A combined risk score enhances prediction of type 1 diabetes among susceptible children

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordType 1 diabetes (T1D)-an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency-often begins early in life when islet autoantibody appearance signals high risk1. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common2,3 and is most severe in the very young4,5, in whom it can be life threatening and difficult to treat6-9. Autoantibody surveillance programs effectively prevent most ketoacidosis10-12 but require frequent evaluations whose expense limits public health adoption13. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible14 because individuals at greatest risk of impending T1D are difficult to identify. To remedy this, we sought accurate, cost-effective estimation of future T1D risk by developing a combined risk score incorporating both fixed and variable factors (genetic, clinical and immunological) in 7,798 high-risk children followed closely from birth for 9.3 years. Compared with autoantibodies alone, the combined model dramatically improves T1D prediction at ≥2 years of age over horizons up to 8 years of age (area under the receiver operating characteristic curve ≥ 0.9), doubles the estimated efficiency of population-based newborn screening to prevent ketoacidosis, and enables individualized risk estimates for better prevention trial selection.National Institutes of Health/National Center for Advancing Translational Sciences Clinical and Translational ScienceDiabetes Research CenterDiabetes UKWellcome TrustJDR

Predictors of the initiation of islet autoimmunity and progression to multiple autoantibodies and clinical diabetes: The TEDDY study.

OBJECTIVE: To distinguish among predictors of seroconversion, progression to multiple autoantibodies and from multiple autoantibodies to type 1 diabetes in young children. RESEARCH DESIGN AND METHODS: Genetically high-risk newborns (n = 8,502) were followed for a median of 11.2 years (interquartile range 9.3-12.6); 835 (9.8%) developed islet autoantibodies and 283 (3.3%) were diagnosed with type 1 diabetes. Predictors were examined using Cox proportional hazards models. RESULTS: Predictors of seroconversion and progression differed, depending on the type of first appearing autoantibody. Male sex, Finnish residence, having a sibling with type 1 diabetes, the HLA DR4 allele, probiotic use before age 28 days, and single nucleotide polymorphism (SNP) rs689_A (INS) predicted seroconversion to IAA-first (having islet autoantibody to insulin as the first appearing autoantibody). Increased weight at 12 months and SNPs rs12708716_G (CLEC16A) and rs2292239_T (ERBB3) predicted GADA-first (autoantibody to GAD as the first appearing). For those having a father with type 1 diabetes, the SNPs rs2476601_A (PTPN22) and rs3184504_T (SH2B3) predicted both. Younger age at seroconversion predicted progression from single to multiple autoantibodies as well as progression to diabetes, except for those presenting with GADA-first. Family history of type 1 diabetes and the HLA DR4 allele predicted progression to multiple autoantibodies but not diabetes. Sex did not predict progression to multiple autoantibodies, but males progressed more slowly than females from multiple autoantibodies to diabetes. SKAP2 and MIR3681HG SNPs are newly reported to be significantly associated with progression from multiple autoantibodies to type 1 diabetes. CONCLUSIONS: Predictors of IAA-first versus GADA-first autoimmunity differ from each other and from the predictors of progression to diabetes

Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young.

Background: Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects. Methods: The 10-factor weighted risk model (HLA, PTPN22, INS, IL2RA, ERBB3, ORMDL3, BACH2, IL27, GLIS3, RNLS), 3-factor model (HLA, PTPN22, INS), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941). Results: Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P=.00006; FDR: P=.0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P=.03; FDR, P=.01). In GP, the restricted 3-factor model was superior to HLA (P=.03), but not different from the 10-factor model (P=.22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P=.12) and performed worse than the 10-factor model (P=.02) Conclusions: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups

Residual beta-cell function in diabetes children followed and diagnosed in the TEDDY study compared to community controls.

