Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis.

AIMS/HYPOTHESIS: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk. METHODS: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation. RESULTS: The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics. CONCLUSIONS/INTERPRETATION: Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Associations between diabetes-related genetic risk scores and residual beta cell function in type 1 diabetes: the GUTDM1 study.

AIMS/HYPOTHESIS: Use of genetic risk scores (GRS) may help to distinguish between type 1 diabetes and type 2 diabetes, but less is known about whether GRS are associated with disease severity or progression after diagnosis. Therefore, we tested whether GRS are associated with residual beta cell function and glycaemic control in individuals with type 1 diabetes. METHODS: Immunochip arrays and TOPMed were used to genotype a cross-sectional cohort (n=479, age 41.7 ± 14.9 years, duration of diabetes 16.0 years [IQR 6.0-29.0], HbA1c 55.6 ± 12.2 mmol/mol). Several GRS, which were originally developed to assess genetic risk of type 1 diabetes (GRS-1, GRS-2) and type 2 diabetes (GRS-T2D), were calculated. GRS-C1 and GRS-C2 were based on SNPs that have previously been shown to be associated with residual beta cell function. Regression models were used to investigate the association between GRS and residual beta cell function, assessed using the urinary C-peptide/creatinine ratio, and the association between GRS and continuous glucose monitor metrics. RESULTS: Higher GRS-1 and higher GRS-2 both showed a significant association with undetectable UCPCR (OR 0.78; 95% CI 0.69, 0.89 and OR 0.84: 95% CI 0.75, 0.93, respectively), which were attenuated after correction for sex and age of onset (GRS-2) and disease duration (GRS-1). Higher GRS-C2 was associated with detectable urinary C-peptide/creatinine ratio (≥0.01 nmol/mmol) after correction for sex and age of onset (OR 6.95; 95% CI 1.19, 40.75). A higher GRS-T2D was associated with less time below range (TBR) (OR for TBR<4% 1.41; 95% CI 1.01 to 1.96) and lower glucose coefficient of variance (ß -1.53; 95% CI -2.76, -0.29). CONCLUSIONS/INTERPRETATION: Diabetes-related GRS are associated with residual beta cell function in individuals with type 1 diabetes. These findings suggest some genetic contribution to preservation of beta cell function.

Diabetes Study of Children of Diverse Ethnicity and Race: Study design.

AIMS: Determining diabetes type in children has become increasingly difficult due to an overlap in typical characteristics between type 1 diabetes (T1D) and type 2 diabetes (T2D). The Diabetes Study in Children of Diverse Ethnicity and Race (DISCOVER) programme is a National Institutes of Health (NIH)-supported multicenter, prospective, observational study that enrols children and adolescents with non-secondary diabetes. The primary aim of the study was to develop improved models to differentiate between T1D and T2D in diverse youth. MATERIALS AND METHODS: The proposed models will evaluate the utility of three existing T1D genetic risk scores in combination with data on islet autoantibodies and other parameters typically available at the time of diabetes onset. Low non-fasting serum C-peptide (<0.6 nmol/L) between 3 and 10 years after diabetes diagnosis will be considered a biomarker for T1D as it reflects the loss of insulin secretion ability. Participating centres are enrolling youth (<19 years old) either with established diabetes (duration 3-10 years) for a cross-sectional evaluation or with recent onset diabetes (duration 3 weeks-15 months) for the longitudinal observation with annual visits for 3 years. Cross-sectional data will be used to develop models. Longitudinal data will be used to externally validate the best-fitting model. RESULTS: The results are expected to improve the ability to classify diabetes type in a large and growing subset of children who have an unclear form of diabetes at diagnosis. CONCLUSIONS: Accurate and timely classification of diabetes type will help establish the correct clinical management early in the course of the disease.

Islet autoantibody frequency in relatives of children with type 1 diabetes who have a type 2 diabetes diagnosis.

