Two single nucleotide polymorphisms identify the highest-risk diabetes HLA genotype: potential for rapid screening

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.

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.

The INNODIA Type 1 Diabetes Natural History Study: a European cohort of newly diagnosed children, adolescents and adults

AIMS/HYPOTHESIS

Type 1 diabetes is an heterogenous condition. Characterising factors explaining differences in an individual's clinical course and treatment response will have important clinical and research implications. Our aim was to explore type 1 diabetes heterogeneity, as assessed by clinical characteristics, autoantibodies, beta cell function and glycaemic outcomes, during the first 12 months from diagnosis, and how it relates to age at diagnosis.

METHODS

Data were collected from the large INNODIA cohort of individuals (aged 1.0-45.0 years) newly diagnosed with type 1 diabetes, followed 3 monthly, to assess clinical characteristics, C-peptide, HbA1c and diabetes-associated antibodies, and their changes, during the first 12 months from diagnosis, across three age groups: <10 years; 10-17 years; and ≥18 years.

RESULTS

The study population included 649 individuals (57.3% male; age 12.1±8.3 years), 96.9% of whom were positive for one or more diabetes-related antibodies. Baseline (IQR) fasting C-peptide was 242.0 (139.0-382.0) pmol/l (AUC 749.3 [466.2-1106.1] pmol/l × min), with levels increasing with age (p<0.001). Over time, C-peptide remained lower in participants aged <10 years but it declined in all age groups. In parallel, glucose levels progressively increased. Lower baseline fasting C-peptide, BMI SD score and presence of diabetic ketoacidosis at diagnosis were associated with lower stimulated C-peptide over time. HbA1c decreased during the first 3 months (p<0.001), whereas insulin requirement increased from 3 months post diagnosis (p<0.001).

CONCLUSIONS/INTERPRETATION

In this large cohort with newly diagnosed type 1 diabetes, we identified age-related differences in clinical and biochemical variables. Of note, C-peptide was lower in younger children but there were no main age differences in its rate of decline.

Human immune phenotyping reveals accelerated aging in type 1 diabetes

The proportions and phenotypes of immune cell subsets in peripheral blood undergo continual and dramatic remodeling throughout the human life span, which complicates efforts to identify disease-associated immune signatures in type 1 diabetes (T1D). We conducted cross-sectional flow cytometric immune profiling on peripheral blood from 826 individuals (stage 3 T1D, their first-degree relatives, those with ≥2 islet autoantibodies, and autoantibody-negative unaffected controls). We constructed an immune age predictive model in unaffected participants and observed accelerated immune aging in T1D. We used generalized additive models for location, shape, and scale to obtain age-corrected data for flow cytometry and complete blood count readouts, which can be visualized in our interactive portal (ImmScape); 46 parameters were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with accelerated immunological aging in T1D included increased CXCR3+ and programmed cell death 1-positive (PD-1+) frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels on memory T cells. Phenotypes associated with T1D after age correction were predictive of T1D status. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.

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.

Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes

Polygenic risk scores (PRS) quantify the genetic liability to disease and are calculated using an individual's genotype profile and disease-specific genome-wide association study (GWAS) summary statistics. Type 1 (T1D) and type 2 (T2D) diabetes both are determined in part by genetic loci. Correctly differentiating between types of diabetes is crucial for accurate diagnosis and treatment. PRS have the potential to address possible misclassification of T1D and T2D. Here we evaluated PRS models for T1D and T2D in European genetic ancestry participants from the UK Biobank (UKB) and then in the Michigan Genomics Initiative (MGI). Specifically, we investigated the utility of T1D and T2D PRS to discriminate between T1D, T2D, and controls in unrelated UKB individuals of European ancestry. We derived PRS models using external non-UKB GWAS. The T1D PRS model with the best discrimination between T1D cases and controls (area under the receiver operator curve [AUC] = 0.805) also yielded the best discrimination of T1D from T2D cases in the UKB (AUC = 0.792) and separation in MGI (AUC = 0.686). In contrast, the best T2D model did not discriminate between T1D and T2D cases (AUC = 0.527). Our analysis suggests that a T1D PRS model based on independent single nucleotide polymorphisms may help differentiate between T1D, T2D, and controls in individuals of European genetic ancestry.

Islet Autoantibody Level Distribution in Type 1 Diabetes and Their Association With Genetic and Clinical Characteristics

CONTEXT

The importance of the autoantibody level at diagnosis of type 1 diabetes (T1D) is not clear.

OBJECTIVE

We aimed to assess the association of glutamate decarboxylase (GADA), islet antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A) autoantibody levels with clinical and genetic characteristics at diagnosis of T1D.

METHODS

We conducted a prospective, cross-sectional study. GADA, IA-2A, and ZnT8A were measured in 1644 individuals with T1D at diagnosis using radiobinding assays. Associations between autoantibody levels and the clinical and genetic characteristics for individuals were assessed in those positive for these autoantibodies. We performed replication in an independent cohort of 449 people with T1D.

RESULTS

GADA and IA-2A levels exhibited a bimodal distribution at diagnosis. High GADA level was associated with older age at diagnosis (median 27 years vs 19 years, P = 9 × 10-17), female sex (52% vs 37%, P = 1 × 10-8), other autoimmune diseases (13% vs 6%, P = 3 × 10-6), and HLA-DR3-DQ2 (58% vs 51%, P = .006). High IA-2A level was associated with younger age of diagnosis (median 17 years vs 23 years, P = 3 × 10-7), HLA-DR4-DQ8 (66% vs 50%, P = 1 × 10-6), and ZnT8A positivity (77% vs 52%, P = 1 × 10-15). We replicated our findings in an independent cohort of 449 people with T1D where autoantibodies were measured using enzyme-linked immunosorbent assays.

CONCLUSION

Islet autoantibody levels provide additional information over positivity in T1D at diagnosis. Bimodality of GADA and IA-2A autoantibody levels highlights the novel aspect of heterogeneity of T1D. This may have implications for T1D prediction, treatment, and pathogenesis.

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.

Estimating disease prevalence in large datasets using genetic risk scores

Clinical classification is essential for estimating disease prevalence but is difficult, often requiring complex investigations. The widespread availability of population level genetic data makes novel genetic stratification techniques a highly attractive alternative. We propose a generalizable mathematical framework for determining disease prevalence within a cohort using genetic risk scores. We compare and evaluate methods based on the means of genetic risk scores' distributions; the Earth Mover's Distance between distributions; a linear combination of kernel density estimates of distributions; and an Excess method. We demonstrate the performance of genetic stratification to produce robust prevalence estimates. Specifically, we show that robust estimates of prevalence are still possible even with rarer diseases, smaller cohort sizes and less discriminative genetic risk scores, highlighting the general utility of these approaches. Genetic stratification techniques offer exciting new research tools, enabling unbiased insights into disease prevalence and clinical characteristics unhampered by clinical classification criteria.

