Genetic Risk For Type 1 Diabetes Research Articles

Immune perturbations in human pancreas lymphatic tissues prior to and after type 1 diabetes onset.

Autoimmune destruction of pancreatic β cells results in type 1 diabetes (T1D), with pancreatic immune infiltrate representing a key feature in this process. Studies of human T1D immunobiology have predominantly focused on circulating immune cells in the blood, while mouse models suggest diabetogenic lymphocytes primarily reside in pancreas-draining lymph nodes (pLN). A comprehensive study of immune cells in human T1D was conducted using pancreas draining lymphatic tissues, including pLN and mesenteric lymph nodes, and the spleen from non-diabetic control, β cell autoantibody positive non-diabetic (AAb+), and T1D organ donors using complementary approaches of high parameter flow cytometry and CITEseq. Immune perturbations suggestive of a proinflammatory environment were specific for T1D pLN and AAb+ pLN. In addition, certain immune populations correlated with high T1D genetic risk independent of disease state. These datasets form an extensive resource for profiling human lymphatic tissue immune cells in the context of autoimmunity and T1D.

Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP).

To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.

Implementation of type 1 diabetes genetic risk screening in children in diverse communities: the Virginia PrIMeD project

BackgroundPopulation screening for risk of type 1 diabetes (T1D) has been proposed to identify those with islet autoimmunity (presence of islet autoantibodies). As islet autoantibodies can be transient, screening with a genetic risk score has been proposed as an entry into autoantibody testing.MethodsChildren were recruited from eight general pediatric and specialty clinics across Virginia with diverse community settings. Recruiters in each clinic obtained informed consent/assent, a medical history, and a saliva sample for DNA extraction in children with and without a history of T1D. A custom genotyping panel was used to define T1D genetic risk based upon associated SNPs in European- and African-genetic ancestry. Subjects at “high genetic risk” were offered a separate blood collection for screening four islet autoantibodies. A follow-up contact (email, mail, and telephone) in one half of the participants determined interest and occurrence of subsequent T1D.ResultsA total of 3818 children aged 2–16 years were recruited, with 14.2% (n = 542) having a “high genetic risk.” Of children with “high genetic risk” and without pre-existing T1D (n = 494), 7.0% (34/494) consented for autoantibody screening; 82.4% (28/34) who consented also completed the blood collection, and 7.1% (2/28) of them tested positive for multiple autoantibodies. Among children with pre-existing T1D (n = 91), 52% (n = 48) had a “high genetic risk.” In the sample of children with existing T1D, there was no relationship between genetic risk and age at T1D onset. A major factor in obtaining islet autoantibody testing was concern over SARS-CoV-2 exposure.ConclusionsMinimally invasive saliva sampling implemented using a genetic risk score can identify children at genetic risk of T1D. Consent for autoantibody screening, however, was limited largely due to the SARS-CoV-2 pandemic and need for blood collection.

Sick individuals, sick populations revisited: a test of the Rose hypothesis for type 2 diabetes disparities

IntroductionThe Rose hypothesis predicts that since genetic variation is greater within than between populations, genetic risk factors will be associated with individuals’ risk of disease but not population disparities, and...

Type 1 Diabetes Genetic Risk Score Differentiates Subgroups of Ketosis-Prone Diabetes.

To determine whether genetic risk for type 1 diabetes (T1D) differentiates the four Aβ subgroups of ketosis-prone diabetes (KPD), where A+ and A- define the presence or absence of islet autoantibodies and β+ and β- define the presence or absence of β-cell function. We compared T1D genetic risk scores (GRS) of patients with KPD across subgroups, race/ethnicity, β-cell function, and glycemia. Among 426 patients with KPD (54% Hispanic, 31% African American, 11% White), rank order of GRS was A+β- > A+β+ = A-β- > A-β+. GRS of A+β- KPD was lower than that of a T1D cohort, and GRS of A-β+ KPD was higher than that of a type 2 diabetes cohort. GRS was lowest among African American patients, with a similar distribution across KPD subgroups. T1D genetic risk delineates etiologic differences among KPD subgroups. Patients with A+β- KPD have the highest and those with A-β+ KPD the lowest GRS.

Early Intervention by Family Physicians to Delay Type 1 Diabetes.

