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

Insights into non-autoimmune type 1 diabetes with 13 novel loci in low polygenic risk score patients

With polygenic risk score (PRS) for autoimmune type 1 diabetes (T1D), this study identified T1D cases with low T1D PRS and searched for susceptibility loci in these cases. Our hypothesis is that genetic effects (likely mediated by relatively rare genetic variants) of non-mainstream (or non-autoimmune) T1D might have been diluted in the previous studies on T1D cases in general. Two cohorts for the PRS modeling and testing respectively were included. The first cohort consisted of 3302 T1D cases and 6181 controls, and the independent second cohort consisted of 3297 T1D cases and 6169 controls. Cases with low T1D PRS were identified using PRSice-2 and compared to controls with low T1D PRS by genome-wide association (GWA) test. Thirteen novel genetic loci with high imputation quality (Quality Score r² > 0.91) were identified of SNPs/SNVs associated with low PRS T1D at genome-wide significance (P ≤ 5.0 × E-08), in addition to 4 established T1D loci, 3 reported loci by our previous study, as well as 9 potential novel loci represented by rare SNVs, but with relatively low imputation quality (Quality Score r² < 0.90). For the 13 novel loci, 9 regions have been reported of association with obesity related traits by previous GWA studies. Three loci encoding long intergenic non-protein coding RNAs (lncRNA), and 2 loci involved in N-linked glycosylation are also highlighted in this study.

Genetic architecture of type 1 diabetes with low genetic risk score informed by 41 unreported loci

Type 1 diabetes (T1D) patients with low genetic risk scores (GRS) may be non-autoimmune or autoimmune mediated by other genetic loci. The T1D-GRS2 provides us an opportunity to look into the genetic architecture of these patients. A total of 18,949 European individuals were included in this study, including 6599 T1D cases and 12,323 controls. 957 (14.5%) T1D patients were identified with low GRS (GRS < 8.43). The genome-wide association study on these patients identified 41 unreported loci. Two loci with common variants and 39 loci with rare variants were identified in this study. This study identified common SNPs associated with both low GRS T1D and expression levels of the interferon-α-induced MNDA gene, indicating the role of viral infection in T1D. Interestingly, 16 of the 41 unreported loci have been linked to autism spectrum disorder (ASD) by previous studies, suggesting that genes residing at these loci may underlie both T1D and autism.

Viruses and Type 1 Diabetes: From Enteroviruses to the Virome

For over a century, viruses have left a long trail of evidence implicating them as frequent suspects in the development of type 1 diabetes. Through vigorous interrogation of viral infections in individuals with islet autoimmunity and type 1 diabetes using serological and molecular virus detection methods, as well as mechanistic studies of virus-infected human pancreatic β-cells, the prime suspects have been narrowed down to predominantly human enteroviruses. Here, we provide a comprehensive overview of evidence supporting the hypothesised role of enteroviruses in the development of islet autoimmunity and type 1 diabetes. We also discuss concerns over the historical focus and investigation bias toward enteroviruses and summarise current unbiased efforts aimed at characterising the complete population of viruses (the “virome”) contributing early in life to the development of islet autoimmunity and type 1 diabetes. Finally, we review the range of vaccine and antiviral drug candidates currently being evaluated in clinical trials for the prevention and potential treatment of type 1 diabetes.

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

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

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide-significant (P < 5 × 10-8) regions, including 36 that are new. We define credible sets of T1D-associated variants and show that they are enriched in immune-cell accessible chromatin, particularly CD4+ effector T cells. Using chromatin-accessibility profiling of CD4+ T cells from 115 individuals, we map chromatin-accessibility quantitative trait loci and identify five regions where T1D risk variants co-localize with chromatin-accessibility quantitative trait loci. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D.

Segregation Analysis of Genotyped and Family-Phased, Long Range MHC Classical Class I and Class II Haplotypes in 5 Families With Type 1 Diabetes Proband in the United Arab Emirates

The classical Human Leucocyte Antigen (HLA) class II haplotypes of the Major Histocompatibility Complex (MHC) that are associated with type 1 diabetes (T1D) were identified in five families from the United Arab Emirates (UAE). Segregation analyses were performed on these 5 families with the disease, 3 with one child and 2 with 2 children diagnosed with T1D. Three HLA-DR4 haplotypes were identified: HLA- DRB1^∗04:01:01-DQB1^∗03:02:01:01; HLA- DRB1^∗04:02:01- DQB1^∗03:02:01; and HLA -DRB1^∗04:05:01-DQB1^∗02:02:01:02. All have previously been identified to be associated with T1D in studies of the Arabian population. In the 10 parents from the 5 families, 9 had at least one HLA-DR4 and HLA-DR3 haplotype which potentially increases the risk of T1D. Of these 9 parents, 3 were heterozygous for HLA-DR4/HLA-DR3 and one was homozygous for HLA-DR3. Two haplotypes that were identified here extend to the HLA class I region were previously designated AH8.2 (HLA -A^∗26-B^∗08-DRB1^∗03) and AH50.2 (HLA -C^∗06-B^∗50-DRB1^∗03:01-DQ^∗02) and associated with diabetes in neighboring North Indian populations. This study provides examples of MHC haplotype analysis in pedigrees to improve our understanding of the genetics of T1D in the understudied population of the UAE.