AIM: This study aimed to evaluate characteristics of autoimmunity in individuals who have a type 2 diagnosis and are relatives of children with type 1 diabetes. METHODS: Pre-diagnosis samples (median 17 months before onset) from relatives who were later diagnosed with type 2 diabetes were measured for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen-2 (IA-2A), zinc transporter 8 (ZnT8A) and insulin (IAA) as well as the type 1 diabetes genetic risk score (GRS2). Associations between islet autoantibodies, insulin treatment and GRS2 were analysed using Fisher's exact and t-tests. RESULTS: Among 226 relatives (64% men; mean age at sampling 41 years; mean age 54 years at diagnosis), 32 (14%) were islet autoantibody-positive for at least one autoantibody more than a decade before diagnosis. Approximately half of these (n = 15) were treated with insulin. GADA-positivity was higher in insulin-treated relatives than in non-insulin-treated relatives (12/18 [67%] vs. 6/18 [33%], p < 0.001). IAA-positivity was observed in 13/32 (41%) of relatives with autoantibodies. GRS2 scores were increased in autoantibody-positive relatives (p = 0.032), but there was no clear evidence for a difference according to treatment (p = 0.072). CONCLUSION: This study highlights the importance of measuring islet autoantibodies, including IAA, in relatives of people with type 1 diabetes to avoid misdiagnosis.

Protocol for the Australian Type 1 Diabetes National Screening Pilot: Assessing the feasibility and acceptability of three general population screening models in children.

AIM: One third of Australian children diagnosed with type 1 diabetes present with life-threatening diabetic ketoacidosis (DKA) at diagnosis. Screening for early-stage, presymptomatic type 1 diabetes, with ongoing follow-up, can substantially reduce this risk (<5% risk). Several screening models are being trialled internationally, without consensus on the optimal approach. This pilot study aims to assess three models for a routine, population-wide screening programme in Australia. METHODS: An implementation science-guided pilot study to evaluate the feasibility, acceptability and costs of three screening models in children will be conducted between July 2022 and June 2024. These models are as follows: (1) Genetic risk-stratified screening using newborn heel prick dried bloodspots, followed by autoantibody testing from 11 months of age; (2) genetic risk-stratified screening of infant (6-12 months) saliva followed by autoantibody testing from 10 months of age; and (3) autoantibody screening using capillary dried bloodspots collected from children aged 2, 6 or 10 years. Cohorts for each model will be recruited from targeted geographic areas across Australia involving ≥2 states per cohort, with a recruitment target of up to 3000 children per cohort (total up to 9000 children). The primary outcome is screening uptake for each cohort. Secondary outcomes include programme feasibility, costs, parental anxiety, risk perception, satisfaction, well-being and quality of life, and health professional attitudes and satisfaction. CONCLUSIONS: This pilot is the first direct comparison of three screening implementation models for general population screening. Findings will provide evidence to inform a potential national screening programme for Australian children. TRIAL REGISTRATION: ACTRN12622000381785.

Random C-Peptide and Islet Antibodies at Onset Predict ß Cell Function Trajectory and Insulin Dependence in Pediatric Diabetes.

OBJECTIVE: Identification of prognostic biomarkers in pediatric diabetes is important for precision medicine. We assessed whether C-peptide and islet autoantibodies are useful to predict the natural history of children with new-onset diabetes. METHODS: We prospectively studied 72 children with new-onset diabetes (median follow-up: 8 months) by applying the Aß classification system ("A+": islet autoantibody positive, "ß+": random serum C-peptide ≥1.3 ng/mL at diagnosis). Beta-cell function was assessed longitudinally with 2 hours postprandial/stimulated urinary C-peptide-to-creatinine ratio (UCPCR) 3-12 weeks (V1) and 6 to 12 months after diagnosis (V2). We obtained a type 1 diabetes genetic risk score for each participant, and compared characteristics at baseline, and clinical outcomes at V2. RESULTS: The cohort was 50% male. Racial distribution was 76.4% White, 20.8% Black, and 2.8% Asian or other races. A total of 46.5% participants were Hispanic. Median age (Q1-Q3) was 12.4 (8.3-14.5) years. The Aß subgroup frequencies were 46 A+ß-(63.9%), 1 A-ß-(1.4%), 4 A+ß+(5.6%), and 21 A-ß+(29.2%). Baseline serum C-peptide correlated with UCPCR at both V1 (r = 0.36, P = .002) and V2 (r = 0.47, P < .001). There were significant subgroup differences in age, race, frequency of diabetic ketoacidosis, and type 1 diabetes genetic risk score (P < .01). At V2, the 2 ß-subgroups had lower UCPCR and higher hemoglobin A1c compared with the 2 ß+ subgroups (P < .001 and P = .02, respectively). Thirty-eight percent of A-ß+ but none of the other subgroups were insulin-independent at V2 (P < .001). CONCLUSION: C-peptide and islet autoimmunity at diagnosis define distinct phenotypes and predict beta-cell function and insulin dependence 6 to 12 months later in racially/ethnically diverse children with new-onset diabetes.

