Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications.

Introduction and Objective: Single-cell (SC) gene expression analysis of pancreatic islets from donors with and without diabetes provides key insights into disease development and heterogeneity. Creating a large atlas through uniform dataset harmonization is essential to overcome challenges such as limited islet availability and inconsistent metadata, experimental methods, and analysis standards. Methods: Sequencing reads generated and/or provided by three independent islet sources (Human Pancreas Analysis Program, Integrated Islet Distribution Program, and Prodo Labs), were collected and aligned. Genotypes and donor metadata were verified to ensure accurate sample identity. Rigorous quality control was employed to select high-quality reads and cells, minimize ambient RNA contamination, and eliminate doublets. During integration, we accounted for key variables such as age, sex, BMI, islet distributor, and sequencing chemistry. Differential expression analysis (DEA) was performed using a latent variable approach. Results: Our comprehensive islet SC atlas combines data from 191 samples across 140 donors (59 female, 81 male), making it the largest number of donors available to date. It represents five phenotypes: 69 non-diabetic, 12 autoantibody-positive non-diabetic, 11 pre-diabetic, 12 T1D, and 36 T2D. It maps 448,935 cells into 13 distinct populations, including alpha cells (43% of total cells), beta (27%), and smaller groups such as immune (0.6%). DEA of the six most abundant cell types revealed 1,805 genes differentially expressed (FDR < 5%), of which 1,402 are novel, between T1D and non-diabetic samples, enriching for pathways such as “adaptive immune response” and “Type I diabetes mellitus”. A notable novel T1D-up-regulated gene in beta cells is OPLAH, which is involved in the glutathione pathway. Conclusion: By integrating data across 191 samples, we created a comprehensive SC map of pancreatic islets, available at www.pankbase.org, and enabled research into diabetes mechanisms and progression. Disclosure H.T. Vu: None. H. Sun: None. S.A. Sharp: None. P. Kudtarkar: None. L. Brusman: None. Y. Wang: None. R. Mao: None. F. Feng: None. S. Corban: None. A.K. Huber: None. J. Jurgens: None. T. Bate: None. D. Jang: None. C.C. Robertson: None. P. Smadbeck: None. Y. Sun: None. M. Brandes: None. J. Liu: None. S. Chen: Stock/Shareholder; Current; iOrganBio Inc. Stock/Shareholder; Ended; Oncobeat. J. Cartailler: None. B.F. Voight: Other - Invited speaker for seminar; They covered expenses (Travel, lodging, food) for the visit.; Ended; Eli Lilly and Company. M. Brissova: None. A.L. Gloyn: Speaker's Bureau; Ended; Novo Nordisk. Other - Spouse is an employee and holds stock options in Roche; Current; Genentech, Inc. K. Gaulton: Other - Spouse employee; Current; Altos Labs. Stock/Shareholder; Current; Neurocrine Biosciences, Inc. S. Parker: Research Support; Current; Pfizer Inc. Consultant; Ended; Novo Nordisk. Funding NIDDK (U24DK138515, U24DK138512)

Background:Acute pancreatitis (AP) is an established risk factor for diabetes, with approximately 20% of children developing either prediabetes or diabetes within one year of their first episode. Little is known about the diabetes pathophysiology or which individuals are at highest risk. We aimed to evaluate whether genetic risk scores (GRS) for type 1 (T1D) and polygenic risk scores (PRS) type 2 diabetes (T2D) are associated with progression to dysglycemia following AP. Methods:Clinical data were available for 123 children (mean age (IQR), 12 (8-15) years; mean body mass index (BMI), 21.8) with AP who were followed for >1 year. Array genotyping coupled with imputation using the TOPMed reference panel was performed. Genetic ancestry was predicted using a random forest classifier. GRS for T1D and T2D were calculated using either an ancestry-appropriate (T1D-GRS) or a multi-ancestry (T2D-PRS) weighted framework. To evaluate risk compared to the population we used predefined GRS thresholds from UK Biobank. Results:Among the 123 subjects, 24 developed dysglycemia (5 with diabetes and 19 with prediabetes). The majority (75.6%, n=93) of children were of European ancestry. Comparison of the T1D-GRS burden with the UK BioBank showed numerically higher proportions for any given threshold. At the top 5% threshold, 9.7% of our cohort were classified as high-risk compared to 5% in UK Biobank (p<0.05). The elevated T1D-GRS could be primarily attributed to non-HLA variants and was more enriched in those testing positive for ≥1 islet-autoantibody. The T2D-PRS was also elevated in the dysglycemic group but only reached statistical significance in those who were obese. Conclusion:These findings highlight the potential role of both T1D-GRS and T2D-PRS in investigating diabetes susceptibility following AP.

