Type 1 Diabetes Risk in African-Ancestry Participants and Utility of an Ancestry-Specific Genetic Risk Score

Enhancing Type 1 Diabetes Immunological Risk Prediction with Continuous Glucose Monitoring and Genetic Profiling

Background: Early identification of individuals at high risk for type 1 diabetes (T1D) is essential for timely intervention. Islet autoantibodies (AB) and continuous glucose monitoring (CGM) reveal early signs of glycemic dysregulation, while T1D genetic risk scores (GRS) further improve disease prediction. We use CGM data and T1D GRS to develop an AB classifier (1 AB vs. ≥2 AB) and predict early T1D risk. Methods: Thirty-nine AB-positive (18 with 1 and 21 with ≥2 AB) healthy relatives of T1D (mean age 22.1 ± 11.1 years, HbA1c 5.3 ± 0.3%, body mass index 24.1 ± 5.8 kg/m2) were enrolled in a National Institutes of Health's (NIH) TrialNet ancillary study. Participants wore CGMs for a week and consumed three standardized liquid mixed meals (SLMM). Post-SLMM CGM glycemic features and T1D GRS were used in a linear support vector machine (SVM) model with recursive feature elimination (RFE) for AB classification, evaluated via fivefold cross-validation using the receiver operating characteristic and precision-recall area under the curve (AUC-ROC/PR). Results: Significant differences between the AB groups were observed in the post-SLMM percent time of glucose >180 mg/dL and GRS (P = 0.020 and P = 0.001, respectively). An SVM model with two RFE-selected features (T1D GRS and incremental AUC) achieved the best performance, classifying 1 versus ≥2 AB individuals with an AUC-ROC of 0.93 (95% confidence interval [CI]: 0.83-1.00) and AUC-PR of 0.89 (95% CI: 0.71-0.99), compared with AUC-ROC of 0.80 (95% CI: 0.46-1.00) and AUC-PR of 0.82 (95% CI: 0.71-0.93) using all features. Conclusions: A machine learning approach combining a 1-week CGM home test and T1D GRS reliably assesses T1D immunological risk, enabling early intervention.

Screening and care for preclinical stage 1-2 type 1 diabetes in first-degree relatives: French expert position statement

The natural history of type 1 diabetes (T1D) evolves from stage 1 (islet autoimmunity with normoglycemia; ICD-10 diagnostic code E10.A1) to stage 2 (autoimmunity with dysglycemia; E10.A2) and subsequent clinical stage 3 (overt hyperglycemia), which is commonly the first time of referral. Autoantibody testing can diagnose T1D at its preclinical stages 1-2 and lead to earlier initiation of care, particularly for first-degree relatives of people living with T1D, who are at higher genetic risk. Preclinical T1D screening and monitoring aims to avoid inaugural ketoacidosis and prolong preservation of endogenous insulin secretion, thereby improving glycemic control and reducing long-term morbidity. Moreover, early management can help coping with T1D and correct modifiable risk factors (obesity, sedentary lifestyle). New treatments currently under clinical deployment or trials also offer the possibility of delaying clinical progression. All these arguments lead to the proposition of a national screening and care pathway open to interested first-degree relatives. This pathway represents a new expertise to acquire for healthcare professionals. By adapting international consensus guidance to the French specificities, the proposed screening strategy involves testing for ≥ 2 autoantibodies (among IAA, anti-GAD, anti-IA-2) in relatives aged 2-45 years. Negative screening (∼95% of cases) should be repeated every 4 years until the age of 12. A management workflow is proposed for relatives screening positive (∼5% of cases), with immuno-metabolic monitoring by autoantibody testing, OGTT, glycemia and/or HbA1c of variable frequency, depending on T1D stage, age, patient preference and available resources, as well as the definition of expert centers for preclinical T1D.

Characteristics of autoantibody-positive individuals without high-risk HLA-DR4-DQ8 or HLA-DR3-DQ2 haplotypes

AIMS/HYPOTHESIS

Many studies of type 1 diabetes pathogenesis focus on individuals with high-risk HLA haplotypes. We tested the hypothesis that, among islet autoantibody-positive individuals, lacking HLA-DRB1*04-DQA1*03-DQB1*0302 (HLA-DR4-DQ8) and/or HLA-DRB1*0301-DQA1*0501-DQB1*0201 (HLA-DR3-DQ2) is associated with phenotypic differences, compared with those who have these high-risk HLA haplotypes.

METHODS

We classified autoantibody-positive relatives of individuals with type 1 diabetes into four groups based on having both HLA-DR4-DQ8 and HLA-DR3-DQ2 (DR3/DR4; n=1263), HLA-DR4-DQ8 but not HLA-DR3-DQ2 (DR4/non-DR3; n=2340), HLA-DR3-DQ2 but not HLA-DR4-DQ8 (DR3/non-DR4; n=1607) and neither HLA-DR3-DQ2 nor HLA-DR4-DQ8 (DRX/DRX; n=1294). Group comparisons included demographics, metabolic markers and the prevalence of autoantibodies against GAD65 (GADA%), IA-2 (IA-2A%) or insulin (IAA%) at enrolment. A p value <0.01 was considered statistically significant.

RESULTS

IA-2A% was lower in the DRX/DRX group (20.9%) than in the DR4/non-DR3 (38.5%, p<0.001) and DR3/DR4 (44.8%, p<0.001) groups, but similar to the DR3/non-DR4 group (20.0%). Conversely, IAA% was similar in the DRX/DRX (43.4%), DR4/non-DR3 (41.1%) and DR3/DR4 (41.0%) groups, but lower in the DR3/non-DR4 group (30.1%, p<0.001). Participants in the DRX/DRX group were older, with a lower prevalence of White participants and a higher prevalence of overweight/obesity, and higher preserved C-peptide (as measured by a lower Index60) than those in the DR3/DR4 group (all comparisons, p<0.005), a lower prevalence of White or non-Hispanic participants and a lower Index60 than those in the DR4/non-DR3 group, and younger age, a higher prevalence of Hispanic participants and a lower Index60 than those in the DR3/non-DR4 group (all comparisons, p<0.005). Among the 1292 participants who progressed to clinical type 1 diabetes, those in the DR3/non-DR4 group had higher GADA%, lower IA-2A% and lower IAA% than the other groups (all comparisons, p<0.01), and those in the DR3/DR4 group had the youngest age at diagnosis (all comparisons, p<0.001).