A genome-wide association study identifies 5 loci associated with frozen shoulder and implicates diabetes as a causal risk factor

Frozen shoulder is a painful condition that often requires surgery and affects up to 5% of individuals aged 40-60 years. Little is known about the causes of the condition, but diabetes is a strong risk factor. To begin to understand the biological mechanisms involved, we aimed to identify genetic variants associated with frozen shoulder and to use Mendelian randomization to test the causal role of diabetes. We performed a genome-wide association study (GWAS) of frozen shoulder in the UK Biobank using data from 10,104 cases identified from inpatient, surgical and primary care codes. We used data from FinnGen for replication and meta-analysis. We used one-sample and two-sample Mendelian randomization approaches to test for a causal association of diabetes with frozen shoulder. We identified five genome-wide significant loci. The most significant locus (lead SNP rs28971325; OR = 1.20, [95% CI: 1.16-1.24], p = 5x10-29) contained WNT7B. This variant was also associated with Dupuytren's disease (OR = 2.31 [2.24, 2.39], p<1x10-300) as were a further two of the frozen shoulder associated variants. The Mendelian randomization results provided evidence that type 1 diabetes is a causal risk factor for frozen shoulder (OR = 1.03 [1.02-1.05], p = 3x10-6). There was no evidence that obesity was causally associated with frozen shoulder, suggesting that diabetes influences risk of the condition through glycemic rather than mechanical effects. We have identified genetic loci associated with frozen shoulder. There is a large overlap with Dupuytren's disease associated loci. Diabetes is a likely causal risk factor. Our results provide evidence of biological mechanisms involved in this common painful condition.

Dissecting polygenic signals from genome-wide association studies on human behaviour

Genome-wide association studies on human behavioural traits are producing large amounts of polygenic signals with significant predictive power and potentially useful biological clues. Behavioural traits are more distal and are less directly under biological control compared with physical characteristics, which makes the associated genetic effects harder to interpret. The results of genome-wide association studies for human behaviour are likely made up of a composite of signals from different sources. While sample sizes continue to increase, we outline additional steps that need to be taken to better delineate the origin of the increasingly stronger polygenic signals. In addition to genetic effects on the traits themselves, the major sources of polygenic signals are those that are associated with correlated traits, environmental effects and ascertainment bias. Advances in statistical approaches that disentangle polygenic effects from different traits as well as extending data collection to families and social circles with better geographical coverage will probably contribute to filling the gap of knowledge between genetic effects and behavioural outcomes.

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

HYPOTHESIS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Development and validation of a prediction model for glucocorticoid-associated osteonecrosis of the femoral head by targeted sequencing

OBJECTIVE

To develop and validate a prediction model based on targeted sequencing for glucocorticoid (GC)-associated osteonecrosis of the femoral head (GA-ONFH) in GC-treated adults.

METHODS

This two-centre retrospective study was conducted between July 2015 and April 2019 at Zhongshan Hospital (training set) and the Sixth People's Hospital (test set) in Shanghai, China. All patients had a history of GC therapy, with a dose exceeding 2000 mg equivalent prednisone within 6 weeks. Patients were divided into two groups according to whether they were diagnosed with GA-ONFH within 2 years after GC initiation. Blood or saliva samples were collected for targeted sequencing of 358 single nucleotide polymorphisms (SNPs) and genetic risk score (GRS) calculating for developing GA-ONFH prediction model. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to evaluate and validate the model.

RESULTS

The training set comprised 117 patients, while test set comprised 30 patients for external validation. Logistic regression analysis showed that GRS was significantly associated with GA-ONFH [OR 1.87, 95% confidence interval (CI) 1.48-2.37]. The ROC and DCA curves showed that the multivariate model considering GRS, age at GC initial, sex and underlying diseases had a discrimination with area under the ROC curve (AUC) of 0.98 (95% CI 0.96-1.00). This model was further externally validated using the test set with an AUC of 0.91 (95% CI 0.81-1.00).

CONCLUSION

Our prediction model comprising GRS, age, sex, and underlying diseases yields valid predictions of GA-ONFH incidence. It may facilitate effective screening and prevention strategies of GA-ONFH.

High-resolution genotyping of HLA class I loci in children with type 1 diabetes and celiac disease

OBJECTIVES

HLA-DQ2 and DQ8 contribute to the strongest risk haplotypes for type 1 diabetes (T1D) and celiac disease (CD). The variation in genetic risk association is likely linked to different HLA class II loci susceptibility, but association studies of HLA class I alleles are scarce. The aim was to investigate HLA class I A, B, and C alleles polymorphisms in children with only T1D, CD, and a subgroup with both T1D and CD (T1D w/CD).

