Abstract
Type 1 diabetes is one of the most common chronic diseases in children and adolescents, but remains unpreventable and incurable. The discovery of insulin, already 100 years ago, embodied a lifesaver for people with type 1 diabetes as it allowed the replacement of all functions of the beta cell. Nevertheless, despite all technological advances, the majority of type 1 diabetic patients fail to reach the recommended target HbA1c levels. The disease-associated complications remain the true burden of affected individuals and necessitate the search for disease prevention and reversal. The recognition that type 1 diabetes is a heterogeneous disease with an etiology in which both the innate and adaptive immune system as well as the insulin-producing beta cells intimately interact, has fostered the idea that treatment to specific molecular or cellular characteristics of the patient groups will be needed. Moreover, robust and reliable biomarkers to detect type 1 diabetes in the early (pre-symptomatic) phases are wanted to preserve functional beta cell mass. The pitfalls of past therapeutics along with the perspectives of current therapies can open up the path for future research.
Introduction
The year 2021 will celebrate the 100th anniversary of the clinical use of insulin. This embodied a lifesaver for people living with type 1 diabetes (T1D), the most common chronic disease in children and young adolescents. But it is not a cure, it is merely a part of our attempts to replace all functions of the beta cells that are destroyed in T1D. This means that therapy is based on symptom control with glucose monitoring and administration of insulin according to algorithms based on food intake, exercise and other external factors. In many people with T1D, tight glycemic control remains an elusive goal, resulting in long-term micro- and macrovascular complications, but also acute complications like hypoglycemic attacks, episodes of severe hyperglycemia and diabetic ketoacidosis. As a result, despite major advances in tools to measure glucose levels and insulin therapy, life years are still lost due to T1D. At present, people with T1D are often treated with insulin analogs and use continuous glucose monitoring systems that can even be integrated with sensors and insulin pumps (i.e. hybrid closed-loop systems) resulting in important steps forward in therapy. Despite these technical advancements, the hope of prevention and a cure for T1D remains. The fact that this has not been achieved to date, is not for a lack of trying. In the process, important lessons have been learned. Here, we describe some of these lessons and open up the path for future research.
Type 1 diabetes: a mistake of the insulin-producing beta cells, the immune system or both?
The dogma that the immune system in T1D mistakenly destroys healthy insulin-producing beta cells in the pancreas, leading to absolute insulin deficiency (Atkinson & Eisenbarth 2001), is being challenged in recent years. Dr Bottazzo in 1986 and Dr Atkinson in 2016 debated the question: 'Death of a beta cell: homicide or suicide?' (Bottazzo 1986, Atkinson et al. 2011). The definitive answer remains unknown, but both sides have strong arguments.
The classic hypothesis, first introduced by Dr Eisenbarth in 1986, proposes that in an individual with a predisposing genetic risk (mostly carried by specific types of major histocompatibility complex (MHC) or human leucocyte antigen (HLA) genes), activation of the immune system by one or multiple environmental triggers results in a rapid destruction of the pancreatic beta cells (Eisenbarth 1986). This implies a malfunction of the immune system as the culprit. The discovery of pancreatic islet cell autoantibodies (ICA), appearing in the period between immune activation and the onset of clinical symptoms, proved to be a landmark discovery in two important aspects. First, it provided a method to predict the onset of diabetes. Furthermore, as different autoantigens (i.e. insulin, glutamic acid decarboxylase (GAD), protein tyrosine phosphatase (IA-2 or ICA512), zinc transporter 8 (ZnT8) and tetraspanin) were discovered, it became clear that beta cell-specific proteins and peptide fragments were targeted by the immune system (Bonifacio 2015). Another strong argument that the immune system may be the causing factor in T1D came from the early observation that transplantation of non-T cell-depleted bone marrow precipitated diabetes (Lampeter et al. 1993).
In this hypothesis, as T1D is due to the aberrant recognition of autoantigens, the most likely cause would be a failure of central tolerance. However, as thymic involution occurs in mammals at puberty, this would imply that T1D would only occur prior to or soon after puberty (Wagner 2016). Moreover, thymic selection appears to be quantitatively similar between healthy individuals and those with T1D (Culina et al. 2018). Yet, there seems to be a major qualitative difference, as thymus-derived regulatory T cells (Tregs) are amongst the primary regulators of T1D (Holohan et al. 2019). Translational errors have been described as a potential source of antigenic peptides to which central immune tolerance is lacking, but to which cytotoxic T cells in the circulation of T1D patients are present, causing destruction of the insulin-producing beta cells (Kracht et al. 2017). In addition to mishaps in the central immune system, in T1D, the peripheral immune regulation appears defective as T cell receptor (TCR) revision increases the T cell repertoire in the periphery. Especially CD40-expressing CD4+ T cells alter their TCR repertoire to become auto-aggressive (Wagner 2016, Vaitaitis et al. 2017). Moreover, beta cell-specific CD8+ T cells are present in equal numbers in the peripheral blood of healthy individuals and T1D patients but are found exclusively in the pancreas of T1D patients (Skowera et al. 2015, Culina et al. 2018). Yet, despite numerical correlates, the phenotypic profile looks different in T1D with hallmarks of an antigen-driven expansion of beta cell-specific CD8+ T cells (Skowera et al. 2015). This observation is supported by the exhaustion of these CD8+ T cells in slow disease progressors (Wiedeman et al. 2020). Another argument is that the HLA region, which enables antigen recognition by antigen-presenting cells, confers the greatest contributor to the genetic susceptibility of T1D (Mathieu et al. 2018).
If, on the other hand, abnormal pancreatic beta cells would be the culprit, it would imply a normal function of the immune system as it then needs to clear these dysfunctional cells. In this perspective, T1D etiology becomes comparable to effective anti-tumor immunity. Several arguments support a primary defect in insulin-producing beta cells. First, recent observations suggest smaller pancreatic volumes in those affected or at risk of T1D (Campbell-Thompson et al. 2019, Virostko et al. 2019). Second, clear signs of beta cell stress can be detected in those on their way to develop T1D, as illustrated by an increased proinsulin-to-insulin ratio (Wasserfall et al. 2017). This increased ratio suggests abnormalities in insulin processing and vesicular trafficking (Rodriguez-Calvo et al. 2017). Moreover, non-specific triggers, like increased metabolic demand or viral infections, have been shown to stress beta cells, inducing endoplasmic reticulum (ER) stress and have been associated with T1D (Eizirik et al. 2013). ER stress eventually results in components of the folding process being hampered, initiating the unfolded protein response (UPR) in order to optimize the folding capacity of the ER. However, if this is unsuccessful, the beta cell is marked as dysfunctional and apoptosis is set in motion. However, UPR by itself has the potential to trigger an inflammatory response that attracts immune cells and initiates the entire cascade leading to T1D (Eizirik et al. 2013). Moreover, ER stress may again increase the visibility of the beta cells to the immune system (Thomaidou et al. 2020). Therefore, ER stress constitutes a major contributory factor to beta cell dysfunction in early T1D (Eizirik et al. 2013). As hyperglycemia stresses the beta cells even further, this may result in a vicious circle, as was proven by the observation that the decline in beta cell function was less in intensively insulin-treated T1D patients (The Diabetes Control and Complications Trial Research Group 1998). The argument of ER stress as an etiologic factor of T1D was reinforced by the observation that in a T1D animal model, the non-obese diabetic (NOD) mouse, avoidance of ER stress resulted in T1D protection (Engin et al. 2013).
