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Objective: The first-appearing β-cell autoantibody has been shown to influence risk of type 1 diabetes (T1D). Here, we assessed the risk of autoantibody spreading to the second-appearing autoantibody and further progression to clinical disease in The Environmental Determinants of Diabetes in the Young (TEDDY) study.Research Design and Methods: Eligible children with increased HLA-DR-DQ genetic risk for T1D were followed quarterly from age 3 months up to 15 years for development of a single first-appearing autoantibody (GAD antibody [GADA], insulin autoantibody [IAA], or insulinoma antigen-2 autoantibody [IA-2A]) and subsequent development of a single second-appearing autoantibody and progression to T1D. Autoantibody positivity was defined as positivity for a specific autoantibody at two consecutive visits confirmed in two laboratories. Zinc transporter 8 autoantibody (ZnT8A) was measured in children who developed another autoantibody.Results: There were 608 children who developed a single first-appearing autoantibody (IAA, n = 282, or GADA, n = 326) with a median follow-up of 12.5 years from birth. The risk of a second-appearing autoantibody was independent of GADA versus IAA as a first-appearing autoantibody (adjusted hazard ratio [HR] 1.12; 95% CI 0.88-1.42; P = 0.36). Second-appearing GADA, IAA, IA-2A, or ZnT8A conferred an increased risk of T1D compared with children who remained positive for a single autoantibody, e.g., IAA or GADA second (adjusted HR 6.44; 95% CI 3.78-10.98), IA-2A second (adjusted HR 16.33; 95% CI 9.10-29.29; P < 0.0001), or ZnT8A second (adjusted HR 5.35; 95% CI 2.61-10.95; P < 0.0001). In children who developed a distinct second autoantibody, IA-2A (adjusted HR 3.08; 95% CI 2.04-4.65; P < 0.0001) conferred a greater risk of progression to T1D as compared with GADA or IAA. Additionally, both a younger initial age at seroconversion and shorter time to the development of the second-appearing autoantibody increased the risk for T1D.Conclusions: The hierarchical order of distinct autoantibody spreading was independent of the first-appearing autoantibody type and was age-dependent and augmented the risk of progression to T1D. [ABSTRACT FROM AUTHOR]

Objective: Assessment of the predictive power of The Environmental Determinants of Diabetes in the Young (TEDDY)-identified risk factors for islet autoimmunity (IA), the type of autoantibody appearing first, and type 1 diabetes (T1D).Research Design and Methods: A total of 7,777 children were followed from birth to a median of 9.1 years of age for the development of islet autoantibodies and progression to T1D. Time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves were calculated to provide estimates of their individual and collective ability to predict IA and T1D.Results: HLA genotype (DR3/4 vs. others) was the best predictor for IA (Youden's index J = 0.117) and single nucleotide polymorphism rs2476601, in PTPN22, was the best predictor for insulin autoantibodies (IAA) appearing first (IAA-first) (J = 0.123). For GAD autoantibodies (GADA)-first, weight at 1 year was the best predictor (J = 0.114). In a multivariate model, the area under the ROC curve (AUC) was 0.678 (95% CI 0.655, 0.701), 0.707 (95% CI 0.676, 0.739), and 0.686 (95% CI 0.651, 0.722) for IA, IAA-first, and GADA-first, respectively, at 6 years. The AUC of the prediction model for T1D at 3 years after the appearance of multiple autoantibodies reached 0.706 (95% CI 0.649, 0.762).Conclusions: Prediction modeling statistics are valuable tools, when applied in a time-until-event setting, to evaluate the ability of risk factors to discriminate between those who will and those who will not get disease. Although significantly associated with IA and T1D, the TEDDY risk factors individually contribute little to prediction. However, in combination, these factors increased IA and T1D prediction substantially. [ABSTRACT FROM AUTHOR]

Objective: The capacity to precisely predict progression to type 1 diabetes (T1D) in young children over a short time span is an unmet need. We sought to develop a risk algorithm to predict progression in children with high‐risk human leukocyte antigen (HLA) genes followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Methods: Logistic regression and 4‐fold cross‐validation examined 38 candidate predictors of risk from clinical, immunologic, metabolic, and genetic data. TEDDY subjects with at least one persistent, confirmed autoantibody at age 3 were analyzed with progression to T1D by age 6 serving as the primary endpoint. The logistic regression prediction model was compared to two non‐statistical predictors, multiple autoantibody status, and presence of insulinoma‐associated‐2 autoantibodies (IA‐2A). Results: A total of 363 subjects had at least one autoantibody at age 3. Twenty‐one percent of subjects developed T1D by age 6. Logistic regression modeling identified 5 significant predictors ‐ IA‐2A status, hemoglobin A1c, body mass index Z‐score, single‐nucleotide polymorphism rs12708716_G, and a combination marker of autoantibody number plus fasting insulin level. The logistic model yielded a receiver operating characteristic area under the curve (AUC) of 0.80, higher than the two other predictors; however, the differences in AUC, sensitivity, and specificity were small across models. Conclusions: This study highlights the application of precision medicine techniques to predict progression to diabetes over a 3‐year window in TEDDY subjects. This multifaceted model provides preliminary improvement in prediction over simpler prediction tools. Additional tools are needed to maximize the predictive value of these approaches. [ABSTRACT FROM AUTHOR]

