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

Predicting autoimmune diseases: A comprehensive review of classic biomarkers and advances in artificial intelligence

Autoimmune diseases comprise a spectrum of disorders characterized by the dysregulation of immune tolerance, resulting in tissue or organ damage and inflammation. Their prevalence has been on the rise, significantly impacting patients' quality of life and escalating healthcare costs. Consequently, the prediction of autoimmune diseases has recently garnered substantial interest among researchers. Despite their wide heterogeneity, many autoimmune diseases exhibit a consistent pattern of paraclinical findings that hold predictive value. From serum biomarkers to various machine learning approaches, the array of available methods has been continuously expanding. The emergence of artificial intelligence (AI) presents an exciting new range of possibilities, with notable advancements already underway. The ultimate objective should revolve around disease prevention across all levels. This review provides a comprehensive summary of the most recent data pertaining to the prediction of diverse autoimmune diseases and encompasses both traditional biomarkers and the latest innovations in AI.

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.

Genetic and Epigenetic Aspects of Type 1 Diabetes Mellitus: Modern View on the Problem

Omics technologies accumulated an enormous amount of data that advanced knowledge about the molecular pathogenesis of type 1 diabetes mellitus and identified a number of fundamental problems focused on the transition to personalized diabetology in the future. Among them, the most significant are the following: (1) clinical and genetic heterogeneity of type 1 diabetes mellitus; (2) the prognostic significance of DNA markers beyond the HLA genes; (3) assessment of the contribution of a large number of DNA markers to the polygenic risk of disease progress; (4) the existence of ethnic population differences in the distribution of frequencies of risk alleles and genotypes; (5) the infancy of epigenetic research into type 1 diabetes mellitus. Disclosure of these issues is one of the priorities of fundamental diabetology and practical healthcare. The purpose of this review is the systemization of the results of modern molecular genetic, transcriptomic, and epigenetic investigations of type 1 diabetes mellitus in general, as well as its individual forms. The paper summarizes data on the role of risk HLA haplotypes and a number of other candidate genes and loci, identified through genome-wide association studies, in the development of this disease and in alterations in T cell signaling. In addition, this review assesses the contribution of differential DNA methylation and the role of microRNAs in the formation of the molecular pathogenesis of type 1 diabetes mellitus, as well as discusses the most currently central trends in the context of early diagnosis of type 1 diabetes mellitus.

Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions

Diabetes is a serious health issue that causes a progressive dysregulation of carbohydrate metabolism due to insufficient insulin hormone, leading to consistently high blood glucose levels. According to the epidemiological data, the prevalence of diabetes has been increasing globally, affecting millions of individuals. It is a long-term condition that increases the risk of various diseases caused by damage to small and large blood vessels. There are two main subtypes of diabetes: type 1 and type 2, with type 2 being the most prevalent. Genetic and molecular studies have identified several genetic variants and metabolic pathways that contribute to the development and progression of diabetes. Current treatments include gene therapy, stem cell therapy, statin therapy, and other drugs. Moreover, recent advancements in therapeutics have also focused on developing novel drugs targeting these pathways, including incretin mimetics, SGLT2 inhibitors, and GLP-1 receptor agonists, which have shown promising results in improving glycemic control and reducing the risk of complications. However, these treatments are often expensive, inaccessible to patients in underdeveloped countries, and can have severe side effects. Peptides, such as glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), are being explored as a potential therapy for diabetes. These peptides are postprandial glucose-dependent pancreatic beta-cell insulin secretagogues and have received much attention as a possible treatment option. Despite these advances, diabetes remains a major health challenge, and further research is needed to develop effective treatments and prevent its complications. This review covers various aspects of diabetes, including epidemiology, genetic and molecular basis, and recent advancements in therapeutics including herbal and synthetic peptides.

Lessons and Gaps in the Prediction and Prevention of Type 1 Diabetes

Type 1 diabetes (T1D) is a serious chronic autoimmune condition. Even though the root cause of T1D development has yet to be determined, enough is known about the natural history of T1D pathogenesis to allow study of interventions that may delay or even prevent the onset of hyperglycemia and clinical T1D. Primary prevention aims to prevent the onset of beta cell autoimmunity in asymptomatic people at high genetic risk for T1D. Secondary prevention strategies aim to preserve functional beta cells once autoimmunity is present, and tertiary prevention aims to initiate and extend partial remission of beta cell destruction after the clinical onset of T1D. The approval of teplizumab in the United States to delay the onset of clinical T1D marks an impressive milestone in diabetes care. This treatment opens the door to a paradigm shift in T1D care. People with T1D risk need to be identified early by measuring T1D related islet autoantibodies. Identifying people with T1D before they have symptoms will facilitate better understanding of pre-symptomatic T1D progression and T1D prevention strategies that may be effective.

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.

Non-HLA Gene Polymorphisms in the Pathogenesis of Type 1 Diabetes: Phase and Endotype Specific Effects

The non-HLA loci conferring susceptibility to type 1 diabetes determine approximately half of the genetic disease risk, and several of them have been shown to affect immune-cell or pancreatic β-cell functions. A number of these loci have shown associations with the appearance of autoantibodies or with progression from seroconversion to clinical type 1 diabetes. In the current study, we have re-analyzed 21 of our loci with prior association evidence using an expanded DIPP follow-up cohort of 976 autoantibody positive cases and 1,910 matched controls. Survival analysis using Cox regression was applied for time periods from birth to seroconversion and from seroconversion to type 1 diabetes. The appearance of autoantibodies was also analyzed in endotypes, which are defined by the first appearing autoantibody, either IAA or GADA. Analyzing the time period from birth to seroconversion, we were able to replicate our previous association findings at PTPN22, INS, and NRP1. Novel findings included associations with ERBB3, UBASH3A, PTPN2, and FUT2. In the time period from seroconversion to clinical type 1 diabetes, prior associations with PTPN2, CD226, and PTPN22 were replicated, and a novel association with STAT4 was observed. Analyzing the appearance of autoantibodies in endotypes, the PTPN22 association was specific for IAA-first. In the progression phase, STAT4 was specific for IAA-first and ERBB3 to GADA-first. In conclusion, our results further the knowledge of the function of non-HLA risk polymorphisms in detailing endotype specificity and timing of disease development.

