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

Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources

This study aims to model genetic, immunologic, metabolomics, and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20 of 42 progressed to diabetes) and 25 control subjects matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Among the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were among the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes.

Clinical and genetic correlates of islet-autoimmune signatures in juvenile-onset type 1 diabetes

AIMS/HYPOTHESIS

Heterogeneity in individuals with type 1 diabetes has become more generally appreciated, but has not yet been extensively and systematically characterised. Here, we aimed to characterise type 1 diabetes heterogeneity by creating immunological, genetic and clinical profiles for individuals with juvenile-onset type 1 diabetes in a cross-sectional study.

METHODS

Participants were HLA-genotyped to determine HLA-DR-DQ risk, and SNP-genotyped to generate a non-HLA genetic risk score (GRS) based on 93 type 1 diabetes-associated SNP variants outside the MHC region. Islet autoimmunity was assessed as T cell proliferation upon stimulation with the beta cell antigens GAD65, islet antigen-2 (IA-2), preproinsulin (PPI) and defective ribosomal product of the insulin gene (INS-DRIP). Clinical parameters were collected retrospectively.

RESULTS

Of 80 individuals, 67 had proliferation responses to one or more islet antigens, with vast differences in the extent of proliferation. Based on the multitude and amplitude of the proliferation responses, individuals were clustered into non-, intermediate and high responders. High responders could not be characterised entirely by enrichment for the highest risk HLA-DR3-DQ2/DR4-DQ8 genotype. However, high responders did have a significantly higher non-HLA GRS. Clinically, high T cell responses to beta cell antigens did not reflect in worsened glycaemic control, increased complications, development of associated autoimmunity or younger age at disease onset. The number of beta cell antigens that an individual responded to increased with disease duration, pointing to chronic islet autoimmunity and epitope spreading.

CONCLUSIONS/INTERPRETATION

Collectively, these data provide new insights into type 1 diabetes disease heterogeneity and highlight the importance of stratifying patients on the basis of their genetic and autoimmune signatures for immunotherapy and personalised disease management.

Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine

During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes. As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management. In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity. We also describe the challenges that will need to be overcome if this potential is to be fully realized.

Genomic variants associated with the number and diameter of muscle fibers in pigs as revealed by a genome-wide association study

Muscle fiber characteristics comprise a set of complex traits that influence the meat quality and lean meat production of livestock. However, the genetic and biological mechanisms regulating muscle fiber characteristics are largely unknown in pigs. Based on a genome-wide association study (GWAS) performed on 421 Large White × Min pig F2 individuals presenting well-characterized phenotypes, this work aimed to detect genome variations and candidate genes for five muscle fiber characteristics: percentage of type I fibers (FIB1P), percentage of type IIA fibers (FIB2AP), percentage of type IIB fibers (FIB2BP), diameter of muscle fibers (DIAMF) and number of muscle fibers per unit area (NUMMF). The GWAS used the Illumina Porcine SNP60K genotypic data, which were analyzed by a mixed model. Seven and 10 single nucleotide polymorphisms (SNPs) were significantly associated with DIAMF and NUMMF, respectively (P < 1.10E-06); no SNP was significantly associated with FIB1P, FIB2AP or FIB2B. For DIAMF, the significant SNPs on chromosome 4 were located in the previously reported quantitative trait loci (QTL) interval. Because the significant SNPs on chromosome 6 were not mapped in the previously reported QTL interval, a putative novel QTL was suggested for this locus. None of the previously reported QTL intervals on chromosomes 6 and 14 harbored significant SNPs for NUMMF; thus, new potential QTLs on these two chromosomes are suggested in the present work. The most significant SNPs associated with DIAMF (ALGA0025682) and NUMMF (MARC0046984) explained 12.02% and 11.59% of the phenotypic variation of these traits, respectively. In addition, both SNPs were validated as associated with DIAMF and NUMMF in Beijing Black pigs (P < 0.01). Some candidate genes or non-coding RNAs, such as solute carrier family 44 member 5 and miR-124a-1 for DIAMF, and coiled-coil serine rich protein 2 for NUMMF, were identified based on their close location to the significant SNPs. This study revealed some genome-wide association variants for muscle fiber characteristics, and it provides valuable information to discover the genetic mechanisms controlling these traits in pigs.

Genetic aspects of type 1 diabetes

Type 1 diabetes mellitus (T1DM) is characterized by autoimmune destruction of pancreatic beta-cells in genetically predisposed individuals, eventually resulting in severe insulin deficiency. It is the most common form of diabetes in children and adolescents. Genetic susceptibility plays a crucial role in development of T1DM. The human leukocyte antigen complex plays a key role in the pathogenesis of T1DM. Furthermore, genome-wide association studies and linkage analysis have recently made a significant contribution to current knowledge relative to the impact of genetics on T1DM development and progression. This review focuses on current knowledge of genetics as a pathogenesis for T1DM. It also discusses mechanisms by which genes influence the risk of developing T1DM as well as the clinical and research applications of genetic risk scores in T1DM.

Age, HLA, and Sex Define a Marked Risk of Organ-Specific Autoimmunity in First-Degree Relatives of Patients With Type 1 Diabetes

OBJECTIVE

Autoimmune diseases can be diagnosed early through the detection of autoantibodies. The aim of this study was to determine the risk of organ-specific autoimmunity in individuals with a family history of type 1 diabetes.

RESEARCH DESIGN AND METHODS

The study cohort included 2,441 first-degree relatives of patients with type 1 diabetes who were prospectively followed from birth to a maximum of 29.4 years (median 13.2 years). All were tested regularly for the development of autoantibodies associated with type 1 diabetes (islet), celiac disease (transglutaminase), or thyroid autoimmunity (thyroid peroxidase). The outcome was defined as an autoantibody-positive status on two consecutive samples.

RESULTS

In total, 394 relatives developed one (n = 353) or more (n = 41) of the three disease-associated autoantibodies during follow-up. The risk by age 20 years was 8.0% (95% CI 6.8-9.2%) for islet autoantibodies, 6.3% (5.1-7.5%) for transglutaminase autoantibodies, 10.7% (8.9-12.5%) for thyroid peroxidase autoantibodies, and 21.5% (19.5-23.5%) for any of these autoantibodies. Each of the three disease-associated autoantibodies was defined by distinct HLA, sex, genetic, and age profiles. The risk of developing any of these autoantibodies was 56.5% (40.8-72.2%) in relatives with HLA DR3/DR3 and 44.4% (36.6-52.2%) in relatives with HLA DR3/DR4-DQ8.

