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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.

BackgroundHeterogeneity exists in type 1 diabetes (T1D) development and presentation. Islet autoantibodies form the foundation for T1D diagnostic and staging efforts. We hypothesized that autoantibodies can be used to identify heterogeneity in T1D before, at, and after diagnosis, and in response to disease modifying therapies. at clinically relevant timepoints throughout T1D progression.MethodsWe performed a systematic review assessing 10 years of original research studies examining relationships between autoantibodies and heterogeneity during disease progression, at the time of diagnosis, after diagnosis, and in response to disease modifying therapies in individuals at risk for T1D or within 1 year of T1D diagnosis.Results10,067 papers were screened. Out of 151 that met data extraction criteria, 90 studies characterized heterogeneity before clinical diagnosis. Autoantibody type/target was most commonly examined, followed by autoantibody number, titer, order of seroconversion, affinity, and novel islet autoantibodies/epitopes. Recurring themes included positive relationships of autoantibody number and specific types and titers with disease progression, differing clinical phenotypes based on the order of autoantibody seroconversion, and interactions with age and genetics. Overall, reporting of autoantibody assay performance was commonly included; however, only 43% (65/151) included information about autoantibody assay standardization efforts. Populations studied were almost exclusively of European ancestry.ConclusionsCurrent evidence most strongly supports the application of autoantibody features to more precisely define T1D before clinical diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly when considered in relation to age and genetic risk, could offer more precise stratification. Increased participation in autoantibody standardization efforts is a critical step to improving future applicability of autoantibody-based precision medicine in T1D.Plain Language SummaryWe performed a systematic review to ascertain whether islet autoantibodies, biomarkers of autoimmunity against insulin-producing cells, could aid in stratifying individuals with different clinical presentations of type 1 diabetes. We found existing evidence most strongly supporting the application of these biomarkers to the period before clinical diagnosis, when certain autoantibody features (number, type) and the age when they develop, can provide important information for patients and care providers on what to expect for future type 1 diabetes progression.

BackgroundType 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Efforts to prevent T1D have focused on modulating immune responses and supporting beta cell health; however, heterogeneity in disease progression and responses to therapies have made these efforts difficult to translate to clinical practice, highlighting the need for precision medicine approaches to T1D prevention.MethodsTo understand the current state of knowledge regarding precision approaches to T1D prevention, we performed a systematic review of randomized-controlled trials from the past 25 years testing disease-modifying therapies in T1D and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument.ResultsWe identified 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss in individuals at disease onset. Seventeen agents tested, mostly immunotherapies, showed benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employed precision analyses to assess features linked to treatment response. Age, measures of beta cell function and immune phenotypes were most frequently tested. However, analyses were typically not prespecified, with inconsistent methods reporting, and tended to report positive findings.ConclusionsWhile the quality of prevention and intervention trials was overall high, low quality of precision analyses made it difficult to draw meaningful conclusions that inform clinical practice. Thus, prespecified precision analyses should be incorporated into the design of future studies and reported in full to facilitate precision medicine approaches to T1D prevention.Plain Language SummaryType 1 diabetes (T1D) results from the destruction of insulin-producing cells in the pancreas, necessitating lifelong insulin dependence. T1D prevention remains an elusive goal, largely due to immense variability in disease progression. Agents tested to date in clinical trials work in a subset of individuals, highlighting the need for precision medicine approaches to prevention. We systematically reviewed clinical trials of disease-modifying therapy in T1D. While age, measures of beta cell function, and immune phenotypes were most commonly identified as factors that influenced treatment response, the overall quality of these studies was low. This review reveals an important need to proactively design clinical trials with well-defined analyses to ensure that results can be interpreted and applied to clinical practice.

