Advertisement

Endocrine

, Volume 55, Issue 2, pp 427–434 | Cite as

Heterogeneity in glucose response curves during an oral glucose tolerance test and associated cardiometabolic risk

  • Adam HulmanEmail author
  • Rebecca K. Simmons
  • Dorte Vistisen
  • Adam G. Tabák
  • Jacqueline M. Dekker
  • Marjan Alssema
  • Femke Rutters
  • Anitra D. M. Koopman
  • Thomas P. J. Solomon
  • John P. Kirwan
  • Torben Hansen
  • Anna Jonsson
  • Anette Prior Gjesing
  • Hans Eiberg
  • Arne Astrup
  • Oluf Pedersen
  • Thorkild I. A. Sørensen
  • Daniel R. Witte
  • Kristine Færch
Original Article

Abstract

We aimed to examine heterogeneity in glucose response curves during an oral glucose tolerance test with multiple measurements and to compare cardiometabolic risk profiles between identified glucose response curve groups. We analyzed data from 1,267 individuals without diabetes from five studies in Denmark, the Netherlands and the USA. Each study included between 5 and 11 measurements at different time points during a 2-h oral glucose tolerance test, resulting in 9,602 plasma glucose measurements. Latent class trajectories with a cubic specification for time were fitted to identify different patterns of plasma glucose change during the oral glucose tolerance test. Cardiometabolic risk factor profiles were compared between the identified groups. Using latent class trajectory analysis, five glucose response curves were identified. Despite similar fasting and 2-h values, glucose peaks and peak times varied greatly between groups, ranging from 7–12 mmol/L, and 35–70 min. The group with the lowest and earliest plasma glucose peak had the lowest estimated cardiovascular risk, while the group with the most delayed plasma glucose peak and the highest 2-h value had the highest estimated risk. One group, with normal fasting and 2-h values, exhibited an unusual profile, with the highest glucose peak and the highest proportion of smokers and men. The heterogeneity in glucose response curves and the distinct cardiometabolic risk profiles may reflect different underlying physiologies. Our results warrant more detailed studies to identify the source of the heterogeneity across the different phenotypes and whether these differences play a role in the development of type 2 diabetes and cardiovascular disease.

Keywords

Oral glucose tolerance test Glucose response curve Cardiometabolic risk Latent class trajectory analysis 

Notes

Acknowledgments

A.H., R.K.S. and D.R.W. are supported by the Danish Diabetes Academy. The Danish Diabetes Academy is funded by the Novo Nordisk Foundation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

