**1. Introduction**

Type 1 diabetes (T1D) is an autoimmune disease that results from the immunetargeted destruction of insulin-producing β-cells in the pancreas [1, 2]. The disease does not discriminate based on age, sex, or race, making it devastating on a global scale. Public health officials approximated that in 2017, 451 million adults were treated with diabetes across the globe [3]. Estimating that by the year 2045, approximately 693 million patients globally will have diabetes; however, only half will have an official diagnosis [3]. T1D results from complex interactions between genes and the environment leading to autoimmune reactions toward pancreatic islet cells (**Figure 1**). The unchecked immune reaction reduces the beta-cell mass, thereby causing insulin insufficiency leading to T1D in these genetically susceptible individuals.

Traditional methods for diagnosis include glucose tolerance testing and monitoring of HbA1c levels in at-risk patients. However, these diagnostic techniques are only

**Figure 1.**

*Susceptibility genes and environmental triggers in development and progression of type-1 diabetes. Created using BioRender.com.*

effective after the onset of diabetes has occurred. Preventative techniques for T1D are currently unavailable due to two main factors: (1) the inability to predict and accurately assess risk for the high-risk population and (2) the etiology of the disease is not well-established [4]. Although higher prevalence is seen in families with established autoimmune diseases, current screening techniques for islet autoantibodies in high-risk first-degree relatives of T1D patients are not very effective, as most cases of T1D occur spontaneously in the general population [5]. In addition, screening large populations poses challenges in both efficacy and funding. Due to these limitations, diabetes research is moving toward personalized prevention strategies, which focus on an individual's unique genetic and environmental risk factors that lead to the progression of T1D [2].

Biomarkers hold the key to unlocking diabetes preventative strategies and monitoring therapeutic outcomes. Over the past several decades, islet cell autoantibodies (ICA) have become an earlier predictor of T1D [2]. Since then, the discovery of other autoantibodies, such as those against specific islet autoantigens insulin and glutamate dehydrogenase (GAD), have continued to improve assays in their predictive value [2]. However, these islet autoantibodies still face the limitation of appearing in later stages of T1D development [2]. In addition, islet autoantibodies have not been useful for assessing therapeutic outcomes [2]. Early detection and prevention of T1D is crucial for the high-risk population and requires the discovery of new biomarkers. These biomarkers can come from metabolic pathways, DNA, RNA, glycans, lipids, and proteins (**Figure 2**). This chapter will address current biomarkers, omic technologies, computational biology, and the use of AI in identifying biomarkers in T1D research, and finally major issues with the discovery as well as validation of T1D biomarkers.
