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Background: Islet transplantation is a promising treatment for type 1 diabetes that aims to restore insulin production and improve glucose control, but long-term graft survival remains a challenge due to immune rejection. Methods: ScRNA-seq data from syngeneic and allogeneic islet transplantation grafts were obtained from GSE198865. Seurat was used for filtering and clustering, and UMAP was used for dimension reduction. Differentially expressed genes were analyzed between syngeneic and allogeneic islet transplantation grafts. Gene set variation analysis (GSVA) was performed on the HALLMARK gene sets from MSigDB. Monocle 2 was used to reconstruct differentiation trajectories, and cytokine signature enrichment analysis was used to compare cytokine responses between syngeneic and allogeneic grafts. Results: Three distinct macrophage clusters (Mø-C1, Mø-C2, and Mø-C3) were identified, revealing complex interactions and regulatory mechanisms within macrophage populations. The significant activation of macrophages in allogeneic transplants was marked by the upregulation of allograft rejection-related genes and pathways involved in inflammatory and interferon responses. GSVA revealed eight pathways significantly upregulated in the Mø-C2 cluster. Trajectory analysis revealed that Mø-C3 serves as a common progenitor, branching into Mø-C1 and Mø-C2. Cytokine signature enrichment analysis revealed significant differences in cytokine responses, highlighting the distinct immunological environments created by syngeneic and allogeneic grafts. Conclusion: This study significantly advances the understanding of macrophage roles within the context of islet transplantation by revealing the interactions between immune pathways and cellular fate processes. The findings highlight potential therapeutic targets for enhancing graft survival and function, emphasizing the importance of understanding the immunological aspects of transplant acceptance and longevity.

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INTRODUCTION: Dual-expressing lymphocytes (DEs) are unique immune cells that express both B cell receptors (BCRs, surface antibody) and T cell receptors (TCRs). In type 1 diabetes, DE antibodies are predominated by one antibody (x-mAb), an IgM monoclonal antibody with a germline-encoded CDR3 that recognizes self-reactive TCRs. We explored if x-mAb and its interacting TCRs have distinct structural features. METHODS: Using bioinformatics, we compared x-mAb and its most common interacting TCRαß to billions of antigen receptor sequences to determine if they were unique or randomly generated. RESULTS: X-mAb represents a unique class of human antibodies with a conserved CDR3 sequence (CARx1-4DTAMVYYFYDW), consisting of a fixed DJH motif (DTAMVYYFDYW) paired with various VH genes. A public TCRß clonotype (CASSPGTEAFF) associated with x-mAb on DEs features two invariant segments, VßD (CASSPGT) and DJß (PGTEAFF), key to two large families of public TCRß clonotypes-CASSPGT-Jßx and CASSPGT-Jßx-formed by recombining the VßD motif with Jß genes and the DJß motif with Vß genes. B cells also use CASSPGT as a VHD motif for public IGH clonotypes (CASSPGT-Jßx). DISCUSSION: DEs, unlike conventional T and B cells, use invariant motifs to create public antibodies and TCRs, a trait previously seen only in cartilaginous fish.

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Background: Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers). Methods: This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7). Results: The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions. Conclusion: Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.

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[This corrects the article DOI: 10.3389/fimmu.2022.825426.].

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Type 1 diabetes (T1D) is a chronic autoimmune condition that affects millions of people worldwide. Insulin pumps or injections are the standard treatment options for this condition. This article provides a comprehensive overview of the several type 1 diabetes treatment options, focusing on oral insulin. The article is divided into parts that include immune-focused treatments, antigen vaccination, cell-directed interventions, cytokine-directed interventions, and non-immunomodulatory adjuvant therapy. Under the section on non-immunomodulatory adjunctive treatment, the benefits and drawbacks of medications such as metformin, amylin, sodium-glucose cotransporter inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 Ras), and verapamil are discussed. The article also discusses the advantages of oral insulin, including increased patient compliance and more dependable and regular blood sugar control. However, several variables, including the enzymatic and physical barriers of the digestive system, impair the administration of insulin via the mouth. Researchers have looked at a few ways to get over these challenges, such as changing the structure of the insulin molecule, improving absorption with the use of absorption enhancers or nanoparticles, and taking oral insulin together with other medications. Even with great advancements in the use of these treatment strategies, T1D still needs improvement in the therapeutic difficulties. Future studies in these areas should focus on creating tailored immunological treatments, looking into combination medications, and refining oral insulin formulations in an attempt to better control Type 1 Diabetes. The ultimate objective is to create accurate, customized strategies that will enhance glycemic management and the quality of life for individuals with the condition.