MATERIALS AND METHODS

HLA class I A, B, and C genes were genotyped using next-generation targeted sequencing. A conditional analysis was performed on 68 children with T1D, 219 children with CD and seven children with T1D w/CD enrolled from a birth cohort study at high genetic risk children from the South of Sweden.

RESULTS

Among 1764 HLA class I allele variants, A*29:02:01 in T1D w/CD was associated with both T1D (OR = 21.42 [1.05, 1322.4], p = 0.0231) and CD (OR = 35 [2.36, 529.12], p = 0.0051) along with C*05:01:01 with both T1D (OR = 5.54 [1.06, 24.8], p = 0.02) and CD (OR = 6.84 [1.46, 26.01], p = 0.0077). No independent effects of HLA-B allele associations were observed in T1D w/CD.

CONCLUSION

Although the distribution of HLA class I alleles differs between children with T1D and CD, the A*29:02:01 and C*05:01:01 alleles showed shared risk association of both diseases.

Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease affecting individuals in the early years of life. Although previous studies have identified genetic loci influencing T1D diagnosis age, these studies did not investigate the genome with high resolution.

OBJECTIVE AND METHODS

We performed a genome-wide meta-analysis for age at diagnosis with cohorts from Finland (Finnish Diabetic Nephropathy Study), the United Kingdom (UK Genetic Resource Investigating Diabetes) and Sardinia. Through SNP associations, transcriptome-wide association analysis linked T1D diagnosis age and gene expression.

RESULTS

We identified two chromosomal regions associated with T1D diagnosis age: multiple independent variants in the HLA region on chromosome 6 and a locus on chromosome 17q12. We performed gene-level association tests with transcriptome prediction models from two whole blood datasets, lymphocyte cell line, spleen, pancreas and small intestine tissues. Of the non-HLA genes, lower PNMT expression in whole blood, and higher IKZF3 and ZPBP2, and lower ORMDL3 and GSDMB transcription levels in multiple tissues were associated with lower T1D diagnosis age (FDR = 0.05). These genes lie on chr17q12 which is associated with T1D, other autoimmune diseases, and childhood asthma. Additionally, higher expression of PHF20L1, a gene not previously implicated in T1D, was associated with lower diagnosis age in lymphocytes, pancreas, and spleen. Altogether, the non-HLA associations were enriched in open chromatin in various blood cells, blood vessel tissues and foetal thymus tissue.

CONCLUSION

Multiple genes on chr17q12 and PHF20L1 on chr8 were associated with T1D diagnosis age and only further studies may elucidate the role of these genes for immunity and T1D onset.

Diagnosis and treatment of type 1 diabetes at the dawn of the personalized medicine era

Type 1 diabetes affects millions of people globally and requires careful management to avoid serious long-term complications, including heart and kidney disease, stroke, and loss of sight. The type 1 diabetes patient cohort is highly heterogeneous, with individuals presenting with disease at different stages and severities, arising from distinct etiologies, and overlaying varied genetic backgrounds. At present, the “one-size-fits-all” treatment for type 1 diabetes is exogenic insulin substitution therapy, but this approach fails to achieve optimal blood glucose control in many individuals. With advances in our understanding of early-stage diabetes development, diabetes stratification, and the role of genetics, type 1 diabetes is a promising candidate for a personalized medicine approach, which aims to apply “the right therapy at the right time, to the right patient”. In the case of type 1 diabetes, great efforts are now being focused on risk stratification for diabetes development to enable pre-clinical detection, and the application of treatments such as gene therapy, to prevent pancreatic destruction in a sub-set of patients. Alongside this, breakthroughs in stem cell therapies hold great promise for the regeneration of pancreatic tissues in some individuals. Here we review the recent initiatives in the field of personalized medicine for type 1 diabetes, including the latest discoveries in stem cell and gene therapy for the disease, and current obstacles that must be overcome before the dream of personalized medicine for all type 1 diabetes patients can be realized.

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.

A Triple Threat? The Role of Diet, Nutrition, and the Microbiota in T1D Pathogenesis

In recent years the role of the intestinal microbiota in health and disease has come to the forefront of medical research. Alterations in the intestinal microbiota and several of its features have been linked to numerous diseases, including type 1 diabetes (T1D). To date, studies in animal models of T1D, as well as studies in human subjects, have linked several intestinal microbiota alterations with T1D pathogenesis. Features that are most often linked with T1D pathogenesis include decreased microbial diversity, the relative abundance of specific strains of individual microbes, and altered metabolite production. Alterations in these features as well as others have provided insight into T1D pathogenesis and shed light on the potential mechanism by which the microbiota plays a role in T1D pathogenesis, yet the underlying factors leading to these alterations remains unknown. One potential mechanism for alteration of the microbiota is through diet and nutrition. Previous studies have shown associations of diet with islet autoimmunity, but a direct contributing factor has yet to be identified. Diet, through introduction of antigens and alteration of the composition and function of the microbiota, may elicit the immune system to produce autoreactive responses that result in the destruction of the beta cells. Here, we review the evidence associating diet induced changes in the intestinal microbiota and their contribution to T1D pathogenesis. We further provide a roadmap for determining the effect of diet and other modifiable factors on the entire microbiota ecosystem, including its impact on both immune and beta cell function, as it relates to T1D. A greater understanding of the complex interactions between the intestinal microbiota and several interacting systems in the body (immune, intestinal integrity and function, metabolism, beta cell function, etc.) may provide scientifically rational approaches to prevent development of T1D and other childhood immune and allergic diseases and biomarkers to evaluate the efficacy of interventions.