12020_2016_1126_MOESM1_ESM.pdf (64 kb)
Supplementary Tables

References

  1. 1.
    K. Faerch, A. Hulman, T.P. Solomon, Heterogeneity of Pre-diabetes and Type 2 Diabetes: Implications for Prediction, Prevention and Treatment Responsiveness. Curr. Diabetes Rev. 12, 30–41 (2016)CrossRefPubMedGoogle Scholar
  2. 2.
    K. Faerch, D.R. Witte, A.G. Tabak, L. Perreault, C. Herder, E.J. Brunner, M. Kivimaki, D. Vistisen, Trajectories of cardiometabolic risk factors before diagnosis of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II cohort study. Lancet Diabetes Endocrinol 1, 43–51 (2013)CrossRefPubMedGoogle Scholar
  3. 3.
    M.A. Abdul-Ghani, V. Lyssenko, T. Tuomi, R.A. Defronzo, L. Groop, The shape of plasma glucose concentration curve during OGTT predicts future risk of type 2 diabetes. Diabetes Metab. Res. Rev. 26, 280–286 (2010)CrossRefPubMedGoogle Scholar
  4. 4.
    M.A. Abdul-Ghani, K. Williams, R.A. DeFronzo, M. Stern, What is the best predictor of future type 2 diabetes? Diabetes Care 30, 1544–1548 (2007)CrossRefPubMedGoogle Scholar
  5. 5.
    A. Alyass, P. Almgren, M. Akerlund, J. Dushoff, B. Isomaa, P. Nilsson, T. Tuomi, V. Lyssenko, L. Groop, D. Meyre, Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts. Diabetologia 58, 87–97 (2015)CrossRefPubMedGoogle Scholar
  6. 6.
    M. Bergman, A. Chetrit, J. Roth, R. Dankner, Dysglycemia and long-term mortality: observations from the Israel study of glucose intolerance, obesity and hypertension. Diabetes Metab. Res. Rev. 31, 368–375 (2015)CrossRefPubMedGoogle Scholar
  7. 7.
    R. Jagannathan, M.A. Sevick, H. Li, D. Fink, R. Dankner, A. Chetrit, J. Roth, M. Bergman, Elevated 1-hour plasma glucose levels are associated with dysglycemia, impaired beta-cell function, and insulin sensitivity: a pilot study from a real world health care setting. Endocrine 52, 172–175 (2016)CrossRefPubMedGoogle Scholar
  8. 8.
    M. Kanauchi, K. Kimura, K. Kanauchi, Y. Saito, Beta-cell function and insulin sensitivity contribute to the shape of plasma glucose curve during an oral glucose tolerance test in non-diabetic individuals. Int. J. Clin. Pract. 59, 427–432 (2005)CrossRefPubMedGoogle Scholar
  9. 9.
    K. Faerch, A. Vaag, J.J. Holst, C. Glumer, O. Pedersen, K. Borch-Johnsen, Impaired fasting glycaemia vs impaired glucose tolerance: similar impairment of pancreatic alpha and beta cell function but differential roles of incretin hormones and insulin action. Diabetologia 51, 853–861 (2008)CrossRefPubMedGoogle Scholar
  10. 10.
    J.M. Rijkelijkhuizen, C.J. Girman, A. Mari, M. Alssema, T. Rhodes, G. Nijpels, P.J. Kostense, P.P. Stein, E.M. Eekhoff, R.J. Heine, J.M. Dekker, Classical and model-based estimates of beta-cell function during a mixed meal vs. an OGTT in a population-based cohort. Diabetes Res. Clin. Pract. 83, 280–288 (2009)CrossRefPubMedGoogle Scholar
  11. 11.
    T.P. Solomon, S.K. Malin, K. Karstoft, S.H. Knudsen, J.M. Haus, M.J. Laye, J.P. Kirwan, Association between cardiorespiratory fitness and the determinants of glycemic control across the entire glucose tolerance continuum. Diabetes Care. 38, 921–929 (2015)CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    L.H. Larsen, A.P. Gjesing, T.I. Sorensen, Y.H. Hamid, S.M. Echwald, S. Toubro, E. Black, A. Astrup, T. Hansen, O. Pedersen, Mutation analysis of the preproghrelin gene: no association with obesity and type 2 diabetes. Clin. Biochem. 38, 420–424 (2005)CrossRefPubMedGoogle Scholar
  13. 13.
    A.P. Gjesing, C.T. Ekstrom, H. Eiberg, S.A. Urhammer, J.J. Holst, O. Pedersen, T. Hansen, Fasting and oral glucose-stimulated levels of glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) are highly familial traits. Diabetologia 55, 1338–1345 (2012)CrossRefPubMedGoogle Scholar
  14. 14.
