2019
DOI: 10.1089/omi.2019.0052
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Glycomics for Type 2 Diabetes Biomarker Discovery: Promise of Immunoglobulin G Subclass-Specific Fragment Crystallizable N-glycosylation in the Uyghur Population

Abstract: 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 … Show more

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Cited by 21 publications
(10 citation statements)
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“…Three types of logistic classification models were applied: (1) The baseline models included age, gender, ethnicity, BMI, and dyslipidemia as the covariates; (2) the glycan-based models included the screened IgG N-glycan traits as the covariates; and (3) the combined models incorporated the screened IgG N-glycan traits on top of the covariates included in the baseline models. The prediction error of the five-fold cross-validation procedure was used to assess the performance of the discriminant models (R package "boot") [32]. The discriminant ability of the models in delineating the HDC from the HTN, T2DM, and healthy individuals was evaluated by receiver operating characteristic (ROC) curve analysis (R package "pROC").…”
Section: Discussionmentioning
confidence: 99%
“…Three types of logistic classification models were applied: (1) The baseline models included age, gender, ethnicity, BMI, and dyslipidemia as the covariates; (2) the glycan-based models included the screened IgG N-glycan traits as the covariates; and (3) the combined models incorporated the screened IgG N-glycan traits on top of the covariates included in the baseline models. The prediction error of the five-fold cross-validation procedure was used to assess the performance of the discriminant models (R package "boot") [32]. The discriminant ability of the models in delineating the HDC from the HTN, T2DM, and healthy individuals was evaluated by receiver operating characteristic (ROC) curve analysis (R package "pROC").…”
Section: Discussionmentioning
confidence: 99%
“…[32] Elevated HbA1c from hyperglycemia is associated with pro-inflammatory glycosylation of IgG Fc in both Type 1 and Type 2 diabetics. [33, 34] Even in non-diabetics HbA1c increases with age[35], and elevated HbA1c predicts a more difficult course of COVID-19. [36]…”
Section: Discussionmentioning
confidence: 99%
“…The focus on IgG is also consistent with the immune facets of both pancreatic cancer and T1D. IgG glycosylation has also been investigated in T2D and DN (Liu et al, 2019;Singh et al, 2020a) Other studies have also focused on other acute phase proteins, including haptoglobin and AGP, as sources of markers for pancreatic diseases (Higai et al, 2003;Nakano et al, 2008;Sarrats et al, 2010;Kontro et al, 2014;Balmaña et al, 2016;Mancera-Arteu et al, 2019;Keser et al, 2021). While the exact mechanisms leading to regulation of these circulating glycoproteins in pancreatic diseases are still unknown, studies have shown their potential as biomarkers, thus warranting further study.…”
Section: Clinical Utility Considerationsmentioning
confidence: 96%
“…IgG glycosylation was investigated in a Uyghur population in China at the IGP level. Analyses identified 27 directly measured and 4 derived glycan traits that were significantly associated with T2D, including decreased bisecting GlcNAc of IgG2 and agalactosylation of IgG4 and increased sialylation of IgG4 and digalactosylation of IgG2 (Liu et al, 2019). AGP glycosylation was investigated in a site-specific and high-throughput manner in T2D plasma at three timepoints.…”
Section: Diabetes Biomarker Discoverymentioning
confidence: 99%