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

Characteristics of autoantibody-positive individuals without high-risk HLA-DR4-DQ8 or HLA-DR3-DQ2 haplotypes

AIMS/HYPOTHESIS

Many studies of type 1 diabetes pathogenesis focus on individuals with high-risk HLA haplotypes. We tested the hypothesis that, among islet autoantibody-positive individuals, lacking HLA-DRB1*04-DQA1*03-DQB1*0302 (HLA-DR4-DQ8) and/or HLA-DRB1*0301-DQA1*0501-DQB1*0201 (HLA-DR3-DQ2) is associated with phenotypic differences, compared with those who have these high-risk HLA haplotypes.

METHODS

We classified autoantibody-positive relatives of individuals with type 1 diabetes into four groups based on having both HLA-DR4-DQ8 and HLA-DR3-DQ2 (DR3/DR4; n=1263), HLA-DR4-DQ8 but not HLA-DR3-DQ2 (DR4/non-DR3; n=2340), HLA-DR3-DQ2 but not HLA-DR4-DQ8 (DR3/non-DR4; n=1607) and neither HLA-DR3-DQ2 nor HLA-DR4-DQ8 (DRX/DRX; n=1294). Group comparisons included demographics, metabolic markers and the prevalence of autoantibodies against GAD65 (GADA%), IA-2 (IA-2A%) or insulin (IAA%) at enrolment. A p value <0.01 was considered statistically significant.

RESULTS

IA-2A% was lower in the DRX/DRX group (20.9%) than in the DR4/non-DR3 (38.5%, p<0.001) and DR3/DR4 (44.8%, p<0.001) groups, but similar to the DR3/non-DR4 group (20.0%). Conversely, IAA% was similar in the DRX/DRX (43.4%), DR4/non-DR3 (41.1%) and DR3/DR4 (41.0%) groups, but lower in the DR3/non-DR4 group (30.1%, p<0.001). Participants in the DRX/DRX group were older, with a lower prevalence of White participants and a higher prevalence of overweight/obesity, and higher preserved C-peptide (as measured by a lower Index60) than those in the DR3/DR4 group (all comparisons, p<0.005), a lower prevalence of White or non-Hispanic participants and a lower Index60 than those in the DR4/non-DR3 group, and younger age, a higher prevalence of Hispanic participants and a lower Index60 than those in the DR3/non-DR4 group (all comparisons, p<0.005). Among the 1292 participants who progressed to clinical type 1 diabetes, those in the DR3/non-DR4 group had higher GADA%, lower IA-2A% and lower IAA% than the other groups (all comparisons, p<0.01), and those in the DR3/DR4 group had the youngest age at diagnosis (all comparisons, p<0.001).

CONCLUSIONS/INTERPRETATION

Autoantibody-positive individuals who lack both high-risk HLA haplotypes (DRX/DRX) or have HLA-DR3-DQ2 but lack HLA-DR4-DQ8 (DR3/non-DR4) have phenotypic differences compared with DR3/DR4 and DR4/non-DR3 individuals, suggesting that there is aetiological heterogeneity in type 1 diabetes.

Time to reframe the disease staging system for type 1 diabetes

In 2015, introduction of a disease staging system offered a framework for benchmarking progression to clinical type 1 diabetes. This model, based on islet autoantibodies (stage 1) and dysglycaemia (stage 2) before type 1 diabetes diagnosis (stage 3), has facilitated screening and identification of people at risk. Yet, there are many limitations to this model as the stages combine a very heterogeneous group of individuals; do not have high specificity for type 1 diabetes; can occur without persistence (ie, reversion to an earlier risk stage); and exclude age and other influential risk factors. The current staging system also infers that individuals at risk of type 1 diabetes progress linearly from stage 1 to stage 2 and subsequently stage 3, whereas such movements are often more complex. With the approval of teplizumab by the US Food and Drug Administration in 2022 to delay type 1 diabetes in people at stage 2, there is a need to refine the definition and accuracy of type 1 diabetes staging. Theoretically, we propose that a type 1 diabetes risk calculator should incorporate any available demographic, genetic, autoantibody, metabolic, and immune data that could be continuously updated. Additionally, we call to action for the field to increase the breadth of knowledge regarding type 1 diabetes risk in non-relatives, adults, and individuals from minority populations.

Inverse relationship between polygenic risk burden and age of onset of autoimmune vitiligo

Vitiligo is a common autoimmune disease characterized by patches of depigmented skin and overlying hair due to destruction of melanocytes in the involved regions. We investigated the relationship between vitiligo risk and vitiligo age of onset (AOO) using a vitiligo polygenic risk score that incorporated the most significant SNPs from genome-wide association studies. We find that vitiligo genetic risk and AOO are strongly inversely correlated; subjects with higher common-variant polygenic risk tend to develop vitiligo at an earlier age. Nevertheless, the correlation is not simple. In individuals who carry a single high-risk major histocompatibility complex class II haplotype, the effect of additional polygenic risk on vitiligo AOO is reduced. Particularly among those with early-AOO vitiligo (onset ≤12 years of age), genetic risk can reflect contributions from high common-variant burden but also rare variants of high effect and sometimes both. While the heritability of vitiligo is relatively high, and we here show that genetic risk factors predict vitiligo AOO, vitiligo is never congenital, and thus environmental triggers also play an important role in disease onset.