In this 'beta cell centric hypothesis', once the beta cell is under attack, a cascade is set into motion, as this inflammatory environment seems to result in the release of additional pro-inflammatory cytokines and chemokines by the beta cells, thus attracting more cells of the immune system (Cardozo et al. 2003). Interestingly, in the presence of inflammation, beta cells overexpress HLA class I molecules creating an additional homing beacon for cytotoxic T cells (Richardson et al. 2016). Recently, it has been shown that stressed beta cells not only misfold insulin, but also misprocess other proteins and peptides, leading to the formation of neo-antigens generating novel epitopes for the immune system. These neo-antigens can even cause epitope spreading in the immune reaction, as demonstrated by the presence of antibodies against these neo-epitopes in people with T1D (James et al. 2018). Inflamed beta cells can furthermore release exosomes (containing, i.e. proinsulin, GAD65, and IA-2), which also have the potential to trigger autoimmune responses (Cianciaruso et al. 2017). A final argument pointing to the beta cell as the true culprit is the fact that current immunotherapies are able to only temporarily slow the decline in beta cell function, suggesting that residual pathogenic mechanisms remain untargeted (Mallone & Eizirik 2020).
Most probably the answer lays in the middle, with T1D being the result of a complex network of dysfunctions both in the beta cell and the immune system, with defects in both innate and adaptive immune regulation, creating ‘the perfect storm’ (Peters et al. 2019). An example of how both sides of the story are connected in T1D is demonstrated by genetic polymorphisms within a single locus encoding the transcription factor basic leucine zipper transcription factor 2 (BACH2). First identified as a key regulator of Tregs, it was thought to be yet another argument of a deficient immune system in T1D (Roychoudhuri et al. 2013). Later, it was shown that BACH2 has an essential anti-apoptotic role in the protection of the insulin-producing beta cells against the cytokine-mediated killing (Marroqui et al. 2014). It appears that BACH2 is moreover downregulated by pro-inflammatory cytokines, resulting in a chain of events in beta cells, already under immune assault (Marroqui et al. 2014). Therefore, downregulation of BACH2 may result in both a failing beta cell and immune system, or just be the result of evolving T1D. This illustrates the complexity of the pathogenesis of T1D (Fig. 1).
Type 1 diabetes is a heterogeneous disease
Despite T1D always resulting in the same clinical endpoint of insulin deficiency and requiring lifelong exogenous insulin substitution, it is actually a collective term of a heterogeneous disease. Clinicians recognize this heterogeneity, with some people being diagnosed early in life, others late in life; some being isolated cases, others having a family history of T1D; some having T1D in the context of multiple autoimmune diseases, others having just T1D; some losing all C-peptide (reflecting functional beta cell mass) in a few months, while others preserving it life-long.
At present, over 60 loci are associated with an increased susceptibility to T1D, with the HLA region as a major contributor (Bakay et al. 2019). The ever-expanding number of associated loci supports the heterogeneity of T1D with, as discussed above, some genes linked to beta cell dysfunction and others to immune cell dysfunction. The genetic complexity is illustrated by the fact that despite the 15-fold increase in the risk of T1D in individuals having a first-degree relative with T1D, the majority of new T1D diagnoses are made in individuals having no known family history for T1D (Pociot & Lernmark 2016). In addition, many people carrying the highest risk HLA haplotypes do not develop T1D (Skyler et al. 2017). These data, together with the observation that the presentation of T1D is changing, with rapid increases in T1D numbers and a shift toward younger ages at disease diagnosis, suggest the presence of external triggers that can differ between different individuals. In an attempt to better understand the nature of these environmental triggers, The Environmental Determinants of Diabetes in the Young (TEDDY) study was initiated. This is a large prospective cohort study in children – beginning at birth, with high-risk genotypes – aiming to identify environmental factors that influence islet autoimmunity and T1D onset (TEDDY Study Group 2008). Yet, despite all these efforts, predicting T1D in a reliable way remains a challenge as a recent 8-year progress report showed that although risk factors can be associated with islet autoimmunity and T1D, they are not precise predictors (Krischer et al. 2019).
At present, the best predictor of progression toward T1D in genetically predisposed individuals, but also in the general population, is the presence of autoantibodies (Primavera et al. 2020). An array of autoantibodies has been described, with heterogeneity in their appearance. Whereas in children insulin autoantibodies (IAA) are most prevalent, in adults GAD autoantibodies (GADA) are the most frequent ones. Again, heterogeneity exists, as the risk depends on antibody titer, affinity, immunoglobulin subclasses and target epitopes on single or multiple islet autoantigens (Bonifacio & Achenbach 2019). Therefore, when using islet autoantibodies in the prediction of T1D onset, it is important to understand their implication. Prospective birth-cohort studies in high-risk children, including the BABYDIAB, DIPP, DAISY and TEDDY trials, have provided major insights into the meaning of antibody appearance in the evolution toward symptom onset. Mostly, T1D evolution starts with the appearance of one autoantibody. This is possible as early as 3 months of age, peaking at the age of 9 months (Krischer et al. 2015). IAA or GADA are most frequently the first autoantibodies, appearing a median period of 7 months before the second autoantibody. Yet, the rate of progression is highly dependent on the hierarchy of antibody appearance (Vehik et al. 2020), with a consensus that the positivity of two or more islet autoantibodies confers a high risk of developing symptomatic T1D (Insel et al. 2015). In longitudinal studies, only 15% of children with one islet autoantibody developed T1D within 10 years, compared to 70% of those with at least two islet autoantibodies (Ziegler et al. 2013). The 30% of high-risk patients that do not progress to T1D within 10 years are termed 'slow progressor' (Long et al. 2018). To complicate things even more, even reversion of islet autoantibody positivity (seroconversion) is possible (Vehik et al. 2016). The islet autoantibodies epitomize the disease heterogeneity of T1D as according to which islet autoantibody appears first and possibly even disappears; the aggressiveness of the autoimmune response can be predicted. Thus, although autoantibodies are at present the ‘best biomarker’, finding better biomarkers is a priority on the path to T1D prevention or cure (see below).
