Type 1 Diabetes Genetic Risk Score Research Articles

Clinical Prediction Models Combining Routine Clinical Measures Have High Accuracy in Identifying Youth-Onset Type 2 Diabetes Defined by Maintained Endogenous Insulin Secretion: The SEARCH for Diabetes in Youth Study.

With high prevalence of obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D). We studied 2,966 youth with diabetes in the prospective SEARCH for Diabetes in Youth study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting C-peptide ≥250 pmol/L (≥0.75 ng/mL) after >3 years' (median 74 months) diabetes duration. Models included clinical measures at the baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL cholesterol), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS). Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with C-peptide ≥0.75 ng/mL (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under the receiver operating characteristic curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope 0.995-0.999). Models retained high performance for predicting retained C-peptide in older youth with obesity (AUCROC 0.88-0.96) and in subgroups defined by self-reported race/ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92). Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with T2D.

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)

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<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<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<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 & 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)

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.

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.

The relationship between islet autoantibody status and the genetic risk of type 1 diabetes in adult-onset type 1 diabetes

Aims/hypothesisThe reason for the observed lower rate of islet autoantibody positivity in clinician-diagnosed adult-onset vs childhood-onset type 1 diabetes is not known. We aimed to explore this by assessing the genetic risk of type 1 diabetes in autoantibody-negative and -positive children and adults.MethodsWe analysed GAD autoantibodies, insulinoma-2 antigen autoantibodies and zinc transporter-8 autoantibodies (ZnT8A) and measured type 1 diabetes genetic risk by genotyping 30 type 1 diabetes-associated variants at diagnosis in 1814 individuals with clinician-diagnosed type 1 diabetes (1112 adult-onset, 702 childhood-onset). We compared the overall type 1 diabetes genetic risk score (T1DGRS) and non-HLA and HLA (DR3-DQ2, DR4-DQ8 and DR15-DQ6) components with autoantibody status in those with adult-onset and childhood-onset diabetes. We also measured the T1DGRS in 1924 individuals with type 2 diabetes from the Wellcome Trust Case Control Consortium to represent non-autoimmune diabetes control participants.ResultsThe T1DGRS was similar in autoantibody-negative and autoantibody-positive clinician-diagnosed childhood-onset type 1 diabetes (mean [SD] 0.274 [0.034] vs 0.277 [0.026], p=0.4). In contrast, the T1DGRS in autoantibody-negative adult-onset type 1 diabetes was lower than that in autoantibody-positive adult-onset type 1 diabetes (mean [SD] 0.243 [0.036] vs 0.271 [0.026], p<0.0001) but higher than that in type 2 diabetes (mean [SD] 0.229 [0.034], p<0.0001). Autoantibody-negative adults were more likely to have the more protective HLA DR15-DQ6 genotype (15% vs 3%, p<0.0001), were less likely to have the high-risk HLA DR3-DQ2/DR4-DQ8 genotype (6% vs 19%, p<0.0001) and had a lower non-HLA T1DGRS (p<0.0001) than autoantibody-positive adults. In contrast to children, autoantibody-negative adults were more likely to be male (75% vs 59%), had a higher BMI (27 vs 24 kg/m2) and were less likely to have other autoimmune conditions (2% vs 10%) than autoantibody-positive adults (all p<0.0001). In both adults and children, type 1 diabetes genetic risk was unaffected by the number of autoantibodies (p>0.3). These findings, along with the identification of seven misclassified adults with monogenic diabetes among autoantibody-negative adults and the results of a sensitivity analysis with and without measurement of ZnT8A, suggest that the intermediate type 1 diabetes genetic risk in autoantibody-negative adults is more likely to be explained by the inclusion of misclassified non-autoimmune diabetes (estimated to represent 67% of all antibody-negative adults, 95% CI 61%, 73%) than by the presence of unmeasured autoantibodies or by a discrete form of diabetes. When these estimated individuals with non-autoimmune diabetes were adjusted for, the prevalence of autoantibody positivity in adult-onset type 1 diabetes was similar to that in children (93% vs 91%, p=0.4).Conclusions/interpretationThe inclusion of non-autoimmune diabetes is the most likely explanation for the observed lower rate of autoantibody positivity in clinician-diagnosed adult-onset type 1 diabetes. Our data support the utility of islet autoantibody measurement in clinician-suspected adult-onset type 1 diabetes in routine clinical practice.Graphical abstract

Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches

ObjectivesWe aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type. Study Design and SettingWe compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE). ResultsApproaches’ accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability. ConclusionModels combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.

