From Prediction to Prevention: The Intricacies of Islet Autoantibodies in Type 1 Diabetes.

Abstract

Purpose of review: This review synthesizes current knowledge on islet autoantibodies (IAs) as predictive biomarkers for type 1 diabetes (T1D), focusing on their role in disease staging, autoantibody patterns, advancements in screening methodologies, and the implications of implementing population-wide screening initiatives.

Recent findings: Autoantibody profiling has refined T1D risk stratification, with progression rates influenced by IA characteristics including number, type, order of appearance, and affinity. While screening efforts initially targeted genetically at-risk groups, approximately 90% of new TID diagnoses occur in individuals without a family history, underscoring the need for broader population-based screening. The approval of teplizumab, a therapy shown to delay clinical T1D onset, represents a paradigm shift by providing an intervention following early identification through screening. Technological advancements have further optimized IA detection and therapeutic strategies. However, challenges such as cost-effectiveness, implementation logistics, and assay standardization remain. T1D is a chronic autoimmune disorder characterized by progressive pancreatic beta-cell destruction, leading to insulin deficiency. The natural history of T1D is typically marked by the appearance of IAs long before clinical symptoms emerge, providing a window for early detection and intervention. Identifying at-risk individuals during this asymptomatic phase can reduce disease severity at clinical onset and facilitate timely application of disease-modifying therapies like teplizumab. Emerging evidence emphasizes that IA characteristics collectively shape disease risk and progression. Advancements in screening technologies and therapies continue to support the integration of IA screening into clinical care, marking a significant step toward effective T1D prevention and management.