Fifty years of HLA-associated type 1 diabetes risk: history, current knowledge, and future directions

More than 50 years have elapsed since the association of human leukocyte antigens (HLA) with type 1 diabetes (T1D) was first reported. Since then, methods for identification of HLA have progressed from cell based to DNA based, and the number of recognized HLA variants has grown from a few to tens of thousands. Current genotyping methodology allows for exact identification of all HLA-encoding genes in an individual's genome, with statistical analysis methods evolving to digest the enormous amount of data that can be produced at an astonishing rate. The HLA region of the genome has been repeatedly shown to be the most important genetic risk factor for T1D, and the original reported associations have been replicated, refined, and expanded. Even with the remarkable progress through 50 years and over 5,000 reports, a comprehensive understanding of all effects of HLA on T1D remains elusive. This report represents a summary of the field as it evolved and as it stands now, enumerating many past and present challenges, and suggests possible paradigm shifts for moving forward with future studies in hopes of finally understanding all the ways in which HLA influences the pathophysiology of T1D.

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.

Use of a type 1 genetic risk score for classification of diabetes type in young Australian adults: the Fremantle Diabetes Study Phase II

The applicability of a UK-validated genetic risk score (GRS) was assessed in 158 participants in the Fremantle Diabetes Study Phase II diagnosed between 20 and <40 years of age with type 1 or type 2 diabetes or latent autoimmune diabetes of adults (LADA). For type 1 versus type 2/LADA, the area under the receiver operating characteristic curve (AUC) was highest for serum C-peptide (0.93) and lowest for the GRS (0.66). Adding age at diagnosis and body mass index to C-peptide increased the AUC minimally (0.96). The GRS appears of modest diabetes diagnostic value in young Australians.

Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes

CONTEXT

Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations.

OBJECTIVE

This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D.

METHODS

Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients.

RESULTS

The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D.

CONCLUSION

PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.

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.

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.

Human immune phenotyping reveals accelerated aging in type 1 diabetes

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

Utility of genetic risk scores in type 1 diabetes

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

Responders to low-dose ATG induce CD4+ T cell exhaustion in type 1 diabetes

BACKGROUNDLow-dose anti-thymocyte globulin (ATG) transiently preserves C-peptide and lowers HbA1c in individuals with recent-onset type 1 diabetes (T1D); however, the mechanisms of action and features of the response remain unclear. Here, we characterized the post hoc immunological outcomes of ATG administration and their potential use as biomarkers of metabolic response to therapy (i.e., improved preservation of endogenous insulin production).METHODSWe assessed gene and protein expression, targeted gene methylation, and cytokine concentrations in peripheral blood following treatment with ATG (n = 29), ATG plus granulocyte colony-stimulating factor (ATG/G-CSF, n = 28), or placebo (n = 31).RESULTSTreatment with low-dose ATG preserved regulatory T cells (Tregs), as measured by stable methylation of FOXP3 Treg-specific demethylation region (TSDR) and increased proportions of CD4+FOXP3+ Tregs (P < 0.001) identified by flow cytometry. While treatment effects were consistent across participants, not all maintained C-peptide. Responders exhibited a transient rise in IL-6, IP-10, and TNF-α (P < 0.05 for all) 2 weeks after treatment and a durable CD4+ exhaustion phenotype (increased PD-1+KLRG1+CD57- on CD4+ T cells [P = 0.011] and PD1+CD4+ Temra MFI [P < 0.001] at 12 weeks, following ATG and ATG/G-CSF, respectively). ATG nonresponders displayed higher proportions of senescent T cells (at baseline and after treatment) and increased methylation of EOMES (i.e., less expression of this exhaustion marker).CONCLUSIONAltogether in these exploratory analyses, Th1 inflammation-associated serum and CD4+ exhaustion transcript and cellular phenotyping profiles may be useful for identifying signatures of clinical response to ATG in T1D.TRIAL REGISTRATIONClinicalTrials.gov NCT02215200.FUNDINGThe Leona M. and Harry B. Helmsley Charitable Trust (2019PG-T1D011), the NIH (R01 DK106191 Supplement, K08 DK128628), NIH TrialNet (U01 DK085461), and the NIH NIAID (P01 AI042288).

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.

A genomic data archive from the Network for Pancreatic Organ donors with Diabetes

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

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.

