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

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

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

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.

Based on systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for diabetes

Purpose

Diabetes and its complications cause a heavy burden of disease worldwide. In recent years, Mendelian randomization (MR) has been widely used to discover the pathogenesis and epidemiology of diseases, as well as to discover new therapeutic targets. Therefore, based on systematic "druggable" genomics, we aim to identify new therapeutic targets for diabetes and analyze its pathophysiological mechanisms to promote its new therapeutic strategies.

Material and method

We used double sample MR to integrate the identified druggable genomics to evaluate the causal effect of quantitative trait loci (eQTLs) expressed by druggable genes in blood on type 1 and 2 diabetes (T1DM and T2DM). Repeat the study using different data sources on diabetes and its complications to verify the identified genes. Not only that, we also use Bayesian co-localization analysis to evaluate the posterior probabilities of different causal variations, shared causal variations, and co-localization probabilities to examine the possibility of genetic confounding. Finally, using diabetes markers with available genome-wide association studies data, we evaluated the causal relationship between established diabetes markers to explore possible mechanisms.

Result

Overall, a total of 4,477 unique druggable genes have been gathered. After filtering using methods such as Bonferroni significance (P<1.90e-05), the MR Steiger directionality test, Bayesian co-localization analysis, and validation with different datasets, Finally, 7 potential druggable genes that may affect the results of T1DM and 7 potential druggable genes that may affect the results of T2DM were identified. Reverse MR suggests that C4B may play a bidirectional role in the pathogenesis of T1DM, and none of the other 13 target genes have a reverse causal relationship. And the 7 target genes in T2DM may each affect the biomarkers of T2DM to mediate the pathogenesis of T2DM.

Conclusion

This study provides genetic evidence supporting the potential therapeutic benefits of targeting seven druggable genes, namely MAP3K13, KCNJ11, REG4, KIF11, CCNE2, PEAK1, and NRBP1, for T2DM treatment. Similarly, targeting seven druggable genes, namely ERBB3, C4B, CD69, PTPN22, IL27, ATP2A1, and LT-β, has The potential therapeutic benefits of T1DM treatment. This will provide new ideas for the treatment of diabetes and also help to determine the priority of drug development for diabetes.

Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives

OBJECTIVE

We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) are associated with C-peptide preservation before type 1 diabetes diagnosis.

METHODS

We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known SNPs and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis.

RESULTS

Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β=-0.06, P<0.0001) and multivariate (β=-0.03, P=0.005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β=-0.11, P=0.002) and multivariate (β=-0.06, P=0.018) analyses.

CONCLUSIONS

The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC prior to diagnosis of type 1 diabetes, with implications for the design of prevention trials.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

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.

Imaging in Type 1 Diabetes, Current Perspectives and Directions

Type 1 diabetes (T1D) is characterized by the autoimmune-mediated attack of insulin-producing beta cells in the pancreas, leading to reliance on exogenous insulin to control a patient's blood glucose levels. As progress is being made in understanding the pathophysiology of the disease and how to better develop therapies to treat it, there is an increasing need for monitoring technologies to quantify beta cell mass and function throughout T1D progression and beta cell replacement therapy. Molecular imaging techniques offer a possible solution through both radiologic and non-radiologic means including positron emission tomography, magnetic resonance imaging, electron paramagnetic resonance imaging, and spatial omics. This commentary piece outlines the role of molecular imaging in T1D research and highlights the need for further applications of such methodologies in T1D.

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Utility of genetic risk scores in type 1 diabetes

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

HLA Class I Association With Autoimmune Diabetes in Chinese People: Distinct Implications in Classic Type 1 Diabetes and LADA

CONTEXT

We aimed to investigate whether human leukocyte antigen (HLA) Class I loci differentially modulated the risk for and clinical features of Chinese people with classic type 1 diabetes (T1D) and latent autoimmune diabetes in adults (LADA).

METHODS

In this case-control study, genotypes of HLA-A, -B, -C, -DRB1, -DQA1, and -DQB1 loci were obtained from 1067 cases with classic T1D, 1062 cases with LADA, and 1107 normal controls using next-generation sequencing.