Over 6.37 million people have died from COVID-19 worldwide, but factors influencing COVID-19-related mortality remain understudied. We aimed to describe and identify risk factors for COVID-19 mortality in the Colorado Center for Personalized Medicine (CCPM) Biobank using integrated data sources, including Electronic Health Records (EHRs). We calculated cause-specific mortality and case-fatality rates for COVID-19 and common pre-existing health conditions defined by diagnostic phecodes and encounters in EHRs. We performed multivariable logistic regression analyses of the association between each pre-existing condition and COVID-19 mortality. Of the 155,859 Biobank participants enrolled as of July 2022, 20,797 had been diagnosed with COVID-19. Of 5334 Biobank participants who had died, 190 were attributed to COVID-19. The case-fatality rate was 0.91% and the COVID-19 mortality rate was 122 per 100,000 persons. The odds of dying from COVID-19 were significantly increased among older men, and those with 14 of the 61 pre-existing conditions tested, including hypertensive chronic kidney disease (OR: 10.14, 95% CI: 5.48, 19.16) and type 2 diabetes with renal manifestations (OR: 5.59, 95% CI: 3.42, 8.97). Male patients who are older and have pre-existing kidney diseases may be at higher risk for death from COVID-19 and may require special care.

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

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 DAISY who developed IA, autoantibodies present on two or more visits. Illumina’s NovaSEQ 600TM was used to quantify gene expression in whole blood. Linear mixed models 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 co-expression network analysis was used to identify co-expression modules. Gene Ontology pathway analysis of the top 150 differentially expressed genes (nominal pLTF, is known to have immunomodulatory properties. Another gene within the module, CAMP, is potentially relevant based on its role in promoting beta cell survival in a murine model. Overall, results provide evidence of alterations in expression of innate immune genes prior to onset of T1D.

Lower blood 25 (OH) D concentration is associated with increased risk of islet autoimmunity (IA) in some cohorts; it is not known if 25 (OH) D metabolites are important after the onset of IA. We investigated novel vitamin D metabolites in development of IA and progression from IA to T1D in the Diabetes Autoimmunity Study in the Young (DAISY) . IA was defined as two or more consecutive visits positive for ≥ 1 autoantibody (IAA, IA-2A, GAD, ZnT8) . In a nested case-control study, we examined plasma vitamin D metabolites at a pre-seroconversion visit in 114 IA cases and 116 controls. We then followed 144 IA-positive children, of whom 46 developed T1D during a mean follow-up of 8.4 years. Plasma was quantified for 25 (OH) D3, 3-epi-25 (OH) D3, 25 (OH) D2, 24,25 (OH) 2D3, and 1,25 (OH) 2D3, using targeted LC-MS/MS. Logistic regression was used to examine pre-seroconversion metabolites and IA, and Cox regression was used to examine metabolites at seroconversion and progression to T1D. Both models adjusted for age, ethnicity, T1D family history and HLA-DR3/4. Higher pre-seroconversion level of 24,25 (OH) 2D3, an inactive catabolite of 25 (OH) D3, was associated with increased odds of IA (OR: 1.35; 95%CI: 1.03,1.78 per SD of 24,25 (OH) 2D3; p=0.031) . The association between 3-epi-25 (OH) D3 at seroconversion and progression from IA to T1D was modified by age at seroconversion. Higher 3-epi-25 (OH) D3 level was more protective against progression to T1D among children who seroconverted at an older age (HR: 0.47; 95%CI: 0.26, 0.85 for children at the 75th percentile of age at seroconversion (ie years)) compared to children who seroconverted at a younger age (HR: 0.88; 95%CI: 0.63, 1.24 for children at the 25th percentile (ie 3 years)) . Neither IA nor progression to T1D was associated with 25 (OH) D3, 25 (OH) D2 or 1,25 (OH) 2D3.Increased 24,25 (OH) 2D3 may play a role in IA development. The age-related heterogeneity of the relationship between 3-epi-25 (OH) D3 and T1D progression requires further investigation. Disclosure S.Kim: None. K.Kechris: None. O.Fiehn: None. M.Rewers: Consultant; Janssen Research & Development, LLC, Medscape, Provention Bio, Inc., Research Support; Dexcom, Inc., JDRF, Roche Diagnostics USA. J.M.Norris: None. N.L.Zwick: None. P.M.Carry: None. B.C.Defelice: None. R.K.Johnson: None. F.Dong: None. T.Buckner: None. L.A.Vanderlinden: None. B.I.Frohnert: Advisory Panel; Provention Bio, Inc. Funding National Institutes of Health (R01-DK104351, R01-DK32493, RI142483)

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 time‐varying 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., Highlights A machine learning model can significantly predict imminent development of islet autoimmunity based on environmental, genetic, and metabolic features.The machine learning algorithm feature selection identified type 1 diabetes‐associated single nucleotide polymorphisms from The Environmental Determinants of the Diabetes in the Young (TEDDY) analysis are correlated to related data from the Diabetes Autoimmunity Study in the Young (DAISY) study.Most of the metabolic features predicting the development of islet autoantibodies belonged to three pathways; lipid oxidation, phospholipase A2 signaling, and pentose phosphate.

