Novel biomarkers and predictive models for type 1 diabetes risk and progression

The management of type 1 diabetes (T1D) has entered a new era of general population screening and preventive interventions. Although islet autoantibodies remain the most established biomarkers for T1D risk assessment and diagnosis, their predictive power is limited by substantial heterogeneity in disease progression. Teplizumab has been applied in high-risk individuals with stage 2 T1D to delay T1D progression and multiple novel preventive strategies are under active investigation. These developments underscore an urgent need for new biomarkers and predictive models that can extend and refine risk assessment beyond the current reliance on established islet autoantibodies.

Although islet autoantibodies are currently the key biomarkers, accurate prediction of T1D progression remains a big challenge.
The heterogeneity of progression creates several critical questions, including:
• After a diagnosis of stage 1 T1D, which markers can identify individuals most likely to progress rapidly to clinical disease
• Before stage 1 T1D diagnosis, how to stratify progression risk in individuals who are positive for only one islet autoantibody
• Are there earlier markers detectable before the appearance of islet autoantibodies that can predict T1D risk?

To address these gaps, researchers are investigating a wide range of candidate biomarkers and predictive models, including novel islet autoantigens and autoantibodies; polygenic and genetic risk scores; peripheral blood transcriptomic, proteomic, metabolomic, and lipidomic signatures; intestinal microbiota; predictive models that integrate multi-omics approaches.

This Research Topic aims to bring together advances on novel biomarkers and predictive models to improve the early prediction, stratification, and prevention of T1D. We welcome the submission of Original Research, Review, Mini Review, and Perspective articles on themes including, but not limited to:

• Novel islet antigens and autoantibodies in T1D pathogenesis

• Genetic risk scores and polygenic modeling of T1D risk

• Transcriptomic and epigenomic signatures for T1D prediction

• Proteomics, metabolomics, and lipidomics in T1D risk stratification

• Intestinal microbiota in T1D development

• Multi-omics integration and machine learning models for T1D prediction

• Translational studies linking biomarkers to preventive interventions