1251-P: Aß Classification Defines Four Distinct Forms of Diabetes in Children

A comprehensive classification accounting for the heterogeneity of pediatric diabetes is lacking. The Aβ classification system, based on islet autoantibody status (A+ or A-) and preserved beta-cell functional reserve (β+ or β-) , was used successfully to define variable phenotypes in adults with Ketosis-prone Diabetes. We set out to investigate the utility of the Aβ classification system in children with new-onset diabetes. “A+” was defined as the presence of ≥1 islet autoantibodies (GAD65, ICA512, insulin or ZnT8 autoantibodies) and "β+" as random C-peptide level ≥0.6 ng/mL at diagnosis. We calculated T1D genetic risk score 2 (T1D GRS2) for each participant, and compared clinical characteristics at diabetes onset and T1D GRS2 between the groups. The study cohort (n=76) was 50% female, 29% non-Hispanic white, 46% Hispanic, 22% non-Hispanic black, and 3% Other. The mean age was 11.7 years and the mean BMI was 58.3%ile. Aβ classification at diagnosis defined the following groups: A+β- (34%) , A+β+ (30%) , A-β+ (33%) and A-β+ (3%) . Results are summarized in Table 1. The Aβ classification system defines phenotypically and genetically distinct diabetes groups in children with new-onset diabetes. This classification system may be useful in clinical settings as well as the design of clinical trials. Disclosure M.Tosur: Advisory Panel; Provention Bio, Inc. S.Deen: None. S.Uysal: n/a. M.Astudillo: None. F.Jahoor: None. W.Hagopian: Research Support; Janssen Research &amp; Development, LLC. R.A.Oram: Consultant; Janssen Research &amp; Development, LLC, Research Support; Randox R &amp; D. M.J.Redondo: Advisory Panel; Provention Bio, Inc. A.Balasubramanyam: None. Funding Texas Children's Pilot Award

Common single-nucleotide polymorphisms combined with a genetic risk score provide new insights regarding the etiology of gestational diabetes mellitus.

Few studies have constructed a genetic risk score (GRS) to predict the risk of gestaional diabetes mellitus (GDM). We tested the hypothesis that single-nucleotide polymorphisms (SNPs) confirmed for diabetes and obesity and the GRS are associated with GDM. We conducted a case-control study comprising 971 GDM cases and 1682 controls from the University of Hong Kong Shenzhen Hospital. A total of 1448 SNPs reported with type 2 diabetes (T2D), type 1 diabetes (T1D), and obesity were selected and the GRS based on SNPs associated with GDM was created. We confirmed that rs10830963 (OR=1.41,95% CI=1.25, 1.59) in MTNR1B and rs2206734 (OR=1.38, 95% CI=1.22, 1.55) in CDKAL1 were strongly associated with the risk of GDM. Compared with participants with GRS based on T2D SNPs in the low tertile, the ORs of GDM across increasing GRS tertiles were 1.63 (95% CI 1.29, 2.06) and 2.72 (95% CI 2.18, 3.38) in the middle and high tertile, respectively. The positive associations between the GRS and the risk of GDM were also observed in GRS based on obesity/waist-to-hip ratio (WHR)/body mass index (BMI) SNPs. The resulting GRS for each allele increase was significantly associated with higher glycemic indices and lower HOMA-B values for GRS based on T2D SNPs, but not for GRS based on T1D SNPs and GRS based on obesity/WHR/BMI SNPs. These findings indicate that GDM may share a common genetic background with T2D and obesity and that SNPs associated with insulin secretion defects have a vital role in the development of GDM.