CONCLUSIONS

Relatives of patients with type 1 diabetes have a very high risk of organ-specific autoimmunity. Appropriate counseling and genetic and autoantibody testing for multiple autoimmune diseases may be warranted for relatives of patients with type 1 diabetes.

The Influence of Type 2 Diabetes-Associated Factors on Type 1 Diabetes

Current efforts to prevent progression from islet autoimmunity to type 1 diabetes largely focus on immunomodulatory approaches. However, emerging data suggest that the development of diabetes in islet autoantibody-positive individuals may also involve factors such as obesity and genetic variants associated with type 2 diabetes, and the influence of these factors increases with age at diagnosis. Although these factors have been linked with metabolic outcomes, particularly through their impact on β-cell function and insulin sensitivity, growing evidence suggests that they might also interact with the immune system to amplify the autoimmune response. The presence of factors shared by both forms of diabetes contributes to disease heterogeneity and thus has important implications. Characteristics that are typically considered to be nonimmune should be incorporated into predictive algorithms that seek to identify at-risk individuals and into the designs of trials for disease prevention. The heterogeneity of diabetes also poses a challenge in diagnostic classification. Finally, after clinically diagnosing type 1 diabetes, addressing nonimmune elements may help to prevent further deterioration of β-cell function and thus improve clinical outcomes. This Perspectives in Care article highlights the role of type 2 diabetes-associated genetic factors (e.g., gene variants at transcription factor 7-like 2 [TCF7L2]) and obesity (via insulin resistance, inflammation, β-cell stress, or all three) in the pathogenesis of type 1 diabetes and their impacts on age at diagnosis. Recognizing that type 1 diabetes might result from the sum of effects from islet autoimmunity and type 2 diabetes-associated factors, their interactions, or both affects disease prediction, prevention, diagnosis, and treatment.

Identification of Novel T1D Risk Loci and Their Association With Age and Islet Function at Diagnosis in Autoantibody-Positive T1D Individuals: Based on a Two-Stage Genome-Wide Association Study

OBJECTIVE

Type 1 diabetes (T1D) is a highly heritable disease with much lower incidence but more adult-onset cases in the Chinese population. Although genome-wide association studies (GWAS) have identified >60 T1D loci in Caucasians, less is known in Asians.

RESEARCH DESIGN AND METHODS

We performed the first two-stage GWAS of T1D using 2,596 autoantibody-positive T1D case subjects and 5,082 control subjects in a Chinese Han population and evaluated the associations between the identified T1D risk loci and age and fasting C-peptide levels at T1D diagnosis.

RESULTS

We observed a high genetic correlation between children/adolescents and adult T1D case subjects (r _g = 0.87), as well as subgroups of autoantibody status (r _g ≥ 0.90). We identified four T1D risk loci reaching genome-wide significance in the Chinese Han population, including two novel loci, rs4320356 near BTN3A1 (odds ratio [OR] 1.26, P = 2.70 × 10^-8) and rs3802604 in GATA3 (OR 1.24, P = 2.06 × 10^-8), and two previously reported loci, rs1770 in MHC (OR 4.28, P = 2.25 × 10^-232) and rs705699 in SUOX (OR 1.46, P = 7.48 × 10^-20). Further fine mapping in the MHC region revealed five independent variants, including another novel locus, HLA-C position 275 (omnibus P = 9.78 × 10^-12), specific to the Chinese population. Based on the identified eight variants, we achieved an area under the curve value of 0.86 (95% CI 0.85-0.88). By building a genetic risk score (GRS) with these variants, we observed that the higher GRS were associated with an earlier age of T1D diagnosis (P = 9.08 × 10^-11) and lower fasting C-peptide levels (P = 7.19 × 10^-3) in individuals newly diagnosed with T1D.

CONCLUSIONS

Our results extend current knowledge on genetic contributions to T1D risk. Further investigations in different populations are needed for genetic heterogeneity and subsequent precision medicine.

New insights on the genetics of type 1 diabetes

PURPOSE OF REVIEW

The genetic risk for type 1 diabetes has been studied for over half a century, with the strong genetic associations of type 1 diabetes forming critical evidence for the role of the immune system in pathogenesis. In this review, we discuss some of the original research leading to recent developments in type 1 diabetes genetics.

RECENT FINDINGS

We examine the translation of polygenic scores for type 1 diabetes into tools for prediction and diagnosis of type 1 diabetes, in particular, when used in combination with other biomarkers and clinical features, such as age and islet-specific autoantibodies. Furthermore, we review the description of age associations with type 1 diabetes genetic risk, and the investigation of loci linked to type 2 diabetes in progression of type 1 diabetes. Finally, we consider current limitations, including the scarcity of data from racial and ethnic minorities, and future directions.

SUMMARY

The development of polygenic risk scores has allowed the integration of type 1 diabetes genetics into diagnosis and prediction. Emerging information on the role of specific genes in subgroups of individuals with the disease, for example, early-onset, mild autoimmunity, and so forth, is facilitating our understanding of the heterogeneity of type 1 diabetes, with the ultimate goal of using genetic information in research and clinical practice.

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.

Oral insulin therapy for primary prevention of type 1 diabetes in infants with high genetic risk: the GPPAD-POInT (global platform for the prevention of autoimmune diabetes primary oral insulin trial) study protocol

INTRODUCTION

The POInT study, an investigator initiated, randomised, placebo-controlled, double-blind, multicentre primary prevention trial is conducted to determine whether daily administration of oral insulin, from age 4.0 months to 7.0 months until age 36.0 months to children with elevated genetic risk for type 1 diabetes, reduces the incidence of beta-cell autoantibodies and diabetes.

METHODS AND ANALYSIS

Infants aged 4.0 to 7.0 months from Germany, Poland, Belgium, UK and Sweden are eligible if they have a >10.0% expected risk for developing multiple beta-cell autoantibodies as determined by genetic risk score or family history and human leucocyte antigen genotype. Infants are randomised 1:1 to daily oral insulin (7.5 mg for 2 months, 22.5 mg for 2 months, 67.5 mg until age 36.0 months) or placebo, and followed for a maximum of 7 years. Treatment and follow-up is stopped if a child develops diabetes. The primary outcome is the development of persistent confirmed multiple beta-cell autoantibodies or diabetes. Other outcomes are: (1) Any persistent confirmed beta-cell autoantibody (glutamic acid decarboxylase (GADA), IA-2A, autoantibodies to insulin (IAA) and zinc transporter 8 or tetraspanin 7), or diabetes, (2) Persistent confirmed IAA, (3) Persistent confirmed GADA and (4) Abnormal glucose tolerance or diabetes.

