, Volume 61, Issue 5, pp 1135–1141 | Cite as

Prediction of clamp-derived insulin sensitivity from the oral glucose insulin sensitivity index

  • Andrea Tura
  • Gaetano Chemello
  • Julia Szendroedi
  • Christian Göbl
  • Kristine Færch
  • Jana Vrbíková
  • Giovanni Pacini
  • Ele Ferrannini
  • Michael Roden



The euglycaemic–hyperinsulinaemic clamp is the gold-standard method for measuring insulin sensitivity, but is less suitable for large clinical trials. Thus, several indices have been developed for evaluating insulin sensitivity from the oral glucose tolerance test (OGTT). However, most of them yield values different from those obtained by the clamp method. The aim of this study was to develop a new index to predict clamp-derived insulin sensitivity (M value) from the OGTT-derived oral glucose insulin sensitivity index (OGIS).


We analysed datasets of people that underwent both a clamp and an OGTT or meal test, thereby allowing calculation of both the M value and OGIS. The population was divided into a training and a validation cohort (n = 359 and n = 154, respectively). After a stepwise selection approach, the best model for M value prediction was applied to the validation cohort. This cohort was also divided into subgroups according to glucose tolerance, obesity category and age.


The new index, called PREDIcted M (PREDIM), was based on OGIS, BMI, 2 h glucose during OGTT and fasting insulin. Bland–Altman analysis revealed a good relationship between the M value and PREDIM in the validation dataset (only 9 of 154 observations outside limits of agreement). Also, no significant differences were found between the M value and PREDIM (equivalence test: p < 0.0063). Subgroup stratification showed that measured M value and PREDIM have a similar ability to detect intergroup differences (p < 0.02, both M value and PREDIM).


The new index PREDIM provides excellent prediction of M values from OGTT or meal data, thereby allowing comparison of insulin sensitivity between studies using different tests.


Glucose clamp Glucose tolerance Insulin resistance Oral glucose tolerance test Prediction model Validation 



Akaike’s information criterion


Glucose infusion rates


Impaired fasting glucose


Impaired glucose tolerance


Leave-one-out cross-validation


Normal glucose tolerance


Mixed-meal test


Oral glucose insulin sensitivity index





The authors wish to thank G. Kacerovsky-Bielesz and A. Brehm (Hanusch-Krankenhaus, Vienna, Austria) and A. Schmid, M. Chmelik and M. Fritsch (Medical University of Vienna, Vienna, Austria) for their help in clarifying some aspects of the data.

Contribution statement

AT analysed the data, developed the model, and wrote the manuscript; GC and CG contributed to the analyses of the data and model development, and drafted the manuscript; JS, KF, JV, EF collected or contributed to collect the data, contributed to the interpretation of the results, and drafted the manuscript; GP contributed to the design of the study, to data analyses and results interpretation, and revised the manuscript critically; MR designed the study, contributed to data analyses and results interpretation, and revised the manuscript critically. All authors approved the final version. MR is the guarantor of this work.


This study was supported in part by the Ministry of Science and Research of the State of North Rhine-Westphalia (MIWF NRW) and the German Federal Ministry of Health (BMG) to the German Diabetes Center (DDZ), by a grant from the Federal Ministry for Research (BMBF) to the German Center for Diabetes Research (DZD e.V.) as well as by grants from the Helmholtz Alliance to Universities (ICEMED), the German Research Foundation (DFG, SFB 1116), German Diabetes Association (DDG) and the Schmutzler-Stiftung. Data on participants recruited in Vienna were analysed in studies supported by the European Foundation for the Study of Diabetes (Novo Nordisk type 2 diabetes grant, GSK grant), the Austrian Science Foundation (P15656), and the Austrian National Bank (OENB 11459) to MR, and by a Research Grant Award by the Austrian Diabetes Association to Gertrud Kacerovsky-Bielesz, Hanusch-Krankenhaus, Vienna, Austria. The Copenhagen study was supported by the Danish Ministry of Science, Technology and Innovation, the Danish Diabetes Association, the Novo Nordisk Foundation, the Foundation of Gerda and Aage Haensch, and by an EXGENESIS grant (005272) from the European Union. The Prague studies were supported by the grant of the European Foundation for the Study of Diabetes (EFSD) and by the Ministry of Health, Czech Republic - conceptual development of research organization (Institute of Endocrinology – EU 00023761). The San Antonio Metabolism Study was supported by funds from the Italian Ministry of University and Scientific Research (2001065883-001).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.


