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Diabetes autoantibodies do not predict progression to diabetes in adults: the Diabetes Prevention Program

D Dabelea1, Y Ma, W C Knowler

  • 1Department of Epidemiology, University of Colorado at Denver, Colorado School of Public Health, Aurora, CO, USA.

Diabetic Medicine : a Journal of the British Diabetic Association
|March 21, 2014
PubMed
Abstract

Insights

Diabetes autoantibodies, such as GAD autoantibodies, do not predict diabetes development in at-risk middle-aged adults. This finding suggests diabetes autoimmunity is not a significant risk factor for progression to diabetes.

Area of Science:

  • Endocrinology
  • Immunology
  • Diabetes Research

Background:

  • Diabetes Prevention Program (DPP) investigated interventions to prevent type 2 diabetes.
  • Autoimmunity plays a role in type 1 diabetes, but its role in type 2 diabetes progression is less clear.
  • Glutamic acid decarboxylase (GAD) 65 autoantibodies are markers of autoimmune diabetes.

Purpose of the Study:

  • To assess if diabetes autoantibodies predict diabetes development in DPP participants.
  • To evaluate the association between GAD 65 autoantibodies and insulinoma-associated-2 autoantibodies with diabetes incidence.

Main Methods:

  • 3050 participants randomized into lifestyle, metformin, or placebo groups.
  • Baseline measurement of GAD 65 and insulinoma-associated-2 autoantibodies.
  • Follow-up for an average of 3.2 years for diabetes development.

Main Results:

  • GAD autoantibodies were present in 4.0% of participants, with variations across racial/ethnic groups.
  • GAD autoantibody positivity did not predict overall diabetes development (aHR 0.98; 95% CI 0.56-1.73).
  • Insulinoma-associated-2 autoantibodies were rare (0.033%).

Conclusions:

  • Diabetes autoimmunity, indicated by GAD and insulinoma-associated-2 autoantibodies, is not a clinically relevant risk factor for diabetes progression in at-risk middle-aged individuals.
  • These findings do not support routine screening for these autoantibodies in this population for predicting diabetes risk.

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