Related Experiment Video
Updated: Feb 4, 2026

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
Published on: July 5, 2022
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.
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.
Abstract:
Progression to clinical type 1 diabetes varies among children who develop β-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal autoantibody profiles by using a novel wavelet-based algorithm. We identified clusters of similar profiles associated with various types of progression among 600 children from The Environmental Determinants of Diabetes in the Young (TEDDY) birth cohort study; these children developed persistent insulin autoantibodies (IAA), GAD autoantibodies (GADA), insulinoma-associated antigen 2 autoantibodies (IA-2A), or a combination of these, and they were followed up prospectively at 3- to 6-month intervals (median follow-up 6.5 years). Children who developed multiple autoantibody types (n = 370) were clustered, and progression from seroconversion to clinical diabetes within 5 years ranged between clusters from 6% (95% CI 0, 17.4) to 84% (59.2, 93.6). Children who seroconverted early in life (median age <2 years) and developed IAA and IA-2A that were stable-positive on follow-up had the highest risk of diabetes, and this risk was unaffected by GADA status. Clusters of children who lacked stable-positive GADA responses contained more boys and lower frequencies of the HLA-DR3 allele. Our novel algorithm allows refined grouping of β-cell autoantibody-positive children who distinctly progressed to clinical type 1 diabetes, and it provides new opportunities in searching for etiological factors and elucidating complex disease mechanisms.
Related Concept Videos
Diabetes Mellitus: Type 2 and Gestational
Diabetes Mellitus: Overview and Type I Subtype
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
Facilitated Transport
Facilitated Transport
Social Facilitation
Facilitated Diffusion
In this process, substrates such as organic compounds and ions interact with a transporter on one side, triggering conformational changes in proteins that enable...