Mutations and variants of ONECUT1 in diabetes

Genes involved in distinct diabetes types suggest shared disease mechanisms. Here we show that One Cut Homeobox 1 (ONECUT1) mutations cause monogenic recessive syndromic diabetes in two unrelated patients, characterized by intrauterine growth retardation, pancreas hypoplasia and gallbladder agenesis/hypoplasia, and early-onset diabetes in heterozygous relatives. Heterozygous carriers of rare coding variants of ONECUT1 define a distinctive subgroup of diabetic patients with early-onset, nonautoimmune diabetes, who respond well to diabetes treatment. In addition, common regulatory ONECUT1 variants are associated with multifactorial type 2 diabetes. Directed differentiation of human pluripotent stem cells revealed that loss of ONECUT1 impairs pancreatic progenitor formation and a subsequent endocrine program. Loss of ONECUT1 altered transcription factor binding and enhancer activity and NKX2.2/NKX6.1 expression in pancreatic progenitor cells. Collectively, we demonstrate that ONECUT1 controls a transcriptional and epigenetic machinery regulating endocrine development, involved in a spectrum of diabetes, encompassing monogenic (recessive and dominant) as well as multifactorial inheritance. Our findings highlight the broad contribution of ONECUT1 in diabetes pathogenesis, marking an important step toward precision diabetes medicine.

A non-coding RNASEH1 gene variant associates with type 1 diabetes and interacts with HLA tagSNPs in families from Colombia

OBJECTIVES

RNASEH1 gene has recently been associated with type 1 diabetes (T1D) in Colombia. The purpose of this study was to fine mapping the putative functional variant in RNASEH1 and testing its interaction with HLA tagSNPs.

METHODS

Two-hundred nuclear families with T1D were included in this study. Probands were tested for GAD65 and IA-2 autoantibodies. Genotyping was performed using 20 coding tagSNPs uncovered through Sanger sequencing (N = 96), in addition to 23 tagSNPs chosen from 1000genomes to cover the extent of the gene region. Also, 45 tagSNPs for classic HLA alleles associated with T1D were also genotyped. The transmission disequilibrium test (TDT) was used to test for association and a multiple testing correction was made using permutation. Interaction between RNASEH1 variants and HLA was evaluated by means of the M-TDT test.

RESULTS

We identified 20 variants (15 were novel) in the 96 patients sequenced. None of these variants were in linkage disequilibrium. In total, 43 RNASEH1 variants were genotyped in the 200 families. Association between T1D and rs7607888 was identified (P = .002). Haplotype analysis involving rs7607888 variant revealed even stronger association with T1D (most significative P = .0003). HLA tagSNPs displayed stronger associations (OR = 6.39, 95% CI = 4.33-9.44, P-value = 9.74E-28). Finally, we found several statistically significant interactions of HLA variants with rs7607888 (P-value ranged from 8.77E-04 to 5.33E-12).

CONCLUSION

Our results verify the association of rs7607888 in RNASEH1 gene with T1D. It is also shown in the interaction between RNASEH1 and HLA for conveying risk to T1D in Northwest Colombia. Work is underway aiming to identify the actual classic HLA alleles associated with the tagSNPs tested here.

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

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

Utility and First Clinical Application of Screening Embryos for Polygenic Disease Risk Reduction

For over 2 decades preimplantation genetic testing (PGT) has been in clinical use to reduce the risk of miscarriage and genetic disease in patients with advanced maternal age and risk of transmitting disease. Recently developed methods of genome-wide genotyping and machine learning algorithms now offer the ability to genotype embryos for polygenic disease risk with accuracy equivalent to adults. In addition, contemporary studies on adults indicate the ability to predict polygenic disorders with risk equivalent to monogenic disorders. Existing biobanks provide opportunities to model the clinical utility of polygenic disease risk reduction among sibling adults. Here, we provide a mathematical model for the use of embryo screening to reduce the risk of type 1 diabetes. Results indicate a 45-72% reduced risk with blinded genetic selection of one sibling. The first clinical case of polygenic risk scoring in human preimplantation embryos from patients with a family history of complex disease is reported. In addition to these data, several common and accepted practices place PGT for polygenic disease risk in the applicable context of contemporary reproductive medicine. In addition, prediction of risk for PCOS, endometriosis, and aneuploidy are of particular interest and relevance to patients with infertility and represent an important focus of future research on polygenic risk scoring in embryos.

Human Leukocyte Antigen (HLA) and Islet Autoantibodies Are Tools to Characterize Type 1 Diabetes in Arab Countries: Emphasis on Kuwait

The incidence rate of type 1 diabetes in Kuwait had been increasing exponentially and has doubled in children ≤ 14 years old within almost two decades. Therefore, there is a dire need for a careful systematic familial cohort study. Several immunogenetic factors affect the pathogenesis of the disease. The human leukocyte antigen (HLA) accounts for the major genetic susceptibility to the disease. The triggering agents initiate disease onset by type 1 destruction of pancreatic β-cells. Both HLA and anti-islet antibodies can be used to characterize, predict susceptibility to the disease, innovate, or delay the β-cell destruction. Evidence from prospective longitudinal studies suggested that the underlying disease process represents a continuum that begins before the symptoms are clinically evident. Autoimmunity of the functional pancreatic β-cells results in symptomatic type 1 diabetes and lifelong insulin dependence. The autoantibodies against glutamic acid decarboxylase (GADA), insulinoma antigen-2 (IA-2A), insulin (IAA), and zinc transporter-8 (ZnT-8A) comprise the most reliable biomarkers for type 1 diabetes in both children and adults. Although Kuwait is the second among the top 10 countries with a high incidence rate of type 1 diabetes, there have been no proper diagnostic and prediction tools as per the World Health Organization. The Kuwaiti Type 1 Diabetes Study (KADS) was initiated to understand the disease pathogenesis as well as the HLA and anti-islet autoantibody profile of type 1 diabetes in Kuwait. Understanding the disease sequela in a homogenous gene pool and highly consanguineous population of Kuwaitis could help solve the challenges and pathogenesis, as well as hasten the prevention, of type 1 diabetes.