CONCLUSIONS/INTERPRETATION

Autoantibody-positive individuals who lack both high-risk HLA haplotypes (DRX/DRX) or have HLA-DR3-DQ2 but lack HLA-DR4-DQ8 (DR3/non-DR4) have phenotypic differences compared with DR3/DR4 and DR4/non-DR3 individuals, suggesting that there is aetiological heterogeneity in type 1 diabetes.

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

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 for Diabetes in Youth study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting C-peptide ≥250 pmol/L (≥0.75 ng/mL) after >3 years' (median 74 months) diabetes duration. Models included clinical measures at the baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL cholesterol), with and without islet autoantibodies (GADA, IA-2A) and a type 1 diabetes genetic risk score (T1DGRS).

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 the receiver operating characteristic curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope 0.995-0.999). Models retained high performance for predicting retained C-peptide in older youth with obesity (AUCROC 0.88-0.96) and in subgroups defined by self-reported race and ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92).

CONCLUSIONS

Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with T2D.

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

A regulatory variant rs9379874 in T1D risk region 6p22.2 affects BTN3A1 expression regulating T cell function

OBJECTIVE

Genome-wide association studies (GWAS) have identified that 6p22.2 region is associated with type 1 diabetes (T1D) risk in the Chinese Han population. This study aims to reveal associations between this risk region and T1D subgroups and related clinical features, and further identify causal variant(s) and target gene(s) in this region.

METHODS

2608 T1D and 4814 healthy controls were recruited from East, Central, and South China. Baseline data and genotyping for rs4320356 were collected. The most likely causal variant and gene were identified by bioinformatics analysis, dual-luciferase reporter assays, expression quantitative trait loci (eQTL), and functional annotation of the non-coding region within the 6p22.2 region.

RESULTS

The leading variant rs4320356 in the 6p22.2 region was associated with T1D risk in the Chinese and Europeans. However, this variant was not significantly associated with islet function or autoimmunity. In silico analysis suggested rs9379874 was the most potential causal variant for T1D risk among thymus, spleen, and T cells, overlapping with the enhancer-related histone mark in multiple T cell subsets. Dual luciferase reporter assay and eQTL showed that the T allele of rs9379874 increased BTN3A1 expression by binding to FOXA1. Public single-cell RNA sequencing analysis indicated that BTN3A1 was related to T-cell activation, ATP metabolism, and cytokine metabolism pathways, which might contribute to T1D development.

CONCLUSION

This study indicates that a functional variant rs9379874 regulates BTN3A1 expression, expanding the genomic landscape of T1D risk and offering a potential target for developing novel therapies.

Fifty years of HLA-associated type 1 diabetes risk: history, current knowledge, and future directions

More than 50 years have elapsed since the association of human leukocyte antigens (HLA) with type 1 diabetes (T1D) was first reported. Since then, methods for identification of HLA have progressed from cell based to DNA based, and the number of recognized HLA variants has grown from a few to tens of thousands. Current genotyping methodology allows for exact identification of all HLA-encoding genes in an individual's genome, with statistical analysis methods evolving to digest the enormous amount of data that can be produced at an astonishing rate. The HLA region of the genome has been repeatedly shown to be the most important genetic risk factor for T1D, and the original reported associations have been replicated, refined, and expanded. Even with the remarkable progress through 50 years and over 5,000 reports, a comprehensive understanding of all effects of HLA on T1D remains elusive. This report represents a summary of the field as it evolved and as it stands now, enumerating many past and present challenges, and suggests possible paradigm shifts for moving forward with future studies in hopes of finally understanding all the ways in which HLA influences the pathophysiology of T1D.

Mitochondrial related variants associated with cardiovascular traits

Introduction

Cardiovascular disease (CVD) is responsible for over 30% of mortality worldwide. CVD arises from the complex influence of molecular, clinical, social, and environmental factors. Despite the growing number of autosomal genetic variants contributing to CVD, the cause of most CVDs is still unclear. Mitochondria are crucial in the pathophysiology, development and progression of CVDs; the impact of mitochondrial DNA (mtDNA) variants and mitochondrial haplogroups in the context of CVD has recently been highlighted.

Aims

We investigated the role of genetic variants in both mtDNA and nuclear-encoded mitochondrial genes (NEMG) in CVD, including coronary artery disease (CAD), hypertension, and serum lipids in the UK Biobank, with sub-group analysis for diabetes.

Methods

We investigated 371,542 variants in 2,527 NEMG, along with 192 variants in 32 mitochondrial genes in 381,994 participants of the UK Biobank, stratifying by presence of diabetes.

Results

Mitochondrial variants showed associations with CVD, hypertension, and serum lipids. Mitochondrial haplogroup J was associated with CAD and serum lipids, whereas mitochondrial haplogroups T and U were associated with CVD. Among NEMG, variants within Nitric Oxide Synthase 3 (NOS3) showed associations with CVD, CAD, hypertension, as well as diastolic and systolic blood pressure. We also identified Translocase Of Outer Mitochondrial Membrane 40 (TOMM40) variants associated with CAD; Solute carrier family 22 member 2 (SLC22A2) variants associated with CAD and CVD; and HLA-DQA1 variants associated with hypertension. Variants within these three genes were also associated with serum lipids.

