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

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.

Familial aggregation and heritability of childhood-onset and adult-onset type 1 diabetes: a Swedish register-based cohort study

BACKGROUND

Type 1 diabetes in children is known to be highly heritable, but much less is known about the heritability of adult-onset type 1 diabetes. Thus, our objective was to compare the familial aggregation and heritability of type 1 diabetes in adults and children.

METHODS

This Swedish nationwide register-based cohort study included individuals born from Jan 1, 1982, to Dec 31, 2010, identified through the Medical Birth Register who were linked to their parents, full siblings, half siblings, and cousins through the Multi-Generation Register (MGR). We excluded multiple births, deaths within the first month of life, individuals who could not be linked to MGR, or individuals with contradictory information on sex. Information on diagnoses of type 1 diabetes was retrieved by linkages to the National Diabetes Register and National Patient Register (1982-2020). Individuals with inconsistent records of diabetes type were excluded. We estimated the cumulative incidence and hazard ratios (HRs) of type 1 diabetes in adults (aged 19-30 years) and children (aged 0-18 years) by family history of type 1 diabetes and the heritability of adult-onset and childhood-onset type 1 diabetes based on tetrachoric correlations, using sibling pairs.

FINDINGS

2 943 832 individuals were born in Sweden during the study period, 2 832 755 individuals were included in the analysis of childhood-onset type 1 diabetes and 1 805 826 individuals were included in the analysis of adult-onset type 1 diabetes. 3240 cases of adult-onset type 1 diabetes (median onset age 23·4 years [IQR 21·1-26·2]; 1936 [59·7%] male and 1304 [40·2%] female) and 17 914 cases of childhood-onset type 1 diabetes (median onset age 9·8 years [6·2-13·3]; 9819 [54·8%] male and 8095 [45·2%] female) developed during follow-up. Having a first-degree relative with type 1 diabetes conferred an HR of 7·21 (95% CI 6·28-8·28) for adult-onset type 1 diabetes and 9·92 (9·38-10·50) for childhood-onset type 1 diabetes. The HR of developing type 1 diabetes before the age 30 years was smaller if a first-degree relative developed type 1 diabetes during adulthood (6·68 [6·04-7·39]) rather than during childhood (10·54 [9·92-11·19]). Similar findings were observed for type 1 diabetes in other relatives. Heritability was lower for adult-onset type 1 diabetes (0·56 [0·45-0·66]) than childhood-onset type 1 diabetes (0·81 [0·77-0·85]).

INTERPRETATION

Adult-onset type 1 diabetes seems to have weaker familial aggregation and lower heritability than childhood-onset type 1 diabetes. This finding suggests a larger contribution of environmental factors to the development of type 1 diabetes in adults than in children and highlights the need to identify and intervene on such factors.

FUNDING

Swedish Research Council, the Swedish Research Council for Health, Working Life and Welfare, Swedish Diabetes Foundation, and the China Scholarship Council.

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.

Increased Frequency of the HLA-DRB1*04:04-DQA1*03-DQB1*03:02 Haplotype Among HLA-DQB1*06:02-Positive Children With Type 1 Diabetes

HLA-DR/DQ haplotypes largely define genetic susceptibility to type 1 diabetes (T1D). The DQB1*06:02-positive haplotype (DR15-DQ602) common in individuals of European ancestry is very rare among children with T1D. Among 4,490 children with T1D in the Finnish Pediatric Diabetes Register, 57 (1.3%) case patients with DQB1*06:02 were identified, in comparison with 26.1% of affected family-based association control participants. There were no differences between DQB1*06:02-positive and -negative children with T1D regarding sex, age, islet autoantibody distribution, or autoantibody levels, but significant differences were seen in the frequency of second class II HLA haplotypes. The most prevalent haplotype present with DQB1*06:02 was DRB1*04:04-DQA1*03-DQB1*03:02, which was found in 27 (47.4%) of 57 children, compared with only 797 (18.0%) of 4,433 among DQB1*06:02-negative case patients (P < 0.001 by χ2 test). The other common risk-associated haplotypes, DRB1*04:01-DQA1*03-DQB1*03:02 and (DR3)-DQA1*05-DQB1*02, were less prevalent in DQB1*06:02-positive versus DQB1*06:02-negative children (P < 0.001). HLA-B allele frequencies did not differ by DQB1*06:02 haplotype between children with T1D and control participants or by DRB1*04:04-DQA1*03-DQB1*03:02 haplotype between DQB1*06:02-positive and -negative children with T1D. The increased frequency of the DRB1*04:04 allele among DQB1*06:02-positive case patients may indicate a preferential ability of the DR404 molecule to present islet antigen epitopes despite competition by DQ602.

