Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

Type 1 diabetes prediction in autoantibody-positive individuals: performance, time and money matter

AIMS/HYPOTHESIS

Efficient prediction of clinical type 1 diabetes is important for risk stratification and monitoring of autoantibody-positive individuals. In this study, we compared type 1 diabetes predictive models for predictive performance, cost and participant time needed for testing.

METHODS

We developed 1943 predictive models using a Cox model based on a type 1 diabetes genetic risk score (GRS2), autoantibody count and types, BMI, age, self-reported gender and OGTT-derived glucose and C-peptide measures. We trained and validated the models using halves of a dataset comprising autoantibody-positive first-degree relatives of individuals with type 1 diabetes (n=3967, 49% female, 14.9 ± 12.1 years of age) from the TrialNet Pathway to Prevention study. The median duration of follow-up was 4.7 years (IQR 2.0-8.1), and 1311 participants developed clinical type 1 diabetes. Models were compared for predictive performances, estimated cost and participant time.

RESULTS

Models that included metabolic measures had best performance, with most exhibiting small performance differences (less than 3% and p>0.05). However, the cost and participant time associated with measuring metabolic variables ranged between US$56 and US$293 and 10-165 min, respectively. The predictive model performance had temporal variability, with the highest GRS2 influence and discriminative power being exhibited in the earliest preclinical stages. OGTT-derived metabolic measures had a similar performance to HbA1c- or Index60-derived models, with an important difference in cost and participant time.

CONCLUSIONS/INTERPRETATION

Cost-performance model analyses identified trade-offs between cost and performance models, and identified cost-minimising options to tailor risk-screening strategies.

Identifying genetically predisposed type 1 diabetes mellitus individuals in a Southern Brazilian population: The construction of a genetic risk score

Single nucleotide polymorphisms (SNPs) in the HLA DR/DQ region have the greatest impact on susceptibility to type 1 diabetes mellitus (T1DM). Non-HLA SNPs interact with the HLA, influencing the risk for T1DM. The aim of this study was to develop a genetic risk score (GRS) based on HLA DR/DQ and non-HLA SNPs to discriminate patients with T1DM. The sample comprised 466 patients with T1DM and 469 controls. The rs689/INS, rs2476601/PTPN22, rs231775/CTLA-4, rs2304256/TYK2, rs2292239/ERBB3, and HLA DR/DQ SNPs were genotyped using real-time PCR. The unweighted GRS (uGRS) was calculated by summing the risk alleles of each SNP and the weighted GRS (wGRS) by multiplying the risk alleles by their odds ratios. The uGRS was higher in T1DM patients than in non-diabetic controls (0.34 ± 0.14 vs. 0.26 ± 0.13, P <0.0001), being positively correlated with HbA1c levels (P <0.0001). wGRSs exhibited higher AUCs than uGRSs. The wGRS containing only HLA DR/DQ SNPs showed an AUC of 0.75 (95% CI 0.72 - 0.78). The wGRS containing both HLA DR/DQ and non-HLA SNPs, adjusted for race, demonstrated the best discriminative power [AUC 0.91 (95% CI 0.89 - 0.93)]. The race adjusted-wGRS, including all SNPs, seems to be a useful genetic tool for assessing individual's predisposition to T1DM.

T helper 17 cells and interleukin-17 immunity in type 1 diabetes: From pathophysiology to targeted immunotherapies

Type 1 diabetes (T1D) is a chronic organ-specific autoimmune disorder characterized by a progressive loss of the insulin-secreting pancreatic beta cells, which ultimately results in insulinopenia, hyperglycemia and lifelong need for exogenous insulin therapy. In the pathophysiological landscape of T1D, T helper 17 cells (Th17 cells) and their hallmark cytokine, interleukin (IL)-17, play pivotal roles from disease onset to disease progression. In this narrative mini-review, we discuss the dynamic interplay between Th17 cells and IL-17 in the context of T1D, providing insights into the underlying immunologic mechanisms contributing to the IL-17-immunity-mediated pancreatic beta-cell destruction. Furthermore, we summarized the main animal and clinical studies that investigated Th17- and IL-17-targeted interventions as promising immunotherapies able to alter the natural history of T1D.

Genetic Associations With C-peptide Levels Before Type 1 Diabetes Diagnosis in At-risk Relatives

OBJECTIVE

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

METHODS

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

RESULTS

Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β = -.06, P < .0001) and multivariate (β = -.03, P = .005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β = -.11, P = .002) and multivariate (β = -.06, P = .018) analyses.