Conclusion

Our study demonstrates the relevance of mitochondrial related variants in the context of CVD. We have linked mitochondrial haplogroup U to CVD, confirmed association of mitochondrial haplogroups J and T with CVD and proposed new markers of hypertension and serum lipids in the context of diabetes. We have also evidenced connections between the etiological pathways underlying CVDs, blood pressure and serum lipids, placing NOS3, SLC22A2, TOMM40 and HLA-DQA1 genes as common nexuses.

Genetic association and machine learning improves discovery and prediction of type 1 diabetes

Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can lead to novel biological and therapeutic discovery and improved risk prediction. In this study, we performed genetic association and fine-mapping analyses in 817,718 European ancestry samples genome-wide and 29,746 samples at the MHC locus, which identified 165 independent risk signals for T1D of which 19 were novel. We used risk variants to train a machine learning model (named T1GRS) to predict T1D, which highly differentiated T1D from non-disease and type 2 diabetes (T2D) in Europeans as well as African Americans at or beyond the level of current standards. We identified extensive non-linear interactions between risk loci in T1GRS, for example between HLA-DQB1*57 and INS, coding and non-coding HLA alleles, and DEXI, INS and other beta cell loci, that provided mechanistic insight and improved risk prediction. T1D individuals formed distinct clusters based on genetic features from T1GRS which had significant differences in age of onset, HbA1c, and renal disease severity. Finally, we provided T1GRS in formats to enhance accessibility of risk prediction to any user and computing environment. Overall, the improved genetic discovery and prediction of T1D will have wide clinical, therapeutic, and research applications.

Untangling the genetics of beta cell dysfunction and death in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is a complex multi-system disease which arises from both environmental and genetic factors, resulting in the destruction of insulin-producing pancreatic beta cells. Over the past two decades, human genetic studies have provided new insight into the etiology of T1D, including an appreciation for the role of beta cells in their own demise.

SCOPE OF REVIEW

Here, we outline models supported by human genetic data for the role of beta cell dysfunction and death in T1D. We highlight the importance of strong evidence linking T1D genetic associations to bona fide candidate genes for mechanistic and therapeutic consideration. To guide rigorous interpretation of genetic associations, we describe molecular profiling approaches, genomic resources, and disease models that may be used to construct variant-to-gene links and to investigate candidate genes and their role in T1D.

MAJOR CONCLUSIONS

We profile advances in understanding the genetic causes of beta cell dysfunction and death at individual T1D risk loci. We discuss how genetic risk prediction models can be used to address disease heterogeneity. Further, we present areas where investment will be critical for the future use of genetics to address open questions in the development of new treatment and prevention strategies for T1D.

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

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 participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%).

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 their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001.

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 those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.

A multi-ancestry genome-wide association study in type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease caused by destruction of the pancreatic β-cells. Genome-wide association (GWAS) and fine mapping studies have been conducted mainly in European ancestry (EUR) populations. We performed a multi-ancestry GWAS to identify SNPs and HLA alleles associated with T1D risk and age at onset. EUR families (N = 3223), and unrelated individuals of African (AFR, N = 891) and admixed (Hispanic/Latino) ancestry (AMR, N = 308) were genotyped using the Illumina HumanCoreExome BeadArray, with imputation to the TOPMed reference panel. The Multi-Ethnic HLA reference panel was utilized to impute HLA alleles and amino acid residues. Logistic mixed models (T1D risk) and frailty models (age at onset) were used for analysis. In GWAS meta-analysis, seven loci were associated with T1D risk at genome-wide significance: PTPN22, HLA-DQA1, IL2RA, RNLS, INS, IKZF4-RPS26-ERBB3, and SH2B3, with four associated with T1D age at onset (PTPN22, HLA-DQB1, INS, and ERBB3). AFR and AMR meta-analysis revealed NRP1 as associated with T1D risk and age at onset, although NRP1 variants were not associated in EUR ancestry. In contrast, the PTPN22 variant was significantly associated with risk only in EUR ancestry. HLA alleles and haplotypes most significantly associated with T1D risk in AFR and AMR ancestry differed from that seen in EUR ancestry; in addition, the HLA-DRB1*08:02-DQA1*04:01-DQB1*04:02 haplotype was 'protective' in AMR while HLA-DRB1*08:01-DQA1*04:01-DQB1*04:02 haplotype was 'risk' in EUR ancestry, differing only at HLA-DRB1*08. These results suggest that much larger sample sizes in non-EUR populations are required to capture novel loci associated with T1D risk.

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

BACKGROUND

Population screening for risk of type 1 diabetes (T1D) has been proposed to identify those with islet autoimmunity (presence of islet autoantibodies). As islet autoantibodies can be transient, screening with a genetic risk score has been proposed as an entry into autoantibody testing.

METHODS

Children were recruited from eight general pediatric and specialty clinics across Virginia with diverse community settings. Recruiters in each clinic obtained informed consent/assent, a medical history, and a saliva sample for DNA extraction in children with and without a history of T1D. A custom genotyping panel was used to define T1D genetic risk based upon associated SNPs in European- and African-genetic ancestry. Subjects at "high genetic risk" were offered a separate blood collection for screening four islet autoantibodies. A follow-up contact (email, mail, and telephone) in one half of the participants determined interest and occurrence of subsequent T1D.

RESULTS

A total of 3818 children aged 2-16 years were recruited, with 14.2% (n = 542) having a "high genetic risk." Of children with "high genetic risk" and without pre-existing T1D (n = 494), 7.0% (34/494) consented for autoantibody screening; 82.4% (28/34) who consented also completed the blood collection, and 7.1% (2/28) of them tested positive for multiple autoantibodies. Among children with pre-existing T1D (n = 91), 52% (n = 48) had a "high genetic risk." In the sample of children with existing T1D, there was no relationship between genetic risk and age at T1D onset. A major factor in obtaining islet autoantibody testing was concern over SARS-CoV-2 exposure.

