Introducing the Endotype Concept to Address the Challenge of Disease Heterogeneity in Type 1 Diabetes

Cluster analysis of adult individuals with type 1 diabetes: Treatment pathways and complications over a five-year follow-up period

AIMS

To identify subgroups of adults with type 1 diabetes and analyse their treatment pathways and risk of diabetes-related complications over a 5-year follow-up.

METHODS

We performed a k-means cluster analysis using the T1DExchange Registry (n = 6,302) to identify subgroups based on demographic and clinical characteristics. Annual reassessments linked treatment trajectories with these clusters, considering drug and technology use. Complication risks were analysed using Cox regression.

RESULTS

Five clusters were identified: 1) A favourable combination of all variables (31.67 %); 2) Longer diabetes duration (22.63 %); 3) Higher HbA1c levels (13.28 %); 4) Higher BMI (15.25 %); 5) Older age at diagnosis (17.17 %). Two-thirds of patients remained in their initial cluster annually. Technology adoption showed improved glycaemic control over time. Cox proportional hazards showed different risk patterns: Cluster 1 had low complication risk; Cluster 2 had the highest risk for retinopathy, coronary artery disease and autonomic neuropathy; Cluster 3 had the highest risk for albuminuria, depression and diabetic ketoacidosis; Cluster 4 had increased risk for multiple complications; Cluster 5 had the highest risk for hypertension and severe hypoglycaemia, with elevated coronary artery disease risk.

CONCLUSIONS

Clinical characteristics can identify subgroups of patients with T1DM showing differences in treatment and complications during follow-up.

Islet autoantibody frequency in relatives of children with type 1 diabetes who have a type 2 diabetes diagnosis

AIM

This study aimed to evaluate characteristics of autoimmunity in individuals who have a type 2 diagnosis and are relatives of children with type 1 diabetes.

METHODS

Pre-diagnosis samples (median 17 months before onset) from relatives who were later diagnosed with type 2 diabetes were measured for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen-2 (IA-2A), zinc transporter 8 (ZnT8A) and insulin (IAA) as well as the type 1 diabetes genetic risk score (GRS2). Associations between islet autoantibodies, insulin treatment and GRS2 were analysed using Fisher's exact and t-tests.

RESULTS

Among 226 relatives (64% men; mean age at sampling 41 years; mean age 54 years at diagnosis), 32 (14%) were islet autoantibody-positive for at least one autoantibody more than a decade before diagnosis. Approximately half of these (n = 15) were treated with insulin. GADA-positivity was higher in insulin-treated relatives than in non-insulin-treated relatives (12/18 [67%] vs. 6/18 [33%], p < 0.001). IAA-positivity was observed in 13/32 (41%) of relatives with autoantibodies. GRS2 scores were increased in autoantibody-positive relatives (p = 0.032), but there was no clear evidence for a difference according to treatment (p = 0.072).

CONCLUSION

This study highlights the importance of measuring islet autoantibodies, including IAA, in relatives of people with type 1 diabetes to avoid misdiagnosis.

Untangling the genetics of beta cell dysfunction and death in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is a complex multi-system disease which arises from both environmental and genetic factors, resulting in the destruction of insulin-producing pancreatic beta cells. Over the past two decades, human genetic studies have provided new insight into the etiology of T1D, including an appreciation for the role of beta cells in their own demise.

SCOPE OF REVIEW

Here, we outline models supported by human genetic data for the role of beta cell dysfunction and death in T1D. We highlight the importance of strong evidence linking T1D genetic associations to bona fide candidate genes for mechanistic and therapeutic consideration. To guide rigorous interpretation of genetic associations, we describe molecular profiling approaches, genomic resources, and disease models that may be used to construct variant-to-gene links and to investigate candidate genes and their role in T1D.

MAJOR CONCLUSIONS

We profile advances in understanding the genetic causes of beta cell dysfunction and death at individual T1D risk loci. We discuss how genetic risk prediction models can be used to address disease heterogeneity. Further, we present areas where investment will be critical for the future use of genetics to address open questions in the development of new treatment and prevention strategies for T1D.

Functional metagenomic analysis reveals potential inflammatory triggers associated with genetic risk for autoimmune disease

To assess functional differences between the microbiomes of individuals with autoimmune risk-associated human leukocyte antigen (HLA) genetics and autoimmune protection-associated HLA, we performed a metagenomic analysis of stool samples from 72 infants in the All Babies in Southeast Sweden general-population cohort and assessed haplotype-peptide binding affinities. Infants with risk-associated HLA DR3-DQ2.5 and DR4-DQ8 had a higher abundance of known pathogen-associated molecular patterns and virulence related genes than infants with protection-associated HLA DR15-DQ6.2. However, there was limited overlap in the type of inflammatory trigger between risk groups. Supported by a high Firmicutes/Bacteroides ratio and differentially abundant flagellated species, genes related to the synthesis of flagella were prominent in those with HLA DR3-DQ2.5. However, this haplotype had a significantly lower likelihood of binding affinity to flagellin peptides. O-antigen biosynthesis genes were significantly correlated with the risk genotypes and absent from protective genotype association, supported by the differential abundance of gram-negative bacteria seen in the risk-associated groups. Genes related to vitamin B biosynthesis stood out in higher abundance in infants with HLA DR3-DQ2.5/DR4-DQ8 heterozygosity compared to those with autoimmune-protective genetics. Prevotella species and genus were significantly abundant in all infant groups with high risk for autoimmune disease. The potential inflammatory triggers associated with genetic risk for autoimmunity have significant implications. These results suggest that certain HLA haplotypes may be creating the opportunity for dysbiosis and subsequent inflammation early in life by clearing beneficial microbes or not clearing proinflammatory microbes. This HLA gatekeeping may prevent genetically at-risk individuals from benefiting from probiotic therapies by restricting the colonization of those beneficial bacteria.

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.

Blood immune cell profiling in adults with longstanding type 1 diabetes is associated with macrovascular complications

Aims/hypothesis

There is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity.

Methods

Adult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects.

Results

Hierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications.

Conclusions

Machine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications.

