Objective: We investigated islet autoimmunity (IA) incidence trends among Colorado children born 1993-2010 in DAISY and TEDDY and whether HLA genotype or early-life environmental exposures contributed to observed increases. Research Design and Methods: We analyzed the risk of IA among 2,734 Colorado children representing three birth cohorts (BC): BC1 (1993-1998), BC2 (1999-Aug.2004), BC3 (Sept.2004-2010). Cox models evaluated IA by BC, with time-varying coefficients, BC-HLA interaction, and stratification by type 1 diabetes family history. Mediation analyses examined age at gluten introduction, weight-for-age Z-score, and maternal smoking. Results: IA increased across BC1-BC3 (7.0%, 10.7%, 12.7%). At age one, moderate-risk HLA participants in BC2 and BC3 had, respectively, 2.57 and 6.19 times the IA risk of BC1. By age five, effects attenuated. No selected environmental exposures mediated the increased incidence. Conclusions: IA increased among Colorado children born 1993-2010, with strongest effects among moderate-risk HLA genotypes in later birth cohorts.

Introduction and Objective: Hyperglycemia leads to hypomethylation of the thioredoxin-interacting protein gene TXNIP, causing its overexpression, oxidative stress, and inflammation. DCCT has demonstrated an association between higher A1c, TXNIP hypomethylation, and microvascular complications. In presymptomatic T1D, TXNIP overexpression in beta cells activates apoptosis and accelerates progression to insulin dependency; however, the role of dysglycemia and methylation is unknown. We explored the relationship between A1c and TXNIP methylation in children with presymptomatic T1D prospectively followed by the DAISY and TEDDY studies. Methods: Multivariable linear regression modeled the association between methylation measured with the 450K or EPIC array and A1c, prior to A1c = 6.5% or T1D diagnosis. Linear mixed effects models determined changes in methylation with age in TEDDY (subjects = 204, samples = 1,407) and DAISY (subjects = 420, samples = 932). We evaluated changes in gene expression due to methylation in TEDDY children via targeted expression quantitative trait methylation analysis. Results: Fifteen of 23 candidate CpGs (65%) reported by DCCT as associated with A1c had directionally concordant A1c-methylation effects in DAISY at pre-clinical levels of A1c (range: 4.7-6.4%). Methylation at cg19266329, near TXNIP, was inversely associated with A1c (est = -0.16; P = 0.01) in DAISY and decreased with age in both DAISY (est = -3.0; P = 0.003) and TEDDY (est = -8.4; P = 1.87e-16). Lower methylation at cg19266329 was associated with higher expression of TXNIP (est = -0.22; P = 0.0003). Conclusion: Youth with islet autoimmunity had dysglycemia-driven methylation patterns similar to those observed in adults with established T1D. Higher A1c resulted in hypomethylation and overexpression of TXNIP, currently a therapeutic target for stage 3 T1D prevention trials using TXNIP inhibitors, e.g., verapamil or SRI-37330 hydrochloride. TXNIP methylation may be an alternative therapeutic target for the prevention of stage 3 T1D and diabetes complications. Disclosure S.E. Ridoux: None. S.D. Slack: None. K. Hohsfield: None. D. Dabelea: None. M. Rewers: Consultant; Current; Sanofi. Advisory Panel; Current; Vertex Pharmaceuticals Incorporated. J. Norris: None. C. Kim: None. R.K. Johnson: None. Funding NIDDK (R01DK032493), NIDDK (R01DK104351), Foundation for Women's Health