ETHICS AND DISSEMINATION

The study is approved by the ethical committees of all participating clinical sites. The results will be disseminated through peer-reviewed journals and conference presentations and will be openly shared after completion of the trial.

TRIAL REGISTRATION NUMBER

NCT03364868.

Genetic Contribution to the Divergence in Type 1 Diabetes Risk Between Children From the General Population and Children From Affected Families

The risk for autoimmunity and subsequently type 1 diabetes is 10-fold higher in children with a first-degree family history of type 1 diabetes (FDR children) than in children in the general population (GP children). We analyzed children with high-risk HLA genotypes (n = 4,573) in the longitudinal TEDDY birth cohort to determine how much of the divergent risk is attributable to genetic enrichment in affected families. Enrichment for susceptible genotypes of multiple type 1 diabetes-associated genes and a novel risk gene, BTNL2, was identified in FDR children compared with GP children. After correction for genetic enrichment, the risks in the FDR and GP children converged but were not identical for multiple islet autoantibodies (hazard ratio [HR] 2.26 [95% CI 1.6-3.02]) and for diabetes (HR 2.92 [95% CI 2.05-4.16]). Convergence varied depending upon the degree of genetic susceptibility. Risks were similar in the highest genetic susceptibility group for multiple islet autoantibodies (14.3% vs .12.7%) and diabetes (4.8% vs. 4.1%) and were up to 5.8-fold divergent for children in the lowest genetic susceptibility group, decreasing incrementally in GP children but not in FDR children. These findings suggest that additional factors enriched within affected families preferentially increase the risk of autoimmunity and type 1 diabetes in lower genetic susceptibility strata.

Cell type-specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes

The rate of decline in insulin secretion after diagnosis with type 1 diabetes (T1D) varies substantially among individuals and with age at diagnosis, but the mechanism(s) behind this heterogeneity are not well understood. We investigated the loss of pancreatic β cell function in new-onset T1D subjects using unbiased whole blood RNA-seq and verified key findings by targeted cell count measurements. We found that patients who lost insulin secretion more rapidly had immune phenotypes ("immunotypes") characterized by higher levels of B cells and lower levels of neutrophils, especially neutrophils expressing primary granule genes. The B cell and neutrophil immunotypes showed strong age dependence, with B cell levels in particular predicting rate of progression in young subjects only. This age relationship suggested that therapy targeting B cells in T1D would be most effective in young subjects with high pretreatment B cell levels, a prediction which was supported by data from a clinical trial of rituximab in new-onset subjects. These findings demonstrate a link between age-related immunotypes and disease outcome in new-onset T1D. Furthermore, our data suggest that greater success could be achieved by targeted use of immunomodulatory therapy in specific T1D populations defined by age and immune characteristics.

Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis

OBJECTIVE

Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the "T1D GRS2") to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.

RESEARCH DESIGN AND METHODS

In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.

RESULTS

The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve [AUC] 0.92; P < 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.

CONCLUSIONS

An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.

GLIS1-3 transcription factors: critical roles in the regulation of multiple physiological processes and diseases

Krüppel-like zinc finger proteins form one of the largest families of transcription factors. They function as key regulators of embryonic development and a wide range of other physiological processes, and are implicated in a variety of pathologies. GLI-similar 1-3 (GLIS1-3) constitute a subfamily of Krüppel-like zinc finger proteins that act either as activators or repressors of gene transcription. GLIS3 plays a critical role in the regulation of multiple biological processes and is a key regulator of pancreatic β cell generation and maturation, insulin gene expression, thyroid hormone biosynthesis, spermatogenesis, and the maintenance of normal kidney functions. Loss of GLIS3 function in humans and mice leads to the development of several pathologies, including neonatal diabetes and congenital hypothyroidism, polycystic kidney disease, and infertility. Single nucleotide polymorphisms in GLIS3 genes have been associated with increased risk of several diseases, including type 1 and type 2 diabetes, glaucoma, and neurological disorders. GLIS2 plays a critical role in the kidney and GLIS2 dysfunction leads to nephronophthisis, an end-stage, cystic renal disease. In addition, GLIS1-3 have regulatory functions in several stem/progenitor cell populations. GLIS1 and GLIS3 greatly enhance reprogramming efficiency of somatic cells into induced embryonic stem cells, while GLIS2 inhibits reprogramming. Recent studies have obtained substantial mechanistic insights into several physiological processes regulated by GLIS2 and GLIS3, while a little is still known about the physiological functions of GLIS1. The localization of some GLIS proteins to the primary cilium suggests that their activity may be regulated by a downstream primary cilium-associated signaling pathway. Insights into the upstream GLIS signaling pathway may provide opportunities for the development of new therapeutic strategies for diabetes, hypothyroidism, and other diseases.

Precision medicine in diabetes prevention, classification and management

Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding of the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In the present review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications. From a clinician's perspective, we attempted to provide a balanced perspective on the utility of genomic medicine in the field of diabetes. Using genetic information to guide management of monogenic forms of diabetes represents the best-known examples of genomic medicine for diabetes. Although major strides have been made in genetic research for diabetes, its complications and pharmacogenetics, ongoing efforts are required to translate these findings into practice by incorporating genetic information into a risk prediction model for prioritization of treatment strategies, as well as using multi-omic analyses to discover novel drug targets with companion diagnostics. Further research is also required to ensure the appropriate use of this information to empower individuals and healthcare professionals to make personalized decisions for achieving the optimal outcome.

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.

Immune Mechanisms and Pathways Targeted in Type 1 Diabetes

PURPOSE OF REVIEW

The immunosuppressive agent cyclosporine was first reported to lower daily insulin dose and improve glycemic control in patients with new-onset type 1 diabetes (T1D) in 1984. While renal toxicity limited cyclosporine's extended use, this observation ignited collaborative efforts to identify immunotherapeutic agents capable of safely preserving β cells in patients with or at risk for T1D.

RECENT FINDINGS

Advances in T1D prediction and early diagnosis, together with expanded knowledge of the disease mechanisms, have facilitated trials targeting specific immune cell subsets, autoantigens, and pathways. In addition, clinical responder and non-responder subsets have been defined through the use of metabolic and immunological readouts. Herein, we review emerging T1D biomarkers within the context of recent and ongoing T1D immunotherapy trials. We also discuss responder/non-responder analyses in an effort to identify therapeutic mechanisms, define actionable pathways, and guide subject selection, drug dosing, and tailored combination drug therapy for future T1D trials.

