2023, Volume 26, Issue 7
Engineering >> 2023, Volume 26, Issue 7 doi: 10.1016/j.eng.2022.12.004
IgG N-Glycosylation Cardiovascular Age Tracks Cardiovascular Risk Beyond Calendar Age
a Centre for Precision Health, Edith Cowan University, Joondalup, WA 6027, Australia
b Beijing Municipal Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China
c Shanghai Fufan Information Technology Co., Ltd., Shanghai 200433, China
d Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC 3086, Australia
# These authors contributed equally to this work.
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Abstract
The use of an altered immunoglobulin G (IgG) N-glycan pattern as an inflammation metric has been reported in subclinical atherosclerosis and metabolic disorders, both of which are important risk factors in cardiovascular health. However, the usable capacity of IgG N-glycosylation profiles for the risk stratification of cardiovascular diseases (CVDs) remains unknown. This study aimed to develop a cardiovascular aging index for tracking cardiovascular risk using IgG N-glycans. This cross-sectional investigation enrolled 1465 individuals aged 40–70 years from the Busselton Healthy and Ageing Study. We stepwise selected the intersection of altered N-glycans using feature-selection methods in machine learning (recursive feature elimination and penalized regression algorithms) and developed an IgG N-glycosylation cardiovascular age (GlyCage) index to reflect the deviation from calendar age attributable to cardiovascular risk. The strongest contributors to GlyCage index were fucosylated N-glycans with bisecting N-acetylglucosamine (GlcNAc) (glycan peak 6 (GP6), FA2B,) and digalactosylated N-glycans with bisecting GlcNAc (GP13, A2BG2). A one-unit increase of GlyCage was significantly associated with a higher Framingham ten-year cardiovascular risk (odds ratio (OR), 1.09; 95% confidence interval (95% CI): 1.05–1.13) and probability of CVDs (OR, 1.07; 95% CI: 1.01–1.13) independent of calendar age. Individuals with excessive GlyCage (exceeding a calendar age > 3 years) had an increased cardiovascular risk and probability of CVDs, with adjusted ORs of 2.22 (95% CI: 1.41–3.53) and 2.71 (95% CI: 1.25–6.41), respectively. The area under curve (AUC) values of discriminating high cardiovascular risk and events were 0.73 and 0.65 for GlyCage index, and 0.65 and 0.63 for calendar age. The GlyCage index developed in this study can thus be used to track cardiovascular health using IgG N-glycosylation profiles. The distance between GlyCage and calendar age independently indicates the cardiovascular risk, suggesting that IgG N-glycosylation plays a role in the pathogenesis of CVDs. The generalization of the observed associations and the predictive capability of GlyCage index require external and longitudinal validation in other populations.
Keywords
IgG N-glycosylation cardiovascular age ; Cardiovascular aging ; Immunoglobulin G ; Glycosylation ; Inflammation ; Feature selection ; Machine learning
SupplementaryMaterials
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