Background Type 1 diabetes is believed to be associated with early genetic and environmental stressors. Epigenetic age acceleration (EAA) is also associated with environmental stressors and the pathogenesis of many chronic diseases. This study explored longitudinal changes in EAA among individuals at high risk for type 1 diabetes.Methods DNA methylation was measured longitudinally in subjects from the Diabetes Autoimmunity Study in the Young cohort, 2547 children born 1993–2006 at high risk for type 1 diabetes. Data were collected before and after islet autoimmunity (IA) seroconversion, a preclinical type 1 diabetes stage. EAA was estimated from DNA methylation using an epigenetic clock appropriate for pediatric blood samples. A linear mixed model was used to test for differences in EAA between 85 type 1 diabetes cases and 85 controls, before and after IA seroconversion.Results Change in EAA significantly differed between cases and controls (p=0.02). EAA significantly decreased in cases, from pre-IA to post-IA seroconversion by 0.367 units (95% CI −0.64 to 0.09, p=0.01), but not in controls (0.045, 95% CI 0.23 to 0.32, p=0.75).Conclusion These results suggest that EAA occurs in children who develop type 1 diabetes prior to IA seroconversion, highlighting the potential role of early environmental stressors in disease pathogenesis.

CONTEXT:Increased levels of 25-hydroxyvitamin D (25OHD) have been protective against islet autoimmunity (IA), a preclinical type 1 diabetes (T1D) disease state. However, the role of 25OHD and downstream metabolites in progression from IA to T1D is not well understood. OBJECTIVE:We hypothesized that downstream vitamin D metabolites and metabolite ratios would be associated with progression from IA to T1D. METHODS:Among participants at high genetic risk for T1D who developed IA (n = 143) in the Diabetes Autoimmunity Study in the Young (DAISY), a T1D birth cohort study, we quantified vitamin D3, 25OHD2, 3-epi-25OHD3, 25OHD3, 24,25(OH)2D3, and 1α,25(OH)2D3 metabolites from plasma samples using LC-MS/MS. We calculated the vitamin D metabolite ratio (VMR) (ie, 24,25(OH)2D3/25OHD3) and the epimer ratio (3-epi-25OHD3/25OHD3). We also tested the correlation between metabolite levels and gene expression in a subset of participants (n = 53). RESULTS:A total of 57/143 progressed to T1D. Higher VMR (hazard ratio (HR) per 1 SD increase: 0.65; 95% CI: 0.49-0.88) and levels of 24,25(OH)2D3 (HR per 1 SD increase: 0.72; 95% CI: 0.55-0.94) at IA seroconversion were associated with a lower risk of progression to T1D, adjusting for seroconversion age, season, HLA-DR3/4 genotype, and ancestry. Functional enrichment analysis suggests higher VMR resulted in a gene expression pattern in whole blood characteristic of decreased activation of inflammatory pathways related to neutrophil infiltration. CONCLUSION:A higher VMR, a more functional measure of vitamin D status, was protective against T1D progression, perhaps by decreasing activation of inflammatory pathways.

INTRODUCTION:Seroconversion (SV) marks islet autoimmunity (IA) onset and preclinical type 1 diabetes (T1D), yet the contributions beyond T and B lymphocytes remain unclear. We evaluated DNA methylation (DNAm)-derived immune cell ratios between T1D cases and controls around SV. RESEARCH DESIGN AND METHODS:High-resolution immune cell-type deconvolution of peripheral blood DNAm from nested case-control samples in the Diabetes Autoimmunity Study in the Young (DAISY; n=151) and the Environmental Determinants of Diabetes in the Young (TEDDY; n=166) estimated immune cell proportions at pre-SV (the latest visit before SV) and at SV (the first visit with persistent detected autoantibodies) to construct immune cell ratios, such as the neutrophil-to-lymphocyte ratio (NLR). Linear models compared T1D cases to matched T1D controls (IA negative) at pre-SV, SV, and the change across time points. RESULTS:From pre-SV to SV, controls showed expected developmental increases in B-memory/naive, B-CD4T-CD8T memory/naive, and NLR, while cases failed to follow these patterns, with attenuated trajectories of 35%, 38%, and 21%, respectively. Pre-SV, cases had 15% higher NLR and 9% lower CD4T/CD8T. At SV, the combined B-CD4T-CD8T memory/naive ratio was 26% reduced in cases. CONCLUSIONS:These patterns may reflect increased neutrophil activation or pancreatic infiltration, altered CD4 and CD8 T cell balance, and delayed or disrupted immune maturation with the persistence or expansion of naive B and T cells or impaired transition to memory B and T subsets following antigen exposure. Our findings highlight early shifts in innate and adaptive immune cell dynamics during T1D pathogenesis and support methylation-derived immune cell ratios as potential biomarkers for risk stratification and mechanistic insight.