Discovery of a drug candidate for GLIS3-associated diabetes

GLIS3 mutations are associated with type 1, type 2, and neonatal diabetes, reflecting a key function for this gene in pancreatic β-cell biology. Previous attempts to recapitulate disease-relevant phenotypes in GLIS3^−/− β-like cells have been unsuccessful. Here, we develop a “minimal component” protocol to generate late-stage pancreatic progenitors (PP2) that differentiate to mono-hormonal glucose-responding β-like (PP2-β) cells. Using this differentiation platform, we discover that GLIS3^−/− hESCs show impaired differentiation, with significant death of PP2 and PP2-β cells, without impacting the total endocrine pool. Furthermore, we perform a high-content chemical screen and identify a drug candidate that rescues mutant GLIS3-associated β-cell death both in vitro and in vivo. Finally, we discovered that loss of GLIS3 causes β-cell death, by activating the TGFβ pathway. This study establishes an optimized directed differentiation protocol for modeling human β-cell disease and identifies a drug candidate for treating a broad range of GLIS3-associated diabetic patients.

GLIS3 mutations are associated with type 1, type 2, and neonatal diabetes. Here, the authors generate mono-hormonal glucose-responding pancreatic β-like cells in vitro and through a screen identify a drug that rescues pancreatic β-like cell death in GLIS3 mutants by inhibiting the abnormally activated TGFβ pathway.

Type 1 diabetes

Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care.

Genetics of type 1 diabetes

Type 1 diabetes (T1D) results from immune-mediated loss of pancreatic beta cells leading to insulin deficiency. It is the most common form of diabetes in children, and its incidence is on the rise. This article reviews the current knowledge on the genetics of T1D. In particular, we discuss the influence of HLA and non-HLA genes on T1D risk and disease progression through the preclinical stages of the disease, and the development of genetic scores that can be applied to disease prediction. Racial/ethnic differences, challenges and future directions in the genetics of T1D are also discussed.

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.

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

BACKGROUND

Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.

METHODS AND FINDINGS

The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%-9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%-54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%-60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations.

CONCLUSIONS

A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials.

Collective effects of common single nucleotide polymorphisms and genetic risk prediction in type 1 diabetes

Type 1 diabetes (T1D) is a common autoimmune disease and may be related to multiple genetic and environmental risk factors. Previous genetic studies have focused on looking for individual polymorphic risk variants. Here, we studied the overall levels of genetic diversity in T1D patients by making use of a previously published study including 1865 cases and 2828 reference samples with genotyping data for 500 K common single nucleotide polymorphisms (SNPs). We determined the minor allele (MA) status of each SNP in the reference samples and calculated the total number of MAs or minor allele contents (MAC) of each individual. We found the average MAC of cases to be greater than that of the reference samples. By focusing on MAs with strong linkage to cases, we further identified a set of 112 SNPs that could predict 19.19% of cases. These results suggest that overall genetic variation over a threshold level may be a risk factor in T1D and provide a new genetic method for predicting the disorder.

Application of a Genetic Risk Score to Racially Diverse Type 1 Diabetes Populations Demonstrates the Need for Diversity in Risk-Modeling

Prior studies identified HLA class-II and 57 additional loci as contributors to genetic susceptibility for type 1 diabetes (T1D). We hypothesized that race and/or ethnicity would be contextually important for evaluating genetic risk markers previously identified from Caucasian/European cohorts. We determined the capacity for a combined genetic risk score (GRS) to discriminate disease-risk subgroups in a racially and ethnically diverse cohort from the southeastern U.S. including 637 T1D patients, 46 at-risk relatives having two or more T1D-related autoantibodies (≥2AAb+), 790 first-degree relatives (≤1AAb+), 68 second-degree relatives (≤1 AAb+), and 405 controls. GRS was higher among Caucasian T1D and at-risk subjects versus ≤ 1AAb+ relatives or controls (P < 0.001). GRS receiver operating characteristic AUC (AUROC) for T1D versus controls was 0.86 (P < 0.001, specificity = 73.9%, sensitivity = 83.3%) among all Caucasian subjects and 0.90 for Hispanic Caucasians (P < 0.001, specificity = 86.5%, sensitivity = 84.4%). Age-at-diagnosis negatively correlated with GRS (P < 0.001) and associated with HLA-DR3/DR4 diplotype. Conversely, GRS was less robust (AUROC = 0.75) and did not correlate with age-of-diagnosis for African Americans. Our findings confirm GRS should be further used in Caucasian populations to assign T1D risk for clinical trials designed for biomarker identification and development of personalized treatment strategies. We also highlight the need to develop a GRS model that accommodates racial diversity.

Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young

BACKGROUND

Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects.

METHODS

The 10-factor weighted risk model (HLA, PTPN22 , INS , IL2RA , ERBB3 , ORMDL3 , BACH2 , IL27 , GLIS3 , RNLS ), 3-factor model (HLA, PTPN22, INS ), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941).

RESULTS

Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P = .00006; FDR: P = .0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3-factor model was superior to HLA (P = .03), but not different from the 10-factor model (P = .22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P = .12) and performed worse than the 10-factor model (P = .02) CONCLUSIONS: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups.

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.

The rs2292239 polymorphism in ERBB3 gene is associated with risk for type 1 diabetes mellitus in a Brazilian population

The Erb-b2 receptor tyrosine kinase 3 (ERBB3) belongs to a family of epidermal growth factor receptors of protein tyrosine kinases, and regulates cell survival, differentiation and proliferation in several cell types. Previous studies have suggested that ERBB3 contributes to T1DM pathogenesis by modulating antigen presenting cell function, autoimmunity and cytokine-induced beta-cell apoptosis. Accordingly, some genome-wide association studies identified ERBB3 gene as a susceptibility locus for T1DM, with the strongest association signal being observed for the rs2292239 single nucleotide polymorphism (SNP) in intron 7 of the gene. Therefore, the aim of the present study was to replicate the association of the ERBB3 rs2292239 SNP with T1DM in a Brazilian population. We analyzed 421 T1DM patients (cases) and 510 nondiabetic subjects (controls). All subjects were self-declared as white. The ERBB3 rs2292239 (A/C) SNP was genotyped by real-time PCR using TaqMan MGB probes. Genotype (P=0.001) and allele (P=0.002) frequencies of the ERBB3 rs2292239 SNP were differently distributed between T1DM patients and nondiabetic controls. Moreover, the A allele was significantly associated with risk for T1DM when considering recessive (OR=1.58, 95% CI 1.11-2.27; P=0.015), additive (OR=1.78, 95% CI 1.21-2.62; P=0.004), and dominant (OR=1.39, 95% CI 1.07-1.81; P=0.016) models of inheritance. However, after adjustment for presence of high-risk HLA DR/DQ genotypes, the rs2292239 SNP remained independently associated with T1DM only for the additive model (OR=1.62, 95% CI 1.02-2.59; P=0.043). Our results suggest that the A/A genotype of the ERBB3 rs2292239 SNP is associated with risk for T1DM in a white Brazilian population.