The β Cell in Diabetes: Integrating Biomarkers With Functional Measures

The pathogenesis of hyperglycemia observed in most forms of diabetes is intimately tied to the islet β cell. Impairments in propeptide processing and secretory function, along with the loss of these vital cells, is demonstrable not only in those in whom the diagnosis is established but typically also in individuals who are at increased risk of developing the disease. Biomarkers are used to inform on the state of a biological process, pathological condition, or response to an intervention and are increasingly being used for predicting, diagnosing, and prognosticating disease. They are also proving to be of use in the different forms of diabetes in both research and clinical settings. This review focuses on the β cell, addressing the potential utility of genetic markers, circulating molecules, immune cell phenotyping, and imaging approaches as biomarkers of cellular function and loss of this critical cell. Further, we consider how these biomarkers complement the more long-established, dynamic, and often complex measurements of β-cell secretory function that themselves could be considered biomarkers.

Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk

Type 1 diabetes (T1D) etiology is complex. We developed a machine learning approach that ranked the tissue-specific transcription regulatory effects for T1D SNPs and estimated their relative contributions to conversion to T1D by integrating case and control genotypes (Wellcome Trust Case Control Consortium and UK Biobank) with tissue-specific expression quantitative trait loci (eQTL) data. Here we show an eQTL (rs6679677) associated with changes to AP4B1-AS1 transcript levels in lung tissue makes the largest gene regulatory contribution to the risk of T1D development. Luciferase reporter assays confirmed allele-specific enhancer activity for the rs6679677 tagged locus in lung epithelial cells (i.e. A549 cells; C > A reduces expression, p = 0.005). Our results identify tissue-specific eQTLs for SNPs associated with T1D. The strongest tissue-specific eQTL effects were in the lung and may help explain associations between respiratory infections and risk of islet autoantibody seroconversion in young children.

The Multifactorial Progression from the Islet Autoimmunity to Type 1 Diabetes in Children

Type 1 Diabetes (T1D) results from autoimmune destruction of insulin producing pancreatic ß-cells. This disease, with a peak incidence in childhood, causes the lifelong need for insulin injections and necessitates careful monitoring of blood glucose levels. However, despite the current insulin therapies, it still shortens life expectancy due to complications affecting multiple organs. Recently, the incidence of T1D in childhood has increased by 3-5% per year in most developed Western countries. The heterogeneity of the disease process is supported by the findings of follow-up studies started early in infancy. The development of T1D is usually preceded by the appearance of autoantibodies targeted against antigens expressed in the pancreatic islets. The risk of T1D increases significantly with an increasing number of positive autoantibodies. The order of autoantibody appearance affects the disease risk. Genetic susceptibility, mainly defined by the human leukocyte antigen (HLA) class II gene region and environmental factors, is important in the development of islet autoimmunity and T1D. Environmental factors, mainly those linked to the changes in the gut microbiome as well as several pathogens, especially viruses, and diet are key modulators of T1D. The aim of this paper is to expand the understanding of the aetiology and pathogenesis of T1D in childhood by detailed description and comparison of factors affecting the progression from the islet autoimmunity to T1D in children.

Untangling the genetic link between type 1 and type 2 diabetes using functional genomics

There is evidence pointing towards shared etiological features between type 1 diabetes (T1D) and type 2 diabetes (T2D) despite both phenotypes being considered genetically distinct. However, the existence of shared genetic features for T1D and T2D remains complex and poorly defined. To better understand the link between T1D and T2D, we employed an integrated functional genomics approach involving extensive chromatin interaction data (Hi-C) and expression quantitative trait loci (eQTL) data to characterize the tissue-specific impacts of single nucleotide polymorphisms associated with T1D and T2D. We identified 195 pleiotropic genes that are modulated by tissue-specific spatial eQTLs associated with both T1D and T2D. The pleiotropic genes are enriched in inflammatory and metabolic pathways that include mitogen-activated protein kinase activity, pertussis toxin signaling, and the Parkinson's disease pathway. We identified 8 regulatory elements within the TCF7L2 locus that modulate transcript levels of genes involved in immune regulation as well as genes important in the etiology of T2D. Despite the observed gene and pathway overlaps, there was no significant genetic correlation between variant effects on T1D and T2D risk using European ancestral summary data. Collectively, our findings support the hypothesis that T1D and T2D specific genetic variants act through genetic regulatory mechanisms to alter the regulation of common genes, and genes that co-locate in biological pathways, to mediate pleiotropic effects on disease development. Crucially, a high risk genetic profile for T1D alters biological pathways that increase the risk of developing both T1D and T2D. The same is not true for genetic profiles that increase the risk of developing T2D. The conversion of information on genetic susceptibility to the protein pathways that are altered provides an important resource for repurposing or designing novel therapies for the management of diabetes.

De-coding genetic risk variants in type 1 diabetes

The conceptual basis for a genetic predisposition underlying the risk for developing type 1 diabetes (T1D) predates modern human molecular genetics. Over half of the genetic risk has been attributed to the human leukocyte antigen (HLA) class II gene region and to the insulin (INS) gene locus - both thought to confer direction of autoreactivity and tissue specificity. Notwithstanding, questions still remain regarding the functional contributions of a vast array of minor polygenic risk variants scattered throughout the genome that likely influence disease heterogeneity and clinical outcomes. Herein, we summarize the available literature related to the T1D-associated coding variants defined at the time of this review, for the genes PTPN22, IFIH1, SH2B3, CD226, TYK2, FUT2, SIRPG, CTLA4, CTSH and UBASH3A. Data from genotype-selected human cohorts are summarized, and studies from the non-obese diabetic (NOD) mouse are presented to describe the functional impact of these variants in relation to innate and adaptive immunity as well as to β-cell fragility, with expression profiles in tissues and peripheral blood highlighted. The contribution of each variant to progression through T1D staging, including environmental interactions, are discussed with consideration of how their respective protein products may serve as attractive targets for precision medicine-based therapeutics to prevent or suspend the development of T1D.