Most participants in large cohorts, such as biobanks, are of European descent. This lack of representation has been an ongoing challenge in genomic research. Understanding the perspectives on genomics research and participation in biobanks of historically underrepresented populations could provide insight into ways to better engage with these groups. We conducted a series of virtual and in-person focus groups with individuals who self-identified as American Indian or Alaska Native (AI/AN), African American/Black (AA/B), or Hispanic/Latino (H/L) and who were enrolled in the Colorado Center for Personalized Medicine (CCPM) biobank. The focus group discussions were centered on participant experiences, including but not limited to their motivations, return of results, and data sharing. There was a total of 23 participants across the six focus groups. The majority of participants identified as AI/AN (60.9%), followed by H/L (39.1%), and AA/B (21.7%); many participants identified with multiple race/ethnicities. The motivations for participating in the biobank included the potential to advance science and health, the potential for return of results, to learn more about one's ancestry, and a few indicated that they were interested in helping the biobank be more representative of all populations. Notably, many expressed positive feedback of the focus groups and felt that their views were valued, illustrating the importance of community-centered work. Our findings can be used to guide recruitment and engagement of biobank participants, especially from diverse backgrounds, contributing to enhanced partnerships advancing knowledge and healthcare.

BACKGROUND:Existing asthma polygenic risk scores (PRSs) have minimal validation in African-ancestry populations, leaving gaps in our understanding of the wide applicability of PRSs. To widen our understanding of the applicability of asthma PRSs, we apply published PRSs in African-ancestry individuals and quantify the extent to which the PRS-asthma relationship is mediated by clinical biomarkers and gene-expression signatures of asthma. METHODS:We applied 22 PRSs from the PGS Catalog in 673 individuals from the Consortium on Asthma among African-Ancestry Populations in the Americas (CAAPA) and calculated the percent of the PRS-asthma relationship that is statistically mediated by clinical and nasal epithelium transcriptomic biomarkers of asthma. Asthma case/control status was defined as ever/never having a doctor's diagnosis of disease. For gene expression mediation analysis, we limited the cases to those with current disease. RESULTS:The PRS (PGS001782) created by the Global Biobank Meta-analysis Initiative (N = 32,658 individuals of African ancestry) performed the best (ΔAUC = 0.104, AUC = 0.657) adjusted for age, sex, study site, and the first two genetic principal components (PC1-2). The PRS's effect on asthma was mediated by total IgE (tIgE) (38.8%, p.adj < 0.0002), multi-allergen ImmunoCAP phadiatop specific IgE (sIgE) (38.7%, p.adj < 0.0002), and eosinophils (7.3%, p.adj = 0.004). Mediation was observed for gene expression modules related to T2 inflammation (21.9%, p.adj < 0.0024), wound healing (11.9%, p.adj = 0.008), and medication response (6.8%, p.adj = 0.049). CONCLUSION:We found the best PRS to be the one derived using the largest sample size and including African-ancestry individuals. Mediation supports the well-documented biology of T2 inflammation in asthma as well as pathophysiological components of asthma like wound healing and medication response.

Background:Genetic control of gene expression in asthma-related tissues is not well-characterized, particularly for African-ancestry populations, limiting advancement in our understanding of the increased prevalence and severity of asthma in those populations. Objective:To create novel transcriptome prediction models for asthma tissues (nasal epithelium and CD4+ T cells) and apply them in transcriptome-wide association study (TWAS) to discover candidate asthma genes. Methods:We developed and validated gene expression prediction databases for unstimulated CD4+ T cells (CD4+T) and nasal epithelium using an elastic net framework. Combining these with existing prediction databases (N=51), we performed TWAS of 9,284 individuals of African-ancestry to identify tissue-specific and cross-tissue candidate genes for asthma. For detailed Methods, please see the Supplemental Methods. Results:Novel databases for CD4+T and nasal epithelial gene expression prediction contain 8,351 and 10,296 genes, respectively, including four asthma loci (SCGB1A1, MUC5AC, ZNF366, LTC4S) not predictable with existing public databases. Prediction performance was comparable to existing databases and was most accurate for populations sharing ancestry with the training set (e.g. African ancestry). From TWAS, we identified 17 candidate causal asthma genes (adjusted P<0.1), including genes with tissue-specific (IL33 in nasal epithelium) and cross-tissue (CCNC and FBXW7) effects. Conclusions:Expression of IL33, CCNC, and FBXW7 may affect asthma risk in African ancestry populations by mediating inflammatory responses. The addition of CD4+T and nasal epithelium prediction databases to the public sphere will improve ancestry representation and power to detect novel gene-trait associations from TWAS.

