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Physiological glucose or ryanodine stimulation of β cell collectives generates a large number of [Ca2+]c events, which can be rapidly assessed with our newly developed automatic image segmentation and [Ca2+]c event identification pipeline. The event durations segregate into three reproducible modes produced by a progressive temporal summation. Using pharmacological tools, we show that activation of ryanodine intracellular Ca2+ receptors is both sufficient and necessary for glucose-dependent [Ca2+]c oscillations in β cell collectives.

Context The oral minimal model is a widely accepted noninvasive tool to quantify both β-cell responsiveness and insulin sensitivity (SI) from glucose, C-peptide, and insulin concentrations during a 3-hour 9-point oral glucose tolerance test (OGTT). Objective Here, we aimed to validate a 2-hour 7-point protocol against the 3-hour OGTT and to test how variation in early sampling frequency impacts estimates of β-cell responsiveness and SI. Methods We conducted a secondary analysis on 15 lean youth with stage 1 type 1 diabetes (T1D; ≥ 2 islet autoantibodies with no dysglycemia) who underwent a 3-hour 9-point OGTT. The oral minimal model was used to quantitate β-cell responsiveness (φtotal) and insulin sensitivity (SI), allowing assessment of β-cell function by the disposition index (DI = φtotal × SI). Seven- and 5-point 2-hour OGTT protocols were tested against the 3-hour 9-point gold standard to determine agreement between estimates of φtotal and its dynamic and static components, SI, and DI across different sampling strategies. Results The 2-hour estimates for the disposition index exhibited a strong correlation with 3-hour measures (r = 0.975; P < .001) with similar results for β-cell responsiveness and SI (r = 0.997 and r = 0.982; P < .001, respectively). The agreement of the 3 estimates between the 7-point 2-hour and 9-point 3-hour protocols fell within the 95% CI on the Bland-Altman grid with a median difference of 16.9% (−35.3 to 32.5), 0.2% (−0.6 to 1.3), and 14.9% (−1.4 to 28.3) for DI, φtotal, and SI. Conversely, the 5-point protocol did not provide reliable estimates of φ dynamic and static components. Conclusion The 2-hour 7-point OGTT is reliable in individuals with stage 1 T1D for assessment of β-cell responsiveness, SI, and DI. Incorporation of these analyses into current 2-hour diabetes staging and monitoring OGTTs offers the potential to more accurately quantify risk of progression in the early stages of T1D.

OBJECTIVE To define the relationship between glucose and C-peptide during the progression to type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS We longitudinally studied glucose and C-peptide response curves (GCRCs), area under curve (AUC) for glucose, and AUC C-peptide from oral glucose tolerance tests (OGTTs), and Index60 (which integrates OGTT glucose and C-peptide values) in Diabetes Prevention Trial–Type 1 (DPT-1) (n = 72) and TrialNet Pathway to Prevention Study (TNPTP) (n = 82) participants who had OGTTs at baseline and follow-up time points before diagnosis. RESULTS Similar evolutions of GCRC configurations were evident between DPT-1 and TNPTP from baseline to 0.5 years prediagnosis. Whereas AUC glucose increased throughout from baseline to 0.5 years prediagnosis, AUC C-peptide increased from baseline until 1.5 years prediagnosis (DPT-1, P = 0.004; TNPTP, P = 0.012) and then decreased from 1.5 to 0.5 years prediagnosis (DPT-1, P = 0.017; TNPTP, P = 0.093). This change was mostly attributable to change in the late AUC C-peptide response (i.e., 60- to 120-min AUC C-peptide). Median Index60 values of DPT-1 (1.44) and TNPTP (1.05) progressors to T1D 1.5 years prediagnosis (time of transition from increasing to decreasing AUC C-peptide) were used as thresholds to identify individuals at high risk for T1D in the full cohort at baseline (5-year risk of 0.75–0.88 for those above thresholds). CONCLUSIONS A transition from an increase to a decrease in AUC C-peptide ∼1.5 years prediagnosis was validated in two independent cohorts. The median Index60 value at that time point can be used as a pathophysiologic-based threshold for identifying individuals at high risk for T1D.

