Type 1 diabetes is a chronic, immune-mediated disease characterised by the destruction of insulinproducing cells. Standardised registry data show that type 1 diabetes incidence has increased 3-4% over the past three decades, supporting the role of environmental factors. Although several factors have been associated with type 1 diabetes, none of the associations are of a magnitude that could explain the rapid increase in incidence alone. Moreover, evidence of changing prevalence of these exposures over time is insufficient. Multiple factors could simultaneously explain the changing type 1 diabetes incidence, or the magnitude of observed associations could have been underestimated because of exposure measurement error, or the mismodelling of complex exposure-time-response relationships. The identification of environmental factors influencing the risk of type 1 diabetes and increased understanding of the cause at the individual level, regardless of the ability to explain the changing incidence at the population level, is important because of the implications for prevention.

DNA methylation may be involved in development of type 1 diabetes (T1D), but previous epigenomewide association studies were conducted among cases with clinically diagnosed diabetes. Using multiple pre-disease peripheral blood samples on the Illumina 450 K and EPIC platforms, we investigated longitudinal methylation differences between 87 T1D cases and 87 controls from the prospective Diabetes Autoimmunity Study in the Young (DAISY) cohort. Change in methylation with age differed between cases and controls in 10 regions. Average longitudinal methylation differed between cases and controls at two genomic positions and 28 regions. Some methylation differences were detectable and consistent as early as birth, including before and after the onset of preclinical islet autoimmunity. Results map to transcription factors, other protein coding genes, and non-coding regions of the genome with regulatory potential. The identification of methylation differences that predate islet autoimmunity and clinical diagnosis may suggest a role for epigenetics in T1D pathogenesis; however, functional validation is warranted. open Scientific RepoRtS | (2020) 10:3721 | https://doi.

OBJECTIVES: To determine the association between the amount of gluten intake in childhood and later celiac disease (CD), for which data are currently scarce. METHODS: The prospective Diabetes Autoimmunity Study in the Young cohort includes 1875 at-risk children with annual estimates of gluten intake (grams/d) from age 1 year. From 1993 through January 2017, 161 children, using repeated tissue transglutaminase (tTGA) screening, were identified with CD autoimmunity (CDA) and persistent tTGA positivity; of these children, 85 fulfilled CD criteria of biopsy-verified histopathology or persistently high tTGA levels. Cox regression, modeling gluten intake between ages 1 and 2 years (i.e., in 1-year-olds), and joint modeling of cumulative gluten intake throughout childhood were used to estimate hazard ratios adjusted for confounders (aHR). RESULTS: Children in the highest third of gluten intake between the ages of 1 and 2 years had a 2-fold greater hazard of CDA (aHR 2.17; 95% confidence interval [CI], 1.22–3.88; P value = 0.01) and CD (aHR 1.96; 95% CI, 0.90–4.24; P value = 0.09) than those in the lowest third. The risk of developing CDA increased by 5% per daily gram increase in gluten intake (aHR 1.05; 95% CI, 1.00–1.09; P value = 0.04) in 1-year-olds. The association between gluten intake in 1-year-olds and later CDA or CD did not differ by the child's human leukocyte antigen genotype. The incidence of CD increased with increased cumulative gluten intake throughout childhood (e.g., aHR 1.15 per SD increase in cumulative gluten intake at age 6; 95% CI, 1.00–1.32; P value = 0.04). DISCUSSION: Gluten intake in 1-year-olds is associated with the future onset of CDA and CD in children at risk for the disease.

Background: The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. TEDDY has collected heterogenous data longitudinally to gain insights into the environmental and biological mechanisms driving the progression to persistent islet autoantibodies. Methods: We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on timevarying metabolomics data integrated with time-invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble-based feature evaluation was utilized to identify a small set of predictive features that can be interrogated to better understand the pathogenesis leading up to persistent islet autoimmunity. Results: The final integrative machine learning model included 42 disparate features, returning a cross-validated receiver operating characteristic area under the curve (AUC) of 0.74 and an AUC of 0.65 on an independent validation dataset. The model identified a principal set of 20 time-invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA-DR genotypes) and two demographic markers (gestational age and exposure to a prebiotic formula). Integration with the metabolome identified 22 supplemental metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies. Conclusions: The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to three pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate, suggesting that these metabolic processes may play a role in triggering islet autoimmunity.

To study the association of gluten intake with development of islet autoimmunity and progression to type 1 diabetes. RESEARCH DESIGN AND METHODSThe Diabetes Autoimmunity Study in the Young (DAISY) follows children with an increased risk of type 1 diabetes. Blood samples were collected at 9, 15, and 24 months of age, and annually thereafter. Islet autoimmunity was defined by the appearance of at least one autoantibody against insulin, IA2, GAD, or ZnT8 (zinc transporter 8) in at least two consecutive blood samples. Using food frequency questionnaires, we estimated the gluten intake (in grams per day) annually from 1 year of age. Cox regression modeling early gluten intake, and joint modeling of the cumulative gluten intake during follow-up, were used to estimate hazard ratios adjusted for confounders (aHR). RESULTSBy August 2017, 1,916 subjects were included (median age at end of follow-up 13.5 years), islet autoimmunity had developed in 178 participants, and 56 of these progressed to type 1 diabetes. We found no association between islet autoimmunity and gluten intake at 1-2 years of age or during follow-up (aHR per 4 g/day increase in gluten intake 1.00, 95% CI 0.85-1.17 and 1.01, 0.99-1.02, respectively). We found similar null results for progression from islet autoimmunity to type 1 diabetes. Introduction of gluten at <4 months of age was associated with an increased risk of progressing from islet autoimmunity to type 1 diabetes compared with introduction at 4-5.9 months (aHR 8.69, 95% CI 1.69-44.8). CONCLUSIONSOur findings indicate no strong rationale to reduce the amount of gluten in high-risk children to prevent development of type 1 diabetes.

The role of diet in type 1 diabetes development is poorly understood. Metabolites, which reflect dietary response, may help elucidate this role. We explored metabolomics and lipidomics differences between 352 cases of islet autoimmunity (IA) and controls in the TEDDY (The Environmental Determinants of Diabetes in the Young) study. We created dietary patterns reflecting pre-IA metabolite differences between groups and examined their association with IA. Secondary outcomes included IA cases positive for multiple autoantibodies (mAb+). The association of 853 plasma metabolites with outcomes was tested at seroconversion to IA, just prior to seroconversion, and during infancy. Key compounds in enriched metabolite sets were used to create dietary patterns reflecting metabolite composition, which were then tested for association with outcomes in the nested case-control subset and the full TEDDY cohort. Unsaturated phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, glucosylceramides, and phospholipid ethers in infancy were inversely associated with mAb+ risk, while dicarboxylic acids were associated with an increased risk. An infancy dietary pattern representing higher levels of unsaturated phosphatidylcholines and phospholipid ethers, and lower sphingomyelins was protective for mAb+ in the nested case-control study only. Characterization of this high-risk infant metabolomics profile may help shape the future of early diagnosis or prevention efforts.

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Improved access to family planning services and increased use of LARC are associated with lower risk of PTB.