Trisomy 21 Is a Cause of Permanent Neonatal Diabetes That Is Autoimmune but Not HLA Associated

Identifying new causes of permanent neonatal diabetes (PNDM) (diagnosis <6 months) provides important insights into β-cell biology. Patients with Down syndrome (DS) resulting from trisomy 21 are four times more likely to have childhood diabetes with an intermediate HLA association. It is not known whether DS can cause PNDM. We found that trisomy 21 was seven times more likely in our PNDM cohort than in the population (13 of 1,522 = 85 of 10,000 observed vs. 12.6 of 10,000 expected) and none of the 13 DS-PNDM patients had a mutation in the known PNDM genes that explained 82.9% of non-DS PNDM. Islet autoantibodies were present in 4 of 9 DS-PNDM patients, but DS-PNDM was not associated with polygenic susceptibility to type 1 diabetes (T1D). We conclude that trisomy 21 is a cause of autoimmune PNDM that is not HLA associated. We propose that autoimmune diabetes in DS is heterogeneous and includes coincidental T1D that is HLA associated and diabetes caused by trisomy 21 that is not HLA associated.

Bronchoalveolar lavage characteristics correlate with HLA tag SNPs in patients with Löfgren's syndrome and other sarcoidosis

Genetic susceptibility for sarcoidosis and Löfgren's syndrome (LS) has been associated with prognosis. Human leukocyte antigen (HLA)-DRB1*03 is over-represented in LS, and is associated with a good prognosis, whereas HLA-DRB1*15-positive patients have a more chronic course of sarcoidosis. These HLA-DRB1 types can be easily tagged by single nucleotide polymorphisms (SNPs). Our aim was to evaluate the association between these tag SNPs and bronchoalveolar lavage (BAL) characteristics. In 29 patients, both complete HLA-DRB1* locus genotyping and SNP tagging was performed in parallel. HLA-DRB1 type was inferred from the presence of *03 tag rs2040410 allele A and referred to as *03. HLA-DRB1*15 was inferred from the presence of tag SNP rs3135388 allele A and referred to as *15. For BAL analysis, 122 patients with LS and 165 patients with non-LS sarcoidosis were included. BAL lymphocyte subsets were analyzed by flow cytometry. The presence of tag SNPs completely corresponded with HLA-DRB1*03/*15 genotypes in all 29 patients in whom both HLA-DRB1* genotyping and SNP tagging was performed. In all patients together, *03⁺ /*15^- patients showed a higher CD4⁺ /CD8⁺ ratio than *03^- /*15⁺ (P = 0·004) and *03^- /*15^- (P = 0·001). LS patients with *03⁺ /*15^- had a lower BAL lymphocyte count compared to *03^- /*15⁺  patients (P = 0·011). Non-LS sarcoidosis patients with *03⁺ /*15^- patients showed a decreased CD103⁺ CD4⁺ /CD4⁺ ratio compared to *03^- /*15⁺  patients (P = 0·045) and *03^- /*15^- patients (P = 0·018). We found that HLA-DRB1*03 and HLA-DRB1*15 can be approximated by genotyping of tag SNPs and corresponds with the degree of lymphocytosis and cell phenotypes in BAL in both LS and non-LS sarcoidosis patients.

Classical HLA alleles tag SNP in families from Antioquia with type 1 diabetes mellitus

Introduction: The HLA region strongly associates with autoimmune diseases, such as type 1 diabetes. An alternative way to test classical HLA alleles is by using tag SNP. A set of tag SNP for several classical HLA alleles has been reported as associated with susceptibility or resistance to this disease in Europeans. Objective: We aimed at validating the methodology based on tag SNP focused on the inference of classical HLA alleles, and at evaluating their association with type 1 diabetes mellitus in a sample of 200 families from Antioquia. Materials and methods: We studied a sample of 200 families from Antioquia. Each family had one or two children with T1D. We genotyped 13 SNPs using tetra-primer ARMS-PCR or PCRRFLP. In addition, we tested the validity of the tag SNP reported for Europeans in 60 individuals from a population of Colombians living in Medellín (CLM) from the 1000 Genomes Project database. Statistical analyses included the Hardy-Weinberg equilibrium, the transmission disequilibrium and the linkage disequilibrium tests. Results: The linkage disequilibrium was low in reported tag SNP and classical HLA alleles in this CLM population. Association analyses revealed both risk and protection factors to develop type 1 diabetes mellitus. Appropriate tag SNPs for the CLM population were determined by using the genotype information available in the 1000 Genome Project database. Conclusions: Although linkage disequilibrium patterns in this CLM population were different from those reported in Europeans, we did find strong evidence of the role of HLA in the development of type 1 diabetes mellitus in the study population.

A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

OBJECTIVE

We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals.

RESEARCH DESIGN AND METHODS

We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables.

RESULTS

Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002).

CONCLUSIONS

The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.

Cross-ethnicity tagging SNPs for HLA alleles associated with adverse drug reaction

Reduction of adverse drug reaction (ADR) incidence through screening of predisposing human leucocyte antigen (HLA) alleles is a promising approach for many widely used drugs. However, application of these associations has been limited by the cost burden of HLA genotyping. Use of single nucleotide polymorphisms (SNPs) that can approximate ('tag') HLA alleles of interest has been proposed as a cost-effective and simple alternative to conventional genotyping. However, most reported SNP tags have not been validated and there is concern regarding clinical utility of this approach due to tagging inconsistency across different populations. We assess the ability of 67 previously reported and 378 novel tagging SNPs, identified here in 5 HLA reference panels, to tag 15 ADR-associated HLA alleles in a panel of 955 ethnically diverse samples. Tags for 8 HLA alleles of interest were identified with 100% sensitivity and >95% specificity. These SNPs may act as a reliable genotyping approach for the routine screening of patients, without the need to account for patient ethnicity.

Meta-genome-wide association studies identify a locus on chromosome 1 and multiple variants in the MHC region for serum C-peptide in type 1 diabetes

AIMS/HYPOTHESIS

The aim of this study was to identify genetic variants associated with beta cell function in type 1 diabetes, as measured by serum C-peptide levels, through meta-genome-wide association studies (meta-GWAS).

METHODS

We performed a meta-GWAS to combine the results from five studies in type 1 diabetes with cross-sectionally measured stimulated, fasting or random C-peptide levels, including 3479 European participants. The p values across studies were combined, taking into account sample size and direction of effect. We also performed separate meta-GWAS for stimulated (n = 1303), fasting (n = 2019) and random (n = 1497) C-peptide levels.