First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual’s unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future. Graphical abstract

Type 2 diabetes (T2D) is associated with loss of transcription factors (TFs) from a subset of failing β-cells. Among these TFs is Pdx1, which controls the expression of numerous genes involved in maintaining β-cell function and identity. Pdx1 activity is modulated by transcriptional coregulators and has recently been shown, through an unbiased screen, to interact with the Chd4 ATPase subunit of the nucleosome remodeling and deacetylase complex. Chd4 contributes to the maintenance of cellular identity and functional status of numerous different cell types. Here, we demonstrated that Pdx1 dynamically interacts with Chd4 under physiological and stimulatory conditions within islet β-cells and established a fundamental role for Chd4 in regulating insulin secretion and modulating numerous Pdx1-bound genes in vitro, including the MafA TF, where we discovered Chd4 is bound to the MafA region 3 enhancer. Furthermore, we found that Pdx1:Chd4 interactions are significantly compromised in islet β-cells under metabolically induced stress in vivo and in human donor tissues with T2D. Our findings establish a fundamental role for Chd4 in regulating insulin secretion and modulating Pdx1-bound genes in vitro, and disruption of Pdx1:Chd4 interactions coincides with β-cell dysfunction associated with T2D.

OBJECTIVES: The metabolic abnormalities that lead to diabetes mellitus (DM) following an episode of acute pancreatitis (AP) have not been extensively studied. This manuscript describes the objectives, hypotheses, and methods of mechanistic studies of glucose metabolism that comprise secondary outcomes of the Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) Study. METHODS: Three months after an index episode of AP, participants without pre-existing DM will undergo baseline testing with an oral glucose tolerance test. Participants will be followed longitudinally in three sub-cohorts with distinct metabolic tests. In the first and largest subcohort, oral glucose tolerance tests will be repeated 12 months after AP and annually to assess changes in β-cell function, insulin secretion, and insulin sensitivity. In the second, mixed meal tolerance tests will be performed at 3 and 12 months, then annually, and following incident DM to assess incretin and pancreatic polypeptide responses. In the third, frequently-sampled intravenous glucose tolerance tests will be performed at 3 months and 12 months to assess the first-phase insulin response and more precisely measure β-cell function and insulin sensitivity. CONCLUSIONS: The DREAM study will comprehensively assess the metabolic and endocrine changes that precede and lead to the development of DM after AP.

BackgroundHeterogeneity exists in type 1 diabetes (T1D) development and presentation. Islet autoantibodies form the foundation for T1D diagnostic and staging efforts. We hypothesized that autoantibodies can be used to identify heterogeneity in T1D before, at, and after diagnosis, and in response to disease modifying therapies. at clinically relevant timepoints throughout T1D progression.MethodsWe performed a systematic review assessing 10 years of original research studies examining relationships between autoantibodies and heterogeneity during disease progression, at the time of diagnosis, after diagnosis, and in response to disease modifying therapies in individuals at risk for T1D or within 1 year of T1D diagnosis.Results10,067 papers were screened. Out of 151 that met data extraction criteria, 90 studies characterized heterogeneity before clinical diagnosis. Autoantibody type/target was most commonly examined, followed by autoantibody number, titer, order of seroconversion, affinity, and novel islet autoantibodies/epitopes. Recurring themes included positive relationships of autoantibody number and specific types and titers with disease progression, differing clinical phenotypes based on the order of autoantibody seroconversion, and interactions with age and genetics. Overall, reporting of autoantibody assay performance was commonly included; however, only 43% (65/151) included information about autoantibody assay standardization efforts. Populations studied were almost exclusively of European ancestry.ConclusionsCurrent evidence most strongly supports the application of autoantibody features to more precisely define T1D before clinical diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly when considered in relation to age and genetic risk, could offer more precise stratification. Increased participation in autoantibody standardization efforts is a critical step to improving future applicability of autoantibody-based precision medicine in T1D.Plain Language SummaryWe performed a systematic review to ascertain whether islet autoantibodies, biomarkers of autoimmunity against insulin-producing cells, could aid in stratifying individuals with different clinical presentations of type 1 diabetes. We found existing evidence most strongly supporting the application of these biomarkers to the period before clinical diagnosis, when certain autoantibody features (number, type) and the age when they develop, can provide important information for patients and care providers on what to expect for future type 1 diabetes progression.