Association between HLA alleles and haplotypes with age at diagnosis of type 1 diabetes in an admixed Brazilian population: A nationwide study

To investigate the potential relationship between HLA alleles and haplotypes and the age at diagnosis of type 1 diabetes (T1DAgeD) in an admixed Brazilian population. This nationwide study was conducted in public clinics across 12 Brazilian cities. We collected demographic and genetic data from 1,600 patients with T1D. DNA samples were utilised to determine genomic ancestry (GA) and perform HLA typings for DRB1, DQA1 and DQB1. We explored allele and haplotype frequencies and GA in patients grouped by T1DAgeD categories (<6 years, ≥6-<11 years, ≥11-<19 years and ≥19 years) through univariate and multivariate analyses and primary component analyses. Additionally, we considered self-reported colour-race and identified a familiar history of T1D in first-degree relatives. The homozygosity index for DRB1~DQA1~DQB1 haplotypes exhibited the highest variation among T1DAgeD groups, and the percentages of Sub-Saharan African and European ancestries showed opposite trends in principal component analysis (PCA) analyses. Regarding the association of alleles and haplotypes with T1DAgeD, risk alleles such as HLA-DQB1*03:02g, -DQA1*03:01g, -02:01g, DRB1*04:05g and -04:02g were more frequently observed in heterozygosity or homozygosity in T1D patients with an early disease onset. Conversely, alleles such as DRB1*07:01g, -13:03g, DQB1*06:02g and DQA1*02:01 were more prevalent in older T1D patients. The combination DR3/DR4.5 was significantly associated with early disease onset. However, gender, GA, familiar history of T1D and self-reported colour-race identity did not exhibit significant associations with the onset of T1D. It is worth noting that the very common risk haplotype DRB1*03:01g~DQA1*05:01g~DQB1*02:01g did not differentiate between T1DAgeD groups. In the admixed Brazilian population, the high-risk haplotype DRB1*04:05~DQA1*03:01~DQB1*03:02 was more prevalent in individuals diagnosed before 6 years of age. In contrast, the protective alleles DQA1*01:02g, DQB1*06:02g, DRB1*07:01g and DRB1*13:03g and haplotypes DRB1*13:03g~DQA1*05:01g~DQB1*03:01g and DRB1*16:02g~DQA1*01:02g~DQB1*05:02g were more frequently observed in patients diagnosed in adulthood. Notably, these associations were independent of factors such as sex, economic status, GA, familiar history of T1D and region of birth in Brazil. These alleles and haplotypes contribute to our understanding of the disease onset heterogeneity and may have implications for early interventions when detected in association with well-known genomic risk or protection factors for T1D.

Integrating patients in time series clinical transcriptomics data

MOTIVATION

Analysis of time series transcriptomics data from clinical trials is challenging. Such studies usually profile very few time points from several individuals with varying response patterns and dynamics. Current methods for these datasets are mainly based on linear, global orderings using visit times which do not account for the varying response rates and subgroups within a patient cohort.

RESULTS

We developed a new method that utilizes multi-commodity flow algorithms for trajectory inference in large scale clinical studies. Recovered trajectories satisfy individual-based timing restrictions while integrating data from multiple patients. Testing the method on multiple drug datasets demonstrated an improved performance compared to prior approaches suggested for this task, while identifying novel disease subtypes that correspond to heterogeneous patient response patterns.

AVAILABILITY AND IMPLEMENTATION

The source code and instructions to download the data have been deposited on GitHub at https://github.com/euxhenh/Truffle.

First-in-human, double-blind, randomized phase 1b study of peptide immunotherapy IMCY-0098 in new-onset type 1 diabetes: an exploratory analysis of immune biomarkers

BACKGROUND

IMCY-0098, a synthetic peptide developed to halt disease progression via elimination of key immune cells in the autoimmune cascade, has shown a promising safety profile for the treatment of type 1 diabetes (T1D) in a recent phase 1b trial. This exploratory analysis of data from that trial aimed to identify the patient biomarkers at baseline associated with a positive response to treatment and examined the associations between immune response parameters and clinical efficacy endpoints (as surrogates for mechanism of action endpoints) using an artificial intelligence-based approach of unsupervised explainable machine learning.

METHODS

We conducted an exploratory analysis of data from a phase 1b, dose-escalation, randomized, placebo-controlled study of IMCY-0098 in patients with recent-onset T1D. Here, a panel of markers of T cell activation, memory T cells, and effector T cell response were analyzed via descriptive statistics. Artificial intelligence-based analyses of associations between all variables, including immune responses and clinical responses, were performed using the Knowledge Extraction and Management (KEM®) v 3.6.2 analytical platform.

RESULTS

The relationship between all available patient data was investigated using unsupervised machine learning implemented in the KEM® environment. Of 15 associations found for the dose C group (450 μg subcutaneously followed by 3 × 225 μg subcutaneously), seven involved human leukocyte antigen (HLA) type, all of which identified improvement/absence of worsening of disease parameters in DR4+ patients and worsening/absence of improvement in DR4- patients. This association with DR4+ and non-DR3 was confirmed using the endpoints normalized area under the curve C-peptide from mixed meal tolerance tests where presence of DR4 HLA haplotype was associated with an improvement in both endpoints. Exploratory immune analysis showed that IMCY-0098 dose B (150 μg subcutaneously followed by 3 × 75 μg subcutaneously) and dose C led to an increase in presumed/potentially protective antigen-specific cytolytic CD4+ T cells and a decrease in pathogenic CD8+ T cells, consistent with the expected mechanism of action of IMCY-0098. The analysis identified significant associations between immune and clinical responses to IMCY-0098.

CONCLUSIONS

Promising preliminary efficacy results support the design of a phase 2 study of IMCY-0098 in patients with recent-onset T1D.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03272269; EudraCT: 2016-003514-27.

Cohort profile: the 'Biomarkers of heterogeneity in type 1 diabetes' study-a national prospective cohort study of clinical and metabolic phenotyping of individuals with long-standing type 1 diabetes in the Netherlands

PURPOSE

The 'Biomarkers of heterogeneity in type 1 diabetes' study cohort was set up to identify genetic, physiological and psychosocial factors explaining the observed heterogeneity in disease progression and the development of complications in people with long-standing type 1 diabetes (T1D).

