Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study
IntroductionType 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...
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2024.1350796/full |
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| author | Shiying Li Iulian Dragan Van Du T. Tran Chun Ho Fung Dmitry Kuznetsov Michael K. Hansen Joline W. J. Beulens Joline W. J. Beulens Leen M. ‘t Hart Leen M. ‘t Hart Leen M. ‘t Hart 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 Guy A. Rutter Guy A. Rutter Mark Ibberson |
| author_facet | Shiying Li Iulian Dragan Van Du T. Tran Chun Ho Fung Dmitry Kuznetsov Michael K. Hansen Joline W. J. Beulens Joline W. J. Beulens Leen M. ‘t Hart Leen M. ‘t Hart Leen M. ‘t Hart 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 Guy A. Rutter Guy A. Rutter Mark Ibberson |
| author_sort | Shiying Li |
| collection | DOAJ |
| description | IntroductionType 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.MethodsCirculating 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.ResultsFrom 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.ConclusionsUsing 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. |
| first_indexed | 2024-03-07T14:31:07Z |
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| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-03-07T14:31:07Z |
| publishDate | 2024-03-01 |
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| series | Frontiers in Endocrinology |
| spelling | doaj.art-8020ce5607d8402da7325136b175bf1e2024-03-06T05:04:54ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922024-03-011510.3389/fendo.2024.13507961350796Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY StudyShiying Li0Iulian Dragan1Van Du T. Tran2Chun Ho Fung3Dmitry Kuznetsov4Michael K. Hansen5Joline W. J. Beulens6Joline W. J. Beulens7Leen M. ‘t Hart8Leen M. ‘t Hart9Leen M. ‘t Hart10Leen M. ‘t Hart11Roderick C. Slieker12Louise A. Donnelly13Mathias J. Gerl14Christian Klose15Florence Mehl16Kai Simons17Petra J. M. Elders18Ewan R. Pearson19Guy A. Rutter20Guy A. Rutter21Guy A. Rutter22Mark Ibberson23Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, CanadaVital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, SwitzerlandVital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, SwitzerlandSection of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United KingdomVital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, SwitzerlandJanssen Research and Development, Philadelphia, PA, United StatesDepartment of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, NetherlandsAmsterdam Public Health, Amsterdam, NetherlandsDepartment of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, NetherlandsAmsterdam Public Health, Amsterdam, NetherlandsDepartment of Cell and Chemical Biology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Cell and Chemical Biology, Leiden University Medical Center, Leiden, NetherlandsDivision of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom0Lipotype GmbH, Dresden, Germany0Lipotype GmbH, Dresden, GermanyVital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland0Lipotype GmbH, Dresden, Germany1Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC–location VUmc, Amsterdam, NetherlandsDivision of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United KingdomCentre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, CanadaSection of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom2Lee Kong Chian School of Medicine, Nan Yang Technological University, Singapore, SingaporeVital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, SwitzerlandIntroductionType 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.MethodsCirculating 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.ResultsFrom 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.ConclusionsUsing 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.https://www.frontiersin.org/articles/10.3389/fendo.2024.1350796/fullmulti-omicstype 2 diabetesglycaemic deteriorationmetabolic syndromelipidomicsproteomics |
| spellingShingle | Shiying Li Iulian Dragan Van Du T. Tran Chun Ho Fung Dmitry Kuznetsov Michael K. Hansen Joline W. J. Beulens Joline W. J. Beulens Leen M. ‘t Hart Leen M. ‘t Hart Leen M. ‘t Hart 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 Guy A. Rutter Guy A. Rutter Mark Ibberson Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study Frontiers in Endocrinology multi-omics type 2 diabetes glycaemic deterioration metabolic syndrome lipidomics proteomics |
| title | Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study |
| title_full | Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study |
| title_fullStr | Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study |
| title_full_unstemmed | Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study |
| title_short | Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study |
| title_sort | multi omics subgroups associated with glycaemic deterioration in type 2 diabetes an imi rhapsody study |
| topic | multi-omics type 2 diabetes glycaemic deterioration metabolic syndrome lipidomics proteomics |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2024.1350796/full |
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