Beyond Stages: Predicting Individual Time Dependent Risk for Type 1 Diabetes.

BACKGROUND: Essentially all individuals with multiple autoantibodies will develop clinical type 1 diabetes. Multiple AABs and normal glucose tolerance define Stage 1 diabetes; abnormal glucose tolerance defines Stage 2. However, the rate of progression within these stages is heterogeneous, necessitating personalized risk calculators to improve clinical implementation. METHODS: We developed 3 models using TrialNet's Pathway to Prevention data to accommodate the reality that not all risk variables are clinically available. The Small model included AAB status, fasting glucose, HbA1c and age, while the Medium and Large models added predictors of disease progression measured via oral glucose tolerance testing. FINDINGS: All models markedly improved granularity regarding personalized risk missing from current categories of stages of T1D. Model derived risk calculations are consistent with the expected reduction of risk with increasing age and increase in risk with higher glucose and lower insulin secretion, illustrating the suitability of the models. Adding glucose and insulin secretion data altered model predicted probabilities within Stages. In those with high 2-hour glucose, a high C-peptide markedly decreased predicted risk; lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of rate of disease progression within Stage 2. CONCLUSIONS: While essentially all those with multiple AABs will develop type 1 diabetes, the rate of progression is heterogeneous and not explained by any individual single risk variable. The model-based probabilities developed here provide an adaptable personalized risk calculator to better inform decisions about how and when to monitor disease progression in clinical practice.

Testing a new platform to screen disease-modifying therapy in type 1 diabetes.

Studies of new therapies to preserve insulin secretion in early type 1 diabetes require several years to recruit eligible subjects and to see a treatment effect; thus, there is interest in alternative study designs to speed this process. Most people with longstanding type 1 diabetes no longer secrete insulin. However, studies from pancreata of those with longstanding T1D show that beta cells staining for insulin can persist for decades after diagnosis, and this is paralleled in work showing proinsulin secretion in individuals with longstanding disease; collectively this suggests that there is a reserve of alive but "sleeping" beta cells. Here, we designed a novel clinical trial platform to test whether a short course of therapy with an agent known to have effects in type 1 diabetes with residual endogenous insulin could transiently induce insulin secretion in those who no longer produce insulin. A therapy that transiently "wakes up" sleeping beta cells might be tested next in a fully powered trial in those with endogenous insulin secretion. In this three-arm non-randomized pilot study, we tested three therapies known to impact disease: two beta-cell supportive agents, liraglutide and verapamil, and an immunomodulatory agent, golimumab. The golimumab treated arm was not fully enrolled due to uncertainties about immunotherapy during the COVID-19 pandemic. Participants had mixed-meal tolerance test (MMTT)-stimulated C-peptide below the quantitation limit (<0.02 ng/mL) at enrollment and received 8 to 12 weeks of therapy. At the completion of therapy, none of the individuals achieved the primary outcome of MMTT-stimulated C-peptide ≥ 0.02 ng/mL. An exploratory outcome of the verapamil arm was MRI-assessed pancreas size, diffusion, and longitudinal relaxation time, which showed repeatability of these measures but no treatment effect. The liraglutide and golimumab arms were registered on clinicaltrials.gov under accession number NCT03632759 and the verapamil arm under accession number NCT05847413. Trail registration: Protocols are registered in ClinicalTrials.gov under accession numbers NCT03632759 and NCT05847413.

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.

OGTT Metrics Surpass Continuous Glucose Monitoring Data for T1D Prediction in Multiple-Autoantibody-Positive Individuals.