RESULTS

In the meta-GWAS for stimulated/fasting/random C-peptide levels, a SNP on chromosome 1, rs559047 (Chr1:238753916, T>A, minor allele frequency [MAF] 0.24-0.26), was associated with C-peptide (p = 4.13 × 10-8), meeting the genome-wide significance threshold (p < 5 × 10-8). In the same meta-GWAS, a locus in the MHC region (rs9260151) was close to the genome-wide significance threshold (Chr6:29911030, C>T, MAF 0.07-0.10, p = 8.43 × 10-8). In the stimulated C-peptide meta-GWAS, rs61211515 (Chr6:30100975, T/-, MAF 0.17-0.19) in the MHC region was associated with stimulated C-peptide (β [SE] = - 0.39 [0.07], p = 9.72 × 10-8). rs61211515 was also associated with the rate of stimulated C-peptide decline over time in a subset of individuals (n = 258) with annual repeated measures for up to 6 years (p = 0.02). In the meta-GWAS of random C-peptide, another MHC region, SNP rs3135002 (Chr6:32668439, C>A, MAF 0.02-0.06), was associated with C-peptide (p = 3.49 × 10-8). Conditional analyses suggested that the three identified variants in the MHC region were independent of each other. rs9260151 and rs3135002 have been associated with type 1 diabetes, whereas rs559047 and rs61211515 have not been associated with a risk of developing type 1 diabetes.

CONCLUSIONS/INTERPRETATION

We identified a locus on chromosome 1 and multiple variants in the MHC region, at least some of which were distinct from type 1 diabetes risk loci, that were associated with C-peptide, suggesting partly non-overlapping mechanisms for the development and progression of type 1 diabetes. These associations need to be validated in independent populations. Further investigations could provide insights into mechanisms of beta cell loss and opportunities to preserve beta cell function.

Clinical and research uses of genetic risk scores in type 1 diabetes

Type 1 diabetes (T1D) is a chronic disease of high blood glucose caused by autoimmune destruction of pancreatic beta cells eventually resulting in severe insulin deficiency. T1D has a significant heritable risk. Genetic associations found are particularly strong in the HLA class II region but T1D is a polygenic disease associated with over 60 loci across the genome. Polygenic risk scores are one method of summing these genetic risk elements as a single continuous variable. This review discusses the clinical and research utility of genetic risk scores in T1D particularly in disease prediction and progression. We also explore creative uses of genetic risk scores in big data and the limitations of using a genetic risk score. The increase in publically available genetic data and rapid fall in costs of genotyping mean that a T1D genetic risk score (T1D GRS) is likely to prove useful for disease prediction, discrimination, investigation of unusual cohorts, and investigation of biology in large datasets where genetic data are available.

Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank

BACKGROUND

Type 1 diabetes is typically considered a disease of children and young adults. Genetic susceptibility to young-onset type 1 diabetes is well defined and does not predispose to type 2 diabetes. It is not known how frequently genetic susceptibility to type 1 diabetes leads to a diagnosis of diabetes after age 30 years. We aimed to investigate the frequency and phenotype of type 1 diabetes resulting from high genetic susceptibility in the first six decades of life.

METHODS

In this cross-sectional analysis, we used a type 1 diabetes genetic risk score based on 29 common variants to identify individuals of white European descent in UK Biobank in the half of the population with high or low genetic susceptibility to type 1 diabetes. We used Kaplan-Meier analysis to evaluate the number of cases of diabetes in both groups in the first six decades of life. We genetically defined type 1 diabetes as the additional cases of diabetes that occurred in the high genetic susceptibility group compared with the low genetic susceptibility group. All remaining cases were defined as type 2 diabetes. We assessed the clinical characteristics of the groups with genetically defined type 1 or type 2 diabetes.

FINDINGS

13 250 (3·5%) of 379 511 white European individuals in UK Biobank had developed diabetes in the first six decades of life. 1286 more cases of diabetes were in the half of the population with high genetic susceptibility to type 1 diabetes than in the half of the population with low genetic susceptibility. These genetically defined cases of type 1 diabetes were distributed across all ages of diagnosis; 537 (42%) were in individuals diagnosed when aged 31-60 years, representing 4% (537/12 233) of all diabetes cases diagnosed after age 30 years. The clinical characteristics of the group diagnosed with type 1 diabetes when aged 31-60 years were similar to the clinical characteristics of the group diagnosed with type 1 diabetes when aged 30 years or younger. For individuals diagnosed with diabetes when aged 31-60 years, the clinical characteristics of type 1 diabetes differed from those of type 2 diabetes: they had a lower BMI (27·4 kg/m2 [95% CI 26·7-28·0] vs 32·4 kg/m2 [32·2-32·5]; p<0·0001), were more likely to use insulin in the first year after diagnosis (89% [476/537] vs 6% [648/11 696]; p<0·0001), and were more likely to have diabetic ketoacidosis (11% [61/537] vs 0·3% [30/11 696]; p<0·0001).

INTERPRETATION

Genetic susceptibility to type 1 diabetes results in non-obesity-related, insulin-dependent diabetes, which presents throughout the first six decades of life. Our results highlight the difficulty of identifying type 1 diabetes after age 30 years because of the increasing background prevalence of type 2 diabetes. Failure to diagnose late-onset type 1 diabetes can have serious consequences because these patients rapidly develop insulin dependency.

FUNDING

Wellcome Trust and Diabetes UK.

Juvenile-Onset Diabetes and Congenital Cataract: "Double-Gene" Mutations Mimicking a Syndromic Diabetes Presentation

Monogenic forms of diabetes may account for 1–5% of all cases of diabetes, and may occur in the context of syndromic presentations. We investigated the case of a girl affected by insulin-dependent diabetes, diagnosed at 6 years old, associated with congenital cataract. Her consanguineous parents and her four other siblings did not have diabetes or cataract, suggesting a recessive syndrome. Using whole exome sequencing of the affected proband, we identified a heterozygous p.R825Q ABCC8 mutation, located at the exact same amino-acid position as the p.R825W recurring diabetes mutation, hence likely responsible for the diabetes condition, and a homozygous p.G71S mutation in CRYBB1, a gene known to be responsible for congenital cataract. Both mutations were predicted to be damaging and were absent or extremely rare in public databases. Unexpectedly, we found that the mother was also homozygous for the CRYBB1 mutation, and both the mother and one unaffected sibling were heterozygous for the ABCC8 mutation, suggesting incomplete penetrance of both mutations. Incomplete penetrance of ABCC8 mutations is well documented, but this is the first report of an incomplete penetrance of a CRYBB1 mutation, manifesting between susceptible subjects (unaffected mother vs. affected child) and to some extent within the patient herself, who had distinct cataract severities in both eyes. Our finding illustrates the importance of family studies to unmask the role of confounding factors such as double-gene mutations and incomplete penetrance that may mimic monogenic syndromes including in the case of strongly evocative family structure with consanguinity.

