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Maternal metabolism during pregnancy shapes offspring health via in utero programming. In the Healthy Start study, we identified five subgroups of pregnant women based on conventional metabolic biomarkers: Reference (n = 360); High HDL-C (n = 289); Dyslipidemic–High TG (n = 149); Dyslipidemic–High FFA (n = 180); Insulin Resistant (IR)–Hyperglycemic (n = 87). These subgroups not only captured metabolic heterogeneity among pregnant participants but were also associated with offspring obesity in early childhood, even among women without obesity or diabetes. Here, we utilize metabolomics data to enrich characterization of the metabolic subgroups and identify key compounds driving between-group differences. We analyzed fasting blood samples from 1065 pregnant women at 18 gestational weeks using untargeted metabolomics. We used weighted gene correlation network analysis (WGCNA) to derive a global network based on the Reference subgroup and characterized distinct metabolite modules representative of the different metabolomic profiles. We used the mummichog algorithm for pathway enrichment and identified key compounds that differed across the subgroups. Eight metabolite modules representing pathways such as the carnitine–acylcarnitine translocase system, fatty acid biosynthesis and activation, and glycerophospholipid metabolism were identified. A module that included 189 compounds related to DHA peroxidation, oxidative stress, and sex hormone biosynthesis was elevated in the Insulin Resistant–Hyperglycemic vs. the Reference subgroup. This module was positively correlated with total cholesterol (R:0.10; p-value < 0.0001) and free fatty acids (R:0.07; p-value < 0.05). Oxidative stress and inflammatory pathways may underlie insulin resistance during pregnancy, even below clinical diabetes thresholds. These findings highlight potential therapeutic targets and strategies for pregnancy risk stratification and reveal mechanisms underlying the developmental origins of metabolic disease risk. [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers). Methods: This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre-vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7). Results: The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions. Conclusion: Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function. [ABSTRACT FROM AUTHOR]

Given the differential risk of type 1 diabetes (T1D) in offspring of affected fathers versus affected mothers and our observation that T1D cases have differential DNA methylation near the imprinted DLGAP2 gene compared to controls, we examined whether methylation near DLGAP2 mediates the association between T1D family history and T1D risk. In a nested case–control study of 87 T1D cases and 87 controls from the Diabetes Autoimmunity Study in the Young, we conducted causal mediation analyses at 12 DLGAP2 region CpGs to decompose the effect of family history on T1D risk into indirect and direct effects. These effects were estimated from two regression models adjusted for the human leukocyte antigen DR3/4 genotype: a linear regression of family history on methylation (mediator model) and a logistic regression of family history and methylation on T1D (outcome model). For 8 of the 12 CpGs, we identified a significant interaction between T1D family history and methylation on T1D risk. Accounting for this interaction, we found that the increased risk of T1D for children with affected mothers compared to those with no family history was mediated through differences in methylation at two CpGs (cg27351978, cg00565786) in the DLGAP2 region, as demonstrated by a significant pure natural indirect effect (odds ratio (OR) = 1.98, 95% confidence interval (CI): 1.06–3.71) and nonsignificant total natural direct effect (OR = 1.65, 95% CI: 0.16–16.62) (for cg00565786). In contrast, the increased risk of T1D for children with an affected father or sibling was not explained by DNA methylation changes at these CpGs. Results were similar for cg27351978 and robust in sensitivity analyses. Lastly, we found that DNA methylation in the DLGAP2 region was associated (P < 0.05) with gene expression of nearby protein-coding genes DLGAP2, ARHGEF10, ZNF596, and ERICH1. Results indicate that the maternal protective effect conferred through exposure to T1D in utero may operate through changes to DNA methylation that have functional downstream consequences. [ABSTRACT FROM AUTHOR]