PARTICIPANTS

Data and samples were collected in two subsets. A prospective cohort of 611 participants aged ≥16 years with ≥5 years T1D duration from four Dutch Diabetes clinics between 2016 and 2021 (median age 32 years; median diabetes duration 12 years; 59% female; mean glycated haemoglobin (HbA1c) 61 mmol/mol (7.7%); 61% on insulin pump; 23% on continuous glucose monitoring (CGM)). Physical assessments were performed, blood and urine samples were collected, and participants completed questionnaires. A subgroup of participants underwent mixed-meal tolerance tests (MMTTs) at baseline (n=169) and at 1-year follow-up (n=104). Genetic data and linkage to medical and administrative records were also available. A second cross-sectional cohort included participants with ≥35 years of T1D duration (currently n=160; median age 64 years; median diabetes duration 45 years; 45% female; mean HbA1c 58 mmol/mol (7.4%); 51% on insulin pump; 83% on CGM), recruited from five centres and measurements, samples and 5-year retrospective data were collected.

FINDINGS TO DATE

Stimulated residual C-peptide was detectable in an additional 10% of individuals compared with fasting residual C-peptide secretion. MMTT measurements at 90 min and 120 min showed good concordance with the MMTT total area under the curve. An overall decrease of C-peptide at 1-year follow-up was observed. Fasting residual C-peptide secretion is associated with a decreased risk of impaired awareness of hypoglycaemia.

FUTURE PLANS

Research groups are invited to consider the use of these data and the sample collection. Future work will include additional hormones, beta-cell-directed autoimmunity, specific immune markers, microRNAs, metabolomics and gene expression data, combined with glucometrics, anthropometric and clinical data, and additional markers of residual beta-cell function.

TRIAL REGISTRATION NUMBER

NCT04977635.

A set of circulating microRNAs belonging to the 14q32 chromosome locus identifies two subgroups of individuals with recent-onset type 1 diabetes

Circulating microRNAs (miRNAs) are linked to the onset and progression of type 1 diabetes mellitus (T1DM), thus representing potential disease biomarkers. In this study, we employed a multiplatform sequencing approach to analyze circulating miRNAs in an extended cohort of prospectively evaluated recent-onset T1DM individuals from the INNODIA consortium. Our findings reveal that a set of miRNAs located within T1DM susceptibility chromosomal locus 14q32 distinguishes two subgroups of individuals. To validate our results, we conducted additional analyses on a second cohort of T1DM individuals, confirming the identification of these subgroups, which we have named cluster A and cluster B. Remarkably, cluster B T1DM individuals, who exhibit increased expression of a set of 14q32 miRNAs, show better glycemic control and display a different blood immunomics profile. Our findings suggest that this set of circulating miRNAs can identify two different T1DM subgroups with distinct blood immunomics at baseline and clinical outcomes during follow-up.

The immunology of type 1 diabetes

Following the seminal discovery of insulin a century ago, treatment of individuals with type 1 diabetes (T1D) has been largely restricted to efforts to monitor and treat metabolic glucose dysregulation. The recent regulatory approval of the first immunotherapy that targets T cells as a means to delay the autoimmune destruction of pancreatic β-cells highlights the critical role of the immune system in disease pathogenesis and tends to pave the way for other immune-targeted interventions for T1D. Improving the efficacy of such interventions across the natural history of the disease will probably require a more detailed understanding of the immunobiology of T1D, as well as technologies to monitor residual β-cell mass and function. Here we provide an overview of the immune mechanisms that underpin the pathogenesis of T1D, with a particular emphasis on T cells.

Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP)

OBJECTIVE

To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates.

RESEARCH DESIGN AND METHODS

Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%).

RESULTS

T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001.

CONCLUSIONS

Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.

The INNODIA Type 1 Diabetes Natural History Study: a European cohort of newly diagnosed children, adolescents and adults

AIMS/HYPOTHESIS

Type 1 diabetes is an heterogenous condition. Characterising factors explaining differences in an individual's clinical course and treatment response will have important clinical and research implications. Our aim was to explore type 1 diabetes heterogeneity, as assessed by clinical characteristics, autoantibodies, beta cell function and glycaemic outcomes, during the first 12 months from diagnosis, and how it relates to age at diagnosis.

METHODS

Data were collected from the large INNODIA cohort of individuals (aged 1.0-45.0 years) newly diagnosed with type 1 diabetes, followed 3 monthly, to assess clinical characteristics, C-peptide, HbA1c and diabetes-associated antibodies, and their changes, during the first 12 months from diagnosis, across three age groups: <10 years; 10-17 years; and ≥18 years.

RESULTS

The study population included 649 individuals (57.3% male; age 12.1±8.3 years), 96.9% of whom were positive for one or more diabetes-related antibodies. Baseline (IQR) fasting C-peptide was 242.0 (139.0-382.0) pmol/l (AUC 749.3 [466.2-1106.1] pmol/l × min), with levels increasing with age (p<0.001). Over time, C-peptide remained lower in participants aged <10 years but it declined in all age groups. In parallel, glucose levels progressively increased. Lower baseline fasting C-peptide, BMI SD score and presence of diabetic ketoacidosis at diagnosis were associated with lower stimulated C-peptide over time. HbA1c decreased during the first 3 months (p<0.001), whereas insulin requirement increased from 3 months post diagnosis (p<0.001).

CONCLUSIONS/INTERPRETATION

In this large cohort with newly diagnosed type 1 diabetes, we identified age-related differences in clinical and biochemical variables. Of note, C-peptide was lower in younger children but there were no main age differences in its rate of decline.

From islet transplantation to beta-cell regeneration: an update on beta-cell-based therapeutic approaches in type 1 diabetes

INTRODUCTION

Type 1 diabetes (T1D) mellitus is an autoimmune disease in which immune cells, predominantly effector T cells, destroy insulin-secreting beta-cells. Beta-cell destruction led to various consequences ranging from retinopathy and nephropathy to neuropathy. Different strategies have been developed to achieve normoglycemia, including exogenous glucose compensation, whole pancreas transplantation, islet transplantation, and beta-cell replacement.

AREAS COVERED

The last two decades of experience have shown that indigenous glucose compensation through beta-cell regeneration and protection is a peerless method for T1D therapy. Tremendous studies have tried to find an unlimited source for beta-cell regeneration, on the one hand, and beta-cell protection against immune attack, on the other hand. Recent advances in stem cell technology, gene editing methods, and immune modulation approaches provide a unique opportunity for both beta-cell regeneration and protection.

