Diabetes

ObjectiveIslet 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. MethodsWe 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 (<18years (n=702), 18-30years (n=524) and >30years (n=588)). FindingsIn 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 <18years, 55%(37/67) of 18-30years and just 23%(34/151) of >30years having type 1 diabetes. Restricting analysis to genetically consistent type 1 diabetes showed similar proportions of positive autoantibodies across age groups (92% <18years, 92% 18-30years, 93% >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. InterpretationOlder 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.

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

Stratifying progression from early-stage type 1 diabetes to clinical disease is essential for optimally timing disease-modifying therapies. We previously developed a progression likelihood score (PLS) that includes quantitative IA-2 autoantibody (IA-2A) measurements. This study aligned IA-2A thresholds used for PLS calculation between the radiobinding assay (RBA) and a commercially available RSR IA-2A ELISA to support broader clinical application. Serum samples from 349 children with stage 1 type 1 diabetes were analyzed using both assays. IA-2A positivity was similar by RBA (61.6%) and ELISA (59.0%). Centile-based alignment of ELISA-positive samples defined thresholds corresponding to RBA IA-2A categories. ELISA-derived PLS low (PLS < 0.5), moderate (PLS 0.5-4.0) and high (PLS > 4.0) risk groups stratified progression to stage 3 disease comparably to RBA-derived groups. The 3-year progression rate for children with an ELISA IA-2A PLS >4.0 was 52.4% (95% CI, 30.5- 66.1), similar to the RBA-derived PLS >4.0 group (58.7%; 95% CI, 37.1-72.8). These results demonstrate that the commercial ELISA can be used for PLS-based risk stratification.

ObjectiveHbA1c thresholds used to define dysglycemia in autoantibody-positive individuals at risk for type 1 diabetes do not account for age-related increases in HbA1c and may overestimate progression risk in adults. We evaluated whether age-adjusted HbA1c or a higher HbA1c threshold improves risk stratification across age groups. Research Design and MethodsWe analyzed 5,024 autoantibody-positive relatives (3,720 children and 1,304 adults) participating in the TrialNet Pathway to Prevention study. Age-related HbA1c effects were modelled using 6,273 adults from the population-based Exeter 10,000 cohort. Progression risk was compared using the standard dysglycemia threshold (HbA1c [&ge;] 5.7% [39 mmol/mol]), age-adjusted HbA1c, and an alternative threshold of HbA1c [&ge;]6.0% (42 mmol/mol). ResultsUsing HbA1c [&ge;] 5.7%, children had higher 1-year progression risk than adults among single autoantibody-positive participants (38% [95% CI 28, 47] vs. 13% [7.2, 19]) and multiple autoantibody-positive participants (55% [49, 60] vs. 38% [27, 47]; both p<0.001). Age adjustment reduced these differences; progression risk was similar among single autoantibody-positive participants (38% [28, 47] vs. 27% [13, 39]; p=0.32), with attenuated differences among multiple autoantibody-positive participants. An HbA1c threshold [&ge;]6.0% yielded comparable progression risk between adults and children across autoantibody subgroups. In post hoc analyses, adults aged <30 years had progression risk similar to children (p=0.1). ConclusionsAge-related variation in HbA1c influences dysglycemia classification in adults at risk for type 1 diabetes. Age-adjusted HbA1c or a higher HbA1c threshold ([&ge;]6.0% [42 mmol/mol]) in adults [&ge;]30 years identifies individuals with progression risk comparable to children and may improve age-specific risk stratification in prevention seungs.