Background: Studies of the role of iron in the risk of type 1 diabetes (T1D) have been inconsistent. Given that iron generates reactive oxygen radicals, which can lead to oxidative damage and apoptosis in the beta cells of the pancreas, we examined whether iron intake was associated with the risk of progressing to T1D in individuals with islet autoimmunity (IA), the pre-clinical phase of T1D. Methods: DAISY is a prospective cohort following 2,547 children at increased risk for IA and progression to T1D. IA is defined as at least two consecutive serum samples positive for at least one autoantibody (insulin, GAD, IA-2, or ZnT8). We measured dietary intake at the time of IA seroconversion in 175 children with IA, and of these, 64 progressed to T1D. We used Cox regression to examine the association between energy-adjusted iron intake and progression to T1D, adjusting for HLA-DR3/4 genotype, race/ethnicity, age at seroconversion, presence of multiple autoantibodies at seroconversion, and multiple vitamin use. In addition, we tested whether this association was modified by vitamin C or calcium intake. Results: In children with IA, high iron intake (as defined as above the 75th percentile, > 20.3 mg/day) was associated with decreased risk of progression to T1D compared to moderate iron intake (as defined by the middle 25-75th percentiles, 12.7-20.3 mg/day) (adjusted hazard ratio (HR): 0.35; 95% confidence interval (CI): 0.15, 0.79). The association between iron intake and T1D was not modified by vitamin C nor calcium intake. In a sensitivity analysis, the removal of six children who had been diagnosed with celiac disease prior to IA seroconversion did not affect this association. Conclusion: Higher iron intake at the time of IA seroconversion is associated with a lower risk of progression to T1D, independent of multivitamin supplement use. Further research that includes plasma biomarkers of iron status is needed to investigate the relationship between iron and the risk of T1D. [ABSTRACT FROM AUTHOR]

Oxylipins, pro-inflammatory and pro-resolving lipid mediators, are associated with the risk of type 1 diabetes (T1D) and may be influenced by diet. This study aimed to develop a nutrient pattern related to oxylipin profiles and test their associations with the risk of T1D among youth. The nutrient patterns were developed with a reduced rank regression in a nested case-control study (n = 335) within the Diabetes Autoimmunity Study in the Young (DAISY), a longitudinal cohort of children at risk of T1D. The oxylipin profiles (adjusted for genetic predictors) were the response variables. The nutrient patterns were tested in the case-control study (n = 69 T1D cases, 69 controls), then validated in the DAISY cohort using a joint Cox proportional hazards model (n = 1933, including 81 T1D cases). The first nutrient pattern (NP1) was characterized by low beta cryptoxanthin, flavanone, vitamin C, total sugars and iron, and high lycopene, anthocyanidins, linoleic acid and sodium. After adjusting for T1D family history, the HLA genotype, sex and race/ethnicity, NP1 was associated with a lower risk of T1D in the nested case-control study (OR: 0.44, p = 0.0126). NP1 was not associated with the risk of T1D (HR: 0.54, p-value = 0.1829) in the full DAISY cohort. Future studies are needed to confirm the nested case-control findings and investigate the modifiable factors for oxylipins. [ABSTRACT FROM AUTHOR]

Longitudinal changes in gene expression during islet autoimmunity (IA) may provide insight into biological processes that explain progression to type 1 diabetes (T1D). We identified individuals from Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, autoantibodies present on two or more visits. Illumina's NovaSeq 6000 was used to quantify gene expression in whole blood. With linear mixed models we tested for changes in expression after IA that differed across individuals who progressed to T1D (progressors) (n = 25), reverted to an autoantibody-negative stage (reverters) (n = 47), or maintained IA positivity but did not develop T1D (maintainers) (n = 66). Weighted gene coexpression network analysis was used to identify coexpression modules. Gene Ontology pathway analysis of the top 150 differentially expressed genes (nominal P < 0.01) identified significantly enriched pathways including leukocyte activation involved in immune response, innate immune response, and regulation of immune response. We identified a module of 14 coexpressed genes with roles in the innate immunity. The hub gene, LTF, is known to have immunomodulatory properties. Another gene within the module, CAMP, is potentially relevant based on its role in promoting β-cell survival in a murine model. Overall, results provide evidence of alterations in expression of innate immune genes prior to onset of T1D. [ABSTRACT FROM AUTHOR]

Objective: Illumina BeadChip arrays are commonly used to generate DNA methylation data for large epidemiological studies. Updates in technology over time create challenges for data harmonization within and between studies, many of which obtained data from the older 450K and newer EPIC platforms. The pre-processing pipeline for DNA methylation is not trivial, and influences the downstream analyses. Incorporating different platforms adds a new level of technical variability that has not yet been taken into account by recommended pipelines. Our study evaluated the performance of various tools on different versions of platform data harmonization at each step of pre-processing pipeline, including quality control (QC), normalization, batch effect adjustment, and genomic inflation. We illustrate our novel approach using 450K and EPIC data from the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Results: We found normalization and probe filtering had the biggest effect on data harmonization. Employing a meta-analysis was an effective and easily executable method for accounting for platform variability. Correcting for genomic inflation also helped with harmonization. We present guidelines for studies seeking to harmonize data from the 450K and EPIC platforms, which includes the use of technical replicates for evaluating numerous pre-processing steps, and employing a meta-analysis. [ABSTRACT FROM AUTHOR]