The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation

We present the Polygenic Score (PGS) Catalog (https://www.PGSCatalog.org ), an open resource of published scores (including variants, alleles and weights) and consistently curated metadata required for reproducibility and independent applications. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with a platform for PGS dissemination, research and translation.

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

Current Developments of Clinical Sequencing and the Clinical Utility of Polygenic Risk Scores in Inflammatory Diseases

In this mini-review, we highlight selected research by the Deutsche Forschungsgemeinschaft (DFG) Cluster of Excellence “Precision Medicine in Chronic Inflammation” focusing on clinical sequencing and the clinical utility of polygenic risk scores as well as its implication on precision medicine in the field of the inflammatory diseases inflammatory bowel disease, atopic dermatitis and coronary artery disease. Additionally, we highlight current developments and discuss challenges to be faced in the future. Exemplary, we point to residual challenges in detecting disease-relevant variants resulting from difficulties in the interpretation of candidate variants and their potential interactions. While polygenic risk scores represent promising tools for the stratification of patient groups, currently, polygenic risk scores are not accurate enough for clinical setting. Precision medicine, incorporating additional data from genomics, transcriptomics and proteomics experiments, may enable the identification of distinct disease pathogeneses. In the future, data-intensive biomedical innovation will hopefully lead to improved patient stratification for personalized medicine.

Three models for the regulation of polygenic scores in reproduction

The past few years have brought significant breakthroughs in understanding human genetics. This knowledge has been used to develop 'polygenic scores' (or 'polygenic risk scores') which provide probabilistic information about the development of polygenic conditions such as diabetes or schizophrenia. They are already being used in reproduction to select for embryos at lower risk of developing disease. Currently, the use of polygenic scores for embryo selection is subject to existing regulations concerning embryo testing and selection. Existing regulatory approaches include 'disease-based' models which limit embryo selection to avoiding disease characteristics (employed in various formats in Australia, the UK, Italy, Switzerland and France, among others), and 'laissez-faire' or 'libertarian' models, under which embryo testing and selection remain unregulated (as in the USA). We introduce a novel 'Welfarist Model' which limits embryo selection according to the impact of the predicted trait on well-being. We compare the strengths and weaknesses of each model as a way of regulating polygenic scores. Polygenic scores create the potential for existing embryo selection technologies to be used to select for a wider range of predicted genetically influenced characteristics including continuous traits. Indeed, polygenic scores exist to predict future intelligence, and there have been suggestions that they will be used to make predictions within the normal range in the USA in embryo selection. We examine how these three models would apply to the prediction of non-disease traits such as intelligence. The genetics of intelligence remains controversial both scientifically and ethically. This paper does not attempt to resolve these issues. However, as with many biomedical advances, an effective regulatory regime must be in place as soon as the technology is available. If there is no regulation in place, then the market effectively decides ethical issues.

The β-Cell Genomic Landscape in T1D: Implications for Disease Pathogenesis

PURPOSE OF REVIEW

Type 1 diabetes (T1D) develops as a consequence of a combination of genetic predisposition and environmental factors. Combined, these events trigger an autoimmune disease that results in progressive loss of pancreatic β cells, leading to insulin deficiency. This article reviews the current knowledge on the genetics of T1D with a specific focus on genetic variation in pancreatic islet regulatory networks and its implication to T1D risk and disease development.

RECENT FINDINGS

Accumulating evidence suggest an active role of β cells in T1D pathogenesis. Based on such observation several studies aimed in mapping T1D risk variants acting at the β cell level. Such studies unravel T1D risk loci shared with type 2 diabetes (T2D) and T1D risk variants potentially interfering with β-cell responses to external stimuli. The characterization of regulatory genomics maps of disease-relevant states and cell types can be used to elucidate the mechanistic role of β cells in the pathogenesis of T1D.

Recent shifts in the genomic ancestry of Mexican Americans may alter the genetic architecture of biomedical traits

People in the Americas represent a diverse continuum of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry increased by an average of ~20% spanning 1940s-1990s in Mexican Americans. These patterns result from complex interactions between several population and cultural factors which shaped patterns of genetic variation and influenced the genetic architecture of complex traits in Mexican Americans. We show for height how polygenic risk scores based on summary statistics from a European-based genome-wide association study perform poorly in Mexican Americans. Our findings reveal temporal changes in population structure within Hispanics/Latinos that may influence biomedical traits, demonstrating a need to improve our understanding of admixed populations.