EXPERT OPINION

Pluripotent stem cell differentiation into the beta-cell is considered an unlimited source for beta-cell regeneration. Devising engineered pancreas-specific regulatory T cells using Chimeric Antigen Receptor (CAR) technology potentiates an effective immune tolerance induction for beta-cell protection. Beta-cell regeneration using pluripotent stem cells and beta-cell protection using pancreas-specific engineered regulatory T cells promises to develop a curative protocol in T1D.

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review

BACKGROUND

Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.

METHODS

We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.

RESULTS

Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.

CONCLUSIONS

Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Immune checkpoint modulators in early clinical development for the treatment of type 1 diabetes

INTRODUCTION

Despite the improvements of insulin therapy, people with type 1 diabetes (T1D) still suffer from a decreased quality of life and life expectancy. The search toward a cure for T1D is therefore still a scorching open field of research.

AREAS COVERED

Tackling the immune checkpoint signaling pathways has gained importance in the field of cancer immunotherapy. The same pathways can be targeted in autoimmunity with an opposite principle: to dampen the exaggerated immune response. In this review, we report a comprehensive excursus on the cellular and molecular mechanisms that lead to loss of immunological tolerance, and recent evidence on the role of immune checkpoint molecules in the development of T1D and their potential application for the mitigation of autoimmune diabetes.

EXPERT OPINION

Contrasting results about the efficacy of immune checkpoint modulators for T1D have been published, with very few molecules from preclinical studies eligible for use in humans. The heterogeneous and complex pathophysiology of T1D may explain the conflicting evidence. Designing clinical trials that acknowledge the pathophysiological and clinical complexity of T1D and that forecast the need of simultaneously tackling different disease pathways will be crucial to enhance the benefits which may be gained by such compounds.

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.

Glycaemia risk index uncovers distinct glycaemic variability patterns associated with remission status in type 1 diabetes

AIMS/HYPOTHESIS

The aim of this work was to define a unique remission status using glycaemia risk index (GRI) and other continuous glucose monitoring (CGM) metrics in individuals with type 1 diabetes for improved phenotyping.

METHODS

A group of 140 individuals with type 1 diabetes were recruited for a cross-sectional study. The participants were categorised into four groups based on their remission status, which was defined as insulin-dose-adjusted A1c (IDAA1c) <9 or C-peptide ≥300 pmol/l: new-onset (n=24); mid-remission (n=44); post-remission (n=44); and non-remission (individuals who did not experience remission, n=28). Participants in the remission phase were referred to as 'remitters', while those who were not in the remission phase were referred to as 'non-remitters', the latter group including new-onset, post-remission and non-remission participants. Clinical variables such as HbA1c, C-peptide and insulin daily dose, as well as IDAA1C and CGM data, were collected. The patterns of CGM metrics were analysed for each group using generalised estimating equations to investigate the glycaemic variability patterns associated with remission status. Then, unsupervised hierarchical clustering was used to place the participants into subgroups based on GRI and other CGM core metrics.

RESULTS

The glycaemic variability patterns associated with remission status were found to be distinct based on the circadian CGM metrics. Remitters showed improved control of blood glucose levels over 14 days within the range of 3.9-10 mmol/l, and lower GRI compared with non-remitters (p<0.001). Moreover, GRI strongly correlated with IDAA1C (r=0.62; p<0.001) and was sufficient to distinguish remitters from non-remitters. Further, four subgroups demonstrating distinct patterns of glycaemic variability associated with different remission status were identified by clustering on CGM metrics: remitters with low risk of dysglycaemia; non-remitters with high risk of hypoglycaemia; non-remitters with high risk of hyperglycaemia; and non-remitters with moderate risk of dysglycaemia.

CONCLUSIONS/INTERPRETATION

GRI, an integrative index, together with other traditional CGM metrics, helps to identify different glycaemic variability patterns; this might provide specifically tailored monitoring and management strategies for individuals in the various subclusters.

Epitope-based precision immunotherapy of Type 1 diabetes

Antigen-specific immunotherapies (ASITs) address important clinical needs in treating autoimmune diseases. However, Type 1 diabetes is a heterogeneous disease wherein patient characteristics influence responsiveness to ASITs. Targeting not only disease-relevant T cell populations, but also specific groups of patients using precision medicine is a new goal toward achieving effective treatment. HLA-restricted peptides provide advantages over protein as antigens, however, methods for profiling antigen-specific T cells need to improve in sensitivity, depth, and throughput to facilitate epitope selection. Delivery approaches are highly diverse, illustrating the many ways relevant antigen-presenting cell populations and anatomical locations can be targeted for tolerance induction. The role of persistence of antigen presentation in promoting durable antigen-specific tolerance requires further investigation. Based on the outcome of ASIT trials, the field is moving toward using patient-specific variations to improve efficacy, but challenges still lie on the path to delivering more effective and safer treatment to the T1D patient population.

Predictors of the clinical severity of T1DM presentation at diagnosis in children and adolescents with type 1 diabetes mellitus (T1DM)

PURPOSE

We aimed to assess factors associated with the presence and severity of ketoacidosis (DKA) at pediatric type 1 diabetes (T1DM) diagnosis, in relation to pancreatic, associated and familial autoimmunity.

METHODS

Antibodies against pancreatic beta-cells, organ specific autoantibodies (thyroid, celiac, and parietal) and family history of autoimmunity were retrospectively evaluated in 116 T1DM patients aged 11.9 ± 4.6 (mean ± SD) years, with disease duration 7.62 ± 3.67 years (mean ± SD).

RESULTS

Most patients (67.2%) presented with DKA at diagnosis. Younger children (< 2 years) had tenfold risk of DKA, compared to older children (12.1-15 years) (OR = 10.8, 95% CI: 1.0-116.9, P = 0.05). Fasting c-peptide levels were lower in the DKA group (OR = 0.26, 95% CI = 0.07-0.89, P = 0.033). The number of anti-pancreatic antibodies at disease onset did not show any significant correlations with the presence (p = 0.889) or severity of DKA (p = 0.863). All patients with multiple autoimmunity (> 2 autoimmune diseases plus T1DM) presented with DKA. Familial autoimmunity acted protectively against DKA manifestation (OR = 0.40, 95% CI = 0.16-1.0, P = 0.051).

CONCLUSIONS

Among newly diagnosed T1DM patients, 67.2% presented with DKA. Younger age, lower c-peptide and the presence of associated autoimmunity were predictive factors of the presence and severity of DKA at diagnosis. High degree of suspicion, due to family history, may prevent DKA development and severity.