Enrollment of underrepresented racial and ethnic groups in the Rare and Atypical Diabetes Network (RADIANT)

Introduction

Diabetes mellitus in underrepresented racial and ethnic groups (URG) is rapidly increasing in incidence and has worse outcomes than diabetes in non-Hispanic White individuals. Rare and Atypical Diabetes Network (RADIANT) established recruitment targets based on the racial and ethnic distribution of the USA to enroll a diverse study population. We examined participation of URG across RADIANT study stages and described strategies to enhance recruitment and retention of URG.

Materials and Methods

RADIANT is a multicenter NIH-funded study of people with uncharacterized forms of atypical diabetes. RADIANT participants consent online and progress through three sequential study stages, as eligible.

Results

We enrolled 601 participants with mean age 44 ± 16.8 years, 64.4% female. At Stage 1, 80.6% were White, 7.2% African American (AA), 12.2% other/more than one race, and 8.4% Hispanic. Enrollment of URG was significantly below preset targets across most stages. Referral sources differed by race (p < 0.001) but not ethnicity (p = 0.15). Most AA participants were referred by RADIANT investigators (58.5% vs. 24.5% in Whites), whereas flyers, news, social media, and family or friends were more frequent referral sources for White individuals (26.4% vs. 12.2% in AA). Ongoing initiatives to increase enrollment of URG in RADIANT include engaging with clinics/hospitals serving URG, screening electronic medical records, and providing culturally competent study coordination and targeted advertisement.

Conclusions

There is low participation of URG in RADIANT, potentially limiting the generalizability of its discoveries. Investigations into barriers and facilitators for recruitment and retention of URG in RADIANT, with implications for other studies, are ongoing.

Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches

OBJECTIVES

We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.

STUDY DESIGN AND SETTING

We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).

RESULTS

Approaches' accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability.

CONCLUSION

Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.

The missing heritability in type 1 diabetes

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

Epidemiology of Type 1 Diabetes

PURPOSE OF REVIEW

Epidemiological research on type 1 diabetes (T1D) has traditionally focussed on the paediatric age group, but recent data in adults has confirmed it to be a disease of all ages with a wide clinical spectrum. We review the epidemiology and clinical features of T1D across the lifespan.

RECENT FINDINGS

While the peak incidence of T1D is still in early adolescence, T1D is now diagnosed more commonly in adulthood than childhood due to increasing recognition of adult-onset T1D and the length of the adult lifespan. It still follows the known geographic variations in incidence, being highest in Northern Europe and lowest in Asia. The onset of T1D in adulthood is usually less acute than in childhood and confers a lower, although still substantial, risk of complications and early mortality. Interventions to delay T1D onset are emerging and screening for those at risk at birth is increasingly available. Type 1 diabetes can develop at any age and may not present with ketosis or an immediate insulin requirement in adults. Macro- and microvascular complications are the greatest cause of excess morbidity and mortality in this population.

Microbial risk score for capturing microbial characteristics, integrating multi-omics data, and predicting disease risk

BACKGROUND

With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction.

METHODS

Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: (1) identifying a sub-community consisting of the signature microbial taxa associated with disease and (2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction.

RESULTS

Through three comprehensive real-data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn's disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa, respectively, are created and validated using the GMHI multi-study cohort. Especially, Crohn's disease MRS achieves AUCs of 0.88 (0.85-0.91) and 0.86 (0.78-0.95) in the discovery and validation cohorts, respectively.

CONCLUSIONS

The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration and provides a great potential in understanding the microbiome's role in disease diagnosis and prognosis. Video Abstract.

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.

Construction and Application of Polygenic Risk Scores in Autoimmune Diseases

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.

Microbial Risk Score for Capturing Microbial Characteristics, Integrating Multi-omics Data, and Predicting Disease Risk

Background

With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction.

Methods

Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: 1) identifying a sub-community consisting of the signature microbial taxa associated with disease, and 2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction.

Results

Through three comprehensive real data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn's disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa respectively are created and validated using the GMHI multi-study cohort. Especially, Crohn's disease MRS achieves AUCs of 0.88 ([0.85-0.91]) and 0.86 ([0.78-0.95]) in the discovery and validation cohorts, respectively.

Conclusions

The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration, and provides great potential in understanding the microbiome's role in disease diagnosis and prognosis.

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.

The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits

Type 2 diabetes has a global prevalence, with epidemiological data suggesting that some populations have a higher risk of developing this disease. However, to date, most genetic studies of type 2 diabetes and related glycaemic traits have been performed in individuals of European ancestry. The same is true for most other complex diseases, largely due to use of 'convenience samples'. Rapid genotyping of large population cohorts and case-control studies from existing collections was performed when the genome-wide association study (GWAS) 'revolution' began, back in 2005. Although global representation has increased in the intervening 15 years, further expansion and inclusion of diverse populations in genetic and genomic studies is still needed. In this review, I discuss the progress made in incorporating multi-ancestry participants in genetic analyses of type 2 diabetes and related glycaemic traits, and associated opportunities and challenges. I also discuss how increased representation of global diversity in genetic and genomic studies is required to fulfil the promise of precision medicine for all.