The RNA-binding profile of the splicing factor SRSF6 in immortalized human pancreatic β-cells

In pancreatic β-cells, the expression of the splicing factor SRSF6 is regulated by GLIS3, a transcription factor encoded by a diabetes susceptibility gene. SRSF6 down-regulation promotes β-cell demise through splicing dysregulation of central genes for β-cells function and survival, but how RNAs are targeted by SRSF6 remains poorly understood. Here, we define the SRSF6 binding landscape in the human pancreatic β-cell line EndoC-βH1 by integrating individual-nucleotide resolution UV cross-linking and immunoprecipitation (iCLIP) under basal conditions with RNA sequencing after SRSF6 knockdown. We detect thousands of SRSF6 bindings sites in coding sequences. Motif analyses suggest that SRSF6 specifically recognizes a purine-rich consensus motif consisting of GAA triplets and that the number of contiguous GAA triplets correlates with increasing binding site strength. The SRSF6 positioning determines the splicing fate. In line with its role in β-cell function, we identify SRSF6 binding sites on regulated exons in several diabetes susceptibility genes. In a proof-of-principle, the splicing of the susceptibility gene LMO7 is modulated by antisense oligonucleotides. Our present study unveils the splicing regulatory landscape of SRSF6 in immortalized human pancreatic β-cells.

Novel genetic risk factors influence progression of islet autoimmunity to type 1 diabetes

Type 1 diabetes arises from the autoimmune destruction of insulin-producing beta-cells of the pancreas, resulting in dependence on exogenously administered insulin to maintain glucose homeostasis. In this study, our aim was to identify genetic risk factors that contribute to progression from islet autoimmunity to clinical type 1 diabetes. We analyzed 6.8 million variants derived from whole genome sequencing of 160 islet autoantibody positive subjects, including 87 who had progressed to type 1 diabetes. The Cox proportional-hazard model for survival analysis was used to identify genetic variants associated with progression. We identified one novel region, 20p12.1 (TASP1; genome-wide P < 5 × 10^-8) and three regions, 1q21.3 (MRPS21-PRPF3), 2p25.2 (NRIR), 3q22.1 (COL6A6), with suggestive evidence of association (P < 8.5 × 10^-8) with progression from islet autoimmunity to type 1 diabetes. Once islet autoimmunity is initiated, functional mapping identified two critical pathways, response to viral infections and interferon signaling, as contributing to disease progression. These results provide evidence that genetic pathways involved in progression from islet autoimmunity differ from those pathways identified once disease has been established. These results support the need for further investigation of genetic risk factors that modulate initiation and progression of subclinical disease to inform efforts in development of novel strategies for prediction and intervention of type 1 diabetes.

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.

Transcription factor GLIS3: Critical roles in thyroid hormone biosynthesis, hypothyroidism, pancreatic beta cells and diabetes

GLI-Similar 3 (GLIS3) is a member of the GLIS subfamily of Krüppel-like zinc finger transcription factors that functions as an activator or repressor of gene expression. Study of GLIS3-deficiency in mice and humans revealed that GLIS3 plays a critical role in the regulation of several biological processes and is implicated in the development of various diseases, including hypothyroidism and diabetes. This was supported by genome-wide association studies that identified significant associations of common variants in GLIS3 with increased risk of these pathologies. To obtain insights into the causal mechanisms underlying these diseases, it is imperative to understand the mechanisms by which this protein regulates the development of these pathologies. Recent studies of genes regulated by GLIS3 led to the identification of a number of target genes and have provided important molecular insights by which GLIS3 controls cellular processes. These studies revealed that GLIS3 is essential for thyroid hormone biosynthesis and identified a critical function for GLIS3 in the generation of pancreatic β cells and insulin gene transcription. These observations raised the possibility that the GLIS3 signaling pathway might provide a potential therapeutic target in the management of diabetes, hypothyroidism, and other diseases. To develop such strategies, it will be critical to understand the upstream signaling pathways that regulate the activity, expression and function of GLIS3. Here, we review the recent progress on the molecular mechanisms by which GLIS3 controls key functions in thyroid follicular and pancreatic β cells and how this causally relates to the development of hypothyroidism and diabetes.

DNA methylation near the INS gene is associated with INS genetic variation (rs689) and type 1 diabetes in the Diabetes Autoimmunity Study in the Young

OBJECTIVE

Mechanisms underlying the role of non-human leukocyte antigen (HLA) genetic risk variants in type 1 diabetes (T1D) are poorly understood. We aimed to test the association between methylation and non-HLA genetic risk.

METHODS

We conducted a methylation quantitative trait loci (mQTL) analysis in a nested case-control study from the Dietary Autoimmunity Study in the Young. Controls (n = 83) were frequency-matched to T1D cases (n = 83) based on age, race/ethnicity, and sample availability. We evaluated 13 non-HLA genetic markers known be associated with T1D. Genome-wide methylation profiling was performed on peripheral blood samples collected prior to T1D using the Illumina 450 K (discovery set) and infinium methylation EPIC beadchip (EPIC validation) platforms. Linear regression models, adjusting for age and sex, were used to test to each single nucleotide polymorphism (SNP) -probe combination. Logistic regression models were used to test the association between T1D and methylation levels among probes with a significant mQTL. A meta-analysis was used to combine odds ratios from the two platforms.