Phenotyping and genotyping initiatives within the Integrated Islet Distribution Program (IIDP), the largest source of human islets for research in the U.S., provide standardized assessment of islet preparations distributed to researchers and enable the integration of multiple data types. Data from islets of the first 299 organ donors without diabetes analyzed using this pipeline highlights substantial heterogeneity in islet cell composition associated with hormone secretory traits, sex, reported race and ethnicity, genetically predicted ancestry, and genetic risk for type 2 diabetes (T2D). While α and β cell composition influenced insulin and glucagon secretory traits, the abundance of δ cells showed the strongest association with insulin secretion and was also associated with the genetic risk score (GRS) for T2D. These findings have important implications for understanding mechanisms underlying diabetes heterogeneity and islet dysfunction and may provide insight into strategies for personalized medicine and β cell replacement therapy.

Introduction and Objective: Some individuals with T2D have islet autoantibodies (aab). To determine if the presence of autoantibodies represents a form of islet-directed autoimmunity, we integrated and compared serologic, functional, genetic, and histologic data from HPAP-T2D donors with (T2Daab+, n=6) or without (T2Daab-, n=47) aab, and donors with no diabetes (ND, N=96). Methods: Donor clinical and demographic data, islet perifusion results, genetic risk scores (GRS), and multiplex immunofluorescent images (CODEX) are available on the HPAP website, PancDB. Immunofluorescent images were quantified using AI-assisted segmentation and classification algorithms in QuPath software. Statistical significance (p<0.05) was tested by ANOVA. Results: Compared to ND donors, T2D donors had higher BMI and T2D-GRS, lower residual C-peptide, but no difference in relative pancreas weight (RPW). Compared to T2Daab-, T2Daab+ (4 GADA+, 2 IA2+) individuals were similar in age, BMI, disease duration, T1D-GRS, and T2D-GRS. T2Daab+ individuals had reduced islet function, with lower C-peptide levels (1.2±0.3 vs 3.7±0.5 ng/mL, p=0.001), lower glucose-stimulated insulin release from isolated islets (1.2±0.1 vs 1.6±0.2 stimulation index, p=0.07), and higher HbA1c (8.8±0.5 vs 7.4±0.3 %, p=0.03) than T2Daab-. RPW of T2Daab+ individuals was intermediate between T2Daab- and HPAP-T1D donors (0.86±0.08 vs 1.03±0.07 vs 0.68±0.04 g/kg, p=0.001). In pancreatic sections, the T cell/islet cell ratio was higher in T2Daab+ than in ND controls (0.38±0.09 vs 0.11±0.02; p=0.03), but was not different between T2Daab- and controls. Sections from one of six T2Daab+ donors was amyloid positive, compared to 24/41 T2Daab-donors being amyloid positive. Conclusion: Among T2D donors, the presence of islet autoantibodies was associated with histologic evidence of latent autoimmunity, reduced islet function, and decreased pancreas size, suggesting that autoimmune processes play a role in their diabetes. Disclosure M.M. Wimalarathne: None. A. Eskaros: None. S. Spearman: None. F. Feng: None. A.L. Hopkirk: None. K.S. Lee: None. P.B. Jackson: None. J. Haynes: None. R. Jenkins: None. R.A. Brantley: None. A. Pandey: None. S.A. Sharp: None. H. Sun: None. E. Manduchi: None. R. O’Flynn: None. N.M. Doliba: None. J. Cartailler: None. S. Shapira: None. C. Dai: None. P. Wilson: None. D.C. Saunders: None. M. Brissova: None. A.L. Gloyn: Speaker's Bureau; Ended; Novo Nordisk. Other - Spouse is an employee and holds stock options in Roche; Current; Genentech, Inc. A. Naji: None. K. Kaestner: None. A.C. Powers: None. J. Wright: None. Funding National Institutes of Health (DK112217, DK123594, DK123716, DK112232), Veterans Administration (BX005910, BX000666)

Polygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we leveraged recent multi-ancestry genome-wide association studies to create a type 1 diabetes multi-ancestry polygenic score (T1D MAPS). We trained the score in the Mass General Brigham (MGB) Biobank (372 individuals with type 1 diabetes) and tested the score in the All of Us program (86 individuals with type 1 diabetes). We evaluated the area under the receiver operating characteristic curve (AUC), and we compared the AUC to two published single-ancestry scores: T1D GRS2EUR and T1D GRSAFR. We also developed an updated score (T1D MAPS2) that combines T1D GRS2EUR and T1D MAPS. Among individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2EUR (0.82) and T1D GRSAFR (0.82). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2EUR (0.89 vs. 0.91). However, T1D MAPS2 performed equivalently to T1D GRS2EUR in European ancestry (0.91 vs. 0.91) and performed better in non-European ancestry (0.90 vs. 0.82). Overall, these findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations.