ARTICLE HIGHLIGHTS

Association of RPS26 gene polymorphism with different types of diabetes in Chinese individuals

AIMS/INTRODUCTION

Different types of diabetes show distinct genetic characteristics, but the specific genetic susceptibility factors remain unclear. Our study aimed to explore the associations between the ribosomal protein S26 (RPS26) gene rs1131017 polymorphisms and susceptibility to type 1 diabetes mellitus, latent autoimmune diabetes in adults (LADA) and type 2 diabetes mellitus in the Chinese Han population, and their correlations with clinical features.

MATERIALS AND METHODS

Genotyping of the rs1131017 variant was carried out for 1,006 type 1 diabetes mellitus patients, 210 LADA patients, 642 type 2 diabetes mellitus patients and 2,099 control individuals.

RESULTS

We found that the rs1131017 C allele was a risk locus for both type 1 diabetes mellitus and LADA (odds ratio [OR] 1.50, 95% confidence interval [CI] 1.33-1.69, P < 0.001; OR 1.31, 95% CI 1.04-1.64, P = 0.021, respectively). Nevertheless, this association was not found for type 2 diabetes mellitus. Carrying the C allele genotype was associated with a lower postprandial C-peptide for type 1 diabetes mellitus (OR 1.41, 95% CI 1.11-1.80, P = 0.006) and lower fasting C-peptide for LADA (OR 1.55, 95% CI 1.01-2.38, P = 0.047). Interestingly, a lower GC frequency was noted for LADA than for type 1 diabetes mellitus, regardless of classification based on age at diagnosis, C-peptide or glutamic acid decarboxylase antibody positivity.

CONCLUSIONS

The RPS26 polymorphism was associated with susceptibility and clinical characteristics of type 1 diabetes mellitus and LADA in the Chinese population, but was not related to type 2 diabetes mellitus. Thus, it might serve as a novel biomarker for particular types of diabetes.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.

Combining polygenic risk scores and human leukocyte antigen variants for personalized risk assessment of type 1 diabetes in the Taiwanese population

AIMS

To analyse the genome-wide association study (GWAS) data of patients with type 1 diabetes mellitus (T1D) in order to develop a risk score for the genetic effects on T1D risk and age at diagnosis in the Taiwanese population.

MATERIALS AND METHODS

We selected 610 patients with T1D and 2511 healthy individuals from an electronic medical record database of more than 300 000 individuals with genetic information, analysed their GWAS data, and developed a polygenic risk score (PRS).

RESULTS

The PRS, based on 149 selected single-nucleotide polymorphisms, could effectively predict T1D risk. A PRS increase was associated with increased T1D risk (odds ratio [OR] 2.09, 95% confidence interval [CI] 1.72-2.55). Moreover, a 1-unit increase in standardized T1D PRS decreased the age at diagnosis by 0.74 years. Combined PRS and human leukocyte antigen (HLA) DQA1*03:02-DQA1*05:01 genotypes could accurately predict T1D risk. In multivariable models, HLA variants and PRS were independent risk factors for T1D risk (OR 3.76 [95% CI 1.54-9.16] and 1.71 [95% CI 1.37-2.13] for HLA DQA1*03:02-DQA1*05:01 and PRS, respectively). In a limited study population of those aged ≤18 years, PRS remained significantly associated with T1D risk. The association between T1D PRS and age at diagnosis was more obvious among males and patients aged ≤18 years.

CONCLUSIONS

Polygenic risk score and HLA variations enable personalized risk estimates, enhance newborn screening efficiency for ketoacidosis prevention, and addresses the gap in data on T1D prediction in isolated Asian populations.