CONTEXT: The value of continuous glucose monitoring (CGM) for monitoring autoantibody (AAB)-positive individuals in clinical trials for progression of type 1 diabetes (T1D) is unknown. OBJECTIVE: Compare CGM with oral glucose tolerance test (OGTT)-based metrics in prediction of T1D. METHODS: At academic centers, OGTT and CGM data from multiple-AAB relatives were evaluated for associations with T1D diagnosis. Participants were multiple-AAB-positive individuals in a TrialNet Pathway to Prevention (TN01) CGM ancillary study (n = 93). The intervention was CGM for 1 week at baseline, 6 months, and 12 months. Receiver operating characteristic (ROC) curves of CGM and OGTT metrics for prediction of T1D were analyzed. RESULTS: Five of 7 OGTT metrics and 29/48 CGM metrics but not HbA1c differed between those who subsequently did or did not develop T1D. ROC area under the curve (AUC) of individual CGM values ranged from 50% to 69% and increased when adjusted for age and AABs. However, the highest-ranking metrics were derived from OGTT: 4/7 with AUC ∼80%. Compared with adjusted multivariable models using CGM data, OGTT-derived variables, Index60 and DPTRS (Diabetes Prevention Trial-Type 1 Risk Score), had higher discriminative ability (higher ROC AUC and positive predictive value with similar negative predictive value). CONCLUSION: Every 6-month CGM measures in multiple-AAB-positive individuals are predictive of subsequent T1D, but less so than OGTT-derived variables. CGM may have feasibility advantages and be useful in some settings. However, our data suggest there is insufficient evidence to replace OGTT measures with CGM in the context of clinical trials.

ABCC8-Related Monogenic Diabetes Presenting Like Type 1 Diabetes in an Adolescent.

Background: Identifying cases of diabetes caused by single gene mutations between the more common type 1 diabetes (T1D) and type 2 diabetes (T2D) is a difficult but important task. We report the diagnosis of ATP-binding cassette transporter sub-family C member 8 (ABCC8)-related monogenic diabetes in a 35-year-old woman with a protective human leukocyte antigen (HLA) allele who was originally diagnosed with T1D at 18 years of age. Case Report: Patient A presented with polyuria, polydipsia, and hypertension at the age of 18 years and was found to have a blood glucose > 500 mg/dL (70-199 mg/dL) and an HbA1C (hemoglobin A1C) >14% (4%-5.6%). She had an unmeasurable C-peptide but no urine ketones. She was diagnosed with T1D and started on insulin therapy. Antibody testing was negative. She required low doses of insulin and later had persistence of low but detectable C-peptide. At the age of 35 years, she was found to have a protective HLA allele, and genetic testing revealed a pathogenic mutation in the ABCC8 gene. The patient was then successfully transitioned to sulfonylurea therapy. Discussion: Monogenic diabetes diagnosed in adolescence typically presents with mild to moderate hyperglycemia, positive family history and, in some cases, other organ findings or dysfunction. The patient in this report presented with very high blood glucose, prompting the diagnosis of T1D. When she was found to have a protective HLA allele, further investigation revealed the mutation in the sulfonylurea receptor gene, ABCC8. Conclusion: Patients suspected of having T1D but with atypical clinical characteristics such as negative autoantibodies, low insulin requirements, and persistence of C-peptide should undergo genetic testing for monogenic diabetes.

SARS-CoV-2 mRNA vaccines decouple anti-viral immunity from humoral autoimmunity.

mRNA-based vaccines dramatically reduce the occurrence and severity of COVID-19, but are associated with rare vaccine-related adverse effects. These toxicities, coupled with observations that SARS-CoV-2 infection is associated with autoantibody development, raise questions whether COVID-19 vaccines may also promote the development of autoantibodies, particularly in autoimmune patients. Here we used Rapid Extracellular Antigen Profiling to characterize self- and viral-directed humoral responses after SARS-CoV-2 mRNA vaccination in 145 healthy individuals, 38 patients with autoimmune diseases, and 8 patients with mRNA vaccine-associated myocarditis. We confirm that most individuals generated robust virus-specific antibody responses post vaccination, but that the quality of this response is impaired in autoimmune patients on certain modes of immunosuppression. Autoantibody dynamics are remarkably stable in all vaccinated patients compared to COVID-19 patients that exhibit an increased prevalence of new autoantibody reactivities. Patients with vaccine-associated myocarditis do not have increased autoantibody reactivities relative to controls. In summary, our findings indicate that mRNA vaccines decouple SARS-CoV-2 immunity from autoantibody responses observed during acute COVID-19.