ObjectivesThe high heritability of type 1 diabetes has enabled the development of polygenic risk scores (PRS) as disease risk screening tools. PRS can identify individuals at the highest genetic risk in a population, who can benefit from autoantibody and metabolic surveillance, to avoid ketoacidosis at diagnosis and access preventive therapies. However, PRS for type 1 diabetes developed from European data perform less well in non-European ancestries. We aimed to develop a PRS with comparable performance among different ancestries. MethodsUsing a the PRS-CSx method, and data from large European, East-Asian, African-American and Hispanic type 1 diabetes GWAS (Ntotal_cases=29,469), we developed a trans-ancestry PRS (TA-PS), combining a non-HLA component incorporating over a million variants, with the HLA component of a published European PRS (GRS2x). We tested the performance of the PRS using AUROC, sensitivity and specificity in a multi-ancestry T1D case-control cohort (Ntotal= 4,657; Nnon-European=556) from Montreal, Canada. We validated our results in two independent T1D case-control cohorts (CHOP-CAG and GRACE) and two population-based cohorts (All of Us and UK Biobank). ResultsIn our multi-ancestry Montreal-based cohort, TA-PS showed an AUROC of 0.89 which was significantly higher from the AUROC of 0.85 of GRS2x. At a 90th percentile cut-off, in African-Americans, the sensitivity of GRS2x was 0.32, compared to 0.56 in Europeans. For TA-PS, we obtained overall better sensitivities, ranging from 0.71 in Europeans to 0.77 in South Asians. TA-PS demonstrated slightly lower albeit acceptable specificity compared to that of GRS2x (> 0.83 across all ancestries). These results were validated in the four independent cohorts. ConclusionWe developed a trans-ancestry PRS that outperformed the European-based GRS2x. Importantly, TA-PS provides a comparable prediction in various ancestries, which supports its use in population-wide screening programs. Research in contextO_ST_ABSWhat is already known about this subject?C_ST_ABS- Polygenic risk scores (PRS) for type 1 diabetes are primarily developed using data from individuals of European ancestry. - The widely used, European-based GRS2 score shows reduced performance in non-European populations, particularly among individuals of African descent. - There are concerns regarding the equity of genetic risk prediction in population-based screening programs for T1D. What is the key question?- Can a trans-ancestry PRS provide accurate and equitable type 1 diabetes risk prediction across ancestries? What are the new findings?- A new trans-ancestry score, TA-PS, was developed by integrating an optimized non-HLA PRS to GRS2. - Compared to GRS2, in multi ancestry case-control and population-based cohorts, TA-PS improves sensitivity across all ancestry groups while maintaining high specificity. How might this impact on clinical practice in the foreseeable future?- TA-PS could provide equitable genetic risk stratification in population-wide screening programs for type 1 diabetes.

Maturity Onset Diabetes of the Young (MODY) can present after the age of 40yrs, but its prevalence, clinical characteristics, and the utility of simple clinical features for selecting cases in this age group remain poorly defined. We analysed whole-exome and clinical data from 51,619 individuals with diabetes diagnosed after 40 years of age from two large cohorts: the UK Biobank (n = 25,012) and the US health system MyCode cohort (n = 26,607). The prevalence of MODY due to pathogenic variants in the ten most common genes was 1 in 191 (0.52%) and 1 in 633 (0.16%) in the UK and US cohorts. For subtypes with treatment implications (GCK, HNF1A, HNF4A, ABCC8, KCNJ11), prevalence was 1 in 234 and 1 in 935, respectively. GCK-MODY was most common, followed by HNF4A and lower-penetrance RFX6. Clinical features of MODY overlapped with both insulin-treated and non-insulin-treated non-MODY diabetes. Applying simple clinical criteria only increased the MODY diagnosis to 2.64% and 0.87% but missed over 86% of cases. MODY is more common than expected in later-onset diabetes but remains difficult to identify using clinical features alone. Further research is needed to develop more effective strategies for selecting individuals with later-onset diabetes for genetic testing. Article HighlightsO_ST_ABSWhy did we undertake this study?C_ST_ABSMODY can present later in life, and diagnosis can enable precision treatment. However, individuals with later-onset diabetes are rarely tested. What specific question did we ask?How common is MODY in people diagnosed with diabetes after 40 years, and can they be identified clinically? What did we find?MODY affects 1 in 191 to 633 individuals with diabetes onset after 40 years, but clinical features alone cannot reliably identify them. What are the implications?MODY is relatively common in later-onset diabetes but difficult to detect clinically, limiting routine genetic testing in this group.