A machine learning based approach to identify carotid subclinical atherosclerosis endotypes

AIMS

To define endotypes of carotid subclinical atherosclerosis.

METHODS AND RESULTS

We integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations.

CONCLUSIONS

We report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.

Distinct transcriptomic profiles in children prior to the appearance of type 1 diabetes-linked islet autoantibodies and following enterovirus infection

Although the genetic basis and pathogenesis of type 1 diabetes have been studied extensively, how host responses to environmental factors might contribute to autoantibody development remains largely unknown. Here, we use longitudinal blood transcriptome sequencing data to characterize host responses in children within 12 months prior to the appearance of type 1 diabetes-linked islet autoantibodies, as well as matched control children. We report that children who present with insulin-specific autoantibodies first have distinct transcriptional profiles from those who develop GADA autoantibodies first. In particular, gene dosage-driven expression of GSTM1 is associated with GADA autoantibody positivity. Moreover, compared with controls, we observe increased monocyte and decreased B cell proportions 9-12 months prior to autoantibody positivity, especially in children who developed antibodies against insulin first. Lastly, we show that control children present transcriptional signatures consistent with robust immune responses to enterovirus infection, whereas children who later developed islet autoimmunity do not. These findings highlight distinct immune-related transcriptomic differences between case and control children prior to case progression to islet autoimmunity and uncover deficient antiviral response in children who later develop islet autoimmunity.

Screening and Prevention of Type 1 Diabetes: Where Are We?

A diagnosis of type 1 diabetes (T1D) and the subsequent requirement for exogenous insulin treatment is associated with considerable acute and chronic morbidity and a substantial effect on patient quality of life. Importantly, a large body of work suggests that early identification of presymptomatic T1D can accurately predict clinical disease, and when paired with education and monitoring, can yield improved health outcomes. Furthermore, a growing cadre of effective disease-modifying therapies provides the potential to alter the natural history of early stages of T1D. In this mini review, we highlight prior work that has led to the current landscape of T1D screening and prevention, as well as challenges and next steps moving into the future of these rapidly evolving areas of patient care.

A standardized metric to enhance clinical trial design and outcome interpretation in type 1 diabetes

The use of a standardized outcome metric enhances clinical trial interpretation and cross-trial comparison. If a disease course is predictable, comparing modeled predictions with outcome data affords the precision and confidence needed to accelerate precision medicine. We demonstrate this approach in type 1 diabetes (T1D) trials aiming to preserve endogenous insulin secretion measured by C-peptide. C-peptide is predictable given an individual's age and baseline value; quantitative response (QR) adjusts for these variables and represents the difference between the observed and predicted outcome. Validated across 13 trials, the QR metric reduces each trial's variance and increases statistical power. As smaller studies are especially subject to random sampling variability, using QR as the outcome introduces alternative interpretations of previous clinical trial results. QR can provide model-based estimates that quantify whether individuals or groups did better or worse than expected. QR also provides a purer metric to associate with biomarker measurements. Using data from more than 1300 participants, we demonstrate the value of QR in advancing disease-modifying therapy in T1D. QR applies to any disease where outcome is predictable by pre-specified baseline covariates, rendering it useful for defining responders to therapy, comparing therapeutic efficacy, and understanding causal pathways in disease.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

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

Baseline plasma proinsulin response to glucose for predicting therapeutic response to otelixizumab in recent-onset type 1 diabetes

AIMS

In recent-onset type 1 diabetes, clamp-derived C-peptide predicts good response to anti-CD3. Elevated proinsulin and proinsulin/C-peptide ratio (PI/CP) suggest increased metabolic/inflammatory beta cell burden. We reanalyzed trial data to compare the ability of baseline acutely glucose-stimulated proinsulin, C-peptide and PI/CP to predict functional outcome.

METHODS

Eighty recent-onset type 1 diabetes patients participated in the placebo-controlled otelixizumab (GSK; NCT00627146) trial. Hyperglycemic clamps were performed at baseline, 6, 12 and 18 months, involving 3 h of induced euglycemia, followed by acutely raising and maintaining glycemia to ≥ 10 mmol/l for 140 min. Plasma proinsulin, C-peptide and PI/CP were determined after acute (minute 0 at 10 mmol/l; PI0, CP0, PI/CP0) and sustained glucose stimulation (AUC between minutes 60-140). Outcome was assessed as change in AUC60-140 C-peptide from baseline.

RESULTS

In multiple linear regression, higher baseline (≥median [P50]) PI0 independently predicted preservation of beta cell function in response to anti-CD3 and interacted significantly with IAA. During follow-up, anti-CD3 tempered a further increase in PI/CP0, but not in PI0. CP0 outperformed PI0 and PI/CP0 for post-treatment monitoring.

CONCLUSIONS

In recent-onset type 1 diabetes, elevated acutely glucose-stimulated proinsulin may complement or replace acutely or sustainedly stimulated C-peptide release for identifying good responders to anti-CD3, but not as outcome measure.

Islet autoimmunity in human type 1 diabetes: initiation and progression from the perspective of the beta cell

Type 1 diabetes results from the poorly understood process of islet autoimmunity, which ultimately leads to the loss of functional pancreatic beta cells. Mounting evidence supports the notion that the activation and evolution of islet autoimmunity in genetically susceptible people is contingent upon early life exposures affecting the islets, especially beta cells. Here, we review some of the recent advances and studies that highlight the roles of these changes as well as antigen presentation and stress response pathways in beta cells in the onset and propagation of the autoimmune process in type 1 diabetes. Future progress in this area holds promise for advancing islet- and beta cell-directed therapies that could be implemented in the early stages of the disease and could be combined with immunotherapies.

