glycemic deterioration and supported by genetic and causal evidence, highlighting their potential as biomarkers for diabetes risk stratification.
Altmetric
Additional Metrics?
Edit extra informations
Login
Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Diabetes ; Glycan-qtl ; Glycomics ; Machine Learning ; Plasma N-glycome ; Prediabetes; N-glycan; Risk; Glycosylation; Biomarkers; Glycoprotein; Prediction; Profiles; Obesity; Cancer
ISSN (print) / ISBN
1475-2840
e-ISSN
1475-2840
Journal
Cardiovascular Diabetology
Quellenangaben
Volume: 24,
Issue: 1,
Article Number: 321
Publisher
BioMed Central
Publishing Place
Campus, 4 Crinan St, London N1 9xw, England
Reviewing status
Peer reviewed
Institute(s)
Institute of Epidemiology (EPI)
Grants
University Hospital of Augsburg - European Union
State of Bavaria
German Federal Ministry of Education and Research (BMBF)
Helmholtz Munich-German Research Center for Environmental Health
China Scholarship Council (CSC)
Helmholtz Zentrum Mnchen - Deutsches Forschungszentrum fr Gesundheit und Umwelt (GmbH) (4209)
State of Bavaria
German Federal Ministry of Education and Research (BMBF)
Helmholtz Munich-German Research Center for Environmental Health
China Scholarship Council (CSC)
Helmholtz Zentrum Mnchen - Deutsches Forschungszentrum fr Gesundheit und Umwelt (GmbH) (4209)