Identifying individuals at risk of early onset type 1 diabetes (diagnosed <2 years) would be highly beneficial in reducing risk of severe diabetic ketoacidosis (DKA) for those with extreme autoimmunity. We aimed to investigate whether genetic variation contributes to heterogeneity in age of type 1 diabetes onset, focusing on those diagnosed <2 years and ages previously defined by histological differences. We carried out association testing on 6773 individuals with type 1 diabetes and tested for heterogeneity in Human Leukocyte Antigen (HLA) variants across stratified age groups (594 diagnosed <2 years, 2241 diagnosed 2-7 years, 3094 diagnosed 7-13 years, 844 diagnosed 13+ years). We used a 67 SNP type 1 diabetes genetic risk score (T1D-GRS) to quantify aggregated genetic risk and assessed its utility in screening for type 1 diabetes <2 years. We observed higher T1D-GRSs as age of onset decreased in type 1 diabetes and found that DR3-DQ2 homozygosity was most strongly associated with <2 years onset (log-OR=4.27). The T1D-GRS showed high discriminative ability for <2 years onset type 1 diabetes onset (AUC=0.94) and correctly identified 88% of type 1 diabetes cases at the 85th population centile. We have shown higher genetic risk for very early onset T1D and suggest T1D-GRSs in newborn screening is likely to be particularly sensitive to those with younger type 1 diabetes onset.

BackgroundDiabetes subtypes with different clinical profiles have been found and replicated in adults. However, clinical heterogeneity of paediatric-onset diabetes remains unexplored. Better capture of different aetiologies of the disease could prove a powerful tool towards precision medicine. MethodsWe performed data-driven clustering analysis in patients with newly diagnosed diabetes (n = 3,064) from the Norwegian Childhood Diabetes Registry. Patients were stratified by autoantibody status and clustered based on five clinical variables: Age at diagnosis, fasting glucose, HbA1c, fasting C-peptide Z-score, and BMI Z-score. We assessed inter-cluster differences regarding severity of disease, early treatment needs, polygenic scores (PS), and serum biomarkers. FindingsWe identified two clusters of autoantibody-positive and three clusters of autoantibody-negative patients: 1) Childhood severe autoimmune diabetes (CSAID; n = 1482, 48{middle dot}37 %), characterized by childhood-onset diabetes, milder disease presentation, unaltered lipidic profiles, higher Type 1 diabetes PS, and autoantibody positivity; 2) Adolescence severe autoimmune diabetes (ASAID; n = 1252, 40{middle dot}86 %) with adolescent-onset of diabetes, more severe disease presentation, higher diabetic ketoacidosis, lipidic profiles signaling towards prolonged metabolic disease, and autoantibody positivity; 3) Childhood severe insulin-deficient diabetes (CSIDD; n = 106, 3{middle dot}46 %) and 4) Adolescence severe insulin-deficient diabetes (ASIDD; n = 144, 4{middle dot}70 %) with similar characteristics to CSAID and ASAID, respectively, but no autoantibody positivity; and 5) Adolescence severe insulin-resistant diabetes (ASIRD; n = 80, 2{middle dot}61 %), the oldest group, with the highest C-peptide, BMI Z-score, and Type 2 diabetes PS. CSAID and ASAID presented similarities to previously described endotypes for type 1 diabetes. InterpretationWe grouped patients with paediatric diabetes into five subgroups with varying clinical severity, genetic risk, and metabolic profiles. Similarities between autoantibody-positive and -negative clusters underscore the importance of adopting a personalised, multivariate approach to diabetes management that extends beyond autoantibody status. We hypothesise that our clusters may be connected to previously described endotypes of type 1 diabetes, facilitating patient classification without the need for pancreatic biopsies. Further understanding of this concept could help define the mechanisms involved in disease initiation, time to diagnosis, and progression.