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Acetylated and butyrylated high amylose starch (HAMS-AB) is a prebiotic shown to be effective in type 1 diabetes (T1D) prevention in mouse models and is safe in adults with established T1D. HAMS-AB alters the gut microbiome profile with increased bacterial fermenters that produce short chain fatty acids (SCFAs) with anti-inflammatory and immune-modulatory effects. We performed a pilot study using a cross-over design to assess the safety and efficacy of 4 weeks of oral HAMS-AB consumption by recently diagnosed (< 2 years of diagnosis) youths with T1D. Seven individuals completed the study. The mean±SD age was 15.0±1.2 years, diabetes duration 19.5±6.3 months, 5/7 were female and 4/7 were White, all with a BMI of < 85th%. The prebiotic was safe. Following prebiotic intake, gut microbiome changes were seen, including a notable increase in the relative abundance of fermenters such as Bifidobacterium and Faecalibacterium. Treatment was also associated with changes in bacterial functional pathways associated with either improved energy metabolism (upregulation of tyrosine metabolism) or anti-inflammatory effects (reduced geraniol degradation). There were no differences in stool SCFA levels. Plasma metabolites associated with improved glycemia, such as hippurate, were significantly increased after treatment and there were positive and significant changes in the immune regulatory function of mucosal associated invariant T cells. There was a significant decrease in the area under the curve glucose but not C-peptide, as measured during a mixed meal tolerance testing, following the prebiotic consumption. In summary, the prebiotic HAMS-AB was safe in adolescents with T1D and showed promising effects on the gut microbiome composition, function and immune regulatory function.

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Experimental animal models of diabetes can be useful for identifying novel targets related to disease, for understanding its physiopathology, and for evaluating emerging antidiabetic treatments. This study aimed to characterize two rat diabetes models: HFD + STZ, a high-fat diet (60% fat) combined with streptozotocin administration (STZ, 35 mg/kg BW), and a model with a single STZ dose (65 mg/kg BW) in comparison with healthy rats. HFD + STZ- induced animals demonstrated a stable hyperglycemia range (350-450 mg/dL), whereas in the STZ-induced rats, we found glucose concentration values with a greater dispersion, ranging from 270 to 510 mg/dL. Moreover, in the HFD + STZ group, the AUC value of the insulin tolerance test (ITT) was found to be remarkably augmented by 6.2-fold higher than in healthy animals (33,687.0 ± 1705.7 mg/dL/min vs. 5469.0 ± 267.6, respectively), indicating insulin resistance (IR). In contrast, a more moderate AUC value was observed in the STZ group (19,059.0 ± 3037.4 mg/dL/min) resulting in a value 2.5-fold higher than the average exhibited by the control group. After microarray experiments on liver tissue from all animals, we analyzed genes exhibiting a fold change value in gene expression <-2 or >2 (p-value <0.05). We found 27,686 differentially expressed genes (DEG), identified the top 10 DEGs and detected 849 coding genes that exhibited opposite expression patterns between both diabetes models (491 upregulated genes in the STZ model and 358 upregulated genes in HFD + STZ animals). Finally, we performed an enrichment analysis of the 849 selected genes. Whereas in the STZ model we found cellular pathways related to lipid biosynthesis and metabolism, in the HFD + STZ model we identified pathways related to immunometabolism. Some phenotypic differences observed in the models could be explained by transcriptomic results; however, further studies are needed to corroborate these findings. Our data confirm that the STZ and the HFD + STZ models are reliable experimental models for human T1D and T2D, respectively. These results also provide insight into alterations in the expression of specific liver genes and could be utilized in future studies focusing on diabetes complications associated with impaired liver function.