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.

243-OR: Integrating Genetic Risk Score and Islet Autoantibody Characteristics in the Predictive Model for Type 1 Diabetes in the TrialNet Study

Background: Type 1 diabetes (T1D) genetic risk score (GRS) and islet autoantibody (AAb) specificity and titers influence T1D risk; however, these factors collected at baseline in cross sectional studies have not been effectively incorporated in the current models for prediction of progression along pre-clinical stages of T1D. Our aim was to develop an updated model of T1D prediction for research and clinical practice. Methods: AAb-positive relatives of individuals with T1D (n=1,233, 52% female, 88% European ancestry, age mean 15 yrs [SD 12.2]) from the TrialNet Pathway to Prevention were followed for a median of 3.1 years (IQR 1.1 -7.1). T1D developed in 498/1,233 (40.4%). T1D GRS, AAb number, AAb combination, AAb titer, age and sex were evaluated on the combined T1D prediction model, estimating its performance using 3-fold cross-validation (10x repeated). Results: Machine learning (survival random forest) and traditional methods (Cox models, extended Cox model) performed equivalently as measured by 5 yr horizon time-dependent AUC ROC (respectively 0.74, 0.74, 0.72), on calibration by integrated Brier score (respectively 0.15, 0.15, 0.16), and by calibration plots. AAb combinations with specific variables predicted T1D. In multivariate analysis, T1D GRS was a significant predictor of T1D in individuals positive for GADA, IAA or both. For any autoantibody combination including IA2A, the IA2A titer was predictive while the T1D GRS was not. Variable importance analyses showed that T1D was predicted by IA2A titer level, followed by T1D GRS and combinations of specific AAbs. Conclusion: We modeled individual estimates of T1D risk with T1D GRS, AAb characteristics, age, and sex. T1D prediction improved by adding T1D GRS and AAb characteristics to other variables, and was similar using machine learning or traditional models. T1D GRS was a significant predictor only in the absence of IA2A. In the presence of IA2A, IA2A titer was the most influential factor. Disclosure L. A. Ferrat: None. M. J. Redondo: Advisory Panel; Self; Provention Bio, Inc. A. Steck: None. H. M. Parikh: None. L. You: None. S. Onengut-gumuscu: None. P. Gottlieb: Advisory Panel; Self; Janssen Research &amp; Development, LLC, Tolerion, Inc., Viacyte, Inc., Other Relationship; Self; ImmunoMolecular Therapeutics, Inc., Research Support; Self; Caladrius Biosciences, Inc., Immune Tolerance Network, Mercia Pharma Inc., National Institute of Diabetes and Digestive and Kidney Diseases, Precigen, Inc., Tolerion, Inc. S. S. Rich: None. J. Krischer: None. R. A. Oram: Consultant; Self; Janssen Research &amp; Development, LLC. Funding National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK121843-01); JDRF (3-SRA-2019-827-S-B)

244-OR: Toward Improved Identification of Adult-Onset Type 1 Diabetes (T1D): Clinical Characteristics Associated with T1D Polygenic Risk Scores in the Million Veteran Program (MVP)