CONCLUSION

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

Looking back at the TEDDY study: lessons and future directions

The goal of the TEDDY (The Environmental Determinants of Diabetes in the Young) study is to elucidate factors leading to the initiation of islet autoimmunity (first primary outcome) and those related to progression to type 1 diabetes mellitus (T1DM; second primary outcome). This Review outlines the key findings so far, particularly related to the first primary outcome. The background, history and organization of the study are discussed. Recruitment and follow-up (from age 4 months to 15 years) of 8,667 children showed high retention and compliance. End points of the presence of autoantibodies against insulin, GAD65, IA-2 and ZnT8 revealed the HLA-associated early appearance of insulin autoantibodies (1-3 years of age) and the later appearance of GAD65 autoantibodies. Competing autoantibodies against tissue transglutaminase (marking coeliac disease autoimmunity) also appeared early (2-4 years). Genetic and environmental factors, including enterovirus infection and gastroenteritis, support mechanistic differences underlying one phenotype of autoimmunity against insulin and another against GAD65. Infant growth and both probiotics and high protein intake affect the two phenotypes differently, as do serious life events during pregnancy. As the end of the TEDDY sampling phase is approaching, major omics approaches are in progress to further dissect the mechanisms that might explain the two possible endotypes of T1DM.

The stage- and subgroup-specific impact of non-HLA polymorphisms on preclinical type 1 diabetes progression

Besides variation within the HLA gene complex determining a major part of genetic susceptibility to Type 1 diabetes, genome-wide association studies have identified over 60 non-HLA loci also contributing to disease risk. While individual single nucleotide polymorphisms (SNPs) have limited predictive power, genetic risk scores (GRS) can identify at-risk individuals. However, current models do not fully capture the heterogeneous progression of asymptomatic islet autoimmunity, especially in autoantibody-positive subjects. In this study, we investigated the additional stage-specific impact of 17 non-HLA loci on previously established prediction models in 448 persistently autoantibody-positive first-degree relatives. Cox regression and Kaplan Meier survival analysis were used to assess their influence on progression from single to multiple autoantibody-positivity, and from there to clinical onset. FUT2 and CTSH significantly accelerated progression of single to multiple autoAb-positivity, but only in presence of insulin autoantibodies and HLA-DQ2/DQ8, respectively. At the stage of multiple autoantibody-positivity, progression to clinical onset was impacted by various non-HLA SNPs either as independent predictors (GLIS3, CENPW, IL2, GSDM, MEG3A, and NRP-1) or through interaction with HLA class I alleles (CLEC16A, NRP-1, TCF7L2), maternal diabetes status (CTSH), or a high-risk autoantibody-profile (CD226). Our data indicate that, unlike for GRS, the weight of distinct non-HLA polymorphisms varies significantly among individuals at risk, depending on disease stage and other stage-specific risk factors. They refine our previous stage-specific prediction models including age, autoantibody-profile, HLA genotype, and other non-HLA SNPs, and emphasize the importance of stratifying accordingly to personalize time-to-event prediction in risk groups, or for preparing or interpreting prevention trials.

Type 1 Diabetes Genetic Risk Scores: History, Application and Future Directions

PURPOSE OF REVIEW

To review the genetics of type 1 diabetes (T1D) and T1D genetic risk scores, focusing on their development, research and clinical applications, and future directions.

RECENT FINDINGS

More than 90 genetic loci have been linked to T1D risk, with approximately half of the genetic risk attributable to the human leukocyte antigen (HLA) locus, along with non-HLA loci that have smaller effects to disease risk. The practical use of T1D genetic risk scores simplifies the complex genetic information, within the HLA and non-HLA regions, by combining the additive effect and interactions of single nucleotide polymorphisms (SNPs) associated with risk. Genetic risk scores have proven to be useful in various aspects, including classifying diabetes (e.g., distinguishing between T1D vs. neonatal, type 2 or other diabetes types), predicting the risk of developing T1D, assessing the prognosis of the clinical course (e.g., determining the risk of developing insulin dependence and glycemic control), and research into the heterogeneity of diabetes (e.g., atypical diabetes). However, there are gaps in our current knowledge including the specific sets of genes that regulate transition between preclinical stages of T1D, response to disease modifying therapies, and other outcomes of interest such as persistence of beta cell function. Several T1D genetic risk scores have been developed and shown to be valuable in various contexts, from classification of diabetes to providing insights into its etiology and predicting T1D risk across different stages of T1D. Further research is needed to develop and validate T1D genetic risk scores that are effective across all populations and ancestries. Finally, barriers such as cost, and training of medical professionals have to be addressed before the use of genetic risk scores can be incorporated into routine clinical practice.