Progression to clinical type 1 diabetes varies among children who develop β-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal autoantibody profiles by using a novel wavelet-based algorithm. We identified clusters of similar profiles associated with various types of progression among 600 children from The Environmental Determinants of Diabetes in the Young (TEDDY) birth cohort study; these children developed persistent insulin autoantibodies (IAA), GAD autoantibodies (GADA), insulinoma-associated antigen 2 autoantibodies (IA-2A), or a combination of these, and they were followed up prospectively at 3- to 6-month intervals (median follow-up 6.5 years). Children who developed multiple autoantibody types (n = 370) were clustered, and progression from seroconversion to clinical diabetes within 5 years ranged between clusters from 6% (95% CI 0, 17.4) to 84% (59.2, 93.6). Children who seroconverted early in life (median age <2 years) and developed IAA and IA-2A that were stable-positive on follow-up had the highest risk of diabetes, and this risk was unaffected by GADA status. Clusters of children who lacked stable-positive GADA responses contained more boys and lower frequencies of the HLA-DR3 allele. Our novel algorithm allows refined grouping of β-cell autoantibody-positive children who distinctly progressed to clinical type 1 diabetes, and it provides new opportunities in searching for etiological factors and elucidating complex disease mechanisms. [ABSTRACT FROM AUTHOR]

OBJECTIVE--To evaluate the long-term intervention effects of oral insulin on the development of type 1 diabetes and to assess the rate of progression to type 1 diabetes before and after oral insulin treatment was stopped in the Diabetes Prevention Trial-Type 1 (DPT-1). RESEARCH DESIGN AND METHODS--The follow-up included subjects who participated in the early intervention of oral insulin (1994-2003) to prevent or delay type 1 diabetes. A telephone survey was conducted in 2009 to determine whether diabetes had been diagnosed and, if not, an oral glucose tolerance test (OGTT), hemoglobin A1c (HbA1c), and autoantibody levels were obtained on all subjects who agreed to participate. RESULTS--Of 372 subjects randomized, 97 developed type 1 diabetes before follow-up; 75% of the remaining 275 subjects were contacted. In the interim, 77 subjects had been diagnosed with type 1 diabetes and 54 of the remainder have had an OGTT; 10 of these were diagnosed with type 1 diabetes, subsequently. Among individuals meeting the original criteria for insulin auto-antibodies (IAAs) (≥80 nU/mL), the overall benefit of oral insulin remained significant (P = 0.05). However, the hazard rate in this group increased (from 6.4% [95% CI 4.5-9.1] to 10.0% [7.1-14.1]) after cessation of therapy, which approximated the rate of individuals treated with placebo (10.2% [7.1-14.6]). CONCLUSIONS--Overall, the oral insulin treatment effect in individuals with confirmed IAA ≥80 nU/mL appeared to be maintained with additional follow-up; however, once therapy stopped, the rate of developing diabetes in the oral insulin group increased to a rate similar to that in the placebo group. [ABSTRACT FROM AUTHOR]

Increased growth in early childhood has been suggested to increase the risk of type 1 diabetes. This study explored the relationship between weight or height and development of persistent islet autoimmunity and progression to type 1 diabetes during the first 4 years of life in 7,468 children at genetic risk for type 1 diabetes followed in Finland, Germany, Sweden, and the U.S. Growth data collected every third month were used to estimate individual growth curves by mixed models. Cox proportional hazards models were used to evaluate body size and risk of islet autoimmunity and type 1 diabetes. In the overall cohort, development of islet autoimmunity (n = 575) was related to weight z scores at 12 months (hazard ratio [HR] 1.16 per 1.14 kg in males or per 1.02 kg in females, 95% CI 1.06-1.27, P < 0.001, false discovery rate [FDR] = 0.008) but not at 24 or 36 months. A similar relationship was seen between weight z scores and development of multiple islet autoantibodies (1 year: HR 1.21, 95% CI 1.08-1.35, P = 0.001, FDR = 0.008; 2 years: HR 1.18, 95% CI 1.06-1.32, P = 0.004, FDR = 0.02). No association was found between weight or height and type 1 diabetes (n = 169). In conclusion, greater weight in the first years of life was associated with an increased risk of islet autoimmunity. [ABSTRACT FROM AUTHOR]

Objective: To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction. Research Design and Methods: RuleFit is used to identify the risk-predictive baseline profile patterns of demographic, immunologic, and metabolic markers, using 356 subjects who were randomized into the control arm of the prospective Diabetes Prevention Trial-Type 1 (DPT-1) study. A novel latent trait model is developed to synthesize these baseline profile patterns for disease risk prediction. The primary outcome was Type 1 Diabetes (T1D) onset. Results: We identified ten baseline profile patterns that were significantly predictive to the disease onset. Using these ten baseline profile patterns, a risk prediction model was built based on the latent trait model, which produced superior prediction performance over existing risk score models for T1D. Conclusion: Our results demonstrated that the underlying disease progression process of T1D can be detected through some risk-predictive patterns of demographic, immunologic, and metabolic markers. A synthesis of these patterns provided accurate prediction of disease onset, leading to more cost-effective design of prevention trials of T1D in the future. [ABSTRACT FROM AUTHOR]