Association of IgG N-glycomics with prevalent and incident type 2 diabetes mellitus from the paradigm of predictive, preventive, and personalized medicine standpoint.

IF 6.5 2区 医学 Q1 Medicine Epma Journal Pub Date : 2022-12-24 eCollection Date: 2023-03-01 DOI:10.1007/s13167-022-00311-3
Xiaoni Meng, Fei Wang, Xiangyang Gao, Biyan Wang, Xizhu Xu, Youxin Wang, Wei Wang, Qiang Zeng
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Abstract

Objectives: Type 2 diabetes mellitus (T2DM), a major metabolic disorder, is expanding at a rapidly rising worldwide prevalence and has emerged as one of the most common chronic diseases. Suboptimal health status (SHS) is considered a reversible intermediate state between health and diagnosable disease. We hypothesized that the time frame between the onset of SHS and the clinical manifestation of T2DM is the operational area for the application of reliable risk assessment tools, such as immunoglobulin G (IgG) N-glycans. From the viewpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS and dynamic monitoring by glycan biomarkers could provide a window of opportunity for targeted prevention and personalized treatment of T2DM.

Methods: Case-control and nested case-control studies were performed and consisted of 138 and 308 participants, respectively. The IgG N-glycan profiles of all plasma samples were detected by an ultra-performance liquid chromatography instrument.

Results: After adjustment for confounders, 22, five, and three IgG N-glycan traits were significantly associated with T2DM in the case-control setting, baseline SHS, and baseline optimal health participants from the nested case-control setting, respectively. Adding the IgG N-glycans to the clinical trait models, the average area under the receiver operating characteristic curves (AUCs) of the combined models based on repeated 400 times fivefold cross-validation differentiating T2DM from healthy individuals were 0.807 in the case-control setting and 0.563, 0.645, and 0.604 in the pooled samples, baseline SHS, and baseline optimal health samples of nested case-control setting, respectively, which presented moderate discriminative ability and were generally better than models with either glycans or clinical features alone.

Conclusions: This study comprehensively illustrated that the observed altered IgG N-glycosylation, i.e., decreased galactosylation and fucosylation/sialylation without bisecting GlcNAc, as well as increased galactosylation and fucosylation/sialylation with bisecting GlcNAc, reflects a pro-inflammatory state of T2DM. SHS is an important window period of early intervention for individuals at risk for T2DM; glycomic biosignatures as dynamic biomarkers have the ability to identify populations at risk for T2DM early, and the combination of evidence could provide suggestive ideas and valuable insight for the PPPM of T2DM.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00311-3.

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从预测、预防和个性化医学的角度看 IgG N-聚类与 2 型糖尿病流行和发病的关系。
目的:2 型糖尿病(T2DM)是一种主要的代谢性疾病,在全球的发病率迅速上升,已成为最常见的慢性疾病之一。亚健康状态(SHS)被认为是介于健康和可诊断疾病之间的一种可逆的中间状态。我们假设,从亚健康状态开始到 T2DM 临床表现之间的时间段是应用可靠的风险评估工具(如免疫球蛋白 G (IgG) N-糖)的操作区域。从预测性、预防性和个性化医学(PPPM/3PM)的角度来看,通过糖类生物标记物早期检测SHS并进行动态监测,可为T2DM的针对性预防和个性化治疗提供机会之窗:方法:分别对 138 名和 308 名参与者进行了病例对照和巢式病例对照研究。所有血浆样本的 IgG N-糖图谱均由超高效液相色谱仪检测:结果:在对混杂因素进行调整后,在病例对照环境、基线SHS和巢式病例对照环境中的基线最佳健康参与者中,分别有22个、5个和3个IgG N-糖特征与T2DM显著相关。将 IgG N-糖加入临床特质模型后,基于重复 400 次五倍交叉验证的综合模型的接收器操作特征曲线下的平均面积(AUC)在病例对照环境中为 0.807,在巢式病例对照环境中为 0.563、0.645和0.604,表现出中等程度的区分能力,总体上优于单独使用糖类或临床特征的模型:本研究全面说明了所观察到的 IgG N-糖基化的改变,即半乳糖基化和岩藻糖基化/糖基化的减少(不含双截面 GlcNAc),以及半乳糖基化和岩藻糖基化/糖基化的增加(含双截面 GlcNAc),反映了 T2DM 的促炎状态。SHS是对T2DM高危人群进行早期干预的重要窗口期;作为动态生物标志物的糖生物特征具有早期识别T2DM高危人群的能力,这些证据的结合可为T2DM的PPPM提供提示性思路和有价值的见解:在线版本包含补充材料,可查阅 10.1007/s13167-022-00311-3。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Epma Journal
Epma Journal Medicine-Biochemistry (medical)
CiteScore
11.30
自引率
23.10%
发文量
0
期刊介绍: PMA Journal is a journal of predictive, preventive and personalized medicine (PPPM). The journal provides expert viewpoints and research on medical innovations and advanced healthcare using predictive diagnostics, targeted preventive measures and personalized patient treatments. The journal is indexed by PubMed, Embase and Scopus.
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