Type 1 diabetes (T1D) is an autoimmune disease mediated by T cells that target and destroy insulin-producing beta cells. Individuals with genetic risk of T1D will progress at variable rates through 3 stages of immune activation and development of islet autoimmunity. Measuring pancreatic islet cell autoantibodies predicts risk for progression that can take weeks to years before the onset of T1D. Screening options available to family physicians can identify persons at risk or in the early stages of T1D, such as first- and second-degree relatives or those with a family history of autoimmune disorders, to ultimately offer proven interventions that may delay or prevent the condition. Screening can reduce emergency room visits, hospitalizations, and intensive care unit admissions for diabetic ketoacidosis, which can be fatal, and can educate and prepare individuals and families for a smoother transition to insulin therapy when necessary. Recent advances in technology and understanding of the immune pathogenesis of T1D has resulted in emerging disease-modifying therapies that are changing how family physicians approach delaying and potentially preventing or reversing the disease.

218-LB: Younger Veterans with Adult-Onset Diabetes Who Have High Type 1 Diabetes (T1D) Genetic Risk Are Disproportionately of Minority Ancestry and Have Features Typical of T1D but Are Often Unrecognized—A Health Disparity

Diabetes in Veterans is usually presumed to be T2D because T1D precludes enlistment, but T1D may present in adults; how T1D genetic risk impacts different racial/ethnic groups is unknown. To assess the contributions of T1D genetic risk, we examined 109,954 Veterans in the VA's Million Veteran Program (MVP), using a 30 SNP T1D genetic risk score (GRS) for those with European, Hispanic, and Asian ancestry, and a separate 7-SNP GRS for those of African ancestry. High T1D genetic risk (GRS ≥90th percentile) was characterized according to age at diabetes onset (20-39, 40-59, and 60+ years). High T1D genetic risk was more common with the earliest onset group (13.2%, 10.0% and 9.0%, respectively), and that group had the highest proportion of minority ancestry (47%, 37%, and 25%), all p&amp;lt;0.001. The three high T1D risk groups had similar follow-up (13, 12, and 11 years), GAD antibodies if tested (39%, 41%, and 40%), and low C-peptide levels if measured (36%, 23%, and 33%). However, typical T1D features were more common with earlier onset: use of insulin (80%, 63%, and 43%), time to insulin (0.2, 2.4, and 1.4 yr), diabetic ketoacidosis (11%, 3%, and 0.8%), and severe hypoglycemia prompting an ED visit (10%, 5%, and 3%), all p&amp;lt;0.001. While earlier age of onset groups had more use of T1D ICD codes (50%, 25% and 13%), testing for GAD antibodies (18%, 4%, and 2%), and testing for C-peptide (24%, 9%, and 3%), respectively, all p&amp;lt;0.001, less than half of any group had diagnostic testing for T1D. Conclusions: Increased T1D genetic risk is associated with earlier age of diabetes onset in adults and increased prevalence of typical T1D characteristics, but similar GAD antibodies and C-peptide levels. Since individuals with high T1D risk and early diabetes onset are disproportionately of minority ancestry, health policies aimed to improve T1D recognition and management are needed to remedy this health disparity. Disclosure P. K. Yang: None. L. S. Phillips: Other Relationship; Diasyst Inc., Research Support; Abbott Diabetes, Janssen Pharmaceuticals, Inc., Pfizer Inc., Kowa Pharmaceuticals America, Inc., GlaxoSmithKline plc. Y. Sun: None. S. Raghavan: None. E. M. Litkowski: None. B. T. Legvold: None. M. K. Rhee: Research Support; Kowa Research Institute, Inc. M. Vujkovic: None. P. Reaven: Research Support; Dexcom, Inc. A. Leong: Other Relationship; Merck &amp; Co., Inc. Funding National Institutes of Health (U01DK098245, P30DK111024, AI156161, DK127083); U.S. Department of Veterans Affairs (I01-CX001025, I01-BX005831, I01-BX003340, CX001899, I01CX001737)

216-LB: Genetic Influences on Beta-Cell Function before Type 1 Diabetes Diagnosis