ISPAD Clinical Practice Consensus Guidelines 2024: Screening, Staging, and Strategies to Preserve Beta-Cell Function in Children and Adolescents with Type 1 Diabetes

The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This guideline serves as an update to the 2022 ISPAD consensus guideline on staging for type 1 diabetes (T1D). Key additions include an evidence-based summary of recommendations for screening for risk of T1D and monitoring those with early-stage T1D. In addition, a review of clinical trials designed to delay progression to Stage 3 T1D and efforts seeking to preserve beta-cell function in those with Stage 3 T1D are included. Lastly, opportunities and challenges associated with the recent US Food and Drug Administration (FDA) approval of teplizumab as an immunotherapy to delay progression are discussed. The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This guideline serves as an update to the 2022 ISPAD consensus guideline on staging for type 1 diabetes (T1D). Key additions include an evidence-based summary of recommendations for screening for risk of T1D and monitoring those with early-stage T1D. In addition, a review of clinical trials designed to delay progression to Stage 3 T1D and efforts seeking to preserve beta-cell function in those with Stage 3 T1D are included. Lastly, opportunities and challenges associated with the recent US Food and Drug Administration (FDA) approval of teplizumab as an immunotherapy to delay progression are discussed.

Infection episodes and islet autoantibodies in children at increased risk for type 1 diabetes before and during the COVID-19 pandemic

OBJECTIVES

To determine the impact of the COVID-19 pandemic on the incidence rates of infection and islet autoimmunity in children at risk for type 1 diabetes.

METHODS

1050 children aged 4 to 7 months with an elevated genetic risk for type 1 diabetes were recruited from Germany, Poland, Sweden, Belgium and the UK. Reported infection episodes and islet autoantibody development were monitored until age 40 months from February 2018 to February 2023.

RESULTS

The overall infection rate was 311 (95% Confidence Interval [CI], 304-318) per 100 person years. Infection rates differed by age, country, family history of type 1 diabetes, and period relative to the pandemic. Total infection rates were 321 per 100 person-years (95% CI 304-338) in the pre-pandemic period (until February 2020), 160 (95% CI 148-173) per 100 person-years in the first pandemic year (March 2020-February 2021; P < 0.001) and 337 (95% CI 315-363) per 100 person-years in subsequent years. Similar trends were observed for respiratory and gastrointestinal infections. Islet autoantibody incidence rates were 1.6 (95% CI 1.0-2.4) per 100 person-years in the pre-pandemic period, 1.2 (95% CI 0.8-1.9) per 100 person-years in the first pandemic year (P = 0.46), and 3.4 (95% CI 2.3-4.8) per 100 person-years in subsequent years (P = 0.005 vs. pre-pandemic year; P < 0.001 vs. first pandemic year).

CONCLUSIONS

The COVID-19 pandemic was associated with significantly altered infection patterns. Islet autoantibody incidence rates increased two-fold when infection rates returned to pre-pandemic levels.

Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements

Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.

Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk of (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb+; (2) when people who are IAb+ are initially identified there is a need for confirmation using a second sample; (3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care.

Consensus Guidance for Monitoring Individuals With Islet Autoantibody-Positive Pre-Stage 3 Type 1 Diabetes

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programs are being increasingly emphasized. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk for (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in nonspecialized settings. To inform this monitoring, JDRF, in conjunction with international experts and societies, developed consensus guidance. Broad advice from this guidance includes the following: 1) partnerships should be fostered between endocrinologists and primary care providers to care for people who are IAb+; 2) when people who are IAb+ are initially identified, there is a need for confirmation using a second sample; 3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; 4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; 5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and 6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasizes significant unmet needs for further research on early-stage type 1 diabetes to increase the rigor of future recommendations and inform clinical care.

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.

Implementation of type 1 diabetes genetic risk screening in children in diverse communities: the Virginia PrIMeD project

BACKGROUND

Population screening for risk of type 1 diabetes (T1D) has been proposed to identify those with islet autoimmunity (presence of islet autoantibodies). As islet autoantibodies can be transient, screening with a genetic risk score has been proposed as an entry into autoantibody testing.