ObjectiveTo explore whether children diagnosed with type 1 diabetes during islet autoantibody surveillance through The Environmental Determinants of Diabetes in the Young (TEDDY) study retain greater islet function than children diagnosed through the community. Methods TEDDY children identified at birth with high-risk human leukocyte antigen and followed every 3months until diabetes diagnosis were compared to age-matched children diagnosed with diabetes in the community. Both participated in long-term follow up after diagnosis. Hemoglobin A1c (HbA1c) and mixed meal tolerance test were performed within 1month of diabetes onset, then at 3, 6, and 12months, and biannually thereafter. ResultsComparison of 43 TEDDY and 43 paired control children showed that TEDDY children often had no symptoms (58%) at diagnosis and none had diabetic ketoacidosis (DKA) compared with 98% with diabetes symptoms and 14% DKA in the controls (P<0.001 and P=0.03, respectively). At diagnosis, mean HbA1c was lower in TEDDY (6.8%, 51mmol/mol) than control (10.5%, 91mmol/mol) children (P<0.0001). TEDDY children had significantly higher area under the curve and peak C-peptide values than the community controls throughout the first year postdiagnosis. Total insulin dose and insulin dose-adjusted A1c were lower throughout the first year postdiagnosis for TEDDY compared with control children. ConclusionsHigher C-peptide levels in TEDDY vs community-diagnosed children persist for at least 12months following diabetes onset and appear to represent a shift in the disease process of about 6months. Symptom-free diagnosis, reduction of DKA, and the potential for immune intervention with increased baseline C-peptide may portend additional long-term benefits of early diagnosis

Pandemrix® vaccination is not associated with increased risk of islet autoimmunity or type 1 diabetes in the TEDDY study children.

Aims/hypothesis: During the A/H1N1 2009 (A/California/04/2009) pandemic, mass vaccination with a squalene-containing vaccine, Pandemrix®, was performed in Sweden and Finland. The vaccination was found to cause narcolepsy in children and young adults with the HLA-DQ 6.2 haplotype. The aim of this study was to investigate if exposure to Pandemrix® similarly increased the risk of islet autoimmunity or type 1 diabetes. Methods: In The Environmental Determinants of Diabetes in the Young (TEDDY) study, children are followed prospectively for the development of islet autoimmunity and type 1 diabetes. In October 2009, when the mass vaccination began, 3401 children at risk for islet autoimmunity and type 1 diabetes were followed in Sweden and Finland. Vaccinations were recorded and autoantibodies against insulin, GAD65 and insulinoma-associated protein 2 were ascertained quarterly before the age of 4 years and semi-annually thereafter. Results: By 5 August 2010, 2413 of the 3401 (71%) children observed as at risk for an islet autoantibody or type 1 diabetes on 1 October 2009 had been vaccinated with Pandemrix®. By 31 July 2016, 232 children had at least one islet autoantibody before 10 years of age, 148 had multiple islet autoantibodies and 96 had developed type 1 diabetes. The risk of islet autoimmunity was not increased among vaccinated children. The HR (95% CI) for the appearance of at least one islet autoantibody was 0.75 (0.55, 1.03), at least two autoantibodies was 0.85 (0.57, 1.26) and type 1 diabetes was 0.67 (0.42, 1.07). In Finland, but not in Sweden, vaccinated children had a lower risk of islet autoimmunity (0.47 [0.29, 0.75]), multiple autoantibodies (0.50 [0.28, 0.90] ) and type 1 diabetes (0.38 [0.20, 0.72]) compared with those who did not receive Pandemrix®. The analyses were adjusted for confounding factors. Conclusions/interpretation: Children with an increased genetic risk for type 1 diabetes who received the Pandemrix® vaccine during the A/H1N1 2009 pandemic had no increased risk of islet autoimmunity, multiple islet autoantibodies or type 1 diabetes. In Finland, the vaccine was associated with a reduced risk of islet autoimmunity and type 1 diabetes

Genetic and environmental interactions modify the risk of diabetes-related autoimmunity by 6 years of age: The TEDDY Study.

OBJECTIVE We tested the associations between genetic background and selected environmental exposures with respect to islet autoantibodies and type 1 diabetes. RESEARCH DESIGN AND METHODS Infants with HLA-DR high-risk genotypes were prospectively followed for diabetesrelated autoantibodies. Single nucleotide polymorphisms (SNPs) came from the Illumina ImmunoChip and environmental exposure data were by parental report. Children were followed to age 6 years. RESULTS Insulin autoantibodies occurred earlier than GAD antibody (GADA) and then declined, while GADA incidence rose and remained constant (significant in HLA-DR4 but not in the DR3/3 children). The presence of SNPs rs2476601 (PTPN22) and rs2292239 (ERBB3) demonstrated increased risk of both autoantibodies to insulin (IAA) only and GADA only. SNP rs689 (INS) was protective of IAA only, but not of GADA only. The rs3757247 (BACH2) SNP demonstrated increased risk of GADA only. Male sex, father or sibling as the diabetic proband, introduction of probiotics under 28 days of age, and weight at age 12 monthswere associated with IAA only, but only father as the diabetic proband and weight at age 12 months were associated with GADA only. Mother as the diabetic proband was not a significant risk factor. CONCLUSIONS These results show clear differences in the initiation of autoimmunity according to genetic factors and environmental exposures that give rise to IAAorGADA as the first appearing indication of autoimmunity

Integration of infant metabolite, genetic and islet autoimmunity signatures to predict type 1 diabetes by 6 years of age.