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Diabetes mellitus, commonly referred to as diabetes, is a group of metabolic disorders characterized by chronic elevation in blood glucose levels, resulting from inadequate insulin production, defective cellular response to extracellular insulin, and/or impaired glucose metabolism. The two main types that account for most diabetics are type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), each with their own pathophysiological features. T1D is an autoimmune condition where the body's immune system attacks and destroys the insulin-producing beta cells in the pancreas. This leads to lack of insulin, a vital hormone for regulating blood sugar levels and cellular glucose uptake. As a result, those with T1D depend on lifelong insulin therapy to control their blood glucose level. In contrast, T2DM is characterized by insulin resistance, where the body's cells do not respond effectively to insulin, coupled with a relative insulin deficiency. This form of diabetes is often associated with obesity, sedentary lifestyle, and/or genetic factors, and it is managed with lifestyle changes and oral medications. Animal models play a crucial role in diabetes research. However, given the distinct differences between T1DM and T2DM, it is imperative for researchers to employ specific animal models tailored to each condition for a better understanding of the impaired mechanisms underlying each condition, and for assessing the efficacy of new therapeutics. In this review, we discuss the distinct animal models used in type 1 and type 2 diabetes mellitus research and discuss their strengths and limitations.

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In the development of Type 1 diabetes (T1D), there are critical states just before drastic changes, and identifying these pre-disease states may predict T1D or provide crucial early-warning signals. Unlike gene expression data, gut microbiome data can be collected noninvasively from stool samples. Gut microbiome sequencing data contain different levels of phylogenetic information that can be utilized to detect the tipping point or critical state in a reliable manner, thereby providing accurate and effective early-warning signals. However, it is still difficult to detect the critical state of T1D based on gut microbiome data due to generally non-significant differences between healthy and critical states. To address this problem, we proposed a new method - microbiome network flow entropy (mNFE) based on a single sample from each individual - for detecting the critical state before seroconversion and abrupt transitions of T1D at various taxonomic levels. The numerical simulation validated the robustness of mNFE under different noise levels. Furthermore, based on real datasets, mNFE successfully identified the critical states and their dynamic network biomarkers (DNBs) at different taxonomic levels. In addition, we found some high-frequency species, which are closely related to the unique clinical characteristics of autoantibodies at the four levels, and identified some non-differential 'dark species' play important roles during the T1D progression. mNFE can robustly and effectively detect the pre-disease states at various taxonomic levels and identify the corresponding DNBs with only a single sample for each individual. Therefore, our mNFE method provides a new approach not only for T1D pre-disease diagnosis or preventative treatment but also for preventative medicine of other diseases by gut microbiome.

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BACKGROUND: Type 1 diabetes (T1D) is a devastating autoimmune disease, and its rising prevalence in the United States and around the world presents a critical problem in public health. While some treatment options exist for patients already diagnosed, individuals considered at risk for developing T1D and who are still in the early stages of their disease pathogenesis without symptoms have no options for any preventive intervention. This is because of the uncertainty in determining their risk level and in predicting with high confidence who will progress, or not, to clinical diagnosis. Biomarkers that assess one's risk with high certainty could address this problem and will inform decisions on early intervention, especially in children where the burden of justifying treatment is high. Single omics approaches (e.g., genomics, proteomics, metabolomics, etc.) have been applied to identify T1D biomarkers based on specific disturbances in association with the disease. However, reliable early biomarkers of T1D have remained elusive to date. To overcome this, we previously showed that parallel multi-omics provides a more comprehensive picture of the disease-associated disturbances and facilitates the identification of candidate T1D biomarkers. METHODS: This paper evaluated the use of machine learning (ML) using data augmentation and supervised ML methods for the purpose of improving the identification of salient patterns in the data and the ultimate extraction of novel biomarker candidates in integrated parallel multi-omics datasets from a limited number of samples. We also examined different stages of data integration (early, intermediate, and late) to assess at which stage supervised parametric models can learn under conditions of high dimensionality and variation in feature counts across different omics. In the late integration scheme, we employed a multi-view ensemble comprising individual parametric models trained over single omics to address the computational challenges posed by the high dimensionality and variation in feature counts across the different yet integrated multi-omics datasets. RESULTS: the multi-view ensemble improves the prediction of case vs. control and finds the most success in flagging a larger consistent set of associated features when compared with chance models, which may eventually be used downstream in identifying a novel composite biomarker signature of T1D risk. CONCLUSIONS: the current work demonstrates the utility of supervised ML in exploring integrated parallel multi-omics data in the ongoing quest for early T1D biomarkers, reinforcing the hope for identifying novel composite biomarker signatures of T1D risk via ML and ultimately informing early treatment decisions in the face of the escalating global incidence of this debilitating disease.