Prevalence of Fabry disease-causing variants in the UK Biobank.

BACKGROUND: Fabry disease is an X-linked lysosomal storage disorder resulting from deficiency of the alpha-galactosidase A enzyme leading to accumulation of globotriaosylceramide in multiple organ sites with prominent cardiovascular and renal involvement. Global prevalence estimates of Fabry disease based on clinical ascertainment range from 1 in 40 000 to 1 in 170 000. We aimed to determine the prevalence of Fabry disease-causing variants in UK Biobank. METHODS: We sought GLA gene variants in exome sequencing data from 200 643 individuals from UK Biobank. We used ACMG/AMP guidelines (American College of Medical Genetics/Association for Molecular Pathology) to classify pathogenicity and compared baseline biomarker data, hospital ICD-10 (International Classification of Diseases version-10) codes, general practitioner records and self-reported health data with those without pathogenic variants. RESULTS: We identified 81 GLA coding variants. We identified eight likely pathogenic variants on the basis of being rare (<1/10 000 individuals) and either previously reported to cause Fabry disease, or being protein-truncating variants. Thirty-six individuals carried one of these variants. In the UK Biobank, the prevalence of likely pathogenic Fabry disease-causing variants is 1/5732 for late-onset disease-causing variants and 1/200 643 for variants causing classic Fabry disease. CONCLUSION: Fabry disease-causing GLA variants are more prevalent in an unselected population sample than the reported prevalence of Fabry disease. These are overwhelmingly variants associated with later onset. It is possible the prevalence of later-onset Fabry disease exceeds current estimates.

Recurrent 17q12 microduplications contribute to renal disease but not diabetes.

BACKGROUND: 17q12 microdeletion and microduplication syndromes present as overlapping, multisystem disorders. We assessed the disease phenotypes of individuals with 17q12 CNV in a population-based cohort. METHODS: We investigated 17q12 CNV using microarray data from 450 993 individuals in the UK Biobank and calculated disease status associations for diabetes, liver and renal function, neurological and psychiatric traits. RESULTS: We identified 11 17q12 microdeletions and 106 microduplications. Microdeletions were strongly associated with diabetes (p=2×10-7) but microduplications were not. Estimated glomerular filtration rate (eGFR mL/min/1.73 m2) was consistently lower in individuals with microdeletions (p=3×10-12) and microduplications (p=6×10-25). Similarly, eGFR <60, including end-stage renal disease, was associated with microdeletions (p=2×10-9, p<0.003) and microduplications (p=1×10-9, p=0.009), respectively, highlighting sometimes substantially reduced renal function in each. Microduplications were associated with decreased fluid intelligence (p=3×10-4). SNP association analysis in the 17q12 region implicated changes to HNF1B as causing decreased eGFR (NC_000017.11:g.37741642T>G, rs12601991, p=4×10-21) and diabetes (NC_000017.11:g.37741165C>T, rs7501939, p=6×10-17). A second locus within the region was also associated with fluid intelligence (NC_000017.11:g.36593168T>C, rs1005552, p=6×10-9) and decreased eGFR (NC_000017.11:g.36558947T>C, rs12150665, p=4×10-15). CONCLUSION: We demonstrate 17q12 microdeletions but not microduplications are associated with diabetes in a population-based cohort, likely caused by HNF1B haploinsufficiency. We show that both 17q12 microdeletions and microduplications are associated with renal disease, and multiple genes within the region likely contribute to renal and neurocognitive phenotypes.

Utility of genetic risk scores in type 1 diabetes.