Immune responses to gut bacteria associated with time to diagnosis and clinical response to T cell-directed therapy for type 1 diabetes prevention

Immune-targeted therapies have efficacy for treatment of autoinflammatory diseases. For example, treatment with the T cell-specific anti-CD3 antibody teplizumab delayed disease onset in participants at high risk for type 1 diabetes (T1D) in the TrialNet 10 (TN-10) trial. However, heterogeneity in therapeutic responses in TN-10 and other immunotherapy trials identifies gaps in understanding disease progression and treatment responses. The intestinal microbiome is a potential source of biomarkers associated with future T1D diagnosis and responses to immunotherapy. We previously reported that antibody responses to gut commensal bacteria were associated with T1D diagnosis, suggesting that certain antimicrobial immune responses may help predict disease onset. Here, we investigated anticommensal antibody (ACAb) responses against a panel of taxonomically diverse intestinal bacteria species in sera from TN-10 participants before and after teplizumab or placebo treatment. We identified IgG2 responses to three species that were associated with time to T1D diagnosis and with teplizumab treatment responses that delayed disease onset. These antibody responses link human intestinal bacteria with T1D progression, adding predictive value to known T1D risk factors. ACAb analysis provides a new approach to elucidate heterogeneity in responses to immunotherapy and identify individuals who may benefit from teplizumab, recently approved by the U.S. Food and Drug Administration for delaying T1D onset.

Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications

BACKGROUND

Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients.

METHODS

We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting.

RESULTS

Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab).

CONCLUSIONS

Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.

Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review

BACKGROUND

Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification.

METHODS

To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument.

RESULTS

We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings.

CONCLUSIONS

While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design.

The T1D Index: Implications of Initial Results, Data Limitations, and Future Development

PURPOSE OF THE REVIEW

Current global information on incidence, prevalence, and mortality of type 1 diabetes (T1D) is limited, particularly in low- and middle-income countries. To address this gap in evidence, JDRF, Life for a Child, International Society for Pediatric and Adolescent Diabetes, and International Diabetes Federation have developed the T1D Index, which uses a Markov mathematical model, and machine learning and all available data to provide global estimates of the burden on T1D. This review assesses the methodology, limitations, current findings, and future directions of the Index.

RECENT FINDINGS

Global prevalence was estimated at 8.4 million in 2021, with 1.5 million <20 years (y). T1D prevalence varied from 1.5 to 534 per 100,000, with T1D accounting for <0.1-17.8% of all diabetes in different countries. A total of 35,000 young people <25 y are estimated to have died at clinical onset of T1D from non-diagnosis. An estimated 435,000 people <25 y were receiving "minimal care." Health-adjusted life years (HALYs) lost for individuals diagnosed with T1D at age 10 y in 2021 ranged from 14 to 55 y. These results show that interventions to reduce deaths from non-diagnosis, and improve access to at least an intermediate care level, are needed to reduce projected life years lost. The results have significant uncertainties due to incomplete data across the required inputs. Obtaining recent incidence, prevalence, and mortality data, as well as addressing data quality issues, misdiagnoses, and the lack of adult data, is essential for maintaining and improving accuracy. The index will be updated regularly as new data become available.

A Plasma miR-193b-365 Signature Combined With Age and Glycemic Status Predicts Response to Lactococcus lactis-Based Antigen-Specific Immunotherapy in New-Onset Type 1 Diabetes

Immunomodulation combined with antigen therapy holds great promise to arrest autoimmune type 1 diabetes, but clinical translation is hampered by a lack of prognostic biomarkers. Low-dose anti-CD3 plus Lactococcus lactis bacteria secreting proinsulin and IL-10 reversed new-onset disease in nonobese diabetic (NOD) mice, yet some mice were resistant to the therapy. Using miRNA profiling, six miRNAs (i.e., miR-34a-5p, miR-125a-3p, miR-193b-3p, miR-328, miR-365-3p, and miR-671-3p) were identified as differentially expressed in plasma of responder versus nonresponder mice before study entry. After validation and stratification in an independent cohort, plasma miR-193b-3p and miR-365-3p, combined with age and glycemic status at study entry, had the best power to predict, with high sensitivity and specificity, poor response to the therapy. These miRNAs were highly abundant in pancreas-infiltrating neutrophils and basophils with a proinflammatory and activated phenotype. Here, a set of miRNAs and disease-associated parameters are presented as a predictive signature for the L. lactis-based immunotherapy outcome in new-onset type 1 diabetes, hence allowing targeted recruitment of trial participants and accelerated trial execution.

ARTICLE HIGHLIGHTS

Low-dose anti-CD3 combined with oral gavage of genetically modified Lactococcus lactis bacteria secreting human proinsulin and IL-10 holds great promise to arrest autoimmune type 1 diabetes, but the absence of biomarkers predicting therapeutic success hampers clinical translation. A set of cell-free circulation miRNAs together with age and glycemia at baseline predicts a poor response after L. lactis-based immunotherapy in nonobese mice with new-onset diabetes. Pancreas-infiltrating neutrophils and basophils are identified as potential cellular sources of discovered miRNAs. The prognostic signature could guide targeted recruitment of patients with newly diagnosed type 1 diabetes in clinical trials with the L. lactis-based immunotherapy.

Characterisation and clinical outcomes in children and adolescents with diabetes according to newly defined subgroups: a cohort study from the DPV registry

Background

Personalised therapy has emerged as a possibly more efficient approach taking disease heterogeneity into account. The aim of this study was to determine whether recently described subgroups of childhood diabetes have prognostic association with diabetes-specific complications and, therefore, might be a basis for personalised therapies.

Methods

We applied a previously developed subgroup classification to pediatric patients (diabetes onset <18 years) from the prospective Diabetes Patient Follow-up (DPV) registry with documented data between January 1, 2000 and March 31, 2022, from diabetes centers in Germany, Austria, Switzerland, and Luxembourg. The classification required information on islet autoantibody status, age, haemoglobin A1c (HbA1c), and body-mass index (BMI-SDS) at disease manifestation, as well as follow up data after 2 and after 4 years, which was available in 22,719 patients. Patients without documented data on these parameters were excluded from the analysis. The cumulative risk of severe hypoglycemia, diabetic ketoacidosis (DKA), retinopathy, and nephropathy were analysed by Kaplan-Meier analyses over a median follow-up of 6.8 years (IQR 4.8-9.6).

Findings

Patients were classified into 10 subgroups (P1-P7 islet autoantibody-positive, n = 19,811; N1-N3 islet autoantibody-negative, n = 2908). The groups varied markedly with respect to specific acute and chronic complications. Severe hypoglycemia was a characteristic feature in young islet autoantibody-positive subgroups P1, P3, P4 (10-year risk 46, 46 and 47%) and the islet autoantibody-negative groups N1, N2 (43 and 46%). Nephropathy was identified in patient groups P2 and P5 (10-year risk 16%), which had features of moderate disease such as preserved C-peptide, low HbA1c, and very low frequency of DKA at diabetes onset. Group P7, which was defined by a high BMI, was associated with poor metabolic control, DKA, and retinopathy. In contrast, islet autoantibody-negative patients with high BMI (N3) had a low risk for all four complications.