METHODS

Children were recruited from eight general pediatric and specialty clinics across Virginia with diverse community settings. Recruiters in each clinic obtained informed consent/assent, a medical history, and a saliva sample for DNA extraction in children with and without a history of T1D. A custom genotyping panel was used to define T1D genetic risk based upon associated SNPs in European- and African-genetic ancestry. Subjects at "high genetic risk" were offered a separate blood collection for screening four islet autoantibodies. A follow-up contact (email, mail, and telephone) in one half of the participants determined interest and occurrence of subsequent T1D.

RESULTS

A total of 3818 children aged 2-16 years were recruited, with 14.2% (n = 542) having a "high genetic risk." Of children with "high genetic risk" and without pre-existing T1D (n = 494), 7.0% (34/494) consented for autoantibody screening; 82.4% (28/34) who consented also completed the blood collection, and 7.1% (2/28) of them tested positive for multiple autoantibodies. Among children with pre-existing T1D (n = 91), 52% (n = 48) had a "high genetic risk." In the sample of children with existing T1D, there was no relationship between genetic risk and age at T1D onset. A major factor in obtaining islet autoantibody testing was concern over SARS-CoV-2 exposure.

CONCLUSIONS

Minimally invasive saliva sampling implemented using a genetic risk score can identify children at genetic risk of T1D. Consent for autoantibody screening, however, was limited largely due to the SARS-CoV-2 pandemic and need for blood collection.

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort

Background

Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk.

Methods

Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA.

Results

All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated.

Conclusions

Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

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

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

The Role of Selenium Nanoparticles in Addressing Diabetic Complications: A Comprehensive Study

Diabetes, as an emerging epidemic, has put forward a significant spotlight on the evolving population worldwide grounded upon the remarkable affliction of healthcare along with economical conflict. Various studies suggested that, in modern society, lack of maintenance of a healthy life style leads to the occurrence of diabetes as insulin resistant, later having a damaging effect on the pancreatic β-cells, suggesting various complications. Furthermore, diabetes management is controversial owing to different opinions based on the prevention of complications. For this purpose, nanostructured materials (NSM) like selenium nanoparticles (SeNPs) have proved their efficiency in the therapeutic management of such serious diseases. This review offers an in- -depth idea regarding the pathophysiology, diagnosis and various conventional therapeutics of type 1 and type 2 diabetes, shedding light on Diabetic Nephropathy (DN), a case study of type 1 diabetes. Moreover, this review provides an exhaustive study by highlighting the economic and healthcare burdens associated with diabetes along with the controversies associated with conventional therapeutic management and the promising role of NSM like selenium nanoparticles (SeNPs), as a novel weapon for encountering such fatal diseases.

Genetic variants in the complement system and their potential link in the aetiology of type 1 diabetes

Type 1 diabetes is an autoimmune disease in which one's own immune system destroys insulin-secreting beta cells in the pancreas. This process results in life-long dependence on exogenous insulin for survival. Both genetic and environmental factors play a role in disease initiation, progression, and ultimate clinical diagnosis of type 1 diabetes. This review will provide background on the natural history of type 1 diabetes and the role of genetic factors involved in the complement system, as several recent studies have identified changes in levels of these proteins as the disease evolves from pre-clinical through to clinically apparent disease.

Challenges of CRISPR/Cas-Based Cell Therapy for Type 1 Diabetes: How Not to Engineer a "Trojan Horse"

Type 1 diabetes mellitus (T1D) is an autoimmune disease caused by the destruction of insulin-producing β-cells in the pancreas by cytotoxic T-cells. To date, there are no drugs that can prevent the development of T1D. Insulin replacement therapy is the standard care for patients with T1D. This treatment is life-saving, but is expensive, can lead to acute and long-term complications, and results in reduced overall life expectancy. This has stimulated the research and development of alternative treatments for T1D. In this review, we consider potential therapies for T1D using cellular regenerative medicine approaches with a focus on CRISPR/Cas-engineered cellular products. However, CRISPR/Cas as a genome editing tool has several drawbacks that should be considered for safe and efficient cell engineering. In addition, cellular engineering approaches themselves pose a hidden threat. The purpose of this review is to critically discuss novel strategies for the treatment of T1D using genome editing technology. A well-designed approach to β-cell derivation using CRISPR/Cas-based genome editing technology will significantly reduce the risk of incorrectly engineered cell products that could behave as a "Trojan horse".

