Advances in Type 1 Diabetes Prediction Using Islet Autoantibodies: Beyond a Simple Count

Michelle So, Cate Speake, Andrea K. Steck, Markus Lundgren, Peter G. Colman, Jerry P. Palmer, Kevan C. Herold, Carla J. Greenbaum

Research output: Contribution to journalReview articlepeer-review

Abstract

Islet autoantibodies are key markers for the diagnosis of type 1 diabetes. Since their discovery, they have also been recognized for their potential to identify at-risk individuals prior to symptoms. To date, risk prediction using autoantibodies has been based on autoantibody number; it has been robustly shown that nearly all multiple-autoantibody-positive individuals will progress to clinical disease. However, longitudinal studies have demonstrated that the rate of progression among multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials and for identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in presymptomatic subjects using immunotherapy, and as the field moves toward population-based screening. There have been many studies investigating islet autoantibody characteristics for their predictive potential, beyond a simple categorical count. Predictive features that have emerged include molecular specifics, such as epitope targets and affinity; longitudinal patterns, such as changes in titer and autoantibody reversion; and sequence-dependent risk profiles specific to the autoantibody and the subject's age. These insights are the outworking of decades of prospective cohort studies and international assay standardization efforts and will contribute to the granularity needed for more sensitive and specific preclinical staging. The aim of this review is to identify the dynamic and nuanced manifestations of autoantibodies in type 1 diabetes, and to highlight how these autoantibody features have the potential to improve study design of trials aiming to predict and prevent disease.

Original languageEnglish
Pages (from-to)584-604
Number of pages21
JournalEndocrine Reviews
Volume42
Issue number5
DOIs
Publication statusPublished - 2021 Oct 1

Subject classification (UKÄ)

  • Endocrinology and Diabetes

Free keywords

  • autoantibody
  • autoimmunity
  • preclinical
  • prediction
  • stages
  • type 1 diabetes

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