Genetic Risk Scores for Type 1 Diabetes Prediction and Diagnosis

PURPOSE OF REVIEW

About 50% of the heritability of type 1 diabetes (T1D) is attributed to human leukocyte antigen (HLA) alleles and the remainder to several (close to 50) non-HLA loci. A current challenge in the field of the genetics of T1D is to apply the knowledge accumulated in the last 40 years towards differential diagnosis and risk assessment.

RECENT FINDINGS

T1D genetic risk scores seek to combine the information from HLA and non-HLA alleles to improve the accuracy of diagnosis, prediction, and prognosis. Here, we describe genetic risk scores that have been developed and validated in various settings and populations. Several genetic scores have been proposed that merge disease risk information from multiple genetic factors to optimize the use of genetic information and ultimately improve prediction and diagnosis of T1D.

GLIS3 rs7020673 and rs10758593 polymorphisms interact in the susceptibility for type 1 diabetes mellitus

AIMS

The transcription factor Gli-similar 3 (GLIS3) plays a key role in the development and maintenance of pancreatic beta cells as well as in the regulation of Insulin gene expression in adults. Accordingly, genome-wide association studies identified GLIS3 as a susceptibility locus for type 1 diabetes mellitus (T1DM) and glucose metabolism traits. Therefore, the aim of this study was to replicate the association of the rs10758593 and rs7020673 single nucleotide polymorphisms (SNPs) in the GLIS3 gene with T1DM in a Brazilian population.

METHODS

Frequencies of the rs7020673 (G/C) and rs10758593 (A/G) SNPs were analyzed in 503 T1DM patients (cases) and in 442 non-diabetic subjects (controls). Haplotypes constructed from the combination of these SNPs were inferred using a Bayesian statistical method.

RESULTS

Genotype and allele frequencies of rs7020673 and rs10758593 SNPs did not differ significantly between case and control groups. However, the frequency of ≥3 minor alleles of the analyzed SNPs in haplotypes was higher in T1DM patients compared to non-diabetic subjects (6.2 vs. 1.6%; P = 0.001). The presence of ≥3 minor alleles remained independently associated with risk of T1DM after adjustment for T1DM high-risk HLA DR/DQ haplotypes, age and ethnicity (OR = 3.684 95% CI 1.220-11.124). Moreover, levels of glycated hemoglobin seem to be higher in T1DM patients with rs10758593 A/A genotype than patients carrying the G allele of this SNP (P = 0.038), although this association was not kept after Bonferroni correction.

CONCLUSIONS

Our results indicate that individually the rs7020673 and rs10758593 SNPs are not significantly associated with T1DM but seem to interact in the predisposition for this disease.

Lower Frequency of HLA-DRB1 Type 1 Diabetes Risk Alleles in Pediatric Patients with MODY

Objective

The aim of this study was to determine the frequency of susceptible HLA-DRB1 alleles for type 1 diabetes in a cohort of pediatric patients with a confirmed genetic diagnosis of MODY.

Materials and Methods

160 families with a proband diagnosed with type 1 diabetes and 74 families with a molecular diagnosis of MODY (61 GCK-MODY and 13 HNF1A-MODY) were categorized at high definition for HLA-DRB1 locus. According to the presence or absence of the susceptible HLA-DRB1 alleles for type 1 diabetes, we considered three different HLA-DRB1 genotypes: 0 risk alleles (no DR3 no DR4); 1 risk allele (DR3 or DR4); 2 risk alleles (DR3 and/or DR4).

Results

Compared with type 1 diabetes, patients with MODY carried higher frequency of 0 risk alleles, OR 22.7 (95% CI: 10.7–48.6) and lower frequency of 1 or 2 risk alleles, OR 0.53 (95% CI: 0.29–0.96) and OR 0.06 (95% CI: 0.02–0.18), respectively.

Conclusions

The frequency of HLA-DRB1 risk alleles for type 1 diabetes is significantly lower in patients with MODY. In children and adolescents with diabetes, the presence of 2 risk alleles (DR3 and/or DR4) reduces the probability of MODY diagnosis, whereas the lack of risk alleles increases it. Therefore, we might consider that HLA-DRB1 provides additional information for the selection of patients with high probability of monogenic diabetes.

A Type 1 Diabetes Genetic Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults

OBJECTIVE

With rising obesity, it is becoming increasingly difficult to distinguish between type 1 diabetes (T1D) and type 2 diabetes (T2D) in young adults. There has been substantial recent progress in identifying the contribution of common genetic variants to T1D and T2D. We aimed to determine whether a score generated from common genetic variants could be used to discriminate between T1D and T2D and also to predict severe insulin deficiency in young adults with diabetes.

RESEARCH DESIGN AND METHODS

We developed genetic risk scores (GRSs) from published T1D- and T2D-associated variants. We first tested whether the scores could distinguish clinically defined T1D and T2D from the Wellcome Trust Case Control Consortium (WTCCC) (n = 3,887). We then assessed whether the T1D GRS correctly classified young adults (diagnosed at 20-40 years of age, the age-group with the most diagnostic difficulty in clinical practice; n = 223) who progressed to severe insulin deficiency <3 years from diagnosis.

RESULTS

In the WTCCC, the T1D GRS, based on 30 T1D-associated risk variants, was highly discriminative of T1D and T2D (area under the curve [AUC] 0.88 [95% CI 0.87-0.89]; P < 0.0001), and the T2D GRS added little discrimination (AUC 0.89). A T1D GRS >0.280 (>50th centile in those with T1D) is indicative of T1D (50% sensitivity, 95% specificity). A low T1D GRS (<0.234, <5th centile T1D) is indicative of T2D (53% sensitivity, 95% specificity). Most discriminative ability was obtained from just nine single nucleotide polymorphisms (AUC 0.87). In young adults with diabetes, T1D GRS alone predicted progression to insulin deficiency (AUC 0.87 [95% CI 0.82-0.92]; P < 0.0001). T1D GRS, autoantibody status, and clinical features were independent and additive predictors of severe insulin deficiency (combined AUC 0.96 [95% CI 0.94-0.99]; P < 0.0001).