BackgroundType 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Efforts to prevent T1D have focused on modulating immune responses and supporting beta cell health; however, heterogeneity in disease progression and responses to therapies have made these efforts difficult to translate to clinical practice, highlighting the need for precision medicine approaches to T1D prevention.MethodsTo understand the current state of knowledge regarding precision approaches to T1D prevention, we performed a systematic review of randomized-controlled trials from the past 25 years testing disease-modifying therapies in T1D and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument.ResultsWe identified 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss in individuals at disease onset. Seventeen agents tested, mostly immunotherapies, showed benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employed precision analyses to assess features linked to treatment response. Age, measures of beta cell function and immune phenotypes were most frequently tested. However, analyses were typically not prespecified, with inconsistent methods reporting, and tended to report positive findings.ConclusionsWhile the quality of prevention and intervention trials was overall high, low quality of precision analyses made it difficult to draw meaningful conclusions that inform clinical practice. Thus, prespecified precision analyses should be incorporated into the design of future studies and reported in full to facilitate precision medicine approaches to T1D prevention.Plain Language SummaryType 1 diabetes (T1D) results from the destruction of insulin-producing cells in the pancreas, necessitating lifelong insulin dependence. T1D prevention remains an elusive goal, largely due to immense variability in disease progression. Agents tested to date in clinical trials work in a subset of individuals, highlighting the need for precision medicine approaches to prevention. We systematically reviewed clinical trials of disease-modifying therapy in T1D. While age, measures of beta cell function, and immune phenotypes were most commonly identified as factors that influenced treatment response, the overall quality of these studies was low. This review reveals an important need to proactively design clinical trials with well-defined analyses to ensure that results can be interpreted and applied to clinical practice.

Inflammation and oxidative stress in pancreatic islets amplify the appearance of various posttranslational modifications to self-proteins. In this study, we identified a select group of carbonylated islet proteins arising before the onset of hyperglycemia in NOD mice. Of interest, we identified carbonyl modification of the prolyl-4-hydroxylase β subunit (P4Hb) that is responsible for proinsulin folding and trafficking as an autoantigen in both human and murine type 1 diabetes. We found that carbonylated P4Hb is amplified in stressed islets coincident with decreased glucose-stimulated insulin secretion and altered proinsulin-to-insulin ratios. Autoantibodies against P4Hb were detected in prediabetic NOD mice and in early human type 1 diabetes prior to the onset of anti-insulin autoimmunity. Moreover, we identify autoreactive CD4+ T-cell responses toward carbonyl-P4Hb epitopes in the circulation of patients with type 1 diabetes. Our studies provide mechanistic insight into the pathways of proinsulin metabolism and in creating autoantigenic forms of insulin in type 1 diabetes.

Objective Metabolic zones were developed to characterize heterogeneity of individuals with islet autoantibodies. Research Design and Methods Baseline 2-hour oral glucose tolerance test (OGTT) data from 6,620 TrialNet Pathway to Prevention Study (TNPTP) autoantibody-positive participants (relatives of individuals with type 1 diabetes) were used to form 25 zones from 5 AUC glucose (AUCGLU) rows and 5 AUC C-peptide (AUCPEP) columns. Zone phenotypes were developed from demographic, metabolic, autoantibody, HLA, and risk data. Results As AUCGLU increased, changes of glucose and C-peptide response curves (GCRCs; from mean glucose and mean C-peptide values at 30,60,90,120 minutes) were similar within the 5 AUCPEP columns. Among the zones, 5-year risk for type 1 diabetes was highly correlated with IA-2A prevalence (r=0.96, p Conclusions Phenotypes of 25 zones formed from AUCGLU and AUCPEP were used to gain insights into type 1 diabetes heterogeneity. Zones were utilized to examine GCRC changes with increasing AUCGLU, associations between risk and autoantibody prevalence, the dependence of glucose as a predictor of risk according to C-peptide, and glucose heterogeneity from contributions of insulin secretion and insulin resistance.

