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

Autor:	Li S; Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada., Dragan I; Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland., Tran VDT; Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland., Fung CH; Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom., Kuznetsov D; Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland., Hansen MK; Janssen Research and Development, Philadelphia, PA, United States., Beulens JWJ; Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands.; Amsterdam Public Health, Amsterdam, Netherlands., Hart LM'; Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands.; Amsterdam Public Health, Amsterdam, Netherlands.; Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands.; Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, Netherlands., Slieker RC; Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands., Donnelly LA; Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom., Gerl MJ; Lipotype GmbH, Dresden, Germany., Klose C; Lipotype GmbH, Dresden, Germany., Mehl F; Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland., Simons K; Lipotype GmbH, Dresden, Germany., Elders PJM; Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC-location VUmc, Amsterdam, Netherlands., Pearson ER; Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom., Rutter GA; Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.; Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom.; Lee Kong Chian School of Medicine, Nan Yang Technological University, Singapore, Singapore., Ibberson M; Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Jazyk:	angličtina
Zdroj:	Frontiers in endocrinology [Front Endocrinol (Lausanne)] 2024 Mar 06; Vol. 15, pp. 1350796. Date of Electronic Publication: 2024 Mar 06 (Print Publication: 2024).
DOI:	10.3389/fendo.2024.1350796
Abstrakt:	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. Competing Interests: GR has received grant funding from, and is a consultant for, Sun Pharmaceuticals Inc. KS is CEO of Lipotype. KS and CK are shareholders of Lipotype. MG is an employee of Lipotype. MH is an employee of Janssen Research & Development. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2024 Li, Dragan, Tran, Fung, Kuznetsov, Hansen, Beulens, Hart, Slieker, Donnelly, Gerl, Klose, Mehl, Simons, Elders, Pearson, Rutter and Ibberson.)
Databáze:	MEDLINE
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