Extended family history of type 1 diabetes in HLA-predisposed children with and without islet autoantibodies

OBJECTIVE

The aim of this study was to explore the extended family history of type 1 diabetes in children at genetic risk and define the impact of a positive family history on the development of islet autoimmunity and type 1 diabetes.

METHODS

The subjects were participants in The Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study and carried increased HLA-conferred risk for type 1 diabetes. The case children (N = 343) were positive for at least one islet autoantibody, and the control children (N = 343) matched by age, gender and class II HLA genotype were negative for islet autoantibodies at the time of data collection. Extended family history of type 1 diabetes was obtained by using a structured questionnaire.

RESULTS

Among children who were autoantibody positive and progressed to type 1 diabetes 62.2% (28/45) had at least one relative with type 1 diabetes. Interestingly, 57.8% of these children (26/45) had such a relative outside the nuclear family compared to 30.7% of children with no autoantibodies (P = .001), 35.2% of those with only classical islet cell antibodies (P = .006), and 35.2% of non-progressors with biochemical autoantibodies (P = 0.011). A positive history of type 1 diabetes in the paternal extended family was more common in children with multiple biochemical autoantibodies compared to those with only one biochemical autoantibody (P = .010). No association between the specificity of the first appearing autoantibody and family history of the disease was found.

CONCLUSIONS

Type 1 diabetes in relatives outside the nuclear family is a significant risk factor for islet autoimmunity and progression to clinical disease in HLA susceptible children.

Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9% mean relative increase in R2). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.

Diversity matters: opportunities in the study of the genetics of psychotic disorders in low- and middle-income countries in Latin America

Lack of diversity regarding genetic and environmental backgrounds weakens the generalization and clinical applicability of research findings on psychotic disorders. Notably, Latin Americans have been generally neglected in genetic studies, comprising less than 2% of genome-wide association study samples. But Latin American populations represent a unique opportunity for research, given the exceptionally high ethnic admixture of this group. Increasing genetic diversity is essential to improve the fine mapping of known regions associated with psychotic disorders, discover novel genetic associations, and replicate studies. Additionally, Latin America is characterized by massive social, political, and economic inequalities, all known risk factors for mental health issues, including psychotic disorders. This article aims to 1) discuss the challenges and advantages of studying Latin America’s particular genetic makeup and environmental context; 2) review previous studies conducted in the region; and 3) describe three Latin American research initiatives in progress: the Neuropsychiatric Genetics of Psychosis in Mexican Populations (NeuroMEX), the Paisa, and the Latin American Network for the Study of Early Psychosis (ANDES) studies.

Diabetes Mellitus Is a Chronic Disease that Can Benefit from Therapy with Induced Pluripotent Stem Cells

Diabetes mellitus (DM) is one of the main causes of morbidity and mortality, with an increasing incidence worldwide. The impact of DM on public health in developing countries has triggered alarm due to the exaggerated costs of the treatment and monitoring of patients with this disease. Considerable efforts have been made to try to prevent the onset and reduce the complications of DM. However, because insulin-producing pancreatic β-cells progressively deteriorate, many people must receive insulin through subcutaneous injection. Additionally, current therapies do not have consistent results regarding the prevention of chronic complications. Leveraging the approval of real-time continuous glucose monitors and sophisticated algorithms that partially automate insulin infusion pumps has improved glycemic control, decreasing the burden of diabetes management. However, these advances are facing physiologic barriers. New findings in molecular and cellular biology have produced an extraordinary advancement in tissue development for the treatment of DM. Obtaining pancreatic β-cells from somatic cells is a great resource that currently exists for patients with DM. Although this therapeutic option has great prospects for patients, some challenges remain for this therapeutic plan to be used clinically. The purpose of this review is to describe the new techniques in cell biology and regenerative medicine as possible treatments for DM. In particular, this review highlights the origin of induced pluripotent cells (iPSCs) and how they have begun to emerge as a regenerative treatment that may mitigate the pathology of this disease.

Next steps in the identification of gene targets for type 1 diabetes

The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment. Graphical abstract.

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.

Defining and Classifying New Subgroups of Diabetes

An etiologically based classification of diabetes is needed to account for the heterogeneity of type 1 and type 2 diabetes (T1D and T2D) and emerging forms of diabetes worldwide. It may be productive for both classification and clinical discovery to consider variant forms of diabetes as a spectrum. Maturity onset diabetes of youth and neonatal diabetes serve as models for etiologically defined, rare forms of diabetes in the spectrum. Ketosis-prone diabetes is a model for more complex forms, amenable to phenotypic dissection. Bioinformatic approaches such as clustering analyses of large datasets and multi-omics investigations of rare and atypical phenotypes are promising avenues to explore and define new subgroups of diabetes.

Genomic risk scores for juvenile idiopathic arthritis and its subtypes

OBJECTIVES

Juvenile idiopathic arthritis (JIA) is an autoimmune disease and a common cause of chronic disability in children. Diagnosis of JIA is based purely on clinical symptoms, which can be variable, leading to diagnosis and treatment delays. Despite JIA having substantial heritability, the construction of genomic risk scores (GRSs) to aid or expedite diagnosis has not been assessed. Here, we generate GRSs for JIA and its subtypes and evaluate their performance.