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

OBJECTIVE

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

SARS-CoV-2 Infection and Development of Islet Autoimmunity in Early Childhood

Importance

The incidence of diabetes in childhood has increased during the COVID-19 pandemic. Elucidating whether SARS-CoV-2 infection is associated with islet autoimmunity, which precedes type 1 diabetes onset, is relevant to disease etiology and future childhood diabetes trends.

Objective

To determine whether there is a temporal relationship between SARS-CoV-2 infection and the development of islet autoimmunity in early childhood.

Design, Setting, and Participants

Between February 2018 and March 2021, the Primary Oral Insulin Trial, a European multicenter study, enrolled 1050 infants (517 girls) aged 4 to 7 months with a more than 10% genetically defined risk of type 1 diabetes. Children were followed up through September 2022.

Exposure

SARS-CoV-2 infection identified by SARS-CoV-2 antibody development in follow-up visits conducted at 2- to 6-month intervals until age 2 years from April 2018 through June 2022.

Main Outcomes and Measures

The development of multiple (≥2) islet autoantibodies in follow-up in consecutive samples or single islet antibodies and type 1 diabetes. Antibody incidence rates and risk of developing islet autoantibodies were analyzed.

Results

Consent was obtained for 885 (441 girls) children who were included in follow-up antibody measurements from age 6 months. SARS-CoV-2 antibodies developed in 170 children at a median age of 18 months (range, 6-25 months). Islet autoantibodies developed in 60 children. Six of these children tested positive for islet autoantibodies at the same time as they tested positive for SARS-CoV-2 antibodies and 6 at the visit after having tested positive for SARS-CoV-2 antibodies. The sex-, age-, and country-adjusted hazard ratio for developing islet autoantibodies when the children tested positive for SARS-CoV-2 antibodies was 3.5 (95% CI, 1.6-7.7; P = .002). The incidence rate of islet autoantibodies was 3.5 (95% CI, 2.2-5.1) per 100 person-years in children without SARS-CoV-2 antibodies and 7.8 (95% CI, 5.3-19.0) per 100 person-years in children with SARS-CoV-2 antibodies (P = .02). Islet autoantibody risk in children with SARS-CoV-2 antibodies was associated with younger age (<18 months) of SARS-CoV-2 antibody development (HR, 5.3; 95% CI, 1.5-18.3; P = .009).

Conclusion and relevance

In young children with high genetic risk of type 1 diabetes, SARS-CoV-2 infection was temporally associated with the development of islet autoantibodies.

Utility of genetic risk scores in type 1 diabetes

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

Heterogeneity and endotypes in type 1 diabetes mellitus

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

ABCC8-Related Monogenic Diabetes Presenting Like Type 1 Diabetes in an Adolescent

Background

Identifying cases of diabetes caused by single gene mutations between the more common type 1 diabetes (T1D) and type 2 diabetes (T2D) is a difficult but important task. We report the diagnosis of ATP-binding cassette transporter sub-family C member 8 (ABCC8)-related monogenic diabetes in a 35-year-old woman with a protective human leukocyte antigen (HLA) allele who was originally diagnosed with T1D at 18 years of age.

Case Report

Patient A presented with polyuria, polydipsia, and hypertension at the age of 18 years and was found to have a blood glucose > 500 mg/dL (70-199 mg/dL) and an HbA1C (hemoglobin A1C) >14% (4%-5.6%). She had an unmeasurable C-peptide but no urine ketones. She was diagnosed with T1D and started on insulin therapy. Antibody testing was negative. She required low doses of insulin and later had persistence of low but detectable C-peptide. At the age of 35 years, she was found to have a protective HLA allele, and genetic testing revealed a pathogenic mutation in the ABCC8 gene. The patient was then successfully transitioned to sulfonylurea therapy.

Discussion

Monogenic diabetes diagnosed in adolescence typically presents with mild to moderate hyperglycemia, positive family history and, in some cases, other organ findings or dysfunction. The patient in this report presented with very high blood glucose, prompting the diagnosis of T1D. When she was found to have a protective HLA allele, further investigation revealed the mutation in the sulfonylurea receptor gene, ABCC8.

Conclusion

Patients suspected of having T1D but with atypical clinical characteristics such as negative autoantibodies, low insulin requirements, and persistence of C-peptide should undergo genetic testing for monogenic diabetes.

Why does the immune system destroy pancreatic β-cells but not α-cells in type 1 diabetes?