ObjectiveWith the high prevalence of pediatric 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). MethodsWe studied 2,966 youth with diabetes in the prospective SEARCH study (diagnosis age [&le;]19 years) to develop prediction models to identify participants with fasting c-peptide [&ge;]250 pmol/L ([&ge;]0.75ng/ml) after >3 years (median 74 months) of 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). ResultsModels using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with c-peptide [&ge;]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). ConclusionPrediction 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.

ContextIdentifying Maturity-Onset Diabetes of the Young (MODY) in patients with diabetes is essential because treatment differs significantly from other forms of diabetes. We identified patients with MODY gene variants and evaluated their clinical characteristics and responses to treatment. Evidence AcquisitionWe identified 106 patients with genetic MODY variants. Demographics, islet autoantibodies at diabetes diagnosis, co-morbidities, and response to treatment by genetic variant were evaluated. Evidence SynthesisPatients diagnosed with MODY variants comprised 4% of the total population with diabetes. Mean age and HbA1c of patients with MODY at diagnosis were 10.5 years and 8.2%, respectively. Surprisingly, diabetic ketoacidosis was a presenting feature for some (n=7, 6.8%), and others with MODY had positive islet cell autoantibodies (n= 7, 6.6%). Variants in HNF1A, GCK, and HNF1B were frequently observed (20%, 22%, and 17% respectively), while rare variants in PDX1, RFX6, BLK, and CNOT1 were uncovered. Initial and follow up treatment of patients with MODY were compared. For each medication (Insulin, Metformin, Sulfonylureas, and GLP-1 receptor agonists), a reduction in HbA1c was observed at follow-up (0.3-21%). Insulin and sulfonylureas were associated with an increase in average BMI (insulin: +8.23%, n=21, sulfonylurea: +0.63%, n=12) at follow-up, metformin was intermediate (-2.46%, n=4), and GLP-1 receptor agonists demonstrated the greatest decrease in BMI (-4.79%, n=4). ConclusionsThe presence of islet autoantibodies or diabetic ketoacidosis does not preclude the diagnosis of MODY. Given the observed improvements in BMI and HbA1c, further investigation into the use of GLP-1 receptor agonists as treatment for MODY should be considered.

The detection of monogenic diabetes illustrates the potential of precision medicine, with treatments tailored to specific genes and diagnosis involving targeted genetic testing. Current detection criteria are derived from White populations. We investigated detection of monogenic diabetes in an unselected multiethnic cohort comprising 1,706 participants diagnosed with diabetes before the age of 30-years. Using broad biomarker criteria (triple pancreatic antibody negative and detectable C-peptide) to select for next generation sequencing of monogenic diabetes genes, we found a non-significantly different minimum cohort prevalence of monogenic diabetes of 2.1% in White, 2.0% in South Asian, 2.5% in African-Caribbean, and 3.6% in Mixed participants. The detection rate, however, varied significantly (17.7% in White, 5.3%in South Asian, 8.0% in African-Caribbean, and 15.2% in Mixed participants, p<0.001). Those without monogenic diabetes showed significant variations in BMI. No difference in phenotype of monogenic diabetes across ancestry groups was observed. Non-white ethnicity participants were significantly more likely to have undiagnosed monogenic diabetes than White with on average a 10-year duration before receiving a correct diagnosis. By applying ancestry-specific BMI cut-offs (White <30, South Asian <27, African-Caribbean and Mixed <35 kg/m{superscript 2}), the overall detection rate increased from 8.8 to 16%, reducing the number needed to test to identify one case from 11 to 6 and boosting detection rates to 39, 11, 9 and 26% in White, South Asian, African-Caribbean and Mixed-ethnicity participants, respectively. These findings were validated in an external real-world dataset. Applying broad biomarker criteria for initial selection, mitigates clinical biases leading to misclassification of monogenic diabetes in non-White ethnicities. However, further tailoring criteria with ethnic-specific BMI cut-offs doubled detection rates, improving cost-effectiveness by minimising unnecessary testing. Our study highlights the need to develop precision medicine approaches accounting for phenotypic variation across diverse populations, to ensure accurate diagnoses and cost-efficient healthcare provision.