Objective:Type 1 diabetes polygenic risk scores (PRS) offer a promising tool for identifying diabetes subtypes in adults with new-onset disease. We aimed to develop a pipeline for the clinical translation of type 1 diabetes PRS to support clinical decision-making within a large health system and to provide publicly available code for applying these methods to future PRS models. Research Design and Methods:We adapted two established type 1 diabetes PRS models: a 67-SNP (GRS2) and a 7-SNP (AA7) score for a clinical genotyping platform and applied them to 73,346 participants in the biobank at the Colorado Center for Personalized Medicine (CCPM). We evaluated the scores' performance differentiating between type 1 and type 2 diabetes in adults using a clinician-curated diabetes phenotyping algorithm and examined associations with diabetes-related clinical data extracted from patients' health records. The impact of technical genotyping missingness on score accuracy and ancestry calibration were assessed independently. Results:Both scores effectively distinguished type 1 from type 2 diabetes across genetically defined ancestry groups (all AUC > 0.80) and demonstrated consistent performance in the UK Biobank (all AUC > 0.75). Individuals in the top quintile of each PRS were enriched for diabetic ketoacidosis (DKA) cases, accounting for nearly half of all DKA cases in the cohort. Additionally, the top quintile showed nearly threefold increased odds of GAD autoantibody positivity (OR = 2.94 [95% CI 2.08-4.17]). Conclusions:Our evaluations demonstrated the potential utility of PRS for diabetes subtyping in a clinical setting. We present a framework of critical steps toward a standardized system for future translation of diabetes PRS to equitable clinical use, along with software to make it possible for others.

Seroconversion (SV) marks the initiation of islet autoimmunity (IA) and pre-clinical phase of type 1 diabetes, yet the contributions of immune cells beyond cytotoxic T cells remain unclear. We applied high-resolution immune cell-type deconvolution using peripheral blood DNA methylation data from nested case-control samples of the Diabetes Autoimmunity Study in the Young (DAISY; n=151) and The Environmental Determinants of Diabetes in the Young (TEDDY; n=166) to estimate immune cell proportions across pre-SV and SV timepoints and construct functional ratios, such as the neutrophil-to-lymphocyte ratio (NLR). Using linear models, we evaluated differences between type 1 diabetes cases and controls at pre-SV, SV, and the change across timepoints. Pre-SV, cases had higher NLR and lower CD4T/CD8T cell ratios. At SV, the combined B-CD4T-CD8T memory/naïve ratio was reduced in cases. From pre-SV to SV, cases showed attenuations in NLR, B-memory/naïve, and B-CD4T-CD8T memory/naïve ratios. These patterns may reflect delayed or disrupted immune maturation with the persistence or expansion of naïve cells or impaired transition to memory subsets following antigen exposure. Our findings highlight early shifts in innate and adaptive immune cell dynamics during type 1 diabetes pathogenesis and support immune cell ratios as potential biomarkers for risk stratification and mechanistic insight.