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.

Molecular genetics of the transcription factor GLIS3 identifies its dual function in beta cells and neurons

The GLIS family zinc finger 3 isoform (GLIS3) is a risk gene for Type 1 and Type 2 diabetes, glaucoma and Alzheimer's disease endophenotype. We identified GLIS3 binding sites in insulin secreting cells (INS1) (FDR q<0.05; enrichment range 1.40-9.11 fold) sharing the motif wrGTTCCCArTAGs, which were enriched in genes involved in neuronal function and autophagy and in risk genes for metabolic and neuro-behavioural diseases. We confirmed experimentally Glis3-mediated regulation of the expression of genes involved in autophagy and neuron function in INS1 and neuronal PC12 cells. Naturally-occurring coding polymorphisms in Glis3 in the Goto-Kakizaki rat model of type 2 diabetes were associated with increased insulin production in vitro and in vivo, suggestive alteration of autophagy in PC12 and INS1 and abnormal neurogenesis in hippocampus neurons. Our results support biological pleiotropy of GLIS3 in pathologies affecting β-cells and neurons and underline the existence of trans‑nosology pathways in diabetes and its co-morbidities.

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.

Can Non-HLA Single Nucleotide Polymorphisms Help Stratify Risk in TrialNet Relatives at Risk for Type 1 Diabetes?

CONTEXT

Genome-wide association studies identified >50 type 1 diabetes (T1D) associated non-human leukocyte antigens (non-HLA) loci.

OBJECTIVE

The purpose of this study was to assess the contribution of non-HLA single nucleotide polymorphisms (SNPs) to risk of disease progression.

DESIGN AND SETTING

The TrialNet Pathway to Prevention Study follows relatives of T1D patients for development of autoantibodies (Abs) and T1D.

PARTICIPANTS

Using the Immunochip, we analyzed 53 diabetes-associated, non-HLA SNPs in 1016 Ab-positive, at-risk non-Hispanic white relatives.

MAIN OUTCOME MEASURE

Effect of SNPs on the development of multiple Abs and T1D.

RESULTS

Cox proportional analyses included all substantial non-HLA SNPs, HLA genotypes, relationship to proband, sex, age at initial screening, initial Ab type, and number. Factors involved in progression from single to multiple Abs included age at screening, relationship to proband, HLA genotypes, and rs3087243 (cytotoxic T lymphocyte antigen-4). Significant factors for diabetes progression included age at screening, Ab number, HLA genotypes, rs6476839 [GLIS family zinc finger 3 (GLIS3)], and rs3184504 [SH2B adaptor protein 3 (SH2B3)]. When glucose area under the curve (AUC) was included, factors involved in disease progression included glucose AUC, age at screening, Ab number, relationship to proband, HLA genotypes, rs6476839 (GLIS3), and rs7221109 (CCR7). In stratified analyses by age, glucose AUC, age at screening, sibling, HLA genotypes, rs6476839 (GLIS3), and rs4900384 (C14orf64) were significantly associated with progression to diabetes in participants <12 years old, whereas glucose AUC, sibling, rs3184504 (SH2B3), and rs4900384 (C14orf64) were significant in those ≥12.

CONCLUSIONS

In conclusion, we identified five non-HLA SNPs associated with increased risk of progression from Ab positivity to disease that may improve risk stratification for prevention trials.

Late-onset islet autoimmunity in childhood: the Diabetes Autoimmunity Study in the Young (DAISY)

AIMS/HYPOTHESIS

We sought to assess the frequency, determinants and prognosis for future diabetes in individuals with islet autoimmunity and whether these factors differ depending on the age of onset of islet autoimmunity.

METHODS

A prospective cohort (n = 2547) of children from the general population who had a high-risk HLA genotype and children who had a first-degree relative with type 1 diabetes were followed for up to 21 years. Those with the persistent presence of one or more islet autoantibodies were categorised as early-onset (<8 years of age, n = 143, median 3.3 years) or late-onset (≥8 years of age, n = 64, median 11.1 years), and were followed for a median of 7.4 and 4.7 years, respectively. Progression to diabetes was evaluated by Kaplan-Meier analysis with logrank test. Factors associated with progression to diabetes were analysed using the parametric accelerated failure time model.

RESULTS

Children with late-onset islet autoimmunity were more likely to be Hispanic or African-American than non-Hispanic white (p = 0.004), and less likely to be siblings of individuals with type 1 diabetes (p = 0.04). The frequencies of the HLA-DR3/4 genotype and non-HLA gene variants associated with type 1 diabetes did not differ between the two groups. However, age and HLA-DR3/4 were important predictors of rate of progression to both the presence of additional autoantibodies and type 1 diabetes. Late-onset islet autoimmunity was more likely to present with a single islet autoantibody (p = 0.01) and revert to an antibody-negative state (p = 0.01). Progression to diabetes was significantly slower in children with late-onset islet autoimmunity (p < 0.001).

CONCLUSIONS/INTERPRETATION

A late onset of islet autoimmunity is more common in African-American and Hispanic individuals. About half of those with late-onset islet autoimmunity progress to show multiple islet autoantibodies and develop diabetes in adolescence or early adulthood. Further investigation of environmental determinants of late-onset autoimmunity may lead to an understanding of and ability to prevent adolescent and adult-onset type 1 diabetes.

Relative contribution of type 1 and type 2 diabetes loci to the genetic etiology of adult-onset, non-insulin-requiring autoimmune diabetes

BACKGROUND

In adulthood, autoimmune diabetes can present as non-insulin-requiring diabetes, termed as 'latent autoimmune diabetes in adults' (LADA). In this study, we investigated established type 1 diabetes (T1D) and type 2 diabetes (T2D) genetic loci in a large cohort of LADA cases to assess where LADA is situated relative to these two well-characterized, classic forms of diabetes.

METHODS

We tested the association of T1D and T2D GWAS-implicated loci in 978 LADA cases and 1057 non-diabetic controls of European ancestry using a linear mixed model. We then compared the associations of T1D and T2D loci between LADA and T1D and T2D cases, respectively. We quantified the difference in genetic risk between each given disease at each locus, and also calculated genetic risk scores to quantify how genetic liability to T1D and T2D distinguished LADA cases from controls.

