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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.

Background/Objective. Growth and obesity have been associated with increased risk of islet autoimmunity (IA) and progression to type 1 diabetes. We aimed to estimate the effect of energy-yielding macronutrient intake on the development of IA through BMI. Research Design and Methods. Genetically at-risk children (n = 5,084) in Finland, Germany, Sweden, and the USA, who were autoantibody negative at 2 years of age, were followed to the age of 8 years, with anthropometric measurements and 3-day food records collected biannually. Of these, 495 (9.7%) children developed IA. Mediation analysis for time-varying covariates (BMI z-score) and exposure (energy intake) was conducted. Cox proportional hazard method was used in sensitivity analysis. Results. We found an indirect effect of total energy intake (estimates: indirect effect 0.13 [0.05, 0.21]) and energy from protein (estimates: indirect effect 0.06 [0.02, 0.11]), fat (estimates: indirect effect 0.03 [0.01, 0.05]), and carbohydrates (estimates: indirect effect 0.02 [0.00, 0.04]) (kcal/day) on the development of IA. A direct effect was found for protein, expressed both as kcal/day (estimates: direct effect 1.09 [0.35, 1.56]) and energy percentage (estimates: direct effect 72.8 [3.0, 98.0]) and the development of GAD autoantibodies (GADA). In the sensitivity analysis, energy from protein (kcal/day) was associated with increased risk for GADA, hazard ratio 1.24 (95% CI: 1.09, 1.53), p = 0.042 . Conclusions. This study confirms that higher total energy intake is associated with higher BMI, which leads to higher risk of the development of IA. A diet with larger proportion of energy from protein has a direct effect on the development of GADA.

AIMS/HYPOTHESIS: Prognostic factors and characteristics of children diagnosed with type 1 diabetes before 6years of age were compared with those diagnosed at 6-13years of age in the TEDDY study. METHODS: Genetically high-risk children (n = 8502) were followed from birth for a median of 9.9years; 328 (3.9%) were diagnosed with type 1 diabetes. Cox proportional hazard model was used to assess the association of prognostic factors with the risk of type 1 diabetes in the two age groups. RESULTS: Children in the younger group tended to develop autoantibodies earlier than those in the older group did (mean age 1.5 vs 3.5years), especially insulin autoantibodies (IAA), which developed earlier than GAD autoantibodies (GADA). Children in the younger group also progressed to diabetes more rapidly than the children in the older group did (mean duration 1.9 vs 5.4 years). Children with autoantibodies first appearing against insulinoma antigen-2 (IA-2A) were found only in the older group. The significant diabetes risk associated with the country of origin in the younger group was no longer significant in the older group. Conversely, the diabetes risk associated with HLA genotypes was statistically significant also in the older group. Initial seroconversion after and before 2years of age was associated with decreased risk for diabetes diagnosis in children positive for multiple autoantibodies, but the diabetes risk did not decrease further with increasing age if initial seroconversion occurred after age 2. Diabetes risk associated with the minor alleles of rs1004446 (INS) was decreased in both the younger and older groups compared with other genotypes (HR 0.67). Diabetes risk was significantly increased with the minor alleles of rs2476601 (PTPN22) (HR 2.04 and 1.72), rs428595 (PPIL2) (HR 2.13 and 2.10), rs113306148 (PLEKHA1) (HR 2.34 and 2.21) and rs73043122 (RNASET2) (HR 2.31 and 2.54) (HR values represent the younger and older groups, respectively). CONCLUSIONS/INTERPRETATIONS: Diabetes at an early age is likely to be preceded by IAA autoantibodies and is a more aggressive form of the disease. Among older children, once multiple autoantibodies have been observed there does not seem to be any association between progression to diabetes and the age of the child or family history. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00279318.

BACKGROUND: Breastfeeding has beneficial effects on numerous health outcomes. OBJECTIVES: We investigated whether breastfeeding duration is associated with the development of early childhood autoimmunity, allergies, or obesity in a multinational prospective birth cohort. METHODS: Infants with genetic susceptibility for type 1 diabetes (n=8676) were followed for the development of autoantibodies to islet autoantigens or transglutaminase, allergies, and for anthropometric measurements to a median age of 8.3 y (IQR: 2.8-10.2y). Information on breastfeeding was collected at 3 mo of age and prospectively thereafter. A propensity score for longer breastfeeding was calculated from the variables that were likely to influence any or exclusive breastfeeding. The risks of developing autoimmunity or allergy were assessed using Cox proportional hazards models, and the risk of obesity at 5.5 y of age was assessed using logistic regression with adjustment by the propensity score. RESULTS: Breastfeeding duration was not associated with a lower risk of either islet or transglutaminase autoimmunity (any breastfeeding>6 mo, adjusted HR: 1.07; 95% CI: 0.96, 1.19; exclusive breastfeeding>3 mo, adjusted HR: 1.03; 95% CI: 0.92, 1.15). Exclusive breastfeeding>3 mo was associated with a decreased risk of seasonal allergic rhinitis (adjusted HR: 0.70; 95% CI: 0.53, 0.92; P6 mo and exclusive breastfeeding>3mo were associated with decreased risk of obesity (adjusted OR: 0.62; 95% CI: 0.47, 0.81; P&nbsp

AIMS/HYPOTHESIS: We aimed to investigate the association between maternal consumption of gluten-containing foods and other selected foods during late pregnancy and offspring risk of islet autoimmunity (IA) and type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study. METHODS: The TEDDY study recruited children at high genetic risk for type 1 diabetes at birth, and prospectively follows them for the development of IA and type 1 diabetes (n=8556). A questionnaire on the mother’s diet in late pregnancy was completed by 3-4 months postpartum. The maternal daily intake was estimated from a food frequency questionnaire for eight food groups: gluten-containing foods, non-gluten cereals, fresh milk, sour milk, cheese products, soy products, lean/medium-fat fish and fatty fish. For each food, we described the distribution of maternal intake among the four participating countries in the TEDDY study and tested the association of tertile of maternal food consumption with risk of IA and type 1 diabetes using forward selection time-to-event Cox regression. RESULTS: By 28 February 2019, 791 cases of IA and 328 cases of type 1 diabetes developed in TEDDY. There was no association between maternal late-pregnancy consumption of gluten-containing foods or any of the other selected foods and risk of IA, type 1 diabetes, insulin autoantibody-first IA or GAD autoantibody-first IA (all p≥0.01). Maternal gluten-containing food consumption in late pregnancy was higher in Sweden (242 g/day), Germany (247 g/day) and Finland (221 g/day) than in the USA (199 g/day) (pairwise p

