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2 型糖尿病:多组学数据的综合分析用于生物标志物发现。

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

机构信息

1 Beijing Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University , Beijing, China .

2 Department of Public Health, School of Medical and Health Sciences, Edith Cowan University , Perth, Australia .

出版信息

OMICS. 2018 Jul;22(7):514-523. doi: 10.1089/omi.2018.0053.

Abstract

Increased fasting plasma glucose (FPG) is an independent risk factor for type 2 diabetes mellitus (T2DM). The development of systems biology technologies for integration of multiomics data is crucial for predicting increased FPG levels. In this case-control study, immunoglobulin (Ig) G glycosylation profiling and genome-wide association analyses were performed on 511 participants, and among them 76 had increased FPG (aged 47.6 ± 6.14 years), and 435 had decreased or fluctuant FPG (aged 47.9 ± 6.08 years). We identified nine single nucleotide polymorphisms (SNPs) in five genes (RPL7AP27, SNX30, SLC39A12, BACE2, and IGFL2) that were significantly associated with increased FPG (odds ratios 1.937-2.393). Moreover, of the 24 glycan peaks (GPs), GPs 3, 8, and 11 presented positive trends with increased FPG levels, whereas GPs 4 and 14 presented negative trends. A significant improvement of predictive power was observed when adding 24 IgG GPs to 9 SNPs with the area under the curve increased from 0.75 to 0.81. This report shows that the combination of candidate SNPs with IgG glycomics offers biomarker potentials for T2DM. The substantial predictive power obtained from integrating genomics and glycomics biomarkers suggests the feasibility of applying such multiomics strategies to enable predictive, preventive, and personalized medicine for T2DM.

摘要

空腹血糖升高(FPG)是 2 型糖尿病(T2DM)的独立危险因素。整合多组学数据的系统生物学技术的发展对于预测 FPG 升高水平至关重要。在这项病例对照研究中,对 511 名参与者进行了 IgG 糖基化谱分析和全基因组关联分析,其中 76 名参与者 FPG 升高(年龄 47.6±6.14 岁),435 名参与者 FPG 降低或波动(年龄 47.9±6.08 岁)。我们在五个基因(RPL7AP27、SNX30、SLC39A12、BACE2 和 IGFL2)中鉴定出九个与 FPG 升高显著相关的单核苷酸多态性(SNP)(比值比 1.937-2.393)。此外,在 24 个糖基化峰(GP)中,GP3、GP8 和 GP11 与 FPG 水平升高呈正相关趋势,而 GP4 和 GP14 呈负相关趋势。当将 24 个 IgG GPs 添加到 9 个 SNP 中时,预测能力显著提高,曲线下面积从 0.75 增加到 0.81。本报告表明,候选 SNP 与 IgG 糖组学的结合为 T2DM 提供了生物标志物潜力。整合基因组学和糖组学生物标志物获得的强大预测能力表明,应用这种多组学策略来实现 T2DM 的预测、预防和个性化医疗是可行的。

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