Type 2 Diabetes Mellitus: Integrative Analysis of Multiomics Data for Biomarker Discovery

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Type 2 diabetes mellitus (T2DM) is a common complex trait arising from interactions among multiple environmental, genomic, and postgenomic factors. We report here the first attempt to investigate the association between immunoglobulin G (IgG) N-glycan patterns, T2DM, and their clinical risk factors in an Australian population. N-glycosylation of proteins is one of the most frequently observed co-and posttranslational modifications, reflecting, importantly, the real-time status of the interplay between the genomic and postgenomic factors. In a community-based case-control study, 849 participants (217 cases and 632 controls) were recruited from an urban community in Busselton, Western Australia. We applied the ultraperformance liquid chromatography method to analyze the composition of IgG N-glycans. We then conducted Spearman's correlation analyses to explore the association between glycan biomarker candidates and clinical risk factors. We performed area under the curve (AUC) analysis of the receiver operating characteristic curves by fivefold cross-validation for clinical risk factors, IgG glycans, and their combination. Two directly measured and four derived glycan peaks were significantly associated with T2DM, after correction for extensive clinical confounders and false discovery rate, thus suggesting that IgG N-glycan traits are highly correlated with T2DM clinical risk factors. Moreover, adding the IgG glycan profiles to fasting blood glucose in the logistic regression model increased the AUC from 0.799 to 0.859. The AUC for IgG glycans alone was 0.623 with a 95% confidence interval 0.580-0.666. In addition, our study provided new evidence of diversity in T2DM complex trait by IgG N-glycan stratification. Six IgG glycan traits were firmly associated with T2DM, which reflects an increased proinflammatory and biological aging status. In summary, our study reports novel associations between the IgG N-glycome and T2DM in an Australian population and the putative role of proinflammatory mechanisms. Furthermore, IgG N-glycomic alterations offer future prospects as inflammatory biomarker candidates for T2DM diagnosis, and monitoring of T2DM progression to cardiovascular disease or renal failure.

Section: Introductionmentioning

confidence: 99%

Type 2 Diabetes Mellitus is Associated with the Immunoglobulin G N-Glycome through Putative Proinflammatory Mechanisms in an Australian Population

Wang

Russell

et al. 2019

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“…Previous glycomics research has shown that IgG Nglycans are associated with the cardiometabolic risk factors, including hypertension (Gao et al, 2017;Liu et al, 2018c;Wang et al, 2016b), T2DM (Ge et al, 2018;Lemmers et al, 2017), central obesity (Russell et al, 2018), and dyslipidemia (Liu et al, 2018a). In this study, we described the association of IgG N-glycans with the 15 cardiometabolic risk factors in a Chinese Han population.…”

Section: Discussionmentioning

confidence: 81%

“…In addition, total plasma N-glycans have been shown to be associated with risk factors of metabolic syndrome (Lu et al, 2011;McLachlan et al, 2016). The inflammatory role of IgG N-glycans, together with their association with the cardiometabolic risk factors, such as aging , central obesity (Russell et al, 2018), dyslipidemia (Liu et al, 2018a), and hyperglycemia (Ge et al, 2018), leads to the hypothesis of this study that the changes to IgG N-glycan profiles are involved in the pathogenesis of cardiometabolic disease by regulating the inflammatory response.…”

Section: Introductionmentioning

confidence: 94%

“…N-glycans attach to the conserved asparagine 297 of both heavy chains in the fragment crystallizable region of IgG and act as a switch between proinflammatory and anti-inflammatory responses of IgG (Gornik et al, 2012;Jefferis, 2009). Changes to the IgG Nglycan profiles are involved in several inflammatory diseases, such as hypertension (Gao et al, 2017;Liu et al, 2018c;Wang et al, 2016b), stroke (Liu et al, 2018b), T2DM (Ge et al, 2018;Lemmers et al, 2017), Parkinson's disease , and rheumatoid arthritis (Sebastian et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Next-Generation (Glycomic) Biomarkers for Cardiometabolic Health: A Community-Based Study of Immunoglobulin G N-Glycans in a Chinese Han Population