OBJECTIVE: Type 1 diabetes is associated with autoantibodies to different organs that include the gut. The objective of the study was to determine the risk of developing gastric parietal cell autoimmunity in relation to other autoimmunity in individuals with a family history of type 1 diabetes. METHODS: Autoantibodies to the parietal cell autoantigen, H+ /K+ ATPase subunit A (ATP4A) was measured in 2218 first-degree relatives of patients with type 1 diabetes, who were prospectively followed from birth for a median of 14.5 years. All were also tested regularly for the development of islet autoantibodies, transglutaminase autoantibodies, and thyroid peroxidase autoantibodies. RESULTS: The cumulative risk to develop ATP4A autoantibodies was 8.1% (95% CI, 6.6-9.6) by age 20 years with a maximum incidence observed at age 2 years. Risk was increased in females (HR, 1.9; 95% CO, 1.3 - 2.8; P = .0004), relatives with the HLA DR4-DQ8/DR4-DQ8 genotype (HR, 3.4; 95% CI, 1.9 - 5.9; P < .0001) and in participants who also had thyroid peroxidase autoantibodies (HR, 3.7; 95% CI, 2.5-5.5; P < .0001). Risk for at least one of ATP4A-, islet-, transglutaminase-, or thyroid peroxidase-autoantibodies was 24.7% (95% CI, 22.6 - 26.7) by age 20 years and was 47.3% (95% CI, 41.3-53.3) in relatives who had an HLA DR3/DR4-DQ8, DR4-DQ8/DR4-DQ8, or DR3/DR3 genotype (P < .0001 vs other genotypes). CONCLUSIONS: Relatives of patients with type 1 diabetes who have risk genotypes are at very high risk for the development of autoimmunity against gastric and other organs.

OBJECTIVE Maternal glycemic dysregulation during pregnancy increases the risk of adverse health outcomes in her offspring; a risk thought to be linearly related to maternal hyperglycemia. It is hypothesized that changes in offspring DNA methylation (DNAm) underline these associations. RESEARCH DESIGN AND METHODS To address this hypothesis, we conducted fixed-effect meta-analyses of epigenome-wide association study (EWAS) results from eight birth cohorts investigating relationships between cord blood DNAm and fetal exposure to maternal glucose (Nmax= 3,503), insulin (Nmax= 2,062), and the area under the curve of glucose (AUCgluc) following oral glucose tolerance tests (OGTT, Nmax= 1,505). We performed look-up analyses for identified CpG dinucleotides (CpGs) in independent observational cohorts to examine associations between DNAm and cardiometabolic traits as well as tissue-specific gene expression. RESULTS Greater maternal AUCgluc was associated with lower cord blood DNAm at neighboring CpGs cg26974062 (β= -0.013 [SE=2.1x10-3], PFDR= 5.1x10-3) and cg02988288 (β= -0.013 [SE=2.3x10-3], PFDR =0.031) in TXNIP. These associations were attenuated in women with GDM. Lower blood DNAm at these two CpGs near TXNIP was associated with multiple metabolic traits later in life, including type 2 diabetes. TXNIP DNAm in liver biopsies was associated with hepatic expression of TXNIP. We observed little evidence of associations between either maternal glucose or insulin and cord blood DNAm. CONCLUSION Maternal hyperglycemia, as reflected by AUCgluc, was associated with lower cord blood DNAm at TXNIP. Associations between DNAm at these CpGs and metabolic traits in subsequent look-up analyses suggest that these may be candidate loci to investigate in future causal and mediation analyses.

OBJECTIVE Biomarkers predicting risk of type 1 diabetes (stage 3) among children with islet autoantibodies are greatly needed to prevent diabetic ketoacidosis and facilitate prevention therapies. RESEARCH DESIGN AND METHODS Children in the prospective The Environmental Determinants of Diabetes in the Young (TEDDY) study (n = 707) with confirmed diabetes-associated autoantibodies (GAD antibody, IA-2A, and/or insulin autoantibody) and two or more HbA1c measurements were followed to diabetes or median age 11.1 years. Once confirmed autoantibody positive, HbA1c was measured quarterly. Cox models and receiver operative characteristic curve analyses revealed the prognostic utility for risk of stage 3 on a relative HbA1c increase from the baseline visit or an oral glucose tolerance test (OGTT) 2-h plasma glucose (2-hPG). This HbA1c approach was then validated in the Type 1 Diabetes TrialNet Pathway to Prevention Study (TrialNet) (n = 1,190). RESULTS A 10% relative HbA1c increase from baseline best marked the increased risk of stage 3 in TEDDY (74% sensitive; 88% specific). Significant predictors of risk for HbA1c change were age and HbA1c at the baseline test, genetic sex, maximum number of autoantibodies, and maximum rate of HbA1c increase by time of change. The multivariable model featuring a HbA1c ≥10% increase and these additional factors revealed increased risk of stage 3 in TEDDY (hazard ratio [HR] 12.74, 95% CI 8.7–18.6, P < 0.0001) and TrialNet (HR 5.09, 95% CI 3.3–7.9, P < 0.0001). Furthermore, the composite model using HbA1c ≥10% increase performed similarly to an OGTT 2-hPG composite model (TEDDY area under the curve [AUC] 0.88 and 0.85, respectively) and to the HbA1c model in TrialNet (AUC 0.82). CONCLUSIONS An increase of ≥10% in HbA1c from baseline is as informative as OGTT 2-hPG in predicting risk of stage 3 in youth with genetic risk and diabetes-associated autoantibodies.

BACKGROUND: Early prediction of childhood type 1 diabetes reduces ketoacidosis at diagnosis and provides opportunities for disease prevention. However, only highly efficient approaches are likely to succeed in public health settings. We sought to identify efficient strategies for initial islet autoantibody screening in children younger than 15 years. METHODS: We harmonised data from five prospective cohorts from Finland (DIPP), Germany (BABYDIAB), Sweden (DiPiS), and the USA (DAISY and DEW-IT) into the Type 1 Diabetes Intelligence (T1DI) cohort. 24 662 children at high risk of diabetes enrolled before age 2 years were included and followed up for islet autoantibodies and diabetes until age 15 years, or type 1 diabetes onset, whichever occurred first. Islet autoantibodies measured included those against glutamic acid decarboxylase, insulinoma antigen 2, and insulin. Main outcomes were sensitivity and positive predictive value (PPV) of detected islet autoantibodies, tested at one or two fixed ages, for diagnosis of clinical type 1 diabetes. FINDINGS: Of the 24 662 participants enrolled in the Type 1 Diabetes Intelligence cohort, 6722 total were followed up to age 15 years or until onset of type 1 diabetes. Type 1 diabetes developed by age 15 years in 672 children, but did not develop in 6050 children. Optimal screening ages for two measurements were 2 years and 6 years, yielding sensitivity of 82% (95% CI 79–86) and PPV of 79% (95% CI 75–80) for diabetes by age 15 years. Autoantibody positivity at the beginning of each test age was highly predictive of diagnosis in the subsequent 2–5 99 year or 6–15-year age intervals. Autoantibodies usually appeared before age 6 years even in children diagnosed with diabetes much later in childhood. INTERPRETATION: Our results show that initial screening for islet autoantibodies at two ages (2 years and 6 years) is sensitive and efficient for public health translation but might require adjustment by country on the basis of population-specific disease characteristics. FUNDING: Juvenile Diabetes Research Foundation.