TCR+/BCR+ dual-expressing cells and their associated public BCR clonotype are not enriched in type 1 diabetes

Ahmed and colleagues recently described a novel hybrid lymphocyte expressing both a B and T cell receptor, termed double expresser (DE) cells. DE cells in blood of type 1 diabetes (T1D) subjects were present at increased numbers and enriched for a public B cell clonotype. Here, we attempted to reproduce these findings. While we could identify DE cells by flow cytometry, we found no association between DE cell frequency and T1D status. We were unable to identify the reported public B cell clone, or any similar clone, in bulk B cells or sorted DE cells from T1D subjects or controls. We also did not observe increased usage of the public clone VH or DH genes in B cells or in sorted DE cells. Taken together, our findings suggest that DE cells and their alleged public clonotype are not enriched in T1D. This Matters Arising paper is in response to Ahmed et al. (2019), published in Cell. See also the response by Ahmed et al. (2021), published in this issue.

Developing mass spectrometry for the quantitative analysis of neuropeptides

INTRODUCTION

Neuropeptides are signaling molecules originating in the neuroendocrine system that can act as neurotransmitters and hormones in many biochemical processes. Their exact function is difficult to characterize, however, due to dependence on concentration, post-translational modifications, and the presence of other comodulating neuropeptides. Mass spectrometry enables sensitive, accurate, and global peptidomic analyses that can profile neuropeptide expression changes to understand their roles in many biological problems, such as neurodegenerative disorders and metabolic function.

AREAS COVERED

We provide a brief overview of the fundamentals of neuropeptidomic research, limitations of existing methods, and recent progress in the field. This review is focused on developments in mass spectrometry and encompasses labeling strategies, post-translational modification analysis, mass spectrometry imaging, and integrated multi-omic workflows, with discussion emphasizing quantitative advancements.

EXPERT OPINION

Neuropeptidomics is critical for future clinical research with impacts in biomarker discovery, receptor identification, and drug design. While advancements are being made to improve sensitivity and accuracy, there is still room for improvement. Better quantitative strategies are required for clinical analyses, and these methods also need to be amenable to mass spectrometry imaging, post-translational modification analysis, and multi-omics to facilitate understanding and future treatment of many diseases.

De-coding genetic risk variants in type 1 diabetes

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

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.

Exocrine Pancreatic Enzymes Are a Serological Biomarker for Type 1 Diabetes Staging and Pancreas Size

Exocrine pancreas abnormalities are increasingly recognized as features of type 1 diabetes. We previously reported reduced serum trypsinogen levels and in a separate study, smaller pancreata at and before disease onset. We hypothesized that three pancreas enzymes (amylase, lipase, and trypsinogen) might serve as serological biomarkers of pancreas volume and risk for type 1 diabetes. Amylase, lipase, and trypsinogen were measured from two independent cohorts, together comprising 800 serum samples from single-autoantibody-positive (1AAb⁺) and multiple-AAb⁺ (≥2AAb⁺) subjects, individuals with recent-onset or established type 1 diabetes, their AAb-negative (AAb^-) first-degree relatives, and AAb^- control subjects. Lipase and trypsinogen were significantly reduced in ≥2AAb⁺, recent-onset, and established type 1 diabetes subjects versus control subjects and 1AAb⁺, while amylase was reduced only in established type 1 diabetes. Logistic regression models demonstrated trypsinogen plus lipase (area under the receiver operating characteristic curve [AUROC] = 81.4%) performed equivalently to all three enzymes (AUROC = 81.4%) in categorizing ≥2AAb⁺ versus 1AAb⁺ subjects. For cohort 2 (n = 246), linear regression demonstrated lipase and trypsinogen levels could individually and collectively serve as indicators of BMI-normalized relative pancreas volume (RPV_BMI, P < 0.001), previously measured by MRI. Serum lipase and trypsinogen levels together provide the most sensitive serological biomarker of RPV_BMI and may improve disease staging in pretype 1 diabetes.

Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes

AIMS

Misclassification of diabetes is common due to an overlap in the clinical features of type 1 and type 2 diabetes. Combined diagnostic models incorporating clinical and biomarker information have recently been developed that can aid classification, but they have not been validated using pancreatic pathology. We evaluated a clinical diagnostic model against histologically defined type 1 diabetes.

METHODS

We classified cases from the Network for Pancreatic Organ donors with Diabetes (nPOD) biobank as type 1 (n = 111) or non-type 1 (n = 42) diabetes using histopathology. Type 1 diabetes was defined by lobular loss of insulin-containing islets along with multiple insulin-deficient islets. We assessed the discriminative performance of previously described type 1 diabetes diagnostic models, based on clinical features (age at diagnosis, BMI) and biomarker data [autoantibodies, type 1 diabetes genetic risk score (T1D-GRS)], and singular features for identifying type 1 diabetes by the area under the curve of the receiver operator characteristic (AUC-ROC).