Iterative advances in understanding of the genetics of type 1 diabetes have identified >70 genetic regions associated with risk of the disease, including strong associations across the HLA class II region that account for >50% of heritability. The increased availability of genetic data combined with the decreased costs of generating these data, have facilitated the development of polygenic scores that aggregate risk variants from associated loci into a single number: either a genetic risk score (GRS) or a polygenic risk score (PRS). PRSs incorporate the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance, whereas GRSs estimate the cumulative contribution of a smaller subset of genetic variants that reach genome-wide significance. Type 1 diabetes GRSs have utility in diabetes classification, aiding discrimination between type 1 diabetes, type 2 diabetes and MODY. Type 1 diabetes GRSs are also being used in newborn screening studies to identify infants at risk of future presentation of the disease. Most early studies of type 1 diabetes genetics have been conducted in European ancestry populations, but, to develop accurate GRSs across diverse ancestries, large case-control cohorts from non-European populations are still needed. The current barriers to GRS implementation within healthcare are mainly related to a lack of guidance and knowledge on integration with other biomarkers and clinical variables. Once these limitations are addressed, there is huge potential for 'test and treat' approaches to be used to tailor care for individuals with type 1 diabetes.

Precision medicine in type 1 diabetes.

First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future.

Congenital beta cell defects are not associated with markers of islet autoimmunity, even in the context of high genetic risk for type 1 diabetes.

AIMS/HYPOTHESIS: A key unanswered question in type 1 diabetes is whether beta cells initiate their own destruction or are victims of an aberrant immune response (beta cell suicide or homicide?). To investigate this, we assessed islet autoantibodies in individuals with congenital beta cell defects causing neonatal diabetes mellitus (NDM). METHODS: We measured autoantibodies to GAD (GADA), islet antigen-2 (IA-2A) and zinc transporter 8 (ZnT8A) in 242 individuals with NDM (median age diagnosed 1.8 months [IQR 0.39-2.9 months]; median age collected 4.6 months [IQR 1.8-27.6 months]; median diabetes duration 2 months [IQR 0.6-23 months]), including 75 whose NDM resulted from severe beta cell endoplasmic reticulum (ER) stress. As a control cohort we also tested samples from 69 diabetes-free individuals (median age collected 9.9 months [IQR 9.0-48.6 months]) for autoantibodies. RESULTS: We found low prevalence of islet autoantibodies in individuals with monogenic NDM; 13/242 (5.4% [95% CI 2.9, 9.0%]) had detectable GADA, IA-2A and/or ZnT8A. This was similar to the proportion in the control participants who did not have diabetes (1/69 positive [1.4%, 95% CI 0.03, 7.8%], p=0.3). Importantly, monogenic individuals with beta cell ER stress had a similar rate of GADA/IA-2A/ZnT8A positivity to non-ER stress aetiologies (2.7% [95% CI 0.3, 9.3%] vs 6.6% [95% CI 3.3, 11.5%] p=0.4). We observed no association between islet autoimmunity and genetic risk, age at testing (including 30 individuals >10 years at testing) or diabetes duration (p>0.4 for all). CONCLUSIONS/INTERPRETATION: Our data support the hypothesis that beta cell stress/dysfunction alone does not lead to the production of islet autoantibodies, even in the context of high-risk HLA types. This suggests that additional factors are required to trigger an autoimmune response towards beta cells.

Applying a genetic risk score for prostate cancer to men with lower urinary tract symptoms in primary care to predict prostate cancer diagnosis: a cohort study in the UK Biobank.

BACKGROUND: Prostate cancer is highly heritable, with >250 common variants associated in genome-wide association studies. It commonly presents with non-specific lower urinary tract symptoms that are frequently associated with benign conditions. METHODS: Cohort study using UK Biobank data linked to primary care records. Participants were men with a record showing a general practice consultation for a lower urinary tract symptom. The outcome measure was prostate cancer diagnosis within 2 years of consultation. The predictor was a genetic risk score of 269 genetic variants for prostate cancer. RESULTS: A genetic risk score (GRS) is associated with prostate cancer in symptomatic men (OR per SD increase = 2.12 [1.86-2.41] P = 3.5e-30). An integrated risk model including age and GRS applied to symptomatic men predicted prostate cancer (AUC 0.768 [0.739-0.796]). Prostate cancer incidence was 8.1% (6.7-9.7) in the highest risk quintile. In the lowest quintile, prostate cancer incidence was <1%. CONCLUSIONS: This study is the first to apply GRS in primary care to improve the triage of symptomatic patients. Men with the lowest genetic risk of developing prostate cancer could safely avoid invasive investigation, whilst those identified with the greatest risk could be fast-tracked for further investigation. These results show that a GRS has potential application to improve the diagnostic pathway of symptomatic patients in primary care.