RESULTS

We identified 10 SNP-methylation probe pairs (false discovery rate (FDR) adjusted P < .05 and validation P < .05). Probes were associated with the GSDMB, C1QTNF6, IL27, and INS genes. The cg03366382 (OR: 1.9, meta-P = .0495), cg21574853 (OR: 2.5, meta-P = .0232), and cg25336198 (odds ratio: 6.6, meta-P = .0081) probes were significantly associated with T1D. The three probes were located upstream from the INS transcription start site.

CONCLUSIONS

We confirmed an association between DNA methylation and rs689 that has been identified in related studies. Measurements in our study preceded the onset of T1D suggesting methylation may have a role in the relationship between INS variation and T1D development.

HLA class II genotyping of admixed Brazilian patients with type 1 diabetes according to self-reported color/race in a nationwide study

The HLA region is responsible for almost 50% of the genetic risk of type 1 diabetes (T1D). However, haplotypes and their effects on risk or protection vary among different ethnic groups, mainly in an admixed population. We aimed to evaluate the HLA class II genetic profile of Brazilian individuals with T1D and its relationship with self-reported color/race. This was a nationwide multicenter study conducted in 10 Brazilian cities. We included 1,019 T1D individuals and 5,116 controls matched for the region of birth and self-reported color/race. Control participants belonged to the bone marrow transplant donor registry of Brazil (REDOME). HLA-class II alleles (DRB1, DQA1, and DQB1) were genotyped using the SSO and NGS methods. The most frequent risk and protection haplotypes were HLA~DRB1*03:01~DQA1*05:01 g~DQB1*02:01 (OR 5.8, p < 0.00001) and HLA~DRB1*07:01~DQA1*02:01~DQB1*02:02 (OR 0.54, p < 0.0001), respectively, regardless of self-reported color/race. Haplotypes HLA~DRB1*03:01~DQA1*05:01 g~DQB1*02:01 and HLA~DRB1*04:02~DQA1*03:01 g~DQB1*03:02 were more prevalent in the self-reported White group than in the Black group (p = 0.04 and p = 0.02, respectively). The frequency of haplotype HLA~DRB1*09:01~DQA1*03:01 g~DQB1*02:02 was higher in individuals self-reported as Black than White (p = <0.00001). No difference between the Brazilian geographical regions was found. Individuals with T1D presented differences in frequencies of haplotypes within self-reported color/race, but the more prevalent haplotypes, regardless of self-reported color/race, were the ones described previously in Europeans. We hypothesize that, in the T1D population of Brazil, although highly admixed, the disease risk alleles come mostly from Europeans as a result of centuries of colonization and migration.

The rs11755527 polymorphism in the BACH2 gene and type 1 diabetes mellitus: case control study in a Brazilian population

Objective Type 1 diabetes mellitus (T1DM) is an autoimmune disorder caused by a complex interaction between environmental and genetic risk factors. BTB domain and CNC homolog 2 (BACH2) gene encodes a transcription factor that acts on the differentiation and formation of B and T lymphocytes. BACH2 is also involved in the suppression of apoptosis and inflammation in pancreatic beta-cells, indicating a role for it in the development of T1DM. Therefore, the aim of this study was to evaluate the association of the BACH2 rs11755527 single nucleotide polymorphism (SNP) with T1DM. Subjects and methods This case-control study comprised 475 patients with T1DM and 598 nondiabetic individuals. The BACH2 rs11755527 (C/G) SNP was genotyped using real-time PCR with TaqMan MGB probes. Results Genotype distributions of rs11755527 SNP were in accordance with frequencies predicted by the Hardy-Weinberg equilibrium in case and control groups and were similar between groups (P = 0.729). The minor allele frequency was 43.6% in cases and 42.5% in controls (P = 0.604). Moreover, the G allele frequency did not differ between groups when considering different inheritance models and adjusting for age, gender, body mass index, and HLA DR/DQ genotypes of high-risk for T1DM. Although, well-known high-risk T1DM HLA DR/DQ genotypes were associated with T1DM in our population [OR= 7.42 (95% CI 3.34 - 17.0)], this association was not influenced by the rs11755527 SNP. Conclusion The BACH2 rs11755527 SNP seems not to be associated with T1DM in a Brazilian population.

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.

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.

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.

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.

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.