Type 1 diabetes (T1D) polygenic risk scores (PRS) are effective tools for discriminating T1D from other diabetes types and predicting T1D risk, with applications in screening and intervention trials. A previously published T1D Genetic Risk Score 2 (GRS2) is widely adopted, but challenges in standardization and accessibility have hindered broader clinical and research utility. To address this, we introduce GRS2x, a standardized and cross-compatible method for accurate T1D PRS calculation, demonstrating genotyping and reference panel independent performance across diverse datasets. GRS2x as a unified approach facilitates accessible and portable measurement of T1D polygenic risk.

HumanIslets.com supports diabetes research by offering easy access to islet phenotyping data, analysis tools, and data download. It includes molecular omics, islet and cellular function assays, tissue processing metadata, and phenotypes from 547 donors. As it expands, the resource aims to improve human islet data quality, usability, and accessibility.

Diabetes mellitus encompasses several disorders, each with differing clinical presentation, prognoses and pathophysiology. Distinct polygenic architectures underlie type 1 diabetes mellitus and type 2 diabetes mellitus, and govern numerous pathophysiological pathways that converge on dysglycaemia. Over the previous decade, polygenic risk scores (PRS) derived from large genome-wide association studies have become broadly recognized for their potential in precision medicine. PRS, and now partitioned polygenic scores generated by clustering of risk variants, can quantify individual genetic predisposition to diabetes mellitus and reveal molecular heterogeneity responsible for variation in clinical presentation and prognoses. In this Review, we examine and contrast progress in the development of type 1 diabetes mellitus PRS and type 2 diabetes mellitus PRS, and discuss paths to further methodological advances. We examine how studies in the past 10 years have harnessed PRS and novel partitioned polygenic scores to reveal insights into diabetes mellitus aetiology and characterize changes in cellular and tissue-specific disease-modifying molecular pathways. Additionally, we discuss advances and opportunities in areas of clinical translation, including improved classification of diabetes mellitus type, screening of those at risk and personalized interventions informed by PRS. Finally, we emphasize the urgent need to overcome ancestry-related challenges and highlight current progress and gaps in ensuring the equitable translation of PRS for diabetes mellitus precision medicine. In this Review, the authors discuss different methods for assessing polygenic risk of diabetes mellitus and the utility of polygenic risk scores in classifying and screening for diabetes mellitus. The current limitations of these scores and ways of overcoming these limitations are also covered.