Adult onset T1D is often misdiagnosed as T2D, leading to poor metabolic control and increased risk of microvascular complications and cardiovascular morbidity and mortality. We examined characteristics associated with a T1D genetic risk score in Veterans, a population where new diagnoses are generally presumed to be T2D, since persons with typical juvenile-onset T1D are excluded from military service. A 30 SNP T1D genetic risk score (GRS), previously validated with the Wellcome Trust Case Control Consortium (WTCCC), was applied to 111,663 Veterans with diabetes in the MVP. Patients were stratified by T1D GRS centile groups (≤5th, &amp;gt;5th-≤36th, &amp;gt;36th-≤50th, and &amp;gt;50th) which corresponded to T1D specificity in the WTCCC of 29%, 53%, 90%, and 95%, respectively. Typical T1D clinical characteristics were significantly more common (p&amp;lt;0.001) across increasing GRS centile groups: diabetic ketoacidosis 1.07%, 1.34%, 1.95%, 3.55%; hypoglycemia prompting an emergency department visit 3.95%, 3.84%, 4.45%, 5.45%; random plasma glucose &amp;lt;50 mg/dl at a primary care visit 8.05%, 7.99%, 9.01%, 12.43%; years to starting outpatient insulin Rx 4.40, 4.31, 3.97, 3.38; any use of a T1D ICD code 16.56%, 17.76%, 20.90%, 27.62%; low C-peptide if tested 2.91%, 9.75%, 18.96%, 32.55%; and GAD antibody (+) if tested 8.80%, 23.39%, 32.73%, 45.00%. Among the 5% tested, years to starting insulin fell significantly (p&amp;lt;0.001) in lowest to highest centile groups, from 2.59 to 1.28 yr for GAD(-), and from 1.77 vs. 0.90 yr for GAD(+). Conclusions: The 10% of US Veterans with diabetes in MVP in the two highest T1D GRS centile groups have characteristics suggestive of T1D, although only 5% were tested for GAD antibodies, and most had no use of T1D diagnosis codes. Since adult-onset T1D may be misdiagnosed as T2D in settings where T2D is more common, genetic analyses and/or clinical characteristics may warrant definitive testing and appropriate management. Disclosure P. K. Yang: None. M. K. Rhee: None. C. Mercado: None. R. A. Oram: Consultant; Self; Janssen Research &amp; Development, LLC. M. Vujkovic: None. P. Reaven: Research Support; Self; AstraZeneca, Dexcom, Inc. K. Cho: None. L. S. Phillips: Other Relationship; Self; Diasyst Inc., Research Support; Self; AbbVie Inc., Eli Lilly and Company, GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Kowa Pharmaceuticals America, Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk Pharma Ltd., Pfizer Inc., Sanofi-Aventis. A. Leong: None. S. Jackson: None. B. R. Charest: None. M. N. Weedon: None. Y. J. Cheng: None. Y. V. Sun: None. S. Raghavan: None. E. M. Litkowski: None. B. T. Legvold: None. Funding U.S. Department of Veterans Affairs (CSP2008, I01CX001899, I01CX001737); National Institutes of Health (R18DK066204, R21AI156161, U01DK091958, U01DK098246, UL1TR002378)

The Absence of Islet Autoantibodies in Clinically Diagnosed Older-Adult Onset Type 1 Diabetes Suggests an Alternative Pathology, Advocating for Routine Testing in This Age Group