Autoimmune loss of beta-cell function (measured by C-peptide) is the hallmark of type 1 diabetes (T1D) targeted by interventions that aim to prevent T1D or its progression after onset. We sought to determine whether T1D genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) that have been previously associated with C-peptide preservation after T1D diagnosis (e.g., SNPs in CLEC16A, G6CP2, INS, JAZF1, PTPN22, SLC30A8 and TCF7L2) influence C-peptide levels before diagnosis. We studied islet autoantibody (Ab)-positive participants in the TrialNet Pathway to Prevention Study who had T1DExomeChip data and assessed the influence of these 12 SNPs and the T1D-GRS2 on area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted between 0-9 months prior to the diagnosis of T1D. Participants (n=702) had a mean age of 13.5±10.3 years, 47% were female, mean BMI was 20.7±6.0 kg/m2, and mean HbA1c 5.4±0.4%. The T1D high-risk HLA-DR3-DQ2 haplotype was present in 47% and the high-risk HLA-DR4-DQ8 haplotype was present in 67% of participants. We performed univariate and multivariate analyses adjusting for BMI, age, sex, number of positive Ab, and the first 3 principal components of ancestry. A higher T1D-GRS2 was associated with lower C-peptide AUC 0-9 months prior to T1D diagnosis in univariate (β=-0.06, P&amp;lt;0.0001) and multivariate (β=-0.03, p=0.008) analyses. Participants with the JAZF1 rs864745 G allele had lower C-peptide AUC 0-9 months prior to T1D diagnosis in univariate (β=-0.10, p=0.003) and multivariate (β=-0.05, p=0.047) analysis. In conclusion, the JAZF1 rs864745 G allele (which has also been associated with type 2 diabetes risk) and higher T1D-GRS2 predict lower C-peptide AUC prior to the diagnosis of T1D. Studies on their effect on response to trials to prevent or delay T1D onset are warranted. Disclosure T. M. Triolo: None. S. S. Rich: None. A. Steck: None. M. J. Redondo: None. H. M. Parikh: None. M. Tosur: Advisory Panel; Provention Bio, Inc. L. A. Ferrat: Consultant; Johnson &amp; Johnson. L. You: None. P. Gottlieb: Advisory Panel; ViaCyte, Inc., Board Member; ImmunoMolecular Therapeutics, Research Support; Imcyse, Hemsley Charitable Trust, Novartis, National Institute of Diabetes and Digestive and Kidney Diseases, Precigen, Inc., Dompé, Nova Pharmaceuticals, Provention Bio, Inc. R. A. Oram: Consultant; Janssen Research &amp; Development, LLC, Research Support; Randox R &amp; D. S. Onengut-gumuscu: None. J. Krischer: None. Funding National Institutes of Health (R01DK121843, R01DK124395)

138-OR: Characterization and Prediction of Genetic Risk of T1D in Individuals without HLA-DR3 or HLA-DR4

The Major Histocompatibility (MHC) class II haplotypes HLA-DR3 and HLA-DR4 confer substantial genetic risk for type 1 diabetes (T1D), where 90% of individuals with T1D carry at least one copy of DR3 and/or DR4. We sought to understand whether the remaining 10% of T1D individuals without DR3 or DR4 haplotypes (non-DR3/DR4) have differences in genetic risk and whether current risk scores are effective for these individuals. We tested for T1D association using 12,716 non-DR3/DR4 samples from five European ancestry cohorts using genotypes imputed into the Michigan HLA and TOPMed v2 reference panels and identified independent signals using stepwise conditional analyses. There were 21 signals with significant effects on T1D, including 13 at the MHC locus and 8 genome-wide, of which 5 were novel. Across all T1D signals, 14 lead variants exhibited significant heterogeneity in effects on T1D in the non-DR3/DR4 subset compared to the full T1D population, including HLA-B*39 and the PTPN22 locus. Nearly 50% of non-DR3/DR4 T1D would be misclassified by a current T1D genetic risk score (T1D GRS1), which is weighted heavily by DR3/DR4 tag variants. We developed a new GRS using the 21 independent signals and determined whether this GRS could better discriminate T1D in individuals with non-DR3/DR4 haplotypes. The non-DR3/DR4 T1D GRS effectively differentiated T1D case and control samples (AUC=0.87) including in samples not in the initial discovery set (AUC=0.86), and improved prediction compared to GRS1 (AUC=0.52 using all controls, AUC=0.79 using only non-DR3/DR4 controls). These findings reveal a subset of T1D cases with heterogeneity in genetic risk and which are not as well represented by current risk scores, and argue that risk scores developed within heterogeneous subsets may enable even more accurate prediction of T1D. Disclosure C.Mcgrail: None. K.J.Gaulton: Consultant; Genentech, Inc., Stock/Shareholder; Neurocrine Biosciences, Inc., Vertex Pharmaceuticals Incorporated. Funding National Institutes of Health (DK120429, DK122607)