Imaging in Type 1 Diabetes, Current Perspectives and Directions

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

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

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

Importance

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

Objective

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

Design, Setting, and Participants

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

Exposure

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

Main Outcomes and Measures

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

Results

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

Conclusion and relevance

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

Utility of genetic risk scores in type 1 diabetes

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

Children diagnosed with presymptomatic type 1 diabetes through public health screening have milder diabetes at clinical manifestation

AIMS/HYPOTHESIS

We aimed to determine whether disease severity was reduced at onset of clinical (stage 3) type 1 diabetes in children previously diagnosed with presymptomatic type 1 diabetes in a population-based screening programme for islet autoantibodies.

METHODS

Clinical data obtained at diagnosis of stage 3 type 1 diabetes were evaluated in 128 children previously diagnosed with presymptomatic early-stage type 1 diabetes between 2015 and 2022 in the Fr1da study and compared with data from 736 children diagnosed with incident type 1 diabetes between 2009 and 2018 at a similar age in the DiMelli study without prior screening.

RESULTS

At the diagnosis of stage 3 type 1 diabetes, children with a prior early-stage diagnosis had lower median HbA1c (51 mmol/mol vs 91 mmol/mol [6.8% vs 10.5%], p<0.001), lower median fasting glucose (5.3 mmol/l vs 7.2 mmol/l, p<0.05) and higher median fasting C-peptide (0.21 nmol/l vs 0.10 nmol/l, p<0.001) compared with children without previous early-stage diagnosis. Fewer participants with prior early-stage diagnosis had ketonuria (22.2% vs 78.4%, p<0.001) or required insulin treatment (72.3% vs 98.1%, p<0.05) and only 2.5% presented with diabetic ketoacidosis at diagnosis of stage 3 type 1 diabetes. Outcomes in children with a prior early-stage diagnosis were not associated with a family history of type 1 diabetes or diagnosis during the COVID-19 pandemic. A milder clinical presentation was observed in children who participated in education and monitoring after early-stage diagnosis.

CONCLUSIONS/INTERPRETATION

Diagnosis of presymptomatic type 1 diabetes in children followed by education and monitoring improved clinical presentation at the onset of stage 3 type 1 diabetes.

Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes

The etiology of type 1 diabetes (T1D) foreshadows the pancreatic islet beta-cell autoimmune pathogenesis that heralds the clinical onset of T1D. Standardized and harmonized tests of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A), and ZnT8 transporter (ZnT8A) allowed children to be followed from birth until the appearance of a first islet autoantibody. In the Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter (Finland, Germany, Sweden, and the United States) observational study, children were identified at birth for the T1D high-risk HLA haploid genotypes DQ2/DQ8, DQ2/DQ2, DQ8/DQ8, and DQ4/DQ8. The TEDDY study was preceded by smaller studies in Finland, Germany, Colorado, Washington, and Sweden. The aims were to follow children at increased genetic risk to identify environmental factors that trigger the first-appearing autoantibody (etiology) and progress to T1D (pathogenesis). The larger TEDDY study found that the incidence rate of the first-appearing autoantibody was split into two patterns. IAA first peaked already during the first year of life and tapered off by 3-4 years of age. GADA first appeared by 2-3 years of age to reach a plateau by about 4 years. Prior to the first-appearing autoantibody, genetic variants were either common or unique to either pattern. A split was also observed in whole blood transcriptomics, metabolomics, dietary factors, and exposures such as gestational life events and early infections associated with prolonged shedding of virus. An innate immune reaction prior to the adaptive response cannot be excluded. Clarifying the mechanisms by which autoimmunity is triggered to either insulin or GAD65 is key to uncovering the etiology of autoimmune T1D.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

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

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

PTPN22 R620W gene editing in T cells enhances low-avidity TCR responses

A genetic variant in the gene PTPN22 (R620W, rs2476601) is strongly associated with increased risk for multiple autoimmune diseases and linked to altered TCR regulation and T cell activation. Here, we utilize Crispr/Cas9 gene editing with donor DNA repair templates in human cord blood-derived, naive T cells to generate PTPN22 risk edited (620W), non-risk edited (620R), or knockout T cells from the same donor. PTPN22 risk edited cells exhibited increased activation marker expression following non-specific TCR engagement, findings that mimicked PTPN22 KO cells. Next, using lentiviral delivery of T1D patient-derived TCRs against the pancreatic autoantigen, islet-specific glucose-6 phosphatase catalytic subunit-related protein (IGRP), we demonstrate that loss of PTPN22 function led to enhanced signaling in T cells expressing a lower avidity self-reactive TCR, but not a high-avidity TCR. In this setting, loss of PTPN22 mediated enhanced proliferation and Th1 skewing. Importantly, expression of the risk variant in association with a lower avidity TCR also increased proliferation relative to PTPN22 non-risk T cells. Together, these findings suggest that, in primary human T cells, PTPN22 rs2476601 contributes to autoimmunity risk by permitting increased TCR signaling and activation in mildly self-reactive T cells, thereby potentially expanding the self-reactive T cell pool and skewing this population toward an inflammatory phenotype.

Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia

The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions. The dataset (n = 1,142) contained demographic and socioeconomic status, genetic and disease history, nutrition history, obstetric history, and maternal characteristics. The comparison between case and control groups showed that most children (cases = 68% and controls = 88%) are from urban areas, 69% (cases) and 66% (control) were delivered after a full-term pregnancy and 31% of cases group were delivered by caesarean, which was higher than the controls (χ2 = 4.12, P-value = 0.042). Models were built using all available environmental and family history factors. The efficacy of models was evaluated using Area Under the Curve, Sensitivity, F Score and Precision. Full logistic regression outperformed other models with Accuracy = 0.77, Sensitivity, F Score and Precision of 0.70, and AUC = 0.83. The most significant KPIs were early exposure to cow's milk (OR = 2.92, P = 0.000), birth weight >4 Kg (OR = 3.11, P = 0.007), residency(rural) (OR = 3.74, P = 0.000), family history (first and second degree), and maternal age >25 years. The results presented here can assist healthcare providers in collecting and monitoring influential KPIs and developing intervention strategies to reduce the childhood T1D incidence rate in Saudi Arabia.

Transferability of the PRS estimates for height and BMI obtained from the European ethnic groups to the Western Russian populations

Genetic data plays an increasingly important role in modern medicine. Decrease in the cost of sequencing with subsequent increase in imputation accuracy, and the accumulation of large amounts of high-quality genetic data enable the creation of polygenic risk scores (PRSs) to perform genotype-phenotype associations. The accuracy of phenotype prediction primarily depends on the overall trait heritability, Genome-wide association studies cohort size, and the similarity of genetic background between the base and the target cohort. Here we utilized 8,664 high coverage genomic samples collected across Russia by "Evogen", a Russian biomedical company, to evaluate the predictive power of PRSs based on summary statistics established on cohorts of European ancestry for basic phenotypic traits, namely height and BMI. We have demonstrated that the PRSs calculated for selected traits in three distinct Russian populations, recapitulate the predictive power from the original studies. This is evidence that GWAS summary statistics calculated on cohorts of European ancestry are transferable onto at least some ethnic groups in Russia.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Progression likelihood score identifies substages of presymptomatic type 1 diabetes in childhood public health screening

AIMS/HYPOTHESIS

The aim of this study was to develop strategies that identify children from the general population who have late-stage presymptomatic type 1 diabetes and may, therefore, benefit from immune intervention.

METHODS

We tested children from Bavaria, Germany, aged 1.75-10 years, enrolled in the Fr1da public health screening programme for islet autoantibodies (n=154,462). OGTT and HbA_1c were assessed in children with multiple islet autoantibodies for diagnosis of presymptomatic stage 1 (normoglycaemia) or stage 2 (dysglycaemia) type 1 diabetes. Cox proportional hazards and penalised logistic regression of autoantibody, genetic, metabolic and demographic information were used to develop a progression likelihood score to identify children with stage 1 type 1 diabetes who progressed to stage 3 (clinical) type 1 diabetes within 2 years.

RESULTS

Of 447 children with multiple islet autoantibodies, 364 (81.4%) were staged. Undiagnosed stage 3 type 1 diabetes, presymptomatic stage 2, and stage 1 type 1 diabetes were detected in 41 (0.027% of screened children), 30 (0.019%) and 293 (0.19%) children, respectively. The 2 year risk for progression to stage 3 type 1 diabetes was 48% (95% CI 34, 58) in children with stage 2 type 1 diabetes (annualised risk, 28%). HbA_1c, islet antigen-2 autoantibody positivity and titre, and the 90 min OGTT value were predictors of progression in children with stage 1 type 1 diabetes. The derived progression likelihood score identified substages corresponding to ≤90th centile (stage 1a, n=258) and >90th centile (stage 1b, n=29; 0.019%) of stage 1 children with a 4.1% (95% CI 1.4, 6.7) and 46% (95% CI 21, 63) 2 year risk of progressing to stage 3 type 1 diabetes, respectively.