    T. Jorgensen, K. Borch-Johnsen, T.F. Thomsen, H. Ibsen, C. Glumer, C. Pisinger, A randomized non-pharmacological intervention study for prevention of ischaemic heart disease: baseline results Inter99. Eur. J. Cardiovasc. Prev. Rehabil. 10, 377–386 (2003)CrossRefPubMedGoogle Scholar
  15. 15.
    L. Andersen, B. Dinesen, P.N. Jorgensen, F. Poulsen, M.E. Roder, Enzyme immunoassay for intact human insulin in serum or plasma. Clin. Chem. 39, 578–582 (1993)PubMedGoogle Scholar
  16. 16.
    C. Proust-Lima, V. Phillips, B. Liquet, Estimation of extended mixed models using latent classes and latent processes: The R package lcmm. https://arxiv.org/pdf/1503.00890.pdf. Accessed 24 Jan 2016
  17. 17.
    R.M. Conroy, K. Pyorala, A.P. Fitzgerald, S. Sans, A. Menotti, G. De Backer, D. De Bacquer, P. Ducimetiere, P. Jousilahti, U. Keil, I. Njolstad, R.G. Oganov, T. Thomsen, H. Tunstall-Pedoe, A. Tverdal, H. Wedel, P. Whincup, L. Wilhelmsen, I.M. Graham, Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur. Heart. J. 24, 987–1003 (2003)CrossRefPubMedGoogle Scholar
  18. 18.
    M. Matsuda, R.A. DeFronzo, Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 22, 1462–1470 (1999)CrossRefPubMedGoogle Scholar
  19. 19.
    D.I. Phillips, P.M. Clark, C.N. Hales, C. Osmond, Understanding oral glucose tolerance: comparison of glucose or insulin measurements during the oral glucose tolerance test with specific measurements of insulin resistance and insulin secretion. Diabet. Med. 11, 286–292 (1994)CrossRefPubMedGoogle Scholar
  20. 20.
    R Development Core Team. R: A language and environment for statistical computing. In. R Foundation for Statistical Computing, Vienna, Austria, (2015)Google Scholar
  21. 21.
    D. Carpenter, S. Dhar, L.M. Mitchell, B. Fu, J. Tyson, N.A.A. Shwan, F. Yang, M.G. Thomas, J.A.L. Armour, Obesity, starch digestion and amylase: association between copy number variants at human salivary (AMY1) and pancreatic (AMY2) amylase genes. Hum. Mol. Gen. 24, 3472–3480 (2015)CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    N. Guess, L. Perreault, A. Kerege, A. Strauss, B.C. Bergman, Dietary Fatty Acids Differentially Associate with Fasting Versus 2-Hour Glucose Homeostasis: Implications for The Management of Subtypes of Prediabetes. PLoS ONE 11, e0150148 (2016)CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    L.P. Lidegaard, A.L. Hansen, N.B. Johansen, D.R. Witte, S. Brage, T. Lauritzen, M.E. Jorgensen, D.L. Christensen, K. Faerch, Physical activity energy expenditure vs cardiorespiratory fitness level in impaired glucose metabolism. Diabetologia. 58, 2709–2717 (2015)CrossRefPubMedGoogle Scholar
  24. 24.
    K. Faerch, K. Borch-Johnsen, A. Vaag, T. Jorgensen, D.R. Witte, Sex differences in glucose levels: a consequence of physiology or methodological convenience? The Inter99 study. Diabetologia 53, 858–865 (2010)CrossRefPubMedGoogle Scholar
  25. 25.
    W. Rathmann, K. Strassburger, G. Giani, A. Doring, C. Meisinger, Differences in height explain gender differences in the response to the oral glucose tolerance test. Diabet. Med. 25, 1374–1375 (2008)PubMedGoogle Scholar
  26. 26.
    C.S. Marathe, C.K. Rayner, K.L. Jones, M. Horowitz, Relationships between gastric emptying, postprandial glycemia, and incretin hormones. Diabetes Care 36, 1396–1405 (2013)CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    K. Faerch, G. Pacini, J.J. Nolan, T. Hansen, A. Tura, D. Vistisen, Impact of glucose tolerance status, sex, and body size on glucose absorption patterns during OGTTs. Diabetes Care 36, 3691–3697 (2013)CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    J.J. Holst, F. Gribble, M. Horowitz, C.K. Rayner, Roles of the Gut in Glucose Homeostasis. Diabetes Care 39, 884–892 (2016)CrossRefPubMedGoogle Scholar
  29. 29.