RESULTS

Despite 4 alleles shared between classic T1D and LADA (protective: A*02:07 and B*46:01; susceptible: B*54:01 and C*08:01), 7 Class I alleles conferred risk exclusively for classic T1D (A*24:02, B*15:02, B*15:18, B*39:01, B*40:06, B*48:01, and C*07:02) whereas only A*02:01 was an additional risk factor for LADA. Class I alleles affected a wide spectrum of T1D clinical features, including positive rate of protein tyrosine phosphatase autoantibody and zinc transporter 8 autoantibody (A*24:02), C-peptide levels (A*24:02), and age at diagnosis (B*46:01, C*01:02, B*15:02, C*07:02, and C*08:01). By contrast, except for the detrimental effect of C*08:01 on C-peptide concentrations in LADA, no other Class I associations with clinical characteristics of LADA could be reported. The addition of Class I alleles refined the risk model consisting only of DR-DQ data in classic T1D while the overall predictive value of the LADA risk model comprising both Class I and II information was relatively low.

CONCLUSION

The attenuated HLA Class I susceptibility to LADA was indicative of a less deleterious immunogenetic nature compared with classic T1D. These autoimmune diabetes-related Class I variants might serve as additional markers in future screening among Chinese people.

Genetics of diabetes

Diabetes mellitus is a complicated disease characterized by a complex interplay of genetic, epigenetic, and environmental variables. It is one of the world's fastest-growing diseases, with 783 million adults expected to be affected by 2045. Devastating macrovascular consequences (cerebrovascular disease, cardiovascular disease, and peripheral vascular disease) and microvascular complications (like retinopathy, nephropathy, and neuropathy) increase mortality, blindness, kidney failure, and overall quality of life in individuals with diabetes. Clinical risk factors and glycemic management alone cannot predict the development of vascular problems; multiple genetic investigations have revealed a clear hereditary component to both diabetes and its related complications. In the twenty-first century, technological advancements (genome-wide association studies, next-generation sequencing, and exome-sequencing) have led to the identification of genetic variants associated with diabetes, however, these variants can only explain a small proportion of the total heritability of the condition. In this review, we address some of the likely explanations for this "missing heritability", for diabetes such as the significance of uncommon variants, gene-environment interactions, and epigenetics. Current discoveries clinical value, management of diabetes, and future research directions are also discussed.

Genome-wide aggregated trans-effects on risk of type 1 diabetes: A test of the "omnigenic" sparse effector hypothesis of complex trait genetics

The "omnigenic" hypothesis postulates that the polygenic effects of common SNPs on a typical complex trait are mediated through trans-effects on expression of a relatively sparse set of effector ("core") genes. We tested this hypothesis in a study of 4,964 cases of type 1 diabetes (T1D) and 7,497 controls by using summary statistics to calculate aggregated (excluding the HLA region) trans-scores for gene expression in blood. From associations of T1D with aggregated trans-scores, nine putative core genes were identified, of which three-STAT1, CTLA4 and FOXP3-are genes in which variants cause monogenic forms of autoimmune diabetes. Seven of these genes affect the activity of regulatory T cells, and two are involved in immune responses to microbial lipids. Four T1D-associated genomic regions could be identified as master regulators via trans-effects on gene expression. These results support the sparse effector hypothesis and reshape our understanding of the genetic architecture of T1D.

The countdown to type 1 diabetes: when, how and why does the clock start?

'The clock to type 1 diabetes has started when islet antibodies are first detected', commented George Eisenbarth with regard to the pathogenesis of type 1 diabetes. This review focuses on 'starting the clock', i.e. the initiation of pre-symptomatic islet autoimmunity/the first appearance of islet autoantibodies. In particular, this review addresses why susceptibility to developing islet autoimmunity is greatest in the first 2 years of life and why beta cells are a frequent target of the immune system during this fertile period. A concept for the development of beta cell autoimmunity in childhood is discussed and three factors are highlighted that contribute to this early predisposition: (1) high beta cell activity and potential vulnerability to stress; (2) high rates of and first exposures to infection; and (3) a heightened immune response, with a propensity for T helper type 1 (Th1) immunity. Arguments are presented that beta cell injury, accompanied by activation of an inflammatory immune response, precedes the initiation of autoimmunity. Finally, the implications for strategies aimed at primary prevention for a world without type 1 diabetes are discussed.

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.