Introduction and Objective: Single-cell RNA sequencing (scRNA-seq) analysis of pancreatic islet tissues is powerful for studying Type 1 and Type 2 Diabetes (T1D and T2D). Despite many existing efforts, individual datasets are modest in size and fragmented across donors, labs and conditions, highlighting the need for a single-cell (SC) atlas that collates information from diverse sources. To address this, we aggregated islet scRNA-seq data from various studies and tissue distribution centers, applied rigorous quality control (QC) and computational integration to create a harmonized resource. Methods: Raw scRNA-seq data from studies utilizing cadaveric islets, generated and/or provided by the Human Pancreas Analysis Program, Integrated Islet Distribution Program, and Prodo Labs, were collected. The sequencing data were aligned, and genotype and metadata checks were conducted to verify donor information. Systematic QCs were implemented to select high-quality cells, adjust for ambient RNA, and identify doublets. Data integration was performed using the Harmony algorithm, adjusting for covariates like sex, BMI, age, study design, tissue sources and sequencing chemistry. Results: Our SC map integrates data from 132 donors (56 female, 76 male) across four phenotypes: 77 non-diabetic, 11 pre-diabetic, 11 T1D, and 33 T2D. It comprises 284 samples covering cell treatments such as SARS-CoV-2 infection and cytokine-induced inflammation. The atlas features 565,388 cells across 13 major cell populations, with high-abundance populations such as alpha and beta cells (39.9% and 28.3% cell counts, respectively), and lower-abundance ones like immune cells (0.64%). Conclusion: By integrating data from multiple sources, we have constructed a comprehensive SC map of islet tissues, available at www.PanKbase.org, to fuel hypothesis testing for diabetes pathophysiology. We are conducting analyses including differential gene expression and cell abundance to gain deeper insights into T1D and T2D mechanisms. H.T.H. Vu: None. H. Sun: None. S. Sharp: None. L. Brusman: None. F. Feng: None. R. Mao: None. Y. Wang: None. S. Corban: None. Y. Huang: None. A.K. Huber: None. A. Shilin: None. D. Jang: None. J. Jurgens: None. C. Robertson: None. T. Nguyen: None. Y. Sun: None. M. Brandes: None. P. Smadbeck: None. S. Narayanaswamy: None. T.S. Bate: None. J. Flannick: None. N. Burtt: None. J. Liu: None. M.L. Stitzel: None. J. Cartailler: None. B.F. Voight: Other Relationship; Eli Lilly and Company. M. Brissova: None. A.L. Gloyn: Other Relationship; Genentech, Inc, Roche Pharmaceuticals. K.J. Gaulton: Stock/Shareholder; Neurocrine biosciences. Other Relationship; Altos labs, Pfizer Inc. Consultant; Genentech, Inc. S.C. Parker: Research Support; Pfizer Inc. National Institute of Diabetes and Digestive and Kidney Diseases (U24DK138515, U24DK138512)

The classification of type 2 diabetes and prediabetes does not consider heterogeneity in the pathophysiology of glucose dysregulation. Here we show that prediabetes is characterized by metabolic heterogeneity, and that metabolic subphenotypes can be predicted by the shape of the glucose curve measured via a continuous glucose monitor (CGM) during standardized oral glucose-tolerance tests (OGTTs) performed in at-home settings. Gold-standard metabolic tests in 32 individuals with early glucose dysregulation revealed dominant or co-dominant subphenotypes (muscle or hepatic insulin-resistance phenotypes in 34% of the individuals, and beta-cell-dysfunction or impaired-incretin-action phenotypes in 40% of them). Machine-learning models trained with glucose time series from OGTTs from the 32 individuals predicted the subphenotypes with areas under the curve (AUCs) of 95% for muscle insulin resistance, 89% for beta-cell deficiency and 88% for impaired incretin action. With CGM-generated glucose curves obtained during at-home OGTTs, the models predicted the muscle-insulin-resistance and beta-cell-deficiency subphenotypes of 29 individuals with AUCs of 88% and 84%, respectively. At-home identification of metabolic subphenotypes via a CGM may aid the risk stratification of individuals with early glucose dysregulation.

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Research Design and Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program (MVP) participants with adult-onset diabetes 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0-<45%), medium (45-<90%), high (90-<95%), or highest (≥95%). Results: 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 they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared to the low risk group, the highest risk group was more likely to have diabetic ketoacidosis (DKA) (low 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 (GAD) antibodies (6.9% vs. 45.2%), all p<0.001. Conclusions: 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 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

Objective 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). Research Design and Methods We studied 2,966 youth with diabetes in the prospective SEARCH study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting c-peptide ≥250 pmol/L (≥0.75ng/ml) after >3 years (median 74 months) diabetes duration. Models included clinical measures at baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL-C), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS). Results 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 receiver operator 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). Conclusion 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 type 2 diabetes.

Human regulatory T cells (Treg) suppress other immune cells. Their dysfunction contributes to the pathophysiology of autoimmune diseases, including type 1 diabetes (T1D). Infusion of Tregs is being clinically evaluated as a novel way to prevent or treat T1D. Genetic modification of Tregs, most notably through the introduction of a chimeric antigen receptor (CAR) targeting Tregs to pancreatic islets, may improve their efficacy. We evaluated CAR targeting of human Tregs to monocytes, a human β cell line and human islet β cells in vitro. Targeting of HLA-A2-CAR (A2-CAR) bulk Tregs to HLA-A2+ cells resulted in dichotomous cytotoxic killing of human monocytes and islet β cells. In exploring subsets and mechanisms that may explain this pattern, we found that CD39 expression segregated CAR Treg cytotoxicity. CAR Tregs from individuals with more CD39low/- Tregs and from individuals with genetic polymorphism associated with lower CD39 expression (rs10748643) had more cytotoxicity. Isolated CD39− CAR Tregs had elevated granzyme B expression and cytotoxicity compared to the CD39+ CAR Treg subset. Genetic overexpression of CD39 in CD39low CAR Tregs reduced their cytotoxicity. Importantly, β cells upregulated protein surface expression of PD-L1 and PD-L2 in response to A2-CAR Tregs. Blockade of PD-L1/PD-L2 increased β cell death in A2-CAR Treg co-cultures suggesting that the PD-1/PD-L1 pathway is important in protecting islet β cells in the setting of CAR immunotherapy. In summary, introduction of CAR can enhance biological differences in subsets of Tregs. CD39+ Tregs represent a safer choice for CAR Treg therapies targeting tissues for tolerance induction.