BACKGROUND:Genetic control of gene expression in asthma-related tissues is not well characterized, particularly for African-ancestry populations, limiting advancement in our understanding of the increased prevalence and severity of asthma in these populations. OBJECTIVE:We sought to create novel transcriptome prediction models for asthma tissues (nasal epithelium and CD4+ T cells) and apply them in a transcriptome-wide association study (TWAS) to discover candidate asthma genes. METHODS:We developed and validated gene expression prediction databases for unstimulated CD4+ T cells and nasal epithelium using an elastic net framework. Combining these with existing prediction databases (N = 51), we performed a TWAS of 9284 individuals of African ancestry to identify tissue-specific and cross-tissue candidate genes for asthma. RESULTS:Novel databases for CD4+ T cells and nasal epithelial gene expression prediction contain 8,351 and 10,296 genes, respectively, including 4 asthma loci (SCGB1A1, MUC5AC, ZNF366, and LTC4S) not predictable with existing public databases. Prediction performance was comparable to existing databases and was most accurate for populations sharing ancestry with the training set (eg, African ancestry). From the TWAS, we identified 17 candidate causal asthma genes (adjusted P < .1), including genes with tissue-specific (IL33 in nasal epithelium) and cross-tissue (CCNC and FBXW7) effects. CONCLUSIONS:Expression of IL33, CCNC, and FBXW7 may affect asthma risk in African- ancestry populations by mediating inflammatory responses. The addition of CD4+ T cell and nasal epithelium prediction databases to the public sphere will improve ancestry representation and power to detect novel gene-trait associations from TWAS.

OBJECTIVE:Multiple studies have reported an inverse association between self-reported smoking during pregnancy and offspring type 1 diabetes (T1D) risk. We investigated the association between DNA methylation (DNAm) smoke exposure scores, parental self-reported smoking, and islet autoimmunity (IA) and T1D risk in children at high risk of T1D. RESEARCH DESIGN AND METHODS:We used longitudinal data from the Diabetes Autoimmunity Study in the Young cohort, including 205 IA case and 206 control participants (87 and 88 were T1D case and control participants, respectively), matched by age, race/ethnicity, and sample availability. DNAm profiles were obtained from cord or peripheral blood using the Infinium Human Methylation 450K or EPIC BeadChip. Three published DNAm smoking scores were calculated at every time point. To estimate in utero smoke exposure, participant-specific intercepts were derived from mixed-effects models of longitudinal DNAm scores. These intercepts strongly correlated with cord blood scores (r = 0.85-0.95; n = 179), indicating their utility as proxies for in utero smoke exposure. Associations with IA/T1D were evaluated using logistic regression, adjusting for HLA-DR3/4, first-degree relative status, and sex. RESULTS:Multivariable models showed both maternally reported smoking during pregnancy and higher DNAm smoking scores to be associated with lower risk of IA and T1D. Maternal smoking showed a strong inverse association with IA (odds ratio [OR] 0.24; 95% CI 0.10-0.54). Rauschert and McCartney DNAm scores showed consistent inverse associations with both outcomes (OR 0.65-0.83 for SD increase). CONCLUSIONS:Our study supports existing literature indicating in utero smoke exposure is associated with reduced IA and T1D risk. Further research is essential to uncover the underlying mechanisms.

Accurate reconstruction of pedigrees from genetic data remains a challenging problem. Many relationship categories (e.g. half-sibships vs avuncular) can be difficult to distinguish without external information. Pedigree inference algorithms are often trained on European-descent families in urban locations. Thus, existing methods tend to perform poorly in endogamous populations for which there may be reticulations within the pedigrees and elevated haplotype sharing. We present a simple, rapid algorithm which initially uses only high-confidence first-degree relationships to seed a machine learning step based on summary statistics of identity-by-descent sharing. One of these statistics, our "haplotype score," is novel and can be used to: (1) distinguish half-sibling pairs from avuncular or grandparent-grandchildren pairs; and (2) assign individuals to ancestor vs descendant generation. We test our approach in a sample of 700 individuals from northern Namibia, sampled from an endogamous population called the Himba. Due to a culture of concurrent relationships in the Himba, there is a high proportion of half-sibships. We accurately identify first through fourth-degree relationships and distinguish between various second-degree relationships: half-sibships, avuncular pairs, and grandparent-grandchildren. We further validate our approach in a second African-descent dataset, the Barbados Asthma Genetics Study, and a European-descent founder population from Quebec. Accurate reconstruction of relatives facilitates estimation of allele frequencies, tracing allele trajectories, improved phasing, heritability and other population genomic questions.