Harnessing Human Pluripotent Stem Cell-Derived Pancreatic In Vitro Models for High-Throughput Toxicity Testing and Diabetes Drug Discovery

The long-standing goals in diabetes research are to improve β-cell survival, functionality and increase β-cell mass. Current strategies to manage diabetes progression are still not ideal for sustained maintenance of normoglycemia, thereby increasing demand for the development of novel drugs. Available pancreatic cell lines, cadaveric islets, and their culture methods and formats, either 2D or 3D, allow for multiple avenues of experimental design to address diverse aims in the research setting. More specifically, these pancreatic cells have been employed in toxicity testing, diabetes drug screens, and with careful curation, can be optimized for use in efficient high-throughput screenings (HTS). This has since spearheaded the understanding of disease progression and related mechanisms, as well as the discovery of potential drug candidates which could be the cornerstone for diabetes treatment. This book chapter will touch on the pros and cons of the most widely used pancreatic cells, including the more recent human pluripotent stem cell-derived pancreatic cells, and HTS strategies (cell models, design, readouts) that can be used for the purpose of toxicity testing and diabetes drug discovery.

Successful integration of newborn genetic testing into UK routine screening using prospective consent to determine eligibility for clinical trials

OBJECTIVE

INGR1D (INvestigating Genetic Risk for type 1 Diabetes) was a type 1 diabetes (T1D) genetic screening study established to identify participants for a primary prevention trial (POInT, Primary Oral Insulin Trial).

METHODS

The majority of participants were recruited by research midwives in antenatal clinics from 18 weeks' gestation. Using the NHS Newborn Bloodspot Screening Programme (NBSP) infrastructure, participants enrolled in INGR1D had an extra sample taken from their day 5 bloodspot card sent for T1D genetic screening. Those at an increased risk of T1D were informed of the result, given education about T1D and the opportunity to take part in POInT.

RESULTS

Between April 2018 and November 2020, 66% of women approached about INGR1D chose to participate. 15 660 babies were enrolled into INGR1D and 14 731 blood samples were processed. Of the processed samples, 157 (1%) had confirmed positive results, indicating an increased risk of T1D, of whom a third (n=49) enrolled into POInT (20 families were unable to participate in POInT due to COVID-19 lockdown restrictions).

CONCLUSION

The use of prospective consent to perform personalised genetic testing on samples obtained through the routine NBSP represents a novel mechanism for clinical genetic research in the UK and provides a model for further population-based genetic studies in the newborn.

Incomplete time-series gene expression in integrative study for islet autoimmunity prediction

Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.

Precision medicine in type 1 diabetes

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

Autoantibodies against ATP4A are a feature of the abundant autoimmunity that develops in first-degree relatives of patients with type 1 diabetes

OBJECTIVE

Type 1 diabetes is associated with autoantibodies to different organs that include the gut. The objective of the study was to determine the risk of developing gastric parietal cell autoimmunity in relation to other autoimmunity in individuals with a family history of type 1 diabetes.

METHODS

Autoantibodies to the parietal cell autoantigen, H⁺ /K⁺ ATPase subunit A (ATP4A) was measured in 2218 first-degree relatives of patients with type 1 diabetes, who were prospectively followed from birth for a median of 14.5 years. All were also tested regularly for the development of islet autoantibodies, transglutaminase autoantibodies, and thyroid peroxidase autoantibodies.

RESULTS

The cumulative risk to develop ATP4A autoantibodies was 8.1% (95% CI, 6.6-9.6) by age 20 years with a maximum incidence observed at age 2 years. Risk was increased in females (HR, 1.9; 95% CI, 1.3-2.8; p = 0.0004), relatives with the HLA DR4-DQ8/DR4-DQ8 genotype (HR, 3.4; 95% CI, 1.9-5.9; p < 0.0001) and in participants who also had thyroid peroxidase autoantibodies (HR, 3.7; 95% CI, 2.5-5.5; p < 0.0001). Risk for at least one of ATP4A-, islet-, transglutaminase-, or thyroid peroxidase-autoantibodies was 24.7% (95% CI, 22.6-26.7) by age 20 years and was 47.3% (95% CI, 41.3-53.3) in relatives who had an HLA DR3/DR4-DQ8, DR4-DQ8/DR4-DQ8, or DR3/DR3 genotype (p < 0.0001 vs. other genotypes).

CONCLUSIONS

Relatives of patients with type 1 diabetes who have risk genotypes are at very high risk for the development of autoimmunity against gastric and other organs.

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.

