Early identification of T1D risk and the onset of autoimmunity provides the basis for a variety of major ongoing studies seeking to prevent or delay the disease. Ongoing research on the natural history of the development of T1D in at risk neonates has shown that early identification of those at risk can foster early diagnosis of T1D and avoid life-threatening diabetic ketoacidosis (DKA). It is estimated that 40-60% of people with T1D in the US still present DKA at the time of the diagnosis. Over the last two decades, multiple prevention clinical trials have been conducted prior to or after the onset of the development of islet autoantibodies in either relatives (adults and children) of individuals with T1D or in children with genetic risk of T1D and recently it has been shown using teplizumab that one can delay the progression to clinical type 1 diabetes in high-risk, nondiabetic relatives of patients with diabetes. However, critical gaps and challenges for prevention of T1D include the lack of cost-effective risk screening and validated biomarkers for precise staging of disease and for optimizing design of prevention clinical trials. Investigators have used a combination of islet autoantibody positivity, autoantibody seroconversion, biomarkers of genetic susceptibility and beta cell functional assays as criteria to select individuals at high risk of developing T1D. However, current technology for identification of at-risk individuals is costly, slow, requires participation of research laboratories, and may not be suitable for public health (general population) screening that would ensue should effective preventative interventions be established. Methods for more efficient identification of individuals at risk of T1D in particular children who may be eligible for preventative intervention would include low cost, high-throughput, fast, easy to use, accurate and predictive assays/devices that could be used at the point of care level and can become widely available. Application of such technologies could facilitate and expedite testing when effective ways to prevent or delay T1D become available and would be essential for identifying individuals who can benefit from such treatments. Population based screening of individuals would be required as most of new cases of T1D (~70-80%) have no affected relatives. Prior initiatives (SBIR) on this area promoted several innovative projects with translational potential with one of them already being tested by ongoing screening programs. As science and technology in this field is continuously evolving, NIDDK considers that is necessary to continue promoting and supporting novel developments in this field as new biomarkers/assays/devices emerge that would improve the identification and categorization of individuals at risk of developing T1D, determine prognosis, monitor progression and assess the efficacy of therapeutic interventions. Examples of topics relevant to this announcement include but are not limited to development of: Techniques or products useful for predicting, preventing or delaying progression of diabetes, including tests for identifying patients at risk, and methods of monitoring disease progression. High-throughput assays (reliable, accurate, cost-effective, highly sensitive/specific, standardized, having rapid turnaround time) for autoantibody detection and other autoimmune/inflammatory/metabolic markers for diagnosis and follow up. Point-of-care low-cost/portable devices for subjects at risk for diabetes and for diagnosis of diabetes. Methods to measure changes in the immune status that may be used to monitor the immune-modulatory activity and beneficial effect of agents tested for the prevention and/or treatment of T1D. Non-invasive imaging as well as other methods/biomarkers for in vivo assessment of pancreatic beta cell mass, function and inflammation to improve diagnosis and prognosis at the earlier clinically silent stages of diabetes (stages 1 and 2) and for follow up/monitoring. High-throughput assays based on biologic pathways likely involved in the pathogenesis of diabetes that could be used to develop novel predictive/diagnostic systems/platforms.