RESULTS

Overall, our results showed that LADA is genetically more similar to T1D, with the exception of an association at the T2D HNF1A locus. Several T1D loci were associated with LADA, including the major histocompatibility complex region, as well as at PTPN22, SH2B3, and INS. Contrary to previous studies, the key T2D risk allele at TCF7L2 (rs7903146-T) had a significantly lower frequency in LADA cases, suggesting that this locus does not play a role in LADA etiology. When constrained on antibody status, the similarity between LADA and T1D became more apparent; however, the HNF1A and TCF7L2 observations persisted.

CONCLUSION

LADA is genetically closer to T1D than T2D, although the genetic load of T1D risk alleles is less than childhood-onset T1D, particularly at the major histocompatibility complex region, potentially accounting for the later disease onset. Our results show that the genetic spectrum of T1D extends into adult-onset diabetes, where it can clinically masquerade as T2D. Furthermore, T2D genetic risk plays a small role in LADA, with a degree of evidence for the HNF1A locus, highlighting the potential for genetic risk scores to contribute towards defining diabetes subtypes.

Emerging roles of GLIS3 in neonatal diabetes, type 1 and type 2 diabetes

GLI-similar 3 (GLIS3), a member of the Krüppel-like zinc finger protein subfamily, is predominantly expressed in the pancreas, thyroid and kidney. Glis3 mRNA can be initially detected in mouse pancreas at embryonic day 11.5 and is largely restricted to β cells, pancreatic polypeptide-expressing cells, as well as ductal cells at later stage of pancreas development. Mutations in GLIS3 cause a neonatal diabetes syndrome, characterized by neonatal diabetes, congenital hypothyroidism and polycystic kidney. Importantly, genome-wide association studies showed that variations of GLIS3 are strongly associated with both type 1 diabetes (T1D) and type 2 diabetes (T2D) in multiple populations. GLIS3 cooperates with pancreatic and duodenal homeobox 1 (PDX1), v-maf musculoaponeurotic fibrosarcoma oncogene family, protein A (MAFA), as well as neurogenic differentiation 1 (NEUROD1) and potently controls insulin gene transcription. GLIS3 also plays a role in β cell survival and likely in insulin secretion. Any perturbation of these functions may underlie all three forms of diabetes. GLIS3, synergistically with hepatocyte nuclear factor 6 (HNF6) and forkhead box A2 (FOXA2), controls fetal islet differentiation via transactivating neurogenin 3 (NGN3) and impairment of this function leads to neonatal diabetes. In addition, GLIS3 is also required for the compensatory β cell proliferation and mass expansion in response to insulin resistance, which if disrupted may predispose to T2D. The increasing understanding of the mechanisms of GLIS3 in β cell development, survival and function maintenance will provide new insights into disease pathogenesis and potential therapeutic target identification to combat diabetes.

Interleukin 1 genetic tests provide no support for reduction of preventive dental care

BACKGROUND

It has been proposed that the PST and PerioPredict genetic tests that are based on polymorphisms in interleukin 1 (IL-1) genes identify a subset of patients who experience fewer tooth extractions if provided with 2 annual preventive visits. Economic analyses indicate rationing preventive care to only "high-risk" genotypes, smokers, patients with diabetes, or combinations of these risk factors would reduce the cost of dental care by $4.8 billion annually in the United States.

METHODS

Data presented in the study that claimed clinical utility for the PST and PerioPredict tests were obtained for reanalysis using logistic regression to assess whether the PST genetic test, smoking, diabetes, or number of preventive visits were risk factors for tooth extraction during a span of 16 years. Consistency of risk classification by the PST (version 1) and PerioPredict (version 2) genetic tests was evaluated in different ethnic groups from the 1000 Genomes database.

RESULTS

Multivariate analyses revealed association of tooth extraction with diabetes (P < .0001), smoking (P < .0001), and number of preventive visits (P = .004), but no support for the PST genetic test (P = .96) nor indication that the benefit of 2 preventive visits was affected by this genetic test (P = .58). Classification of risk was highly inconsistent between the PST (version 1) and PerioPredict (version 2) genetic tests.

CONCLUSIONS

Two annual preventive visits were supported as beneficial for all patients, and there was no evidence that the IL-1 PST genetic test has any effect on tooth extraction risk or influences the benefits of 2 annual preventive visits.

PRACTICAL IMPLICATIONS

Neither IL-1 PST nor PerioPredict genetic tests are useful for rationing preventive dental care. Further research is needed to identify genetic biomarkers with robust clinical validity and clinical utility to effectively personalize the practice of dentistry.

Type 1 Diabetes Prevention: A Goal Dependent on Accepting a Diagnosis of an Asymptomatic Disease

Type 1 diabetes, a disease defined by absolute insulin deficiency, is considered a chronic autoimmune disorder resulting from the destruction of insulin-producing pancreatic β-cells. The incidence of childhood-onset type 1 diabetes has been increasing at a rate of 3%-5% per year globally. Despite the introduction of an impressive array of therapies aimed at improving disease management, no means for a practical "cure" exist. This said, hope remains high that any of a number of emerging technologies (e.g., continuous glucose monitoring, insulin pumps, smart algorithms), alongside advances in stem cell biology, cell encapsulation methodologies, and immunotherapy, will eventually impact the lives of those with recently diagnosed or established type 1 diabetes. However, efforts aimed at reversing insulin dependence do not address the obvious benefits of disease prevention. Hence, key "stretch goals" for type 1 diabetes research include identifying improved and increasingly practical means for diagnosing the disease at earlier stages in its natural history (i.e., early, presymptomatic diagnosis), undertaking such efforts in the population at large to optimally identify those with presymptomatic type 1 diabetes, and introducing safe and effective therapeutic options for prevention.

Rapidly rising incidence of Type 1 diabetes in children and adolescents aged 0-19 years in Zhejiang, China, 2007 to 2013

AIMS

To investigate the incidence rates and trends in Type 1 diabetes in children and adolescents aged 0-19 years in the registered Zhejiang population over the period 2007-2013 by age, sex and calendar year.

METHODS

In total, 611 individuals with newly diagnosed Type 1 diabetes were identified from 30 districts in Zhejiang province over the study period. Annual incidence and 95% confidence intervals (CI) by age group and sex were calculated per 100 000 person-years. Trends in diabetes incidence and the associations of age and sex with Type 1 diabetes were assessed using Poisson regression models.