A genomic data archive from the Network for Pancreatic Organ donors with Diabetes

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

Association of time spent in outdoor light and genetic susceptibility with the risk of type 2 diabetes

To explore the joint association of time spent in outdoor light and genetic susceptibility with the risk of type 2 diabetes (T2D). A total of 395,809 individuals of European ancestry with diabetes-free at baseline in the UK Biobank were included. Time spent in outdoor light on a typical day in summer or winter was obtained from the questionnaire. T2D genetic risk was quantified via the polygenic risk score (PRS) and divided into three levels based on tertiles (lower, intermediate, and higher). T2D cases were ascertained according to the hospital records of diagnoses. After the median follow-up of 12.55 years, the association of outdoor light time and T2D risk demonstrated a nonlinear (J–shaped) trend. Compared to individuals with an average of 1.5–2.5 h/day of outdoor light, individuals who spent <1.5 h/day or >2.5 h/day in outdoor light both had an elevated risk of T2D, and the risk of T2D related to <1.5 h/day outdoor light time was much higher (hazard ratio [HR] = 1.10, 95 % confidence interval [CI]: 1.06 to 1.15). After combining with PRS, in comparison with the lower PRS – average 1.5–2.5 h/day outdoor light group (reference), the higher PRS – <1.5 h/day outdoor light group had the highest T2D risk (HR = 2.74, 95 % CI: 2.55 to 2.94), the higher PRS – >2.5 h/day outdoor light group also had a higher risk of T2D (HR = 2.58, 95 % CI: 2.43 to 2.74). The interaction between average outdoor light time and genetic susceptibility for T2D was statistically significant (Paverage for interaction <0.001). We found that optimal outdoor light time may modify the genetic risk for T2D. This suggests the T2D risk related to genetic factors could be prevented by spending optimal outdoor light time.

HLA-DQ-conferred risk for type 1 diabetes does not alter neutralizing antibody response to a widely used enterovirus vaccine, the poliovirus vaccine.

This study investigated whether children with HLA-DQ-conferred risk for type 1 diabetes (T1D) have an altered immune response to the widely-used enterovirus vaccine, namely poliovirus vaccine, and whether initiation of autoimmunity to pancreatic islets modulates this response. Neutralizing antibodies induced by the inactivated poliovirus vaccine against poliovirus type 1 (Salk) were analysed as a marker of protective immunity at the age of 18 months in a prospective birth cohort. No differences were observed in antibody titers between children with and without genetic risk for T1D (odds ratio [OR] = 0.90 [0.83, 1.06], p = 0.30). In the presence of the genetic risk, no difference was observed between children with and without islet autoimmunity (OR = 1.00 [0.78, 1.28], p = 1.00). This did not change when only children with the autoimmunity before 18 months of age were included in the analyses (OR = 1.00 [0.85, 1.18], p = 1.00). No effect was observed when groups were stratified based on autoantigen specificity of the first-appearing autoantibody (IAA or GADA). The children in each comparison group were matched for sex, calendar year and month of birth, and municipality. Accordingly, we found no indication that children who are at risk to develop islet autoimmunity would have a compromised humoral immune response which could have increased their susceptibility for enterovirus infections. In addition, the proper immune response supports the idea of testing novel enterovirus vaccines for the prevention of T1D among these individuals.

Longitudinal MRI Shows Progressive Decline in Pancreas Size and Altered Pancreas Shape in Type 1 Diabetes.

Individuals with type 1 diabetes (T1D) have a smaller pancreas, but longitudinal changes in pancreas size and shape are unclear. We monitored changes in pancreas size and shape after diagnosis with T1D. We conducted a prospective cohort study at an academic medical center between 2014 and 2022. Individuals with T1D (n = 91) or controls (n = 90) underwent magnetic resonance imaging (MRI) of the pancreas, including longitudinal MRI in 53 individuals with new-onset T1D. Interventions included MRI and continuous glucose monitoring (CGM). Pancreas size and shape were measured from MRI. For participants who used CGM, measures of glycemic variability were calculated. On longitudinal imaging, pancreas volume and pancreas volume index normalized for body weight declined during the first year after diagnosis. Pancreas volume index continued to decline through the fifth year after diagnosis. A cross-sectional study of individuals with diabetes duration up to 60 years demonstrated that pancreas size in adults negatively correlated with age and disease duration, whereas pancreas volume and pancreas volume index remained stable in controls. Pancreas volume index correlated inversely with low blood glucose index, a measure of risk for hypoglycemia. Pancreas shape was altered in individuals with T1D and further diverged from controls over the first 5 years after diagnosis. Pancreas size and shape are altered in nondiabetic individuals at genetic risk for T1D. Combined pancreas size and shape analysis better distinguished the pancreas of individuals with T1D from controls than size alone. Pancreas size declines most rapidly near the clinical diagnosis of T1D and continues to decline throughout adulthood. Declines in pancreas size are accompanied by changes in pancreas shape.