IntroductionThe Global Platform for the Prevention of Autoimmune Diabetes-SINT1A Study is designed as a randomised, placebo-controlled, double-blind, multicentre, multinational, primary prevention study aiming to assess whether daily administration of Bifidobacterium infantis from age 7 days to 6 weeks until age 12 months to children with elevated genetic risk for type 1 diabetes reduces the cumulative incidence of beta-cell autoantibodies in childhood.Methods and analysisInfants aged 7 days to 6 weeks from Germany, Poland, Belgium, UK and Sweden are eligible for study participation if they have a >10.0% expected risk for developing multiple beta-cell autoantibodies by age 6 years as determined by genetic risk score or family history and HLA genotype. Infants are randomised 1:1 to daily administration of B. infantis EVC001 or placebo until age 12 months and followed for a maximum of 5.5 years thereafter. The primary outcome is the development of persistent confirmed multiple beta-cell autoantibodies. Secondary outcomes are (1) Any persistent confirmed beta-cell autoantibody, defined as at least one confirmed autoantibody in two consecutive samples, including insulin autoantibodies, glutamic acid decarboxylase, islet tyrosine phosphatase 2 or zinc transporter 8, (2) Diabetes, (3) Transglutaminase autoantibodies associated with coeliac disease, (4) Respiratory infection rate in first year of life during supplementation and (5) Safety. Exploratory outcomes include allergy, antibody response to vaccines, alterations of the gut microbiome or blood metabolome, stool pH and calprotectin.Ethics and disseminationThe study was approved by the local ethical committees of the Technical University Munich, Medical Faculty, the Technische Universität Dresden, the Medizinische Hochschule Hannover, the Medical University of Warsaw, EC Research UZ Leuven and the Swedish ethical review authority. The results will be disseminated through peer-reviewed journals and conference presentations and will be openly shared after completion of the study.Trial registration numberNCT04769037.

Aims/hypothesis We studied the association of plasma ascorbic acid with the risk of developing islet autoimmunity and type 1 diabetes and examined whether SNPs in vitamin C transport genes modify these associations. Furthermore, we aimed to determine whether the SNPs themselves are associated with the risk of islet autoimmunity or type 1 diabetes. Methods We used a risk set sampled nested case–control design within an ongoing international multicentre observational study: The Environmental Determinants of Diabetes in the Young (TEDDY). The TEDDY study followed children with increased genetic risk from birth to endpoints of islet autoantibodies (350 cases, 974 controls) and type 1 diabetes (102 cases, 282 controls) in six clinical centres. Control participants were matched for family history of type 1 diabetes, clinical centre and sex. Plasma ascorbic acid concentration was measured at ages 6 and 12 months and then annually up to age 6 years. SNPs in vitamin C transport genes were genotyped using the ImmunoChip custom microarray. Comparisons were adjusted for HLA genotypes and for background population stratification. Results Childhood plasma ascorbic acid (mean ± SD 10.76 ± 3.54 mg/l in controls) was inversely associated with islet autoimmunity risk (adjusted OR 0.96 [95% CI 0.92, 0.99] per +1 mg/l), particularly islet autoimmunity, starting with insulin autoantibodies (OR 0.94 [95% CI 0.88, 0.99]), but not with type 1 diabetes risk (OR 0.93 [95% Cl 0.86, 1.02]). The SLC2A2 rs5400 SNP was associated with increased risk of type 1 diabetes (OR 1.77 [95% CI 1.12, 2.80]), independent of plasma ascorbic acid (OR 0.92 [95% CI 0.84, 1.00]). Conclusions/interpretation Higher plasma ascorbic acid levels may protect against islet autoimmunity in children genetically at risk for type 1 diabetes. Further studies are warranted to confirm these findings. Data availability The datasets generated and analysed during the current study will be made available in the NIDDK Central Repository at https://www.niddkrepository.org/studies/teddy.

Aims/hypothesis: Psychological stress has long been considered a possible trigger of type 1 diabetes, although prospective studies examining the link between psychological stress or life events during pregnancy and the child’s type 1 diabetes risk are rare. The objective of this study was to examine the association between life events during pregnancy and first-appearing islet autoantibodies (IA) in young children, conditioned by the child’s type 1 diabetes-related genetic risk. Methods: The IA status of 7317 genetically at-risk The Environmental Determinants of Diabetes in the Young (TEDDY) participants was assessed every 3months from 3months to 4years, and bi-annually thereafter. Reports of major life events during pregnancy were collected at study inception when the child was 3months of age and placed into one of six categories. Life events during pregnancy were examined for association with first-appearing insulin (IAA) (N = 222) or GAD (GADA) (N = 209) autoantibodies in the child until 6years of age using proportional hazard models. Relative excess risk due to interaction (RERI) by the child’s HLA-DR and SNP profile was estimated. Results: Overall, 65% of mothers reported a life event during pregnancy; disease/injury (25%), serious interpersonal (28%) and job-related (25%) life events were most common. The association of life events during pregnancy differed between IAA and GADA as the first-appearing autoantibody. Serious interpersonal life events correlated with increased risk of GADA-first only in HLA-DR3 children with the BACH2-T allele (HR 2.28, p < 0.0001), an additive interaction (RERI 1.87, p = 0.0004). Job-related life events were also associated with increased risk of GADA-first among HLA-DR3/4 children (HR 1.53, p = 0.04) independent of serious interpersonal life events (HR 1.90, p = 0.002), an additive interaction (RERI 1.19, p = 0.004). Job-related life events correlated with reduced risk of IAA-first (HR 0.55, p = 0.004), particularly in children with the BTNL2-GG allele (HR 0.48; 95% CI 0.31, 0.76). Conclusions/interpretation: Specific life events during pregnancy are differentially related to IAA vs GADA as first-appearing IA and interact with different HLA and non-HLA genetic factors, supporting the concept of different endotypes underlying type 1 diabetes. However, the mechanisms underlying these associations remain to be discovered. Life events may be markers for other yet-to-be-identified factors important to the development of first-appearing IA. Graphical abstract: [Figure not available: see fulltext.]