Heterogeneity also exists in the functional pancreatic beta cell mass both at T1D onset and during disease progression (Campbell-Thompson et al. 2016). A general observation is that children lose their functional beta cell mass, as measured by C-peptide, more rapidly than adults do. Some individuals with longstanding T1D retain measurable levels of serum C-peptide and harbor insulin-positive islets in their pancreas even decades after diagnosis (Keenan et al. 2010, Wasserfall et al. 2017). Some arguments exist to suggest that in some individuals beta cells can proliferate, be generated from ductal cells or transdifferentiate from other islet cells like alpha cells, but no clear explanation is available for the heterogeneity of beta cell mass (Saunders & Powers 2016, Lam et al. 2017).
Finally, also in the infiltration type around the beta cells (i.e. insulitis) heterogeneity can be seen. Whereas initiatives like nPOD show that most individuals with T1D have only subtle infiltration, in contrast to the observations on massive infiltration in the NOD mouse, heterogeneity in the composition of the infiltrate has been described (Atkinson et al. 2020). Recently, two distinct profiles, according to the proportion of CD20+ B cells, were observed in the immune infiltrate, related to the age of the person and the rate of beta cell loss. The proportion of B cells in the immune infiltrate and the associated rate of beta cell loss was higher in younger patients (diagnosed before the age of 7 years) and lower in older patients (diagnosed after the age of 13 years) (Leete et al. 2016). The same pattern of two distinct profiles was also detected in the peripheral blood as both pro-inflammatory (defined by multi-autoantibody and interferon-(IFN)-γ positivity) and controlled (defined by pauci-autoantibody and interleukin (IL)-10 positivity) responses were observed in newly diagnosed T1D patients (Arif et al. 2014). This translates to younger patients having approximately 10–15% of residual insulin-containing islets at the time of T1D diagnosis to 40% in older patients (Leete et al. 2016). Moreover, it implicates a significant role of beta cell dysfunction rather than death in these patients.
Lessons learned from intervention studies
Why have we not cured T1D yet? Our plea starts by saying that we have not tried our very best. This is supported by the dazzling list of studies already performed in people with new-onset T1D or unaffected high-risk family members. Large consortia have been established worldwide to perform multicenter trials. Examples are the Type 1 Diabetes TrialNet (an in 2001 established National Institutes of Health (NIH)-funded and Juvenile Diabetes Research Foundation (JDRF)-supported international clinical trial network that emerged from the Diabetes Prevention Trial Type 1 (DPT-1)) and the more recent INNODIA consortium (a European partnership between academic institutions, industrial partners and patient organizations) (Mathieu 2018). Most interventions to date have either targeted the immune system (by general or specific immune suppression or modulation), or were based on strategies aimed at the induction of tolerance toward proteins or peptides relevant to T1D.
Non-antigen-specific immunotherapy, based on immunosuppression, is the oldest of these approaches. Cyclosporine A was the first drug to show the ability to induce disease remission in people with new-onset T1D. However, it was also the first to expose the major obstacles associated with this strategy, namely disease recurrence and adverse effects associated with systemic immunosuppressive drugs (reviewed in Flores et al. 2019). A flurry of immune-modulatory agents has been tested that allowed drawing important conclusions. As such, it has become apparent that anti-inflammatory interventions, targeting single cytokines (like TNF-α or IL-1) are not successful in T1D (Donath et al. 2019). In a recent meta-analysis, it was demonstrated that of all interventions, two are by far the most successful in preventing functional beta cell decline: anti-thymocyte globulin (ATG) and teplizumab (anti-CD3 antibody) (Jacobsen et al. 2020).
ATG is an old immune modulator widely used in organ transplantation. However, the doses of ATG that proved to be effective in delaying beta cell loss in new-onset T1D patients were lower than those used for transplantation. Indeed, higher doses (6.5 mg/kg) were not effective in contrast to low doses (2.5 mg/kg) (Gitelman et al. 2016, Haller et al. 2019). A possible explanation for the protective effect of the low dose is that low doses cause a transient T cell depletion followed by T cell reconstitution, resulting in a shift toward tolerance induction as demonstrated by an increase in Tregs (Lu et al. 2011).
Teplizumab fits in the concept of using more specific anti-T cell antibodies in this T cell-mediated autoimmune disease. First used in animals in 1988 and a first-in-men trial (safety testing) already in 1989, the story of anti-CD3 monoclonal antibodies is a prime example of the importance of choosing the correct study protocol and endpoints (reviewed in Chatenoud 2019). After all, it started as a success story showing complete and permanent remission of T1D in animals. The initial clinical trials using humanized anti-CD3 monoclonal antibodies (i.e. teplizumab or the aglycosylated otelixizumab) were encouraging, showing preservation of beta cell function (Herold et al. 2002, Keymeulen et al. 2005). Large phase III trials for both should have been the icing on the cake, but failed. For otelixizumab, this was the DEFEND trial and was unsuccessful most probably as a result of the decision to reduce the dose to 15 times less than the one previously proven to be effective (Ambery et al. 2014, Aronson et al. 2014). It was later suggested that the maximum target engagement was only achieved at a dose 6 times higher than the one used in the original DEFEND trial, but it was the end of otelixizumab (Vlasakakis et al. 2019). The large phase III trial for teplizumab was the PROTÉGÉ trial and also failed. This time, the probable reason for failing was the study population and the choice of the endpoint (insulin requirement) (Sherry et al. 2011). Later, the AbATE trial chose its endpoints more wisely and was able to demonstrate that teplizumab preserved C-peptide in people with new-onset T1D with even up to 7 years after diagnosis a reduced decline in C-peptide in responders (Hagopian et al. 2013, Perdigoto et al. 2019). Predicting the response to teplizumab remains challenging and underlines the complexity of T1D and the need for biomarkers of therapeutic success. Still, the early start of therapy (at a moment when more beta cells are still present) positively impacted therapeutic response (Herold et al. 2013). Furthermore, responders were shown to express a class of partially exhausted T cells (defined by the expression of EOMES and inhibitory factors like TIGIT and KLRG1) (Long et al. 2017). The signs of exhaustion might serve as a signature of prevention of disease deterioration. Moreover, the induction of exhaustion was also demonstrated to be a hallmark in distinguishing slow and fast disease progressors (Wiedeman et al. 2020). Recently, aiming to preserve even more pancreatic beta cells, teplizumab was given to non-diabetic, high-risk relatives of people with T1D (defined by two or more diabetes-related autoantibodies – stage 2) and this was able to delay progression to symptom onset in T1D up to 3 years (Herold et al. 2019, Sims et al. 2020).