METHODS

We examined three case/control cohorts (UK, US-based and Australia) with genome-wide single nucleotide polymorphism (SNP) genotypes. We trained GRSs for JIA and its subtypes using lasso-penalised linear models in cross-validation on the UK cohort, and externally tested it in the other cohorts.

RESULTS

The JIA GRS alone achieved cross-validated area under the receiver operating characteristic curve (AUC)=0.670 in the UK cohort and externally-validated AUCs of 0.657 and 0.671 in the US-based and Australian cohorts, respectively. In logistic regression of case/control status, the corresponding odds ratios (ORs) per standard deviation (SD) of GRS were 1.831 (1.685 to 1.991) and 2.008 (1.731 to 2.345), and were unattenuated by adjustment for sex or the top 10 genetic principal components. Extending our analysis to JIA subtypes revealed that the enthesitis-related JIA had both the longest time-to-referral and the subtype GRS with the strongest predictive capacity overall across data sets: AUCs 0.82 in UK; 0.84 in Australian; and 0.70 in US-based. The particularly common oligoarthritis JIA also had a GRS that outperformed those for JIA overall, with AUCs of 0.72, 0.74 and 0.77, respectively.

CONCLUSIONS

A GRS for JIA has potential to augment clinical JIA diagnosis protocols, prioritising higher-risk individuals for follow-up and treatment. Consistent with JIA heterogeneity, subtype-specific GRSs showed particularly high performance for enthesitis-related and oligoarthritis JIA.

Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

The convergence of advances in medical science, human biology, data science and technology has enabled the generation of new insights into the phenotype known as 'diabetes'. Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment) and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e. monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realise its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.

Temporal trends in diabetic ketoacidosis at diagnosis of paediatric type 1 diabetes between 2006 and 2016: results from 13 countries in three continents

AIMS/HYPOTHESIS

The aim of this work was to evaluate geographical variability and trends in the prevalence of diabetic ketoacidosis (DKA), between 2006 and 2016, at the diagnosis of childhood-onset type 1 diabetes in 13 countries over three continents.

METHODS

An international retrospective study on DKA at diagnosis of diabetes was conducted. Data on age, sex, date of diabetes diagnosis, ethnic minority status and presence of DKA at diabetes onset were obtained from Australia, Austria, Czechia, Denmark, Germany, Italy, Luxembourg, New Zealand, Norway, Slovenia, Sweden, USA and the UK (Wales). Mean prevalence was estimated for the entire period, both overall and by country, adjusted for sex and age group. Temporal trends in annual prevalence of DKA were estimated using logistic regression analysis for each country, before and after adjustment for sex, age group and ethnic minority status.

RESULTS

During the study period, new-onset type 1 diabetes was diagnosed in 59,000 children (median age [interquartile range], 9.0 years [5.5-11.7]; male sex, 52.9%). The overall adjusted DKA prevalence was 29.9%, with the lowest prevalence in Sweden and Denmark and the highest in Luxembourg and Italy. The adjusted DKA prevalence significantly increased over time in Australia, Germany and the USA while it decreased in Italy. Preschool children, adolescents and children from ethnic minority groups were at highest risk of DKA at diabetes diagnosis in most countries. A significantly higher risk was also found for females in Denmark, Germany and Slovenia.

CONCLUSIONS/INTERPRETATION

DKA prevalence at type 1 diabetes diagnosis varied considerably across countries, albeit it was generally high and showed a slight increase between 2006 and 2016. Increased awareness of symptoms to prevent delay in diagnosis is warranted, especially in preschool children, adolescents and children from ethnic minority groups.

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

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

New Insights into the Genetics of Latent Autoimmune Diabetes in Adults

PURPOSE OF REVIEW

Diabetes is a spectrum of clinical manifestations, including latent autoimmune diabetes in adults (LADA). However, it has been questioned whether LADA exists or simply is a group of misclassified type 1 diabetes (T1D) and type 2 diabetes (T2D) patients. This review will provide an updated overview of the genetics of LADA, highlight what genetics tell us about LADA as a diabetes subtype, and point to future directions in the study of LADA.

RECENT FINDINGS

Recent studies have verified the genetic overlap between LADA and both T1D and T2D and have contributed identification of a novel LADA-specific locus, namely, PFKFB3, and subtype-specific signatures in the HLA region. Genetic risk scores comprising T1D-risk variants have been shown to be a promising tool for discriminating diabetes subtypes and identifying patients rapidly progressing to insulin dependence. Genetic data support the existence of LADA, but further studies are needed to fully determine the place of LADA in the diabetes spectrum.

IgA Nephropathy Genetic Risk Score to Estimate the Prevalence of IgA Nephropathy in UK Biobank

Background

IgA nephropathy (IgAN) is the commonest glomerulonephritis worldwide. Its prevalence is difficult to estimate, as people with mild disease do not commonly receive a biopsy diagnosis. We aimed to generate an IgA nephropathy genetic risk score (IgAN-GRS) and estimate the proportion of people with hematuria who had IgAN in the UK Biobank (UKBB).