A classification and regression tree analysis identifies subgroups of childhood type 1 diabetes

Background

Diabetes in childhood and adolescence includes autoimmune and non-autoimmune forms with heterogeneity in clinical and biochemical presentations. An unresolved question is whether there are subtypes, endotypes, or theratypes within these forms of diabetes.

Methods

The multivariable classification and regression tree (CART) analysis method was used to identify subgroups of diabetes with differing residual C-peptide levels in patients with newly diagnosed diabetes before 20 years of age (n=1192). The robustness of the model was assessed in a confirmation and prognosis cohort (n=2722).

Findings

The analysis selected age, haemoglobin A1c (HbA1c), and body mass index (BMI) as split parameters that classified patients into seven islet autoantibody-positive and three autoantibody-negative groups. There were substantial differences in genetics, inflammatory markers, diabetes family history, lipids, 25-OH-Vitamin D3, insulin treatment, insulin sensitivity and insulin autoimmunity among the groups, and the method stratified patients with potentially different pathogeneses and prognoses. Interferon-ɣ and/or tumour necrosis factor inflammatory signatures were enriched in the youngest islet autoantibody-positive groups and in patients with the lowest C-peptide values, while higher BMI and type 2 diabetes characteristics were found in older patients. The prognostic relevance was demonstrated by persistent differences in HbA1c at 7 years median follow-up.

Interpretation

This multivariable analysis revealed subgroups of young patients with diabetes that have potential pathogenetic and therapeutic relevance.

Funding

The work was supported by funds from the German Federal Ministry of Education and Research (01KX1818; FKZ 01GI0805; DZD e.V.), the Innovative Medicine Initiative 2 Joint Undertaking INNODIA (grant agreement No. 115797), the German Robert Koch Institute, and the German Diabetes Association.

Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years

CONTEXT

Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease.

OBJECTIVE

This work aimed to determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be used to predict the likelihood that a child will develop T1D by age 6 years.

METHODS

Newborns with human leukocyte antigen (HLA) typing were enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). TEDDY ascertained children in Finland, Germany, Sweden, and the United States. TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at ages 3, 6, and 9 months, 11.4% of whom progressed to T1D by age 6 years. The main outcome measure was a diagnosis of T1D as diagnosed by American Diabetes Association criteria.

RESULTS

Machine learning-based feature selection yielded classifiers based on disparate demographic, immunologic, genetic, and metabolite features. The accuracy of the model using all available data evaluated by the area under a receiver operating characteristic curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the area under the curve significantly. Metabolomics had the largest value when evaluating the accuracy at a low false-positive rate.

CONCLUSION

The metabolite features identified as important for progression to T1D by age 6 years point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood.

The rs705708 A allele of the ERBB3 gene is associated with lower prevalence of diabetic retinopathy and arterial hypertension and with improved renal function in type 1 diabetic patients

INTRODUCTION

The Erb-b2 receptor tyrosine kinase 3 (ERBB3) is involved in autoimmune processes related to type 1 diabetes mellitus (T1DM) pathogenesis. Accordingly, some studies have suggested that single nucleotide polymorphisms (SNPs) in the ERBB3 gene confer risk for T1DM. Proliferation-associated protein 2G4 (PA2G4) is another candidate gene for this disease because it regulates cell proliferation and adaptive immunity. Moreover, PA2G4 regulates ERBB3. To date, no study has evaluated the association of PA2G4 SNPs and T1DM.

AIM

To evaluate the association of ERBB3 rs705708 (G/A) and PA2G4 rs773120 (C/T) SNPs with T1DM and its clinical and laboratory characteristics.

METHODS

This case-control study included 976 white subjects from Southern Brazil, categorized into 501 cases with T1DM and 475 non-diabetic controls. The ERBB3 and PA2G4 SNPs were genotyped by allelic discrimination-real-time PCR.

RESULTS

ERBB3 rs705708 and PA2G4 rs773120 SNPs were not associated with T1DM considering different inheritance models and also when controlling for covariables. However, T1DM patients carrying the ERBB3 rs705708 A allele developed T1DM at an earlier age vs. G/G patients. Interestingly, in the T1DM group, the rs705708 A allele was associated with lower prevalence of diabetic retinopathy and arterial hypertension as well as with improved renal function (higher estimated glomerular filtration rate and lower urinary albumin excretion levels) compared to G/G patients.