Here we report a case where the manifestations of insulin-dependent diabetes occurred following SARS-CoV-2 infection in a young individual in the absence of autoantibodies typical for type 1 diabetes mellitus. Specifically, a 19-year-old white male presented at our emergency department with diabetic ketoacidosis, C-peptide level of 0.62 µg l–1, blood glucose concentration of 30.6 mmol l–1 (552 mg dl–1) and haemoglobin A1c of 16.8%. The patient´s case history revealed probable COVID-19 infection 5–7 weeks before admission, based on a positive test for antibodies against SARS-CoV-2 proteins as determined by enzyme-linked immunosorbent assay. Interestingly, the patient carried a human leukocyte antigen genotype (HLA DR1-DR3-DQ2) considered to provide only a slightly elevated risk of developing autoimmune type 1 diabetes mellitus. However, as noted, no serum autoantibodies were observed against islet cells, glutamic acid decarboxylase, tyrosine phosphatase, insulin and zinc-transporter 8. Although our report cannot fully establish causality between COVID-19 and the development of diabetes in this patient, considering that SARS-CoV-2 entry receptors, including angiotensin-converting enzyme 2, are expressed on pancreatic β-cells and, given the circumstances of this case, we suggest that SARS-CoV-2 infection, or COVID-19, might negatively affect pancreatic function, perhaps through direct cytolytic effects of the virus on β-cells. The authors report the case of a young patient who displayed insulin-dependent diabetes after SARS-CoV-2 infection in the absence of autoantibodies indicative of autoimmune type 1 diabetes.

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.

Children’s plasma metabolome, especially lipidome, reflects gene regulation and dietary exposures, heralding the development of islet autoantibodies (IA) and type 1 diabetes (T1D). The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8,676 newborns by screening of HLA-DR-DQ genotypes at six clinical centers in four countries, profiled metabolome, and measured concentrations of ascorbic acid, 25-hydroxyvitamin D [25(OH)D], and erythrocyte membrane fatty acids following birth until IA seroconversion under a nested case-control design. We grouped children having an initial autoantibody only against insulin (IAA-first) or GAD (GADA-first) by unsupervised clustering of temporal lipidome, identifying a subgroup of children having early onset of each initial autoantibody, i.e., IAA-first by 12 months and GADA-first by 21 months, consistent with population-wide early seroconversion age. Differential analysis showed that infants having reduced plasma ascorbic acid and cholesterol experienced IAA-first earlier, while early onset of GADA-first was preceded by reduced sphingomyelins at infancy. Plasma 25(OH)D prior to either autoantibody was lower in T1D progressors compared with nonprogressors, with simultaneous lower diglycerides, lysophosphatidylcholines, triglycerides, and alanine before GADA-first. Plasma ascorbic acid and 25(OH)D at infancy were lower in HLA-DR3/DR4 children among IA case subjects but not in matched control subjects, implying gene expression dysregulation of circulating vitamins as latent signals for IA or T1D progression.

The risk for autoimmunity and subsequently type 1 diabetes is 10-fold higher in children with a first-degree family history of type 1 diabetes (FDR children) than in children in the general population (GP children). We analyzed children with high-risk HLA genotypes (n = 4,573) in the longitudinal TEDDY birth cohort to determine how much of the divergent risk is attributable to genetic enrichment in affected families. Enrichment for susceptible genotypes of multiple type 1 diabetes–associated genes and a novel risk gene, BTNL2, was identified in FDR children compared with GP children. After correction for genetic enrichment, the risks in the FDR and GP children converged but were not identical for multiple islet autoantibodies (hazard ratio [HR] 2.26 [95% CI 1.6–3.02]) and for diabetes (HR 2.92 [95% CI 2.05–4.16]). Convergence varied depending upon the degree of genetic susceptibility. Risks were similar in the highest genetic susceptibility group for multiple islet autoantibodies (14.3% vs .12.7%) and diabetes (4.8% vs. 4.1%) and were up to 5.8-fold divergent for children in the lowest genetic susceptibility group, decreasing incrementally in GP children but not in FDR children. These findings suggest that additional factors enriched within affected families preferentially increase the risk of autoimmunity and type 1 diabetes in lower genetic susceptibility strata.

Children at increased genetic risk for type 1 diabetes (T1D) after environmental exposures may develop pancreatic islet autoantibodies (IA) at a very young age. Metabolic profile changes over time may imply responses to exposures and signal development of the first IA. Our present research in The Environmental Determinants of Diabetes in the Young (TEDDY) study aimed to identify metabolome-wide signals preceding the first IA against GAD (GADA-first) or against insulin (IAA-first). We profiled metabolomes by mass spectrometry from children’s plasma at 3-month intervals after birth until appearance of the first IA. A trajectory analysis discovered each first IA preceded by reduced amino acid proline and branched-chain amino acids (BCAAs), respectively. With independent time point analysis following birth, we discovered dehydroascorbic acid (DHAA) contributing to the risk of each first IA, and γ-aminobutyric acid (GABAs) associated with the first autoantibody against insulin (IAA-first). Methionine and alanine, compounds produced in BCAA metabolism and fatty acids, also preceded IA at different time points. Unsaturated triglycerides and phosphatidylethanolamines decreased in abundance before appearance of either autoantibody. Our findings suggest that IAA-first and GADA-first are heralded by different patterns of DHAA, GABA, multiple amino acids, and fatty acids, which may be important to primary prevention of T1D.

OBJECTIVE To examine duration of breastfeeding and timing of complementary foods and risk of islet autoimmunity (IA). RESEARCH DESIGN AND METHODS The Environmental Determinants of Diabetes in the Young (TEDDY) study prospectively follows 8,676 children with increased genetic risk of type 1 diabetes (T1D) in the U.S., Finland, Germany, and Sweden. This study included 7,563 children with at least 9 months of follow-up. Blood samples were collected every 3 months from birth to evaluate IA, defined as persistent, confirmed positive antibodies to insulin (IAAs), GAD, or insulinoma antigen-2. We examined the associations between diet and the risk of IA using Cox regression models adjusted for country, T1D family history, HLA genotype, sex, and early probiotic exposure. Additionally, we investigated martingale residuals and log-rank statistics to determine cut points for ages of dietary exposures. RESULTS Later introduction of gluten was associated with increased risk of any IA and IAA. The hazard ratios (HRs) for every 1-month delay in gluten introduction were 1.05 (95% CI 1.01, 1.10; P = 0.02) and 1.08 (95% CI 1.00, 1.16; P = 0.04), respectively. Martingale residual analysis suggested that the age at gluten introduction could be grouped as 9 months. The risk of IA associated with introducing gluten before 4 months of age was lower (HR 0.68; 95% CI 0.47, 0.99), and the risk of IA associated with introducing it later than the age of 9 months was higher (HR 1.57; 95% CI 1.07, 2.31) than introduction between 4 and 9 months of age. CONCLUSIONS The timing of gluten-containing cereals and IA should be studied further.

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.

