Algorithm based on immunogenetic predisposition

Abstract

Type 1 diabetes mellitus (T1DM) is an autoimmune disease that develops from a complex interaction between genetic, immunological, and environmental factors. The ISPAD 2024 guidelines highlight the importance of algorithms based on immunogenetic predisposition as key tools for early detection and risk stratification of progression to clinical T1DM.

Genetic risk is strongly influenced by HLA haplotypes, especially DR3-DQ2 and DR4-DQ8, which account for approximately 50% of the inherited risk. In addition, non-HLA genes such as INS and PTPN22 also contribute significantly. The combination of these factors in genetic risk scores has been shown to improve sensitivity (70–80%) and specificity (85–90%) in predicting islet autoimmunity and T1DM.

The detection of autoantibodies against beta-cell antigens (IAA, GADA, IA-2A, and ZnT8A) allows for the identification of preclinical stages of the disease. The presence of two or more autoantibodies in asymptomatic individuals defines stage 1, while the onset of dysglycemia marks stage 2. These markers, combined with genetic and metabolic data (HbA1c, fasting glucose, OGTT), allow for the development of predictive algorithms that guide follow-up and early intervention.

Population-based and family screening programs, supported by these algorithms, have been shown to significantly reduce the incidence of diabetic ketoacidosis at the time of diagnosis. Furthermore, they have facilitated the inclusion of patients in prevention clinical trials, such as the use of teplizumab, an anti-CD3 antibody approved by the Food and Drug Administration (FDA) to delay progression from stage 2 to 3.

Together, immunogenetic predisposition-based algorithms enable precision medicine, aimed at intervening before irreversible beta-cell damage occurs, thereby improving patients' prognosis and quality of life.

In conclusion, immunogenetic predisposition-based algorithms represent a powerful tool for anticipating the onset of T1D, facilitating early interventions, personalized education, and participation in preventive clinical trials.