Methods

We calculated an IgAN-GRS using 14 single-nucleotide polymorphisms (SNPs) drawn from the largest European Genome-Wide Association Study (GWAS) and validated the IgAN-GRS in 464 biopsy-proven IgAN European cases from the UK Glomerulonephritis DNA Bank (UKGDB) and in 379,767 Europeans in the UKBB. We used the mean of IgAN-GRS to calculate the proportion of potential IgAN in 14,181 with hematuria and other nonspecific renal phenotypes from 379,767 Europeans in the UKBB.

Results

The IgAN-GRS was higher in the IgAN cohort (4.30; 95% confidence interval [95% CI: 4.23–4.38) than in controls (3.98; 3.97–3.98; P < 0.0001). The mean GRS in UKBB participants with hematuria (n = 12,858) was higher (4.04; 4.02–4.06) than UKBB controls (3.98; 3.97–3.98; P < 0.0001) and higher in those with hematuria, hypertension, and microalbuminuria (n = 1323) (4.07; 4.02–4.13) versus (3.98; 3.97–3.98; P = 0.0003). Using the difference in these means, we estimated that IgAN accounted for 19% of noncancer hematuria and 28% with hematuria, hypertension, and microalbuminuria in UKBB.

Conclusions

We used an IgAN-GRS to estimate the prevalence of IgAN contributing to common phenotypes that are not always biopsied. The noninvasive use of polygenic risk in this setting may have further utility to identify likely etiology of nonspecific renal phenotypes in large population cohorts.

The emerging field of polygenic risk scores and perspective for use in clinical care

Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.

Precision Medicine in Diabetes: A Consensus Report From the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

The convergence of advances in medical science, human biology, data science, and technology has enabled the generation of new insights into the phenotype known as "diabetes." Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment), and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e., monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realize its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.

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

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

Systematic Genetic Study of Youth With Diabetes in a Single Country Reveals the Prevalence of Diabetes Subtypes, Novel Candidate Genes, and Response to Precision Therapy

Identifying gene variants causing monogenic diabetes (MD) increases understanding of disease etiology and allows for implementation of precision therapy to improve metabolic control and quality of life. Here, we aimed to assess the prevalence of MD in youth with diabetes in Lithuania, uncover potential diabetes-related gene variants, and prospectively introduce precision treatment. First, we assessed all pediatric and most young-adult patients with diabetes in Lithuania (n = 1,209) for diabetes-related autoimmune antibodies. We then screened all antibody-negative patients (n = 153) using targeted high-throughput sequencing of >300 potential candidate genes. In this group, 40.7% had MD, with the highest percentage (100%) in infants (diagnosis at ages 0-12 months), followed by those diagnosed at ages >1-18 years (40.3%) and >18-25 years (22.2%). The overall prevalence of MD in youth with diabetes in Lithuania was 3.5% (1.9% for GCK diabetes, 0.7% for HNF1A, 0.2% for HNF4A and ABCC8, 0.3% for KCNJ11, and 0.1% for INS). Furthermore, we identified likely pathogenic variants in 11 additional genes. Microvascular complications were present in 26% of those with MD. Prospective treatment change was successful in >50% of eligible candidates, with C-peptide >252 pmol/L emerging as the best prognostic factor.

Classification of Type 2 Diabetes Genetic Variants and a Novel Genetic Risk Score Association With Insulin Clearance

CONTEXT

Genome-wide association studies have identified more than 450 single nucleotide polymorphisms (SNPs) for type 2 diabetes (T2D).

OBJECTIVE

To facilitate use of these SNPs in future genetic risk score (GRS)-based analyses, we aimed to classify the SNPs based on physiology. We also sought to validate GRS associations with insulin-related traits in deeply phenotyped Mexican Americans.

DESIGN, SETTING, AND PARTICIPANTS

A total of 457 T2D SNPs from the literature were assigned physiologic function based on association studies and cluster analyses. All SNPs (All-GRS), beta-cell (BC-GRS), insulin resistance (IR-GRS), lipodystrophy (Lipo-GRS), and body mass index plus lipids (B + L-GRS) were evaluated for association with diabetes and indices of insulin secretion (from oral glucose tolerance test), insulin sensitivity and insulin clearance (from euglycemic clamp), and adiposity and lipid markers in 1587 Mexican Americans.

RESULTS

Of the 457 SNPs, 52 were classified as BC, 30 as IR, 12 as Lipo, 12 as B + L, whereas physiologic function of 351 was undefined. All-GRS was strongly associated with T2D. Among nondiabetic Mexican Americans, BC-GRS was associated with reduced insulinogenic index, IR-GRS was associated with reduced insulin sensitivity, and Lipo-GRS was associated with reduced adiposity. B + L-GRS was associated with increased insulin clearance. The latter did not replicate in an independent cohort wherein insulin clearance was assessed by a different method.