We examined the association between plasma 25-hydroxyvitamin D [25(OH)D] concentration and islet autoimmunity (IA) and whether vitamin D gene polymorphisms modify the effect of 25(OH)D on IA risk. We followed 8,676 children at increased genetic risk of type 1 diabetes at six sites in the U.S. and Europe. We defined IA as positivity for at least one autoantibody (GADA, IAA, or IA-2A) on two or more visits. We conducted a risk set sampled nested case-control study of 376 IA case subjects and up to 3 control subjects per case subject. 25(OH)D concentration was measured on all samples prior to, and including, the first IA positive visit. Nine polymorphisms in VDR, CYP24A, CYP27B1, GC, and RXRA were analyzed as effect modifiers of 25(OH)D. Adjusting for HLA-DR-DQ and ancestry, higher childhood 25(OH)D was associated with lower IA risk (odds ratio = 0.93 for a 5 nmol/L difference; 95% CI 0.89, 0.97). Moreover, this association was modified by VDR rs7975232 (interaction P = 0.0072), where increased childhood 25(OH)D was associated with a decreasing IA risk based upon number of minor alleles: 0 (1.00; 0.93, 1.07), 1 (0.92; 0.89, 0.96), and 2 (0.86; 0.80, 0.92). Vitamin D and VDR may have a combined role in IA development in children at increased genetic risk for type 1 diabetes.

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 diabetes-related 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 months were 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 IAA or GADA as the first appearing indication of autoimmunity.

OBJECTIVE This study investigates two-phase growth patterns in early life and their association with development of islet autoimmunity (IA) and type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS The Environmental Determinants of Diabetes in the Young (TEDDY) study followed 7,522 genetically high-risk children in Sweden, Finland, Germany, and the U.S. from birth for a median of 9.0 years (interquartile range 5.7–10.6) with available growth data. Of these, 761 (10.1%) children developed IA and 290 (3.9%) children were diagnosed with T1D. Bayesian two-phase piecewise linear mixed models with a random change point were used to estimate children’s individual growth trajectories. Cox proportional hazards models were used to assess the effects of associated growth parameters on the risks of IA and progression to T1D. RESULTS A higher rate of weight gain in infancy was associated with increased IA risk (hazard ratio [HR] 1.09 [95% CI 1.02, 1.17] per 1 kg/year). A height growth pattern with a lower rate in infancy (HR 0.79 [95% CI 0.70, 0.90] per 1 cm/year), higher rate in early childhood (HR 1.48 [95% CI 1.22, 1.79] per 1 cm/year), and younger age at the phase transition (HR 0.76 [95% CI 0.58, 0.99] per 1 month) was associated with increased risk of progression from IA to T1D. A higher rate of weight gain in early childhood was associated with increased risk of progression from IA to T1D (HR 2.57 [95% CI 1.34, 4.91] per 1 kg/year) in children with first-appearing GAD autoantibody only. CONCLUSIONS Growth patterns in early life better clarify how specific growth phases are associated with the development of T1D.

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 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)

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

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)

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.

Type 1 diabetes, a disease defined by absolute insulin deficiency, is considered a chronic autoimmune disorder resulting from the destruction of insulin-producing pancreatic β-cells. The incidence of childhood-onset type 1 diabetes has been increasing at a rate of 3%–5% per year globally. Despite the introduction of an impressive array of therapies aimed at improving disease management, no means for a practical “cure” exist. This said, hope remains high that any of a number of emerging technologies (e.g., continuous glucose monitoring, insulin pumps, smart algorithms), alongside advances in stem cell biology, cell encapsulation methodologies, and immunotherapy, will eventually impact the lives of those with recently diagnosed or established type 1 diabetes. However, efforts aimed at reversing insulin dependence do not address the obvious benefits of disease prevention. Hence, key “stretch goals” for type 1 diabetes research include identifying improved and increasingly practical means for diagnosing the disease at earlier stages in its natural history (i.e., early, presymptomatic diagnosis), undertaking such efforts in the population at large to optimally identify those with presymptomatic type 1 diabetes, and introducing safe and effective therapeutic options for prevention.

Progression to type 1 diabetes in children and adolescents is not uniform. Based on individual genetic background and environment, islet autoimmunity may develop at variable age, exhibit different autoantibody profiles and progress to clinical diabetes at variable rates. Here, we aimed to quantify the qualitative dynamics of sequential islet autoantibody profiles in order to identify longitudinal patterns that stratify progression rates to type 1 diabetes in multiple-autoantibody-positive children. Qualitative changes in antibody status on follow-up and progression rate to diabetes were analysed in 88 children followed from birth in the prospective BABYDIAB study who developed multiple autoantibodies against insulin (IAA), GAD (GADA), insulinoma-associated antigen-2 (IA-2A) and/or zinc transporter 8 (ZnT8A). An algorithm was developed to define similarities in sequential autoantibody profiles and hierarchical clustering was performed to group children with similar profiles. We defined nine clusters that distinguished children with respect to their sequential profiles of IAA, GADA, IA-2A and ZnT8A. Progression from first autoantibody appearance to clinical diabetes between clusters ranged from 6% (95% CI [0, 16.4]) to 73% (28.4, 89.6) within 5 years. Delayed progression was observed in children who were positive for only two autoantibodies, and for a cluster of 12 children who developed three or four autoantibodies but were IAA-negative in their last samples, nine of whom lost IAA positivity during follow-up. Among all children who first seroconverted to IAA positivity and developed at least two other autoantibodies (n = 57), the 10 year risk of diabetes was 23% (0, 42.9) in those who became IAA-negative during follow-up compared with 76% (58.7, 85.6) in those who remained IAA-positive (p = 0.004). The novel clustering approach provides a tool for stratification of islet autoantibody-positive individuals that has prognostic relevance, and new opportunities in elucidating disease mechanisms. Our data suggest that losing IAA reactivity is associated with delayed progression to type 1 diabetes in multiple-islet-autoantibody-positive children.

Ein Schwangerschaftsdiabetes erhoht das Risiko, spater im Leben an einem Typ-2-Diabetes zu erkranken. Jede zweite Frau mit Gestationsdiabetes mellitus (GDM) entwickelt innerhalb von 10 Jahren nach der Entbindung einen Typ-2-Diabetes, obwohl sich ihr Blutzuckerspiegel nach der Schwangerschaft zunachst wieder normalisiert. Zahlreiche Risikofaktoren, wie z. B. Insulintherapie wahrend der Schwangerschaft und Adipositas in der fruhen Schwangerschaft, werden mit der Entwicklung eines Typ-2-Diabetes postpartum in Verbindung gebracht. Wissenschaftler vom Institut fur Diabetesforschung, Helmholtz Zentrum Munchen, haben nun einen Risiko-Score entwickelt, der eine gute Abschatzung des Typ-2-Diabetes Risikos postpartum anhand weniger, leicht erfassbarer klinischer Variablen ermoglicht. Eine Risikoabschatzung ist essenziell fur die adaquate Planung von Kontrolluntersuchungen beim betreuenden Arzt.