The transcriptional activity of Pdx1 is modulated by a diverse array of coregulatory factors that govern chromatin accessibility, histone modifications, and nucleosome distribution. We previously identified the Chd4 subunit of the nucleosome remodeling and deacetylase complex as a Pdx1-interacting factor. To identify how loss of Chd4 impacts glucose homeostasis and gene expression programs in β-cells in vivo, we generated an inducible β-cell–specific Chd4 knockout mouse model. Removal of Chd4 from mature islet β-cells rendered mutant animals glucose intolerant, in part due to defects in insulin secretion. We observed an increased ratio of immature-to-mature insulin granules in Chd4-deficient β-cells that correlated with elevated levels of proinsulin both within isolated islets and from plasma following glucose stimulation in vivo. RNA sequencing and assay for transposase-accessible chromatin with sequencing showed that lineage-labeled Chd4-deficient β-cells have alterations in chromatin accessibility and altered expression of genes critical for β-cell function, including MafA, Slc2a2, Chga, and Chgb. Knockdown of CHD4 from a human β-cell line revealed similar defects in insulin secretion and alterations in several β-cell–enriched gene targets. These results illustrate how critical Chd4 activities are in controlling genes essential for maintaining β-cell function. Article Highlights Pdx1–Chd4 interactions were previously shown to be compromised in β-cells from human donors with type 2 diabetes. β-Cell–specific removal of Chd4 impairs insulin secretion and leads to glucose intolerance in mice. Expression of key β-cell functional genes and chromatin accessibility are compromised in Chd4-deficient β-cells. Chromatin remodeling activities enacted by Chd4 are essential for β-cell function under normal physiological conditions.

Alternative splicing (AS) within the β-cell has been proposed as one potential pathway that may exacerbate autoimmunity and unveil novel immunogenic epitopes in type 1 diabetes (T1D). We used a computational strategy to prioritize pathogenic splicing events in human islets treated with interleukin-1β plus interferon-γ as an ex vivo model of T1D and coupled this analysis with a k-mer–based approach to predict RNA-binding proteins involved in AS. In total, 969 AS events were identified in cytokine-treated islets, with a majority (44.8%) involving a skipped exon. ExonImpact identified 129 events predicted to affect protein structure. AS occurred with high frequency in MHC class II–related mRNAs, and targeted quantitative PCR validated reduced inclusion of exon 5 in the MHC class II gene HLA-DMB. Single-molecule RNA fluorescence in situ hybridization confirmed increased HLA-DMB splicing in β-cells from human donors with established T1D and autoantibody positivity. Serine/arginine-rich splicing factor 2 was implicated in 37.2% of potentially pathogenic events, including exon 5 exclusion in HLA-DMB. Together, these data suggest that dynamic control of AS plays a role in the β-cell response to inflammatory signals during T1D evolution.

The release of peptide hormones is predominantly regulated by a transient increase in cytosolic Ca

The oral minimal model is a widely accepted noninvasive tool to quantify both β-cell responsiveness and insulin sensitivity from glucose, C-peptide and insulin concentrations during a 3-h 9-point oral glucose tolerance test (OGTT). Here, we aimed to validate a 2-h 7-point protocol against the 3-h OGTT and to test how variation in early sampling frequency impacts estimates of β-cell responsiveness and insulin sensitivity.We conducted a secondary analysis on 15 lean youth with stage 1 T1D (≥2islet autoantibodies with no dysglycemia) who underwent a 3-h 9-point OGTT. The oral minimal model was used to quantitate β-cell responsiveness (φtotal) and insulin sensitivity (SI), allowing assessment of β-cell function by the disposition index (DI=φtotal × SI). Seven- and 5-point 2-h OGTT protocols were tested against the 3-h 9-point gold standard to determine agreement between estimates of φtotal and its dynamic and static components, SI, and DI across different sampling strategies.The 2-h estimates for the disposition index exhibited a strong correlation with 3-h measures (r = 0.975, p 0.001) with similar results for β-cell responsiveness and insulin sensitivity (r = 0.997 and r = 0.982, p 0.001, respectively). The agreement of the three estimates between the 7-point 2-h and 9-point 3-h protocols fell within the 95% CI on the Bland-Altman grid with a median difference of 16.9%(-35.3, 32.5), 0.2%(-0.6, 1.3) and 14.9%(-1.4, 28.3) for DI, φtotal and SI. Conversely, the 5-point protocol did not provide reliable estimates of φ dynamic and static components.The 2-h 7-point OGTT is reliable in subjects with Stage 1 T1D for assessment of β-cell responsiveness, insulin sensitivity, and disposition index. Incorporation of these analyses into current 2-h diabetes staging and monitoring OGTTs offers the potential to more accurately quantify risk of progression in the early stages of T1D.

SummaryLoss of pancreatic β cell mass, identity, and function contribute to the development of diabetes. Here, we show that the endoplasmic reticulum (ER) calcium sensor, stromal interaction molecule 1 (STIM1), is critical for the maintenance of β cell function in female mice. When mice with β cell-specific deletion of STIM1 (STIM1Δβ) were challenged with high-fat diet, β cell dysfunction was observed in female, but not male, mice. Impaired glucose tolerance was accompanied by reductions in β cell mass, a concomitant increase in α cell mass, and significant reductions in the expression of markers of β cell maturity, including MafA and UCN3. Mechanistic assays demonstrated that the sexually dimorphic phenotype observed in STIM1Δβ mice was due in part to loss of signaling through the noncanonical 17-β estradiol receptor, GPER1. Together, these data suggest that STIM1 orchestrates pancreatic β cell function and identity through GPER1-mediated estradiol signaling.