CONCLUSIONS

Although no association was observed between the ERBB3 rs705708 and PA2G4 rs773120 SNPs and T1DM, the rs705708 A allele was associated, for the first time in literature, with lower prevalence of diabetic retinopathy and arterial hypertension. Additionally, this SNP was associated with improved renal function.

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

OBJECTIVE

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Subgroups of patients with young-onset type 2 diabetes in India reveal insulin deficiency as a major driver

AIM/HYPOTHESIS

Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes.

METHODS

We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205).

RESULTS

Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy.

CONCLUSIONS /INTERPRETATION

Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.

Polygenic risk scores: An overview from bench to bedside for personalised medicine

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.

Low C-peptide together with a high glutamic acid decarboxylase autoantibody level predicts progression to insulin dependence in latent autoimmune diabetes in adults: The HUNT study

AIM

To search for risk factors that could predict progression in latent autoimmune diabetes in adults (LADA) and compare them with those for type 2 diabetes.

MATERIALS AND METHODS

This study included 175 participants with LADA (autoantibody positive, without insulin treatment ≥1 year after diagnosis) and 2331 participants with type 2 diabetes (autoantibody negative, without insulin treatment ≥1 year after diagnosis) from the HUNT2 and HUNT3 surveys. We used Cox regression models and receiver operating characteristic curve analysis to identify predictive factors for progression to insulin dependency within 10 years.

RESULTS

Low C-peptide levels (<0.3 nmol/L) predicted progression to insulin dependency within 10 years in both LADA (hazard ratio [HR] 6.40 [95% CI, 2.02-20.3]) and type 2 diabetes (HR 5.01 [95% CI, 3.53-7.10]). In addition, a high glutamic acid decarboxylase autoantibody (GADA) level (HR 5.37 [95% CI, 1.17-24.6]) predicted progression in LADA. Together, these two factors had a discriminatory power between non-progressors and progressors of area under the curve (AUC) of 0.86 (95% CI, 0.80-0.93). In type 2 diabetes, younger age at diagnosis (<50 years: HR 2.83 [95% CI, 1.56-5.15]; 50-69 years: HR 2.11 [95% CI, 1.19-3.74]), high HbA1c levels (≥53 mmol/mol, HR 2.44 [95% CI, 1.72-3.46]), central obesity (HR 1.65 [95% CI, 1.06-2.55]) and a body mass index of more than 30 kg/m² (HR 1.73 [95% CI, 1.23-2.41]) were independent predictors. Together with C-peptide they reached an AUC of 0.79 (95% CI, 0.76-0.82).

CONCLUSION

Factors predicting progression to insulin dependence are partly similar and partly dissimilar between LADA and type 2 diabetes. A constellation of low C-peptide and high GADA levels identifies LADA patients who are probable to progress to insulin dependence.

Incidence of Type 1 Diabetes May Be Underestimated in the Chinese Population: Evidence From 21.7 Million People Between 2007 and 2017

OBJECTIVE

Previous reports of the annual incidence of type 1 diabetes (T1D) in China were conducted using retrospective hospital cases, which may not reflect the reality. This longitudinal study estimated T1D incidence in a Chinese population of 21.7 million from 2007 to 2017.

RESEARCH DESIGN AND METHODS

A population-based registry of T1D was performed by the Beijing Municipal Health Commission Information Center. Annual incidence and 95% CIs were calculated by age group and sex. The association of sex with T1D incidence and predicted new cases of T1D were assessed using Poisson regression models. Annual percentage change and average annual percentage of change were assessed using Joinpoint regression.

RESULTS

Overall, there were 6,875 individuals who developed T1D from 2007 to 2017 in this population. T1D incidence (/100,000 persons) (95% CI) significantly increased from 2.72 (2.51, 2.93) in 2007 to 3.60 (3.38, 3.78) in 2017 (P < 0.001). The T1D onset peak was in the 10-14-year-old age group. While no significant trend was found in the 0-14- and 15-29-year-old age groups, T1D incidence markedly increased from 1.87 to 3.52 in the ≥30-year-old age group (P < 0.05). The prevalence of diabetic ketoacidosis at diagnosis was highest in the 0-4-year-old age group. We predicted new cases of T1D will increase 1.57-fold over the next decade.

CONCLUSIONS

T1D incidence in this large Chinese population is higher than has been reported previously. From 2007 to 2017, although the incidence peak was in the 10-14-year age group, the T1D incidence increased sharply in adults but not in youth.