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.

Background Oxylipins are formed from oxidation of omega-6 (n6) and omega-3 (n3) fatty acids (FAs). Evidence for inflammatory effects comes mostly from adults. Methods Oxylipins from n6 FA (27 n6-oxylipins) and n3 FA (12 n3-oxylipins) were measured through ultra-high-performance liquid chromatography-mass spectrometry (LC-MS/MS) in plasma from 111 children at risk of type 1 diabetes (age 1-17 years) studied longitudinally. Oxylipin precursor FAs (arachidonic acid, linoleic acid, alpha-linolenic acid, docosahexaenoic acid, eicosapentaenoic acid) were measured in red blood cell (RBC) membrane and plasma. Precursor FAs dietary intake was measured through food frequency questionnaire and environmental tobacco smoke (ETS) through questionnaires. Linear mixed models were used to test oxylipins with predictors. Results Age associated with 15 n6- and 6 n3-oxylipins; race/ethnicity associated with 3 n6- and 1 n3-oxylipins; sex associated with 2 n6-oxylipins. ETS associated with lipoxin-A4. Oxylipins associated with precursor FAs in plasma more often than RBC. RBC levels and dietary intake of precursor FAs more consistently associated with n3-oxylipins than with n6-oxylipins. Conclusions In healthy children, oxylipin levels change with age. Oxylipins associated with precursor FAs more often in plasma than RBC or diet, suggesting that inflammatory regulation leading to FA release into plasma may also be a determinant of oxylipin generation. Impact This is the first study to examine predictors of oxylipins in healthy children at risk of type 1 diabetes.In healthy children at risk of type 1 diabetes, many oxylipins change with age, and most oxylipins do not differ by sex or race/ethnicity.Environmental tobacco smoke exposure was associated with the presence of lipoxin A4.Omega-6- and omega-3-related oxylipin levels were consistently associated with their respective precursor fatty acid levels measured in the plasma.Proportionally more omega-3 compared to omega-6 oxylipins were associated with dietary intake and red blood cell membrane levels of the respective precursor fatty acid.

BackgroundCharacterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources such as the electronic health record (EHR) or convenience sample surveys.ObjectiveTo describe testing behaviors, symptoms, impact, vaccination status and case ascertainment during the COVID-19 pandemic using integrated data sources.MethodsIn summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM, N = 180,599) about their experience with COVID-19. Prevalence of testing, symptoms, and the impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Using the Electronic Health Record (EHR), we compared COVID-19 case ascertainment and characteristics in the EHR versus the survey.ResultsOf the 25,063 survey respondents (13.9%), 42.5% had been tested for COVID-19 and of those, 12.8% tested positive. Nearly half of those tested had symptoms and/or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (54.6%) and family life (48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. After integration with EHR data up to the time of the survey completion, 4.0% of survey respondents (n=1,006) had discordant COVID-19 case status between the EHR and the survey. Using all longitudinal EHR and survey data, we identified 11,472 COVID-positive cases among Biobank participants (6.4%). In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic.ConclusionsIntegrated data assets such as the Biobank at the CCPM are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic.