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A combination therapy of preproinsulin (PPI) and immunomodulators (TGFß+IL10) orally delivered via genetically modified Salmonella and anti-CD3 promoted glucose balance in in NOD mice with recent onset diabetes. The Salmonella bacteria were modified to express the diabetes-associated antigen PPI controlled by a bacterial promoter in conjunction with over-expressed immunomodulating molecules. The possible mechanisms of action of this vaccine to limit autoimmune diabetes remained undefined. In mice, the vaccine prevented and reversed ongoing diabetes. The vaccine-mediated beneficial effects were associated with increased numbers of antigen-specific CD4+CD25+Foxp3+ Tregs, CD4+CD49b+LAG3+ Tr1-cells, and tolerogenic dendritic-cells (tol-DCs) in the spleens and lymphatic organs of treated mice. Despite this, the immune response to Salmonella infection was not altered. Furthermore, the vaccine effects were associated with a reduction in islet-infiltrating lymphocytes and an increase in the islet beta-cell mass. This was associated with increased serum levels of the tolerogenic cytokines (IL10, IL2, and IL13) and chemokine ligand 2 (CCL2) and decreased levels of inflammatory cytokines (IFNγ, GM-CSF, IL6, IL12, and TNFα) and chemokines (CXCL1, CXCL2, and CXCL5). Overall, the data suggest that the Salmonella-based vaccine modulates the immune response, reduces inflammation, and promotes tolerance specifically to an antigen involved in autoimmune diabetes.

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Depression is more common in youth with type 1 diabetes (T1D) compared to youth without diabetes. This study aims to assess the efficacy of Competent Adulthood Transition with Cognitive Humanistic and Interpersonal Teaching (CATCH-IT), an internet-based cognitive behavioral therapy (CBT) intervention, in adolescents with T1D and depressive symptoms. Adolescents (13 to 17 years old) with T1D and mild (score 5-9) or moderate (score 10-14) depressive symptoms on Patient Health Questionnaire-Adolescent (PHQ-A) screening assessment were recruited to participate and received online access to the CATCH-IT modules for 6 months (requested to complete in 12 weeks). Statistical analyses included paired t-test for changes in Center for Epidemiologic Studies Depression Scale (CES-D), PHQ-A, Problem Areas in Diabetes-Teen version (PAID-T), and hemoglobin A1c (HbA1c). Nineteen patients were consented, 15 met inclusion criteria and received the intervention. In the seven participants that completed the modules, there was a trend towards improvements in PHQ-A, CES-D and HbA1c. Participants provided robust qualitative feedback on the modules and areas for improvement in subsequent iterations, such as inclusion of diabetes-related content. Given the prevalence of depression in diabetes, feasible, low resource interventions are needed. Internet programs such as CATCH-IT can serve as an effective first line intervention in this high-risk population. A modified version of CATCH-IT tailored for adolescents with T1D may be beneficial in this patient population.

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BACKGROUND: Advances in treatment could mitigate the expected adverse changes in the body composition of children and adolescents with type 1 diabetes (T1D). OBJECTIVES: To examine the evolution of weight status and body composition and their association with glycaemic control and partial clinical remission in youth with T1D. METHODS: Ninety-nine participants with T1D (median age 9.5 years [interquartile range 7.3, 12.9], 59.6% boys) were longitudinally followed for 3 years since diagnosis. Data at seven pre-determined time points were extracted from medical files. Outcome measures included body mass index (BMI) z-scores, muscle-to-fat ratio (MFR) z-scores, haemoglobin A1c (HbA1c) levels, continuous glucose monitoring metrics, and insulin dose-adjusted HbA1c (IDAA1c) levels. RESULTS: The BMI z-scores increased significantly (p < 0.001) for both sexes, with no significant change in MFR z-scores over time. The girls had higher BMI z-scores (p < 0.001) and lower MFR z-scores than the boys (p = 0.016). The mean HbA1c levels decreased during the first month and at 3 months since diagnosis (p < 0.001), then plateaued and achieved a median overall HbA1c of 7.1% for the entire cohort. At 12 months, 37 participants (37.6%) were in partial clinical remission, as evidenced by IDAA1c ≤ 9. The odds of partial clinical remission at 2 years increased by 2.1-fold for each standard deviation increase in the MFR z-score (p < 0.001). Higher MFR z-scores were associated with better metabolic control. CONCLUSIONS: Integration of body composition assessments could mitigate adverse body changes in paediatric patients with T1D.