RESULTS

Diagnostic models validated well against histologically defined type 1 diabetes. The model combining clinical features, islet autoantibodies and T1D-GRS was strongly discriminative of type 1 diabetes, and performed better than clinical features alone (AUC-ROC 0.97 vs. 0.95; P = 0.03). Histological classification of type 1 diabetes was concordant with serum C-peptide [median < 17 pmol/l (limit of detection) vs. 1037 pmol/l in non-type 1 diabetes; P < 0.0001].

CONCLUSIONS

Our study provides robust histological evidence that a clinical diagnostic model, combining clinical features and biomarkers, could improve diabetes classification. Our study also provides reassurance that a C-peptide-based definition of type 1 diabetes is an appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible. Parts of this study were presented in abstract form at the Network for Pancreatic Organ Donors Conference, Florida, USA, 19-22 February 2019 and Diabetes UK Professional Conference, Liverpool, UK, 6-8 March 2019.

Challenges in the diagnosis of diabetes type in pediatrics

The incidence of diabetes, both type 1 and type 2, is increasing. Health outcomes in pediatric diabetes are currently poor, with trends indicating that they are worsening. Minority racial/ethnic groups are disproportionately affected by suboptimal glucose control and have a higher risk of acute and chronic complications of diabetes. Correct clinical management starts with timely and accurate classification of diabetes, but in children this is becoming increasingly challenging due to high prevalence of obesity and shifting demographic composition. The growing obesity epidemic complicates classification by obesity's effects on diabetes. Since the prevalence and clinical characteristics of diabetes vary among racial/ethnic groups, migration between countries leads to changes in the distribution of diabetes types in a certain geographical area, challenging the clinician's ability to classify diabetes. These challenges must be addressed to correctly classify diabetes and establish an appropriate treatment strategy early in the course of disease for all. This may be the first step in improving diabetes outcomes across racial/ethnic groups. This review will discuss the pitfalls in the current diabetes classification scheme that is leading to increasing overlap between diabetes types and heterogeneity within each type. It will also present proposed alternative classification schemes and approaches to understanding diabetes type that may improve the timely and accurate classification of pediatric diabetes type.

Immunomodulatory Dual-Sized Microparticle System Conditions Human Antigen Presenting Cells Into a Tolerogenic Phenotype In Vitro and Inhibits Type 1 Diabetes-Specific Autoreactive T Cell Responses

Current monotherapeutic agents fail to restore tolerance to self-antigens in autoimmune individuals without systemic immunosuppression. We hypothesized that a combinatorial drug formulation delivered by a poly-lactic-co-glycolic acid (PLGA) dual-sized microparticle (dMP) system would facilitate tunable drug delivery to elicit immune tolerance. Specifically, we utilized 30 µm MPs to provide local sustained release of granulocyte-macrophage colony-stimulating factor (GM-CSF) and transforming growth factor β1 (TGF-β1) along with 1 µm MPs to facilitate phagocytic uptake of encapsulated antigen and 1α,25(OH)2 Vitamin D3 (VD3) followed by tolerogenic antigen presentation. We previously demonstrated the dMP system ameliorated type 1 diabetes (T1D) and experimental autoimmune encephalomyelitis (EAE) in murine models. Here, we investigated the system's capacity to impact human cell activity in vitro to advance clinical translation. dMP treatment directly reduced T cell proliferation and inflammatory cytokine production. dMP delivery to monocytes and monocyte-derived dendritic cells (DCs) increased their expression of surface and intracellular anti-inflammatory mediators. In co-culture, dMP-treated DCs (dMP-DCs) reduced allogeneic T cell receptor (TCR) signaling and proliferation, while increasing PD-1 expression, IL-10 production, and regulatory T cell (Treg) frequency. To model antigen-specific activation and downstream function, we co-cultured TCR-engineered autoreactive T cell "avatars," with dMP-DCs or control DCs followed by β-cell line (ßlox5) target cells. For G6PC2-specific CD8+ avatars (clone 32), dMP-DC exposure reduced Granzyme B and dampened cytotoxicity. GAD65-reactive CD4+ avatars (clone 4.13) exhibited an anergic/exhausted phenotype with dMP-DC presence. Collectively, these data suggest this dMP formulation conditions human antigen presenting cells toward a tolerogenic phenotype, inducing regulatory and suppressive T cell responses.

Type 1 diabetes in Africa: an immunogenetic study in the Amhara of North-West Ethiopia

AIMS/HYPOTHESIS

We aimed to characterise the immunogenic background of insulin-dependent diabetes in a resource-poor rural African community. The study was initiated because reports of low autoantibody prevalence and phenotypic differences from European-origin cases with type 1 diabetes have raised doubts as to the role of autoimmunity in this and similar populations.

