| Affiliation: | 1.Research Unit of Molecular Epidemiology, Helmholtz Zentrum München,German Research Center for Environmental Health,Neuherberg,Germany;2.Institute of Epidemiology II, Helmholtz Zentrum München,German Research Center for Environmental Health,Neuherberg,Germany;3.German Center for Diabetes Research (DZD),München-Neuherberg,Germany;4.Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health,Neuherberg,Germany;5.Department of Internal Medicine–Diabetes Center,VU University Medical Center,Amsterdam,the Netherlands;6.Department of Biological Psychology,Vrije Universiteit,Amsterdam,the Netherlands;7.Department of Epidemiology,German Institute of Human Nutrition Potsdam-Rehbruecke,Nuthetal,Germany;8.Department of Molecular Epidemiology,Leiden University Medical Center,Leiden,the Netherlands;9.Max Planck Institute for Biology of Ageing,Cologne,Germany;10.Institute of Diabetes Research, Helmholtz Zentrum München,German Research Center for Environmental Health,Neuherberg,Germany;11.Forschergruppe Diabetes, Klinikum rechts der Isar,Technische Universit?t München,Neuherberg,Germany;12.Department of Molecular Cell Biology,Leiden University Medical Center,Leiden,the Netherlands;13.Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences,University of Copenhagen,Copenhagen,Denmark;14.Department of Biophysics and Physiology,Weill Cornell Medical College in Qatar,Doha,Qatar;15.Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München,German Research Center for Environmental Health,Neuherberg,Germany;16.Institute for Clinical Diabetology, German Diabetes Center,Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf,Düsseldorf,Germany;17.Institute for Biometrics and Epidemiology, German Diabetes Center,Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf,Düsseldorf,Germany;18.Department of Endocrinology and Diabetology, Medical Faculty,Heinrich Heine University Düsseldorf,Düsseldorf,Germany;19.Department of Gerontology and Geriatrics,Leiden University Medical Center,Leiden,the Netherlands;20.Department of Bio and Health Informatics,Technical University of Denmark,Kongens Lyngby,Denmark;21.Department of Molecular Epidemiology,German Institute of Human Nutrition Potsdam-Rehbruecke,Nuthetal,Germany;22.Molecular Epidemiology Research Group,Max Delbrück Center for Molecular Medicine,Berlin Buch,Germany;23.Oxford Centre for Diabetes, Endocrinology and Metabolism,University of Oxford, Churchill Hospital,Oxford,UK;24.Wellcome Trust Centre for Human Genetics,University of Oxford,Oxford,UK;25.Oxford NIHR Biomedical Research Centre,Churchill Hospital,Oxford,UK;26.Division of Molecular and Clinical Medicine, School of Medicine,University of Dundee,Dundee,UK;27.Institute of Experimental Genetics,Technical University of Munich,Freising-Weihenstephan,Germany;28.Department of Epidemiology and Biostatistics,VU University Medical Center,Amsterdam,the Netherlands |
| Abstract: | ![]()
Aims/hypothesisCirculating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes.MethodsWe measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case–control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders.ResultsThere were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10?7). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10?3) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [β 0.97 ± 0.09], p = 1.0 × 10?27). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [β 0.45 ± 0.06]; p = 1.3 × 10?15), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose).Conclusions/interpretationIn this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors. |