CONCLUSIONS

Minimally invasive saliva sampling implemented using a genetic risk score can identify children at genetic risk of T1D. Consent for autoantibody screening, however, was limited largely due to the SARS-CoV-2 pandemic and need for blood collection.

Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes

CONTEXT

Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations.

OBJECTIVE

This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D.

METHODS

Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients.

RESULTS

The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D.

CONCLUSION

PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.

Immune responses to gut bacteria associated with time to diagnosis and clinical response to T cell-directed therapy for type 1 diabetes prevention

Immune-targeted therapies have efficacy for treatment of autoinflammatory diseases. For example, treatment with the T cell-specific anti-CD3 antibody teplizumab delayed disease onset in participants at high risk for type 1 diabetes (T1D) in the TrialNet 10 (TN-10) trial. However, heterogeneity in therapeutic responses in TN-10 and other immunotherapy trials identifies gaps in understanding disease progression and treatment responses. The intestinal microbiome is a potential source of biomarkers associated with future T1D diagnosis and responses to immunotherapy. We previously reported that antibody responses to gut commensal bacteria were associated with T1D diagnosis, suggesting that certain antimicrobial immune responses may help predict disease onset. Here, we investigated anticommensal antibody (ACAb) responses against a panel of taxonomically diverse intestinal bacteria species in sera from TN-10 participants before and after teplizumab or placebo treatment. We identified IgG2 responses to three species that were associated with time to T1D diagnosis and with teplizumab treatment responses that delayed disease onset. These antibody responses link human intestinal bacteria with T1D progression, adding predictive value to known T1D risk factors. ACAb analysis provides a new approach to elucidate heterogeneity in responses to immunotherapy and identify individuals who may benefit from teplizumab, recently approved by the U.S. Food and Drug Administration for delaying T1D onset.

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

OBJECTIVE

To determine whether genetic risk for type 1 diabetes (T1D) differentiates the four Aβ subgroups of ketosis-prone diabetes (KPD), where A+ and A- define the presence or absence of islet autoantibodies and β+ and β- define the presence or absence of β-cell function.

RESEARCH DESIGN AND METHODS

We compared T1D genetic risk scores (GRS) of patients with KPD across subgroups, race/ethnicity, β-cell function, and glycemia.

RESULTS

Among 426 patients with KPD (54% Hispanic, 31% African American, 11% White), rank order of GRS was A+β- > A+β+ = A-β- > A-β+. GRS of A+β- KPD was lower than that of a T1D cohort, and GRS of A-β+ KPD was higher than that of a type 2 diabetes cohort. GRS was lowest among African American patients, with a similar distribution across KPD subgroups.

CONCLUSIONS

T1D genetic risk delineates etiologic differences among KPD subgroups. Patients with A+β- KPD have the highest and those with A-β+ KPD the lowest GRS.

Clinical prediction models combining routine clinical measures identify participants with youth-onset diabetes who maintain insulin secretion in the range associated with type 2 diabetes: The SEARCH for Diabetes in Youth Study

Objective

With 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).

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) 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).

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.

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores - a new resource for diabetes precision medicine

OBJECTIVE

The study aimed to develop and validate algorithms for identifying people with type 1 and type 2 diabetes in the All of Us Research Program (AoU) cohort, using electronic health record (EHR) and survey data.

RESEARCH DESIGN AND METHODS

Two sets of algorithms were developed, one using only EHR data (EHR), and the other using a combination of EHR and survey data (EHR+). Their performance was evaluated by testing their association with polygenic scores for both type 1 and type 2 diabetes.

RESULTS

For type 1 diabetes, the EHR-only algorithm showed a stronger association with T1D polygenic score (p=3×10-5) than the EHR+. For type 2 diabetes, the EHR+ algorithm outperformed both the EHR-only and the existing AoU definition, identifying additional cases (25.79% and 22.57% more, respectively) and showing stronger association with T2D polygenic score (DeLong p=0.03 and 1×10-4, respectively).

CONCLUSIONS

We provide new validated definitions of type 1 and type 2 diabetes in AoU, and make them available for researchers. These algorithms, by ensuring consistent diabetes definitions, pave the way for high-quality diabetes research and future clinical discoveries.

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.

Heterogeneity and endotypes in type 1 diabetes mellitus

Despite major advances over the past decade, prevention and treatment of type 1 diabetes mellitus (T1DM) remain suboptimal, with large and unexplained variations in individual responses to interventions. The current classification schema for diabetes mellitus does not capture the complexity of this disease or guide clinical management effectively. One of the approaches to achieve the goal of applying precision medicine in diabetes mellitus is to identify endotypes (that is, well-defined subtypes) of the disease each of which has a distinct aetiopathogenesis that might be amenable to specific interventions. Here, we describe epidemiological, clinical, genetic, immunological, histological and metabolic differences within T1DM that, together, suggest heterogeneity in its aetiology and pathogenesis. We then present the emerging endotypes and their impact on T1DM prediction, prevention and treatment.

Deep integrative models for large-scale human genomics

Polygenic risk scores (PRSs) are expected to play a critical role in precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. We developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large-scale genomics data. The framework supports multi-task learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual-level data from the UK Biobank, the GLN model demonstrated a competitive performance compared to established neural network architectures, particularly for certain traits, showcasing its potential in modeling complex genetic relationships. Furthermore, the GLN model outperformed linear PRS methods for Type 1 Diabetes, likely due to modeling non-additive genetic effects and epistasis. This was supported by our identification of widespread non-additive genetic effects and epistasis in the context of T1D. Finally, we constructed PRS models that integrated genotype, blood, urine, and anthropometric data and found that this improved performance for 93% of the 290 diseases and disorders considered. EIR is available at https://github.com/arnor-sigurdsson/EIR.