The rs2304256 Polymorphism in TYK2 Gene Is Associated with Protection for Type 1 Diabetes Mellitus

BACKGROUND

Tyrosine kinase 2 (TYK2) is a candidate gene for type 1 diabetes mellitus (T1DM) since it plays an important role in regulating apoptotic and pro-inflammatory pathways in pancreatic β-cells through modulation of the type I interferon signaling pathway. The rs2304256 single nucleotide polymorphism (SNP) in TYK2 gene has been associated with protection for different autoimmune diseases. However, to date, only two studies have evaluated the association between this SNP and T1DM, with discordant results. This study thus aimed to investigate the association between the TYK2 rs2304256 SNP and T1DM in a Southern Brazilian population.

METHODS

This case-control study comprised 478 patients with T1DM and 518 non-diabetic subjects. The rs2304256 (C/A) SNP was genotyped by real-time polymerase chain reaction technique using TaqMan minor groove binder (MGB) probes.

RESULTS

Genotype and allele frequencies of the rs2304256 SNP differed between T1DM patients and non-diabetic subjects (P<0.0001 and P=0.001, respectively). Furthermore, the A allele was associated with protection against T1DM under recessive (odds ratio [OR], 0.482; 95% confidence interval [CI], 0.288 to 0.806) and additive (OR, 0.470; 95% CI, 0.278 to 0.794) inheritance models, adjusting for human leukocyte antigen (HLA) DR/DQ genotypes, gender, and ethnicity.

CONCLUSION

The A/A genotype of TYK2 rs2304256 SNP is associated with protection against T1DM in a Southern Brazilian population.

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.

Monogenic Causes in the Type 1 Diabetes Genetics Consortium Cohort: Low Genetic Risk for Autoimmunity in Case Selection

HYPOTHESIS

About 1% of patients clinically diagnosed as type 1 diabetes have non-autoimmune monogenic diabetes. The distinction has important therapeutic implications but, given the low prevalence and high cost of testing, selecting patients to test is important. We tested the hypothesis that low genetic risk for type 1 diabetes can substantially contribute to this selection.

METHODS

As proof of principle, we examined by exome sequencing families with 2 or more children, recruited by the Type 1 Diabetes Genetics Consortium (T1DGC) and selected for negativity for 2 autoantibodies and absence of risk human leukocyte antigen haplotypes.

RESULTS

We examined 46 families that met the criteria. Of the 17 with an affected parent, 7 (41.2%) had actionable monogenic variants. Of 29 families with no affected parent, 14 (48.3%) had such variants, including 5 with recessive pathogenic variants of WFS1 but no report of other features of Wolfram syndrome. Our approach diagnosed 55.8% of the estimated number of monogenic families in the entire T1DGC cohort, by sequencing only 11.1% of the autoantibody-negative ones.

CONCLUSIONS

Our findings justify proceeding to large-scale prospective screening studies using markers of autoimmunity, even in the absence of an affected parent. We also confirm that nonsyndromic WFS1 variants are common among cases of monogenic diabetes misdiagnosed as type 1 diabetes.

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

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

The association of HLA-DP loci with autoimmune diabetes in Chinese

AIMS

To determine if HLA-DP loci independently contribute to classic type 1 diabetes (T1D) of all ages, childhood-onset T1D and latent autoimmune diabetes in adults (LADA) among Chinese Han population.

METHODS

A total of 518 patients with classic T1D (Among them 180 participants manifested T1D between 1 and 14 years), 519 patients with LADA and 527 normal controls were genotyped for both HLA-DPA1 and -DPB1 loci. The frequencies of DP alleles and haplotypes in patients were directly compared to those in controls, followed by adjustment for linkage disequilibrium (LD) with DR-DQ haplotypes.

RESULTS

In the direct comparison, DPA1*01:03, DPB1*04:01 and DPA1*01:03-DPB1*04:01 showed disease-predisposing effects in both the overall T1D group and the childhood-onset T1D group mainly due to their conjunction with the known susceptible DR3 haplotype. Conditioning on DR-DQ haplotypes, only DPA1*02:02-DPB1*02:02 significantly increased T1D risk among those diagnosed during childhood (OR = 2.02, 95% CI = 1.35-3.01). Whether or not adjusted for LD, no statistically significant HLA-DP association could be observed for LADA.