Interpretation

Subgrouping of childhood diabetes at diabetes onset provided prognostic value for the development of acute and chronic diabetes-specific complications.

Funding

The DPV initiative is supported by The German Ministry of Education and Research (BMBF) within the German Center for Diabetes Research, the diabetes surveillance of the Robert Koch Institute, the German Diabetes Association (DDG) and INNODIA.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

The incidence of type 1 diabetes in children under 15 years of age is rising again-a nationwide study

International incidence rates (IRs) and trends of childhood type 1 diabetes (T1D) vary. Recent data from Ireland and other high incidence countries suggested a stabilisation in IRs of T1D in children aged under 15 years. Our primary objective was to report the IR of T1D in children in Ireland from 2019 to 2021 and evaluate if age, sex and season of diagnosis had changed. Incident cases of T1D in those aged under 15 years were identified prospectively by clinicians nationally and reported to the Irish Childhood Diabetes National Register (ICDNR). Following case verification, capture-recapture methodology was applied, and IRs calculated. Numbers of children including age, sex and season of diagnosis per year were evaluated. There were 1027 cases, 542 males (53%). The direct standardised incidence rates (SIRs) increased by 21% overall and were 31.1, 32.2 and 37.6/100,000/year, respectively, with no significant sex difference. The highest IRs were in the 10-14-year category until 2021, then changed to the 5-9-year category (40% of cases). Whilst autumn and winter remain dominant diagnostic seasons, seasonality differed in 2021 with a greater number presenting in spring.

CONCLUSION

The incidence of childhood T1D in Ireland is increasing, observed prior to the COVID-19 pandemic, and shifting to an earlier age at diagnosis for the first time. The pattern of seasonality also appears to have changed. This may reflect an increased severity of diabetes with important implications for healthcare providers.

WHAT IS KNOWN

• Ireland has a very high incidence of T1D in childhood, which had stabilised following a rapid rise, similar to other high incidence countries. • The incidence rate is consistently highest in older children (10-14 years).

WHAT IS NEW

• Irish IR is no longer stable and has increased again, with the highest incidence occurring in the younger 5-9 age category for the first time. • The seasonality of diagnosis has changed during the COVID-19 pandemic years of 2020-2021.

Glibenclamide for Brain Contusions: Contextualizing a Promising Clinical Trial Design that Leverages an Imaging-Based TBI Endotype

TBI heterogeneity is recognized as a major impediment to successful translation of therapies that could improve morbidity and mortality after injury. This heterogeneity exists on multiple levels including primary injury, secondary injury/host-response, and recovery. One widely accepted type of primary-injury related heterogeneity is pathoanatomic-the intracranial compartment that is predominantly affected, which can include any combination of subdural, subarachnoid, intraparenchymal, diffuse axonal, intraventricular and epidural hemorrhages. Intraparenchymal contusions carry the highest risk for progression. Contusion expansion is one of the most important drivers of death and disability after TBI. Over the past decade, there has been increasing evidence of the role of the sulfonylurea-receptor 1-transient receptor potential melastatin 4 (SUR1-TRPM4) channel in secondary injury after TBI, including progression of both cerebral edema and intraparenchymal hemorrhage. Inhibition of SUR1-TRPM4 with glibenclamide has shown promising results in preclinical models of contusional TBI with benefits against cerebral edema, secondary hemorrhage progression of the contusion, and improved functional outcome. Early-stage human research supports the key role of this pathway in contusion expansion and suggests a benefit with glibenclamide inhibition. ASTRAL is an ongoing international multi-center double blind multidose placebo-controlled phase-II clinical trial evaluating the safety and efficacy of an intravenous formulation of glibenclamide (BIIB093). ASTRAL is a unique and innovative study that addresses TBI heterogeneity by limiting enrollment to patients with the TBI pathoanatomic endotype of brain contusion and using contusion-expansion (a mechanistically linked secondary injury) as its primary outcome. Both criteria are consistent with the strong supporting preclinical and molecular data. In this narrative review, we contextualize the development and design of ASTRAL, including the need to address TBI heterogeneity, the scientific rationale underlying the focus on brain contusions and contusion-expansion, and the preclinical and clinical data supporting benefit of SUR1-TRPM4 inhibition in this specific endotype. Within this framework, we summarize the current study design of ASTRAL which is sponsored by Biogen and actively enrolling with a goal of 160 participants.

Plasma amino acid signatures define types of pediatric diabetes

BACKGROUND & AIMS

Metabolic biomarkers with pathophysiological relevance is lacking in pediatric diabetes. We aimed to identify novel metabolic biomarkers in pediatric type 1 (T1D) and type 2 diabetes (T2D). We hypothesized that (1) targeted plasma metabolomics, focused on plasma amino acid concentrations, could identify distinctively altered patterns in children with T1D or T2D, and (2) there are specific changes in concentrations of metabolites related to branch chain amino acids (BCAA) and arginine metabolism in children with T2D.

METHODS

In a pilot study, we enrolled children with T1D (n = 15) and T2D (n = 13), and healthy controls (n = 15). Fasting plasma amino acid concentrations were measured by ultra-performance liquid chromatography, and compared between the groups after adjustment for confounding factors.

RESULTS

The mean age (SD) of participants was 16.4 (0.9) years. There were no group differences in age, gender, race/ethnicity, or 24-h protein intake. Mean BMI percentile was higher in the T2D than the T1D group or controls (p < 0.001). The T2D group had lower arginine, citrulline, glutamine, glycine, phenylalanine, methionine, threonine, asparagine and symmetric dimethylarginine (SDMA) but higher aspartate than controls, after adjusting for BMI percentiles (all p < 0.05). Children with T2D also had lower glycine but higher ornithine, proline, leucine, isoleucine, valine, total BCAA, lysine and tyrosine than those with T1D after adjusting for confounding factors (all p < 0.05). Children with T1D had lower phenylalanine, methionine, threonine, glutamine, tyrosine, asymmetric dimethylarginine (ADMA) and SDMA than controls (all p < 0.05).

CONCLUSIONS

Children with T2D and T1D have distinct fasting plasma amino acid signatures that suggest varying pathogenic mechanisms and could serve as biomarkers for these conditions.

