Time-Resolved Autoantibody Profiling Facilitates Stratification of Preclinical Type 1 Diabetes in Children

David Endesfelder1, Wolfgang Zu Castell2,3, Ezio Bonifacio4,5

  • 1Scientific Computing Research Unit, Helmholtz Zentrum München, Munich, Germany.

Diabetes
|October 12, 2018
PubMed

Insights

Children developing multiple islet autoantibodies show varied type 1 diabetes progression. A novel algorithm identified distinct risk groups, aiding the search for disease causes.

Area of Science:

  • Immunology
  • Endocrinology
  • Pediatrics

Background:

  • Type 1 diabetes progression varies in children with islet autoantibodies.
  • Autoantibody patterns may influence disease progression and etiology.
  • Understanding these patterns is crucial for predicting and preventing type 1 diabetes.

Purpose of the Study:

  • To model complex longitudinal autoantibody profiles in children.
  • To identify clusters of autoantibody profiles associated with distinct type 1 diabetes progression.
  • To explore potential etiological factors linked to specific autoantibody patterns.

Main Methods:

  • Utilized a novel wavelet-based algorithm to analyze longitudinal autoantibody data.
  • Clustered autoantibody profiles from 600 children in the TEDDY birth cohort study.
  • Followed participants prospectively for a median of 6.5 years, monitoring for clinical type 1 diabetes.

Main Results:

  • Identified distinct clusters of autoantibody profiles with varying 5-year progression rates to clinical diabetes (6% to 84%).
  • Children with early-onset, stable insulin autoantibodies (IAA) and insulinoma-associated antigen 2 autoantibodies (IA-2A) had the highest diabetes risk.
  • Lack of stable GAD autoantibodies (GADA) was associated with more boys and lower HLA-DR3 allele frequency.

Conclusions:

  • A novel algorithm refines classification of autoantibody-positive children, revealing distinct type 1 diabetes progression pathways.
  • This approach offers new avenues for investigating type 1 diabetes etiology and complex mechanisms.
  • Specific autoantibody profiles, particularly early IAA and IA-2A, are strong predictors of rapid disease progression.

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