ContextDiagnosing Maturity-Onset Diabetes of the Young (MODY) is clinically important for treatment and prognosis. However, phenotype-based studies of MODY are prone to ascertainment bias, limiting accurate estimates of its population prevalence and phenotypic spectrum. ObjectiveTo apply a genotype-first approach to determine the population prevalence, penetrance, and all-cause mortality associated with MODY. MethodsWe analysed exome sequencing and clinical data from 454,275 UK Biobank participants to identify pathogenic variants in 10 established MODY genes. We assessed variant prevalence, age-dependent diabetes penetrance, and all-cause mortality by genetic aetiology over a mean follow-up of 13.4 years. ResultsPathogenic MODY variants were present in 1 in 1,052 individuals and accounted for 1.48% of diabetes cases diagnosed before age 40. GCK variants were the most frequent (1 in 2,787), demonstrating high penetrance (mean HbA1c 8.8 mmol/mol higher; 94.5% with prediabetes or diabetes) but no significant association with all-cause mortality (P=0.09). Variants in other MODY genes showed lower penetrance, with 12% of carriers developing diabetes by age 40 and 31.6% by age 60 and showed no increase in all-cause mortality (P=0.89). Penetrance varied by genetic aetiology, with HNF1A showing the highest penetrance and PDX1, NEUROD1, and RFX6 the lowest. Parental history of diabetes and polygenic risk for type 2 diabetes were important modifiers of penetrance (Hazard ratios 2.54 and 1.52 respectively, P<3.9x10-3). ConclusionsThis large-scale genotype-first study provides novel insights into MODY in the population. These findings have broad implications for genetic counselling, personalised treatment strategies, and healthcare resource allocation.

BackgroundOral administration of antigen can induce immunological tolerance. Insulin is a key autoantigen in childhood type 1 diabetes with insulin autoimmunity often appearing in the first years of life. Here, oral insulin was given as antigen-specific immunotherapy before the onset of autoimmunity in children from age 6 months to assess its safety and actions on immunity and the gut microbiome. MethodsA phase I/II randomized controlled trial was performed in 44 islet autoantibody-negative children aged 6 months to 2 years with genetic risk for type 1 diabetes. Children were randomized 1:1 to daily oral insulin (7.5 mg with dose escalation to 67.5 mg) or placebo for 12 months. Primary outcome was safety and immune efficacy pre-specified as hypoglycemia and induction of antibody or T cell responses to insulin, respectively. ResultsOral insulin was well tolerated with no changes in metabolic variables. Immune responses to insulin were observed in both children who received insulin (55%) and placebo (67%), and were modified by the INSULIN gene. Among children with type 1 diabetes-susceptible INSULIN genotype, antibody responses to insulin were more frequent in insulin-treated (cumulative response, 75.8%) as compared to placebo-treated children (18.2%; P=0.0085), and T cell responses to insulin were modified by treatment-independent inflammatory episodes. Changes in the microbiome were related to INSULIN genotype. ConclusionThe study demonstrated that oral insulin immunotherapy in young genetically at-risk children was safe and engaged the adaptive immune system in an INSULIN genotype-dependent manner, and linked inflammatory episodes to the activation of insulin-responsive T cells. Trial registrationClinicaltrials.gov NCT02547519 FundingGerman Center for Diabetes Research (DZD e.V.), Juvenile Diabetes Research Foundation (JDRF, grant 1-SRA-2018-546-S-B), Federal Ministry of Education and Research (BMBF, grant FKZ01KX1818).

ObjectivePolygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we developed a multi-ancestry polygenic score to accurately predict type 1 diabetes risk across diverse populations. Research Design and MethodsWe used 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. ResultsAmong individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2EUR (0.82, P = 0.04) and T1D GRSAFR (0.82, P = 0.007). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2EUR (0.89 vs. 0.91, P = 0.02). However, T1D MAPS2 performed equivalently to T1D GRS2EUR in European ancestry (0.91 vs. 0.91, P = 0.45) while still performing better in non-European ancestry (0.90 vs. 0.82, P = 0.04). ConclusionsA novel polygenic score improves type 1 diabetes risk prediction in non-European ancestry while maintaining high predictive power in European ancestry. These findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations. Article HighlightsO_LIWhy did we undertake this study? Type 1 diabetes polygenic scores are highly predictive of disease risk, but their performance varies based on genetic ancestry. C_LIO_LIWhat is the specific question(s) we wanted to answer? Can we develop a polygenic score that accurately predicts type 1 diabetes risk across diverse populations? C_LIO_LIWhat did we find? Our novel polygenic score performs similarly to existing scores in European populations, and it demonstrates superior performance in non-European populations. C_LIO_LIWhat are the implications of our findings? This polygenic score will improve prediction of type 1 diabetes risk in genetically diverse populations. C_LI

Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes. Plan Language SummarySince monogenic diabetes misclassification can occur and lead to missed opportunities for optimal management, and several diagnostic technologies are available, we systematically review the yield of monogenic diabetes using different criteria to select people with diabetes for genetic testing and the technologies used.

BackgroundGenetic risk scores (GRS) for type 1 diabetes (T1D) have been developed primarily in European populations, limiting their generalisability across ancestries. Indians differ from Europeans in clinical characteristics of T1D and overall genetic architecture, yet systematic evaluation of T1D GRS performance in multi-regional Indian cohorts is lacking. MethodsThe study included 597 T1D patients and 3347 non-diabetic controls from different regions in India. Genotyping, imputation, quality control analysis, and construction of the 67-SNPs T1D GRS were performed using standardised pipelines. Discriminative performance was assessed using Receiver Operative Curve-Area under Curve (ROC-AUC) analysis, and optimal thresholds were derived using Youdens index. HLA-DQ diplotype frequencies were compared, and association analysis was conducted using multivariable logistic regression. FindingsT1D GRS showed consistent discriminative performance across Indian cohorts [ROC-AUC=0.84 (range=0{middle dot}78-0{middle dot}87)], supporting its comprehensive use for T1D classification in India. Notably, its performance was lower in islet cell autoantibody (IA) negative compared with IA positive T1D patients (ROC-AUC, 0{middle dot}75 vs 0{middle dot}85) and in adult-onset than in childhood-onset patients (0{middle dot}74 vs 0{middle dot}84). We observed a lower frequency of protective HLA-DQ diplotypes and a strong association of HLA-DQ81 containing diplotypes in childhood-onset T1D. Application of an India-specific T1D GRS score improved the sensitivity than the European cut-off. InterpretationT1D GRS is a valuable unified diagnostic tool in Indians, but its performance varies by islet cell autoantibody status and age at onset, likely reflecting population-specific HLA architecture. European-derived T1D GRS thresholds under-classify the genetic risk, highlighting the importance of ancestry-aware optimisation in Indians. FundingCDRC grant CDRC202111026 and CSIR Intramural Grant P50. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrevious studies have shown that a 67-SNPs T1D genetic risk score (GRS) can distinguish T1D patients from non-diabetic controls and other forms of diabetes, but its performance varies across ancestries. Islet cell autoantibodies (IA) have important diagnostic value for classifying type 1 diabetes (T1D). However, their prevalence in India varies widely, with up to one-quarter of patients testing negative, limiting their clinical utility. Evidence supporting the use of the T1D GRS in India, combined with IA antibodies status is limited to a single cohort representing one linguistic group. The applicability of T1D GRS across multi-centric clinical settings has not been systematically evaluated. Added value of this studyThis study validates the 67-SNPs T1D GRS across multiple Indian cohorts representing major linguistic groups, supporting its use as a unified diagnostic tool. Differences in T1DGRS performance between childhood-and adult-onset T1D are linked to enrichment of protective HLA-DQ diplotypes in adult-onset disease, providing genetic insight into disease heterogeneity. The study also demonstrates that European-derived GRS thresholds systematically under-classify genetic risk in Indians and the population-specific threshold is essential. Implications of all the available evidenceThe European-derived T1D GRS can be applied across Indian clinical settings with consistent discriminative performance. However, its utility is influenced by islet cell autoantibody status and the age at onset of disease. Ancestry-aware threshold optimisation substantially improves diagnostic accuracy and is essential for equitable implementation of T1D GRS in Indians. Larger studies are needed to identify population-specific risk variants and further refine genetic tools for clinical diagnosis.

