Background: There is an unmet need for accurate cost-effective estimation of future T1D risk. Methods: We derived a combined T1D prediction model using 7883 children followed closely from birth for a median of 9 yr, considering T1D genetic risk score (GRS), family T1D history (FH), standard islet autoantibodies (IA), race, birth circumstances, early growth and nutrient status. T1D developed in 326. Results: Machine learning and traditional methods (Cox models) performed equivalently as measured by time-dependent AUC ROC. Future T1D was accurately predicted by combining only 3 variables: GRS, FH and IA status. Accuracy of combined scores increased with age at scoring. By age 2, they were highly predictive for T1D in the next 5 years (AUC >0.91, 95% CI 0.88-0.95) (see X on Figure). A 2-yr-old with 2 IA, positive FH and high GRS (>12) would have T1D risk over the next 1, 3 and 5 years of 14% (8-19%), 36% (25-45%) and 51% (39%-61%) respectively. A 2-yr-old with 1 IA, no FH and moderate GRS (10-11) has T1D risk of 0.8% (0.6-1.2%), 2.6% (1.9-3.2%) and 4.3% (3.4%-5.2%) respectively. After newborn genetic screening, only simple venous sampling in routine healthcare settings is required. Conclusion: This approach allows updated individual risk estimates by age, and in the future may enable release of low risk individuals from surveillance long after initial newborn screening for more cost-efficient population based pediatric T1D prediction. Disclosure L.A. Ferrat: None. K. Vehik: None. S.A. Sharp: None. Å. Lernmark: None. A. Ziegler: None. M. Rewers: None. J. She: None. J. Toppari: None. B. Akolkar: None. J. Krischer: None. M.N. Weedon: None. S.S. Rich: None. R.A. Oram: Other Relationship; Self; Randox Laboratories Ltd. W. Hagopian: Research Support; Self; Novo Nordisk A/S. Funding National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; JDRF; Centers for Disease Control and Prevention

To ascertain whether skin pigmentation type and sensitivity to ultraviolet (UV) light are associated with susceptibility to type I (insulin-dependent) diabetes, 55 type I diabetic patients were examined, 38 new-onset and 17 long-term cases. They were compared to 72 control subjects of the same geographic region and nationality. To evaluate the individual skin pigmentation type, a standardized questionnaire was developed. Reactivity to UV light was determined by a stepwise-graded UV irradiation. Significantly more diabetic patients in southern Germany had blue eyes than nondiabetic control subjects (55 vs. 26%, P less than 0.01), and significantly more diabetic patients had a low-pigment eye color (blue or green) than control subjects (66 vs. 38%, P less than 0.01). In addition, more fair skin color was noted among diabetic versus control subjects (84 vs. 60%, P less than 0.01). In response to UV irradiation, diabetic patients more often showed an increased UV-light sensitivity than control subjects (83 vs. 23%, P less than 0.001). The relative risk for susceptibility to type I diabetes in subjects with low-pigment eye color was 3.1, in subjects with fair skin type 3.4, and in subjects with increased UV-light sensitivity 5.8. The highest risk for the development of diabetes was seen in subjects who had low-pigment eye color and/or increased UV-light sensitivity (95 vs. 51%, P = 0.00002, odds ratio 17.4). We conclude that a low-pigment skin type may predispose for the development of type I diabetes.

This study investigates two-phase growth patterns in early life and their associations with development of islet autoimmunity (IA) and type 1 diabetes (T1D). There were 7521 genetically high-risk children from Sweden, Finland, U.S. and Germany followed from birth for a median of 9.0 (IQR 5.7-10.6) years with available growth data. Of these, 761 children developed IA and 290 children progressed to T1D. Bayesian two-phase piecewise linear mixed models with a random change point were used to evaluate distinct growth phases in early life (Figure 1). Cox proportional hazard models were used to assess growth phase effects on risk of IA and progression to T1D. A higher rate of weight gain (kg/year) in the first phase was associated with an increased IA risk (HR =1.10, 95% CI 1.02, 1.18). A height growth pattern with a slower growth rate (cm/year) in the first phase, a higher growth rate in the second phase and younger age (months) at the phase transition was associated with an increased risk of progression from IA to T1D (HR=0.80, 95% CI 0.70, 0.91; HR=1.47, 95% CI 1.21, 1.78; HR=0.69, 95% CI 0.52, 0.93, respectively). A higher rate of weight gain in the second phase was associated with an increased risk of progression from IA to T1D (HR=2.54, 95% CI 1.36, 4.74) in children with first appearing GADA-only. Considering growth by phases in early life better clarified how specific growth phases contribute to the risk of IA and T1D. Disclosure X. Liu: None. K. Vehik: None. Y. Huang: None. H. Elding Larsson: None. J. Toppari: None. A. Ziegler: None. J. She: None. M. Rewers: None. W. Hagopian: Research Support; Self; Novo Nordisk A/S. B. Akolkar: None. J. Krischer: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; Centers for Disease Control and Prevention; JDRF; University of Florida (UL1TR000064); University of Colorado (UL1TR001082)

Type 1 diabetes (T1D) associated genetic factors affect risk of islet autoantibodies (IA), but non-genetic factors show conflicting associations. Mechanistic interactions may play a role. Under a sufficient-component causal framework, we used a rule-based discovery method to investigate if genetic factors, early environment and first appearing IA (IAA or GADA) act synergistically to mark different disease mechanisms. TEDDY children (n=7512) were followed until age 6 years for IA development (n=518). Rules differentiating IAA-first (n=258) from GADA-first (n=243) were identified by a rule discovery algorithm (RuleFit) and examined in logistic regression models. Rule components were assessed for additive interaction on IAA-first and GADA-first separately using Relative Excess Risk due to Interaction (RERI) calculated from Cox regression models. Here we show 2 of the 5 top rules, the first involving the child having CTLA4-AA (rs231775) and mother with a gestational respiratory but no skin infection (rule1, OR=5.6, 95% CI=2.6-12.0, p<0.0001); the second involving child having BACH2-T (rs3757247) and sufficient weight gain by age 3 months (rule2, OR=0.47, 95% CI=0.30-0.72, p<0.0001). Each differentiated IAA from GADA, and rule components showed interaction on absolute risk of IAA-first or GADA-first. Figure 1. Gene-environmental interaction with first appearing IA may mark different mechanisms. Disclosure K.F. Lynch: None. T. Feng: None. X. Qian: None. W. Hagopian: Research Support; Self; Novo Nordisk A/S. Å. Lernmark: None. A. Ziegler: None. J. Toppari: None. M. Rewers: None. J. She: None. D. Schatz: None. B. Akolkar: None. J. Krischer: None. S. Huang: None. K. Vehik: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; Centers for Disease Control and Prevention; JDRF; University of Florida (UL1TR000064); University of Colorado (UL1TR001082)

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