Immune biomarkers of type 1 diabetes are many and diverse. Some of these, such as the autoantibodies, are well established but not discriminative enough to deal with the heterogeneity inherent to type 1 diabetes progression. As an alternative, high hopes are placed on T cell assays, which give insight into the cells that actually target the beta cell or play a crucial role in maintaining tolerance. These assays are approaching a level of robustness that may allow for solid conclusions on both disease progression and therapeutic efficacy of immune interventions. In addition, 'omics' approaches to biomarker discovery are rapidly progressing. The potential emergence of novel biomarkers creates a need for the introduction of bioinformatics and 'big data' analysis systems for the integration of the multitude of biomarker data that will be available, to translate these data into clinical tools. It is worth noting that it is unlikely that the same markers will apply to all individuals. Instead, individualised signatures of biomarkers, combining autoantibodies, T cell profiles and other biomarkers, will need to be used to classify at-risk patients into various categories, thus enabling personalised prediction, prevention and treatment approaches. To achieve this goal, the standardisation of assays for biomarker discovery, the integration of analyses and data from biomarker studies and, most importantly, the careful clinical characterisation of individuals providing samples for these studies are critical. Longitudinal sample-collection initiatives, like INNODIA, should lead to novel biomarker discovery, not only providing a better understanding of type 1 diabetes onset and progression, but also yielding biomarkers of therapeutic efficacy of interventions to prevent or arrest type 1 diabetes.
Keywords: Autoantibodies; Bioinformatics; Biomarker; Immune; Review; T cell assays; Type 1 diabetes.