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10.1007/s00125-017-4436-7.pdf 549,08KB
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1000 Titel
  • Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study
1000 Autor/in
  1. Molnos, Sophie |
  2. Wahl, Simone |
  3. Haid, Mark |
  4. Eekhoff, E. Marelise W. |
  5. Pool, René |
  6. Floegel, Anna |
  7. Deelen, Joris |
  8. Much, Daniela |
  9. Prehn, Cornelia |
  10. Breier, Michaela |
  11. Draisma, Harmen H. |
  12. van Leeuwen, Nienke |
  13. Simonis-Bik, Annemarie M. C. |
  14. Jonsson, Anna |
  15. Willemsen, Gonneke |
  16. Bernigau, Wolfgang |
  17. Wang-Sattler, Rui |
  18. Suhre, Karsten |
  19. Peters, Annette |
  20. Thorand, Barbara |
  21. Herder, Christian |
  22. Rathmann, Wolfgang |
  23. Roden, Michael |
  24. Gieger, Christian |
  25. Kramer, Mark H. H. |
  26. van Heemst, Diana |
  27. Pedersen, Helle K. |
  28. Gudmundsdottir, Valborg |
  29. Schulze, Matthias B. |
  30. Pischon, Tobias |
  31. de Geus, Eco J. C. |
  32. Böing, Heiner |
  33. Boomsma, Dorret I. |
  34. Ziegler, Anette G. |
  35. Slagboom, P. Eline |
  36. Hummel, Sandra |
  37. Beekman, Marian |
  38. Grallert, Harald |
  39. Brunak, Søren |
  40. McCarthy, Mark I. |
  41. Gupta, Ramneek |
  42. Pearson, Ewan R. |
  43. Adamski, Jerzy |
  44. 't Hart, Leen M. |
1000 Erscheinungsjahr 2017
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2017-10-25
1000 Erschienen in
1000 Quellenangabe
  • 61(1):117-129
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2017
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s00125-017-4436-7 |
1000 Ergänzendes Material
  • https://link.springer.com/article/10.1007/s00125-017-4436-7#SupplementaryMaterial |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
lokal Epidemiology
lokal Type 2 diabetes
lokal Prediction of diabetes
lokal Metabolomics
lokal Insulin secretion
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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1000 Label
1000 Förderer
  1. Innovative Medicines Initiative Joint Undertaking |
  2. European Union |
  3. EFPIA companies |
  4. Netherlands Organization for Health Research and Development |
  5. German Federal Ministry of Education and Research (BMBF) |
  6. Helmholtz Association |
  7. German Diabetes Association |
  8. Helmholtz International Research Group |
  9. German Federal Ministry of Health (BMG) |
  10. Ministry of Innovation, Science, Research and Technology (MIWF) of the State North Rhine-Westphalia |
  11. Weill Cornell Medicine (Qatar) |
  12. Qatar Foundation |
  13. European Research Council |
  14. Dutch government |
  15. Netherlands Organization for Scientific Research |
  16. SenterNovem |
  17. Centre for Medical Systems Biology |
  18. Netherlands Consortium for Healthy Ageing |
  19. Netherlands Genomics Initiative |
  20. Netherlands Organization for Scientific Research (NWO) |
1000 Fördernummer
  1. 115317
  2. FP6 036894; FP7 259679
  3. -
  4. 113102006
  5. -
  6. -
  7. -
  8. HIRG-0018
  9. -
  10. -
  11. -
  12. -
  13. 230374
  14. NWO 184.021.007
  15. NWO 480-04-004; NWO/SPI 56-464-14192
  16. IGE01014; IGE05007
  17. -
  18. 05040202; 050-060-810
  19. -
  20. -
1000 Förderprogramm
  1. DIRECT
  2. Seventh Framework Programme; Network of Excellence Lifespan
  3. -
  4. Priority Medicines Elderly Program
  5. -
  6. Helmholtz Initiative Personalized Medicine (iMED)
  7. -
  8. -
  9. -
  10. -
  11. Biomedical Research Program
  12. -
  13. -
  14. BBMRI-NL (Research Infrastructure)
  15. -
  16. Innovation-Oriented Research Program on Genomics