CONCLUSION

HLA-DP is implicated in the pathogenesis of childhood-onset T1D in Chinese, independent of the predominant DR-DQ loci and might serve as additional markers in genetic models for the recognition of those genetically at-risk individuals.

First-appearing islet autoantibodies for type 1 diabetes in young children: maternal life events during pregnancy and the child's genetic risk

AIMS/HYPOTHESIS

Psychological stress has long been considered a possible trigger of type 1 diabetes, although prospective studies examining the link between psychological stress or life events during pregnancy and the child's type 1 diabetes risk are rare. The objective of this study was to examine the association between life events during pregnancy and first-appearing islet autoantibodies (IA) in young children, conditioned by the child's type 1 diabetes-related genetic risk.

METHODS

The IA status of 7317 genetically at-risk The Environmental Determinants of Diabetes in the Young (TEDDY) participants was assessed every 3 months from 3 months to 4 years, and bi-annually thereafter. Reports of major life events during pregnancy were collected at study inception when the child was 3 months of age and placed into one of six categories. Life events during pregnancy were examined for association with first-appearing insulin (IAA) (N = 222) or GAD (GADA) (N = 209) autoantibodies in the child until 6 years of age using proportional hazard models. Relative excess risk due to interaction (RERI) by the child's HLA-DR and SNP profile was estimated.

RESULTS

Overall, 65% of mothers reported a life event during pregnancy; disease/injury (25%), serious interpersonal (28%) and job-related (25%) life events were most common. The association of life events during pregnancy differed between IAA and GADA as the first-appearing autoantibody. Serious interpersonal life events correlated with increased risk of GADA-first only in HLA-DR3 children with the BACH2-T allele (HR 2.28, p < 0.0001), an additive interaction (RERI 1.87, p = 0.0004). Job-related life events were also associated with increased risk of GADA-first among HLA-DR3/4 children (HR 1.53, p = 0.04) independent of serious interpersonal life events (HR 1.90, p = 0.002), an additive interaction (RERI 1.19, p = 0.004). Job-related life events correlated with reduced risk of IAA-first (HR 0.55, p = 0.004), particularly in children with the BTNL2-GG allele (HR 0.48; 95% CI 0.31, 0.76).

CONCLUSIONS/INTERPRETATION

Specific life events during pregnancy are differentially related to IAA vs GADA as first-appearing IA and interact with different HLA and non-HLA genetic factors, supporting the concept of different endotypes underlying type 1 diabetes. However, the mechanisms underlying these associations remain to be discovered. Life events may be markers for other yet-to-be-identified factors important to the development of first-appearing IA.

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.

Residual β cell function in long-term type 1 diabetes associates with reduced incidence of hypoglycemia

BACKGROUNDWe investigated residual β cell function in Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study participants with an average 35-year duration of type 1 diabetes mellitus (T1DM).METHODSSerum C-peptide was measured during a 4-hour mixed-meal tolerance test. Associations with metabolic outcomes and complications were explored among nonresponders (all C-peptide values after meal <0.003 nmol/L) and 3 categories of responders, classified by peak C-peptide concentration (nmol/L) as high (>0.2), intermediate (>0.03 to ≤0.2), and low (≥ 0.003 to ≤0.03).RESULTSOf the 944 participants, 117 (12.4%) were classified as responders. Residual C-peptide concentrations were associated with higher DCCT baseline concentrations of stimulated C-peptide (P value for trend = 0.0001). Residual C-peptide secretion was not associated with current or mean HbA1c, HLA high-risk haplotypes for T1DM, or the current presence of T1DM autoantibodies. The proportion of subjects with a history of severe hypoglycemia was lower with high (27%) and intermediate (48%) residual C-peptide concentrations than with low (74%) and no (70%) residual C-peptide concentrations (P value for trend = 0.0001). Responders and nonresponders demonstrated similar rates of advanced microvascular complications.CONCLUSIONβ Cell function can persist in long-duration T1DM. With a peak C-peptide concentration of >0.03 nmol/L, we observed clinically meaningful reductions in the prevalence of severe hypoglycemia.TRIAL REGISTRATIONClinicalTrials.gov NCT00360815 and NCT00360893.FUNDINGDivision of Diabetes Endocrinology and Metabolic Diseases of the National Institute of Diabetes and Digestive and Kidney Diseases (DP3-DK104438, U01 DK094176, and U01 DK094157).

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.