Abatacept for Delay of Type 1 Diabetes Progression in Stage 1 Relatives at Risk: A Randomized, Double-Masked, Controlled Trial.

OBJECTIVE: Previous studies showed that inhibiting lymphocyte costimulation reduces declining ß-cell function in individuals newly diagnosed with type 1 diabetes. We tested whether abatacept would delay or prevent progression of type 1 diabetes from normal glucose tolerance (NGT) to abnormal glucose tolerance (AGT) or to diabetes and the effects of treatment on immune and metabolic responses. RESEARCH DESIGN AND METHODS: We conducted a phase 2, randomized, placebo-controlled, double-masked trial of abatacept in antibody-positive participants with NGT who received monthly abatacept/placebo infusions for 12 months. The end point was AGT or diabetes, assessed by oral glucose tolerance tests. RESULTS: A total of 101 participants received abatacept and 111 placebo. Of these, 81 (35 abatacept and 46 placebo) met the end point of AGT or type 1 diabetes diagnosis (hazard ratio 0.702; 95% CI 0.452, 1.09; P = 0.11) The C-peptide responses to oral glucose tolerance tests were higher in the abatacept arm (P < 0.03). Abatacept reduced the frequency of inducible T-cell costimulatory (ICOS)+ PD1+ T-follicular helper (Tfh) cells during treatment (P < 0.0001), increased naive CD4+ T cells, and also reduced the frequency of CD4+ regulatory T cells (Tregs) from the baseline (P = 0.0067). Twelve months after treatment, the frequency of ICOS+ Tfh, naive CD4+ T cells, and Tregs returned to baseline. CONCLUSIONS: Although abatacept treatment for 1 year did not significantly delay progression to glucose intolerance in at-risk individuals, it impacted immune cell subsets and preserved insulin secretion, suggesting that costimulation blockade may modify progression of type 1 diabetes.

Barriers to Screening: An Analysis of Factors Impacting Screening for Type 1 Diabetes Prevention Trials.

Context: Participants with stage 1 or 2 type 1 diabetes (T1D) qualify for prevention trials, but factors involved in screening for such trials are largely unknown. Objective: To identify factors associated with screening for T1D prevention trials. Methods: This study included TrialNet Pathway to Prevention participants who were eligible for a prevention trial: oral insulin (TN-07, TN-20), teplizumab (TN-10), abatacept (TN-18), and oral hydroxychloroquine (TN-22). Univariate and multivariate logistic regression models were used to examine participant, site, and study factors at the time of prevention trial accrual. Results: Screening rates for trials were: 50% for TN-07 (584 screened/1172 eligible), 9% for TN-10 (106/1249), 24% for TN-18 (313/1285), 17% for TN-20 (113/667), and 28% for TN-22 (371/1336). Younger age and male sex were associated with higher screening rates for prevention trials overall and for oral therapies. Participants with an offspring with T1D showed lower rates of screening for all trials and oral drug trials compared with participants with other first-degree relatives as probands. Site factors, including larger monitoring volume and US site vs international site, were associated with higher prevention trial screening rates. Conclusions: Clear differences exist between participants who screen for prevention trials and those who do not screen and between the research sites involved in prevention trial screening. Participant age, sex, and relationship to proband are significantly associated with prevention trial screening in addition to key site factors. Identifying these factors can facilitate strategic recruitment planning to support rapid and successful enrollment into prevention trials.

CGM Metrics Identify Dysglycemic States in Participants From the TrialNet Pathway to Prevention Study.

