title: Profiling IgG N-glycans as potential biomarker of chronological and biological ages: A community-based study in a Han Chinese population.
abstract: As an important post-translation modifying process, glycosylation significantly affects the structure and function of immunoglobulin G (IgG) molecules and is essential in many steps of the inflammatory cascade. Studies have demonstrated the potential of using glycosylation features of IgG as a component of predictive biomarkers for chronological age in several European populations, whereas no study has been reported in Chinese. Herein, we report various patterns of changes in IgG glycosylation associated with age by analyzing IgG glycosylation in 701 community-based Han Chinese (244 males, 457 females; 23-68 years old). Eleven IgG glycans, including FA2B, A2G1, FA2[6]G1, FA2[3]G1, FA2[6]BG1, FA2[3]BG1, A2G2, A2BG2, FA2G2, FA2G2S1, and FA2G2S2, change considerably with age and specific combinations of these glycan features can explain 23.3% to 45.4% of the variance in chronological age in this population. This indicates that these combinations of glycan features provide more predictive information than other single markers of biological age such as telomere length. In addition, the clinical traits such as fasting plasma glucose and aspartate aminotransferase associated with biological age are strongly correlated with the combined glycan features. We conclude that IgG glycosylation appears to correlate with both chronological and biological ages, and thus its possible role in the aging process merits further study.
authors.affiliation: Beijing Municipal Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia Genos Glycobiology Research Laboratory, Zagreb, Croatia Physical Examination Center, Xuanwu Hospital, Capital Medical University, Beijing, China Centre for Population Health Sciences, Medical School, University of Edinburgh, Edinburgh, UK.
authors.forename: Xinwei
authors.initials: X
authors.lastname: Yu
authors.forename: Youxin
authors.initials: Y
authors.lastname: Wang
authors.forename: Jasminka
authors.initials: J
authors.lastname: Kristic
authors.forename: Jing
authors.initials: J
authors.lastname: Dong
authors.forename: Xi
authors.initials: X
authors.lastname: Chu
authors.forename: Siqi
authors.initials: S
authors.lastname: Ge
authors.forename: Hao
authors.initials: H
authors.lastname: Wang
authors.forename: Honghong
authors.initials: H
authors.lastname: Fang
authors.forename: Qing
authors.initials: Q
authors.lastname: Gao
authors.forename: Di
authors.initials: D
authors.lastname: Liu
authors.forename: Zhongyao
authors.initials: Z
authors.lastname: Zhao
authors.forename: Hongli
authors.initials: H
authors.lastname: Peng
authors.forename: Maja
authors.initials: M
authors.lastname: Pucic Bakovic
authors.forename: Lijuan
authors.initials: L
authors.lastname: Wu
authors.forename: Manshu
authors.initials: M
authors.lastname: Song
authors.forename: Igor
authors.initials: I
authors.lastname: Rudan
authors.forename: Harry
authors.initials: H
authors.lastname: Campbell
authors.forename: Gordan
authors.initials: G
authors.lastname: Lauc
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000378:metabolism; D002681:China / Q000208:ethnology; D003430:Cross-Sectional Studies; D005260:Female; D006031:Glycosylation; D006801:Humans; D007074:Immunoglobulin G / Q000378:metabolism*; D008136:Longevity; D008297:Male; D008875:Middle Aged; D011134:Polysaccharides / Q000378:metabolism*; D006023:Glycoproteins
chemical_list: D015415:Biomarkers; D007074:Immunoglobulin G; D011134:Polysaccharides; C046179:glycosylated IgG; D006023:Glycoproteins