Age of Diagnosis Does Not Alter the Presentation or Progression of Robustly Defined Adult-Onset Type 1 Diabetes.

To determine whether presentation, progression, and genetic susceptibility of robustly defined adult-onset type 1 diabetes (T1D) are altered by diagnosis age. We compared the relationship between diagnosis age and presentation, C-peptide loss (annual change in urine C-peptide-creatinine ratio [UCPCR]), and genetic susceptibility (T1D genetic risk score [GRS]) in adults with confirmed T1D in the prospective StartRight study, 1,798 adults with new-onset diabetes. T1D was defined in two ways: two or more positive islet autoantibodies (of GAD antibody, IA-2 antigen, and ZnT8autoantibody) irrespective of clinical diagnosis (n = 385) or one positive islet autoantibody and a clinical diagnosis of T1D (n = 180). In continuous analysis, age of diagnosis was not associated with C-peptide loss for either definition of T1D (P > 0.1), with mean (95% CI) annual C-peptide loss in those diagnosed before and after 35 years of age (median age of T1D defined by two or more positive autoantibodies): 39% (31-46) vs. 44% (38-50) with two or more positive islet autoantibodies and 43% (33-51) vs. 39% (31-46) with clinician diagnosis confirmed by one positive islet autoantibody (P > 0.1). Baseline C-peptide and T1D GRS were unaffected by age of diagnosis or T1D definition (P > 0.1). In T1D defined by two or more autoantibodies, presentation severity was similar in those diagnosed before and after 35 years of age: unintentional weight loss, 80% (95% CI 74-85) vs. 82% (76-87); ketoacidosis, 24% (18-30) vs. 19% (14-25); and presentation glucose, 21 mmol/L (19-22) vs. 21 mmol/L (20-22) (all P ≥ 0.1). Despite similar presentation, older adults were less likely to be diagnosed with T1D, insulin-treated, or admitted to hospital. When adult-onset T1D is robustly defined, the presentation characteristics, progression, and T1D genetic susceptibility are not altered by age of diagnosis.

Polygenic risk for type 2 diabetes, lifestyle, metabolic health, and cardiovascular disease: a prospective UK Biobank study

BackgroundFew studies have examined associations between genetic risk for type 2 diabetes (T2D), lifestyle, clinical risk factors, and cardiovascular disease (CVD). We aimed to investigate the association of and potential interactions among genetic risk for T2D, lifestyle behavior, and metabolic risk factors with CVD.MethodsA total of 345,217 unrelated participants of white British descent were included in analyses. Genetic risk for T2D was estimated as a genome-wide polygenic risk score constructed from > 6 million genetic variants. A favorable lifestyle was defined in terms of four modifiable lifestyle components, and metabolic health status was determined according to the presence of metabolic syndrome components.ResultsDuring a median follow-up of 8.9 years, 21,865 CVD cases (6.3%) were identified. Compared with the low genetic risk group, participants at high genetic risk for T2D had higher rates of overall CVD events, CVD subtypes (coronary artery disease, peripheral artery disease, heart failure, and atrial fibrillation/flutter), and CVD mortality. Individuals at very high genetic risk for T2D had a 35% higher risk of CVD than those with low genetic risk (HR 1.35 [95% CI 1.19 to 1.53]). A significant gradient of increased CVD risk was observed across genetic risk, lifestyle, and metabolic health status (P for trend > 0.001). Those with favorable lifestyle and metabolically healthy status had significantly reduced risk of CVD events regardless of T2D genetic risk. This risk reduction was more apparent in young participants (≤ 50 years).ConclusionsGenetic risk for T2D was associated with increased risks of overall CVD, various CVD subtypes, and fatal CVD. Engaging in a healthy lifestyle and maintaining metabolic health may reduce subsequent risk of CVD regardless of genetic risk for T2D.

Genetic trade-offs between complex diseases and longevity.

Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly studied. Here, we constructed polygenic risk scores (PRSs) for 225 complex diseases/traits and evaluated their relationships with human longevity in a cohort with 2178 centenarians and 2299 middle‐aged individuals. Lower genetic risks of stroke and hypotension were observed in centenarians, while higher genetic risks of schizophrenia (SCZ) and type 2 diabetes (T2D) were detected in long‐lived individuals. We further stratified PRSs into cell‐type groups and significance‐level groups. The results showed that the immune component of SCZ genetic risk was positively linked to longevity, and the renal component of T2D genetic risk was the most deleterious. Additionally, SNPs with very small p‐values (p ≤ 1x10‐5) for SCZ and T2D were negatively correlated with longevity. While for the less significant SNPs (1x10‐5 < p ≤ 0.05), their effects on disease and longevity were positively correlated. Overall, we identified genetically informed positive and negative factors for human longevity, gained more insights on the accumulation of disease risk alleles during evolution, and provided evidence for the theory of genetic trade‐offs between complex diseases and longevity.

1254-P: Genetic Prediction of C-Peptide Trajectory before Type 1 Diabetes Diagnosis in TrialNet

Modeling the course of C-peptide decline in autoantibody (Ab) positive individuals is important for type 1 diabetes (T1D) prediction and implementation of T1D prevention trials. Single nucleotide polymorphisms in TCF7L2, JAZF1, SLC30A8, INS, PTPRK, G6CP2, CLEC16, PTPN22 and HLA genes are associated with persistent C-peptide at or after T1D diagnosis. We sought to determine whether 12 SNPs in these genes and the T1D genetic risk score-2 (GRS2) can predict C-peptide trajectory before diagnosis of T1D. We studied Ab positive at-risk participants in the TrialNet Pathway to Prevention Study who had ImmunoChip data (N=1217, age at initial screen (mean±SD) 16.1±12.7 years, 51.5% female, 81.0% non-Hispanic white) . Over a mean follow up of 3.4±2. years, 255 (21.0%) developed multiple Ab and 336 (27.6%) developed clinical T1D. We analyzed the influence of these 12 SNPs and the T1D GRS2 on C-peptide AUC during progression from single to multiple Abs, from single Ab to clinical T1D, and from multiple Abs to clinical T1D. Analyses were adjusted for baseline C-peptide AUC, age, glucose AUC, BMI Z-score and HbA1c; the presence of high-risk HLA haplotypes (DR3 and DR4-DQ8) ; and the first 3 principal components. The type 2 diabetes (T2D) -associated TCF7L2 rs7901695 and rs4506565 SNPs were significantly associated with higher C-peptide AUC during progression from multiple Abs to T1D (p&amp;lt;0.04) and neared significance for the single-to-multiple Ab transition (p=0.05) . Additionally, lower T1D GRS2 was significantly associated with higher C-peptide AUC during progression from multiple Abs to T1D (p=0.01) . We observed trend associations of lower C-peptide AUC with JAZF1 SNP rs864745 in both single Ab-to-T1D and multiple Ab-to-T1D progression (both p=0.06) , and with SNPs in SLC30A8 and INS in multiple Abs to T1D (p=0.and 0.08, respectively) . In conclusion, T2D-associated SNPs in the TCF7L2 gene and lower T1D GRS2 predict higher C-peptide particularly in progression from multiple Abs to clinical T1D. Disclosure T.M.Triolo: None. M.J.Redondo: Advisory Panel; Provention Bio, Inc. H.M.Parikh: None. M.Tosur: Advisory Panel; Provention Bio, Inc. P.Gottlieb: Advisory Panel; Janssen Research &amp; Development, LLC, ViaCyte, Inc., Other Relationship; IM Therapeutics, Research Support; Caladrius Biosciences, Inc., Immune Tolerance Network, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk, Precigen, Inc., Tolerion, Inc. R.A.Oram: Consultant; Janssen Research &amp; Development, LLC, Research Support; Randox R &amp; D. S.Onengut-gumuscu: None. J.Krischer: None. S.S.Rich: None. A.Steck: None. Funding NIH K12 DK094712NIH RDK121843NIH RDK124395

188-LB: Deep Learning–Based Population Screening of Type 1 Diabetes and Celiac Disease Genetic Risk from Blood Spots at Birth