BACKGROUND Characterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources, such as electronic health records (EHRs) or cross-sectional surveys. OBJECTIVE This study aims to describe testing behaviors, symptoms, impact, vaccination status, and case ascertainment during the COVID-19 pandemic using integrated data sources. METHODS In summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM; N=180,599) about their experience with COVID-19. The prevalence of testing, symptoms, and impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Survey respondents who reported receiving a positive COVID-19 test result were considered a “confirmed case” of COVID-19. Using EHRs, we compared COVID-19 case ascertainment and characteristics in EHRs versus the survey. Positive cases were identified in EHRs using the International Statistical Classification of Diseases, 10th revision (ICD-10) diagnosis codes, health care encounter types, and encounter primary diagnoses. RESULTS Of the 25,063 (13.9%) survey respondents, 10,661 (42.5%) had been tested for COVID-19, and of those, 1366 (12.8%) tested positive. Nearly half of those tested had symptoms or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (n=13,688, 54.6%) and family life (n=12,233, 48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. Of the 10,249 individuals who responded to vaccination questions from version 2 of the survey (summer 2021), 9770 (95.3%) had received the vaccine. After integration with EHR data up to the time of the survey completion, 1006 (4%) of the survey respondents had a discordant COVID-19 case status between EHRs and the survey. Using all longitudinal EHR and survey data, we identified 11,472 (6.4%) COVID-19-positive cases among Biobank participants. In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic. CONCLUSIONS We found that the COVID-19 pandemic has had far-reaching and varying effects among our Biobank participants. Integrated data assets, such as the Biobank at the CCPM, are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic.

Background Characterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources, such as electronic health records (EHRs) or cross-sectional surveys. Objective This study aims to describe testing behaviors, symptoms, impact, vaccination status, and case ascertainment during the COVID-19 pandemic using integrated data sources. Methods In summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM; N=180,599) about their experience with COVID-19. The prevalence of testing, symptoms, and impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Survey respondents who reported receiving a positive COVID-19 test result were considered a “confirmed case” of COVID-19. Using EHRs, we compared COVID-19 case ascertainment and characteristics in EHRs versus the survey. Positive cases were identified in EHRs using the International Statistical Classification of Diseases, 10th revision (ICD-10) diagnosis codes, health care encounter types, and encounter primary diagnoses. Results Of the 25,063 (13.9%) survey respondents, 10,661 (42.5%) had been tested for COVID-19, and of those, 1366 (12.8%) tested positive. Nearly half of those tested had symptoms or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (n=13,688, 54.6%) and family life (n=12,233, 48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. Of the 10,249 individuals who responded to vaccination questions from version 2 of the survey (summer 2021), 9770 (95.3%) had received the vaccine. After integration with EHR data up to the time of the survey completion, 1006 (4%) of the survey respondents had a discordant COVID-19 case status between EHRs and the survey. Using all longitudinal EHR and survey data, we identified 11,472 (6.4%) COVID-19-positive cases among Biobank participants. In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic. Conclusions We found that the COVID-19 pandemic has had far-reaching and varying effects among our Biobank participants. Integrated data assets, such as the Biobank at the CCPM, are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic.

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.

BACKGROUND: Integration of metabolomics with genetics may advance understanding of disease pathogenesis but has been underutilized in asthma genetic studies. OBJECTIVE: We aimed to discover new genetic effects in asthma and to characterize the molecular consequences of asthma genetic risk through integration with the metabolome in a homogenous population. METHODS: From fasting serum samples collected on 348 Tangier Island residents, we quantified 2,612 compounds using untargeted metabolomics. Genotyping was performed using Illumina’s MEGA array imputed to the TOPMed reference panel. To prioritize metabolites for genome-wide association analysis (GWAS), we performed a metabolome-wide association study with asthma, selecting asthma-associated metabolites with heritability q-value75%) for a shared causal variant between three metabolites and asthma, including the polyamine acisoga and variants in LPP, and derivative leukotriene B4 and intergenic variants in chr10p14. CONCLUSION: We identified novel metabolite quantitative trait loci with asthma associations. Identification and characterization of these genetically-driven metabolites may provide insight into the functional consequences of genetic risk factors for asthma.

Lipid mediators, bioactive products of polyunsaturated fatty acid metabolism, contribute to inflammation initiation and resolution in allergic diseases; however, their presence in lung-related biosamples has not been fully described.We aimed to quantify lipid mediators in the nasal airway epithelium and characterize preliminary associations with asthma.Using liquid chromatography-mass spectrometry, we conducted a pilot study to quantify 56 lipid mediators from nasal epithelial samples collected from 11 female participants of an outpatient asthma clinic and community controls (aged 30-55 years). We examined the presence of each compound using descriptive statistics to test whether lipid mediators could distinguish subjects with asthma (n = 8) from control subjects (n = 3) using linear regression and partial least squares discriminant analysis.Fifteen lipid mediators were detectable in all samples, including resolvin (Rv) D5 (RvD5), with the highest median concentrations (in pg/μg protein) of 13-HODE (126.481), 15-HETE (32.869), and 13-OxoODE (13.251). From linear regression adjusted for age, prostaglandin ELipid mediators are detectable in the nasal epithelium, and their levels distinguish asthma cases from controls.