Capturing the real-world benefit of residual ß-cell function during clinically important time-periods in established Type 1 diabetes.

AIMS: Many individuals with type 1 diabetes retain residual ß-cell function, with increased endogenous insulin secretion associated with reduced hyperglycaemia, hypoglycaemia and glycaemic variability. However, it is unknown when these improvements occur during the day. Dysglycaemia is common in overnight and postprandial periods and associated with diabetes complications. Therefore, this study aimed to determine the influence of residual ß-cell function upon nocturnal and postprandial glycaemic control in established type 1 diabetes. METHODS: Under free-living conditions, 66 participants wore a blinded continuous glucose monitor (CGM), kept a food diary, and completed a stimulated urine C-peptide creatinine (UCPCR) test. Nocturnal, and postprandial CGM outcomes (participant means and discrete event analysis) were compared between UCPCR groups: undetectable (Cpepund ), low (Cpeplow : 0.001-0.19 nmol/mmol) and high (Cpephigh : ≥0.2 nmol/mmol). RESULTS: Greater ß-cell function was associated with incremental improvements in glycaemia. Cpephigh spent significantly greater time in normoglycaemia than Cpepund overnight (76 ± 20% vs. 58 ± 20%, p = 0.005) and 0-300 mins postprandially (68 ± 22% vs. 51 ± 22%, p = 0.045), while also having reducing nocturnal variability (SD 1.12 ± 0.41 vs. 1.52 ± 0.43 mmol/L, p = 0.010). Analysis of individual events, controlling for diabetes duration, BMI, basal insulin, use of a continuous or flash glucose monitor and (for postprandial) meal type, carbohydrate and bolus insulin intake, replicated the group findings, additionally demonstrating Cpepund had increased hyperglycaemia versus Cpeplow overnight and increased postprandial hypoglycaemic events compared with Cpephigh . For all participants, breakfast had a significantly higher incremental area under the curve than lunch and dinner. CONCLUSIONS: Residual ß-cell function is associated with improved nocturnal and postprandial glycaemic control. These data may be of clinical importance for identifying specific periods and individuals where further glycaemic management strategies would be beneficial.

Estimation of Early Graft Function Using the BETA-2 Score Following Clinical Islet Transplantation.

Little is known about how early islet graft function evolves in the clinical setting. The BETA-2 score is a validated index of islet function that can be calculated from a single blood sample and lends itself to frequent monitoring of graft function. In this study, we characterized early graft function by calculating weekly BETA-2 score in recipients who achieved insulin independence after single transplant (group 1, n = 8) compared to recipients who required a second transplant before achieving insulin independence (group 2, n = 7). We also determined whether graft function 1-week post-transplant was associated with insulin independence in individuals who received initial transplant between 2000-2017 (n = 125). Our results show that graft function increased rapidly reaching a plateau 4-6 weeks post-transplant. The BETA-2 score was higher in group 1 compared to group 2 as early as 1-week post-transplant (15 + 3 vs. 9 + 2, p = 0.001). In an unselected cohort, BETA-2 at 1-week post-transplant was associated with graft survival as defined by insulin independence during median follow up of 12 months (range 2-119 months) with greater survival among those with BETA-2 score >10 (p < 0.001, log-rank test). These findings suggest that primary graft function is established within 4-6 weeks post-transplant and graft function at 1-week post-transplant predicts long-term transplant outcomes.

DR15-DQ6 remains dominantly protective against type 1 diabetes throughout the first five decades of life.