CONCLUSIONS/INTERPRETATION

Public health screening for islet autoantibodies found 0.027% of children to have undiagnosed clinical type 1 diabetes and 0.038% to have undiagnosed presymptomatic stage 2 or stage 1b type 1 diabetes, with 50% risk to develop clinical type 1 diabetes within 2 years.

Elevations in blood glucose before and after the appearance of islet autoantibodies in children

The etiology of type 1 diabetes has polygenic and environmental determinants that lead to autoimmune responses against pancreatic β cells and promote β cell death. The autoimmunity is considered silent without metabolic consequences until late preclinical stages,and it remains unknown how early in the disease process the pancreatic β cell is compromised. To address this, we investigated preprandial nonfasting and postprandial blood glucose concentrations and islet autoantibody development in 1,050 children with high genetic risk of type 1 diabetes. Pre- and postprandial blood glucose decreased between 4 and 18 months of age and gradually increased until the final measurements at 3.6 years of age. Determinants of blood glucose trajectories in the first year of life included sex, body mass index, glucose-related genetic risk scores, and the type 1 diabetes-susceptible INS gene. Children who developed islet autoantibodies had early elevations in blood glucose concentrations. A sharp and sustained rise in postprandial blood glucose was observed at around 2 months prior to autoantibody seroconversion, with further increases in postprandial and, subsequently, preprandial values after seroconversion. These findings show heterogeneity in blood glucose control in infancy and early childhood and suggest that islet autoimmunity is concurrent or subsequent to insults on the pancreatic islets.

How dysregulation of the immune system promotes diabetes mellitus and cardiovascular risk complications

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by persistent hyperglycemia due to insulin resistance or failure to produce insulin. Patients with DM develop microvascular complications that include chronic kidney disease and retinopathy, and macrovascular complications that mainly consist in an accelerated and more severe atherosclerosis compared to the general population, increasing the risk of cardiovascular (CV) events, such as stroke or myocardial infarction by 2- to 4-fold. DM is commonly associated with a low-grade chronic inflammation that is a known causal factor in its development and its complications. Moreover, it is now well-established that inflammation and immune cells play a major role in both atherosclerosis genesis and progression, as well as in CV event occurrence. In this review, after a brief presentation of DM physiopathology and its macrovascular complications, we will describe the immune system dysregulation present in patients with type 1 or type 2 diabetes and discuss its role in DM cardiovascular complications development. More specifically, we will review the metabolic changes and aberrant activation that occur in the immune cells driving the chronic inflammation through cytokine and chemokine secretion, thus promoting atherosclerosis onset and progression in a DM context. Finally, we will discuss how genetics and recent systemic approaches bring new insights into the mechanisms behind these inflammatory dysregulations and pave the way toward precision medicine.

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

Over 75 genetic loci within and outside of the HLA region influence type 1 diabetes risk. Genetic risk scores (GRS), which facilitate the integration of complex genetic information, have been developed in type 1 diabetes and incorporated into models and algorithms for classification, prognosis, and prediction of disease and response to preventive and therapeutic interventions. However, the development and validation of GRS across different ancestries is still emerging, as is knowledge on type 1 diabetes genetics in populations of diverse genetic ancestries. In this Review, we provide a summary of the current evidence on the evolutionary genetic variation in type 1 diabetes and the racial and ethnic differences in type 1 diabetes epidemiology, clinical characteristics, and preclinical course. We also discuss the influence of genetics on type 1 diabetes with differences across ancestries and the development and validation of GRS in various populations.

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

Background

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

Methods

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

Findings

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

Interpretation

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

Funding

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

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

CONTEXT

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Personalized Immunotherapies for Type 1 Diabetes: Who, What, When, and How?

Our understanding of the immunopathological features of type 1 diabetes (T1D) has greatly improved over the past two decades and has shed light on disease heterogeneity dictated by multiple immune, metabolic, and clinical parameters. This may explain the limited effects of immunotherapies tested so far to durably revert or prevent T1D, for which life-long insulin replacement remains the only therapeutic option. In the era of omics and precision medicine, offering personalized treatment could contribute to turning this tide. Here, we discuss how to structure the selection of the right patient at the right time for the right treatment. This individualized therapeutic approach involves enrolling patients at a defined disease stage depending on the target and mode of action of the selected drug, and better stratifying patients based on their T1D endotype, reflecting intrinsic disease aggressiveness and immune context. To this end, biomarker screening will be critical, not only to help stratify patients and disease stage, but also to select the best predicted responders ahead of treatment and at early time points during clinical trials. This strategy could contribute to increase therapeutic efficacy, notably through the selection of drugs with complementary effects, and to further develop precision multi-hit medicine.