Marie M, Stickney K, VanBuecken D, Vellek B, Webber C, Allen L, Bollyk J, Hilderman N, Ismail H, Lamola S, Sanda S, Vendettuoli H, Tridgell D, Monzavi R, Bock M, Fisher L, Halvorson M, Jeandron D, Kim M, Wood J, Geffner M, Kaufman F, Parkman R, Salazar C, Goland R, Clynes R, Cook S, Freeby M, Gallagher MP, Gandica R, Greenberg E, Kurland A, Pollak S, Wolk A, Chan M, Koplimae L, Levine E, Smith K, Trast J, DiMeglio L, Blum J, Evans-Molina C, Hufferd R, Jagielo B, Kruse C, Patrick V, Rigby M, Spall M, Swinney K, Terrell J, Christner L, Ford L, Lynch S, Menendez M, Merrill P, Pescovitz M, Rodriguez H, Alleyn C, Baidal D, Fay S, Gaglia J, Resnick B, Szubowicz S, Weir G, Benjamin R, Conboy D, deManbey A, Jackson R, Jalahej H, Orban T, Ricker A, Wolfsdorf J, Zhang HH, Wilson D, Aye T, Baker B, Barahona K, Buckingham B, Esrey K, Esrey T, Fathman G, Snyder R, Aneja B, Chatav M, Espinoza O, Frank E, Liu J, Perry J, Pyle R, Rigby A, Riley K, Soto A, Gitelman S, Adi S, Anderson M, Berhel A, Breen K, Fraser K, Gerard-Gonzalez A, Jossan P, Lustig R, Moassesfar S, Mugg A, Ng D, Prahalod P, Rangel-Lugo M, Sanda S, Tarkoff J, Torok C, Wesch R, Aslan I, Buchanan J, Cordier J, Hamilton C, Hawkins L, Ho T, Jain A, Ko K, Lee T, Phelps S, Rosenthal S, Sahakitrungruang T, Stehl L, Taylor L, Wertz M, Wong J, Philipson L, Briars R, Devine N, Littlejohn E, Grant T, Gottlieb P, Klingensmith G, Steck A, Alkanani A, Bautista K, Bedoy R, Blau A, Burke B, Cory L, Dang M, Fitzgerald-Miller L, Fouts A, Gage V, Garg S, Gesauldo P, Gutin R, Hayes C, Hoffman M, Ketchum K, Logsden-Sackett N, Maahs D, Messer L, Meyers L, Michels A, Peacock S, Rewers M, Rodriguez P, Sepulbeda F, Sippl R, Steck A, Taki I, Tran BK, Tran T, Wadwa RP, Zeitler P, Barker J, Barry S, Birks L, Bomsburger L, Bookert T, Briggs L, Burdick P, Cabrera R, Chase P, Cobry E, Conley A, Cook G, Daniels J, DiDomenico D, Eckert J, Ehler A, Eisenbarth G, Fain P, Fiallo-Scharer R, Frank N, Goettle H, Haarhues M, Harris S, Horton L, Hutton J, Jeffrrey J, Jenison R, Jones K, Kastelic W, King MA, Lehr D, Lungaro J, Mason K, Maurer H, Nguyen L, Proto A, Realsen J, Schmitt K, Schwartz M, Skovgaard S, Smith J, Vanderwel B, Voelmle M, Wagner R, Wallace A, Walravens P, Weiner L, Westerhoff B, Westfall E, Widmer K, Wright H, Schatz D, Abraham A, Atkinson M, Cintron M, Clare-Salzler M, Ferguson J, Haller M, Hosford J, Mancini D, Rohrs H, Silverstein J, Thomas J, Winter W, Cole G, Cook R, Coy R, Hicks E, Lewis N, Marks J, Pugliese A, Blaschke C, Matheson D, Sanders-Branca N, Sosenko J, Arazo L, Arce R, Cisneros M, Sabbag S, Moran A, Gibson C, Fife B, Hering B, Kwong C, Leschyshyn J, Nathan B, Pappenfus B, Street A, Boes MA, Eck SP, Finney L, Fischer TA, Martin A, Muzamhindo CJ, Rhodes M, Smith J, Wagner J, Wood B, Becker D, Delallo K, Diaz A, Elnyczky B, Libman I, Pasek B, Riley K, Trucco M, Copemen B, Gwynn D, Toledo F, Rodriguez H, Bollepalli S, Diamond F, Eyth E, Henson D, Lenz A, Shulman D, Raskin P, Adhikari S, Dickson B, Dunnigan E, Lingvay I, Pruneda L, Ramos-Roman M, Raskin P, Rhee C, Richard J, Siegelman M, Sturges D, Sumpter K, White P, Alford M, Arthur J, Aviles-Santa ML, Cordova E, Davis R, Fernandez S, Fordan S, Hardin T, Jacobs A, Kaloyanova P, Lukacova-Zib I, Mirfakhraee S, Mohan A, Noto H, Smith O, Torres N, Wherrett D, Balmer D, Eisel L, Kovalakovska R, Mehan M, Sultan F, Ahenkorah B, Cevallos J, Razack N, Ricci MJ, Rhode A, Srikandarajah M, Steger R, Russell WE, Black M, Brendle F, Brown A, Moore D, Pittel E, Robertson A, Shannon A, Thomas JW, Herold K, Feldman L, Sherwin R, Tamborlane W, Weinzimer S, Toppari J, Kallio T, Kärkkäinen M, Mäntymäki E, Niininen T, Nurmi B, Rajala P, Romo M, Suomenrinne S, Näntö-Salonen K, Simell O, Simell T, Bosi E, Battaglia M, Bianconi E, Bonfanti R, Grogan P, Laurenzi A, Martinenghi S, Meschi F, Pastore M, Falqui L, Muscato MT, Viscardi M, Castleden H, Farthing N, Loud S, Matthews C, McGhee J, Morgan A, Pollitt J, Elliot-Jones R, Wheaton C, Knip M, Siljander H, Suomalainen H, Colman P, Healy F, Mesfin S, Redl L, Wentworth J, Willis J, Farley M, Harrison L, Perry C, Williams F, Mayo