Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study
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 T...
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| Published in | Frontiers in endocrinology (Lausanne) Vol. 15; p. 1350796 |
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| Main Authors | , , , , , , , , , , , , , , , , , |
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| Language | English |
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06.03.2024
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| Abstract | 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. |
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| AbstractList | 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. 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.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.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.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.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.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.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.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. 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. |
| Author | Gerl, Mathias J. Hart, Leen M. ‘t Klose, Christian Pearson, Ewan R. Li, Shiying Elders, Petra J. M. Tran, Van Du T. Dragan, Iulian Slieker, Roderick C. Mehl, Florence Rutter, Guy A. Kuznetsov, Dmitry Hansen, Michael K. Simons, Kai Fung, Chun Ho Donnelly, Louise A. Beulens, Joline W. J. Ibberson, Mark |
| AuthorAffiliation | 2 Vital-IT Group, SIB Swiss Institute of Bioinformatics , Lausanne , Switzerland 5 Department of Epidemiology and Data Sciences, Amsterdam University Medical Center , Amsterdam , Netherlands 11 Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC–location VUmc , Amsterdam , Netherlands 6 Amsterdam Public Health , Amsterdam , Netherlands 7 Department of Cell and Chemical Biology, Leiden University Medical Center , Leiden , Netherlands 8 Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center , Leiden , Netherlands 3 Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London , London , United Kingdom 10 Lipotype GmbH , Dresden , Germany 9 Division of Population Health & Genomics, School of Medicine, University of Dundee , Dundee , United Kingdom 1 Centre de Recherche du CHUM, Faculty of Medicine, University of Montrea |
| AuthorAffiliation_xml | – name: 3 Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London , London , United Kingdom – name: 9 Division of Population Health & Genomics, School of Medicine, University of Dundee , Dundee , United Kingdom – name: 2 Vital-IT Group, SIB Swiss Institute of Bioinformatics , Lausanne , Switzerland – name: 4 Janssen Research and Development , Philadelphia, PA , United States – name: 6 Amsterdam Public Health , Amsterdam , Netherlands – name: 5 Department of Epidemiology and Data Sciences, Amsterdam University Medical Center , Amsterdam , Netherlands – name: 8 Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center , Leiden , Netherlands – name: 10 Lipotype GmbH , Dresden , Germany – name: 7 Department of Cell and Chemical Biology, Leiden University Medical Center , Leiden , Netherlands – name: 12 Lee Kong Chian School of Medicine, Nan Yang Technological University , Singapore , Singapore – name: 1 Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal , Montreal, QC , Canada – name: 11 Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC–location VUmc , Amsterdam , Netherlands |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38510703$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.2337/db20-1281 10.3389/fgene.2019.00020 10.2337/dc13-1995 10.1046/j.1525-1381.1999.99220.x 10.1038/s41591-022-01791-6 10.1038/s41588-018-0241-6 10.1186/1742-7622-10-12 10.1016/j.jacc.2015.12.047 10.1038/s41540-017-0045-9 10.1016/S0950-3293(01)00026-X 10.1038/s41591-022-01790-7 10.1007/s00125-021-05490-8 10.1101/2020.11.17.386813 10.1016/S2213-8587(18)30051-2 10.1038/nmeth.2810 10.21037/atm-20-3962 10.1093/ije/dyx140 10.1136/bmjopen-2016-015599 10.1093/bioinformatics/btq166 10.1007/s00125-022-05732-3 10.2337/diacare.21.12.2191 10.1007/s00125-017-4210-x 10.1093/ije/dyq111 10.1016/S0950-3293(99)00069-5 10.1038/s41467-023-38148-7 10.1093/ije/dyu188 10.1093/ije/dyx180 10.2310/JIM.0b013e3181bca9d2 10.1093/bioinformatics/btx378 |
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| Copyright | Copyright © 2024 Li, Dragan, Tran, Fung, Kuznetsov, Hansen, Beulens, Hart, Slieker, Donnelly, Gerl, Klose, Mehl, Simons, Elders, Pearson, Rutter and Ibberson. Copyright © 2024 Li, Dragan, Tran, Fung, Kuznetsov, Hansen, Beulens, Hart, Slieker, Donnelly, Gerl, Klose, Mehl, Simons, Elders, Pearson, Rutter and Ibberson 2024 Li, Dragan, Tran, Fung, Kuznetsov, Hansen, Beulens, Hart, Slieker, Donnelly, Gerl, Klose, Mehl, Simons, Elders, Pearson, Rutter and Ibberson |
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| Keywords | metabolic syndrome multi-omics glycaemic deterioration type 2 diabetes proteomics lipidomics |
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| Snippet | Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these... IntroductionType 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper... |
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| StartPage | 1350796 |
| SubjectTerms | Diabetes Mellitus, Type 2 - metabolism Endocrinology glycaemic deterioration Humans Insulin Resistance lipidomics metabolic syndrome multi-omics Multiomics Proteomics type 2 diabetes |
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| Title | Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/38510703 https://www.proquest.com/docview/2973105050 https://pubmed.ncbi.nlm.nih.gov/PMC10951062 https://doaj.org/article/8020ce5607d8402da7325136b175bf1e |
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