OBJECTIVE We studied longitudinal differences between progressors and nonprogressors to type 1 diabetes with similar and substantial baseline risk. RESEARCH DESIGN AND METHODS Changes in 2-h oral glucose tolerance test indices were used to examine variability in diabetes progression in the Diabetes Prevention Trial–Type 1 (DPT-1) study (n = 246) and Type 1 Diabetes TrialNet Pathway to Prevention study (TNPTP) (n = 503) among autoantibody (Ab)+ children (aged RESULTS Longitudinal analyses revealed annualized area under the curve (AUC) of C-peptide increases in nonprogressors versus decreases in progressors (P ≤ 0.026 for DPT-1 and TNPTP). Vector indices for AUC glucose and AUC C-peptide changes (on a two-dimensional grid) also differed significantly (P < 0.001). Despite marked baseline metabolic impairment of nonprogressors, changes in AUC C-peptide, AUC glucose, AUC C-peptide–to–AUC glucose ratio (AUC ratio), and Index60 did not differ from Ab− relatives during follow-up. Divergence between nonprogressors and progressors occurred by 6 months from baseline in both cohorts (AUC glucose, P ≤ 0.007; AUC ratio, P ≤ 0.034; Index60, P < 0.001; vector indices of change, P < 0.001). Differences in 6-month change were positively associated with greater diabetes risk (respectively, P < 0.001, P ≤ 0.019, P < 0.001, and P < 0.001) in DPT-1 and TNPTP, except AUC ratio, which was inversely associated with risk (P < 0.001). CONCLUSIONS Novel findings show that even with similarly abnormal baseline risk, progressors had appreciably more metabolic impairment than nonprogressors within 6 months and that the measures showing impairment were predictive of type 1 diabetes. Longitudinal metabolic patterns did not differ between nonprogressors and Ab− relatives, suggesting persistent β-cell responsiveness in nonprogressors.

Aims/hypothesis The circadian clock influences both diabetes and immunity. Our goal in this study was to characterise more thoroughly the circadian patterns of immune cell populations and cytokines that are particularly relevant to the immune pathology of type 1 diabetes and thus fill in a current gap in our understanding of this disease. Methods Ten individuals with established type 1 diabetes (mean disease duration 11 years, age 18–40 years, six female) participated in a circadian sampling protocol, each providing six blood samples over a 24 h period. Results Daily ranges of population frequencies were sometimes large and possibly clinically significant. Several immune populations, such as dendritic cells, CD4 and CD8 T cells and their effector memory subpopulations, CD4 regulatory T cells, B cells and cytokine IL-6, exhibited statistically significant circadian rhythmicity. In a comparison with historical healthy control individuals, but using shipped samples, we observed that participants with type 1 diabetes had statistically significant phase shifts occurring in the time of peak occurrence of B cells (+4.8 h), CD4 and CD8 T cells (~ +5 h) and their naive and effector memory subsets (~ +3.3 to +4.5 h), and regulatory T cells (+4.1 h). An independent streptozotocin murine experiment confirmed the phase shifting of CD8 T cells and suggests that circadian dysrhythmia in type 1 diabetes might be an effect and not a cause of the disease. Conclusions/interpretation Future efforts investigating this newly described aspect of type 1 diabetes in human participants are warranted. Peripheral immune populations should be measured near the same time of day in order to reduce circadian-related variation. Graphical abstract

Inflammation and oxidative stress in pancreatic islets amplify the appearance of various post-translational modifications (PTMs) to self-proteins. Herein, we identified a select group of carbonylated islet proteins arising before the onset of hyperglycemia in non-obese diabetic mice. Of interest, we identified carbonyl modification of the prolyl-4-hydroxylase beta subunit (P4Hb) that is responsible for proinsulin folding and trafficking as an autoantigen in both human and murine type 1 diabetes. We found the carbonylated P4Hb is amplified in stressed islets coincident with decreased glucose-stimulated insulin secretion and altered proinsulin to insulin ratios. Autoantibodies against P4Hb were detected in prediabetic NOD mice and in early human type 1 diabetes prior to the onset of anti-insulin autoimmunity. Moreover, we identify autoreactive CD4+ T cell responses toward carbonyl-P4Hb epitopes in the circulation of patients with type 1 diabetes. Our studies provide mechanistic insight into the pathways of proinsulin metabolism and in creating autoantigenic forms of insulin in type 1 diabetes.