Human immune phenotyping reveals accelerated aging in type 1 diabetes

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

The pathogenic "symphony" in type 1 diabetes: A disorder of the immune system, β cells, and exocrine pancreas

Type 1 diabetes (T1D) is widely considered to result from the autoimmune destruction of insulin-producing β cells. This concept has been a central tenet for decades of attempts seeking to decipher the disorder's pathogenesis and prevent/reverse the disease. Recently, this and many other disease-related notions have come under increasing question, particularly given knowledge gained from analyses of human T1D pancreas. Perhaps most crucial are findings suggesting that a collective of cellular constituents-immune, endocrine, and exocrine in origin-mechanistically coalesce to facilitate T1D. This review considers these emerging concepts, from basic science to clinical research, and identifies several key remaining knowledge voids.

Heterogeneity and endotypes in type 1 diabetes mellitus

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

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

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

Microbes as triggers and boosters of Type 1 Diabetes - Mediation by molecular mimicry

AIMS

Type 1 diabetes is characterized by steadily increasing incidence and largely obscured pathogenesis. Molecular mimicry is well-established as trigger for different autoimmune pathologies, but obscurely explored in the context of T1D. The presented study explores the underestimated role of molecular mimicry in T1D-etiology/progression in search for etiologic factors among human pathogens and commensals.

METHODS

A comprehensive immunoinformatics analysis of T1D-specific experimental T-cell epitopes across bacterial, fungal, and viral proteomes was performed, coupled with MHC-restricted mimotope validation and docking of most potent epitopes/mimotopes to T1D-high-risk MHCII molecules. In addition, re-analysis of the publicly available T1D-microbiota dataset was performed, including samples at the pre-T1D disease stage.

RESULTS

A number of bacterial pathogens/commensals were tagged as putative T1D triggers/boosters, including ubiquitous gut residents. The prediction of most likely mimicked epitopes revealed heat-shock proteins as most potent autoantigens for autoreactive T-cell priming via molecular mimicry. Docking revealed analogous interactions for predicted bacterial mimotopes and corresponding experimental epitopes. Finally, re-analysis of T1D gut microbiota datasets prompted pre-T1D as most significantly different/dysbiotic, compared to other explored categories (T1D stage/controls).

CONCLUSIONS

Obtained results support the unrecognized role of molecular mimicry in T1D, suggesting that autoreactive T-cell priming might be the triggering factor of disease development.

Type 1 diabetes

Type 1 diabetes is a chronic disease caused by autoimmune destruction of pancreatic β cells. Individuals with type 1 diabetes are reliant on insulin for survival. Despite enhanced knowledge related to the pathophysiology of the disease, including interactions between genetic, immune, and environmental contributions, and major strides in treatment and management, disease burden remains high. Studies aimed at blocking the immune attack on β cells in people at risk or individuals with very early onset type 1 diabetes show promise in preserving endogenous insulin production. This Seminar will review the field of type 1 diabetes, highlighting recent progress within the past 5 years, challenges to clinical care, and future directions in research, including strategies to prevent, manage, and cure the disease.

High-Throughput Analysis of Neutrophil Extracellular Trap Levels in Subtypes of People with Type 1 Diabetes

Neutrophils might play an important role in the pathogenesis of autoimmune diseases, including type 1 diabetes (T1D), by contributing to immune dysregulation via a highly inflammatory program called neutrophil extracellular trap (NET) formation or NETosis, involving the extrusion of chromatin entangled with anti-microbial proteins. However, numerous studies reported contradictory data on NET formation in T1D. This might in part be due to the inherent heterogeneity of the disease and the influence of the disease developmental stage on neutrophil behavior. Moreover, there is a lack of a standardized method to measure NETosis in an unbiased and robust manner. In this study, we employed the Incucyte^® ZOOM live-cell imaging platform to study NETosis levels in various subtypes of adult and pediatric T1D donors compared to healthy controls (HC) at baseline and in response to phorbol-myristate acetate (PMA) and ionomycin. Firstly, we determined that the technique allows for an operator-independent and automated quantification of NET formation across multiple time points, which showed that PMA and ionomycin induced NETosis with distinct kinetic characteristics, confirmed by high-resolution microscopy. NETosis levels also showed a clear dose-response curve to increasing concentrations of both stimuli. Overall, using Incucyte^® ZOOM, no aberrant NET formation was observed over time in the different subtypes of T1D populations, irrespective of age, compared to HC. These data were corroborated by the levels of peripheral NET markers in all study participants. The current study showed that live-cell imaging allows for a robust and unbiased analysis and quantification of NET formation in real-time. Peripheral neutrophil measures should be complemented with dynamic quantification of NETing neutrophils to make robust conclusions on NET formation in health and disease.

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

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

Lessons and Gaps in the Prediction and Prevention of Type 1 Diabetes

Type 1 diabetes (T1D) is a serious chronic autoimmune condition. Even though the root cause of T1D development has yet to be determined, enough is known about the natural history of T1D pathogenesis to allow study of interventions that may delay or even prevent the onset of hyperglycemia and clinical T1D. Primary prevention aims to prevent the onset of beta cell autoimmunity in asymptomatic people at high genetic risk for T1D. Secondary prevention strategies aim to preserve functional beta cells once autoimmunity is present, and tertiary prevention aims to initiate and extend partial remission of beta cell destruction after the clinical onset of T1D. The approval of teplizumab in the United States to delay the onset of clinical T1D marks an impressive milestone in diabetes care. This treatment opens the door to a paradigm shift in T1D care. People with T1D risk need to be identified early by measuring T1D related islet autoantibodies. Identifying people with T1D before they have symptoms will facilitate better understanding of pre-symptomatic T1D progression and T1D prevention strategies that may be effective.

Determination of autoantibodies in type 2 diabetes: one simple way to improve classification

This letter aims to increase interest in better classification of type 2 diabetes. This can be done in a simple and cheap way via determination of autoantibodies. Autoantibody analysis can be used to detect the 7-12% of people with diabetes that is phenotypic of type 2 diabetes but is, in fact, latent autoimmune diabetes in adults (LADA), which may be regarded as a variant of type 1 diabetes. This may help to explain why some individuals with type 2 diabetes do not go into remission after reducing their weight, while others do, and why remission sometimes ends earlier than expected. Improved classification of diabetes may play an important role in determining adequate therapy.