A, Paxton J, Thompson V, Volin L, Fenton C, Carr L, Lemon E, Swank M, Luidens M, Salgam M, Sharma V, Schade D, King C, Carano R, Heiden J, Means N, Holman L, Thomas I, Madrigal D, Muth T, Martin C, Plunkett C, Ramm C, Auchus R, Lane W, Avots E, Buford M, Hale C, Hoyle J, Lane B, Muir A, Shuler S, Raviele N, Ivie E, Jenkins M, Lindsley K, Hansen I, Fadoju D, Felner E, Bode B, Hosey R, Sax J, Jefferies C, Mannering S, Prentis R, She J, Stachura M, Hopkins D, Williams J, Steed L, Asatapova E, Nunez S, Knight S, Dixon P, Ching J, Donner T, Longnecker S, Abel K, Arcara K, Blackman S, Clark L, Cooke D, Plotnick L, Levin P, Bromberger L, Klein K, Sadurska K, Allen C, Michaud D, Snodgrass H, Burghen G, Chatha S, Clark C, Silverberg J, Wittmer C, Gardner J, LeBoeuf C, Bell P, McGlore O, Tennet H, Alba N, Carroll M, Baert L, Beaton H, Cordell E, Haynes A, Reed C, Lichter K, McCarthy P, McCarthy S, Monchamp T, Roach J, Manies S, Gunville F, Marosok L, Nelson T, Ackerman K, Rudolph J, Stewart M, McCormick K, May S, Falls T, Barrett T, Dale K, Makusha L, McTernana C, Penny-Thomas K, Sullivan K, Narendran P, Robbie J, Smith D, Christensen R, Koehler B, Royal C, Arthur T, Houser H, Renaldi J, Watsen S, Wu P, Lyons L, House B, Yu J, Holt H, Nation M, Vickers C, Watling R, Heptulla R, Trast J, Agarwal C, Newell D, Katikaneni R, Gardner C, Del Rio A, Logan A, Collier H, Rishton C, Whalley G, Ali A, Ramtoola S, Quattrin T, Mastrandea L, House A, Ecker M, Huang C, Gougeon C, Ho J, Pacuad D, Dunger D, May J, O’Brien C, Acerini C, Salgin B, Thankamony A, Williams R, Buse J, Fuller G, Duclos M, Tricome J, Brown H, Pittard D, Bowlby D, Blue A, Headley T, Bendre S, Lewis K, Sutphin K, Soloranzo C, Puskaric J, Madison H, Rincon M, Carlucci M, Shridharani R, Rusk B, Tessman E, Huffman D, Abrams H, Biederman B, Jones M, Leathers V, Brickman W, Petrie P, Zimmerman D, Howard J, Miller L, Alemzadeh R, Mihailescu D, Melgozza-Walker R, Abdulla N, Boucher-Berry C, Ize-Ludlow D, Levy R, Swenson Brousell C, Scott R, Heenan H, Lunt H, Kendall D, Willis J, Darlow B, Crimmins N, Edler D, Weis T, Schultz C, Rogers D, Latham D, Mawhorter C, Switzer C, Spencer W, Konstantnopoulus P, Broder S, Klein J, Bachrach B, Gardner M, Eichelberger D, Knight L, Szadek L, Welnick G, Thompson B, Hoffman R, Revell A, Cherko J, Carter K, Gilson E, Haines J, Arthur G, Bowen B, Zipf W, Graves P, Lozano R, Seiple D, Spicer K, Chang A, Fregosi J, Harbinson J, Paulson C, Stalters S, Wright P, Zlock D, Freeth A, Victory J, Maheshwari H, Maheshwari A, Holmstrom T, Bueno J, Arguello R, Ahern J, Noreika L, Watson V, Hourse S, Breyer P, Kissel C, Nicholson Y, Pfeifer M, Almazan S, Bajaj J, Quinn M, Funk K, McCance J, Moreno E, Veintimilla R, Wells A, Cook J, Trunnel S, Transue D, Surhigh J, Bezzaire D, Moltz K, Zacharski E, Henske J, Desai S, Frizelis K, Khan F, Sjoberg R, Allen K, Manning P, Hendry G, Taylor B, Jones S, Couch R, Danchak R, Lieberman D, Strader W, Bencomo M, Bailey T, Bedolla L, Roldan C, Moudiotis C, Vaidya B, Anning C, Bunce S, Estcourt S, Folland E, Gordon E, Harrill C, Ireland J, Piper J, Scaife L, Sutton K, Wilkins S, Costelloe M, Palmer J, Casas L, Miller C, Burgard M, Erickson C, Hallanger-Johnson J, Clark P, Taylor W, Galgani J, Banerjee S, Banda C, McEowen D, Kinman R, Lafferty A, Gillett S, Nolan C, Pathak M, Sondrol L, Hjelle T, Hafner S, Kotrba J, Hendrickson R, Cemeroglu A, Symington T, Daniel M, Appiagyei-Dankah Y, Postellon D, Racine M, Kleis L, Barnes K, Godwin S, McCullough H, Shaheen K, Buck G, Noel L, Warren M, Weber S, Parker S, Gillespie I, Nelson B, Frost C, Amrhein J, Moreland E, Hayes A, Peggram J, Aisenberg J, Riordan M, Zasa J, Cummings E, Scott K, Pinto T, Mokashi A, McAssey K, Helden E, Hammond P, Dinning L, Rahman S, Ray S, Dimicri C, Guppy S, Nielsen H, Vogel C, Ariza C, Morales L, Chang Y, Gabbay R, Ambrocio L, Manley L, Nemery R, Charlton W, Smith P, Kerr L, Steindel-Kopp B, Alamaguer M, Tabisola-Nuesca E, Pendersen A, Larson N, Cooper-Olviver H, Chan D, Fitz-Patrick D, Carreira T, Park Y, Ruhaak R, Liljenquist D. 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.