CONCLUSIONS

A T1D GRS can accurately identify young adults with diabetes who will require insulin treatment. This will be an important addition to correctly classifying individuals with diabetes when clinical features and autoimmune markers are equivocal.

Gut microbiota, obesity and diabetes

The central role of the intestinal microbiota in the progression and, equally, prevention of metabolic dysfunction is becoming abundantly apparent. The symbiotic relationship between intestinal microbiota and host ensures appropriate development of the metabolic system in humans. However, disturbances in composition and, in turn, functionality of the intestinal microbiota can disrupt gut barrier function, a trip switch for metabolic endotoxemia. This low-grade chronic inflammation, brought about by the influx of inflammatory bacterial fragments into circulation through a malfunctioning gut barrier, has considerable knock-on effects for host adiposity and insulin resistance. Conversely, recent evidence suggests that there are certain bacterial species that may interact with host metabolism through metabolite-mediated stimulation of enteric hormones and other systems outside of the gastrointestinal tract, such as the endocannabinoid system. When the abundance of these keystone species begins to decline, we see a collapse of the symbiosis, reflected in a deterioration of host metabolic health. This review will investigate the intricate axis between the microbiota and host metabolism, while also addressing the promising and novel field of probiotics as metabolic therapies.

The genetic and regulatory architecture of ERBB3-type 1 diabetes susceptibility locus

The study aimed to explore the role of ERBB3 in type 1 diabetes (T1D). We examined whether genetic variation of ERBB3 (rs2292239) affects residual β-cell function in T1D cases. Furthermore, we examined the expression of ERBB3 in human islets, the effect of ERBB3 knockdown on apoptosis in insulin-producing INS-1E cells and the genetic and regulatory architecture of the ERBB3 locus to provide insights to how rs2292239 may confer disease susceptibility. rs2292239 strongly correlated with residual β-cell function and metabolic control in children with T1D. ERBB3 locus associated lncRNA (NONHSAG011351) was found to be expressed in human islets. ERBB3 was expressed and down-regulated by pro-inflammatory cytokines in human islets and INS-1E cells; knockdown of ERBB3 in INS-1E cells decreased basal and cytokine-induced apoptosis. Our data suggests an important functional role of ERBB3 and its potential regulators in the β-cells and may constitute novel targets to prevent β-cell destruction in T1D.

Shared Genetic Basis for Type 1 Diabetes, Islet Autoantibodies, and Autoantibodies Associated With Other Immune-Mediated Diseases in Families With Type 1 Diabetes

Type 1 diabetes (T1D) is a polygenic autoimmune disease that is often present with autoantibodies directed against pancreatic islet proteins. Many genetic susceptibility loci are shared with other autoimmune or immune-mediated diseases that also cosegregate in families with T1D. The aim of this study was to investigate whether susceptibility loci identified in genome-wide association studies (GWAS) of T1D were also associated with autoantibody positivity in individuals with diabetes. Fifty single nucleotide polymorphisms (SNPs) were genotyped in 6,556 multiethnic cases collected by the Type 1 Diabetes Genetics Consortium (T1DGC). These were tested for association with three islet autoantibodies-against autoantibodies to GAD (GADA), IA-2 (IA-2A), and zinc transporter 8 (ZnT8A)-and autoantibodies against thyroid peroxidase (TPOA) in autoimmune thyroid disease, gastric parietal cells (PCA) in autoimmune gastritis, transglutaminase (TGA) in celiac disease, and 21-hydroxylase (21-OHA) in autoimmune hypoadrenalism. In addition to the MHC region, we identify SNPs in five susceptibility loci (IFIH1, PTPN22, SH2B3, BACH2, and CTLA4) as significantly associated with more than one autoantibody at a false discovery rate less than 5%. IFIH1/2q24 demonstrated the most unrestricted association, as significant association was demonstrated for PCA, TPOA, GADA, 21-OHA, and IA-2A. In addition, 11 loci were significantly associated with a single autoantibody.

Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes

AIMS/HYPOTHESIS

More than 40 regions of the human genome confer susceptibility for type 1 diabetes and could be used to establish population screening strategies. The aim of our study was to identify weighted sets of SNP combinations for type 1 diabetes prediction.

METHODS

We applied multivariable logistic regression and Bayesian feature selection to the Type 1 Diabetes Genetics Consortium (T1DGC) dataset with genotyping of HLA plus 40 SNPs within other type 1 diabetes-associated gene regions in 4,574 cases and 1,207 controls. We tested the weighted models in an independent validation set (765 cases, 423 controls), and assessed their performance in 1,772 prospectively followed children.

RESULTS

The inclusion of 40 non-HLA gene SNPs significantly improved the prediction of type 1 diabetes over that provided by HLA alone (p = 3.1 × 10(-25)), with a receiver operating characteristic AUC of 0.87 in the T1DGC set, and 0.84 in the validation set. Feature selection identified HLA plus nine SNPs from the PTPN22, INS, IL2RA, ERBB3, ORMDL3, BACH2, IL27, GLIS3 and RNLS genes that could achieve similar prediction accuracy as the total SNP set. Application of this ten SNP model to prospectively followed children was able to improve risk stratification over that achieved by HLA genotype alone.

CONCLUSIONS

We provided a weighted risk model with selected SNPs that could be considered for recruitment of infants into studies of early type 1 diabetes natural history or appropriately safe prevention.

Improving prediction of type 1 diabetes by testing non-HLA genetic variants in addition to HLA markers

OBJECTIVE

The purpose of this study was to explore whether non-human leukocyte antigen (non-HLA) genetic markers can improve type 1 diabetes(T1D) prediction in a prospective cohort with high-risk HLA-DR,DQ genotypes.

METHODS

The Diabetes Autoimmunity Study in the Young (DAISY) follows prospectively for the development of T1D and islet autoimmunity (IA)children at increased genetic risk. A total of 1709 non-Hispanic White DAISY participants have been genotyped for 27 non-HLA single nucleotide polymorphisms (SNPs) and one microsatellite.