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.

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

OBJECTIVE: 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 DAISY cohort includes 1875 at-risk children with annual estimates of gluten intake (grams/day) from age one year. From 1993 through January 2017, 161 children were, via repeated tissue transglutaminase (tTGA) screening, identified with CD autoimmunity (CDA, persistent tTGA positivity); of these, 85 children fulfilled CD criteria of biopsy-verified histopathology or persistently high tTGA levels. Cox regression, modeling gluten intake between ages one and two years (i.e., in one-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 one and two years had a 2-fold greater hazard of CDA (aHR 2.17; 95%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 one-year-olds. The association between gluten intake in one-year-olds and later CDA or CD did not differ by the child’s HLA genotype. The incidence of CD increased with increased cumulative gluten intake throughout childhood (e.g., aHR 1.15 per standard deviation increase in cumulative gluten intake at age six; 95%CI, 1.00–1.32; P-value=0.04). CONCLUSIONS: Gluten intake in one-year-olds is associated with the future onset of CDA and CD in children at risk for the disease.

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.

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 &lt, 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.

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.

PURPOSE: Autoimmune diseases co-occur, perhaps due to common risk factors. The age at gluten introduction and gluten intake in early childhood have been associated with the autoimmunity preceding celiac disease (CD) and type 1 diabetes (T1D). We explored their associations with the development of thyroid autoimmunity. METHODS: DAISY has prospectively followed children at increased risk for T1D and CD since 1993. During follow-up, 107 children developed thyroid autoimmunity, defined as positivity for autoantibodies against thyroid peroxidase on at least two study visits. Age at gluten introduction was ascertained from food history interviews every 3 months until 15 months of age. Gluten intake (grams/day) at age 1-2 years was estimated using a food frequency questionnaire. RESULTS: From multivariable Cox regression, there was no association between the age of gluten introduction nor the amount of gluten intake and development of thyroid autoimmunity. However, females (Hazard Ratio=2.19, 95%CI: 1.46, 3.27) and cases of islet autoimmunity (HR=2.20, 95%CI: 1.39, 3.50) were significantly more likely to develop thyroid autoimmunity, while exposure to environmental tobacco smoke decreased the risk (HR=0.46, 95%CI: 0.30, 0.71). CONCLUSION: Neither the age of gluten introduction nor the amount of gluten consumed in early childhood is associated with risk of thyroid autoimmunity.

Type 1 diabetes is a chronic, immune-mediated disease characterised by the destruction of insulin-producing 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.

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.

There is need for a single assay able to quantify the most biologically active metabolite, 1α,25-dihydroxy-vitamin-D3, and the recently discovered biologically distinct C3-epimers of 25OHD, in addition to traditional vitamin D metabolites. We developed a method of chromatographic separation and absolute quantification of the following ten forms of vitamin D: 3-epi-25OHD3, 25OHD3, 3-epi-25OHD2, 25OHD2, 1α,25(OH)(2)D3, 24R,25(OH)(2)D3, 23R,25(OH)(2)D3, 1α,25(OH)(2)D2, D3, and D2 by single extraction and injection. Chemical derivatization followed by liquid chromatography using a charged surface hybrid C18 column and subsequent tandem mass spectrometry was utilized to detect and quantify each metabolite. This method is remarkable as a cooled column was required to achieve chromatographic resolution of epimers. Validation of each metabolite was performed at four concentrations and revealed inter- and intraday precision and accuracy below 15% across three consecutive days of analysis. After validation, this method was applied to analyze the blood plasma from 739 samples from 352 subjects (8mo to 20yr), 79 pooled plasma samples, and 10 NIST SRM972a samples. Healthy control samples (n =357) were used to investigate developmentally associated changes in vitamin D metabolite concentrations during early life. This method yields excellent linearity (R(2) ≥0.99) across concentrations encompassing the biological range of many metabolites including 1α,25(OH)(2)D3. Concentrations of 25OHD2 and 24R,25(OH)(2)D3 were significantly (q ≤0.05) lower in infants compared to both children and adolescents. The percentage of 3-epi-25OHD3 in total 25OHD3 was significantly lower (q ≥0.009) in post-puberty subjects. Here we present a single assay capable of separating and quantifying ten vitamin D metabolites including C3-epimers of 25OHD, and quantifying 1α,25-dihydroxy-vitamin-D3 at and below concentrations observed in human plasma (LLOQ