Reversion of islet autoimmunity (IA) may point to mechanisms that prevent IA progression. We followed 199 individuals who developed IA during the Diabetes Autoimmunity Study in the Young. Untargeted metabolomics was performed in serum samples following IA. Cox proportional hazards models were used to test whether the metabolites (2,487) predicted IA reversion: two or more consecutive visits negative for all autoantibodies. We conducted a principal components analysis (PCA) of the top metabolites; |hazard ratio (HR) >1.25| and nominal P < 0.01. Phosphatidylcholine (16:0_18:1(9Z)) was the strongest individual metabolite (HR per 1 SD 2.16, false discovery rate (FDR)-adjusted P = 0.0037). Enrichment analysis identified four clusters (FDR P < 0.10) characterized by an overabundance of sphingomyelin (d40:0), phosphatidylcholine (16:0_18:1(9Z)), phosphatidylcholine (30:0), and l-decanoylcarnitine. Overall, 63 metabolites met the criteria for inclusion in the PCA. PC1 (HR 1.4, P < 0.0001), PC2 (HR 0.85, P = 0.0185), and PC4 (HR 1.28, P = 0.0103) were associated with IA reversion. Given the potential influence of diet on the metabolome, we investigated whether nutrients were correlated with PCs. We identified 20 nutrients that were correlated with the PCs (P < 0.05). Total sugar intake was the top nutrient. Overall, we identified an association between phosphatidylcholine, sphingomyelin, and carnitine levels and reversion of IA. [ABSTRACT FROM AUTHOR]

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Objective: Our aim was to elucidate the role of diet in type 1 diabetes (T1D) by examining combinations of nutrient intake in the progression from islet autoimmunity (IA) to T1D. Methods: We measured 2457 metabolites and dietary intake at the time of seroconversion in 132 IA‐positive children in the prospective Diabetes Autoimmunity Study in the Young. IA was defined as the first of two consecutive visits positive for at least one autoantibody (insulin, GAD, IA‐2, or ZnT8). By December 2018, 40 children progressed to T1D. Intakes of 38 nutrients were estimated from semiquantitative food frequency questionnaires. We tested the association of each metabolite with progression to T1D using multivariable Cox regression. Nutrient patterns that best explained variation in candidate metabolites were identified using reduced rank regression (RRR), and their association with progression to T1D was tested using Cox regression adjusting for age at seroconversion and high‐risk HLA genotype. Results: In stepwise selection, 22 nutrients significantly predicted at least two of the 13 most significant metabolites associated with progression to T1D, and were included in RRR. A nutrient pattern corresponding to intake lower in linoleic acid, niacin, and riboflavin, and higher in total sugars, explained 18% of metabolite variability. Children scoring higher on this metabolite‐related nutrient pattern at seroconversion had increased risk for progressing to T1D (HR = 3.17, 95%CI = 1.42‐7.05). Conclusions: Combinations of nutrient intake reflecting candidate metabolites are associated with increased risk of T1D, and may help focus dietary prevention efforts. [ABSTRACT FROM AUTHOR]

Objective: Mechanisms underlying the role of non‐human leukocyte antigen (HLA) genetic risk variants in type 1 diabetes (T1D) are poorly understood. We aimed to test the association between methylation and non‐HLA genetic risk. Methods: We conducted a methylation quantitative trait loci (mQTL) analysis in a nested case‐control study from the Dietary Autoimmunity Study in the Young. Controls (n = 83) were frequency‐matched to T1D cases (n = 83) based on age, race/ethnicity, and sample availability. We evaluated 13 non‐HLA genetic markers known be associated with T1D. Genome‐wide methylation profiling was performed on peripheral blood samples collected prior to T1D using the Illumina 450 K (discovery set) and infinium methylation EPIC beadchip (EPIC validation) platforms. Linear regression models, adjusting for age and sex, were used to test to each single nucleotide polymorphism (SNP) ‐probe combination. Logistic regression models were used to test the association between T1D and methylation levels among probes with a significant mQTL. A meta‐analysis was used to combine odds ratios from the two platforms. Results: We identified 10 SNP‐methylation probe pairs (false discovery rate (FDR) adjusted P <.05 and validation P <.05). Probes were associated with the GSDMB, C1QTNF6, IL27, and INS genes. The cg03366382 (OR: 1.9, meta‐P =.0495), cg21574853 (OR: 2.5, meta‐P =.0232), and cg25336198 (odds ratio: 6.6, meta‐P =.0081) probes were significantly associated with T1D. The three probes were located upstream from the INS transcription start site. Conclusions: We confirmed an association between DNA methylation and rs689 that has been identified in related studies. Measurements in our study preceded the onset of T1D suggesting methylation may have a role in the relationship between INS variation and T1D development. [ABSTRACT FROM AUTHOR]