TULA-family proteins: Jacks of many trades and then some

UBASH3/STS/TULA is a novel two-member family, which exerts several key regulatory effects in multiple cell types. UBASH3B/STS-1/TULA-2 is a highly active protein tyrosine phosphatase; its major target appears to be a specific regulatory site of protein tyrosine kinases of the Syk family, dephosphorylation of which inhibits Syk and Zap-70 kinases and suppresses receptor signaling mediated by these kinases. UBASH3A/STS-2/TULA exhibits substantial homology to UBASH3B/STS-1/TULA-2, but possesses only a small fraction of phosphatase activity of UBASH3B/STS-1/TULA-2, and thus, its regulatory effect may be based also on the phosphatase-independent mechanisms. Critical physiologic effects of these proteins have been demonstrated in T lymphocytes, platelets, stem cells, and other important cell types. These proteins have also been shown to play a key role in such pathologic conditions as autoimmunity, cancer, and thrombosis. The review focuses on the recent studies of this important family of cellular regulators.

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.

Identification of non-HLA genes associated with development of islet autoimmunity and type 1 diabetes in the prospective TEDDY cohort

Traditional linkage analysis and genome-wide association studies have identified HLA and a number of non-HLA genes as genetic factors for islet autoimmunity (IA) and type 1 diabetes (T1D). However, the relative risk associated with previously identified non-HLA genes is usually very small as measured in cases/controls from mixed populations. Genetic associations for IA and T1D may be more accurately assessed in prospective cohorts. In this study, 5806 subjects from the TEDDY (The Environmental Determinants of Diabetes in the Young) study, an international prospective cohort study, were genotyped for 176,586 SNPs on the ImmunoChip. Cox proportional hazards analyses were performed to discover the SNPs associated with the risk for IA, T1D, or both. Three regions were associated with the risk of developing any persistent confirmed islet autoantibody: one known region near SH2B3 (HR = 1.35, p = 3.58 × 10-7) with Bonferroni-corrected significance and another known region near PTPN22 (HR = 1.46, p = 2.17 × 10-6) and one novel region near PPIL2 (HR = 2.47, p = 9.64 × 10-7) with suggestive evidence (p < 10-5). Two known regions (PTPN22: p = 2.25 × 10-6, INS; p = 1.32 × 10-7) and one novel region (PXK/PDHB: p = 8.99 × 10-6) were associated with the risk for multiple islet autoantibodies. First appearing islet autoantibodies differ with respect to association. Two regions (INS: p = 5.67 × 10-6 and TTC34/PRDM16: 6.45 × 10-6) were associated if the fist appearing autoantibody was IAA and one region (RBFOX1: p = 8.02 × 10-6) was associated if the first appearing autoantibody was GADA. The analysis of T1D identified one region already known to be associated with T1D (INS: p = 3.13 × 10-7) and three novel regions (RNASET2, PLEKHA1, and PPIL2; 5.42 × 10-6 > p > 2.31 × 10-6). These results suggest that a number of low frequency variants influence the risk of developing IA and/or T1D and these variants can be identified by large prospective cohort studies using a survival analysis approach.

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.

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.

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.

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.

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.

Genetics and its potential to improve type 1 diabetes care

PURPOSE OF REVIEW

The genetic basis of type 1 diabetes (T1D) is being characterized through DNA sequence variation and cell type specificity. This review discusses the current understanding of the genes and variants implicated in risk of T1D and how genetic information can be used in prediction, intervention and components of clinical care.

RECENT FINDINGS

Fine mapping and functional studies has provided resolution of the heritable basis of T1D risk, incorporating novel insights on the dominant role of human leukocyte antigen (HLA) genes as well as the lesser impact of non-HLA genes. Evaluation of T1D-associated single nucleotide polymorphisms (SNPs), there is enrichment of genetic effects restricted to specific immune cell types (CD4 and CD8 T cells, CD19 B cells and CD34 stem cells), suggesting pathways to improved prediction. In addition, T1D-associated SNPs have been used to generate genetic risk scores (GRS) as a tool to distinguish T1D from type 2 diabetes (T2D) and to provide prediagnostic data to target those for autoimmunity screening (e.g. islet autoantibodies) as a prelude for continuous monitoring and entry into intervention trials.

SUMMARY

Genetic susceptibility accounts for nearly one-half of the risk for T1D. Although the T1D-associated SNPs in white populations account for nearly 90% of the genetic risk, with high sensitivity and specificity, the low prevalence of T1D makes the T1D GRS of limited utility. However, identifying those with highest genetic risk may permit early and targeted immune monitoring to diagnose T1D months prior to clinical onset.

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.

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.

Factors affecting long-term efficacy of T regulatory cell-based therapy in type 1 diabetes

Background

Recent studies suggest that immunotherapy using T regulatory cells (Tregs) prolongs remission in type 1 diabetes (T1DM). Here, we report factors that possibly affect the efficacy of this treatment.

Methods

The metabolic and immune background of 12 children with recently diagnosed T1DM, as well as that of untreated subjects, during a 2-year follow-up is presented. Patients were treated with up to 30 × 10⁶/kg b.w. of autologous expanded CD3⁺CD4⁺CD25^highCD127⁻ Tregs.

Results

The disease progressed and all patients were insulin-dependent 2 years after inclusion. The β-cell function measured by c-peptide levels and the use of insulin were the best preserved in patients treated with two doses of Tregs (3/6 in remission), less so after one dose (1/6 in remission) and the worst in untreated controls (no remissions). Increased levels of Tregs could be seen in peripheral blood after their adoptive transfer together with the shift from naïve CD62L⁺CD45RA⁺ to memory CD62L⁺CD45RA⁻ Tregs. Increasing serum levels of proinflammatory cytokines were found: IL6 increased in all subjects, while IL1 and TNFα increased only in untreated group. Therapeutic Tregs were dependent on IL2, and their survival could be improved by other lymphocytes.

Conclusions

The disease progression was associated with changing proportions of naïve and memory Tregs and slowly increasing proinflammatory activity, which was only partially controlled by the administered Tregs. The therapeutic cells were highly dependent on IL2. We conclude that the therapy should be administered at the earliest to protect the highest possible mass of islets and also to utilize the preserved content of Tregs in the earlier phases of T1DM.

Trial registrationhttp://www.controlled-trials.com/ISRCTN06128462; registered retrospectively

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-1090-7) contains supplementary material, which is available to authorized users.

A novel approach for the analysis of longitudinal profiles reveals delayed progression to type 1 diabetes in a subgroup of multiple-islet-autoantibody-positive children

AIMS/HYPOTHESIS

Progression to type 1 diabetes in children and adolescents is not uniform. Based on individual genetic background and environment, islet autoimmunity may develop at variable age, exhibit different autoantibody profiles and progress to clinical diabetes at variable rates. Here, we aimed to quantify the qualitative dynamics of sequential islet autoantibody profiles in order to identify longitudinal patterns that stratify progression rates to type 1 diabetes in multiple-autoantibody-positive children.

METHODS

Qualitative changes in antibody status on follow-up and progression rate to diabetes were analysed in 88 children followed from birth in the prospective BABYDIAB study who developed multiple autoantibodies against insulin (IAA), GAD (GADA), insulinoma-associated antigen-2 (IA-2A) and/or zinc transporter 8 (ZnT8A). An algorithm was developed to define similarities in sequential autoantibody profiles and hierarchical clustering was performed to group children with similar profiles.