RESULTS

The mean annual age-standardized incidence of diabetes was 2.02/100 000 person-years (95% CI: 1.92-2.12), with an average annual increase of 12.0% (95% CI: 7.6-16.6%) over the study period. The risk for Type 1 diabetes in girls was estimated to be 1.25 (95% CI: 1.07-1.47) times higher than that in boys. Compared with those aged 0-4 years, the 5-9, 10-14 and 15-19 years age groups were at significantly greater risk, with adjusting incidence rate ratios of 3.54, 6.58 and 5.39, respectively. The mean age at diagnosis decreased significantly from 12.85 years in 2007 to 11.21 years in 2013. A steep rise in diabetes incidence was observed in the under 5 years age group, which showed the greatest increase at 33.61%.

CONCLUSIONS

The incidence of diabetes in Zhejiang was relatively low, although rapidly rising trends have been found in recent years, particularly in younger children. Further monitoring and research are urgently required to better understand possible environmental risk factors and formulate preventive strategies.

Aldehyde dehydrogenase 1a3 defines a subset of failing pancreatic β cells in diabetic mice

Insulin-producing β cells become dedifferentiated during diabetes progression. An impaired ability to select substrates for oxidative phosphorylation, or metabolic inflexibility, initiates progression from β-cell dysfunction to β-cell dedifferentiation. The identification of pathways involved in dedifferentiation may provide clues to its reversal. Here we isolate and functionally characterize failing β cells from various experimental models of diabetes and report a striking enrichment in the expression of aldehyde dehydrogenase 1 isoform A3 (ALDH(+)) as β cells become dedifferentiated. Flow-sorted ALDH(+) islet cells demonstrate impaired glucose-induced insulin secretion, are depleted of Foxo1 and MafA, and include a Neurogenin3-positive subset. RNA sequencing analysis demonstrates that ALDH(+) cells are characterized by: (i) impaired oxidative phosphorylation and mitochondrial complex I, IV and V; (ii) activated RICTOR; and (iii) progenitor cell markers. We propose that impaired mitochondrial function marks the progression from metabolic inflexibility to dedifferentiation in the natural history of β-cell failure.

Type 1 Diabetes Genetic Risk Score: A Novel Tool to Discriminate Monogenic and Type 1 Diabetes

Distinguishing patients with monogenic diabetes from those with type 1 diabetes (T1D) is important for correct diagnosis, treatment, and selection of patients for gene discovery studies. We assessed whether a T1D genetic risk score (T1D-GRS) generated from T1D-associated common genetic variants provides a novel way to discriminate monogenic diabetes from T1D. The T1D-GRS was highly discriminative of proven maturity-onset diabetes of young (MODY) (n = 805) and T1D (n = 1,963) (receiver operating characteristic area under the curve 0.87). A T1D-GRS of >0.280 (>50th T1D centile) was indicative of T1D (94% specificity, 50% sensitivity). We then analyzed the T1D-GRS of 242 white European patients with neonatal diabetes (NDM) who had been tested for all known NDM genes. Monogenic NDM was confirmed in 90, 59, and 8% of patients with GRS <5th T1D centile, 50-75th T1D centile, and >75th T1D centile, respectively. Applying a GRS 50th T1D centile cutoff in 48 NDM patients with no known genetic cause identified those most likely to have a novel monogenic etiology by highlighting patients with probable early-onset T1D (GRS >50th T1D centile) who were diagnosed later and had less syndromic presentation but additional autoimmune features compared with those with proven monogenic NDM. The T1D-GRS is a novel tool to improve the use of biomarkers in the discrimination of monogenic diabetes from T1D.

Monogenic Diabetes: What It Teaches Us on the Common Forms of Type 1 and Type 2 Diabetes

To date, more than 30 genes have been linked to monogenic diabetes. Candidate gene and genome-wide association studies have identified > 50 susceptibility loci for common type 1 diabetes (T1D) and approximately 100 susceptibility loci for type 2 diabetes (T2D). About 1-5% of all cases of diabetes result from single-gene mutations and are called monogenic diabetes. Here, we review the pathophysiological basis of the role of monogenic diabetes genes that have also been found to be associated with common T1D and/or T2D. Variants of approximately one-third of monogenic diabetes genes are associated with T2D, but not T1D. Two of the T2D-associated monogenic diabetes genes-potassium inward-rectifying channel, subfamily J, member 11 (KCNJ11), which controls glucose-stimulated insulin secretion in the β-cell; and peroxisome proliferator-activated receptor γ (PPARG), which impacts multiple tissue targets in relation to inflammation and insulin sensitivity-have been developed as major antidiabetic drug targets. Another monogenic diabetes gene, the preproinsulin gene (INS), is unique in that INS mutations can cause hyperinsulinemia, hyperproinsulinemia, neonatal diabetes mellitus, one type of maturity-onset diabetes of the young (MODY10), and autoantibody-negative T1D. Dominant heterozygous INS mutations are the second most common cause of permanent neonatal diabetes. Moreover, INS gene variants are strongly associated with common T1D (type 1a), but inconsistently with T2D. Variants of the monogenic diabetes gene Gli-similar 3 (GLIS3) are associated with both T1D and T2D. GLIS3 is a key transcription factor in insulin production and β-cell differentiation during embryonic development, which perturbation forms the basis of monogenic diabetes as well as its association with T1D. GLIS3 is also required for compensatory β-cell proliferation in adults; impairment of this function predisposes to T2D. Thus, monogenic forms of diabetes are invaluable "human models" that have contributed to our understanding of the pathophysiological basis of common T1D and T2D.

A common variant association study reveals novel susceptibility loci for low HDL-cholesterol levels in ethnic Arabs

The genetic susceptibility to acquiring low high density lipoprotein-cholesterol (LHDLC) levels is not completely elucidated yet. In this study, we performed a common variant association study for harboring this trait in ethnic Arabs. We employed the Affymetrix high-density Axiom Genome-Wide ASI Array (Asian population) providing a coverage of 598,000 single nucleotide variations (SNPs) to genotype 5495 individuals in a two-phase study involving discovery and validation sets of experiments. The rs1800775 [1.31 (1.22-1.42); p = 3.41E-12] in the CETP gene and rs359027 [1.26 (1.16-1.36); p = 2.55E-08] in the LMCD1 gene were significantly associated with LHDLC levels. Furthermore, rs3104435 [1.26 (1.15-1.38); p = 1.19E-06] at the MATN1 locus, rs9835344 [1.16 (1.08-1.26); p = 8.75E-06] in the CNTN6 gene, rs1559997 [1.3 (1.14-1.47); p = 9.48E-06] in the SDS gene and rs1670273 [1.2 (1.1-1.31); p = 4.81E-06] in the DMN/SYNM gene exhibited suggestive association with the disorder. Seven other variants including rs1147169 in the PLCL1 gene, rs10248618 in the DNAH11, rs476155 in the GLIS3, rs7024300 in the ABCA1, intergenic rs10836699, rs11603691 in P2RX3 and rs750134 in CORO1C gene exhibited borderline protective properties. Validation and joint meta-analysis resulted in rs1800775, rs3104435 and rs359027 retaining their predisposing properties, while rs10836699 and rs11603691 showed protective properties. Our data show several predisposing variants across the genome for LHDLC levels in ethnic Arabs.