CONCLUSIONS

Supporting their utility, BC-GRS, IR-GRS, and Lipo-GRS, based on SNPs discovered largely in Europeans, exhibited expected associations in Mexican Americans. The novel association of B + L-GRS with insulin clearance suggests that impaired ability to reduce insulin clearance in compensation for IR may play a role in the pathogenesis of T2D. Whether this applies to other ethnic groups remains to be determined.

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.

Genetic Discrimination Between LADA and Childhood-Onset Type 1 Diabetes Within the MHC

OBJECTIVE

The MHC region harbors the strongest loci for latent autoimmune diabetes in adults (LADA); however, the strength of association is likely attenuated compared with that for childhood-onset type 1 diabetes. In this study, we recapitulate independent effects in the MHC class I region in a population with type 1 diabetes and then determine whether such conditioning in LADA yields potential genetic discriminators between the two subtypes within this region.

RESEARCH DESIGN AND METHODS

Chromosome 6 was imputed using SNP2HLA, with conditional analysis performed in type 1 diabetes case subjects (n = 1,985) and control subjects (n = 2,219). The same approach was applied to a LADA cohort (n = 1,428) using population-based control subjects (n = 2,850) and in a separate replication cohort (656 type 1 diabetes case, 823 LADA case, and 3,218 control subjects).

RESULTS

The strongest associations in the MHC class II region (rs3957146, β [SE] = 1.44 [0.05]), as well as the independent effect of MHC class I genes, on type 1 diabetes risk, particularly HLA-B*39 (β [SE] = 1.36 [0.17]), were confirmed. The conditional analysis in LADA versus control subjects showed significant association in the MHC class II region (rs3957146, β [SE] = 1.14 [0.06]); however, we did not observe significant independent effects of MHC class I alleles in LADA.

CONCLUSIONS

In LADA, the independent effects of MHC class I observed in type 1 diabetes were not observed after conditioning on the leading MHC class II associations, suggesting that the MHC class I association may be a genetic discriminator between LADA and childhood-onset type 1 diabetes.

New Frontiers in the Treatment of Type 1 Diabetes

Type 1 diabetes is an autoimmune disease caused by the immune-mediated destruction of pancreatic β cells that results in lifelong absolute insulin deficiency. For nearly a century, insulin replacement has been the only therapy for most people living with this disease. Recent advances in technology and our understanding of β cell development, glucose metabolism, and the underlying immune pathogenesis of the disease have led to innovative therapeutic and preventative approaches. A paradigm shift in immunotherapy development toward the targeting of islet-specific immune pathways involved in tolerance has driven the development of therapies that may allow for the prevention or reversal of this disease while avoiding toxicities associated with historical approaches that were broadly immunosuppressive. In this review, we discuss successes, failures, and emerging pharmacological therapies for type 1 diabetes that are changing how we approach this disease, from improving glycemic control to developing the "holy grail" of disease prevention.

Exploring the Genomic Architectures of Health, Physical Traits and Antisocial Behavioral Outcomes: A Brief Report

A widely replicated finding across the behavioral sciences is that antisocial behaviors correlate with an array of health problems. Less clear, however, is the precise nature of this association. There is reason to suspect that a direct causal link exists between incarceration-a consequence of some antisocial behaviors-and certain negative health outcomes, for instance. However, it might be the case that broader phenotypes like antisocial behavior may correlate with certain health and physiological traits at a genomic level. We explore this possibility from a theoretical vantage point, while also presenting some preliminary data from existing secondary sources. Tentatively, no significant genetic correlations emerged across a host of health, physiological, and wellbeing outcomes after correction for multiple testing. However, more work is needed exploring this topic. We propose that future studies should make use of larger, more diverse samples and examine the genetic overlap between homogeneous clusters of antisocial behavioral subtypes and disease traits or symptoms.

Utility and First Clinical Application of Screening Embryos for Polygenic Disease Risk Reduction

For over 2 decades preimplantation genetic testing (PGT) has been in clinical use to reduce the risk of miscarriage and genetic disease in patients with advanced maternal age and risk of transmitting disease. Recently developed methods of genome-wide genotyping and machine learning algorithms now offer the ability to genotype embryos for polygenic disease risk with accuracy equivalent to adults. In addition, contemporary studies on adults indicate the ability to predict polygenic disorders with risk equivalent to monogenic disorders. Existing biobanks provide opportunities to model the clinical utility of polygenic disease risk reduction among sibling adults. Here, we provide a mathematical model for the use of embryo screening to reduce the risk of type 1 diabetes. Results indicate a 45-72% reduced risk with blinded genetic selection of one sibling. The first clinical case of polygenic risk scoring in human preimplantation embryos from patients with a family history of complex disease is reported. In addition to these data, several common and accepted practices place PGT for polygenic disease risk in the applicable context of contemporary reproductive medicine. In addition, prediction of risk for PCOS, endometriosis, and aneuploidy are of particular interest and relevance to patients with infertility and represent an important focus of future research on polygenic risk scoring in embryos.

Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine

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