Autoantibodies to pancreatic beta cell proteins are markers of asymptomatic type 1 diabetes. The aim was to determine whether autoantibodies to the beta cell protein tetraspanin 7 would improve the ability to identify autoimmunity against pancreatic beta cells.Full length and external domain fragments of tetraspanin 7 were expressed as luciferase-tagged fusion proteins and used in immunoprecipitation assays to measure autoantibodies in samples from 363 patients with type 1 diabetes at onset of disease, 503 beta cell autoantibody negative first-degree relatives of patients, and 212 relatives with autoantibodies to insulin, glutamic acid decarboxylase, insulinoma antigen 2 or zinc transporter 8.Antibody binding was observed against the full length and external domains of tetraspanin 7, and was strongest against the full length protein. Autoantibodies that could be inhibited by untagged tetraspanin 7 were detected in 5 (1%) of 503 autoantibody negative relatives, 3 (3.2%) of 94 autoantibody negative patients, 95 (35.3%) of 269 autoantibody positive patients, 1 (1%) of 98 single autoantibody positive relatives and 25 (21.9%) of 114 multiple autoantibody positive relatives. Progression to diabetes did not differ between multiple autoantibody positive relatives with and without tetraspanin 7 autoantibodies.Tetraspanin 7 is an autoantigen in type 1 diabetes. Tetraspanin 7 autoantibodies are a marker of type 1 diabetes, but provide minor additional value to existing autoantibodies in identifying beta cell autoimmunity.

OBJECTIVE Autoimmune diseases can be diagnosed early through the detection of autoantibodies. The aim of this study was to determine the risk of organ-specific autoimmunity in individuals with a family history of type 1 diabetes. RESEARCH DESIGN AND METHODS The study cohort included 2,441 first-degree relatives of patients with type 1 diabetes who were prospectively followed from birth to a maximum of 29.4 years (median 13.2 years). All were tested regularly for the development of autoantibodies associated with type 1 diabetes (islet), celiac disease (transglutaminase), or thyroid autoimmunity (thyroid peroxidase). The outcome was defined as an autoantibody-positive status on two consecutive samples. RESULTS In total, 394 relatives developed one (n = 353) or more (n = 41) of the three disease-associated autoantibodies during follow-up. The risk by age 20 years was 8.0% (95% CI 6.8–9.2%) for islet autoantibodies, 6.3% (5.1–7.5%) for transglutaminase autoantibodies, 10.7% (8.9–12.5%) for thyroid peroxidase autoantibodies, and 21.5% (19.5–23.5%) for any of these autoantibodies. Each of the three disease-associated autoantibodies was defined by distinct HLA, sex, genetic, and age profiles. The risk of developing any of these autoantibodies was 56.5% (40.8–72.2%) in relatives with HLA DR3/DR3 and 44.4% (36.6–52.2%) in relatives with HLA DR3/DR4-DQ8. CONCLUSIONS Relatives of patients with type 1 diabetes have a very high risk of organ-specific autoimmunity. Appropriate counseling and genetic and autoantibody testing for multiple autoimmune diseases may be warranted for relatives of patients with type 1 diabetes.

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.

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

Aims/hypothesis The beta cell protein tetraspanin 7 is a target of autoantibodies in individuals with type 1 diabetes. The aim of this study was to identify autoantibody epitope-containing regions and key residues for autoantibody binding.Methods Autoantibody epitope regions were identified by immunoprecipitation of luciferase-tagged single or multiple tetraspanin 7 domains using tetraspanin 7 antibody-positive sera. Subsequently, amino acids (AAs) relevant for autoantibody binding were identified by single AA mutations.Results In tetraspanin 7 antibody-positive sera, antibody binding was most frequent to tetraspanin 7 proteins that contained the NH2-terminal cytoplasmic domain 1 (C1; up to 39%) or COOH-terminal C3 (up to 22%). Binding to C3 was more frequent when the domain was expressed along with the flanking transmembrane domain, suggesting that conformation is likely to be important. Binding to external domains was not observed. Single AA mutations of C3 identified residues Y246, E247 and R239 as critical for COOH-terminal binding of 9/10, 10/10 and 8/10 sera tested, respectively. Mutation of cysteines adjacent to the transmembrane domain at either residues C235 or C236 resulted in both decreased (8/178 and 15/178 individuals, respectively; >twofold decrease) and increased (30/178 and 13/178 individuals, respectively; >twofold increase) binding in participant sera vs wild-type protein.Conclusions/interpretation We hypothesise that conformation and, potentially, modification of protein terminal ends of tetraspanin 7 may be important for autoantibody binding in type 1 diabetes.

Aims/hypothesis Adult-onset type 1 diabetes, in which the 65 kDa isoform of GAD (GAD65) is a major autoantigen, has a broad clinical phenotype encompassing variable need for insulin therapy. This study aimed to evaluate whether autoantibodies against N-terminally truncated GAD65 more closely defined a type 1 diabetes phenotype associated with insulin therapy. Methods Of 1114 participants with adult-onset diabetes from the Action LADA (latent autoimmune diabetes in adults) study with sufficient sera, we selected those designated type 1 (n = 511) or type 2 diabetes (n = 603) and retested the samples in radiobinding assays for human full-length GAD65 autoantibodies (f-GADA) and N-terminally truncated (amino acids 96–585) GAD65 autoantibodies (t-GADA). Individuals’ clinical phenotypes were analysed according to antibody binding patterns. Results Overall, 478 individuals were f-GADA-positive, 431 were t-GADA-positive and 628 were negative in both assays. Risk of insulin treatment was augmented in t-GADA-positive individuals (OR 4.69 [95% CI 3.57, 6.17]) compared with f-GADA-positive individuals (OR 3.86 [95% CI 2.95, 5.06]), irrespective of diabetes duration. Of 55 individuals who were f-GADA-positive but t-GADA-negative, i.e. with antibody binding restricted to the N-terminus of GAD65, the phenotype was similar to type 2 diabetes with low risk of progression to insulin treatment. Compared with these individuals with N-terminal GAD65-restricted GADA, t-GADA-positive individuals were younger at diagnosis (p = 0.005), leaner (p