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AIM: Much focus of immunotherapy for type 1 diabetes (T1D) has been devoted on selectively boosting regulatory T (Treg) cells using low dose IL-2 due to their constitutive expression of IL-2Rα, CD25. However, several clinical trials using a low dose of IL-2 only showed a limited improvement of metabolic control. It can therefore be hypothesized that further decreasing IL-2 dosage may increase the selective responsiveness of Treg cells. METHODS: We induced experimental T1D using multiple low dose streptozotocin (STZ) injections and treated the mice with an ultra-low dose IL-2 (uIL-2, approximately 7-fold lower than low dose). Immune response was studied using multicolor flow cytometry. RESULTS: We found that uIL-2 did not protect STZ mice from developing hyperglycemia. It did neither increase Treg cell proportions, nor did it correct the phenotypic shift of Treg cells seen in T1D. It only partially decreased the proportion of IFN-γ+ T cells. Likewise, uIL-2 also did not protect the dysfunction of regulatory B (Breg) cells. Strikingly, when administered in combination with an anti-inflammatory cytokine IL-35, uIL-2 abrogated IL-35's protective effect. Low dose IL-2, on the other hand, protected half of the STZ mice from developing hyperglycemia. No difference was found in the Treg and Breg response, and it only tended to decrease CD80 expression in macrophages and dendritic cells. CONCLUSION: In conclusion, further decreasing IL-2 dosage may not be a suitable approach for T1D therapy, and the limited success suggests that an alternative low dose IL-2 therapy strategy or other immunotherapies should be considered.

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The onset of chronic diseases in childhood represents a stressful event for both young patients and their caregivers. In this context, coping strategies play a fundamental role in dealing with illness-related challenges. Although numerous studies have explored coping strategies employed by parents of children with chronic diseases, there remains a gap in the understanding of children's coping strategies and their correlation with their and their parents' anxiety. This study aims to investigate coping strategies and their interaction with anxiety in groups of young patients with cancer, type 1 diabetes (T1D), and their respective caregivers, in comparison to healthy children and caregivers. We recruited a total of 61 control children, 33 with cancer, and 56 with T1D, 7 to 15 years old, along with their mothers. Each participant completed a customized survey and standardized questionnaires. No significant differences emerged in coping strategies used by children among the different groups. However, when examining the association between coping strategy and anxiety, we found specific patterns of interaction between children's use of coping strategies and their and their mothers' anxiety levels. This study underscores the importance of an illness-specific approach to gain deeper insights into this topic and develop targeted interventions aimed at enhancing the psychological well-being of these vulnerable populations.

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BACKGROUND: Accumulating observational studies have identified associations between type 1 diabetes (T1D) and polycystic ovary syndrome (PCOS). Still, the evidence about the causal effect of this association is uncertain. METHODS: We performed a two-sample Mendelian randomization (MR) analysis to test for the causal association between T1D and PCOS using data from a large-scale biopsy-confirmed genome-wide association study (GWAS) in European ancestries. We innovatively divided T1D into nine subgroups to be analyzed separately, including: type1 diabetes wide definition, type1 diabetes early onset, type 1 diabetes with coma, type 1 diabetes with ketoacidosis, type 1 diabetes with neurological complications, type 1 diabetes with ophthalmic complications, type 1 diabetes with peripheral circulatory complications, type 1 diabetes with renal complications, and type 1 diabetes with other specified/multiple/unspecified complications. GWAS data for PCOS were obtained from a large-scale GWAS (10,074 cases and 103,164 controls) for primary analysis and the IEU consortium for replication and meta-analysis. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. RESULTS: Following rigorous instrument selection steps, the number of SNPs finally used for T1D nine subgroups varying from 6 to 36 was retained in MR estimation. However, we did not observe evidence of causal association between type 1 diabetes nine subgroups and PCOS using the IVW analysis, MR-Egger regression, and weighted median approaches, and all P values were > 0.05 with ORs near 1. Subsequent replicates and meta-analyses also yielded consistent results. A number of sensitivity analyses also did not reveal heterogeneity and pleiotropy, including Cochran's Q test, MR-Egger intercept test, MR-PRESSO global test, leave-one-out analysis, and funnel plot analysis. CONCLUSION: This is the first MR study to investigate the causal relationship between type 1 diabetes and PCOS. Our findings failed to find substantial causal effect of type 1 diabetes on risk of PCOS. Further randomized controlled studies and MR studies are necessary.

