This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Hypothesis: Type 1 diabetes (T1D) is a disease that results from the destruction of pancreatic [unreadable]-cells. It can occur at any age, but its incidence is highest in children and adolescents. Although all people are susceptible, relatives of individuals with T1D are at much greater risk for development of the disease. It is clear that genetic factors contribute to most cases of T1D. Associated human leukocyte antigens (HLA) have been found to be strongly predictive of T1D. However, environmental factors also appear to contribute to the disorder. This is evident in studies of twin pairs. If one identical twin has T1D, although the other twin is clearly at high risk, it is far from inevitable that T1D will develop. Also, epidemiological studies have suggested that other factors such as viral infections may be involved in the pathogenesis of T1D. There is good evidence that cell-mediated immunity contributes to the pathogenesis of T1D. In addition, most patients who develop T1D have at least one autoantibody to islet cell antigens. These autoantibodies include anti-GAD65, anti-ICA512, ICA and IAA. When relatives of T1D patients develop these autoantibodies, they are at greater risk for T1D. The risk becomes even greater when subtle metabolic abnormalities are evident. The goals of the TrialNet Natural History Study of the Development of T1D are to both gain information about the pathogenesis and natural history of T1D and to facilitate the recruitment and assessment of individuals who might qualify for T1D prevention trials. Importantly, the information obtained from this study will serve to enable the development and implementation of prevention trials. The design of the TrialNet Natural History Study of the Development of T1D as described in this protocol is intended to achieve these goals. Specific Aims: 1. To determine the risk for the occurrence of T1D according to oral glucose tolerance tests (OGTT), C-peptide levels, biochemical autoantibodies (anti-GAD65, anti-ICA512 and IAA), islet cell autoantibodies (ICA), markers of cell-mediated immunity, intravenous glucose tolerance tests (IVGTT), and HLA genetic markers that are associated with risk for T1D. 2. To examine the accuracy of TrialNet risk assessment procedures for predicting future T1D. 3. To determine the prevalence of impaired glucose tolerance and ICA positivity in individuals with at least one positive biochemical autoantibody test. 4. To characterize the progression of immunologic abnormalities in the development of T1D by serially studying biochemical autoantibodies, ICA, and markers of cell-mediated immunity. 5. To characterize the progression of metabolic decompensation in the development of T1D by serially studying insulin, C-peptide and glucose levels, and to identify immunologic and other factors associated with this decompensation. 6. To determine the incidence of severe acute metabolic decompensation as the initial clinical presentation in individuals who have been identified as being at increased risk for T1D. 7. To identify individuals who qualify for TrialNet prevention trials for T1D. 8. To accrue additional information about immunologic and metabolic factors related to the pathogenesis of T1D by analyzing stored blood samples. The Immune Tolerance Network will function as a core laboratory for TrialNet for the development of specialized immunologic procedures. 9. To accrue additional information about genetic markers associated with risk for the development of T1D by analyzing stored blood samples. 10. For those who participated in the DPT-1 study, to examine associations of characteristics (e.g. demographics, immunologic, metabolic, etc.) assessed during the DPT-1 study with characteristics and outcomes assessed in TrialNet.