DNA methylation may be involved in development of type 1 diabetes (T1D), but previous epigenome-wide 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.

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 CONCLUSIONS Our findings indicate no strong rationale to reduce the amount of gluten in high-risk children to prevent development of type 1 diabetes.

Background: Few studies have examined gluten intake as a risk factor for islet autoimmunity (IA) and type 1 diabetes (T1D). Our aim was to study the association of gluten intake with development of IA and progression to T1D, accounting for HLA genotype. Methods: The Diabetes Autoimmunity Study in the Young (DAISY) follows children with an increased risk of T1D based on presence of a first-degree relative with T1D, or HLA genotype. HLA genotypes were categorized in three groups as DR3/4 (DR3/4-DQ2/8), DR3 (DR3/3 or 3/X), and DR4 (DR4/4, 4/X or X/X, where X was neither 3 nor 4). Blood samples were collected at age 9, 15 and 24 months, and annually thereafter. IA was defined by the appearance of at least one autoantibody against insulin, IA-2, GAD or ZnT8 in at least two consecutive blood samples; T1D was defined using ADA criteria. Using FFQs we estimated the gluten intake (g/d) annually from 1 year of age. Cox regression was used to estimate hazard ratios adjusted for potential confounders. Results: This report includes 1,916 subjects followed from birth to the median age 13.5 y; 178 developed IA and 56 of these progressed to T1D. At the first assessment (median age 1.4 y) the average daily gluten intake was 11.4 g (SD: 5.6). The amount of gluten intake did not predict risk of IA (HR per 4 g 0.93; 95% CI 0.78-1.11) or progression from IA to T1D (HR 1.30; 95% CI 0.94-1.78). Four grams of gluten equal to a slice of bread. However, we found a significant interaction with HLA (p=0.02), where the association between gluten and IA were significantly different between all groups. Moreover, we found a significant interaction for progression to T1D (p=0.04) where in children with IA, gluten intake was associated with T1D among children with DR3/4 (HR 1.66 95% CI 1.10-2.51), but not in children with the other HLA groups. Conclusion: Gluten intake may have different effects on development of IA and progression from IA to T1D depending on HLA genotype. Disclosure N.A. Lund-Blix: None. F. Dong: None. K. Marild: None. A.E. Baron: None. J.A. Seifert: None. K. Waugh: None. G. Joner: None. K. Stordal: None. G. Tapia: None. L.C. Stene: None. R.K. Johnson: None. M. Rewers: None. J. Norris: None.

Objectives. We assessed whether living in counties with Title X clinics and increased use of long-acting reversible contraception (LARC) in Colorado are associated with decreased risk of adverse birth outcomes. Methods. We linked Title X clinic counties to the Colorado birth data set by using the mother’s county of residence. We compared low birth weight (LBW) and preterm birth (PTB) in 2008 and 2012, in counties with and without Title X clinics. We compared the relationship between LARC use and the incidence of LBW or PTB in 2012 for women living in counties with Title X clinics. Results. For women living in counties with Title X clinics, the odds of PTB were significantly lower in 2012 compared with 2008 (odds ratio = 0.85; 95% confidence interval = 0.81, 0.89; interaction P = .02). For women living in Title X clinic counties in 2012, a higher proportion of LARC use (> 12.4%) was significantly associated with decreased risk of PTB (P = .02) compared with a low proportion of LARC use (≤ 4.96%). Conclusions. Improved access to family planning services and increased use of LARC are associated with lower risk of PTB.