METHODS

A study of consecutive, unselected cases of recently diagnosed, insulin-dependent diabetes (n = 236, ≤35 years) and control participants (n = 200) was carried out in the ethnic Amhara of rural North-West Ethiopia. We assessed their demographic and socioeconomic characteristics, and measured non-fasting C-peptide, diabetes-associated autoantibodies and HLA-DRB1 alleles. Leveraging genome-wide genotyping, we performed both a principal component analysis and, given the relatively modest sample size, a provisional genome-wide association study. Type 1 diabetes genetic risk scores were calculated to compare their genetic background with known European type 1 diabetes determinants.

RESULTS

Patients presented with stunted growth and low BMI, and were insulin sensitive; only 15.3% had diabetes onset at ≤15 years. C-peptide levels were low but not absent. With clinical diabetes onset at ≤15, 16-25 and 26-35 years, 86.1%, 59.7% and 50.0% were autoantibody positive, respectively. Most had autoantibodies to GAD (GADA) as a single antibody; the prevalence of positivity for autoantibodies to IA-2 (IA-2A) and ZnT8 (ZnT8A) was low in all age groups. Principal component analysis showed that the Amhara genomes were distinct from modern European and other African genomes. HLA-DRB1*03:01 (p = 0.0014) and HLA-DRB1*04 (p = 0.0001) were positively associated with this form of diabetes, while HLA-DRB1*15 was protective (p < 0.0001). The mean type 1 diabetes genetic risk score (derived from European data) was higher in patients than control participants (p = 1.60 × 10-7). Interestingly, despite the modest sample size, autoantibody-positive patients revealed evidence of association with SNPs in the well-characterised MHC region, already known to explain half of type 1 diabetes heritability in Europeans.

CONCLUSIONS/INTERPRETATION

The majority of patients with insulin-dependent diabetes in rural North-West Ethiopia have the immunogenetic characteristics of autoimmune type 1 diabetes. Phenotypic differences between type 1 diabetes in rural North-West Ethiopia and the industrialised world remain unexplained.

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.

Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk: Clinical Perspectives and Basic Implications

An individual's susceptibility to atherosclerotic cardiovascular disease is influenced by numerous clinical and lifestyle factors, motivating the multifaceted approaches currently endorsed for primary and secondary cardiovascular disease prevention. With growing knowledge of the genetic basis of atherosclerotic cardiovascular disease-in particular, coronary artery disease-and its contribution to disease pathogenesis, there is increased interest in understanding the potential clinical utility of a genetic predictor that might further refine the assessment and management of atherosclerotic cardiovascular disease risk. Rapid scientific and technological advances have enabled widespread genotyping efforts and dynamic research in the field of coronary artery disease genetic risk prediction. In this review, we describe how genomic analyses of coronary artery disease have been leveraged to create polygenic risk scores. We then discuss evaluations of the clinical utility of these scores, pertinent mechanistic insights gleaned, and practical considerations relevant to the implementation of polygenic risk scores in the health care setting.

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.

The risk of progression to type 1 diabetes is highly variable in individuals with multiple autoantibodies following screening

AIMS/HYPOTHESIS

Young children who develop multiple autoantibodies (mAbs) are at very high risk for type 1 diabetes. We assessed whether a population with mAbs detected by screening is also at very high risk, and how risk varies according to age, type of autoantibodies and metabolic status.

METHODS

Type 1 Diabetes TrialNet Pathway to Prevention participants with mAbs (n = 1815; age, 12.35 ± 9.39 years; range, 1-49 years) were analysed. Type 1 diabetes risk was assessed according to age, autoantibody type/number (insulin autoantibodies [IAA], glutamic acid decarboxylase autoantibodies [GADA], insulinoma-associated antigen-2 autoantibodies [IA-2A] or zinc transporter 8 autoantibodies [ZnT8A]) and Index60 (composite measure of fasting C-peptide, 60 min glucose and 60 min C-peptide). Cox regression and cumulative incidence curves were utilised in this cohort study.

RESULTS

Age was inversely related to type 1 diabetes risk in those with mAbs (HR 0.97 [95% CI 0.96, 0.99]). Among participants with 2 autoantibodies, those with GADA had less risk (HR 0.35 [95% CI 0.22, 0.57]) and those with IA-2A had higher risk (HR 2.82 [95% CI 1.76, 4.51]) of type 1 diabetes. Those with IAA and GADA had only a 17% 5 year risk of type 1 diabetes. The risk was significantly lower for those with Index60 <1.0 (HR 0.23 [95% CI 0.19, 0.30]) vs those with Index60 values ≥1.0. Among the 12% (225/1815) ≥12.0 years of age with GADA positivity, IA-2A negativity and Index60 <1.0, the 5 year risk of type 1 diabetes was 8%.

