Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study

Shiying Li, Iulian Dragan, Van Du T. Tran, Chun Ho Fung, Dmitry Kuznetsov, Michael K. Hansen, Joline W. J. Beulens, Leen M. 't Hart, Roderick C. Slieker, Louise A. Donnelly, Mathias J. Gerl, Christian Klose, Florence Mehl, Kai Simons, Petra J. M. Elders, Ewan R. Pearson, Guy A. Rutter (Lead / Corresponding author), Mark Ibberson (Lead / Corresponding author)

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Abstract

Introduction: 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. Methods: 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. Results: 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, p=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. Conclusions: 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.

Original languageEnglish
Article number1350796
Number of pages9
JournalFrontiers in endocrinology
Volume15
DOIs
Publication statusPublished - 6 Mar 2024

Keywords

  • glycaemic deterioration
  • lipidomics
  • metabolic syndrome
  • multi-omics
  • proteomics
  • type 2 diabetes

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism

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