RESULTS

In multivariate analyses adjusting for family history and HLA-DR3/4 genotype, PTPN22 (rs2476601) and two UBASH3A (rs11203203 and rs9976767) SNPs were associated with development of IA [hazard ratio(HR)=1.87, 1.55, and 1.54, respectively, all p ≤ 0.003], while GLIS3 and IL2RA showed borderline association with development of IA. INS,UBASH3A, and IFIH1 were significantly associated with progression from IA to diabetes (HR=1.65, 1.44, and 1.47, respectively, all p ≤ 0.04), while PTPN22 and IL27 showed borderline association with progression from IA to diabetes. In survival analysis, 45% of general population DAISY children with PTPN22 rs2476601 TT or HLA-DR3/4 and UBASH3A rs11203203 AA developed diabetes by age 15, compared with 3% of children with all other genotypes (p<0.0001). Addition of non-HLA markers to HLA-DR3/4,DQ8 did not improve diabetes prediction in first-degree relatives.

CONCLUSION

Addition of PTPN22 and UBASH3A SNPs to HLA-DR,DQ genotyping can improve T1D risk prediction.

Beyond genetics. Influence of dietary factors and gut microbiota on type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease ultimately leading to destruction of insulin secreting β-cells in the pancreas. Genetic susceptibility plays an important role in T1D etiology, but even mono-zygotic twins only have a concordance rate of around 50%, underlining that other factors than purely genetic are involved in disease development. Here we review the influence of dietary and environmental factors on T1D development in humans as well as animal models. Even though data are still inconclusive, there are strong indications that gut microbiota dysbiosis plays an important role in T1D development and evidence from animal models suggests that gut microbiota manipulation might prove valuable in future prevention of T1D in genetically susceptible individuals.

Association of TCF7L2 variation with single islet autoantibody expression in children with type 1 diabetes

BACKGROUND

The transcription factor 7-like 2 (TCF7L2) gene has the strongest genetic association with type 2 diabetes. TCF7L2 also associates with latent autoimmune diabetes in adults, which often presents with a single islet autoantibody, but not with classical type 1 diabetes.

METHODS

We aimed to test if TCF7L2 is associated with single islet autoantibody expression in pediatric type 1 diabetes. We studied 71 prospectively recruited children who had newly diagnosed type 1 diabetes and evidence of islet autoimmunity, that is, expressed ≥1 islet autoantibody to insulin, glutamic acid decarboxylase 65, islet cell autoantigen 512, or zinc transporter 8. TCF7L2 rs7903146 alleles were identified. Data at diagnosis were cross-sectionally analyzed.

RESULTS

We found that 21.1% of the children with autoimmune type 1 diabetes expressed a single islet autoantibody. The distribution of TCF7L2 rs7903146 genotypes in children with a single autoantibody (n=15) was 40% CC, 26.7% CT and 33.3% TT, compared with children with ≥2 islet autoantibodies (50% CC, 42.9% CT and 7.1% TT, p=0.024). Furthermore, compared with children with ≥2 autoantibodies, single-autoantibody children had characteristics reflecting milder autoimmune destruction of β-cells. Restricting to lean children (body mass index<85th centile; n=36), 45.5% of those expressing a single autoantibody were rs7903146 TT homozygotes, compared with 0% of those with ≥2 autoantibodies (p<0.0001).

CONCLUSION

These results suggest that, in children with only mild islet autoimmunity, mechanisms associated with TCF7L2 genetic variation contribute to diabetogenesis, and this contribution is larger in the absence of obesity.

The next big idea

George S. Eisenbarth will remain in our memories as a brilliant scientist and great collaborator. His quest to discover the cause and prevention of type 1 (autoimmune) diabetes started from building predictive models based on immunogenetic markers. Despite his tremendous contributions to our understanding of the natural history of pre-type 1 diabetes and potential mechanisms, George left us with several big questions to answer before his quest is completed.

Definition of high-risk type 1 diabetes HLA-DR and HLA-DQ types using only three single nucleotide polymorphisms

Evaluating risk of developing type 1 diabetes (T1D) depends on determining an individual's HLA type, especially of the HLA DRB1 and DQB1 alleles. Individuals positive for HLA-DRB1*03 (DR3) or HLA-DRB1*04 (DR4) with DQB1*03:02 (DQ8) have the highest risk of developing T1D. Currently, HLA typing methods are relatively expensive and time consuming. We sought to determine the minimum number of single nucleotide polymorphisms (SNPs) that could rapidly define the HLA-DR types relevant to T1D, namely, DR3/4, DR3/3, DR4/4, DR3/X, DR4/X, and DRX/X (where X is neither DR3 nor DR4), and could distinguish the highest-risk DR4 type (DR4-DQ8) as well as the non-T1D-associated DR4-DQB1*03:01 type. We analyzed 19,035 SNPs of 10,579 subjects (7,405 from a discovery set and 3,174 from a validation set) from the Type 1 Diabetes Genetics Consortium and developed a novel machine learning method to select as few as three SNPs that could define the HLA-DR and HLA-DQ types accurately. The overall accuracy was 99.3%, area under curve was 0.997, true-positive rates were >0.99, and false-positive rates were <0.001. We confirmed the reliability of these SNPs by 10-fold cross-validation. Our approach predicts HLA-DR/DQ types relevant to T1D more accurately than existing methods and is rapid and cost-effective.

Dominant suppression of Addison's disease associated with HLA-B15

CONTEXT

Autoimmune Addison's disease (AD) is the major cause of primary adrenal failure in developed nations. Autoantibodies to 21-hydroxylase (21OH-AA) are associated with increased risk of progression to AD. Highest genetic risk is associated with the Major Histocompatibility region (MHC), specifically human leukocyte antigen (HLA)-DR3 haplotypes (containing HLA-B8) and HLA-DR4.

OBJECTIVE

The objective of the study was the further characterization of AD risk associated with MHC alleles.

DESIGN, SETTING, AND PARTICIPANTS

MHC genotypes were determined for HLA-DRB1, DQA1, DQB1, MICA, HLA-B, and HLA-A in 168 total individuals with 21OH-AA (85 with AD at referral and 83 with positive 21OH-AA but without AD at referral).

MAIN OUTCOME MEASURE(S)

Genotype was evaluated in 21OH-AA-positive individuals. Outcomes were compared with general population controls and type 1 diabetes patients.

RESULTS

In HLA-DR4+ individuals, HLA-B15 was found in only one of 55 (2%) with AD vs. 24 of 63 (40%) 21OH-AA-positive nonprogressors (P = 2 × 10(-7)) and 518 of 1558 (33%) HLA-DR4 patients with type 1 diabetes (P = 1 × 10(-8)). On prospective follow-up, none of the HLA-B15-positive, 21-hydroxylase-positive individuals progressed to AD vs. 25% non-HLA-B15 autoantibody-positive individuals by life table analysis (P = 0.03). Single nucleotide polymorphism analysis revealed the HLA-DR/DQ region associated with risk and HLA-B15 were separated by multiple intervening single-nucleotide polymorphism haplotypes.