OBJECTIVE: Continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes. We aimed to determine whether CGM metrics provide additional insights into progression to clinical stage 3 type 1 diabetes. RESEARCH DESIGN AND METHODS: One hundred five relatives of individuals in type 1 diabetes probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) from the TrialNet Pathway to Prevention study underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The baseline data are reported here. Three groups were evaluated: individuals with 1) stage 2 type 1 diabetes (n = 42) with two or more diabetes-related autoantibodies and abnormal OGTT; 2) stage 1 type 1 diabetes (n = 53) with two or more diabetes-related autoantibodies and normal OGTT; and 3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10). RESULTS: Multiple CGM metrics were associated with progression to stage 3 type 1 diabetes. Specifically, spending ≥5% time with glucose levels ≥140 mg/dL (P = 0.01), ≥8% time with glucose levels ≥140 mg/dL (P = 0.02), ≥5% time with glucose levels ≥160 mg/dL (P = 0.0001), and ≥8% time with glucose levels ≥160 mg/dL (P = 0.02) were all associated with progression to stage 3 disease. Stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; spent more time with glucose values over 120, 140, and 160 mg/dL; and had greater variability. CONCLUSIONS: CGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 type 1 diabetes.

Risk Modeling to Reduce Monitoring of an Autoantibody-Positive Population to Prevent DKA at Type 1 Diabetes Diagnosis.

CONTEXT: The presence of islet autoimmunity identifies individuals likely to progress to clinical type 1 diabetes (T1D). In clinical research studies, autoantibody screening followed by regular metabolic monitoring every 6 months reduces incidence of diabetic ketoacidosis (DKA) at diagnosis. OBJECTIVE: We hypothesized that DKA reduction can be achieved on a population basis with a reduced frequency of metabolic monitoring visits. We reasoned that prolonged time between the development of T1D and the time of clinical diagnosis ("undiagnosed time") would more commonly result in DKA and thus that limiting undiagnosed time would decrease DKA. METHODS: An analysis was conducted of data from TrialNet's Pathway to Prevention (PTP), a cross-sectional longitudinal study that identifies and follows at-risk relatives of people with T1D. PTP is a population-based study enrolling across multiple countries. A total of 6193 autoantibody (AAB)-positive individuals participated in PTP from March 2004 to April 2019. We developed models of progression to clinical diagnosis for pediatric and adult populations with single or multiple AAB, and summarized results using estimated hazard rate. An optimal monitoring visit schedule was determined for each model to achieve a minimum average level of undiagnosed time for each population. RESULTS: Halving the number of monitoring visits usually conducted in research studies is likely to substantially lower the population incidence of DKA at diagnosis of T1D. CONCLUSION: Our study has clinical implications for the metabolic monitoring of at-risk individuals. Fewer monitoring visits would reduce the clinical burden, suggesting a path toward transitioning monitoring beyond the research setting.

Recognition of mRNA Splice Variant and Secretory Granule Epitopes by CD4+ T Cells in Type 1 Diabetes.

A recent discovery effort resulted in identification of novel splice variant and secretory granule antigens within the HLA class I peptidome of human islets and documentation of their recognition by CD8+ T cells from peripheral blood and human islets. In the current study, we applied a systematic discovery process to identify novel CD4+ T cell epitopes derived from these candidate antigens. We predicted 145 potential epitopes spanning unique splice junctions and within conventional secretory granule antigens and measured their in vitro binding to DRB1*04:01. We generated HLA class II tetramers for the 35 peptides with detectable binding and used these to assess immunogenicity and isolate T cell clones. Tetramers corresponding to peptides with verified immunogenicity were then used to label T cells specific for these putative epitopes in peripheral blood. T cells that recognize distinct epitopes derived from a cyclin I splice variant, neuroendocrine convertase 2, and urocortin-3 were detected at frequencies that were similar to those of an immunodominant proinsulin epitope. Cells specific for these novel epitopes predominantly exhibited a Th1-like surface phenotype. Among the three epitopes, responses to the cyclin I peptide exhibited a distinct memory profile. Responses to neuroendocrine convertase 2 were detected among pancreatic infiltrating T cells. These results further establish the contribution of unconventional antigens to the loss of tolerance in autoimmune diabetes.

IL-6-targeted therapies to block the cytokine or its receptor drive distinct alterations in T cell function.

