Author Identifiers

ORCID: 0000-0002-2540-7902

Date of Award

5-20-2019

Degree Type

Thesis

Degree Name

Doctor of Philosophy

School

School of Medical and Health Sciences

First Advisor

Professor Wei Wang

Second Advisor

Dr Angus Stewart

Field of Research Code

1101, 1117

Abstract

Objective

Type 2 diabetes (T2DM) is a complicated and comprehensive disease that has many metabolic facets with its prevalence increasing with age. The inflammation hypothesis of T2DM was proposed at the American Diabetes Association’s 65th scientific session and subsequently validated in many studies. Immunoglobulin G (IgG) is the main component of antibody formation after a secondary immune response and therefore plays an important role in the inflammation cascade and in the occurrence and development of age-related diseases. N-glycosylation, an important post-translation modifying process, significantly affects the function of IgG molecules, including complement activation, receptor binding properties, molecular recognition and aggregation. As T2DM is a low-grade chronic inflammatory disease and IgG N-glycosylation involves in the inflammation cascade, we hypothesised that IgG N-glycans could be involved in pathophysiological process of T2DM. To provide clues to the molecular mechanisms, and screen intervention targets and search for early diagnosis marker of T2DM, we conducted an observational study analyzing the correlation between IgG N-glycans and aging, and age-related disease, T2DM.

Methods

This was an observational study with a longitudinal design. A community-based cohort in Beijing Xicheng district was established in 2012, and 701 participants aged from 23 to 68 (244 males and 457 females) were recruited. Demographic information and 39 clinical traits (7 anthropometric, 10 biochemical and 22 hematological) were collected and blood was sampled for plasma IgG N-glycans analysis using Ultra performance liquid chromatography (UPLC). From the glycan testing platform, 22 IgG N-glycan chromatographic peaks were detected and then 22 basic glycan traits (GP1-2, GP4-19, GP21-24) were calculated from the relative contributions of individual peaks to the total IgG N-glyans. Based on the 22 basic glycan traits, 54 derived glycan traits (17 derived basic glycan traits, 14 neutral glycan traits, 23 derived neutral glycan traits) were obtained for describing glycosylation character in details.

In 2014, a sample of 516 participants (160 males and 356 females), aged from 26 to 66 were re-investigated in a follow-up survey and the same clinical traits were collected. All the statistical analyses were performed with SPSS 23.0 and R programming language software. All reported P values were 2-sided and PN-glycans and level of fasting plasma glucose (FPG). Canonical correlation analysis (CAA) was used to determine the integrate correlation between IgG N-glycans and clinical traits. The potential of IgG N-glycans to be a biomarker of T2DM was analysed by receiver-operating characteristic (ROC) curve and decision tree methods. The relationship between IgG N-glycan traits and age was assessed by all-subsets regression, binominal regression and permutation test.

Results

1. Screening IgG N-Glycans for type 2 diabetes

A sample of 701 participants from the baseline survey was categorised into three groups according to their FPG levels: an NGR (normal glucose regulation) group (FPGN-glycans among the three groups of participants, the low level of galactosylation was found in people with high levels of FPG. The results of Spearman’s rank correlation analyses showed a decreased level of galactosylation with increasing of age. In addition, CCA indicated that IgG N-glycan traits highly correlated with the clinical traits such as age, waist circumference, waist-hip ratio, FPG, triglyceride, creatinine and urea (canonical correlations coefficient=0.662, P

A sample of 516 participants in the 2nd follow-up study was categorised into two groups according to the changes of their FPG (ΔFPG): A ΔFPG>0 group and a ΔFPG≤0 group. After comparison and correlation analysis of IgG N-glycans between the two groups, the low level of fucosylation was found in males with increased level of FPG (P

2. Association between IgG N-glycans and chronological and biological ages

As the correlation between IgG N-glycans and FPG altered with age, and age accounted for the largest canonical loading among risk factors of T2DM, we investigated the patterns of changes in IgG glycan profiles associated with age by analyzing IgG glycosylation in 701 community-based Han Chinese. Nineteen IgG N-glycan traits, including glycan peaks (GP) GP1, 2, 4-15, 17-19, 21, 23, change considerably with age and specific combinations of these glycan features, named “glycan age”, can explain 23.3% to 45.4% of the variance in chronological age in this population. In addition, the clinical traits (FPG and aspartate aminotransferase) which associated with biological age were strongly correlated with the “glycan age”.

Conclusions

1. IgG N-glycans showed significant variability in a Chinese Han population and significantly correlated with the level and the changes of FPG and other clinical traits associated with T2DM. The decision tree analysis could be appropriate in building a diagnostic model based on the IgG N-Glycan profiles to distinguish people with different FPG levels. Therefore, IgG N-glycosylation could be a potential early diagnostic biomarker of T2DM.

2. The study showed that most of IgG glycans were significantly related with chronological age in Han Chinese population and we investigated the extensive patterns of changes in IgG N-glycans with chronological age. The age predictive models consisted of 12 glycan structures and explained 23.2% to 45.4% of variance in chronological age. The remaining variance in these glycans was associated with the clinical traits related to biological age. Therefore, IgG glycosylation seems to correlate with both chronological and biological ages and thus contributes to the aging process. Models that can indicate biological age are of significant interest for prevention, diagnosis, and monitoring of aging and age-related diseases.

Available for download on Thursday, May 23, 2024

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