Background Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes. Methods and findings The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%–6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%–4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%–13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%–4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%–9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%–3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%–54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%–60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case–control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations. Conclusions A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials., Anette-Gabriele Ziegler and colleagues report their novel genetic risk score for identifying infants with a high risk of developing type 1 diabetes., Author summary Why was this study done? Prevention of childhood diseases such as type 1 diabetes is of medical importance. Prevention of type 1 diabetes might be best achieved by intervention prior to the development of islet autoantibodies, which define a pre-symptomatic disease stage. Early intervention requires tools such as measures of genetic risk that identify future cases. Risk for type 1 diabetes in the absence of a family history is currently identified by HLA genotyping, with maximum identified risk reaching around 5%. Genetic scores derived from multiple risk loci may improve risk stratification for pre-symptomatic type 1 diabetes. What did the researchers do and find? Two previously proposed genetic scores for type 1 diabetes risk were calculated for over 3,000 children without a family history of type 1 diabetes but with 1 of the 2 highest-risk HLA genotypes (heterozygous DR3 and DR4-DQ8 or homozygous DR4-DQ8) participating in the TEDDY cohort study, which prospectively follows children from birth for the development of islet autoantibodies and diabetes. We found that both of the genetic scores, and a merged genetic score that combined the features of both, stratified the risk for islet autoantibodies and diabetes in the children. The upper quartile of the merged genetic score was associated with a >10% risk for the pre-symptomatic stage of multiple islet autoantibodies, and almost half the children who developed pre-symptomatic or symptomatic diabetes were identified by this score. What do these findings mean? Combining genetic information from multiple risk loci can improve the prediction of diseases such as type 1 diabetes. A genetic risk score model is proposed that could be used to recruit infants into early type 1 diabetes primary prevention trials. The model provides a new paradigm for genetic screening and selection of at-risk infants that, together with family history and HLA genotyping, could identify up to 25% of future childhood cases of type 1 diabetes from less than 1% of newborns.

Insights from prospective, longitudinal studies of individuals at risk for developing type 1 diabetes have demonstrated that the disease is a continuum that progresses sequentially at variable but predictable rates through distinct identifiable stages prior to the onset of symptoms. Stage 1 is defined as the presence of β-cell autoimmunity as evidenced by the presence of two or more islet autoantibodies with normoglycemia and is presymptomatic, stage 2 as the presence of β-cell autoimmunity with dysglycemia and is presymptomatic, and stage 3 as onset of symptomatic disease. Adoption of this staging classification provides a standardized taxonomy for type 1 diabetes and will aid the development of therapies and the design of clinical trials to prevent symptomatic disease, promote precision medicine, and provide a framework for an optimized benefit/risk ratio that will impact regulatory approval, reimbursement, and adoption of interventions in the early stages of type 1 diabetes to prevent symptomatic disease.

The purpose of this review was to describe the potential for general childhood population-based screening of risk of symptomatic type 1 diabetes (T1D) RECENT FINDINGS: The earliest stages of T1D can be identified and risk and rate of progression to symptomatic disease can be estimated by the presence of multiple islet autoantibodies and glucose intolerance (dysglycemia) in individuals screened for risk. Screening for human leukocyte antigen risk genotypes in neonates with follow-up detection of islet autoantibodies in childhood has been explored. An alternative approach of general childhood population-based detection of autoantibodies at well child visits provides an approach to detect a high proportion of children who will develop T1D. The Fr1da study was launched in Bavaria in 2015 to explore this concept.General childhood population-based screening for risk of T1D will allow detection of an at-risk population that can participate in natural history studies to better understand disease pathogenesis and intervention trials to prevent symptomatic disease and will provide a framework for public health-based prevention of childhood-onset T1D.

Islet autoantibodies, in addition to elevated blood glucose, define type 1 diabetes. These autoantibodies are detectable for a variable period of time before diabetes onset. Thus, the occurrence of islet autoantibodies is associated with the beginning of the disease process. The age at, and order in, which autoantibodies appear may be associated with different genetic backgrounds or environmental exposures, or both.Infants with HLA-DR high-risk genotypes (DR3/4, DR4/4, DR4/8 and DR3/3) were enrolled and prospectively followed with standardised autoantibody assessments quarterly throughout the first 4 years of life and then semi-annually thereafter.Autoantibodies appeared in 549/8,503 (6.5%) children during 34,091 person-years of follow-up. Autoantibodies at 3 (0.1%) and 6 (0.2%) months of age were rare. Of the 549, 43.7% had islet autoantibodies to insulin (IAA) only, 37.7% had glutamic acid decarboxylase autoantibodies (GADA) only, 13.8% had both GADA and IAA only, 1.6% had insulinoma antigen-2 only and 3.1% had other combinations. The incidence of IAA only peaked within the first year of life and declined over the following 5 years, but GADA only increased until the second year and remained relatively constant. GADA only were more common than IAA only in HLA-DR3/3 children but less common in HLA-DR4/8 children.Islet autoantibodies can occur very early in life and the order of appearance was related to HLA-DR-DQ genotype.

BackgroundProgression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown.MethodsIn 341 islet autoantibody-positive children with the human leucocyte antigen (HLA) DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype from the prospective TEDDY (The Environmental Determinants of Diabetes in the Young) study, we investigated whether a genetic risk score that had previously been shown to predict islet autoimmunity is also associated with disease progression.ResultsIslet autoantibody-positive children with a genetic risk score in the lowest quartile had a slower progression from single to multiple autoantibodies (p=0.018), from single autoantibodies to diabetes (p=0.004), and by trend from multiple islet autoantibodies to diabetes (p=0.06). In a Cox proportional hazards analysis, faster progression was associated with an increased genetic risk score independently of HLA genotype (HR for progression from multiple autoantibodies to type 1 diabetes, 1.27, 95% CI 1.02 to 1.58 per unit increase), an earlier age of islet autoantibody development (HR, 0.68, 95% CI 0.58 to 0.81 per year increase in age) and female sex (HR, 1.94, 95% CI 1.28 to 2.93).ConclusionsGenetic risk scores may be used to identify islet autoantibody-positive children with high-risk HLA genotypes who have a slow rate of progression to subsequent stages of autoimmunity and type 1 diabetes.

Importance Evidence is lacking regarding the consequences of antibiotic use in early life and the risk of certain autoimmune diseases. Objective To test the association between early-life antibiotic use and islet or celiac disease (CD) autoimmunity in genetically at-risk children prospectively followed up for type 1 diabetes (T1D) or CD. Design, Setting, and Participants HLA-genotyped newborns from Finland, Germany, Sweden, and the United States were enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY) study between November 20, 2004, and July 8, 2010. The dates of analysis were November 20, 2004, to August 31, 2014. Individuals from the general population and those having a first-degree relative with T1D were enrolled if they had 1 of 9 HLA genotypes associated with a risk for T1D. Exposures Parental reports of the most common antibiotics (cephalosporins, penicillins, and macrolides) used between age 3 months and age 4 years were recorded prospectively. Main Outcomes and Measures Islet autoimmunity and CD autoimmunity were defined as being positive for islet or tissue transglutaminase autoantibodies at 2 consecutive clinic visits at least 3 months apart. Hazard ratios and 95% CIs calculated from Cox proportional hazards regression models were used to assess the relationship between antibiotic use in early life before seroconversion and the development of autoimmunity. Results Participants were 8495 children (49.0% female) and 6558 children (48.7% female) enrolled in the TEDDY study who were tested for islet and tissue transglutaminase autoantibodies, respectively. Exposure to and frequency of use of any antibiotic assessed in this study in early life or before seroconversion did not influence the risk of developing islet autoimmunity or CD autoimmunity. Cumulative use of any antibiotic during the first 4 years of life was not associated with the appearance of any autoantibody (hazard ratio [HR], 0.98; 95% CI, 0.95-1.01), multiple islet autoantibodies (HR, 0.99; 95% CI, 0.95-1.03), or the transglutaminase autoantibody (HR, 1.00; 95% CI, 0.98-1.02). Conclusions and Relevance The use of the most prescribed antibiotics during the first 4 years of life, regardless of geographic region, was not associated with the development of autoimmunity for T1D or CD. These results suggest that a risk of islet or tissue transglutaminase autoimmunity need not influence the recommendations for clinical use of antibiotics in young children at risk for T1D or CD.