RESULTS

We defined nine clusters that distinguished children with respect to their sequential profiles of IAA, GADA, IA-2A and ZnT8A. Progression from first autoantibody appearance to clinical diabetes between clusters ranged from 6% (95% CI [0, 16.4]) to 73% (28.4, 89.6) within 5 years. Delayed progression was observed in children who were positive for only two autoantibodies, and for a cluster of 12 children who developed three or four autoantibodies but were IAA-negative in their last samples, nine of whom lost IAA positivity during follow-up. Among all children who first seroconverted to IAA positivity and developed at least two other autoantibodies (n = 57), the 10 year risk of diabetes was 23% (0, 42.9) in those who became IAA-negative during follow-up compared with 76% (58.7, 85.6) in those who remained IAA-positive (p = 0.004).

CONCLUSIONS/INTERPRETATION

The novel clustering approach provides a tool for stratification of islet autoantibody-positive individuals that has prognostic relevance, and new opportunities in elucidating disease mechanisms. Our data suggest that losing IAA reactivity is associated with delayed progression to type 1 diabetes in multiple-islet-autoantibody-positive children.

The relationship between BMI and insulin resistance and progression from single to multiple autoantibody positivity and type 1 diabetes among TrialNet Pathway to Prevention participants

AIMS/HYPOTHESIS

The incidence of type 1 diabetes is increasing at a rate of 3-5% per year. Genetics cannot fully account for this trend, suggesting an influence of environmental factors. The accelerator hypothesis proposes an effect of metabolic factors on type 1 diabetes risk. To test this in the TrialNet Pathway to Prevention (PTP) cohort, we analysed the influence of BMI, weight status and insulin resistance on progression from single to multiple islet autoantibodies (Aab) and progression from normoglycaemia to diabetes.

METHODS

HOMA1-IR was used to estimate insulin resistance in Aab-positive PTP participants. Cox proportional hazards models were used to evaluate the effects of BMI, BMI percentile (BMI%), weight status and HOMA1-IR on the progression of autoimmunity or the development of diabetes.

RESULTS

Data from 1,310 single and 1,897 multiple Aab-positive PTP participants were included. We found no significant relationships between BMI, BMI%, weight status or HOMA1-IR and the progression from one to multiple Aabs. Similarly, among all Aab-positive participants, no significant relationships were found between BMI, weight status or HOMA1-IR and progression to diabetes. Diabetes risk was modestly increased with increasing BMI% among the entire cohort, in obese participants 13-20 years of age and with increasing HOMA1-IR in adult Aab-positive participants.

CONCLUSIONS/INTERPRETATION

Analysis of the accelerator hypothesis in the TrialNet PTP cohort does not suggest a broad influence of metabolic variables on diabetes risk. Efforts to identify other potentially modifiable environmental factors should continue.

Predictors of slow progression to diabetes in children with multiple islet autoantibodies

Although most children with multiple islet autoantibodies develop type 1 diabetes, rate of progression is highly variable. The goal of this study was to explore potential factors involved in rate of progression to diabetes in children with multiple islet autoantibodies. The Diabetes Autoimmunity Study in the Young (DAISY) has followed 118 children with multiple islet autoantibodies for progression to diabetes. After excluding 27 children currently diabetes-free but followed for <10 years, the study population was grouped into: rapid progressors (N = 39) who developed diabetes in <5 years; moderate progressors (N = 25), diagnosed with diabetes within 5-10 years; and slow progressors (N = 27), diabetes-free for >10 years. Islet autoimmunity appeared at 4.0 ± 3.5, 3.2 ± 1.8 and 5.8 ± 3.1 years of age in rapid, moderate and slow progressors, respectively (p = 0.006). Insulin autoantibody levels were lower in slow progressors compared to moderate and rapid progressors. The groups did not differ by gender, ethnicity, family history, susceptibility HLA and non-HLA genes. The rate of development of individual islet autoantibodies including mIAA, GADA, IA-2A and ZnT8A were all slower in the slow versus moderate/rapid progressors. In multivariate analyses, older age at seroconversion and lower initial mIAA levels independently predicted slower progression to diabetes. Later onset of islet autoimmunity and lower autoantibody levels predicted slower progression to diabetes among children with multiple islet autoantibodies. These factors may need to be considered in the design of trials to prevent type 1 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.

Non-HLA gene effects on the disease process of type 1 diabetes: From HLA susceptibility to overt disease

In addition to the HLA region numerous other gene loci have shown association with type 1 diabetes. How these polymorphisms exert their function has not been comprehensively described, however. We assessed the effect of 39 single nucleotide polymorphisms (SNP) on the development of autoantibody positivity, on progression from autoantibody positivity to clinical disease and on the specificity of the antibody initiating the autoimmune process in 521 autoantibody-positive and 989 control children from a follow-up study starting from birth. Interestingly, PTPN2 rs45450798 gene polymorphism was observed to strongly affect the progression rate of beta-cell destruction after the appearance of humoral beta-cell autoimmunity. Moreover, primary autoantigen dependent associations were also observed as effect of the IKZF4-ERBB3 region on the progression rate of β-cell destruction was restricted to children with GAD antibodies as their first autoantibody whereas the effect of the INS rs 689 polymorphism was observed among subjects with insulin as the primary autoantigen. In the whole study cohort, INS rs689, PTPN22 rs2476601 and IFIH1 rs1990760 polymorphisms were associated with the appearance of beta-cell autoantibodies. These findings provide new insights into the role of genetic factors implicated in the pathogenesis of type 1 diabetes. The effect of some of the gene variants is restricted to control the initiation of β-cell autoimmunity whereas others modify the destruction rate of the β-cells. Furthermore, signs of primary autoantigen-related pathways were detected.