    M. Lind, J. Tuomilehto, M. Uusitupa, O. Nerman, J. Eriksson, P. Ilanne-Parikka, S. Keinanen-Kiukaanniemi, M. Peltonen, A. Pivodic, J. Lindstrom, The association between HbA1c, fasting glucose, 1-hour glucose and 2-hour glucose during an oral glucose tolerance test and cardiovascular disease in individuals with elevated risk for diabetes. PLoS ONE 9, e109506 (2014)CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Glucose tolerance and cardiovascular mortality, comparison of fasting and 2-hour diagnostic criteria. Arch. Intern. Med. 161, 397–405 (2001)CrossRefGoogle Scholar
  31. 31.
    S. Soulimane, D. Simon, W.H. Herman, C. Lange, C.M. Lee, S. Colagiuri, J.E. Shaw, P.Z. Zimmet, D. Magliano, S.R. Ferreira, Y. Dong, L. Zhang, T. Jorgensen, J. Tuomilehto, V. Mohan, D.L. Christensen, L. Kaduka, J.M. Dekker, G. Nijpels, C.D. Stehouwer, O. Lantieri, W.Y. Fujimoto, D.L. Leonetti, M.J. McNeely, K. Borch-Johnsen, E.J. Boyko, D. Vistisen, B. Balkau, HbA1c, fasting and 2 h plasma glucose in current, ex- and never-smokers: a meta-analysis. Diabetologia 57, 30–39 (2014)CrossRefPubMedGoogle Scholar
  32. 32.
    M. Manco, S. Panunzi, D.P. Macfarlane, A. Golay, O. Melander, T. Konrad, J.R. Petrie, G. Mingrone, One-hour plasma glucose identifies insulin resistance and beta-cell dysfunction in individuals with normal glucose tolerance: cross-sectional data from the Relationship between Insulin Sensitivity and Cardiovascular Risk (RISC) study. Diabetes Care 33, 2090–2097 (2010)CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    L. Perreault, S.E. Kahn, C.A. Christophi, W.C. Knowler, R.F. Hamman, Regression from pre-diabetes to normal glucose regulation in the diabetes prevention program. Diabetes Care 32, 1583–1588 (2009)CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Adam Hulman
    • 1
    • 2
    • 3
    Email author
  • Rebecca K. Simmons
    • 2
    • 4
    • 5
  • Dorte Vistisen
    • 6
  • Adam G. Tabák
    • 7
    • 8
  • Jacqueline M. Dekker
    • 9
    • 10
  • Marjan Alssema
    • 9
    • 11
  • Femke Rutters
    • 9
    • 10
  • Anitra D. M. Koopman
    • 9
    • 10
  • Thomas P. J. Solomon
    • 12
    • 13
  • John P. Kirwan
    • 14
  • Torben Hansen
    • 15
  • Anna Jonsson
    • 15
  • Anette Prior Gjesing
    • 15
  • Hans Eiberg
    • 16
  • Arne Astrup
    • 17
  • Oluf Pedersen
    • 15
  • Thorkild I. A. Sørensen
    • 15
    • 18
  • Daniel R. Witte
    • 1
    • 2
  • Kristine Færch
    • 6
  1. 1.Department of Public Health, Section of EpidemiologyAarhus UniversityAarhusDenmark
  2. 2.Danish Diabetes AcademyOdenseDenmark
  3. 3.Department of Medical Physics and InformaticsUniversity of SzegedSzegedHungary
  4. 4.Department of Public Health, Section of General PracticeAarhus UniversityAarhusDenmark
  5. 5.MRC Epidemiology UnitUniversity of Cambridge School of Clinical MedicineCambridgeUK
  6. 6.Steno Diabetes CenterGentofteDenmark
  7. 7.1st Department of MedicineSemmelweis University Faculty of MedicineBudapestHungary
  8. 8.Department of Epidemiology and Public HealthUniversity College LondonLondonUK
  9. 9.Department of Biostatistics and EpidemiologyVU Medical CenterAmsterdamNetherlands
  10. 10.EMGO+ Institute for Health and Care ResearchVU Medical CenterAmsterdamNetherlands
  11. 11.Unilever Research and DevelopmentVlaardingenNetherlands
  12. 12.School of Sport, Exercise and Rehabilitation SciencesUniversity of BirminghamEdgbastonUK
  13. 13.Institute for Metabolism and Systems ResearchUniversity of BirminghamEdgbastonUK
  14. 14.Department of PathobiologyLerner Research Institute, Cleveland ClinicOhioUSA
  15. 15.Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
  16. 16.Department of Cellular and Molecular MedicineUniversity of CopenhagenCopenhagenDenmark
  17. 17.Department of Nutrition, Exercise and SportsUniversity of CopenhagenCopenhagenDenmark
  18. 18.Institute of Preventive MedicineFrederiksberg and Bispebjerg University Hospital, The Capital RegionCopenhagenDenmark

Personalised recommendations