  1. 1.
    DeFronzo RA (2009) Banting lecture. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes. Diabetes 58:773–795CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Szendroedi J, Phielix E, Roden M (2011) The role of mitochondria in insulin resistance and type 2 diabetes mellitus. Nat Rev Endocrinol 13:92–103Google Scholar
  3. 3.
    Ferrannini E (2006) Is insulin resistance the cause of the metabolic syndrome? Ann Med 38:42–51CrossRefPubMedGoogle Scholar
  4. 4.
    DeFronzo RA, Tobin JD, Andres R (1979) Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Phys 237:E214–E223Google Scholar
  5. 5.
    Tura A, Sbrignadello S, Succurro E, Groop L, Sesti G, Pacini G (2010) An empirical index of insulin sensitivity from short IVGTT: validation against the minimal model and glucose clamp indices in patients with different clinical characteristics. Diabetologia 53:144–152 Erratum in Diabetologia 2010;53:1245CrossRefPubMedGoogle Scholar
  6. 6.
    Pacini G, Mari A (2003) Methods for clinical assessment of insulin sensitivity and beta cell function. Best Pract Res Clin Endocrinol Metab 17:305–322CrossRefPubMedGoogle Scholar
  7. 7.
    Mari A, Pacini G, Murphy E, Ludvik B, Nolan JJ (2001) A model-based method for assessing insulin sensitivity from the oral glucose tolerance test. Diabetes Care 24:539–548CrossRefPubMedGoogle Scholar
  8. 8.
    Mari A, Pacini G, Brazzale AR, Ahrén B (2005) Comparative evaluation of simple insulin sensitivity methods based on the oral glucose tolerance test. Diabetologia 48:748–751CrossRefPubMedGoogle Scholar
  9. 9.
    Szendroedi J, Yoshimura T, Phielix E et al (2014) Role of diacylglycerol activation of PKCθ in lipid-induced muscle insulin resistance in humans. Proc Natl Acad Sci U S A 111:9597–9602CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Szendroedi J, Kaul K, Kloock L et al (2014) Lower fasting muscle mitochondrial activity relates to hepatic steatosis in humans. Diabetes Care 37:468–474CrossRefPubMedGoogle Scholar
  11. 11.
    Faerch K, Vaag A, Holst JJ, Glümer C, Pedersen O, Borch-Johnsen K (2008) 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 51:853–861CrossRefPubMedGoogle Scholar
  12. 12.
    Bradnova O, Kyrou I, Hainer V et al (2014) Laparoscopic greater curvature plication in morbidly obese women with type 2 diabetes: effects on glucose homeostasis, postprandial triglyceridemia and selected gut hormones. Obes Surg 24:718–726CrossRefPubMedGoogle Scholar
  13. 13.
    Vrbíková J, Cibula D, Dvoráková K et al (2004) Insulin sensitivity in women with polycystic ovary syndrome. J Clin Endocrinol Metab 89:2942–2945CrossRefPubMedGoogle Scholar
  14. 14.
    Ferrannini E, Gastaldelli A, Miyazaki Y, Matsuda M, Mari A, DeFronzo RA (2005) β-cell function in subjects spanning the range from normal glucose tolerance to overt diabetes: a new analysis. J Clin Endocrinol Metab 90:493–500CrossRefPubMedGoogle Scholar
  15. 15.
    Mari A, Gastaldelli A, Foley JE, Pratley RE, Ferrannini E (2005) Beta-cell function in mild type 2 diabetic patients: effects of 6-month glucose lowering with nateglinide. Diabetes Care 28:1132–1138CrossRefPubMedGoogle Scholar
  16. 16.
    American Diabetes Association (2010) Diagnosis and classification of diabetes mellitus. Diabetes Care 33(Suppl 1):S62–S629CrossRefPubMedCentralGoogle Scholar
  17. 17.
    Crowther PS, Cox RJ (2005) A method for optimal division of data sets for use in neural networks. In: Khosla R, Howlett RJ, Jain LC (eds) Knowledge-based intelligent information and engineering systems. KES 2005. Lecture notes in computer science, vol. 3684, Springer, Berlin, Heidelberg, pp 1–7Google Scholar
  18. 18.
    Hocking RR (1976) The analysis and selection of variables in linear regression. Biometrics 32:1–49CrossRefGoogle Scholar
  19. 19.
    Robinson AP, Froese RE (2004) Model validation using equivalence tests. Ecol Model 176:349–358CrossRefGoogle Scholar
  20. 20.
    Michiels S, Le Maitre A, Buyse M et al (2009) Surrogate endpoints for overall survival in locally advanced head and neck cancer: meta-analyses of individual patient data. Lancet Oncol 10:341–350CrossRefPubMedGoogle Scholar
  21. 21.
    Tam CS, Xie W, Johnson WD, Cefalu WT, Redman LM, Ravussin E (2012) Defining insulin resistance from hyperinsulinemic-euglycemic clamps. Diabetes Care 35:1605–1610CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Andrea Tura
    • 1
  • Gaetano Chemello
    • 1
  • Julia Szendroedi
    • 2
    • 3
    • 4
  • Christian Göbl
    • 5
  • Kristine Færch
    • 6
  • Jana Vrbíková
    • 7
  • Giovanni Pacini
    • 1
  • Ele Ferrannini
    • 8
  • Michael Roden
    • 2
    • 3
    • 4
  1. 1.Metabolic UnitCNR Institute of NeurosciencePadovaItaly
  2. 2.Division of Endocrinology and Diabetology, Medical FacultyHeinrich-Heine UniversityDüsseldorfGermany
  3. 3.Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes ResearchDüsseldorfGermany
  4. 4.German Center for Diabetes Research (DZD)München-NeuherbergGermany
  5. 5.Department of Obstetrics and Gynecology, Division of Obstetrics and Feto-maternal MedicineMedical University of ViennaViennaAustria
  6. 6.Steno Diabetes Center CopenhagenGentofteDenmark
  7. 7.Institute of EndocrinologyPragueCzech Republic
  8. 8.CNR Institute of Clinical PhysiologyPisaItaly

Personalised recommendations