Increased circulating levels of incompletely processed insulin (i.e. proinsulin) are observed clinically in both type 1 and type 2 diabetes; however, the mechanisms underlying impaired proinsulin processing remain incompletely understood. Here, we identify the sarcoendoplasmic reticulum Ca2+ ATPase-2 (SERCA2) pump and β cell ER Ca2+ as key regulators of systemic glucose tolerance and proinsulin processing. We generated mice with a β cell-specific SERCA2 deletion (βS2KO) and SERCA2 deficient INS-1 cells to show that SERCA2 loss increases systemic and pancreatic levels of proinsulin protein and leads to aberrant localization of proinsulin within the proximal β cell secretory pathway. These defects in proinsulin processing were linked to reduced maturation of the proinsulin processing enzymes PC1/3 and PC2, suggesting a model whereby chronic ER Ca2+ depletion in the β cell, which is observed in many pathological conditions, impairs the spatial regulation of prohormone trafficking, processing, and maturation within the β cell secretory pathway.

The Integrated Islet Distribution Program (IIDP) is currently the largest source of human islets for research in the U.S. The IIDP Human Islet Phenotyping Program (HIPP) provides standardized islet assessment and makes these data available to researchers. Since 2016, HIPP has phenotyped 337 islet samples from nondiabetic organ donors (40%F / 60%M, 45±12 years, BMI 29.0±5.4; 52% European, 26% Latino, 8% African, 3% Asian, and 11% other ancestry) . To define the islet structure-function relationships, we integrated physiological profiling of hormone secretion with islet cell composition measured by immunohistochemistry­. We found that % β cells varied significantly (range 34 - 92%; CV 21%) and had a strong negative correlation with % α (range 3 - 59%; CV 33%; r -0.95; p Disclosure M. Brissova: None. G. Poffenberger: None. D. C. Saunders: None. A. C. Powers: None. J. C. Niland: None. C. Evans-molina: Advisory Panel; Avotres Inc., DiogenX, Isla Technologies, MaiCell Therapeutics, Provention Bio, Inc., Other Relationship; Astellas Pharma Inc., Dompé, Lilly, Research Support; BMS, Nimbus Therapeutics. H. H. Durai: None. S. Mei: None. A. Coldren: None. C. Davis: None. C. Reihsmann: None. A. L. Hopkirk: None. D. Gibson: None. R. Aramandla: None.

It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes.In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history.The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR.This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.

The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We used an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray data sets generated using human islets from donors with diabetes and islets where type 1 (T1D) and type 2 (T2D) diabetes had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. In parallel, we generated an RNA-sequencing data set from human islets treated with brefeldin A (BFA), a known GA stress inducer. Overlapping the T1D and T2D groups with the BFA data set, we identified 120 and 204 differentially expressed genes, respectively. In both the T1D and T2D models, pathway analyses revealed that the top pathways were associated with GA integrity, organization, and trafficking. Quantitative RT-PCR was used to validate a common signature of GA stress that included ATF3, ARF4, CREB3, and COG6. Taken together, these data indicate that GA-associated genes are dysregulated in diabetes and identify putative markers of β-cell GA stress.

Purpose of review Emerging data have suggested that β-cell dysfunction may exacerbate the development and progression of type 1 diabetes (T1D). In this review, we highlight clinical and preclinical studies suggesting a role for β-cell dysfunction during the evolution of T1D and suggest agents that may promote β-cell health in T1D. Recent findings Metabolic abnormalities exist years before development of hyperglycemia and exhibit a reproducible pattern reflecting progressive deterioration of β-cell function and increases in β-cell stress and death. Preclinical studies indicate that T1D may be prevented by modification of pathways impacting intrinsic β-cell stress and antigen presentation. Recent findings suggest that differences in metabolic phenotypes and β-cell stress may reflect differing endotypes of T1D. Multiple pathways representing potential drug targets have been identified, but most remain to be tested in human populations with preclinical disease. Summary This cumulative body of work shows clear evidence that β-cell stress, dysfunction, and death are harbingers of impending T1D and likely contribute to progression of disease and insulin deficiency. Treatment with agents targeting β-cell health could augment interventions with immunomodulatory therapies but will need to be tested in intervention studies with endpoints carefully designed to capture changes in β-cell function and health.