  17. -
  18. -
  19. -
  20. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Innovative Medicines Initiative Joint Undertaking |
    1000 Förderprogramm DIRECT
    1000 Fördernummer 115317
  2. 1000 joinedFunding-child
    1000 Förderer European Union |
    1000 Förderprogramm Seventh Framework Programme; Network of Excellence Lifespan
    1000 Fördernummer FP6 036894; FP7 259679
  3. 1000 joinedFunding-child
    1000 Förderer EFPIA companies |
    1000 Förderprogramm -
    1000 Fördernummer -
  4. 1000 joinedFunding-child
    1000 Förderer Netherlands Organization for Health Research and Development |
    1000 Förderprogramm Priority Medicines Elderly Program
    1000 Fördernummer 113102006
  5. 1000 joinedFunding-child
    1000 Förderer German Federal Ministry of Education and Research (BMBF) |
    1000 Förderprogramm -
    1000 Fördernummer -
  6. 1000 joinedFunding-child
    1000 Förderer Helmholtz Association |
    1000 Förderprogramm Helmholtz Initiative Personalized Medicine (iMED)
    1000 Fördernummer -
  7. 1000 joinedFunding-child
    1000 Förderer German Diabetes Association |
    1000 Förderprogramm -
    1000 Fördernummer -
  8. 1000 joinedFunding-child
    1000 Förderer Helmholtz International Research Group |
    1000 Förderprogramm -
    1000 Fördernummer HIRG-0018
  9. 1000 joinedFunding-child
    1000 Förderer German Federal Ministry of Health (BMG) |
    1000 Förderprogramm -
    1000 Fördernummer -
  10. 1000 joinedFunding-child
    1000 Förderer Ministry of Innovation, Science, Research and Technology (MIWF) of the State North Rhine-Westphalia |
    1000 Förderprogramm -
    1000 Fördernummer -
  11. 1000 joinedFunding-child
    1000 Förderer Weill Cornell Medicine (Qatar) |
    1000 Förderprogramm Biomedical Research Program
    1000 Fördernummer -
  12. 1000 joinedFunding-child
    1000 Förderer Qatar Foundation |
    1000 Förderprogramm -
    1000 Fördernummer -
  13. 1000 joinedFunding-child
    1000 Förderer European Research Council |
    1000 Förderprogramm -
    1000 Fördernummer 230374
  14. 1000 joinedFunding-child
    1000 Förderer Dutch government |
    1000 Förderprogramm BBMRI-NL (Research Infrastructure)
    1000 Fördernummer NWO 184.021.007
  15. 1000 joinedFunding-child
    1000 Förderer Netherlands Organization for Scientific Research |
    1000 Förderprogramm -
    1000 Fördernummer NWO 480-04-004; NWO/SPI 56-464-14192
  16. 1000 joinedFunding-child
    1000 Förderer SenterNovem |
    1000 Förderprogramm Innovation-Oriented Research Program on Genomics
    1000 Fördernummer IGE01014; IGE05007
  17. 1000 joinedFunding-child
    1000 Förderer Centre for Medical Systems Biology |
    1000 Förderprogramm -
    1000 Fördernummer -
  18. 1000 joinedFunding-child
    1000 Förderer Netherlands Consortium for Healthy Ageing |
    1000 Förderprogramm -
    1000 Fördernummer 05040202; 050-060-810
  19. 1000 joinedFunding-child
    1000 Förderer Netherlands Genomics Initiative |
    1000 Förderprogramm -
    1000 Fördernummer -
  20. 1000 joinedFunding-child
    1000 Förderer Netherlands Organization for Scientific Research (NWO) |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6407058.rdf
1000 Erstellt am 2018-03-08T11:04:29.627+0100
1000 Erstellt von 284
1000 beschreibt frl:6407058
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Mon Mar 08 15:16:05 CET 2021
1000 Objekt bearb. Mon Mar 08 15:16:05 CET 2021
1000 Vgl. frl:6407058
1000 Oai Id
  1. oai:frl.publisso.de:frl:6407058 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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