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BACKGROUND: This study explored the effects of hyperbaric oxygen therapy (HBOT) on the cardiovascular system and oxidative stress in streptozotocin-induced diabetic rats. Wistar albino rats were divided into four groups: DM group (diabetic rats), DM+HBOT group (diabetic rats exposed to HBOT for 1 h daily, five days a week, at 2.8 atmosphere absolute (ATA) with 100% oxygen for two weeks), DM+INS group (diabetic rats treated with neutral protamine hagedorn (NPH) insulin at a dosage of 3-5 U/day), and DM+HBOT+INS group (diabetic rats treated with both NPH insulin and HBOT for two weeks). METHODS: Evaluations included glycemic control, oxidative stress parameters, and cardiac function measurements. RESULTS: NPH insulin treatment reduced blood glucose levels, although normoglycemia was not achieved. The DM+HBOT+INS group demonstrated the lowest pro-oxidative marker levels. NPH insulin treatment improved cardiac function, and combination therapy effectively restored cardiac function in diabetic animals. CONCLUSIONS: NPH insulin treatment reduced hyperglycemia and improved cardiac function in diabetic rats. The combined approach of NPH insulin and HBOT resulted in decreased pro-oxidative markers. These findings provide valuable insights for managing cardiovascular complications and oxidative stress in diabetes.

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Background: Technology use, including continuous glucose monitoring (CGM) and insulin pump therapy, is associated with improved outcomes in youth with type 1 diabetes (T1D). In 2017 CGM was universally funded for youth with T1D in Australia. In contrast, pump access is primarily accessed through private health insurance, self-funding or philanthropy. The study aim was to investigate the use of diabetes technology across different socioeconomic groups in Australian youth with T1D, in the setting of two contrasting funding models. Methods: A cross-sectional evaluation of 4957 youth with T1D aged <18 years in the national registry was performed to determine technology use. The Index of Relative Socio-Economic Disadvantage (IRSD) derived from Australian census data is an area-based measure of socioeconomic status (SES). Lower quintiles represent greater disadvantage. IRSD based on most recent postcode of residence was used as a marker of SES. A multivariable generalised linear model adjusting for age, diabetes duration, sex, remoteness classification, and location within Australia was used to determine the association between SES and device use. Results: CGM use was lower in IRSD quintile 1 in comparison to quintiles 2 to 5 (p<0.001) where uptake across the quintiles was similar. A higher percentage of pump use was observed in the least disadvantaged IRSD quintiles. Compared to the most disadvantaged quintile 1, pump use progressively increased by 16% (95% CI: 4% to 31%) in quintile 2, 19% (6% to 33%) in quintile 3, 35% (21% to 50%) in quintile 4 and 51% (36% to 67%) in the least disadvantaged quintile 5. Conclusion: In this large national dataset, use of diabetes technologies was found to differ across socioeconomic groups. For nationally subsidised CGM, use was similar across socioeconomic groups with the exception of the most disadvantaged quintile, an important finding requiring further investigation into barriers to CGM use within a nationally subsidised model. User pays funding models for pump therapy result in lower use with socioeconomic disadvantage, highlighting inequities in this funding approach. For the full benefits of diabetes technology to be realised, equitable access to pump therapy needs to be a health policy priority.

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Type 1 diabetes (T1D) is a chronic, progressive autoinflammatory disorder resulting from the breakdown of self-tolerance and unrestrained ß cell-reactive immune response. Activation of immune cells is initiated in islet and amplified in lymphoid tissues, especially those pancreatic draining lymph nodes (PLNs). The knowledge of PLNs as the hub of aberrant immune response is continuously being replenished and renewed. Here we provide a PLN-centered view of T1D pathogenesis and emphasize that PLNs integrate signal inputs from the pancreas, gut, viral infection or peripheral circulation, undergo immune remodeling within the local microenvironment and export effector cell components into pancreas to affect T1D progression. In accordance, we suggest that T1D intervention can be implemented by three major ways: cutting off the signal inputs into PLNs (reduce inflammatory ß cell damage, enhance gut integrity and control pathogenic viral infections), modulating the immune activation status of PLNs and blocking the outputs of PLNs towards pancreatic islets. Given the dynamic and complex nature of T1D etiology, the corresponding intervention strategy is thus required to be comprehensive to ensure optimal therapeutic efficacy.

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CONTEXT: Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations. OBJECTIVE: This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D. METHODS: Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients. RESULTS: The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D. CONCLUSION: PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.

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