Advances in Type 1 Diabetes Prediction Using Islet Autoantibodies: Beyond a Simple Count

Abstract

Islet autoantibodies are key markers for the diagnosis of type 1 diabetes. Since their discovery, they have also been recognized for their potential to identify at-risk individuals prior to symptoms. To date, risk prediction using autoantibodies has been based on autoantibody number; it has been robustly shown that nearly all multiple-autoantibody-positive individuals will progress to clinical disease. However, longitudinal studies have demonstrated that the rate of progression amongst multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials, and identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in pre-symptomatic subjects using immunotherapy, and as the field moves towards population-based screening. There have been many studies investigating islet autoantibody characteristics for their predictive potential, beyond a simple categorical count. Predictive features that have emerged include molecular specifics such as epitope targets and affinity; longitudinal patterns such as changes in titer and autoantibody reversion; and sequence-dependent risk profiles specific to the autoantibody and the subject’s age. These insights are the outworking of decades of prospective cohort studies and international assay standardization efforts and will contribute to the granularity needed for more sensitive and specific pre-clinical staging. The aim of this review is to identify the dynamic and nuanced manifestations of autoantibodies in type 1 diabetes, and to highlight how these autoantibody features have the potential to improve study design of trials aiming to predict and prevent disease.

Screening for Type 1 Diabetes in the General Population: A Status Report and Perspective

“…Subsequently, other antigens, including GAD65, were recognized, and methods such as radioimmunoprecipitation were used to identify islet cell proteins recognized by antibodies (16)(17)(18). The methods to measure biochemically defined AAs to insulin (IAA), GAD65 (GAD antibody [GADA]), ZnT8 (ZnT8A), and a protein tyrosine phosphatase (ICA512A or IA2A) have previously been reviewed (19).…”

Precision medicine in type 1 diabetes

“…We are now able to study the complex environmental, metabolomic, virome, molecular and microbiome associations in type 1 diabetes progression. A large number of association studies have highlighted the complex interplay between immune abnormalities, genetics and the environment [32][33][34]. We now have more markers of beta cell stress and dysfunction and increasing evidence of the complex interplay between the environment, beta cells and the immune system.…”

α Cell dysfunction in islets from nondiabetic, glutamic acid decarboxylase autoantibody–positive individuals

“…The presence of 2 or more AAbs confers a 70% risk of developing T1D within 10 years and nearly 100% over the lifetime of the individual. The factors involved in the rate of progression are poorly understood, although a younger age at seroconversion, a higher number of positive AAbs, and higher levels of IAA and IA-2A AAbs have been associated with a more rapid rate of progression to T1D ( 2 – 4 ). In contrast, T1D develops in just 15% of individuals who are single AAb + within 10 years of follow-up, with the anti–glutamic acid decarboxylase AAb (GADA) being by far the most common presenting AAb ( 2 , 3 ).…”