CONCLUSIONS

HLA-B15 is not associated with protection from 21OH-AA formation but is associated with protection from progression to AD in 21OH-AA-positive individuals. To our knowledge, this is one of the most dramatic examples of genetic disease suppression in individuals who already have developed autoantibodies and of novel dominant suppression of an autoimmune disease by a class I HLA allele.

Prediction and prevention of Type 1 diabetes mellitus

Type 1A diabetes mellitus (T1DM) is caused by autoimmune islet β-cell destruction with consequent severe insulin deficiency. We can now predict the development of T1DM by determining four biochemically characterized islet autoantibodies, namely those antibodies against insulin, glutamic acid decarboxylase 65, insulinoma antigen (IA)-2 (ICA512) and the zinc transporter ZnT8. We can also prevent T1DM in animal models, but the final goal is the prevention of T1DM in humans. Multiple clinical trials are underway investigating methods to prevent β-cell destruction.

Accurate HLA type inference using a weighted similarity graph

BACKGROUND

The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true.

RESULTS

In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test.

CONCLUSIONS

Our algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.

Novel gene associations in type 1 diabetes

Recent genome-wide association studies have been able to identify multiple new gene loci affecting type 1 diabetes susceptibility, but the impact of these new defined loci seems to decrease in parallel with their number. The HLA gene region remains the main nominator of genetic susceptibility, although the identity of important genes and especially the mechanisms of their action are still largely unclear. Products of HLA and most other known risk genes are involved in regulation of the immune system in accordance with the autoimmune nature of the disease. The multitude of genes involved in the pathogenesis implies complex pathways where multiple steps in each may be essential in turning the balance of immune response to beta-cell destructing autoimmunity.

Estimation of genetic risk for Type 1 diabetes mellitus in newborns on dried blood spot

BACKGROUND

The main contribution to genetic susceptibility for Type 1 Diabetes Mellitus (T1DM) is conferred by the Human Leukocyte Antigens (HLA).

AIM

We evaluated the feasibility of large scale screening on Dried Blood Spot (DBS) to estimate the genetic risk for T1DM in newborns.

SUBJECTS AND METHODS

Peripheral blood DBS samples from 256 newborns, were genotyped for HLA DRB1 and DQB1 alleles identification by a commercially available assay based on a dissociation enhancer lanthanide fluorescence system available in many newborn screening laboratories. Results were compared with those obtained in two wide multicentric studies on cord blood (DIABFIN and PREVEFIN).

RESULTS

Genotyping on DBS revealed 6 subjects at high risk for T1DM, 99 at moderate risk for T1DM and the remaining at low risk for T1DM. We found 100% concordance between both techniques for HLA-DQB1 and DRB1 determination, confirming the feasibility of large scale screening on DBS.

CONCLUSIONS

DBSs represent a resource for future studies about new genetics markers. This assay for estimate the genetic risk of T1DM on DBS showed an excellent sensitivity, specificity and accuracy compared with conventional techniques. Moreover, this assay resulted less expensive, and it could be easily performed on material already collected for newborn screening programs.

Banting Lecture 2009: An unfinished journey: molecular pathogenesis to prevention of type 1A diabetes

The Banting Medal for Scientific Achievement Award is the American Diabetes Association's highest scientific award and honors an individual who has made significant, long-term contributions to the understanding of diabetes, its treatment, and/or prevention. The award is named after Nobel Prize winner Sir Frederick Banting, who codiscovered insulin treatment for diabetes.

Dr. Eisenbarth received the American Diabetes Association's Banting Medal for Scientific Achievement at the Association's 69th Scientific Sessions, June 5–9, 2009, in New Orleans, Louisiana. He presented the Banting Lecture, An Unfinished Journey—Type 1 Diabetes—Molecular Pathogenesis to Prevention , on Sunday, June 7, 2009.

Capture of type 1 diabetes-susceptible HLA DR-DQ haplotypes in Japanese subjects using a tag single nucleotide polymorphism

OBJECTIVE

To identify type 1 diabetes-susceptible HLA DR-DQ haplotypes using tag single nucleotide polymorphisms (SNPs) and to estimate the disease risk using these tag SNPs.

RESEARCH DESIGN AND METHODS

Five tag SNPs were typed in a total of 211 Japanese subjects including 201 patients with type 1 diabetes who had already been typed for HLA-DRB1, -DQA1, and -DQB1 alleles and 300 control subjects.

RESULTS

Tag SNP rs2395185 captured haplotypes involving all DR4 specificities and DR9 specificity with a sensitivity of 98.5% and specificity of 94.9%. Using the T allele of rs2395185, we obtained an odds ratio (95% CI) of 2.87 (2.21-3.74) for type 1 diabetes. In addition, rs3129888 captured haplotypes involving HLA-DRB1*0802 with a sensitivity of 92.3% and specificity of 98.9%.

CONCLUSIONS

Typing of two tag SNPs (rs2395185 and rs3129888) may be useful for the screening of Japanese subjects at genetic risk of type 1 diabetes.

Exploring the diabetogenicity of the HLA-B18-DR3 CEH: independent association with T1D genetic risk close to HLA-DOA

The objective of this study was to identify additional diabetes susceptibility markers in the MHC that could be responsible for the differential diabetogenicity of different HLA-DR3 CEHs. High-resolution SNP genotyping of the MHC was carried out in 15 type 1 diabetes (T1D) patients and 39 non-diabetic controls, homozygous for DR3-DQ2 and with one copy of the A(*)30-B(*)18-MICA(*)4-F1C30-DRB1(*)0301-DQB1(*)0201-DPB1(*)0202 HLA haplotype. Significantly associated SNPs were replicated in an independent sample of 554 T1D patients and 841 controls without HLA matching. Electrophoretic mobility shift assay was used to show a functional effect of an associated SNP. Seven SNPs showed evidence of association in the initial discovery experiment. Upon replication, only rs419434 (upstream HLA-DOA gene) remained significant. A functional variant (rs432375) in complete LD with rs419434 was shown to affect USF-1 binding and could be responsible for the association signal in the region. We have identified a new susceptibility locus within the MHC with a modest contribution to T1D (OR=1.93; CI: 1.52-2.44; P=10(-8)) that is independent of HLA-DRB1 locus.