Obesity and type 2 diabetes (T2D) are metabolic diseases with shared pathophysiology. Traditional polygenic risk scores (PRS) have focused on these conditions individually, yet the single disease approach falls short in capturing the full dimension of metabolic dysfunction. We derived biologically enriched metabolic PRS (MetPRS), a composite score that uses multi-ancestry genome-wide association studies of 22 metabolic traits from over 10 million people. MetPRS, optimized to predict obesity (O-MetPRS) and T2D (D-MetPRS), was validated in the UK Biobank (UKB, n=15,000), and tested in UKB hold-out set (n=49,377), then externally tested in 3 cohorts - All of Us (n=245,394), Mass General Brigham (MGB) Biobank (n=53,306), and a King Faisal Specialist Hospital and Research Center cohort (n=6,416). O-MetPRS and D-MetPRS outperformed existing PRSs in predicting obesity and T2D across 6 ancestries (European, African, East Asian, South Asian, Latino/admixed American, and Middle Eastern). O-MetPRS and D-MetPRS also predicted morbidities and downstream complications of obesity and T2D, as well as the use of GLP-1 receptor agonists in contemporary practice. Among 37,329 MGB participants free of T2D and obesity at baseline, those in the top decile of O-MetPRS had a 103% relatively higher chance, and those in the top decile of D-MetPRS had an 80% relatively higher chance of receiving a GLP-1 receptor agonist prescription compared to individuals at the population median of MetPRS. The biologically enriched MetPRS is poised to have an impact across all layers of clinical utility, from predicting morbidities to informing management decisions.

OBJECTIVEThe clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic score (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. RESEARCH DESIGN AND METHODSWe tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): 1) age and sex, 2) age, sex, BMI, systolic blood pressure, and family history of diabetes; 3) all variables in (2) and random glucose; 4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. RESULTSPGS was associated with incident diabetes in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk [(PGS-CRS interaction p=0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)]. CONCLUSIONSGenetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.

Novel subphenotypes of type 2 diabetes mellitus (T2DM) are associated with differences in response to treatment and risk of complications. The most widely replicated approach identified four subphenotypes (severe insulin-deficient diabetes [SIDD], severe insulin-resistant diabetes [SIRD], mild obesity-related diabetes [MOD], and mild age-related diabetes [MARD]). However, the widespread clinical application of this model is hindered by the limited availability of fasting insulin and glucose measurements in routine clinical settings. To address this, we pooled data of adults ([&ge;]18 years) with newly diagnosed T2DM from six cohort studies (n = 3,377) to perform de novo clustering and developed classification algorithms for each of the four subphenotypes using nine variables routinely collected in electronic health records (EHRs). After operationalizing the classification algorithms on the Epic Cosmos Research Platform, we identified that among the 727,076 newly diagnosed diabetes cases, 21.6% were classified as SIDD, 23.8% as MOD, and 40.9% as MARD. Individuals classified as SIDD were more likely to receive insulin and incretin mimetics treatment and had higher risks for microvascular complications (retinopathy, neuropathy, nephropathy). Our findings underscore the heterogeneity in newly diagnosed T2DM and validated T2DM subphenotypes in routine EHR systems. This offers possibilities for the subsequent development of treatment strategies tailored to subphenotypes.