A perplexing feature of type 1 diabetes (T1D) is that the immune system destroys pancreatic β-cells but not neighbouring α-cells, even though both β-cells and α-cells are dysfunctional. Dysfunction, however, progresses to death only for β-cells. Recent findings indicate important differences between these two cell types. First, expression of BCL2L1, a key antiapoptotic gene, is higher in α-cells than in β-cells. Second, endoplasmic reticulum (ER) stress-related genes are differentially expressed, with higher expression levels of pro-apoptotic CHOP in β-cells than in α-cells and higher expression levels of HSPA5 (which encodes the protective chaperone BiP) in α-cells than in β-cells. Third, expression of viral recognition and innate immune response genes is higher in α-cells than in β-cells, contributing to the enhanced resistance of α-cells to coxsackievirus infection. Fourth, expression of the immune-inhibitory HLA-E molecule is higher in α-cells than in β-cells. Of note, α-cells are less immunogenic than β-cells, and the CD8+ T cells invading the islets in T1D are reactive to pre-proinsulin but not to glucagon. We suggest that this finding is a result of the enhanced capacity of the α-cell to endure viral infections and ER stress, which enables them to better survive early stressors that can cause cell death and consequently amplify antigen presentation to the immune system. Moreover, the processing of the pre-proglucagon precursor in enteroendocrine cells might favour immune tolerance towards this potential self-antigen compared to pre-proinsulin.

Prevention of Type 1 Diabetes: Current Perspective

People living with type 1 Diabetes (T1D) and their families have poor perception of health related quality of life. Therapies for T1D are becoming better with time, but they still involve a lot of effort. Prevention of T1D, if successful, has potential to change lives of millions of families across the globe. Type 1 diabetes is an autoimmune disease with underlying genetic predisposition for autoimmunity against beta cell antigens upon exposure to an environmental trigger. Identifying underlying primary antigen responsible for initiating autoimmune cascade, avoiding environmental trigger and modifying immunity has all been used as strategies for preventing or delaying onset of type 1 diabetes. Primary prevention for type 1 diabetes is hindered by difficulty in identifying at-risk population and also due to lack of effective preventive strategy. Secondary prevention, in children with presence of autoimmunity, has recently received a boost with approval of Teplizumab, an immunity modifying drug by its Anti-CD3 action. Application of preventive strategies would also change based on country specific incidence, prevalence and availability of health resources. In current review, an update on preventive strategies for type 1 diabetes is being discussed as well as their applicability in Indian context.

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

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

Type 1 diabetes and inborn errors of immunity: Complete strangers or 2 sides of the same coin?

Type 1 diabetes (T1D) is a polygenic disease and does not follow a mendelian pattern. Inborn errors of immunity (IEIs), on the other hand, are caused by damaging germline variants, suggesting that T1D and IEIs have nothing in common. Some IEIs, resulting from mutations in genes regulating regulatory T-cell homeostasis, are associated with elevated incidence of T1D. The genetic spectrum of IEIs is gradually being unraveled; consequently, molecular pathways underlying human monogenic autoimmunity are being identified. There is an appreciable overlap between some of these pathways and the genetic variants that determine T1D susceptibility, suggesting that after all, IEI and T1D are 2 sides of the same coin. The study of monogenic IEIs with a variable incidence of T1D has the potential to provide crucial insights into the mechanisms leading to T1D. These insights contribute to the definition of T1D endotypes and explain disease heterogeneity. In this review, we discuss the interconnected pathogenic pathways of autoimmunity, β-cell function, and primary immunodeficiency. We also examine the role of environmental factors in disease penetrance as well as the circumstantial evidence of IEI drugs in preventing and curing T1D in individuals with IEIs, suggesting the repositioning of these drugs also for T1D therapy.

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

OBJECTIVE

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Precision Medicine in Type 1 Diabetes

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

Association analysis between the TLR9 gene polymorphism rs352140 and type 1 diabetes

Background

To a great extent, genetic factors contribute to the susceptibility to type 1 diabetes (T1D) development, and by triggering immune imbalance, Toll-like receptor (TLR) 9 is involved in the development of T1D. However, there is a lack of evidence supporting a genetic association between polymorphisms in the TLR9 gene and T1D.