CONCLUSIONS/INTERPRETATION

Type 1 diabetes risk varies substantially according to age, autoantibody type and metabolic status in individuals screened for mAbs. An appreciable proportion of older children and adults with mAbs appear to have a low risk of progressing to type 1 diabetes at 5 years. With this knowledge, clinical trials of type 1 diabetes prevention can better target those most likely to progress.

Innate inflammation drives NK cell activation to impair Treg activity

IL-12 and IL-18 synergize to promote TH1 responses and have been implicated as accelerators of autoimmune pathogenesis in type 1 diabetes (T1D). We investigated the influence of these cytokines on immune cells involved in human T1D progression: natural killer (NK) cells, regulatory T cells (Tregs), and cytotoxic T lymphocytes (CTL). NK cells from T1D patients exhibited higher surface CD226 versus controls and lower CD25 compared to first-degree relatives and controls. Changes in NK cell phenotype towards terminal differentiation were associated with cytomegalovirus (CMV) seropositivity, while possession of IL18RAP, IFIH1, and IL2RA T1D-risk variants impacted NK cell activation as evaluated by immuno-expression quantitative trait loci (eQTL) analyses. IL-12 and IL-18 stimulated NK cells from healthy donors exhibited enhanced specific killing of myelogenous K562 target cells. Moreover, activated NK cells increased expression of NKG2A, NKG2D, CD226, TIGIT and CD25, which enabled competition for IL-2 upon co-culture with Tregs, resulting in Treg downregulation of FOXP3, production of IFNγ, and loss of suppressive function. We generated islet-autoreactive CTL "avatars", which upon exposure to IL-12 and IL-18, upregulated IFNγ and Granzyme-B leading to increased lymphocytotoxicity of a human β-cell line in vitro. These results support a model for T1D pathogenesis wherein IL-12 and IL-18 synergistically enhance CTL and NK cell cytotoxic activity and disrupt immunoregulation by Tregs.

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

AIMS/HYPOTHESIS

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

METHODS

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

RESULTS

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

CONCLUSIONS/INTERPRETATION

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

Common genetic variation and risk of osteosarcoma in a multi-ethnic pediatric and adolescent population

Osteosarcoma, a malignant primary bone tumor most commonly diagnosed in children and adolescents, has a poorly understood genetic etiology. Genome-wide association studies (GWAS) and candidate-gene analyses have identified putative risk variants in subjects of European ancestry. However, despite higher incidence among African-American and Hispanic children, little is known regarding common heritable variation that contributes to osteosarcoma incidence and clinical presentation across racial/ethnic groups. In a multi-ethnic sample of non-Hispanic white, Hispanic, African-American and Asian/Pacific Islander children (537 cases, 2165 controls), we performed association analyses assessing previously-reported loci for osteosarcoma risk and metastasis, including meta-analysis across racial/ethnic groups. We also assessed a previously described association between genetic predisposition to longer leukocyte telomere length (LTL) and osteosarcoma risk in this independent multi-ethnic dataset. In our sample, we were unable to replicate previously-reported loci for osteosarcoma risk or metastasis detected in GWAS of European-ancestry individuals in either ethnicity-stratified analyses or meta-analysis across ethnic groups. Our analyses did confirm that genetic predisposition to longer LTL is a risk factor for osteosarcoma (ORmeta: 1.22; 95% CI: 1.09-1.36; P = 3.8 × 10-4), and the strongest effect was seen in Hispanic subjects (OR: 1.32; 95% CI: 1.12-1.54, P = 6.2 × 10-4). Our findings shed light on the replicability of osteosarcoma risk loci across ethnicities and motivate further characterization of these genetic factors in diverse clinical cohorts.

Type 1 diabetes genetic risk score discriminates between monogenic and Type 1 diabetes in children diagnosed at the age of <5 years in the Iranian population

AIM

To examine the extent to which discriminatory testing using antibodies and Type 1 diabetes genetic risk score, validated in European populations, is applicable in a non-European population.

METHODS

We recruited 127 unrelated children with diabetes diagnosed between 9 months and 5 years from two centres in Iran. All children underwent targeted next-generation sequencing of 35 monogenic diabetes genes. We measured three islet autoantibodies (islet antigen 2, glutamic acid decarboxylase and zinc transporter 8) and generated a Type 1 diabetes genetic risk score in all children.

RESULTS

We identified six children with monogenic diabetes, including four novel mutations: homozygous mutations in WFS1 (n=3), SLC19A2 and SLC29A3, and a heterozygous mutation in GCK. All clinical features were similar in children with monogenic diabetes (n=6) and in the rest of the cohort (n=121). The Type 1 diabetes genetic risk score discriminated children with monogenic from Type 1 diabetes [area under the receiver-operating characteristic curve 0.90 (95% CI 0.83-0.97)]. All children with monogenic diabetes were autoantibody-negative. In children with no mutation, 59 were positive to glutamic acid decarboxylase, 39 to islet antigen 2 and 31 to zinc transporter 8. Measuring zinc transporter 8 increased the number of autoantibody-positive individuals by eight.

