Diabetologia

, Volume 56, Issue 9, pp 1919–1924

The use of intermediate endpoints in the design of type 1 diabetes prevention trials

  • Jeffrey P. Krischer
  • the Type 1 Diabetes TrialNet Study Group
Article

Abstract

Aims/hypothesis

This paper presents a rationale for the selection of intermediate endpoints to be used in the design of type 1 diabetes prevention clinical trials.

Methods

Relatives of individuals diagnosed with type 1 diabetes were enrolled on the TrialNet Natural History Study and screened for diabetes-related autoantibodies. Those with two or more such autoantibodies were analysed with respect to increased HbA1c, decreased C-peptide following an OGTT, or abnormal OGTT values as intermediate markers of disease progression.

Results

Over 2 years, a 10% increase in HbA1c, and a 20% or 30% decrease in C-peptide from baseline, or progression to abnormal OGTT, occurred with a frequency between 20% and 41%. The 3- to 5-year risk of type 1 diabetes following each intermediate endpoint was high, namely 47% to 84%. The lower the incidence of the endpoint being reached, the higher the risk of diabetes. A diabetes prevention trial using these intermediate endpoints would require a 30% to 50% smaller sample size than one using type 1 diabetes as the endpoint.

Conclusions/interpretation

The use of an intermediate endpoint in diabetes prevention is based on the generally held view of disease progression from initial occurrence of autoantibodies through successive immunological and metabolic changes to manifest type 1 diabetes. Thus, these markers are suitable for randomised phase 2 trials, which can more rapidly screen promising new therapies, allowing them to be subsequently confirmed in definitive phase 3 trials.

Keywords

Clinical trial C-peptide Dysglycaemia HbA1c Intermediate endpoints Prevention Type 1 diabetes 

Abbreviations

ICAs

Islet cell autoantibodies

ZnT8A

Zinc transporter autoantibodies

Supplementary material

125_2013_2960_MOESM1_ESM.pdf (26 kb)
ESM Appendix(PDF 26 kb)

References

  1. 1.
    Atkinson MA, Eisenbarth GS (2001) Type 1 diabetes: new perspectives on disease pathogenesis and treatment. Lancet 358:221–229PubMedCrossRefGoogle Scholar
  2. 2.
    American Diabetes Association (2011) Diagnosis and classification of diabetes mellitus. Diabetes Care 34:62–69CrossRefGoogle Scholar
  3. 3.
    Sosenko JM, Palmer JP, Greenbaum CJ et al (2006) Patterns of metabolic progression to type 1 diabetes in the Diabetes Prevention Trial-Type 1. Diabetes Care 29:643–649PubMedCrossRefGoogle Scholar
  4. 4.
    Sosenko JM, Palmer JP, Rafkin-Mervis L et al (2008) Glucose and C-peptide changes in the perionset period of type 1 diabetes in the Diabetes Prevention Trial-Type 1. Diabetes Care 31:2188–2192PubMedCrossRefGoogle Scholar
  5. 5.
    TRIGR Investigators (2007) Study design of the trial to reduce IDDM in the genetically at risk (TRIGR). Pediatr Diabetes 8:117–137CrossRefGoogle Scholar
  6. 6.
    Skyler JS, Krischer JP, Wolfsdorf J et al (2005) Effects of oral insulin in relatives of patients with type 1 diabetes: The Diabetes Prevention Trial-Type 1. Diabetes Care 28:1068–1076PubMedCrossRefGoogle Scholar
  7. 7.
    Mahon JL, Sosenko JM, Rafkin-Mervis L et al (2009) The TrialNet natural history study of the development of type 1 diabetes: objectives, design, and initial results. Pediatr Diabetes 10:97–104PubMedCrossRefGoogle Scholar
  8. 8.
    Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Amer Statist Assn 53:457–481CrossRefGoogle Scholar
  9. 9.
    Greenwood M (1926) The natural duration of cancer. Reports on public health and medical subjects, vol. 33. Her Majesty's Stationery Office, London, pp 1–26Google Scholar
  10. 10.
    Fleming TR (2005) Surrogate endpoints and FDA's accelerated approval process. Health Aff (Millwood) 24:67–78CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jeffrey P. Krischer
    • 1
  • the Type 1 Diabetes TrialNet Study Group
  1. 1.Division of Informatics and Biostatistics, Department of PediatricsUniversity of South FloridaTampaUSA

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