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

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

Association between HLA Class II Alleles/Haplotypes and Genomic Ancestry in Brazilian Patients with Type 1 Diabetes: A Nationwide Exploratory Study

We aimed to identify HLA-DRB1, -DQA1, and -DQB1 alleles/haplotypes associated with European, African, or Native American genomic ancestry (GA) in admixed Brazilian patients with type 1 diabetes (T1D). This exploratory nationwide study enrolled 1599 participants. GA percentage was inferred using a panel of 46 ancestry informative marker-insertion/deletion. Receiver operating characteristic curve analysis (ROC) was applied to identify HLA class II alleles related to European, African, or Native American GA, and showed significant (p < 0.05) accuracy for identifying HLA risk alleles related to European GA: for DRB1*03:01, the area under the curve was (AUC) 0.533; for DRB1*04:01 AUC = 0.558, for DRB1*04:02 AUC = 0.545. A better accuracy for identifying African GA was observed for the risk allele DRB1*09:01AUC = 0.679 and for the protective alleles DRB1*03:02 AUC = 0.649, DRB1*11:02 AUC = 0.636, and DRB1*15:03 AUC = 0.690. Higher percentage of European GA was observed in patients with risk haplotypes (p < 0.05). African GA percentage was higher in patients with protective haplotypes (p < 0.05). Risk alleles and haplotypes were related to European GA and protective alleles/haplotypes to African GA. Future studies with other ancestry markers are warranted to fill the gap in knowledge regarding the genetic origin of T1D in highly admixed populations such as that found in Brazil.

Polygenic Scores Help Reduce Racial Disparities in Predictive Accuracy of Automated Type 1 Diabetes Classification Algorithms

OBJECTIVE

Automated algorithms to identify individuals with type 1 diabetes using electronic health records are increasingly used in biomedical research. It is not known whether the accuracy of these algorithms differs by self-reported race. We investigated whether polygenic scores improve identification of individuals with type 1 diabetes.

RESEARCH DESIGN AND METHODS

We investigated two large hospital-based biobanks (Mass General Brigham [MGB] and BioMe) and identified individuals with type 1 diabetes using an established automated algorithm. We performed medical record reviews to validate the diagnosis of type 1 diabetes. We implemented two published polygenic scores for type 1 diabetes (developed in individuals of European or African ancestry). We assessed the classification algorithm before and after incorporating polygenic scores.

RESULTS

The automated algorithm was more likely to incorrectly assign a diagnosis of type 1 diabetes in self-reported non-White individuals than in self-reported White individuals (odds ratio 3.45; 95% CI 1.54-7.69; P = 0.0026). After incorporating polygenic scores into the MGB Biobank, the positive predictive value of the type 1 diabetes algorithm increased from 70 to 97% for self-reported White individuals (meaning that 97% of those predicted to have type 1 diabetes indeed had type 1 diabetes) and from 53 to 100% for self-reported non-White individuals. Similar results were found in BioMe.

CONCLUSIONS

Automated phenotyping algorithms may exacerbate health disparities because of an increased risk of misclassification of individuals from underrepresented populations. Polygenic scores may be used to improve the performance of phenotyping algorithms and potentially reduce this disparity.

Precision Medicine in Type 1 Diabetes

Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.

Type 1 Diabetes Mellitus and Autoimmune Diseases: A Critical Review of the Association and the Application of Personalized Medicine

Type 1 Diabetes Mellitus (T1DM) is a common hyperglycemic disease characterized by the autoimmune destruction of insulin-producing beta cells of the pancreas. Various attempts have been made to understand the complex interplay of genetic and environmental factors which lead to the development of the autoimmune response in an individual. T1DM is frequently associated with other autoimmune illnesses, the most common being autoimmune thyroid disorders affecting more than 90% of people with T1D and autoimmune disorders. Antithyroid antibodies are present in around 20% of children with T1D at the start of the illness and are more frequent in girls. Patients with T1DM often have various other co-existing multi-system autoimmune disorders including but not limited to thyroid diseases, parathyroid diseases, celiac disease, vitiligo, gastritis, skin diseases, and rheumatic diseases. It is a consistent observation in clinics that T1DM patients have other autoimmune disorders which in turn affect their prognosis. Concomitant autoimmune illness might affect diabetes care and manifest itself clinically in a variety of ways. A thorough understanding of the complex pathogenesis of this modern-day epidemic and its association with other autoimmune disorders has been attempted in this review in order to delineate the measures to prevent the development of these conditions and limit the morbidity of the afflicted individuals as well. The measures including antibody screening in susceptible individuals, early identification and management of other autoimmune disorders, and adoption of personalized medicine can significantly enhance the quality of life of these patients. Personalized medicine has recently gained favor in the scientific, medical, and public domains, and is frequently heralded as the future paradigm of healthcare delivery. With the evolution of the ‘omics’, the individualization of therapy is not only closer to reality but also the need of the hour.

The missing heritability in type 1 diabetes

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

Towards a global view of multiple sclerosis genetics

Multiple sclerosis (MS) is a neuroimmunological disorder of the CNS with a strong heritable component. The genetic architecture of MS susceptibility is well understood in populations of European ancestry. However, the extent to which this architecture explains MS susceptibility in populations of non-European ancestry remains unclear. In this Perspective article, we outline the scientific arguments for studying MS genetics in ancestrally diverse populations. We argue that this approach is likely to yield insights that could benefit individuals with MS from all ancestral groups. We explore the logistical and theoretical challenges that have held back this field to date and conclude that, despite these challenges, inclusion of participants of non-European ancestry in MS genetics studies will ultimately be of value to all patients with MS worldwide.

Type 1 diabetes in diverse ancestries and the use of genetic risk scores

Over 75 genetic loci within and outside of the HLA region influence type 1 diabetes risk. Genetic risk scores (GRS), which facilitate the integration of complex genetic information, have been developed in type 1 diabetes and incorporated into models and algorithms for classification, prognosis, and prediction of disease and response to preventive and therapeutic interventions. However, the development and validation of GRS across different ancestries is still emerging, as is knowledge on type 1 diabetes genetics in populations of diverse genetic ancestries. In this Review, we provide a summary of the current evidence on the evolutionary genetic variation in type 1 diabetes and the racial and ethnic differences in type 1 diabetes epidemiology, clinical characteristics, and preclinical course. We also discuss the influence of genetics on type 1 diabetes with differences across ancestries and the development and validation of GRS in various populations.