BackgroundType 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).MethodsThis 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).ResultsThe 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.ConclusionDifferentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.

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 Asthma has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omic signatures of asthma focusing on populations of African ancestry. RNASeq and DNA methylation data are generated from nasal epithelium including cases (current asthma, N = 253) and controls (never-asthma, N = 283) from 7 different geographic sites to identify differentially expressed genes (DEGs) and gene networks. We identify 389 DEGs; the top DEG, FN1, was downregulated in cases (q = 3.26 × 10−9) and encodes fibronectin which plays a role in wound healing. The top three gene expression modules implicate networks related to immune response (CEACAM5; p = 9.62 × 10−16 and CPA3; p = 2.39 × 10−14) and wound healing (FN1; p = 7.63 × 10−9). Multi-omic analysis identifies FKBP5, a co-chaperone of glucocorticoid receptor signaling known to be involved in drug response in asthma, where the association between nasal epithelium gene expression is likely regulated by methylation and is associated with increased use of inhaled corticosteroids. This work reveals molecular dysregulation on three axes – increased Th2 inflammation, decreased capacity for wound healing, and impaired drug response – that may play a critical role in asthma within the African Diaspora.

BACKGROUND:Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.METHODS:We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.RESULTS:Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.CONCLUSIONS:Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Introduction: A family history of type 1 diabetes (T1D) increases T1D risk, but the increase is lower for maternal compared to paternal T1D. We aimed to identify epigenetic markers of this parent-of-origin effect by testing whether the effect of DNA methylation on T1D risk differs by T1D family history in The Environmental Determinants of Diabetes in the Young (TEDDY) Study. Methods: For 106 T1D cases and 99 matched controls in TEDDY, methylation was measured in 1,424 peripheral blood samples collected prospectively from 3-75 months of age using the MethylationEPIC Beadchip. Following data processing, we performed an epigenome-wide association study across 534,790 CpGs using linear regression adjusted for age, sex, and HLA-DR3/4. We used an interaction term to test whether the difference in mean longitudinal methylation (%) between T1D cases and controls differed by T1D family history (affected: mother, N=19; father or sibling, N=50; none, N=136). Results: We identified 141 CpGs where the effect of methylation on T1D risk differed by T1D family history (FDR-adjusted Pinteraction<0.01). Among children exposed to maternal T1D in utero, methylation levels differed between cases and controls; however, in those with no T1D family history or with an affected father or sibling there was no difference in methylation. In those with an affected mother, the largest effect sizes included hypomethylation in T1D cases near glucose metabolism genes ASTN2 (-11.3%) and ACOT7 (-7.8%), and hypermethylation near PTEN (11.3%), a key insulin signaling gene. Over 24% (35/141) of CpGs localized in previously identified loci exhibiting allele-specific methylation, implicating genetic-epigenetic interplay. Conclusion: We identified epigenetic changes preceding T1D that differ by T1D family history. At these loci, methylation differences occurred only among children exposed to T1D in utero and localized near glucose metabolism genes, suggesting epigenetic mechanisms may be involved in the long-described maternal effect in T1D risk. Disclosure R.K. Johnson: None. S.D. Slack: None. L.A. Vanderlinden: None. K. Hohsfield: None. P.M. Carry: None. S. Onengut-Gumuscu: None. S.S. Rich: None. M. Rewers: Advisory Panel; Sanofi. Other Relationship; Sanofi. Consultant; Janssen Pharmaceuticals, Inc. Research Support; Juvenile Diabetes Research Foundation (JDRF). Consultant; Provention Bio, Inc. Research Support; Hemsley Charitable Trust, National Institute of Diabetes and Digestive and Kidney Diseases. K. Kechris: None. J.M. Norris: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (2103-05094). The TEDDY Study is a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Juvenile Diabetes Research Foundation (JDRF), and Centers for Disease Control and Prevention (CDC).