The vicious circle of dying beta cells in type 1 diabetes and therapeutic opportunities to interfere at different disease stages. In a genetically predisposed individual, undefined factor(s) (depicted in orange) will trigger type 1 diabetes by the induction of beta cell stress, leading to compensatory responses that ultimately are deleterious for the beta cells, and by activation of inappropriate immune responses toward the beta cells (stage 0). Once pancreatic beta cells are failing, a vicious circle advances as a result of the release of (neo)antigens and cytokines/chemokines (stages 1–2), reinforcing the ongoing autoimmune responses and contributing to (complete) destruction of functional pancreatic beta cells (stage 3). The constant interaction between beta cells and immune system may vary in individuals at risk, leading to disease heterogeneity. As this destruction process is highly immunogenic, it is by itself a trigger to reinitiate this cascade in adjoining (so far) healthy islets. This figure again emphasizes the need for the identification of robust and reliable biomarkers (depicted in green) as their implementation would offer an opportunity to detect type 1 diabetes in early disease stages, resulting in a better efficacy of therapeutics. Therapeutic approaches (depicted in purple) can be designed for each disease stage. As to date, none has shown prevention or long-term disease reversal, indicating that lasting prevention and/or cure will depend on combination therapies. GAD, glutamic acid decarboxylase; GADA, glutamic acid decarboxylase antibodies; IA-2A, tyrosine phosphatase-related islet antigen 2; IAA, insulin autoantibodies.
Citation: Journal of Endocrinology 249, 2; 10.1530/JOE-20-0552
The final goal in T1D is the re-establishment of antigen-specific tolerance. Thus, many trials have been conducted in an attempt to introduce T1D-relevant antigens to induce specific tolerance. Some of these trials have used insulin administered orally or GAD in aluminium hydroxide (alum) administered subcutaneously (Beam et al. 2017). Despite the fact that none demonstrated convincingly beta cell protection, the major lesson learned from these trials is that the antigen-specific interventions tested were safe (Roep et al. 2019). To obtain superior antigen-based tolerance induction, trials have been designed to combine antigen therapy with low-grade immune modulation. For proinsulin, based on successful animal trials (Takiishi et al. 2017, Cook et al. 2020), this is the ongoing study by Precigen ActoBio, combining teplizumab with AG019 Actobiotics™, an oral capsule consisting of genetically engineered Lactococcus lactis, modified to deliver human proinsulin together with the tolerance inducing cytokine human IL-10 (clinical trial identifier: NCT03751007, EudraCT 2017-002871-24).
As GAD in alum administered subcutaneously did not result in convincing results, this concept was revived in the DIAGNODE-1 trial, designed to administer GAD in alum into lymph nodes (a more targeted approach), but also in combination with vitamin D for low-grade immunomodulation (Tavira et al. 2018). Based on relatively promising results, GAD in alum is now being tested in combination with ibuprofen (DIABGAD; clinical trial identifier: NCT01785108), etanercept (EDCR; clinical trial identifier: NCT02464033), or GABA (GABA/Diamyd; clinical trial identifier: NCT02002130) as anti-inflammatory component. To elucidate the immunomodulatory role of vitamin D in DIAGNODE-1, further research is ongoing (clinical trial identifier: NCT03345004).
Road to a cure – need for biomarkers
As the majority of pancreatic beta cell destruction happens during the pre-symptomatic stage, this emphasizes that therapeutic approaches should preferentially start during the pre-symptomatic period (Atkinson & Eisenbarth 2001). To date, the standard biomarkers are genetic markers and autoantibodies. Genetic biomarkers in T1D are mainly based on HLA typing with the highest HLA-DR and -DQ, that is HLA-DR3/4 and HLA-DQ8,genotypes present in 30–40% of T1D individuals, increasing the risk over 10-fold compared to the background population (present in 2–3%) (Mathieu et al. 2018). Autoantibodies are, as described above, robust biomarkers only useful for discriminating T1D from other types of diabetes and by itself predict an increased risk for T1D as the significance of the presence of ICA is dependent on the a priori probability of a true result (e.g. genetic high-risk individual) (Bonifacio & Achenbach 2019).
This highlights the need for true biomarkers in T1D and makes it the major focus of research in INNODIA. Over 50 clinical centers in Europe, both pediatric and adult clinics, are collecting samples from new-onset T1D patients and from unaffected family members of people living with T1D. In a natural history study, spanning several years, modular interrogation platforms for analysis of cellular and molecular features of beta cell and immune cell biomarkers have been established. These include proteomes, lipidomes, and metabolomes, as well as a full immunomes and RNA analyses. INNODIA is in the final stages of performing an integrated multi-omics natural history study on samples of new-onset T1D individuals. Of importance, these biomarker analyses are also included in the clinical trials running in the INNODIA network, thus not only opening the path to biomarkers of disease, but also raising hope for the discovery of biomarkers of therapeutic effect and success of interventions.
Based on the observations described above, suggesting a role in the pathogenesis of T1D for both the beta cells and the immune system, interventions with the highest probability of success should be based on combinations of interventions. The most straightforward is as previously described the combination of immune modulation with antigen-based tolerance induction. Other appealing combinations would be agents improving beta cell health together with immune modulators. As such, a recent study combined liraglutide, a GLP-1 receptor agonist and an antibody targeting the cytokine IL-21. Results were intriguing, with additive effects observed during therapy, but all effect was lost after stopping the agents (Von Herrath et al. 2020).
To reduce inflammation at the onset, anti-inflammatory drugs may be added to the treatment regimens. A combination of abatacept and rituximab is currently being tested in T1D prevention (stage 2) (clinical trial identifier: NCT03929601, UC4DK117009), as both abatacept and rituximab were shown to slow the decline of beta cell function in new-onset T1D, driven by an effect seen early after treatment initiation (Pescovitz et al. 2009, Orban et al. 2011). However, not always does a combination therapy result in a better efficacy as demonstrated by the addition of pegylated granulocyte colony-stimulating factor (GCSF) to low-dose ATG, which seemed to diminish the benefits provided by low-dose ATG (Haller et al. 2019).
The Achilles heel in the design of novel studies is the lack of strong animal models for T1D to guide clinical trial design together with the long duration and large sample size needed for robust clinical trial design. Here, adaptive trial designs on the backbone of a master protocol may be the answer. In INNODIA, a Master Protocol has been designed that will be the guide for all studies in INNODIA, allowing comparison between studies and allowing adaptive trial design (Dunger et al. 2020). As such, the INNODIA Master Protocol uses the same inclusion criteria, visit schedule, duration, sample collection for standardized efficacy and mechanistic studies throughout all studies. The inclusion/exclusion criteria can be adapted to the requirements of specific interventions, but the clinical and mechanistic evaluations remain largely unchanged, providing the primary outcomes and the potential for a more detailed analysis of variability in intervention response. Additional study visits and sample collection allow studies of toxicology and pharmacokinetic or dynamic analyses. Appealing in T1D to drive progress are adaptive trial design strategies such as dose finding, dropping study arms, inclusion of additional treatments, options to share controls and potentially inclusion of data from the natural history studies.
Trials should be conducted in the most relevant populations: young adults, adolescents and in particular children. We must admit that by drawing conclusions on interventions in adults, where the course of the disease is very different from that in children (see part on heterogeneity), we may be missing some interventions that could have worked in children. Thus, we, as researchers and clinicians, need to convince regulators and industry that even early-phase trials should be conducted in children, of course, taking into account all possible safety measures.