Construction and Application of Polygenic Risk Scores in Autoimmune Diseases

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.

Randomised controlled trials on prevention, diagnosis and treatment of diabetes in African countries: a systematic review

OBJECTIVES

The epidemiological transition from infectious to chronic diseases leads to novel challenges in African health systems. The prevalence of diabetes mellitus (DM) is increasing dramatically. Undiagnosed and undertreated DM leads to numerous complications including end-organ damage and death. Our objectives were to collect the best locally generated evidence on DM interventions, identify knowledge gaps and determine underexplored research areas.

DESIGN

A systematic review and meta-analysis of randomised controlled trials.

PARTICIPANTS AND SETTING

African patients in primary, secondary and tertiary prevention, diagnosis and treatment DM type 1 (DM1), type 2 (DM2) and gestational DM (GDM).

OUTCOME

All-cause mortality, glycaemic control, complications, quality of life, hospital admission, treatment adherence and costs.

DATA SOURCES

Articles published in MEDLINE Ovid, CENTRAL, CINAHL, African Journals Online and African Index Medicus and the International Clinical Trials Registry Platform in English language without time restrictions. The systematic search was last updated in October 2020.

RESULTS

Out of 3736 identified publications, we included 60 eligible studies conducted in 15 countries, 75% were conducted in urban healthcare settings, including 10 112 participants. We included 8 studies on DM1, 6 on GDM, 2 on pre-DM, 37 on mainly DM2 including 7 on DM-related complications. The design of the studied intervention was heterogeneous with a focus on educational strategies. The other studies investigated the efficacy of nutritional strategies including food supplementations, pharmacological strategies and strategies to enhance physical activity. Seven studies included interventions on DM-related complications.

CONCLUSIONS

Research activities increased in recent years, but available evidence is still not representative for all African countries. There is a big lack of evidence in primary healthcare and rural settings, implementation research, pharmacological interventions, especially in poorer countries. Nevertheless, the identified studies offer a variety of effective interventions that can inform medical care and future research.

PROSPERO REGISTRATION NUMBER

CRD42019122785.

Utility of Diabetes Type-Specific Genetic Risk Scores for the Classification of Diabetes Type Among Multiethnic Youth

OBJECTIVE

Genetic risk scores (GRS) aid classification of diabetes type in White European adult populations. We aimed to assess the utility of GRS in the classification of diabetes type among racially/ethnically diverse youth in the U.S.

RESEARCH DESIGN AND METHODS

We generated type 1 diabetes (T1D)- and type 2 diabetes (T2D)-specific GRS in 2,045 individuals from the SEARCH for Diabetes in Youth study. We assessed the distribution of genetic risk stratified by diabetes autoantibody positive or negative (DAA+/-) and insulin sensitivity (IS) or insulin resistance (IR) and self-reported race/ethnicity (White, Black, Hispanic, and other).

RESULTS

T1D and T2D GRS were strong independent predictors of etiologic type. The T1D GRS was highest in the DAA+/IS group and lowest in the DAA-/IR group, with the inverse relationship observed with the T2D GRS. Discrimination was similar across all racial/ethnic groups but showed differences in score distribution. Clustering by combined genetic risk showed DAA+/IR and DAA-/IS individuals had a greater probability of T1D than T2D. In DAA- individuals, genetic probability of T1D identified individuals most likely to progress to absolute insulin deficiency.

CONCLUSIONS

Diabetes type-specific GRS are consistent predictors of diabetes type across racial/ethnic groups in a U.S. youth cohort, but future work needs to account for differences in GRS distribution by ancestry. T1D and T2D GRS may have particular utility for classification of DAA- children.

Improved genetic risk scoring algorithm for type 1 diabetes prediction

BACKGROUND

Precise risk prediction of type 1 diabetes (T1D) facilitates early intervention and identification of risk factors prior to irreversible beta-islet cell destruction, and can significantly improve T1D prevention and clinical care. Sharp et al. developed a genetic risk scoring (GRS) system for T1D (T1D-GRS2) capable of predicting T1D risk in children of European ancestry. The T1D-GRS2 was developed on the basis of causal genetic variants, thus may be applicable to minor populations, while a trans-ethnic GRS for T1D may avoid the exacerbation of health disparities due to the lack of genomic information in minorities.

METHODS

Here, we describe a T1D-GRS2 calculator validated in two independent cohorts, including African American children and European American children. Participants were recruited by the Center for Applied Genomics at the Children's Hospital of Philadelphia.

RESULTS

It demonstrates that GRS2 is applicable to the T1D risk prediction in the AA cohort, while population-specific thresholds are needed for different populations.

CONCLUSIONS

The study highlights the potential to further improve T1D-GRS2 performance with the inclusion of additional genetic markers.

HLA-DRB1 and -DQB1 Alleles, Haplotypes and Genotypes in Emirati Patients with Type 1 Diabetes Underscores the Benefits of Evaluating Understudied Populations

Background: HLA class II (DR and DQ) alleles and antigens have historically shown strong genetic predisposition to type 1 diabetes (T1D). This study evaluated the association of DRB1 and DQB1 alleles, genotypes, and haplotypes with T1D in United Arab Emirates. Materials and Methods: Study subjects comprised 149 patients with T1D, and 147 normoglycemic control subjects. Cases and controls were Emiratis and were HLA-DRB1 and -DQB1 genotyped using sequence-based typing. Statistical analysis was performed using Bridging Immunogenomic Data-Analysis Workflow Gaps R package. Results: In total, 15 DRB1 and 9 DQB1 alleles were identified in the study subjects, of which the association of DRB1*03:01, DRB1*04:02, DRB1*11:01, DRB1*16:02, and DQB1*02:01, DQB1*03:02, DQB1*03:01, and DQB1*06:01 with altered risk of T1D persisted after correcting for multiple comparisons. Two-locus haplotype analysis identified DRB1*03:01∼DQB1*02:01 [0.44 vs. 0.18, OR (95% CI) = 3.44 (2.33-5.1), Pc = 3.48 × 10^-10]; DRB1*04:02∼DQB1*03:02 [0.077 vs. 0.014, OR = 6.06 (2.03-24.37), Pc = 2.3 × 10^-3] and DRB1*04:05∼DQB1*03:02 [0.060 vs. 0.010, OR = 6.24 (1.79-33.34), Pc = 0.011] as positively associated, and DRB1*16:02∼DQB1*05:02 [0.024 vs. 0.075, OR = 0.3 (0.11-0.74), Pc = 0.041] as negatively associated with T1D, after applying Bonferroni correction. Furthermore, the highest T1D risk was observed for DR3/DR4 [0.104 vs. 0.006, OR = 25.03 (8.23-97.2), Pc = 2.6 × 10^-10], followed by DR3/DR3 [0.094 vs. 0.010, OR = 8.72 (3.17-25.32), Pc = 3.18 × 10^-8] diplotypes. Conclusion: While DRB1 and DQB1 alleles and haplotypes associated with T1D in Emiratis showed similarities to Caucasian and non-Caucasian populations, several alleles and haplotypes associated with T1D in European, African, and Asian populations, were not observed. This underscores the contribution of ethnic diversity and possible diverse associations between DRB1 and DQB1 and T1D across different populations.

Toward an Improved Classification of Type 2 Diabetes: Lessons From Research into the Heterogeneity of a Complex Disease

CONTEXT

Accumulating evidence indicates that type 2 diabetes (T2D) is phenotypically heterogeneous. Defining and classifying variant forms of T2D are priorities to better understand its pathophysiology and usher clinical practice into an era of "precision diabetes."

EVIDENCE ACQUISITION AND METHODS

We reviewed literature related to heterogeneity of T2D over the past 5 decades and identified a range of phenotypic variants of T2D. Their descriptions expose inadequacies in current classification systems. We attempt to link phenotypically diverse forms to pathophysiology, explore investigative methods that have characterized "atypical" forms of T2D on an etiological basis, and review conceptual frameworks for an improved taxonomy. Finally, we propose future directions to achieve the goal of an etiological classification of T2D.

EVIDENCE SYNTHESIS

Differences among ethnic and racial groups were early observations of phenotypic heterogeneity. Investigations that uncover complex interactions of pathophysiologic pathways leading to T2D are supported by epidemiological and clinical differences between the sexes and between adult and youth-onset T2D. Approaches to an etiological classification are illustrated by investigations of atypical forms of T2D, such as monogenic diabetes and syndromes of ketosis-prone diabetes. Conceptual frameworks that accommodate heterogeneity in T2D include an overlap between known diabetes types, a "palette" model integrated with a "threshold hypothesis," and a spectrum model of atypical diabetes.

CONCLUSION

The heterogeneity of T2D demands an improved, etiological classification scheme. Excellent phenotypic descriptions of emerging syndromes in different populations, continued clinical and molecular investigations of atypical forms of diabetes, and useful conceptual models can be utilized to achieve this important goal.

Exploring the Epigenetic Regulatory Role of m6A-Associated SNPs in Type 2 Diabetes Pathogenesis

Purpose

Genetic factors in type 2 diabetes (T2D) pathogenesis have been widely explored by the genome-wide association studies (GWAS), identifying a great amount of susceptibility loci. With the development of high-resolution sequencing, the N(6)-methyladenosine (m6A) RNA modification has been proved to be affected by genetic variation. In this study, we identified the T2D-associated m6A-SNPs from T2D GWAS data and explored the underlying mechanism of the pathogenesis of T2D.

Methods

We examined the association of m6A-SNPs with T2D among large-scale T2D GWAS summary statistics and further performed multi-omics integrated analysis to explore the potential role of the identified m6A-SNPs in T2D pathogenesis.

Results

Among the 15,124 T2D-associated m6A-SNPs, 71 of them reach the genome-wide significant threshold (5.0e-05). The leading SNP rs4993986 (C>G), which is located near the m6A modification site at the 3' end of the HLA-DQB1 transcript, is expected to participate in the pathogenesis of T2D by influencing m6A modification to regulate the HLA-DQB1 expression.

Conclusion

The current study has suggested a potential correlation between m6A-SNPs and T2D pathogenesis and also provided new insights into the pathogenic mechanism of the T2D susceptibility loci identified by GWAS.

Partners in Crime: Beta-Cells and Autoimmune Responses Complicit in Type 1 Diabetes Pathogenesis

Type 1 diabetes (T1D) is an autoimmune disease characterized by autoreactive T cell-mediated destruction of insulin-producing pancreatic beta-cells. Loss of beta-cells leads to insulin insufficiency and hyperglycemia, with patients eventually requiring lifelong insulin therapy to maintain normal glycemic control. Since T1D has been historically defined as a disease of immune system dysregulation, there has been little focus on the state and response of beta-cells and how they may also contribute to their own demise. Major hurdles to identifying a cure for T1D include a limited understanding of disease etiology and how functional and transcriptional beta-cell heterogeneity may be involved in disease progression. Recent studies indicate that the beta-cell response is not simply a passive aspect of T1D pathogenesis, but rather an interplay between the beta-cell and the immune system actively contributing to disease. Here, we comprehensively review the current literature describing beta-cell vulnerability, heterogeneity, and contributions to pathophysiology of T1D, how these responses are influenced by autoimmunity, and describe pathways that can potentially be exploited to delay T1D.

A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response

Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.