Methods

In total, 1513 individuals, including T1D patients (n=738) and healthy control individuals (n=775), from the Han Chinese population were recruited for an association analysis of the rs352140 polymorphism of the TLR9 gene and T1D. rs352140 was genotyped by MassARRAY. The allele and genotype distributions of rs352140 in the T1D and healthy groups and those in different T1D subgroups were analyzed by the chi-squared test and binary logistic regression model. The chi-square test and Kruskal-Wallis H test were performed to explore the association between genotype and phenotype in T1D patients.

Results

The allele and genotype distributions of rs352140 were significantly different in T1D patients and healthy control individuals (p=0.019, p=0.035). Specifically, the T allele and TT genotype of rs352140 conferred a higher risk of T1D (OR=1.194, 95% CI=1.029-1.385, p=0.019, OR=1.535, 95% CI=1.108-2.126, p=0.010). The allele and genotype distributions of rs352140 were not significantly different between childhood-onset and adult-onset T1D and between T1D with a single islet autoantibody and T1D with multiple islet autoantibodies (p=0.603, p=0.743). rs352140 was associated with T1D susceptibility according to the recessive and additive models (p=0.015, p=0.019) but was not associated with T1D susceptibility in the dominant and overdominant models (p=0.117, p=0.928). Moreover, genotype-phenotype association analysis showed that the TT genotype of rs352140 was associated with higher fasting C-peptide levels (p=0.017).

Conclusion

In the Han Chinese population, the TLR9 polymorphism rs352140 is associated with T1D and is a risk factor for susceptibility to T1D.

Type 1 diabetes - What's new in prevention and therapeutic strategies?

Type 1 diabetes (T1D) is an autoimmune disorder, and insulin deficiency is the result of b-cell dysfunction. Treatment of type 1 diabetes requires constant parenteral insulin administration, which can be very burdensome for the patient. Meticulous use of insulin therapy does not protect the patient against complications. Hence, the search for other methods of treatment as well as ways of preventing the onset of diabetes has been ongoing for a long time. The main obstacle in the implementation of the prevention task is the need to identify people at risk of developing diabetes before the start of autoimmunity. It seems that primary prevention is still unrealistic at the moment, because we do not know all the factors leading to the activation of autoimmunity processes. Research on the use of late secondary prevention in people who develop glucose tolerance disorders or in the early period after the onset of type 1 diabetes are at the most advanced stage. Gene therapy is another attempt at an alternative treatment and prevention of type 1 diabetes and still requires further research. Recent years have brought a lot of information about the nature of type 1 diabetes and the mechanisms leading to its development. However, it has not yet been established what factors decide about the initiation of autoimmunity and what determines the dynamics of these processes.

Diabetes-related antibody-testing is a valuable screening tool for identifying monogenic diabetes - A survey from the worldwide SWEET registry

AIMS

To evaluate access to screening tools for monogenic diabetes in paediatric diabetes centres across the world and its impact on diagnosis and clinical outcomes of children and youth with genetic forms of diabetes.

METHODS

79 centres from the SWEET diabetes registry including 53,207 children with diabetes participated in a survey on accessibility and use of diabetes related antibodies, c-peptide and genetic testing.

RESULTS

73, 63 and 62 participating centres had access to c-peptide, antibody and genetic testing, respectively. Access to antibody testing was associated with higher proportion of patients with rare forms of diabetes identified with monogenic diabetes (54 % versus 17 %, p = 0.01), lower average whole clinic HbA1c (7.7[Q1,Q2: 7.3-8.0]%/61[56-64]mmol/mol versus 9.2[8.6-10.0]%/77[70-86]mmol/mol, p < 0.001) and younger age at onset (8.3 [7.3-8.8] versus 9.7 [8.6-12.7] years p < 0.001). Additional access to c-peptide or genetic testing was not related to differences in age at onset or HbA1c outcome.

CONCLUSIONS

Clinical suspicion and antibody testing are related to identification of different types of diabetes. Implementing access to comprehensive antibody screening may provide important information for selecting individuals for further genetic evaluation. In addition, worse overall clinical outcomes in centers with limited diagnostic capabilities indicate they may also need support for individualized diabetes management.

TRIAL REGISTRATION

NCT04427189.