AIMS/HYPOTHESIS: Among white European children developing type 1 diabetes, the otherwise common HLA haplotype DR15-DQ6 is rare, and highly protective. Adult-onset type 1 diabetes is now known to represent more overall cases than childhood onset, but it is not known whether DR15-DQ6 is protective in older-adult-onset type 1 diabetes. We sought to quantify DR15-DQ6 protection against type 1 diabetes as age of onset increased. METHODS: In two independent cohorts we assessed the proportion of type 1 diabetes cases presenting through the first 50 years of life with DR15-DQ6, compared with population controls. In the After Diabetes Diagnosis Research Support System-2 (ADDRESS-2) cohort (n = 1458) clinician-diagnosed type 1 diabetes was confirmed by positivity for one or more islet-specific autoantibodies. In UK Biobank (n = 2502), we estimated type 1 diabetes incidence rates relative to baseline HLA risk for each HLA group using Poisson regression. Analyses were restricted to white Europeans and were performed in three groups according to age at type 1 diabetes onset: 0-18 years, 19-30 years and 31-50 years. RESULTS: DR15-DQ6 was protective against type 1 diabetes through to age 50 years (OR < 1 for each age group, all p < 0.001). The following ORs for type 1 diabetes, relative to a neutral HLA genotype, were observed in ADDRESS-2: age 5-18 years OR 0.16 (95% CI 0.08, 0.31); age 19-30 years OR 0.10 (0.04, 0.23); and age 31-50 years OR 0.37 (0.21, 0.68). DR15-DQ6 also remained highly protective at all ages in UK Biobank. Without DR15-DQ6, the presence of major type 1 diabetes high-risk haplotype (either DR3-DQ2 or DR4-DQ8) was associated with increased risk of type 1 diabetes. CONCLUSIONS/INTERPRETATION: HLA DR15-DQ6 confers dominant protection from type 1 diabetes across the first five decades of life.

The clinical consequences of heterogeneity within and between different diabetes types.

Advances in molecular methods and the ability to share large population-based datasets are uncovering heterogeneity within diabetes types, and some commonalities between types. Within type 1 diabetes, endotypes have been discovered based on demographic (e.g. age at diagnosis, race/ethnicity), genetic, immunological, histopathological, metabolic and/or clinical course characteristics, with implications for disease prediction, prevention, diagnosis and treatment. In type 2 diabetes, the relative contributions of insulin resistance and beta cell dysfunction are heterogeneous and relate to demographics, genetics and clinical characteristics, with substantial interaction from environmental exposures. Investigators have proposed approaches that vary from simple to complex in combining these data to identify type 2 diabetes clusters relevant to prognosis and treatment. Advances in pharmacogenetics and pharmacodynamics are also improving treatment. Monogenic diabetes is a prime example of how understanding heterogeneity within diabetes types can lead to precision medicine, since phenotype and treatment are affected by which gene is mutated. Heterogeneity also blurs the classic distinctions between diabetes types, and has led to the definition of additional categories, such as latent autoimmune diabetes in adults, type 1.5 diabetes and ketosis-prone diabetes. Furthermore, monogenic diabetes shares many features with type 1 and type 2 diabetes, which make diagnosis difficult. These challenges to the current classification framework in adult and paediatric diabetes require new approaches. The 'palette model' and the 'threshold hypothesis' can be combined to help explain the heterogeneity within and between diabetes types. Leveraging such approaches for therapeutic benefit will be an important next step for precision medicine in diabetes. Graphical abstract.

Studies of insulin and proinsulin in pancreas and serum support the existence of aetiopathological endotypes of type 1 diabetes associated with age at diagnosis.

AIMS/HYPOTHESIS: It is unclear whether type 1 diabetes is a single disease or if endotypes exist. Our aim was to use a unique collection of pancreas samples recovered soon after disease onset to resolve this issue. METHODS: Immunohistological analysis was used to determine the distribution of proinsulin and insulin in the islets of pancreas samples recovered soon after type 1 diabetes onset (<2 years) from young people diagnosed at age <7 years, 7-12 years and ≥13 years. The patterns were correlated with the insulitis profiles in the inflamed islets of the same groups of individuals. C-peptide levels and the proinsulin:C-peptide ratio were measured in the circulation of a cohort of living patients with longer duration of disease but who were diagnosed in these same age ranges. RESULTS: Distinct patterns of proinsulin localisation were seen in the islets of people with recent-onset type 1 diabetes, which differed markedly between children diagnosed at <7 years and those diagnosed at ≥13 years. Proinsulin processing was aberrant in most residual insulin-containing islets of the younger group but this was much less evident in the group ≥13 years (p < 0.0001). Among all individuals (including children in the middle [7-12 years] range) aberrant proinsulin processing correlated with the assigned immune cell profiles defined by analysis of the lymphocyte composition of islet infiltrates. C-peptide levels were much lower in individuals diagnosed at <7 years than in those diagnosed at ≥13 years (median <3 pmol/l, IQR <3 to <3 vs 34.5 pmol/l, IQR <3-151; p < 0.0001), while the median proinsulin:C-peptide ratio was increased in those with age of onset <7 years compared with people diagnosed aged ≥13 years (0.18, IQR 0.10-0.31) vs 0.01, IQR 0.009-0.10 pmol/l; p < 0.0001). CONCLUSIONS/INTERPRETATION: Among those with type 1 diabetes diagnosed under the age of 30 years, there are histologically distinct endotypes that correlate with age at diagnosis. Recognition of such differences should inform the design of future immunotherapeutic interventions designed to arrest disease progression.