CONCLUSIONS

The present study provides the first evidence that Type 1 diabetes genetic risk score can be used to distinguish monogenic from Type 1 diabetes in an Iranian population with a large number of consanguineous unions. This test can be used to identify children with a higher probability of having monogenic diabetes who could then undergo genetic testing. Identification of these individuals would reduce the cost of treatment and improve the management of their clinical course.

Early-life factors contributing to type 1 diabetes

The incidence of type 1 diabetes has increased since the mid-twentieth century at a rate that is too rapid to be attributed to genetic predisposition alone. While the disease can occur at any age, mounting evidence from longitudinal cohort studies of at-risk children indicate that type 1 diabetes associated autoantibodies can be present from the first year of life, and that those who develop type 1 diabetes at a young age have a more aggressive form of the disease. This corroborates the hypothesis that environmental exposures in early life contribute to type 1 diabetes risk, whether related to maternal influences on the fetus during pregnancy, neonatal factors or later effects during infancy and early childhood. Studies to date show a range of environmental triggers acting at different time points, suggesting a multifactorial model of genetic and environmental factors in the pathogenesis of type 1 diabetes, which integrally involves a dialogue between the immune system and pancreatic beta cells. For example, breastfeeding may have a weak protective effect on type 1 diabetes risk, while use of an extensively hydrolysed formula does not. Additionally, exposure to being overweight pre-conception, both in utero and postnatally, is associated with increased risk of type 1 diabetes. Epidemiological, clinical and pathological studies in humans support a role for viral infections, particularly enteroviruses, in type 1 diabetes, but definitive proof is lacking. The role of the early microbiome and its perturbations in islet autoimmunity and type 1 diabetes is the subject of investigation in ongoing cohort studies. Understanding the interactions between environmental exposures and the human genome and metagenome, particularly across ethnically diverse populations, will be critical for the development of future strategies for primary prevention of type 1 diabetes.

What Have Slow Progressors Taught Us About T1D-Mind the Gap!

PURPOSE OF REVIEW

Progression rate from islet autoimmunity to clinical diabetes is unpredictable. In this review, we focus on an intriguing group of slow progressors who have high-risk islet autoantibody profiles but some remain diabetes free for decades.

RECENT FINDINGS

Birth cohort studies show that islet autoimmunity presents early in life and approximately 70% of individuals with multiple islet autoantibodies develop clinical symptoms of diabetes within 10 years. Some "at risk" individuals however progress very slowly. Recent genetic studies confirm that approximately half of type 1 diabetes (T1D) is diagnosed in adulthood. This creates a conundrum; slow progressors cannot account for the number of cases diagnosed in the adult population. There is a large "gap" in our understanding of the pathogenesis of adult onset T1D and a need for longitudinal studies to determine whether there are "at risk" adults in the general population; some of whom are rapid and some slow adult progressors.

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

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

New insights on the genetics of type 1 diabetes

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Towards clinical utility of polygenic risk scores

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.

Type 1 Diabetes Risk in African-Ancestry Participants and Utility of an Ancestry-Specific Genetic Risk Score

OBJECTIVE

Genetic risk scores (GRS) have been developed that differentiate individuals with type 1 diabetes from those with other forms of diabetes and are starting to be used for population screening; however, most studies were conducted in European-ancestry populations. This study identifies novel genetic variants associated with type 1 diabetes risk in African-ancestry participants and develops an African-specific GRS.

RESEARCH DESIGN AND METHODS

We generated single nucleotide polymorphism (SNP) data with the ImmunoChip on 1,021 African-ancestry participants with type 1 diabetes and 2,928 control participants. HLA class I and class II alleles were imputed using SNP2HLA. Logistic regression models were used to identify genome-wide significant (P < 5.0 × 10-8) SNPs associated with type 1 diabetes in the African-ancestry samples and validate SNPs associated with risk in known European-ancestry loci (P < 2.79 × 10-5).

RESULTS

African-specific (HLA-DQA1*03:01-HLA-DQB1*02:01) and known European-ancestry HLA haplotypes (HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01, HLA-DRB1*04:01-HLA-DQA1*03:01-HLA-DQB1*03:02) were significantly associated with type 1 diabetes risk. Among European-ancestry defined non-HLA risk loci, six risk loci were significantly associated with type 1 diabetes in subjects of African ancestry. An African-specific GRS provided strong prediction of type 1 diabetes risk (area under the curve 0.871), performing significantly better than a European-based GRS and two polygenic risk scores in independent discovery and validation cohorts.

CONCLUSIONS

Genetic risk of type 1 diabetes includes ancestry-specific, disease-associated variants. The GRS developed here provides improved prediction of type 1 diabetes in African-ancestry subjects and a means to identify groups of individuals who would benefit from immune monitoring for early detection of islet autoimmunity.