Wang

et al. 2019

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Cardiovascular disease is a common complex trait that calls for next-generation biomarkers for precision diagnostics and therapeutics. The most common type of post-translational protein modification involves glycosylation. Glycans participate in key intercellular and intracellular functions, such as protein quality control, cell adhesion, cell-cell recognition, signal transduction, cell proliferation, and cell differentiation. In this context, immunoglobulin G (IgG) N-glycans affect the anti-inflammatory and proinflammatory responses of IgG, and are associated with cardiometabolic risk factors such as aging, central obesity, dyslipidemia, and hyperglycemia. Yet, the role of such glycomic biomarkers requires evaluation in diverse world populations. We report here original observations on association of IgG N-glycan biosignatures with 15 cardiometabolic risk factors in a community-based cross-sectional study conducted in 701 Chinese Han participants. After controlling for age and sex, we found that the 16, 21, and 18 IgG N-glycan traits were significantly different in participants with and without metabolic syndrome, hypertriglyceridemic waist phenotype, or abdominal obesity, respectively. The canonical correlation analysis showed that IgG N-glycan profiles were significantly associated with cardiometabolic risk factors (r = 0.469, p < 0.001). Classification models based on IgG N-glycan traits were able to differentiate participants with (1) metabolic syndrome, (2) hypertriglyceridemic waist phenotype, or (3) abdominal obesity from controls, with an area under receiver operating characteristic curves (AUC) of 0.632 (95% confidence interval [CI], 0.574-0.691, p < 0.001), 0.659 (95% CI, 0.587-0.730, p < 0.001), and 0.610 (95% CI, 0.565-0.656, p < 0.001), respectively. These new data suggest that IgG N-glycans may play an important role in cardiometabolic disease pathogenesis by regulating the proinflammatory or anti-inflammatory responses of IgG. Looking into the future, IgG N-glycan biosignatures warrant further research in other world population samples with a view to applications in clinical cardiology and public health practice.

“…The integration of glycomics with other system science biomarkers might offer further hope for future innovation in diagnosis and treatment of T2DM (Ge et al, 2018), a disease with broad public health importance worldwide. Finally, it is noteworthy that ''Population Glycomics'' is an emerging approach to biomarker discovery for common complex diseases.…”

Section: Discussionmentioning

confidence: 99%

Glycomics for Type 2 Diabetes Biomarker Discovery: Promise of Immunoglobulin G Subclass-Specific Fragment Crystallizable N-glycosylation in the Uyghur Population

Liu

Mamatyusupu

Štambuk

et al. 2019

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Aberrant immunoglobulin G (IgG) N-glycosylation offers new prospects to detect changes in cell metabolism and by extension, for biomarker discovery in type 2 diabetes mellitus (T2DM). However, past studies did not analyze the individual IgG subclasses in relation to T2DM pathophysiology. We report here original findings through a comparison of the IgG subclass-specific fragment crystallizable (Fc) glycan biosignatures in 115 T2DM patients with 122 healthy controls within the Uyghur population in China. IgG Fc glycosylation profiles were analyzed using nano-liquid chromatography-mass spectrometry to exclude changes attributed to fragment antigen binding N-glycosylation. After correction for clinical covariates, 27 directly measured and 4 derived glycan traits of the IgG subclass-specific N-glycopeptides were significantly associated with T2DM. Furthermore, we observed in T2DM a decrease in bisecting N-acetylglucosamine of IgG2 and agalactosylation of IgG4, and an increase in sialylation of IgG4 and digalactosylation of IgG2. Classification model based on IgG subclass-specific N-glycan traits was able to distinguish patients with T2DM from controls with an area under the receiver operating characteristic curve of 0.927 (95% confidence interval 0.894-0.960, p < 0.001). In conclusion, a robust association between the IgG subclass-specific Fc N-glycomes and T2DM was observed in the Uyghur population sample in China, suggesting a potential for the IgG Fc glycosylation as a biomarker candidate for type 2 diabetes. The integration of glycomics with other system science biomarkers might offer further hope for innovation in diagnosis and treatment of T2DM in the future. Finally, it is noteworthy that ''Population Glycomics'' is an emerging approach to biomarker discovery for common complex diseases.