Objective: Islet autoantibodies at diagnosis are not well studied in older-adult onset (>30years) type 1 diabetes due to difficulties of accurate diagnosis. We used a type 1 diabetes genetic risk score (T1DGRS) to identify type 1 diabetes aiming to evaluate the prevalence and pattern of autoantibodies in older-adult onset type 1 diabetes. Methods: We used a 30 variant T1DGRS in 1866 white-European individuals to genetically confirm a clinical diagnosis of new onset type 1 diabetes. We then assessed the prevalence and pattern of GADA, IA2A and ZnT8A within genetically consistent type 1 diabetes across three age groups ( 30years (n=588)). Findings: In autoantibody positive cases T1DGRS was consistent with 100% type 1 diabetes in each age group. Conversely in autoantibody negative cases, T1DGRS was consistent with 93%(56/60) of 30years having type 1 diabetes. Restricting analysis to genetically consistent type 1 diabetes showed similar proportions of positive autoantibodies across age groups (92% 30years)[p=0.87]. GADA was the most common autoantibody in older-adult onset type 1 diabetes, identifying 95% of autoantibody positive cases versus 72% in those <18years. Interpretation: Older adult-onset type 1 diabetes has identical rates but different patterns of positive autoantibodies to childhood onset. In clinically suspected type 1 diabetes in older-adults, absence of autoantibodies strongly suggests non-autoimmune diabetes. Our findings suggest the need to change guidelines from measuring islet autoantibodies where there is diagnostic uncertainty to measuring at least GADA in all suspected adult type 1 diabetes cases. Funding Statement: NJT is funded by a Wellcome Trust funded GW4 PhD (220601/Z/20/Z). M.N.W. is supported by the Wellcome Trust Institutional Support Fund (WT097835MF). KAP is a Wellcome Trust fellow (219606/Z/19/Z) and the genetic risk score analysis in ADDRESS-2 was funded by Diabetes wellness Research foundation pump priming grant. ADDRESS-2 is co-funded by Diabetes UK (grant numbers 09-0003919, 15-005234 & 19/0006119) and the Juvenile Diabetes Research Foundation (grant numbers 9-2010-407 & 3-SRA-2015-35-A-N), supported by the NIHR Clinical Research Network and hosted by Imperial College London. S.M. is currently supported by a Future Leaders Mentorship award from the European Federation for the Study of Diabetes. NO is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Imperial College London. DJ is funded by the NIHR CRN. Declaration of Interests: We declare no competing interest. Ethics Approval Statement:Ethics for the ADDRESS2 study was granted by the South Central – Berkshire NHS Research Ethics Committee on the 03/10/2010, ref: 10/H0505/85. Ethics for monogenic testing in the ADDRESS2 cohort by the East of England – Essex Research Ethics Committee on the 14/7/2016, ref 16/EE/0306

Simposio 1: Diabetes monogénicas

SIMPOSIO 1: Cuando la diabetes no es autoinmuneDiabetes monogénicas Contar con un diagnóstico de precisión del subtipo de diabetes mellitus (DM) es de vital importancia. Esto se refleja en el pequeño subgrupo de DM que se diagnostica antes de los 6 meses de edad, llamadas DM neonatales. Aquellas causadas por mutaciones en el canal de potasio Kir6.2 son un ejemplo extraordinario de farmacogenética, ya que la transferencia en el tratamiento de insulina a altas dosis de sulfonilureas mejora no sólo el control glucémico, sino también el pronóstico neurológico. El diagnóstico del subtipo de DM se complejiza en el adolescente y el adulto joven (20-40 años). En este grupo etario, el algoritmo propuesto por Florez -que tiene en cuenta el fenotipo, péptido C, presencia de autoanticuerpos y el escore genético de DM1 (T1D GRS)- es de gran utilidad y, de esta forma, podemos llegar al diagnóstico del subtipo de DM monogénica más frecuente: la DM MODY (Maturity Onset Diabetes in Youngs). Se trata de un síndrome heterogéneo, desde un punto de vista genético, metabólico y clínico, cuyo denominador común es la hiposecreción de insulina como factor desencadenante primario.

Type 1 diabetes can present before the age of 6\xa0months and is characterised by autoimmunity and rapid loss of beta cells

Aims/hypothesisDiabetes diagnosed at <6 months of age is usually monogenic. However, 10–15% of affected infants do not have a pathogenic variant in one of the 26 known neonatal diabetes genes. We characterised infants diagnosed at <6 months of age without a pathogenic variant to assess whether polygenic type 1 diabetes could arise at early ages.MethodsWe studied 166 infants diagnosed with type 1 diabetes at <6 months of age in whom pathogenic variants in all 26 known genes had been excluded and compared them with infants with monogenic neonatal diabetes (n = 164) or children with type 1 diabetes diagnosed at 6–24 months of age (n = 152). We assessed the type 1 diabetes genetic risk score (T1D-GRS), islet autoantibodies, C-peptide and clinical features.ResultsWe found an excess of infants with high T1D-GRS: 38% (63/166) had a T1D-GRS >95th centile of healthy individuals, whereas 5% (8/166) would be expected if all were monogenic (p < 0.0001). Individuals with a high T1D-GRS had a similar rate of autoantibody positivity to that seen in individuals with type 1 diabetes diagnosed at 6–24 months of age (41% vs 58%, p = 0.2), and had markedly reduced C-peptide levels (median <3 pmol/l within 1 year of diagnosis), reflecting rapid loss of insulin secretion. These individuals also had reduced birthweights (median z score −0.89), which were lowest in those diagnosed with type 1 diabetes at <3 months of age (median z score −1.98).Conclusions/interpretationWe provide strong evidence that type 1 diabetes can present before the age of 6 months based on individuals with this extremely early-onset diabetes subtype having the classic features of childhood type 1 diabetes: high genetic risk, autoimmunity and rapid beta cell loss. The early-onset association with reduced birthweight raises the possibility that for some individuals there was reduced insulin secretion in utero. Comprehensive genetic testing for all neonatal diabetes genes remains essential for all individuals diagnosed with diabetes at <6 months of age.Graphical abstract

Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes.

Misclassification of diabetes is common due to an overlap in the clinical features of type 1 and type 2 diabetes. Combined diagnostic models incorporating clinical and biomarker information have recently been developed that can aid classification, but they have not been validated using pancreatic pathology. We evaluated a clinical diagnostic model against histologically defined type 1 diabetes. We classified cases from the Network for Pancreatic Organ donors with Diabetes (nPOD) biobank as type 1 (n=111) or non-type 1 (n=42) diabetes using histopathology. Type 1 diabetes was defined by lobular loss of insulin-containing islets along with multiple insulin-deficient islets. We assessed the discriminative performance of previously described type 1 diabetes diagnostic models, based on clinical features (age at diagnosis, BMI) and biomarker data [autoantibodies, type 1 diabetes genetic risk score (T1D-GRS)], and singular features for identifying type 1 diabetes by the area under the curve of the receiver operator characteristic (AUC-ROC). Diagnostic models validated well against histologically defined type 1 diabetes. The model combining clinical features, islet autoantibodies and T1D-GRS was strongly discriminative of type 1 diabetes, and performed better than clinical features alone (AUC-ROC 0.97 vs. 0.95; P=0.03). Histological classification of type 1 diabetes was concordant with serum C-peptide [median <17pmol/l (limit of detection) vs. 1037pmol/l in non-type 1 diabetes; P<0.0001]. Our study provides robust histological evidence that a clinical diagnostic model, combining clinical features and biomarkers, could improve diabetes classification. Our study also provides reassurance that a C-peptide-based definition of type 1 diabetes is an appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible. Parts of this study were presented in abstract form at the Network for Pancreatic Organ Donors Conference, Florida, USA, 19-22 February 2019 and Diabetes UK Professional Conference, Liverpool, UK, 6-8 March 2019.

Type 1 diabetes genetic risk score is discriminative of diabetes in non-Europeans: evidence from a study in India

Type 1 diabetes (T1D) is a significant problem in Indians and misclassification of T1D and type 2 diabetes (T2D) is a particular problem in young adults in this population due to the high prevalence of early onset T2D at lower BMI. We have previously shown a genetic risk score (GRS) can be used to discriminate T1D from T2D in Europeans. We aimed to test the ability of a T1D GRS to discriminate T1D from T2D and controls in Indians. We studied subjects from Pune, India of Indo-European ancestry; T1D (n = 262 clinically defined, 200 autoantibody positive), T2D (n = 345) and controls (n = 324). We used the 9 SNP T1D GRS generated in Europeans and assessed its ability to discriminate T1D from T2D and controls in Indians. We compared Indians with Europeans from the Wellcome Trust Case Control Consortium study; T1D (n = 1963), T2D (n = 1924) and controls (n = 2938). The T1D GRS was discriminative of T1D from T2D in Indians but slightly less than in Europeans (ROC AUC 0.84 v 0.87, p < 0.0001). HLA SNPs contributed the majority of the discriminative power in Indians. A T1D GRS using SNPs defined in Europeans is discriminative of T1D from T2D and controls in Indians. As with Europeans, the T1D GRS may be useful for classifying diabetes in Indians.