Aims/hypothesis Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes. Methods We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case–control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders. Results There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p

BACKGROUND AND OBJECTIVES: Few birth cohorts have prospectively followed development of type 1 diabetes (T1D) and celiac disease (CD) autoimmunities to determine timing, extent of co-occurrence, and associated genetic and demographic factors. METHODS: In this prospective birth cohort study, 8676 children at high genetic risk of both diseases were enrolled and 5891 analyzed in median follow-up of 66 months. Along with demographic factors and HLA-DR-DQ, genotypes for HLA-DPB1 and 5 non-HLA loci conferring risk of both T1D and CD were analyzed. RESULTS: Development of persistent islet autoantibodies (IAs) and tissue transglutaminase autoantibodies (tTGAs), as well as each clinical disease, was evaluated quarterly from 3 to 48 months of age and semiannually thereafter. IAs alone appeared in 367, tTGAs alone in 808, and both in 90 children. Co-occurrence significantly exceeded the expected rate. IAs usually, but not always, appeared earlier than tTGAs. IAs preceding tTGAs was associated with increasing risk of tTGAs (hazard ratio [HR]: 1.48; 95% confidence interval [CI]: 1.15–1.91). After adjusting for country, sex, family history, and all other genetic loci, significantly greater co-occurrence was observed in children with a T1D family history (HR: 2.80), HLA-DR3/4 (HR: 1.94) and single-nucleotide polymorphism rs3184504 at SH2B3 (HR: 1.53). However, observed co-occurrence was not fully accounted for by all analyzed factors. CONCLUSIONS: In early childhood, T1D autoimmunity usually precedes CD autoimmunity. Preceding IAs significantly increases the risk of subsequent tTGAs. Co-occurrence is greater than explained by demographic factors and extensive genetic risk loci, indicating that shared environmental or pathophysiological mechanisms may contribute to the increased risk.

Aims: The onset of clinical type 1 diabetes (T1D) is preceded by the occurrence of disease-specific autoantibodies. The level of autoantibody titers is known to be associated with progression time from the first emergence of autoantibodies to the onset of clinical symptoms, but detailed analyses of this complex relationship are lacking. We aimed to fill this gap by applying advanced statistical models. Methods: We investigated data of 613 children from the prospective TEDDY study who were persistent positive for IAA, GADA and/or IA2A autoantibodies. We used a novel approach of Bayesian joint modeling of longitudinal and survival data to assess the potentially time- and covariate-dependent association between the longitudinal autoantibody titers and progression time to T1D. Results: For all autoantibodies we observed a positive association between the titers and the T1D progression risk. This association was estimated as time-constant for IA2A, but decreased over time for IAA and GADA. For example the hazard ratio [95% credibility interval] for IAA (per transformed unit) was 3.38 [2.66, 4.38] at 6 months after seroconversion, and 2.02 [1.55, 2.68] at 36 months after seroconversion. Conclusions: These findings indicate that T1D progression risk stratification based on autoantibody titers should focus on time points early after seroconversion. Joint modeling techniques allow for new insights into these associations.

OBJECTIVE Gut microbiome dysbiosis is associated with numerous diseases, including type 1 diabetes. This pilot study determines how geographical location affects the microbiome of infants at high risk for type 1 diabetes in a population of homogenous HLA class II genotypes. RESEARCH DESIGN AND METHODS High-throughput 16S rRNA sequencing was performed on stool samples collected from 90 high-risk, nonautoimmune infants participating in The Environmental Determinants of Diabetes in the Young (TEDDY) study in the U.S., Germany, Sweden, and Finland. RESULTS Study site–specific patterns of gut colonization share characteristics across continents. Finland and Colorado have a significantly lower bacterial diversity, while Sweden and Washington state are dominated by Bifidobacterium in early life. Bacterial community diversity over time is significantly different by geographical location. CONCLUSIONS The microbiome of high-risk infants is associated with geographical location. Future studies aiming to identify the microbiome disease phenotype need to carefully consider the geographical origin of subjects.

ObjectiveTo understand the association between life stress, postpartum depression (PD), maternal perception of her child's risk for type 1 diabetes (T1D) and a mother's anxiety about her child's T1D risk in mothers of genetically at risk children in The Environmental Determinants of Diabetes in the Young (TEDDY) study. MethodsA short form of the state component (SAI) of the State-Trait Anxiety Inventory, negative life events (LE), the Edinburgh Postnatal Depression Scale (EPDS), and one question about the child's risk of developing T1D risk perceptions (RP) were given to mothers at the 6-month TEDDY clinic visit. The relationship between the four measures was modeled using multiple regressions. ResultsControlling for sociodemographic factors, significant country differences in SAI, LE, EPDS, and RP emerged. LE - particularly interpersonal LE - had a strong association to maternal anxiety about the baby's risk of diabetes. Both evidence of PD and accurate risk perceptions (RPs) about the child's T1D risk were associated with increased maternal anxiety about the child's T1D risk. ConclusionHeightened maternal anxiety in response to the news that a child is at increased risk for T1D is common. Mothers who have experienced recent negative LE, who experience PD and who accurately understand their child's risk may be particularly vulnerable to high levels of anxiety. The findings reported here need to be confirmed in future prospective studies. (Less)

Aims The Environmental Determinants of Diabetes in the Young planned biomarker discovery studies on longitudinal samples for persistent confirmed islet cell autoantibodies and type 1 diabetes using dietary biomarkers, metabolomics, microbiome/viral metagenomics and gene expression. Methods This article describes the details of planning The Environmental Determinants of Diabetes in the Young biomarker discovery studies using a nested case-control design that was chosen as an alternative to the full cohort analysis. In the frame of a nested case-control design, it guides the choice of matching factors, selection of controls, preparation of external quality control samples and reduction of batch effects along with proper sample allocation. Results and conclusion Our design is to reduce potential bias and retain study power while reducing the costs by limiting the numbers of samples requiring laboratory analyses. It also covers two primary end points (the occurrence of diabetes-related autoantibodies and the diagnosis of type 1 diabetes). The resulting list of case-control matched samples for each laboratory was augmented with external quality control samples. Copyright (C) 2013 John Wiley & Sons, Ltd. (Less)