Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Front Endocrinol (Lausanne). 2024 Mar 6;15:1350796. doi: 10.3389/fendo.2024.1350796. eCollection 2024.
Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma.
Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.
From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, =3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.
Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.
2 型糖尿病（T2D）的发病、进展和结局在个体之间存在显著差异。多组学分析可能有助于更深入地了解这些差异，并最终促进个体化治疗。在这里，我们采用一种无监督的“自下而上”方法，仅根据来自血浆的组学数据尝试对 T2D 患者进行分组。
使用相似网络融合（Similarity Network Fusion）分析来自两个独立临床队列（Hoorn 糖尿病护理系统（DCS）和苏格兰泰赛德遗传学糖尿病审计和研究（GoDARTS））的循环血浆脂质组学和蛋白质组学数据。使用逻辑回归和 Cox 回归模型分析所得患者网络，以探讨血浆组学特征与临床特征之间的关系。
在来自两个队列的总共 1134 名受试者中，使用 180 种循环血浆脂质和 1195 种蛋白质水平将患者分为两组。这两组在血糖恶化方面存在差异（危险比=0.56；0.73），胰岛素敏感性和分泌（C 肽，DCS 和 GoDARTS 分别为 3.7e-11；2.5e-06；稳态模型评估 2（HOMA2）-B；-IR；-S，p=0.0008；4.2e-11；1.1e-09，仅在 DCS 中）。分离两组的主要分子特征包括三酰甘油、神经鞘磷脂、testican-1 和白细胞介素 18 受体。
使用无监督的基于网络的融合方法对来自两个独立队列的血浆脂质组学和蛋白质组学数据进行分析，我们能够识别出 T2D 患者在疾病严重程度方面存在差异的两个亚组。在这些亚组中鉴定的分子特征为 T2D 疾病机制提供了新的见解，并可能为其提供新的预后标志物。