CONTEXT: Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease. OBJECTIVE: Determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be utilized to predict the likelihood that a child will develop T1D by the age of 6 years. DESIGN: Newborns with HLA typing enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). SETTING: TEDDY ascertained children in Finland, Germany, Sweden, and the United States. PATIENTS: TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at 3, 6 and 9 months of age, 11.4% of which progressed to T1D by the age of 6. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Diagnosis of T1D as diagnosed by American Diabetes Association criteria. RESULTS: Machine learning-based feature selection yielded classifiers based on disparate demographic, immunologic, genetic and metabolite features. The accuracy of the model utilizing all available data evaluated by the Area Under a Receiver Operating Characteristic Curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the AUC significantly. Metabolomics had the largest value when evaluating the accuracy at a low false positive rate. CONCLUSIONS: The metabolite features identified as important for progression to T1D by age 6 point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood

Family adjustment to diabetes diagnosis in children: Can participation in a study on type 1 diabetes genetic risk be helpful?

BACKGROUND: Diagnosis of type 1 diabetes often causes a negative psychological impact on families. We examined whether parents and children enrolled in The Environmental Determinants of Diabetes in the Young (TEDDY) study differ in their psychological adjustment to diabetes diagnosis compared to children diagnosed with diabetes in the community. METHODS: TEDDY follows 8676 children at genetic risk for type 1 diabetes from birth. Fifty-four TEDDY children diagnosed with diabetes and 54 age-matched community control children diagnosed with diabetes were enrolled. Participants were aged 3 to 10 years and study visits occurred at 3, 6, and 12 months postdiagnosis. Psychological measures included an adapted diabetes-specific State Anxiety Inventory, the Pediatric Quality of Life Inventory-Diabetes Module, and the Pediatric Inventory for Parents, which measures frequency and difficulty of parenting stress. RESULTS: A generalized estimating equation analysis based on a difference score between TEDDY children and community controls found no significant differences between TEDDY parents and community controls on parent diabetes-specific anxiety (P = .30). However, TEDDY children exhibited better diabetes-specific quality of life (P = .03) and TEDDY parents reported lower frequency (P = .004) and difficulty (P = .008) of parenting stress compared to community controls. CONCLUSIONS: Children diagnosed with at-risk for type 1 diabetes who have previously enrolled in research monitoring have improved diabetes quality of life and lower parenting stress postdiagnosis compared to children diagnosed in the community. Families in follow-up studies may be more prepared if their child is diagnosed with diabetes

The influence of type 1 diabetes genetic susceptibility regions, age, sex, and family history to the progression from multiple autoantibodies to type 1 diabetes: A TEDDY Study Report.

This paper seeks to determine whether factors related to autoimmunity risk remain significant after the initiation of two or more diabetes-related autoantibodies and continue to contribute to T1D risk among autoantibody positive children in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Characteristics included are age at multiple autoantibody positivity, sex, selected high-risk HLA-DR-DQ genotypes, relationship to a family member with T1D, autoantibody at seroconversion, INS gene (rs1004446_A), and non-HLA gene polymorphisms identified by the Type 1 Diabetes Genetics Consortium. The risk of progression to T1D was not different among those with or without a family history of T1D (p=0.39) nor HLA-DR-DQ genotypes (p=0.74). Age at developing multiple autoantibodies (HR=0.96 per 1 month increase in age, 95% CI=0.95, 0.97, p<0.001) and the type of first autoantibody (when more than a single autoantibody was the first appearing indication of seroconversion [p=0.006]) were statistically significant. Female sex was also a significant risk factor (p=0.03). Three SNPs were associated with increased diabetes risk (rs10517086_A, [p=0.03], rs1534422_G, [p=0.006], and rs2327832_G in TNFAIP3 [p=0.03]), and one with decreased risk (rs1004446_A in INS, [p=0.006]). The TEDDY data suggest that non-HLA gene polymorphisms may play a different role in the initiation of autoimmunity than they do in progression to T1D once autoimmunity has appeared. The strength of these associations may be related to the age of the population and the high-risk HLA-DR-DQ subtypes studied