Therapeutics that inhibit IL-6 at different points in its signaling pathway are in clinical use, yet whether the immunological effects of these interventions differ based on their molecular target is unknown. We performed short-term interventions in individuals with type 1 diabetes using anti-IL-6 (siltuximab) or anti-IL-6 receptor (IL-6R; tocilizumab) therapies and investigated the impact of this in vivo blockade on T cell fate and function. Immune outcomes were influenced by the target of the therapeutic intervention (IL-6 versus IL-6R) and by peak drug concentration. Tocilizumab reduced ICOS expression on T follicular helper cell populations and T cell receptor-driven (TCR-driven) STAT3 phosphorylation. Siltuximab reversed resistance to Treg-mediated suppression and increased TCR-driven phosphorylated STAT3 and production of IL-10, IL-21, and IL-27 by T effectors. Together, these findings indicate that the context of IL-6 blockade in vivo drives distinct T cell-intrinsic changes that may influence therapeutic outcomes.

The SARS-CoV-2 Delta variant induces an antibody response largely focused on class 1 and 2 antibody epitopes.

Exposure histories to SARS-CoV-2 variants and vaccinations will shape the specificity of antibody responses. To understand the specificity of Delta-elicited antibody immunity, we characterize the polyclonal antibody response elicited by primary or mRNA vaccine-breakthrough Delta infections. Both types of infection elicit a neutralizing antibody response focused heavily on the receptor-binding domain (RBD). We use deep mutational scanning to show that mutations to the RBD's class 1 and class 2 epitopes, including sites 417, 478, and 484-486 often reduce binding of these Delta-elicited antibodies. The anti-Delta antibody response is more similar to that elicited by early 2020 viruses than the Beta variant, with mutations to the class 1 and 2, but not class 3 epitopes, having the largest effects on polyclonal antibody binding. In addition, mutations to the class 1 epitope (e.g., K417N) tend to have larger effects on antibody binding and neutralization in the Delta spike than in the D614G spike, both for vaccine- and Delta-infection-elicited antibodies. These results help elucidate how the antigenic impacts of SARS-CoV-2 mutations depend on exposure history.

Cutting Edge: Effect of Disease-Modifying Therapies on SARS-CoV-2 Vaccine-Induced Immune Responses in Multiple Sclerosis Patients.

Multiple sclerosis (MS) is a demyelinating inflammatory disease of the CNS treated by diverse disease-modifying therapies that suppress the immune system. Severe acute respiratory syndrome coronavirus 2 mRNA vaccines have been very effective in immunocompetent individuals, but whether MS patients treated with modifying therapies are afforded the same protection is not known. This study determined that dimethyl fumarate caused a momentary reduction in anti-Spike (S)-specific Abs and CD8 T cell response. MS patients treated with B cell-depleting (anti-CD20) or sphingosine 1-phosphate receptor agonist (fingolimod) therapies lack significant S-specific Ab response. Whereas S-specific CD4 and CD8 T cell responses were largely compromised by fingolimod treatment, T cell responses were robustly generated in anti-CD20-treated MS patients, but with a reduced proportion of CD4+CXCR5+ circulating follicular Th cells. These data provide novel information regarding vaccine immune response in patients with autoimmunity useful to help improve vaccine effectiveness in these populations.

The SARS-CoV-2 Delta variant induces an antibody response largely focused on class 1 and 2 antibody epitopes.

Exposure histories to SARS-CoV-2 variants and vaccinations will shape the specificity of antibody responses. To understand the specificity of Delta-elicited antibody immunity, we characterize the polyclonal antibody response elicited by primary or mRNA vaccine-breakthrough Delta infections. Both types of infection elicit a neutralizing antibody response focused heavily on the receptor-binding domain (RBD). We use deep mutational scanning to show that mutations to the RBD's class 1 and class 2 epitopes, including sites 417, 478, and 484-486 often reduce binding of these Delta-elicited antibodies. The anti-Delta antibody response is more similar to that elicited by early 2020 viruses than the Beta variant, with mutations to the class 1 and 2, but not class 3 epitopes, having the largest effects on polyclonal antibody binding. In addition, mutations to the class 1 epitope (e.g., K417N) tend to have larger effects on antibody binding and neutralization in the Delta spike than in the D614G spike, both for vaccine- and Delta-infection-elicited antibodies. These results help elucidate how the antigenic impacts of SARS-CoV-2 mutations depend on exposure history.