Introduction: Screening for type 1 diabetes (T1D) genetic risk in early life can prevent life threatening complications such as diabetic ketoacidosis and allow cost-effective recruitment into intervention and immunotherapy trials. Celiac disease (CD) frequently presents as a comorbidity in those with T1D and shares a strong genetic basis with T1D. Single nucleotide polymorphism (SNP) based genetic risk scores (GRS) for both T1D and CD have shown to be very predictive of future disease but are difficult to generate and typically require SNP array genotyping. We aimed to develop a combined GRS screening panel that could be genotyped from a single dried blood spot at birth. Methods: We developed assays for proxy SNPs of common HLA-DQ haplotypes reported in previous GRS and additional loci from the most recent available genome-wide association studies (GWAS) . We used backwards stepwise regression to identify a subset of variants able to be genotyped with DNA eluted from 800 6mm dried blood spots. We developed and validated neural network models to quantify genetic risk of both T1D and CD using T1DGC and UK Celiac case-control SNP array data, validated in UK Biobank. Assays were developed with LGC Genomics and validated on 675 Seattle area samples. Results: The complete panel consisted of 71 validated SNP assays, including 11 backup variants for key loci. We generated neural nework models which demonstrate equivalent or greater predictive power (AUC: T1D=0.914, CD=0.893) to previously published GRS yet require much less expertise to apply. We have developed an algorithm for preparation of raw genotyping data and subsequent generation of GRS requiring little expertise to apply. Conclusion: A 71 SNP blood-spot screening panel is highly effective at screening genetic risk associated with T1D and CD at birth. Using a neural network model the panel enables widely available, easy to generate and inexpensive population screening of genetic risk for T1D and CD. Disclosure S. A. Sharp: None. J. M. Locke: None. Y. Xu: None. D. P. Fraser: None. L. A. Ferrat: None. M. N. Weedon: None. M. Inouye: None. R. A. Oram: Consultant; Janssen Research &amp; Development, LLC, Research Support; Randox R &amp; D. W. Hagopian: Research Support; Janssen Research &amp; Development, LLC. Funding Diabetes UK (16/0005529) JDRF (3-SRA-2019-827-S-B)

Growth in Children with HLA-Conferred Susceptibility to Type 1 Diabetes.

The incidence of type 1 diabetes (T1D) is increasing throughout the world. This trend may be explained by the accelerator hypothesis. Our study investigated growth, its biochemical markers, and their associations with the development of diabetes-associated autoantibodies (DAAB) in 219 children with genetic risk for T1D. Subjects were divided into risk groups based on their human leukocyte antigen genotype. Children in the moderate- to high-risk group were significantly taller when corrected to mid-parental height and had a lower insulin-like growth factor 1 (IGF-1)/IGF-1 binding protein (IGFBP-3) molar ratio than those in the low-risk group (corrected height standard deviation score 0.22±0.93 vs. −0.04±0.84, P<0.05; molar ratio 0.199±0.035 vs. 0.211+0.039, P<0.05). Children with DAAB tended to be taller and to have a higher body mass index than those with no DAAB. Our results suggest that the accelerator hypothesis explaining the increasing incidence of T1D may not solely be dependent on environmental factors, but could be partially genetically determined.

Genetic analysis for type 1 diabetes genes in juvenile dermatomyositis unveils genetic disease overlap.

JDM is a serious autoimmune and complex genetic disease. Another autoimmune genetic disease, type 1 diabetes (T1D), has been observed for significantly increased prevalence in families with JDM, while increased JDM risk has also been observed in T1D cases. This study aimed to study whether these two autoimmune diseases, JDM and T1D, share common genetic susceptibility. From 169 JDM families, 121 unrelated cases with European ancestry (EA) were identified by genome-wide genotyping, principal component analysis and identical-by-descent (IBD) analysis. T1D genetic risk score (GRS) were calculated in these cases and were compared with 361 EA T1D cases and 1943 non-diabetes EA controls. A total of 113 cases of the 121 unrelated European cases were sequenced by whole exome sequencing. We observed increased T1D GRS in JDM cases (P = 9.42E-05). Using whole exome sequencing, we uncovered the T1D genes, phospholipase B1, cystic fibrosis transmembrane conductance regulator, tyrosine hydroxylase, CD6 molecule, perforin 1 and dynein axonemal heavy chain 2, potentially associated with JDM by the burden test of rare functional coding variants. Novel mechanisms of JDM related to these T1D genes are suggested by this study, which may imply novel therapeutic targets for JDM and warrant further study.