The aim of this study was to determine the effectiveness of the Type 1 Diabetes Genetic Risk Score (T1D GRS) for the prioritisation of children with newly diagnosed hyperglycaemia for genetic testing of monogenic diabetes. MethodsA cohort of 808 children and adolescents with newly diagnosed hyperglycaemia were collected. All underwent standard clinical follow-up and genetic testing based on the knowledge and means accessible at the time. In this cohort and in 189 control subjects (165 monogenic diabetes patients and 24 healthy individuals), we assessed the T1D GRS2 and 10 SNP GRS scores. We assessed the T1D GRS2 cut-off in our cohort and investigated its utility in addition to negative autoantibody status for the prioritisation of cases for genetic testing for monogenic diabetes. Genetic testing included Sanger sequencing, panel sequencing and MLPA. ResultsApplying T1D GRS2 in addition to negative autoantibodies on the newly diagnosed hyperglycaemia cohort substantially decreased the number of unnecessarily tested cases. The pick-up rate was increased three-fold, while the sensitivity of the prioritisation decreased only slightly from 77.8% to 72.2% when compared with autoantibodies alone. The majority of monogenic diabetes cases that escaped this prioritisation for genetic testing had low levels of a single autoantibody and were most probably false positives in the autoantibody testing. The monogenic cases that would not be prioritised using GRS2 and autoantibodies were diagnosed based on clinical phenotype. On the other hand, two monogenic diabetes cases with HNF1B-MODY were not originally diagnosed and were identified only thanks to their low GRS2 value. ConclusionsUsing T1D GRS in combination with autoantibody testing is effective in decreasing the number of unnecessarily genetically tested cases. This approach, used in addition to the standard clinical evaluation, can be a valuable tool in the early selection of suitable candidates for molecular testing for monogenic diabetes. Research in ContextO_LIWhat is already known about this subject? O_LIDiagnosing monogenic diabetes is important, because gene-tailored treatment is available. In children and adolescents, monogenic diabetes has to be differentiated mostly from type 1 diabetes. C_LIO_LIIndividuals with type 1 diabetes and monogenic diabetes have a different genetic risk for type 1 diabetes. C_LIO_LIThe genetic risk score for type 1 diabetes (T1D GRS) can be computed from disease-associated polymorphisms. C_LI C_LIO_LIWhat is the key question? O_LIWhat is the utility of T1D GRS as a tool for the prioritisation of cases for genetic testing of monogenic diabetes? C_LI C_LIO_LIWhat are the new findings? O_LIMore than half of the children with diabetes with negative autoantibodies (type 2 diabetes excluded) and T1D GRS2 below a cut-off was confirmed genetically as having monogenic diabetes. C_LIO_LIThe combination of T1D GRS2 + negative autoantibodies increased the pick-up rate by genetic testing three-fold compared with negative autoantibodies alone, with only small decrease in sensitivity of the prioritisation. C_LIO_LILow T1D GRS can draw interest to cases that would otherwise not be suspected of having monogenic diabetes. C_LI C_LIO_LIHow might this affect clinical practice in the foreseeable future? O_LIChildren and adolescents with confirmed diabetes and normal BMI, negative autoantibodies and low T1D GRS can be prioritised for genetic testing soon after diabetes diagnosis. C_LI C_LI