Finally, a robust definition of therapeutic success is needed. This definition may be different at different stages of the disease: in people with just a genetic risk, success could be the prevention of autoimmunity (defined by prevention of biomarker appearance, e.g. autoantibodies) whereas in people with new-onset T1D success could be prevention of decline in functional beta cell mass as measured by stimulated C-peptide. In the latter group, the presence of C-peptide is associated with fewer complications in the long-term and a smoother glycemic control in the immediate term.
Conclusion
Type 1 diabetes is one of the most common and severe chronic diseases in children, adolescents and young adults, and in desperate need of disease-modifying therapy. For 100 years now, the gold standard treatment consists of lifelong insulin treatment with the aim to maintain a normal glucose homeostasis. The discovery of insulin was pioneering, transforming T1D from a quickly fatal disease to an incurable, chronic disease. However, as the majority of T1D patients fail to reach recommended target HbA1c levels, the burden of disease-associated complications remains important. This should stimulate the search for disease-modifying therapies. As this review describes, we have already traveled a long way, but the road remains long and full of obstacles, the most important being the unidentified culprit of T1D (beta cell or immune system or both). Moreover, even if we can identify the culprit, we still need to discover the exact mechanism that sets everything in motion. Nevertheless, progress in our understanding is made and results of clinical intervention trials are promising, showing a temporary deferral of disease onset or transient preservation of functional beta cell mass (as defined by C-peptide) by several interventions. Temporary or transient mostly means a couple of months to even a couple of years, but for now, to many people living with T1D these carefree couple of months to years already mean a lot.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of this review.
Funding
This work did not receive any specific grant from any funding agency in the public, commercial or non-profit sector that could be perceived as prejudicing the impartiality of the research reported.
References
Ambery P, Donner TW, Biswas N, Donaldson J, Parkin J & Dayan CM 2014 Efficacy and safety of low-dose otelixizumab anti-CD3 monoclonal antibody in preserving c-peptide secretion in adolescent type 1 diabetes: defend-2, a randomized, placebo-controlled, double-blind, multi-centre study. Diabetic Medicine 31 399–402. (https://doi.org/10.1111/dme.12361)
Arif S, Leete P, Nguyen V, Marks K, Nor NM, Estorninho M, Kronenberg-Versteeg D, Bingley PJ, Todd JA & Guy C et al. 2014 Blood and islet phenotypes indicate immunological heterogeneity in type 1 diabetes. Diabetes 63 3835–3845. (https://doi.org/10.2337/db14-0365)
Aronson R, Gottlieb PA, Christiansen JS, Donner TW, Bosi E, Bode BW, Pozzilli PDEFEND Investigator Group 2014 Low-dose otelixizumab anti-CD3 monoclonal antibody DEFEND-1 study: results of the randomized phase III study in recent-onset human type 1 diabetes. Diabetes Care 37 2746–2754. (https://doi.org/10.2337/dc13-0327)
Atkinson MA & Eisenbarth GS 2001 Type 1 diabetes: new perspectives on disease pathogenesis and treatment. Lancet 358 221–229. (https://doi.org/10.1016/S0140-6736(0105415-0)
Atkinson MA, Bluestone JA, Eisenbarth GS, Hebrok M, Herold KC, Accili D, Pietropaolo M, Arvan PR, Von Herrath M & Markel DS et al. 2011 How does type 1 diabetes develop?: the notion of homicide or beta-cell suicide revisited. Diabetes 60 1370–1379. (https://doi.org/10.2337/db10-1797)
Atkinson MA, Campbell-Thompson M, Kusmartseva I & Kaestner KH 2020 Organisation of the human pancreas in health and in diabetes. Diabetologia 63 1966–1973. (https://doi.org/10.1007/s00125-020-05203-7)
Bakay M, Pandey R, Grant SFA & Hakonarson H 2019 The genetic contribution to type 1 diabetes. Current Diabetes Reports 19 116. (https://doi.org/10.1007/s11892-019-1235-1)
Beam CA, Maccallum C, Herold KC, Wherrett DK, Palmer J, Ludvigsson JType 1 Diabetes TrialNet Study Group 2017 GAD vaccine reduces insulin loss in recently diagnosed type 1 diabetes: findings from a Bayesian meta-analysis. Diabetologia 60 43–49. (https://doi.org/10.1007/s00125-016-4122-1)
Bonifacio E 2015 Predicting type 1 diabetes using biomarkers. Diabetes Care 38 989–996. (https://doi.org/10.2337/dc15-0101)
Bonifacio E & Achenbach P 2019 Birth and coming of age of islet autoantibodies. Clinical and Experimental Immunology 198 294–305. (https://doi.org/10.1111/cei.13360)
Bottazzo GF 1986 Lawrence lecture. Death of a beta cell: homicide or suicide? Diabetic Medicine 3 119–130. (https://doi.org/10.1111/j.1464-5491.1986.tb00722.x)
Campbell-Thompson M, Fu A, Kaddis JS, Wasserfall C, Schatz DA, Pugliese A & Atkinson MA 2016 Insulitis and beta-cell mass in the natural history of type 1 diabetes. Diabetes 65 719–731. (https://doi.org/10.2337/db15-0779)
Campbell-Thompson ML, Filipp SL, Grajo JR, Nambam B, Beegle R, Middlebrooks EH, Gurka MJ, Atkinson MA, Schatz DA & Haller MJ 2019 Relative pancreas volume is reduced in first-degree relatives of patients with type 1 diabetes. Diabetes Care 42 281–287. (https://doi.org/10.2337/dc18-1512)
Cardozo AK, Proost P, Gysemans C, Chen MC, Mathieu C & Eizirik DL 2003 IL-1beta and IFN-gamma induce the expression of diverse chemokines and IL-15 in human and rat pancreatic islet cells, and in islets from pre-diabetic nod mice. Diabetologia 46 255–266. (https://doi.org/10.1007/s00125-002-1017-0)
Chatenoud L 2019 A future for CD3 antibodies in immunotherapy of type 1 diabetes. Diabetologia 62 578–581. (https://doi.org/10.1007/s00125-018-4808-7)
Cianciaruso C, Phelps EA, Pasquier M, Hamelin R, Demurtas D, Alibashe Ahmed M, Piemonti L, Hirosue S, Swartz MA & De Palma M et al. 2017 Primary human and rat beta-cells release the intracellular autoantigens GAD65, IA-2, and proinsulin in exosomes together with cytokine-induced enhancers of immunity. Diabetes 66 460–473. (https://doi.org/10.2337/db16-0671)