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.

Primary prevention of beta-cell autoimmunity and type 1 diabetes - The Global Platform for the Prevention of Autoimmune Diabetes (GPPAD) perspectives

OBJECTIVE

Type 1 diabetes can be identified by the presence of beta-cell autoantibodies that often arise in the first few years of life. The purpose of this perspective is to present the case for primary prevention of beta-cell autoimmunity and to provide a study design for its implementation in Europe.

METHODS

We examined and summarized recruitment strategies, enrollment rates, and outcomes in published TRIGR, FINDIA and BABYDIET primary prevention trials, and the TEDDY intensive observational study. A proposal for a recruitment and implementation strategy to perform a phase II/III primary prevention randomized controlled trial in infants with genetic risk for developing beta-cell autoimmunity is outlined.

RESULTS

Infants with a family history of type 1 diabetes (TRIGR, BABYDIET, TEDDY) and infants younger than age 3 months from the general population (FINDIA, TEDDY) were enrolled into these studies. All studies used HLA genotyping as part of their eligibility criteria. Predicted beta-cell autoimmunity risk in the eligible infants ranged from 3% (FINDIA, TEDDY general population) up to 12% (TRIGR, BABYDIET). Amongst eligible infants, participation was between 38% (TEDDY general population) and 97% (FINDIA). Outcomes, defined as multiple beta-cell autoantibodies, were consistent with predicted risks. We subsequently modeled recruitment into a randomized controlled trial (RCT) that could assess the efficacy of oral insulin treatment as adapted from the Pre-POINT pilot trial. The RCT would recruit infants with and without a first-degree family history of type 1 diabetes and be based on general population genetic risk testing. HLA genotyping and, for the general population, genotyping at additional type 1 diabetes susceptibility SNPs would be used to identify children with around 10% risk of beta-cell autoimmunity. The proposed RCT would have 80% power to detect a 50% reduction in multiple beta-cell autoantibodies by age 4 years at a two-tailed alpha of 0.05, and would randomize around 1160 infants to oral insulin or placebo arms in order to fulfill this. It is estimated that recruitment would require testing of between 400,000 and 500,000 newborns or infants.

CONCLUSION

It is timely and feasible to establish a platform for primary prevention trials for type 1 diabetes in Europe. This multi-site European infrastructure would perform RCTs, supply data coordination and biorepository, provide cohorts for mechanistic and observational studies, and increase awareness for autoimmune diabetes.

Genetics of long-term treatment outcome in bipolar disorder

Bipolar disorder (BD) shows one of the strongest genetic predispositions among psychiatric disorders and the identification of reliable genetic predictors of treatment response could significantly improve the prognosis of the disease. The present study investigated genetic predictors of long-term treatment-outcome in 723 patients with BD type I from the STEP-BD (Systematic Treatment Enhancement Program for Bipolar Disorder) genome-wide dataset. BD I patients with >6months of follow-up and without any treatment restriction (reflecting a natural setting scenario) were included. Phenotypes were the total and depressive episode rates and the occurrence of one or more (hypo)manic/mixed episodes during follow-up. Quality control of genome-wide data was performed according to standard criteria and linear/logistic regression models were used as appropriate under an additive hypothesis. Top genes were further analyzed through a pathway analysis. Genes previously involved in the susceptibility to BD (DFNB31, SORCS2, NRXN1, CNTNAP2, GRIN2A, GRM4, GRIN2B), antidepressant action (DEPTOR, CHRNA7, NRXN1), and mood stabilizer or antipsychotic action (NTRK2, CHRNA7, NRXN1) may affect long-term treatment outcome of BD. Promising findings without previous strong evidence were TRAF3IP2-AS1, NFYC, RNLS, KCNJ2, RASGRF1, NTF3 genes. Pathway analysis supported particularly the involvement of molecules mediating the positive regulation of MAPK cascade and learning/memory processes. Further studies focused on the outlined genes may be helpful to provide validated markers of BD treatment outcome.

Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association

Insights from prospective, longitudinal studies of individuals at risk for developing type 1 diabetes have demonstrated that the disease is a continuum that progresses sequentially at variable but predictable rates through distinct identifiable stages prior to the onset of symptoms. Stage 1 is defined as the presence of β-cell autoimmunity as evidenced by the presence of two or more islet autoantibodies with normoglycemia and is presymptomatic, stage 2 as the presence of β-cell autoimmunity with dysglycemia and is presymptomatic, and stage 3 as onset of symptomatic disease. Adoption of this staging classification provides a standardized taxonomy for type 1 diabetes and will aid the development of therapies and the design of clinical trials to prevent symptomatic disease, promote precision medicine, and provide a framework for an optimized benefit/risk ratio that will impact regulatory approval, reimbursement, and adoption of interventions in the early stages of type 1 diabetes to prevent symptomatic disease.

Immunogenetics of type 1 diabetes: A comprehensive review

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing beta cells in the pancreas. Prevention of T1D will require the ability to detect and modulate the autoimmune process before the clinical onset of disease. Genetic screening is a logical first step in identification of future patients to test prevention strategies. Susceptibility to T1D includes a strong genetic component, with the strongest risk attributable to genes that encode the classical Human Leukocyte Antigens (HLA). Other genetic loci, both immune and non-immune genes, contribute to T1D risk; however, the results of decades of small and large genetic linkage and association studies show clearly that the HLA genes confer the most disease risk and protection and can be used as part of a prediction strategy for T1D. Current predictive genetic models, based on HLA and other susceptibility loci, are effective in identifying the highest-risk individuals in populations of European descent. These models generally include screening for the HLA haplotypes "DR3" and "DR4." However, genetic variation among racial and ethnic groups reduces the predictive value of current models that are based on low resolution HLA genotyping. Not all DR3 and DR4 haplotypes are high T1D risk; some versions, rare in Europeans but high frequency in other populations, are even T1D protective. More information is needed to create predictive models for non-European populations. Comparative studies among different populations are needed to complete the knowledge base for the genetics of T1D risk to enable the eventual development of screening and intervention strategies applicable to all individuals, tailored to their individual genetic background. This review summarizes the current understanding of the genetic basis of T1D susceptibility, focusing on genes of the immune system, with particular emphasis on the HLA genes.