Context There is an unmet need for biomarkers of pancreatic beta-cell death to improve early diagnosis of type 1 diabetes, enroll subjects into clinical trials, and assess treatment response. To address this need, several groups developed assays measuring insulin deoxyribonucleic acid (DNA) with unmethylated CpG sites in cell-free DNA. Unmethylated insulin DNA should be derived predominantly from beta-cells and indicate ongoing beta-cell death. Objective To assess the performance of three unmethylated insulin DNA assays. Design and Participants Plasma or serum samples from 13 subjects undergoing total pancreatectomy and islet autotransplantation were coded and provided to investigators to measure unmethylated insulin DNA. Samples included a negative control taken post-pancreatectomy but pretransplant, and a positive control taken immediately following islet infusion. We assessed technical reproducibility, linearity, and persistence of detection of unmethylated insulin DNA for each assay. Results All assays discriminated between the negative sample and samples taken directly from the islet transplant bag; 2 of 3 discriminated negative samples from those taken immediately after islet infusion. When high levels of unmethylated insulin DNA were present, technical reproducibility was generally good for all assays. Conclusions The measurement of beta cell cell-free DNA, including insulin, is a promising approach, warranting further testing and development in those with or at-risk for type 1 diabetes, as well as in other settings where understanding the frequency or kinetics of beta cell death could be useful.

Several unrelated findings led us to hypothesize that induction of autoimmunity is a consequence of a prior major inflammatory event in individuals with susceptible HLA phenotypes and elevated sensitivity to cytokines and free fatty acids (FFA). We observed provocative enhanced responsiveness of cultured human fibroblasts from individuals with type 1 diabetes (T1D), but not control subjects, to FFA and the inflammatory cytokines TNFα and IL1-β. Major infections increase inflammatory cytokines as well as circulating FFA. Endotoxin-treated animal models of sepsis also exhibit elevated inflammatory cytokines that inhibit FFA oxidation and elevate FFA. The pancreatic β-cell possesses low reactive oxygen species (ROS) scavenging capacity and responds to both elevated FFA and cytokines with increased ROS production, a combination that increases exocytosis and trafficking of secretory vesicles to the plasma membrane. Increased trafficking is accompanied by increased cycling of secretory granule proteins and may be linked with increased surface presentation of granule proteins to the immune system. We propose that this ultimately targets β-cell granular proteins at the cell surface and is consistent with the preponderance of autoantibodies to granule proteins. Our hypothesis encourages testing of potential early therapeutic interventions to prevent progression of β-cell destruction.

The risk of progressing from autoantibody positivity to type 1 diabetes is inversely related to age. Separately, whether age influences patterns of C-peptide loss or changes in insulin sensitivity in autoantibody-positive individuals who progress to stage 3 type 1 diabetes is unclear.Beta cell function and insulin sensitivity were determined by modelling of OGTTs performed in 658 autoantibody-positive participants followed longitudinally in the Diabetes Prevention Trial-Type 1 (DPT-1). In this secondary analysis of DPT-1 data, time trajectories of beta cell function and insulin sensitivity were analysed in participants who progressed to type 1 diabetes (progressors) to address the impact of age on patterns of metabolic progression to diabetes.Among the entire DPT-1 cohort, the highest discriminant age for type 1 diabetes risk was 14 years, with participants aged14 years being twice as likely to progress to type 1 diabetes as those aged ≥14 years. At study entry, beta cell glucose sensitivity was impaired to a similar extent in progressors aged14 years and progressors aged ≥14 years. From study entry to stage 3 type 1 diabetes onset, beta cell glucose sensitivity and insulin sensitivity declined in both progressor groups. However, there were no significant differences in the yearly rate of decline in either glucose sensitivity (-13.7 [21.2] vs -11.9 [21.5] pmol minOur data indicate that during progression to stage 3 type 1 diabetes, rates of change in declining glucose and insulin sensitivity are not significantly different between progressors aged14 years and progressors aged ≥14 years. These data suggest there is a predictable course of declining metabolic function during the progression to type 1 diabetes that is not influenced by age.

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the “single disease” approach appears untenable, as does the notion of individualizing each single patient’s care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.