Cook DP, Cunha JPMCM, Martens PJ, Sassi G, Mancarella F, Ventriglia G, Sebastiani G, Vanherwegen AS, Atkinson MA & Van Huynegem K et al. 2020 Intestinal delivery of proinsulin and IL-10 via Lactococcus lactis combined with low-dose anti-CD3 restores tolerance outside the window of acute type 1 diabetes diagnosis. Frontiers in Immunology 11 1103. (https://doi.org/10.3389/fimmu.2020.01103)
Culina S, Lalanne AI, Afonso G, Cerosaletti K, Pinto S, Sebastiani G, Kuranda K, Nigi L, Eugster A & Osterbye T et al. 2018 Islet-reactive CD8(+) T cell frequencies in the pancreas, but not in blood, distinguish type 1 diabetic patients from healthy donors. Science Immunology 3 eaao4013. (https://doi.org/10.1126/sciimmunol.aao4013)
Donath MY, Dinarello CA & Mandrup-Poulsen T 2019 Targeting innate immune mediators in type 1 and type 2 diabetes. Nature Reviews: Immunology 19 734–746. (https://doi.org/10.1038/s41577-019-0213-9)
Dunger DB, Bruggraber SF, Mander AP, Tree T, Jaroslaw Chmura P, Knip MJ, Schulte AM & Mathieu C 2020 Innodia master protocol for the evaluation of investigational medicinal products in children, adolescents and adults with newly diagnosed type 1 diabetes. In Paper presented at: European Association for the Study of Diabetes 56th Annual Meeting; 21–25 September 2020; Virtual. (available at: https://www.innodia.eu/downloads/pdf/2020-easd-overview.pdf)
Eisenbarth GS 1986 Type I diabetes mellitus. A chronic autoimmune disease. New England Journal of Medicine 314 1360–1368. (https://doi.org/10.1056/NEJM198605223142106)
Eizirik DL, Miani M & Cardozo AK 2013 Signalling danger: endoplasmic reticulum stress and the unfolded protein response in pancreatic islet inflammation. Diabetologia 56 234–241. (https://doi.org/10.1007/s00125-012-2762-3)
Engin F, Yermalovich A, Nguyen T, Hummasti S, Fu W, Eizirik DL, Mathis D & Hotamisligil GS 2013 Restoration of the unfolded protein response in pancreatic beta cells protects mice against type 1 diabetes. Science Translational Medicine 5 211ra156. (https://doi.org/10.1126/scitranslmed.3006534)
Flores C, Fouquet G, Moura IC, Maciel TT & Hermine O 2019 Lessons to learn from low-dose cyclosporin-A: a new approach for unexpected clinical applications. Frontiers in Immunology 10 588. (https://doi.org/10.3389/fimmu.2019.00588)
Gitelman SE, Gottlieb PA, Felner EI, Willi SM, Fisher LK, Moran A, Gottschalk M, Moore WV, Pinckney A & Keyes-Elstein L et al. 2016 Antithymocyte globulin therapy for patients with recent-onset type 1 diabetes: 2 year results of a randomised trial. Diabetologia 59 1153–1161. (https://doi.org/10.1007/s00125-016-3917-4)
Hagopian W, Ferry RJ Jr, Sherry N, Carlin D, Bonvini E, Johnson S, Stein KE, Koenig S, Daifotis AG & Herold KC et al. 2013 Teplizumab preserves c-peptide in recent-onset type 1 diabetes: two-year results from the randomized, placebo-controlled protege trial. Diabetes 62 3901–3908. (https://doi.org/10.2337/db13-0236)
Haller MJ, Long SA, Blanchfield JL, Schatz DA, Skyler JS, Krischer JP, Bundy BN, Geyer SM, Warnock MV & Miller JL et al. 2019 Low-dose anti-thymocyte globulin preserves c-peptide, reduces HbA1c, and increases regulatory to conventional T-cell ratios in new-onset type 1 diabetes: two-year clinical trial data. Diabetes 68 1267–1276. (https://doi.org/10.2337/db19-0057)
Herold KC, Hagopian W, Auger JA, Poumian-Ruiz E, Taylor L, Donaldson D, Gitelman SE, Harlan DM, Xu D & Zivin RA et al. 2002 Anti-CD3 monoclonal antibody in new-onset type 1 diabetes mellitus. New England Journal of Medicine 346 1692–1698. (https://doi.org/10.1056/NEJMoa012864)
Herold KC, Gitelman SE, Ehlers MR, Gottlieb PA, Greenbaum CJ, Hagopian W, Boyle KD, Keyes-Elstein L, Aggarwal S & Phippard D et al. 2013 Teplizumab (anti-CD3 mAb) treatment preserves c-peptide responses in patients with new-onset type 1 diabetes in a randomized controlled trial: metabolic and immunologic features at baseline identify a subgroup of responders. Diabetes 62 3766–3774. (https://doi.org/10.2337/db13-0345)
Herold KC, Bundy BN, Long SA, Bluestone JA, Dimeglio LA, Dufort MJ, Gitelman SE, Gottlieb PA, Krischer JP & Linsley PS et al. 2019 An anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes. New England Journal of Medicine 381 603–613. (https://doi.org/10.1056/NEJMoa1902226)
Holohan DR, Van Gool F & Bluestone JA 2019 Thymically-derived foxp3+ regulatory T cells are the primary regulators of type 1 diabetes in the non-obese diabetic mouse model. PLoS ONE 14 e0217728. (https://doi.org/10.1371/journal.pone.0217728)
Insel RA, Dunne JL, Atkinson MA, Chiang JL, Dabelea D, Gottlieb PA, Greenbaum CJ, Herold KC, Krischer JP & Lernmark Å et al. 2015 Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care 38 1964–1974. (https://doi.org/10.2337/dc15-1419)
Jacobsen LM, Bundy BN, Greco MN, Schatz DA, Atkinson MA, Brusko TM, Mathews CE, Herold KC, Gitelman SE & Krischer JP et al. 2020 Comparing beta cell preservation across clinical trials in recent-onset type 1 diabetes. Diabetes Technology and Therapeutics 22 948–953. (https://doi.org/10.1089/dia.2020.0305)
James EA, Pietropaolo M & Mamula MJ 2018 Immune recognition of beta-cells: neoepitopes as key players in the loss of tolerance. Diabetes 67 1035–1042. (https://doi.org/10.2337/dbi17-0030)
Keenan HA, Sun JK, Levine J, Doria A, Aiello LP, Eisenbarth G, Bonner-Weir S & King GL 2010 Residual insulin production and pancreatic ss-cell turnover after 50 years of diabetes: Joslin medalist study. Diabetes 59 2846–2853. (https://doi.org/10.2337/db10-0676)
Keymeulen B, Vandemeulebroucke E, Ziegler AG, Mathieu C, Kaufman L, Hale G, Gorus F, Goldman M, Walter M & Candon S et al. 2005 Insulin needs after CD3-antibody therapy in new-onset type 1 diabetes. New England Journal of Medicine 352 2598–2608. (https://doi.org/10.1056/NEJMoa043980)
Kracht MJ, Van Lummel M, Nikolic T, Joosten AM, Laban S, Van Der Slik AR, Van Veelen PA, Carlotti F, De Koning EJ & Hoeben RC et al. 2017 Autoimmunity against a defective ribosomal insulin gene product in type 1 diabetes. Nature Medicine 23 501–507. (https://doi.org/10.1038/nm.4289)
