Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

Sophie Molnos, Simone Wahl, Mark Haid, E. Marelise W. Eekhoff, René Pool, Anna Floegel, Joris Deelen, Daniela Much, Cornelia Prehn, Michaela Breier, Harmen H. Draisma, Nienke van Leeuwen, Annemarie M.C. Simonis-Bik, Anna Jonsson, Gonneke Willemsen, Wolfgang Bernigau, Rui Wang-Sattler, Karsten Suhre, Annette Peters, Barbara ThorandChristian Herder, Wolfgang Rathmann, Michael Roden, Christian Gieger, Mark H.H. Kramer, Diana van Heemst, Helle K. Pedersen, Valborg Gudmundsdottir, Matthias B. Schulze, Tobias Pischon, Eco J.C. de Geus, Heiner Boeing, Dorret I. Boomsma, Anette G. Ziegler, P. Eline Slagboom, Sandra Hummel, Marian Beekman, Harald Grallert, Søren Brunak, Mark I. McCarthy, Ramneek Gupta, Ewan R. Pearson, Jerzy Adamski, Leen M. ’t Hart*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)117-129
Number of pages13
Issue number1
Publication statusPublished - 1 Jan 2018

Bibliographical note

Funding Information:
Funding This work was partly funded by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contributions (; the Netherlands Organization for Health Research and Development (Priority Medicines Elderly Program 113102006); a grant from the German Federal Ministry of Education and Research (BMBF) to the German Centre Diabetes Research (DZD); a grant from the Helmholtz Initiative Personalized Medicine (iMED); grants from the German Diabetes Association and the Helmholtz International Research Group (Helmholtz HIRG-0018); the German Diabetes Centre is funded by the German Federal Ministry of Health (BMG) and the Ministry of Innovation, Science, Research and Technology (MIWF) of the State North Rhine-Westphalia. KS was supported by ‘Biomedical Research Program’ funds at Weill Cornell Medicine in Qatar, a programme funded by the Qatar Foundation. The NTR is supported by the European Research Council (grant 230374), by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007) and the Netherlands Organization for Scientific Research (grant NWO 480-04-004, NWO/SPI 56-464-14192). The LLS was financially supported by the Innovation-Oriented Research Program on Genomics (SenterNovem IGE01014 and IGE05007), the Centre for Medical Systems Biology and the Netherlands Consortium for Healthy Ageing (grant 05040202 and 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO), by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021. 007), the European Union-funded Network of Excellence Lifespan (FP6 036894) and the European Union’s Seventh Framework Programme (FP7/ 2007-2011) under grant agreement no. 259679.

Publisher Copyright:
© 2017, The Author(s).

Other keywords

  • Epidemiology
  • Insulin secretion
  • Metabolomics
  • Prediction of diabetes
  • Type 2 diabetes


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