Extracellular Vesicles in Type 1 Diabetes: A Versatile Tool

Type 1 diabetes is a chronic autoimmune disease affecting nearly 35 million people. This disease develops as T-cells continually attack the β-cells of the islets of Langerhans in the pancreas, which leads to β-cell death, and steadily decreasing secretion of insulin. Lowered levels of insulin minimize the uptake of glucose into cells, thus putting the body in a hyperglycemic state. Despite significant progress in the understanding of the pathophysiology of this disease, there is a need for novel developments in the diagnostics and management of type 1 diabetes. Extracellular vesicles (EVs) are lipid-bound nanoparticles that contain diverse content from their cell of origin and can be used as a biomarker for both the onset of diabetes and transplantation rejection. Furthermore, vesicles can be loaded with therapeutic cargo and delivered in conjunction with a transplant to increase cell survival and long-term outcomes. Crucially, several studies have linked EVs and their cargos to the progression of type 1 diabetes. As a result, gaining a better understanding of EVs would help researchers better comprehend the utility of EVs in regulating and understanding type 1 diabetes. EVs are a composition of biologically active components such as nucleic acids, proteins, metabolites, and lipids that can be transported to particular cells/tissues through the blood system. Through their varied content, EVs can serve as a flexible aid in the diagnosis and management of type 1 diabetes. In this review, we provide an overview of existing knowledge about EVs. We also cover the role of EVs in the pathogenesis, detection, and treatment of type 1 diabetes and the function of EVs in pancreas and islet β-cell transplantation.

Predicting age at onset of type 1 diabetes in children using regression, artificial neural network and Random Forest: A case study in Saudi Arabia

The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A reliable estimate of the age at onset of T1D in children would facilitate intervention plans for medical practitioners to reduce the problems with delayed diagnosis of T1D. This paper has utilised Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Random Forest (RF) to model and predict the age at onset of T1D in children in Saudi Arabia (S.A.) which is ranked as the 7th for the highest number of T1D and 5th in the world for the incidence rate of T1D. De-identified data between (2010-2020) from three cities in S.A. were used to model and predict the age at onset of T1D. The best subset model selection criteria, coefficient of determination, and diagnostic tests were deployed to select the most significant variables. The efficacy of models for predicting the age at onset was assessed using multi-prediction accuracy measures. The average age at onset of T1D is 6.2 years and the most common age group for onset is (5-9) years. Most of the children in the sample (68%) are from urban areas of S.A., 75% were delivered after a full term pregnancy length and 31% were delivered through a cesarean section. The models of best fit were the MLR and RF models with R2 = (0.85 and 0.95), the root mean square error = (0.25 and 0.15) and mean absolute error = (0.19 and 0.11) respectively for logarithm of age at onset. This study for the first time has utilised MLR, ANN and RF models to predict the age at onset of T1D in children in S.A. These models can effectively aid health care providers to monitor and create intervention strategies to reduce the impact of T1D in children in S.A.

Roles of extracellular vesicles associated non-coding RNAs in Diabetes Mellitus

Extracellular vesicles (EVs), especially exosomes (50 to 150 nm), have been shown to play important roles in a wide range of physiological and pathological processes, including metabolic diseases such as Diabetes Mellitus (DM). In the last decade, several studies have demonstrated how EVs are involved in cell-to-cell communication. EVs are enriched in proteins, mRNAs and non-coding RNAs (miRNAs, long non-coding RNAs and circRNAS, among others) which are transferred to recipient cells and may have a profound impact in either their survival or functionality. Several studies have pointed out the contribution of exosomal miRNAs, such as miR-l42-3p and miR-26, in the development of Type 1 and Type 2 DM (T1DM and T2DM), respectively. In addition, some miRNA families such as miR-let7 and miR-29 found in exosomes have been associated with both types of diabetes, suggesting that they share common etiological features. The knowledge about the role of exosomal long non-coding RNAs in this group of diseases is more immature, but the exosomal lncRNA MALAT1 has been found to be elevated in the plasma of individuals with T2DM, while more than 169 lncRNAs were reported to be differentially expressed between healthy donors and people with T1DM. Here, we review the current knowledge about exosomal non-coding RNAs in DM and discuss their potential as novel biomarkers and possible therapeutic targets.

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

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