Krischer JP, Lynch KF, Schatz DA, Ilonen J, Lernmark Å, Hagopian WA, Rewers MJ, She JX, Simell OG & Toppari J et al. 2015 The 6 year incidence of diabetes-associated autoantibodies in genetically at-risk children: the teddy study. Diabetologia 58 980–987. (https://doi.org/10.1007/s00125-015-3514-y)
Krischer JP, Liu X, Vehik K, Akolkar B, Hagopian WA, Rewers MJ, She JX, Toppari J, Ziegler AG & Lernmark Å et al. 2019 Predicting islet cell autoimmunity and type 1 diabetes: an 8-year teddy study progress report. Diabetes Care 42 1051–1060. (https://doi.org/10.2337/dc18-2282)
Lam CJ, Jacobson DR, Rankin MM, Cox AR & Kushner JA 2017 Beta cells persist in T1D pancreata without evidence of ongoing beta-cell turnover or neogenesis. Journal of Clinical Endocrinology and Metabolism 102 2647–2659. (https://doi.org/10.1210/jc.2016-3806)
Lampeter EF, Homberg M, Quabeck K, Schaefer UW, Wernet P, Bertrams J, Grosse-Wilde H, Gries FA & Kolb H 1993 Transfer of insulin-dependent diabetes between hla-identical siblings by bone marrow transplantation. Lancet 341 1243–1244. (https://doi.org/10.1016/0140-6736(9391148-f)
Leete P, Willcox A, Krogvold L, Dahl-Jorgensen K, Foulis AK, Richardson SJ & Morgan NG 2016 Differential insulitic profiles determine the extent of beta-cell destruction and the age at onset of type 1 diabetes. Diabetes 65 1362–1369. (https://doi.org/10.2337/db15-1615)
Long SA, Thorpe J, Herold KC, Ehlers M, Sanda S, Lim N, Linsley PS, Nepom GT & Harris KM 2017 Remodeling T cell compartments during anti-CD3 immunotherapy of type 1 diabetes. Cellular Immunology 319 3–9. (https://doi.org/10.1016/j.cellimm.2017.07.007)
Long AE, Wilson IV, Becker DJ, Libman IM, Arena VC, Wong FS, Steck AK, Rewers MJ, Yu L & Achenbach P et al. 2018 Characteristics of slow progression to diabetes in multiple islet autoantibody-positive individuals from five longitudinal cohorts: the snail study. Diabetologia 61 1484–1490. (https://doi.org/10.1007/s00125-018-4591-5)
Lu Y, Suzuki J, Guillioli M, Umland O & Chen Z 2011 Induction of self-antigen-specific FOXP3+ regulatory T cells in the periphery by lymphodepletion treatment with anti-mouse thymocyte globulin in mice. Immunology 134 50–59. (https://doi.org/10.1111/j.1365-2567.2011.03466.x)
Mallone R & Eizirik DL 2020 Presumption of innocence for beta cells: why are they vulnerable autoimmune targets in type 1 diabetes? Diabetologia 63 1999–2006. (https://doi.org/10.1007/s00125-020-05176-7)
Marroqui L, Santin I, Dos Santos RS, Marselli L, Marchetti P & Eizirik DL 2014 BACH2, a candidate risk gene for type 1 diabetes, regulates apoptosis in pancreatic beta-cells via jnk1 modulation and crosstalk with the candidate gene PTPN2. Diabetes 63 2516–2527. (https://doi.org/10.2337/db13-1443)
Mathieu C 2018 Are we there yet? Finding ways to work together on T1D. Diabetes Care 41 667–669. (https://doi.org/10.2337/dci17-0065)
Mathieu C, Lahesmaa R, Bonifacio E, Achenbach P & Tree T 2018 Immunological biomarkers for the development and progression of type 1 diabetes. Diabetologia 61 2252–2258. (https://doi.org/10.1007/s00125-018-4726-8)
Orban T, Bundy B, Becker DJ, Dimeglio LA, Gitelman SE, Goland R, Gottlieb PA, Greenbaum CJ, Marks JB & Monzavi R et al. 2011 Co-stimulation modulation with abatacept in patients with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled trial. Lancet 378 412–419. (https://doi.org/10.1016/S0140-6736(1160886-6)
Perdigoto AL, Preston-Hurlburt P, Clark P, Long SA, Linsley PS, Harris KM, Gitelman SE, Greenbaum CJ, Gottlieb PA & Hagopian W et al. 2019 Treatment of type 1 diabetes with teplizumab: clinical and immunological follow-up after 7 years from diagnosis. Diabetologia 62 655–664. (https://doi.org/10.1007/s00125-018-4786-9)
Pescovitz MD, Greenbaum CJ, Krause-Steinrauf H, Becker DJ, Gitelman SE, Goland R, Gottlieb PA, Marks JB, Mcgee PF & Moran AM et al. 2009 Rituximab, B-lymphocyte depletion, and preservation of beta-cell function. New England Journal of Medicine 361 2143–2152. (https://doi.org/10.1056/NEJMoa0904452)
Peters L, Posgai A & Brusko TM 2019 Islet-immune interactions in type 1 diabetes: the nexus of beta cell destruction. Clinical and Experimental Immunology 198 326–340. (https://doi.org/10.1111/cei.13349)
Pociot F & Lernmark Å 2016 Genetic risk factors for type 1 diabetes. Lancet 387 2331–2339. (https://doi.org/10.1016/S0140-6736(1630582-7)
Primavera M, Giannini C & Chiarelli F 2020 Prediction and prevention of type 1 diabetes. Frontiers in Endocrinology 11 248. (https://doi.org/10.3389/fendo.2020.00248)
Richardson SJ, Rodriguez-Calvo T, Gerling IC, Mathews CE, Kaddis JS, Russell MA, Zeissler M, Leete P, Krogvold L & Dahl-Jorgensen K et al. 2016 Islet cell hyperexpression of hla class I antigens: a defining feature in type 1 diabetes. Diabetologia 59 2448–2458. (https://doi.org/10.1007/s00125-016-4067-4)
Rodriguez-Calvo T, Zapardiel-Gonzalo J, Amirian N, Castillo E, Lajevardi Y, Krogvold L, Dahl-Jorgensen K & Von Herrath MG 2017 Increase in pancreatic proinsulin and preservation of beta-cell mass in autoantibody-positive donors prior to type 1 diabetes onset. Diabetes 66 1334–1345. (https://doi.org/10.2337/db16-1343)
Roep BO, Wheeler DCS & Peakman M 2019 Antigen-based immune modulation therapy for type 1 diabetes: the era of precision medicine. Lancet: Diabetes and Endocrinology 7 65–74. (https://doi.org/10.1016/S2213-8587(1830109-8)
Roychoudhuri R, Hirahara K, Mousavi K, Clever D, Klebanoff CA, Bonelli M, Sciume G, Zare H, Vahedi G & Dema B et al. 2013 BACH2 represses effector programs to stabilize t(reg)-mediated immune homeostasis. Nature 498 506–510. (https://doi.org/10.1038/nature12199)
Saunders D & Powers AC 2016 Replicative capacity of beta-cells and type 1 diabetes. Journal of Autoimmunity 71 59–68. (https://doi.org/10.1016/j.jaut.2016.03.014)
Sherry N, Hagopian W, Ludvigsson J, Jain SM, Wahlen J, Ferry RJ Jr, Bode B, Aronoff S, Holland C & Carlin D et al. 2011 Teplizumab for treatment of type 1 diabetes (Protégé study): 1-year results from a randomised, placebo-controlled trial. Lancet 378 487–497. (https://doi.org/10.1016/S0140-6736(1160931-8)
Sims EK, Bundy BN, Stier KD, Serti E, Lim N, Nepom GT, Evans-Molina C & Herold KC 2020 277-or: teplizumab reverses the loss of c-peptide in relatives at risk for type 1 diabetes. Diabetes 69 277-OR.