1683-P: Analysis of Type 1 Diabetes Genetic Risk Score Shows 1 in 8 People with Clinically Diagnosed Adult-Onset T1D Are Misdiagnosed, and Presenting Features at Diagnosis Do Not Identify Those Misdiagnosed

Aims: Accurate initial diagnosis of adult-onset type 1 diabetes (T1D) is important. We aimed to estimate the rate of misdiagnosis in clinically-diagnosed adult T1D. We also assessed the utility of Islet autoantibodies and presenting features to identify misdiagnosed-T1D. Methods: We used a T1D-genetic risk score (T1D-GRS) to estimate misdiagnosed-T1D in 1112 white-Europeans adults clinically-diagnosed with T1D (age diagnosis &amp;gt;18y) from the ADDRESS-2 study. We used 1924 gold-standard T2D as a non-T1D control. All patients were recruited &amp;lt;6m from diagnosis and insulin treated. We assessed clinical features and autoantibodies (GADA, IA-2A, ZNT8A) in misdiagnosed and true-T1D. Results: People with one (n=309), two (n=285) or three (n=300) autoantibodies had a similar T1D-GRS suggesting people with ≥1 antibody positive had true-T1D (mean GRS 0.270 vs. 0.269 vs. 0.273, p=0.13). People without antibody had T1D-GRS (0.243, n=218) between true-T1D (0.271, n=894, ≥1ab+ve) and T2D (0.229, n=1924, p&amp;lt;0.0001) suggesting presence of non-T1D in antibody negative group. The estimated proportion of non-T1D in antibody negative group was 67% (n=146/218) which was 13% of the whole cohort (95% CI 11-15%). Misdiagnosis was higher in people diagnosed 30-75y (21%) vs. 18-30y (6%, p&amp;lt;0.001). Misdiagnosed-T1D and true-T1D had similar rates of polyuria (91% vs. 95%), weight loss (85% vs. 88%) and DKA (38% vs. 43%) (all p&amp;gt;0.05). Misdiagnosed-T1D were more likely to be male (81% vs. 59%), older (40y vs. 32y), had higher BMI (28 vs. 24) and HbA1c (105 vs. 91 mmol/mol) compared to true-T1D (all p&amp;lt;0.001). Conclusion: Initial misdiagnosis of T1D is common in people diagnosed &amp;gt;30y (21%). When clinically-diagnosed with T1D at diagnosis, negative islet autoantibodies but not severe hyperglycaemia/DKA identify misdiagnosed people. Disclosure K.A. Patel: None. N. Thomas: None. M.N. Weedon: None. H.C. Walkey: None. A. Kaur: None. A.J. Williams: None. S. Misra: None. N. Oliver: Advisory Panel; Self; Roche Diabetes Care. Research Support; Self; Dexcom, Inc., Roche Diabetes Care. Speaker's Bureau; Self; Dexcom, Inc., Sanofi. D.G. Johnston: None. A.T. Hattersley: None. Funding Diabetes UK; JDRF; Diabetes Research &amp; Wellness Foundation

210-OR: A Combined Method Improves Risk Prediction for Childhood Type 1 Diabetes in the TEDDY Study