We examined whether change in added sugar intake is associated with change in δ13C, a novel sugar biomarker, in thirty-nine children aged 5–10 years selected from a Colorado (USA) prospective cohort of children at increased risk for type 1 diabetes. Reported added sugar intake via FFQ and δ13C in erythrocytes were measured at two time points a median of 2 years apart. Change in added sugar intake was associated with change in the δ13C biomarker, where for every 1-g increase in added sugar intake between the two time points, there was an increase in δ13C of 0⋅0082 (P = 0⋅0053), independent of change in HbA1c and δ15N. The δ13C biomarker may be used as a measure of compliance in an intervention study of children under the age of 10 years who are at increased risk for type 1 diabetes, in which the goal was to reduce dietary sugar intake. [ABSTRACT FROM AUTHOR]

Objective: To study the association of gluten intake with development of islet autoimmunity and progression to type 1 diabetes.Research Design and Methods: The 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).Results: By 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).Conclusions: Our findings indicate no strong rationale to reduce the amount of gluten in high-risk children to prevent development of type 1 diabetes. [ABSTRACT FROM AUTHOR]

We assessed associations between infant diet (e.g., breastfeeding and introduction to solid foods) and DNA methylation in infancy and childhood. We measured DNA methylation in peripheral blood collected in infancy (9–15 months of age) in 243 children; and in a subset of 50 children, we also measured methylation in childhood (6–9 years of age) to examine persistence, and at birth (in cord blood) to examine temporality. We performed multivariable linear regression of infant diet on the outcome of methylation using epigenome-wide and candidate site approaches. We identified six novel CpG sites associated with breastfeeding duration using an EWAS approach. One differentially methylated site presented directionally consistent associations with breastfeeding (cg00574958, CPT1A) in infancy and childhood but not at birth. Two differentially methylated sites in infancy (cg19693031, TXNIP; cg23307264, KHSRP) were associated with breastfeeding and were not present at birth; however, these associations did not persist into childhood. Associations between infant diet and methylation in infancy at three sites (cg22369607, AP001525.1; cg2409200, TBCD; cg27173510, PGBD5) were also present at birth, suggesting the influence of exposures other than infant diet. Infant diet exposures are associated with persistent methylation differences in CPT1A, which may be one mechanism behind infant diet's long-term health effects. [ABSTRACT FROM AUTHOR]

Environmental factors including viruses, diet, and the metabolome have been linked with the appearance of islet autoimmunity (IA) that precedes development of type 1 diabetes (T1D). We measured global DNA methylation (DNAm) and untargeted metabolomics prior to IA and at the time of seroconversion to IA in 92 IA cases and 91 controls from the Diabetes Autoimmunity Study in the Young (DAISY). Causal mediation models were used to identify seven DNAm probe-metabolite pairs in which the metabolite measured at IA mediated the protective effect of the DNAm probe measured prior to IA against IA risk. These pairs included five DNAm probes mediated by histidine (a metabolite known to affect T1D risk), one probe (cg01604946) mediated by phostidyl choline p-32:0 or o-32:1, and one probe (cg00390143) mediated by sphingomyelin d34:2. The top 100 DNAm probes were over-represented in six reactome pathways at the FDR <0.1 level (q = 0.071), including transport of small molecules and inositol phosphate metabolism. While the causal pathways in our mediation models require further investigation to better understand the biological mechanisms, we identified seven methylation sites that may improve our understanding of epigenetic protection against T1D as mediated by the metabolome. [ABSTRACT FROM AUTHOR]