Skowera A, Ladell K, Mclaren JE, Dolton G, Matthews KK, Gostick E, Kronenberg-Versteeg D, Eichmann M, Knight RR & Heck S et al. 2015 beta-Cell-specific CD8 T cell phenotype in type 1 diabetes reflects chronic autoantigen exposure. Diabetes 64 916–925. (https://doi.org/10.2337/db14-0332)
Skyler JS, Bakris GL, Bonifacio E, Darsow T, Eckel RH, Groop L, Groop PH, Handelsman Y, Insel RA & Mathieu C et al. 2017 Differentiation of diabetes by pathophysiology, natural history, and prognosis. Diabetes 66 241–255. (https://doi.org/10.2337/db16-0806)
Takiishi T, Cook DP, Korf H, Sebastiani G, Mancarella F, Cunha JP, Wasserfall C, Casares N, Lasarte JJ & Steidler L et al. 2017 Reversal of diabetes in nod mice by clinical-grade proinsulin and IL-10-secreting Lactococcus lactis in combination with low-dose anti-CD3 depends on the induction of FOXP3-positive T cells. Diabetes 66 448–459. (https://doi.org/10.2337/db15-1625)
Tavira B, Barcenilla H, Wahlberg J, Achenbach P, Ludvigsson J & Casas R 2018 Intralymphatic glutamic acid decarboxylase-alum administration induced th2-like-specific immunomodulation in responder patients: a pilot clinical trial in type 1 diabetes. Journal of Diabetes Research 2018 9391845. (https://doi.org/10.1155/2018/9391845)
TEDDY Study Group 2008 The environmental determinants of diabetes in the young (teddy) study. Annals of the New York Academy of Sciences 1150 1–13. (https://doi.org/10.1155/2018/9391845)
The Diabetes Control and Complications Trial Research Group 1998 Effect of intensive therapy on residual beta-cell function in patients with type 1 diabetes in the diabetes control and complications trial. A randomized controlled tiral. Annals of Internal Medicine 128 517–523. (https://doi.org/10.7326/0003-4819-128-7-199804010-00001)
Thomaidou S, Kracht MJL, Van Der Slik A, Laban S, De Koning EJ, Carlotti F, Hoeben RC, Roep BO & Zaldumbide A 2020 beta-Cell stress shapes ctl immune recognition of preproinsulin signal peptide by posttranscriptional regulation of endoplasmic reticulum aminopeptidase. Diabetes 69 670–680. (https://doi.org/10.2337/db19-0984)
Vaitaitis GM, Waid DM, Yussman MG & Wagner DH Jr 2017 CD40-mediated signalling influences trafficking, T-cell receptor expression, and T-cell pathogenesis, in the NOD model of type 1 diabetes. Immunology 152 243–254. (https://doi.org/10.1111/imm.12761)
Vehik K, Lynch KF, Schatz DA, Akolkar B, Hagopian W, Rewers M, She JX, Simell O, Toppari J & Ziegler AG et al. 2016 Reversion of beta-cell autoimmunity changes risk of type 1 diabetes: TEDDY study. Diabetes Care 39 1535–1542. (https://doi.org/10.2337/dc16-0181)
Vehik K, Bonifacio E, Lernmark Å, Yu L, Williams A, Schatz D, Rewers M, She JX, Toppari J & Hagopian W et al. 2020 Hierarchical order of distinct autoantibody spreading and progression to type 1 diabetes in the TEDDY study. Diabetes Care 43 2066–2073. (https://doi.org/10.2337/dc19-2547)
Virostko J, Williams J, Hilmes M, Bowman C, Wright JJ, Du L, Kang H, Russell WE, Powers AC & Moore DJ 2019 Pancreas volume declines during the first year after diagnosis of type 1 diabetes and exhibits altered diffusion at disease onset. Diabetes Care 42 248–257. (https://doi.org/10.2337/dc18-1507)
Vlasakakis G, Napolitano A, Barnard R, Brown K, Bullman J, Inman D, Keymeulen B, Lanham D, Leirens Q & Macdonald A et al. 2019 Target engagement and cellular fate of otelixizumab: a repeat dose escalation study of an anti-CD3epsilon mab in new-onset type 1 diabetes mellitus patients. British Journal of Clinical Pharmacology 85 704–714. (https://doi.org/10.1111/bcp.13842)
Von Herrath MG, Bain SC, Bode BW, Clausen JO, Coppieters K, Gaysina L, Gumprecht J, Hansen TK, Mathieu C & Portillo CM et al. 2020 278-or: efficacy and safety of anti-interleukin (IL)-21 in combination with liraglutide in adults recently diagnosed with type 1 diabetes. Diabetes 69 278-OR. (https://doi.org/10.2337/db20-278-OR)
Wagner DH 2016 Of the multiple mechanisms leading to type 1 diabetes, T cell receptor revision may play a prominent role (is type 1 diabetes more than a single disease?). Clinical and Experimental Immunology 185 271–280. (https://doi.org/10.1111/cei.12819)
Wasserfall C, Nick HS, Campbell-Thompson M, Beachy D, Haataja L, Kusmartseva I, Posgai A, Beery M, Rhodes C & Bonifacio E et al. 2017 Persistence of pancreatic insulin mrna expression and proinsulin protein in type 1 diabetes pancreata. Cell Metabolism 26 568.e3–575.e3. (https://doi.org/10.1016/j.cmet.2017.08.013)
Wiedeman AE, Muir VS, Rosasco MG, Deberg HA, Presnell S, Haas B, Dufort MJ, Speake C, Greenbaum CJ & Serti E et al. 2020 Autoreactive CD8+ T cell exhaustion distinguishes subjects with slow type 1 diabetes progression. Journal of Clinical Investigation 130 480–490. (https://doi.org/10.1172/JCI126595)
Ziegler AG, Rewers M, Simell O, Simell T, Lempainen J, Steck A, Winkler C, Ilonen J, Veijola R & Knip M et al. 2013 Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA 309 2473–2479. (https://doi.org/10.1001/jama.2013.6285)