Characterising the age-dependent effects of risk factors on type 1 diabetes progression.

AIMS/HYPOTHESIS: Age is known to be one of the most important stratifiers of disease progression in type 1 diabetes. However, what drives the difference in rate of progression between adults and children is poorly understood. Evidence suggests that many type 1 diabetes disease predictors do not have the same effect across the age spectrum. Without a comprehensive analysis describing the varying risk profiles of predictors over the age continuum, researchers and clinicians are susceptible to inappropriate assessment of risk when examining populations of differing ages. We aimed to systematically assess and characterise how the effect of key type 1 diabetes risk predictors changes with age. METHODS: Using longitudinal data from single- and multiple-autoantibody-positive at-risk individuals recruited between the ages of 1 and 45 years in TrialNet's Pathway to Prevention Study, we assessed and visually characterised the age-varying effect of key demographic, immune and metabolic predictors of type 1 diabetes by employing a flexible spline model. Two progression outcomes were defined: participants with single autoantibodies (n=4893) were analysed for progression to multiple autoantibodies or type 1 diabetes, and participants with multiple autoantibodies were analysed (n=3856) for progression to type 1 diabetes. RESULTS: Several predictors exhibited significant age-varying effects on disease progression. Amongst single-autoantibody participants, HLA-DR3 (p=0.007), GAD65 autoantibody positivity (p=0.008), elevated BMI (p=0.007) and HOMA-IR (p=0.002) showed a significant increase in effect on disease progression with increasing age. Insulin autoantibody positivity had a diminishing effect with older age in single-autoantibody-positive participants (p<0.001). Amongst multiple-autoantibody-positive participants, male sex (p=0.002) was associated with an increase in risk for progression, and HLA DR3/4 (p=0.05) showed a decreased effect on disease progression with older age. In both single- and multiple-autoantibody-positive individuals, significant changes in HR with age were seen for multiple measures of islet function. Risk estimation using prediction risk score Index60 was found to be better at a younger age for both single- and multiple-autoantibody-positive individuals (p=0.007 and p<0.001, respectively). No age-varying effect was seen for prediction risk score DPTRS (p=0.861 and p=0.178, respectively). Multivariable analyses suggested that incorporating the age-varying effect of the individual components of these validated risk scores has the potential to enhance the risk estimate. CONCLUSIONS/INTERPRETATION: Analysing the age-varying effect of disease predictors improves understanding and prediction of type 1 diabetes disease progression, and should be leveraged to refine prediction models and guide mechanistic studies.

IL-6 receptor blockade does not slow ß cell loss in new-onset type 1 diabetes.