A combined risk score enhances prediction of type 1 diabetes among susceptible children

Type 1 diabetes (T1D)-an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency-often begins early in life when islet autoantibody appearance signals high risk1. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common2,3 and is most severe in the very young4,5, in whom it can be life threatening and difficult to treat6-9. Autoantibody surveillance programs effectively prevent most ketoacidosis10-12 but require frequent evaluations whose expense limits public health adoption13. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible14 because individuals at greatest risk of impending T1D are difficult to identify. To remedy this, we sought accurate, cost-effective estimation of future T1D risk by developing a combined risk score incorporating both fixed and variable factors (genetic, clinical and immunological) in 7,798 high-risk children followed closely from birth for 9.3 years. Compared with autoantibodies alone, the combined model dramatically improves T1D prediction at ≥2 years of age over horizons up to 8 years of age (area under the receiver operating characteristic curve ≥ 0.9), doubles the estimated efficiency of population-based newborn screening to prevent ketoacidosis, and enables individualized risk estimates for better prevention trial selection.

Pancreatic β-cells in type 1 and type 2 diabetes mellitus: different pathways to failure

Loss of functional β-cell mass is the key mechanism leading to the two main forms of diabetes mellitus - type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Understanding the mechanisms behind β-cell failure is critical to prevent or revert disease. Basic pathogenic differences exist in the two forms of diabetes mellitus; T1DM is immune mediated and T2DM is mediated by metabolic mechanisms. These mechanisms differentially affect early β-cell dysfunction and eventual fate. Over the past decade, major advances have been made in the field, mostly delivered by studies on β-cells in human disease. These advances include studies of islet morphology and human β-cell gene expression in T1DM and T2DM, the identification and characterization of the role of T1DM and T2DM candidate genes at the β-cell level and the endoplasmic reticulum stress signalling that contributes to β-cell failure in T1DM (mostly IRE1 driven) and T2DM (mostly PERK-eIF2α dependent). Here, we review these new findings, focusing on studies performed on human β-cells or on samples obtained from patients with diabetes mellitus.

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

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

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.

Immune checkpoint inhibitor diabetes mellitus: a novel form of autoimmune diabetes

Autoimmune diabetes mellitus is a rare but significant side effect of treatment with immune checkpoint inhibitors. Immune checkpoint inhibitor-induced diabetes mellitus (CPI-DM) is characterized by acute onset of dramatic hyperglycemia with severe insulin deficiency and occurrence following exposure to programmed cell death-1/programmed cell death ligand-1 (PD-1/PD-L1) inhibitors rather than cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) inhibitors. As a growing number of patients undergo immunotherapy, further understanding of the characteristics of CPI-DM patients is needed for improved prognostic and diagnostic application in order to reduce overall morbidity for this already at-risk population. Additionally, understanding of the features and mechanisms of CPI-DM may contribute to understanding mechanisms of spontaneous type I diabetes mellitus (T1DM). Here, we summarize the clinical features of CPI-DM and interrogate the genetic and cellular mechanisms that may contribute to the disease, as well as the clinical challenges for predicting and treating these patients as increasing cancer immunotherapies reach clinical utility.

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.

Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

Analysis of potential hub genes involved in the pathogenesis of Chinese type 1 diabetic patients

Background

Type 1 diabetes is an autoimmune disease strongly related to genetic factors. Although studies on T1D susceptibility genes have achieved great progress, the molecular mechanism of T1D remains to be explained.

Methods

To explore the underlying mechanisms of T1D, bioinformatic analysis based on a microarray database was used to determine the key biomarkers of T1D as well as their biofunctions and interactions. The microarray database GSE55100 was downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were processed by packages in R Software. The database for Annotation, Visualization, and Integrated Discovery (DAVID, version 6.8) was used to conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction network was analyzed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the module analysis was performed using Cytoscape.

Results

Seventy-eight DEGs and 13 hub genes were identified. The biofunctions and pathways of these DEGs were enriched in immune response, extracellular exosome, cytokine activity and antigen processing and presentation. Thirteen DEGs with MCODE score ≥2 were selected as hub genes including MMP9, ARG1, CAMP, CHI3L1, CRISP3, SLPI, LCN2, PGLYRP1, LTF, RETN, CEACAM1, CEACAM8 and MS4A3.

Conclusions

The identification and analyses of the DEGs and hub genes from database GSE55100 provide novel prospectives of the pathogenesis of T1D.