The Role of Electronic Health Records in Advancing Genomic Medicine

Recent advances in genomic technology and widespread adoption of electronic health records (EHRs) have accelerated the development of genomic medicine, bringing promising research findings from genome science into clinical practice. Genomic and phenomic data, accrued across large populations through biobanks linked to EHRs, have enabled the study of genetic variation at a phenome-wide scale. Through new quantitative techniques, pleiotropy can be explored with phenome-wide association studies, the occurrence of common complex diseases can be predicted using the cumulative influence of many genetic variants (polygenic risk scores), and undiagnosed Mendelian syndromes can be identified using EHR-based phenotypic signatures (phenotype risk scores). In this review, we trace the role of EHRs from the development of genome-wide analytic techniques to translational efforts to test these new interventions to the clinic. Throughout, we describe the challenges that remain when combining EHRs with genetics to improve clinical care.

Genetic ancestry inferred from autosomal and Y chromosome markers and HLA genotypes in Type 1 Diabetes from an admixed Brazilian population

This study aimed to investigate the relationship between genetic ancestry inferred from autosomal and Y chromosome markers and HLA genotypes in patients with Type 1 Diabetes from an admixed Brazilian population. Inference of autosomal ancestry; HLA-DRB1, -DQA1 and -DQB1 typifications; and Y chromosome analysis were performed. European autosomal ancestry was about 50%, followed by approximately 25% of African and Native American. The European Y chromosome was predominant. The HLA-DRB1*03 and DRB1*04 alleles presented risk association with T1D. When the Y chromosome was European, DRB1*03 and DRB1*04 homozygote and DRB1*03/DRB1*04 heterozygote genotypes were the most frequent. The results suggest that individuals from Maranhão have a European origin as their major component; and are patrilineal with greater frequency from the R1b haplogroup. The predominance of the HLA-DRB1*03 and DRB1*04 alleles conferring greater risk in our population and being more frequently related to the ancestry of the European Y chromosome suggests that in our population, the risk of T1D can be transmitted by European ancestors of our process miscegenation. However, the Y sample sizes of Africans and Native Americans were small, and further research should be conducted with large mixed sample sizes to clarify this possible association.

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide-significant (P < 5 × 10-8) regions, including 36 that are new. We define credible sets of T1D-associated variants and show that they are enriched in immune-cell accessible chromatin, particularly CD4+ effector T cells. Using chromatin-accessibility profiling of CD4+ T cells from 115 individuals, we map chromatin-accessibility quantitative trait loci and identify five regions where T1D risk variants co-localize with chromatin-accessibility quantitative trait loci. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D.

Genetic Composition and Autoantibody Titers Model the Probability of Detecting C-Peptide Following Type 1 Diabetes Diagnosis

We and others previously demonstrated that a type 1 diabetes genetic risk score (GRS) improves the ability to predict disease progression and onset in at-risk subjects with islet autoantibodies. Here, we hypothesized that GRS and islet autoantibodies, combined with age at onset and disease duration, could serve as markers of residual β-cell function following type 1 diabetes diagnosis. Generalized estimating equations were used to investigate whether GRS along with insulinoma-associated protein-2 autoantibody (IA-2A), zinc transporter 8 autoantibody (ZnT8A), and GAD autoantibody (GADA) titers were predictive of C-peptide detection in a largely cross-sectional cohort of 401 subjects with type 1 diabetes (median duration 4.5 years [range 0-60]). Indeed, a combined model with incorporation of disease duration, age at onset, GRS, and titers of IA-2A, ZnT8A, and GADA provided superior capacity to predict C-peptide detection (quasi-likelihood information criterion [QIC] = 334.6) compared with the capacity of disease duration, age at onset, and GRS as the sole parameters (QIC = 359.2). These findings support the need for longitudinal validation of our combinatorial model. The ability to project the rate and extent of decline in residual C-peptide production for individuals with type 1 diabetes could critically inform enrollment and benchmarking for clinical trials where investigators are seeking to preserve or restore endogenous β-cell function.

Next steps in the identification of gene targets for type 1 diabetes

The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment. Graphical abstract.

Challenges in the diagnosis of diabetes type in pediatrics

The incidence of diabetes, both type 1 and type 2, is increasing. Health outcomes in pediatric diabetes are currently poor, with trends indicating that they are worsening. Minority racial/ethnic groups are disproportionately affected by suboptimal glucose control and have a higher risk of acute and chronic complications of diabetes. Correct clinical management starts with timely and accurate classification of diabetes, but in children this is becoming increasingly challenging due to high prevalence of obesity and shifting demographic composition. The growing obesity epidemic complicates classification by obesity's effects on diabetes. Since the prevalence and clinical characteristics of diabetes vary among racial/ethnic groups, migration between countries leads to changes in the distribution of diabetes types in a certain geographical area, challenging the clinician's ability to classify diabetes. These challenges must be addressed to correctly classify diabetes and establish an appropriate treatment strategy early in the course of disease for all. This may be the first step in improving diabetes outcomes across racial/ethnic groups. This review will discuss the pitfalls in the current diabetes classification scheme that is leading to increasing overlap between diabetes types and heterogeneity within each type. It will also present proposed alternative classification schemes and approaches to understanding diabetes type that may improve the timely and accurate classification of pediatric diabetes type.

Defining and Classifying New Subgroups of Diabetes

An etiologically based classification of diabetes is needed to account for the heterogeneity of type 1 and type 2 diabetes (T1D and T2D) and emerging forms of diabetes worldwide. It may be productive for both classification and clinical discovery to consider variant forms of diabetes as a spectrum. Maturity onset diabetes of youth and neonatal diabetes serve as models for etiologically defined, rare forms of diabetes in the spectrum. Ketosis-prone diabetes is a model for more complex forms, amenable to phenotypic dissection. Bioinformatic approaches such as clustering analyses of large datasets and multi-omics investigations of rare and atypical phenotypes are promising avenues to explore and define new subgroups of diabetes.