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

AIMS/HYPOTHESIS: Diabetes 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. METHODS: We 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. RESULTS: We 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/INTERPRETATION: We 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.

Beta cells in type 1 diabetes: mass and function; sleeping or dead?

Histological analysis of donor pancreases coupled with measurement of serum C-peptide in clinical cohorts has challenged the idea that all beta cells are eventually destroyed in type 1 diabetes. These findings have raised a number of questions regarding how the remaining beta cells have escaped immune destruction, whether pools of 'sleeping' or dysfunctional beta cells could be rejuvenated and whether there is potential for new growth of beta cells. In this Review, we describe histological and in vivo evidence of persistent beta cells in type 1 diabetes and discuss the limitations of current methods to distinguish underlying beta cell mass in comparison with beta cell function. We highlight that evidence for new beta cell growth in humans many years from diagnosis is limited, and that this growth may be very minimal if at all present. We review recent contributions to the debate around beta cell abnormalities contributing to the pathogenesis of type 1 diabetes. We also discuss evidence for restoration of beta cell function, as opposed to mass, in recent-onset type 1 diabetes, but highlight the absence of data supporting functional recovery in the setting of long-duration diabetes. Finally, future areas of research are suggested to help resolve the source and phenotype of residual beta cells that persist in some, but not all, people with type 1 diabetes.

Type 1 diabetes defined by severe insulin deficiency occurs after 30 years of age and is commonly treated as type 2 diabetes.

AIMS/HYPOTHESIS: Late-onset type 1 diabetes can be difficult to identify. Measurement of endogenous insulin secretion using C-peptide provides a gold standard classification of diabetes type in longstanding diabetes that closely relates to treatment requirements. We aimed to determine the prevalence and characteristics of type 1 diabetes defined by severe endogenous insulin deficiency after age 30 and assess whether these individuals are identified and managed as having type 1 diabetes in clinical practice. METHODS: We assessed the characteristics of type 1 diabetes defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (non-fasting C-peptide <200 pmol/l) in 583 participants with insulin-treated diabetes, diagnosed after age 30, from the Diabetes Alliance for Research in England (DARE) population cohort. We compared characteristics with participants with retained endogenous insulin secretion (>600 pmol/l) and 220 participants with severe insulin deficiency who were diagnosed under age 30. RESULTS: Twenty-one per cent of participants with insulin-treated diabetes who were diagnosed after age 30 met the study criteria for type 1 diabetes. Of these participants, 38% did not receive insulin at diagnosis, of whom 47% self-reported type 2 diabetes. Rapid insulin requirement was highly predictive of severe endogenous insulin deficiency: 85% required insulin within 1 year of diagnosis, and 47% of all those initially treated without insulin who progressed to insulin treatment within 3 years of diagnosis had severe endogenous insulin deficiency. Participants with late-onset type 1 diabetes defined by development of severe insulin deficiency had similar clinical characteristics to those with young-onset type 1 diabetes. However, those with later onset type 1 diabetes had a modestly lower type 1 diabetes genetic risk score (0.268 vs 0.279; p < 0.001 [expected type 2 diabetes population median, 0.231]), a higher islet autoantibody prevalence (GAD-, islet antigen 2 [IA2]- or zinc transporter protein 8 [ZnT8]-positive) of 78% at 13 years vs 62% at 26 years of diabetes duration; (p = 0.02), and were less likely to identify as having type 1 diabetes (79% vs 100%; p < 0.001) vs those with young-onset disease. CONCLUSIONS/INTERPRETATION: Type 1 diabetes diagnosed over 30 years of age, defined by severe insulin deficiency, has similar clinical and biological characteristics to that occurring at younger ages, but is frequently not identified. Clinicians should be aware that patients progressing to insulin within 3 years of diagnosis have a high likelihood of type 1 diabetes, regardless of initial diagnosis.