BackgroundIL-6 receptor (IL-6R) signaling drives development of T cell populations important to type 1 diabetes pathogenesis. We evaluated whether blockade of IL-6R with monoclonal antibody tocilizumab would slow loss of residual ß cell function in newly diagnosed type 1 diabetes patients.MethodsWe conducted a multicenter, randomized, placebo-controlled, double-blind trial with tocilizumab in new-onset type 1 diabetes. Participants were screened within 100 days of diagnosis. Eligible participants were randomized 2:1 to receive 7 monthly doses of tocilizumab or placebo. The primary outcome was the change from screening in the mean AUC of C-peptide collected during the first 2 hours of a mixed meal tolerance test at week 52 in pediatric participants (ages 6-17 years).ResultsThere was no statistical difference in the primary outcome between tocilizumab and placebo. Immunophenotyping showed reductions in downstream signaling of the IL-6R in T cells but no changes in CD4 memory subsets, Th17 cells, Tregs, or CD4+ T effector cell resistance to Treg suppression. A DC subset decreased during therapy but regressed to baseline once therapy stopped. Tocilizumab was well tolerated.ConclusionTocilizumab reduced T cell IL-6R signaling but did not modulate CD4+ T cell phenotypes or slow loss of residual ß cell function in newly diagnosed individuals with type 1 diabetes.Trial RegistrationClinicalTrials.gov NCT02293837.FundingNIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and National Institute of Allergy and Infectious Diseases (NIAID) UM1AI109565, UL1TR000004 from NIH/National Center for Research Resources (NCRR) Clinical and Translational Science Award (CTSA), NIH/NIDDK P30DK036836, NIH/NIDDK U01DK103266, NIH/NIDDK U01DK103266, 1UL1TR000064 from NIH/NCRR CTSA, NIH/National Center for Advancing Translational Sciences (NCATS) UL1TR001878, UL1TR002537 from NIH/CTSA; National Health and Medical Research Council Practitioner Fellowship (APP1136735), NIH/NIDDK U01-DK085476, NIH/CTSA UL1-TR002494, Indiana Clinical and Translational Science Institute Award UL1TR002529, Vanderbilt Institute for Clinical and Translational Research UL1TR000445. NIH/NCATS UL1TR003142, NIH/CTSA program UL1-TR002494, Veteran Affairs Administration, and 1R01AI132774.

Insulin is necessary but not sufficient: changing the therapeutic paradigm in type 1 diabetes.

Despite the clear evidence that type 1 diabetes (T1D) begins well before hyperglycemia is evident, there are no clinically available disease-modifying therapies for early-stage disease. However, following the exciting results of the Teplizumab Prevention Study, the first study to demonstrate that overt T1D can be delayed with immunotherapy, there is renewed optimism that in the future, T1D will be treated before hyperglycemia develops. A different treatment paradigm is needed, as a majority of people with T1D do not meet the glycemic targets that are associated with a lower risk of T1D complications and therefore remain vulnerable to complications and shortened life expectancy. The following review will outline the history and current status of immunotherapy for T1D and highlight some challenges and ideas for the future. Although such efforts have been worldwide, we will focus particularly on the activities of Diabetes TrialNet, a National Institutes of Health consortium launched in 2004.

Disease-Modifying Therapies in Type 1 Diabetes: A Look into the Future of Diabetes Practice.

The novel understanding that the presence of multiple islet autoantibodies, indicating islet autoimmunity, inevitably leads to type 1 diabetes mellitus (T1DM) has necessitated the development of a new staging classification system for the condition. Coupled with an improved understanding of the disease course, the realization that T1DM appears to be more heterogeneous than previously thought has led to unique opportunities to develop more targeted therapies that may be applied even before the onset of dysglycemia or symptoms. To date, several therapies have been trialed to delay or halt disease progression in both presymptomatic and clinical T1DM, each demonstrating varying degrees of effectiveness, toxicity, and utility. Key research supports the eventual implementation of immunotherapy in autoimmune diabetes, potentially calling for a paradigm shift among care providers. It will likely be necessary to develop new approaches to trial design and to address potential barriers to progress before an effective treatment for the disease may be achieved.

Emerging Concepts on Disease-Modifying Therapies in Type 1 Diabetes.

PURPOSE OF REVIEW: This review seeks to characterize emerging concepts related to disease-modifying therapy in type 1 diabetes. RECENT FINDINGS: We begin by describing the new understanding that islet autoimmunity, as identified by the presence of islet autoantibodies, inevitably leads to clinical type 1 diabetes. This understanding informs the new staging paradigm for type 1 diabetes, which suggests that type 1 diabetes may be recognized and diagnosed long before symptoms develop. Although it is known that nearly all individuals with established islet autoimmunity will eventually develop symptomatic type 1 diabetes (T1D), individual characteristics such as age and biomarker profile may predict rate of disease progression and response to treatment and may therefore be used to individualize therapy. Key research supports the use of immunotherapy in TID, although a paradigm shift is necessary before immunotherapy may transition from clinical trials to clinical practice. Recent and ongoing research as it relates to these concepts is described throughout.