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Research Design and Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program (MVP) participants with adult-onset diabetes 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0-<45%), medium (45-<90%), high (90-<95%), or highest (≥95%). Results: 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 they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared to the low risk group, the highest risk group was more likely to have diabetic ketoacidosis (DKA) (low 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 (GAD) antibodies (6.9% vs. 45.2%), all p<0.001. Conclusions: 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 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

A Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is used increasingly in research studies. We aim to assess differences between WGS-based T1DGRS and array-based T1DGRS, focusing on variations across genetic ancestries. We generated 67-variant T1DGRS from 149,265 individuals from UK Biobank with WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes. WGS-based T1DGRS showed strong correlation to GRS from TOPMed-imputed array genotypes (r = 0.99), with a slightly lower mean (-0.0028 SD, p < 10− 31). Correlation was lower in both non-European populations and GRS from 1000 Genomes-imputed array genotypes (r ranging between 0.95–0.98). This can lead to between 6–29% re-categorisation of individuals at clinical risk thresholds using the array-based GRS in non-European populations. Compared to Europeans, WGS-based T1DGRS was much lower for African and South Asian populations. In conclusion, WGS is a viable approach for generating T1DGRS and TOPMed-imputed genotypes offer a cost-effective alternative. The observed variations in T1DGRS at the population-level among different genetic ancestries cautions against indiscriminate use of European-centric T1DGRS risk thresholds in clinical practice and advocates the need for ancestry-specific or pan-ancestry standards.

Abstract A Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is now increasingly used in clinical and research studies. We investigated the concordance between WGS-based and array-based T1DGRS across genetic ancestries in 149,265 UK Biobank participants using WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes. In the overall cohort, WGS-based T1DGRS demonstrated strong correlation with TOPMed-imputed array-based score (r = 0.996, average WGS-based score 0.0028 standard deviations (SD) lower, p < 10− 31), while showing lower correlation with 1000 Genomes-imputed array-based scores (r = 0.981, 0.043 SD lower in WGS, p < 10− 300). Ancestry-stratified analyses between WGS-based and TOPMed-imputed array-based score showed the highest correlation with European ancestry (r = 0.996, 0.044 SD lower in WGS, p < 10− 300) followed by African ancestry (r = 0.989, 0.0193 SD lower in WGS, p < 10− 14) and South Asian ancestry (r = 0.986, 0.0129 SD lower in WGS, p < 10 − 6). These differences were more pronounced when comparing WGS based score with 1000 Genomes-imputed array-based scores (r = 0.982, 0.975, 0.957 for European, South Asian, African respectively). Population-level analysis using WGS-based T1DGRS revealed significant ancestry-based stratification, with European ancestry individuals showing the highest scores, followed by South Asian (average 0.28 SD lower than Europeans, p < 10− 58) and African ancestry individuals (average 0.89 SD lower than Europeans, p < 10− 300). Notably, when applying the European ancestry-derived 90th centile risk threshold, only 0.71% (95% CI 0.41–1.13) of African ancestry individuals and 6.4% (95% CI 5.6–7.2) of South Asian individuals were identified as high-risk, substantially below the expected 10%. In conclusion, while WGS is viable for generating T1DGRS, with TOPMed-imputed genotypes offering a cost-effective alternative, the persistence of ancestry-based variations in T1DGRS distribution even using whole genome sequencing emphasises the need for ancestry-specific or pan-ancestry standards in clinical practice.

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Research Design and Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program (MVP) participants with adult-onset diabetes 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0-<45%), medium (45-<90%), high (90-<95%), or highest (≥95%). Results: 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 they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared to the low risk group, the highest risk group was more likely to have diabetic ketoacidosis (DKA) (low 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 (GAD) antibodies (6.9% vs. 45.2%), all p<0.001. Conclusions: 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 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