Background: There is an unmet need for accurate cost-effective estimation of future T1D risk. Methods: We derived a combined T1D prediction model using 7883 children followed closely from birth for a median of 9 yr, considering T1D genetic risk score (GRS), family T1D history (FH), standard islet autoantibodies (IA), race, birth circumstances, early growth and nutrient status. T1D developed in 326. Results: Machine learning and traditional methods (Cox models) performed equivalently as measured by time-dependent AUC ROC. Future T1D was accurately predicted by combining only 3 variables: GRS, FH and IA status. Accuracy of combined scores increased with age at scoring. By age 2, they were highly predictive for T1D in the next 5 years (AUC &amp;gt;0.91, 95% CI 0.88-0.95) (see X on Figure). A 2-yr-old with 2 IA, positive FH and high GRS (&amp;gt;12) would have T1D risk over the next 1, 3 and 5 years of 14% (8-19%), 36% (25-45%) and 51% (39%-61%) respectively. A 2-yr-old with 1 IA, no FH and moderate GRS (10-11) has T1D risk of 0.8% (0.6-1.2%), 2.6% (1.9-3.2%) and 4.3% (3.4%-5.2%) respectively. After newborn genetic screening, only simple venous sampling in routine healthcare settings is required. Conclusion: This approach allows updated individual risk estimates by age, and in the future may enable release of low risk individuals from surveillance long after initial newborn screening for more cost-efficient population based pediatric T1D prediction. Disclosure L.A. Ferrat: None. K. Vehik: None. S.A. Sharp: None. Å. Lernmark: None. A. Ziegler: None. M. Rewers: None. J. She: None. J. Toppari: None. B. Akolkar: None. J. Krischer: None. M.N. Weedon: None. S.S. Rich: None. R.A. Oram: Other Relationship; Self; Randox Laboratories Ltd. W. Hagopian: Research Support; Self; Novo Nordisk A/S. Funding National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; JDRF; Centers for Disease Control and Prevention

82-OR: A T1D Genetic Risk Score Combined with Clinical Features and Autoantibodies Enables Accurate Diabetes Classification in a Racial/Ethnically Diverse Population: The Search for Diabetes in Youth Study

Accurate classification of diabetes type guides correct treatment. We recently showed a type 1 diabetes (T1D) genetic risk score (GRS-1), combined with clinical features and biomarkers discriminates between T1D and type 2 (T2D) and MODY in European adults. We aimed to test the ability of an improved 67 SNP T1D score (GRS-2) to discriminate T1D in an ethnically diverse U.S. pediatric population. We included 1818 SEARCH Study participants who had a C-peptide at a median (IQR) of 8 (6-9) years post diagnosis. T1D was defined based on severe insulin deficiency (fasting C-peptide &amp;lt;0.25 ng/ml) at follow-up. We generated genetic risk scores from SNP data, and assessed discriminative power of the T1D GRS-2 using the area under the receiver operating curve (AUC) in the three largest ethnic groups in SEARCH: white (n=1101, 95% T1D), black (n=228, 59% T1D) and Hispanic (n=257, 77% T1D). We then assessed the discriminative power of GRSs combined with autoantibody and clinical data. The T1D GRS-2 was discriminative of T1D across all three ethnic groups AUC (95% CI) for GRS-1 vs. GRS-2 was 0.88 (0.84, 0.93) vs. 0.88 (0.83, 0.92) in white, 0.82 (0.77, 0.87) vs. 0.87 (0.83, 0.92) in blacks, and 0.87 (0.82, 0.92) vs. 0.9 (0.86, 0.95) in Hispanics). Combined risk scores were most accurate at classifying T1D. AUC for different scores were: 0.93 (0.91, 0.95) for type assigned by provider at diagnosis, 0.95 (0.93, 0.96) for age and BMI, 0.95 (0.94, 0.97) for GAD/IA-2/ZNT8 autoantibodies, and 0.99 (0.99, 1.00) for a combined approach using autoantibodies, GRS-2 and clinical features. These results were similar across all race/ethnic groups. A new T1D GRS-2 is discriminative of T1D in a racial/ethnically diverse pediatric population. Using GRS2 in a combined model of clinical features, autoantibodies and genetics offers near perfect classification of T1D and could be used to accurately classify diabetes type. Disclosure R.A. Oram: Other Relationship; Self; Randox Laboratories Ltd. S.A. Sharp: None. C. Pihoker: None. L.A. Ferrat: None. G. Imperatore: None. S. Saydah: None. A.H. Williams: None. L.E. Wagenknecht: None. J.M. Lawrence: None. M.N. Weedon: None. R. Dagostino: Consultant; Self; Acelity, Amgen Inc. W. Hagopian: Research Support; Self; Novo Nordisk A/S. J. Divers: None. D. Dabelea: None. Funding National Institutes of Health (UC4DK108173)