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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 Art der Datei
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 Type 2 diabetes
lokal Metabolomics
lokal Prediction of diabetes
lokal Epidemiology
lokal Insulin secretion
1000 Fachgruppe
  1. Biologie |
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 Fri Jan 31 02:59:40 CET 2020
1000 Objekt bearb. Fri Jul 27 11:20:29 CEST 2018
1000 Vgl. frl:6407058
1000 Oai Id
  1. oai:frl.publisso.de:frl:6407058 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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