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Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study
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Molnos-2018(MetaboliteRatios&T2D).pdf | Published version | 549.08 kB | Adobe PDF | View/Open |
Title: | Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study |
Authors: | Molnos, S Wahl, S Haid, M Eekhoff, EMW Pool, R Floegel, A Deelen, J Much, D Prehn, C Breier, M Draisma, HH Van Leeuwen, N Simonis-Bik, AMC Jonsson, A Willemsen, G Bernigau, W Wang-Sattler, R Suhre, K Peters, A Thorand, B Herder, C Rathmann, W Roden, M Gieger, C Kramer, MHH Van Heemst, D Pedersen, HK Gudmundsdottir, V Schulze, MB Pischon, T De Geus, EJC Boeing, H Boomsma, DI Ziegler, AG Slagboom, PE Hummel, S Beekman, M Grallert, H Brunak, S McCarthy, MI Gupta, R Pearson, ER Adamski, J 't Hart, LM |
Item Type: | Journal Article |
Abstract: | AIMS/HYPOTHESIS: Circulating 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. METHODS: We 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. RESULTS: There 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/INTERPRETATION: In 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. |
Issue Date: | 25-Oct-2017 |
Date of Acceptance: | 28-Jul-2017 |
URI: | http://hdl.handle.net/10044/1/58119 |
DOI: | https://dx.doi.org/10.1007/s00125-017-4436-7 |
ISSN: | 0012-186X |
Publisher: | Springer Verlag |
Start Page: | 117 |
End Page: | 129 |
Journal / Book Title: | Diabetologia |
Volume: | 61 |
Issue: | 1 |
Copyright Statement: | © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Keywords: | Epidemiology Insulin secretion Metabolomics Prediction of diabetes Type 2 diabetes 1103 Clinical Sciences 1114 Paediatrics And Reproductive Medicine 1117 Public Health And Health Services Endocrinology & Metabolism |
Publication Status: | Published |
Conference Place: | Germany |
Appears in Collections: | Department of Medicine (up to 2019) |