The 2019 report of a randomized, placebo-controlled clinical trial demonstrating that immune therapy can delay the onset of clinical type 1 diabetes (T1D) in antibody-positive relatives by a median ...of 2 years stands out as a landmark in the decades-long effort to prevent T1D. With this important step achieved, it is now time to consider what is needed to bring disease-modifying therapy for prevention or delay of T1D to clinical use from this point. Long considered a chicken and egg problem (why screen for T1D risk when we have no therapy, and how can we develop therapies without more screening), we now have the opportunity to break this impasse. The purpose of this article is to place this clinical trial result in context, highlighting key foundational studies leading to this accomplishment, addressing the current gaps, and suggesting that a key next step for prevention of T1D is to screen and monitor relatives for T1D risk in the context of clinical care.
Insights from prospective, longitudinal studies of individuals at risk for developing type 1 diabetes have demonstrated that the disease is a continuum that progresses sequentially at variable but ...predictable rates through distinct identifiable stages prior to the onset of symptoms. Stage 1 is defined as the presence of β-cell autoimmunity as evidenced by the presence of two or more islet autoantibodies with normoglycemia and is presymptomatic, stage 2 as the presence of β-cell autoimmunity with dysglycemia and is presymptomatic, and stage 3 as onset of symptomatic disease. Adoption of this staging classification provides a standardized taxonomy for type 1 diabetes and will aid the development of therapies and the design of clinical trials to prevent symptomatic disease, promote precision medicine, and provide a framework for an optimized benefit/risk ratio that will impact regulatory approval, reimbursement, and adoption of interventions in the early stages of type 1 diabetes to prevent symptomatic disease.
Type 1 diabetes is a chronic autoimmune disease that leads to destruction of insulin-producing beta cells and dependence on exogenous insulin for survival. Some interventions have delayed the loss of ...insulin production in patients with type 1 diabetes, but interventions that might affect clinical progression before diagnosis are needed.
We conducted a phase 2, randomized, placebo-controlled, double-blind trial of teplizumab (an Fc receptor-nonbinding anti-CD3 monoclonal antibody) involving relatives of patients with type 1 diabetes who did not have diabetes but were at high risk for development of clinical disease. Patients were randomly assigned to a single 14-day course of teplizumab or placebo, and follow-up for progression to clinical type 1 diabetes was performed with the use of oral glucose-tolerance tests at 6-month intervals.
A total of 76 participants (55 72% of whom were ≤18 years of age) underwent randomization - 44 to the teplizumab group and 32 to the placebo group. The median time to the diagnosis of type 1 diabetes was 48.4 months in the teplizumab group and 24.4 months in the placebo group; the disease was diagnosed in 19 (43%) of the participants who received teplizumab and in 23 (72%) of those who received placebo. The hazard ratio for the diagnosis of type 1 diabetes (teplizumab vs. placebo) was 0.41 (95% confidence interval, 0.22 to 0.78; P = 0.006 by adjusted Cox proportional-hazards model). The annualized rates of diagnosis of diabetes were 14.9% per year in the teplizumab group and 35.9% per year in the placebo group. There were expected adverse events of rash and transient lymphopenia. KLRG1+TIGIT+CD8+ T cells were more common in the teplizumab group than in the placebo group. Among the participants who were HLA-DR3-negative, HLA-DR4-positive, or anti-zinc transporter 8 antibody-negative, fewer participants in the teplizumab group than in the placebo group had diabetes diagnosed.
Teplizumab delayed progression to clinical type 1 diabetes in high-risk participants. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT01030861.).
Aims:
Our study aims were to determine the frequency of MODY mutations (HNF1A, HNF4A, glucokinase) in a diverse population of youth with diabetes and to assess how well clinical features identify ...youth with maturity-onset diabetes of the young (MODY).
Methods:
The SEARCH for Diabetes in Youth study is a US multicenter, population-based study of youth with diabetes diagnosed at age younger than 20 years. We sequenced genomic DNA for mutations in the HNF1A, HNF4A, and glucokinase genes in 586 participants enrolled in SEARCH between 2001 and 2006. Selection criteria included diabetes autoantibody negativity and fasting C-peptide levels of 0.8 ng/mL or greater.
Results:
We identified a mutation in one of three MODY genes in 47 participants, or 8.0% of the tested sample, for a prevalence of at least 1.2% in the pediatric diabetes population. Of these, only 3 had a clinical diagnosis of MODY, and the majority was treated with insulin. Compared with the MODY-negative group, MODY-positive participants had lower FCP levels (2.2 ± 1.4 vs 3.2 ± 2.1 ng/mL, P < .01) and fewer type 2 diabetes-like metabolic features. Parental history of diabetes did not significantly differ between the 2 groups.
Conclusions/Interpretation:
In this systematic study of MODY in a large pediatric US diabetes cohort, unselected by referral pattern or family history, MODY was usually misdiagnosed and incorrectly treated with insulin. Although many type 2 diabetes-like metabolic features were less common in the mutation-positive group, no single characteristic identified all patients with mutations. Clinicians should be alert to the possibility of MODY diagnosis, particularly in antibody-negative youth with 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.
We aimed to describe the natural history of residual insulin secretion in Type 1 Diabetes TrialNet participants over 4 years from diagnosis and relate this to previously reported alternative clinical ...measures reflecting β-cell secretory function.
Data from 407 subjects from 5 TrialNet intervention studies were analyzed. All subjects had baseline stimulated C-peptide values of ≥0.2 nmol/L from mixed-meal tolerance tests (MMTTs). During semiannual visits, C-peptide values from MMTTs, HbA1c, and insulin doses were obtained.
The percentage of individuals with stimulated C-peptide of ≥0.2 nmol/L or detectable C-peptide of ≥0.017 nmol/L continued to diminish over 4 years; this was markedly influenced by age. At 4 years, only 5% maintained their baseline C-peptide secretion. The expected inverse relationships between C-peptide and HbA1c or insulin doses varied over time and with age. Combined clinical variables, such as insulin-dose adjusted HbA1c (IDAA1C) and the relationship of IDAA1C to C-peptide, also were influenced by age and time from diagnosis. Models using these clinical measures did not fully predict C-peptide responses. IDAA1C ≤9 underestimated the number of individuals with stimulated C-peptide ≥0.2 nmol/L, especially in children.
Current trials of disease-modifying therapy for type 1 diabetes should continue to use C-peptide as a primary end point of β-cell secretory function. Longer duration of follow-up is likely to provide stronger evidence of the effect of disease-modifying therapy on preservation of β-cell function.
OBJECTIVE:--β-Cell function in type 1 diabetes clinical trials is commonly measured by C-peptide response to a secretagogue in either a mixed-meal tolerance test (MMTT) or a glucagon stimulation test ...(GST). The Type 1 Diabetes TrialNet Research Group and the European C-peptide Trial (ECPT) Study Group conducted parallel randomized studies to compare the sensitivity, reproducibility, and tolerability of these procedures. RESEARCH DESIGN AND METHODS--In randomized sequences, 148 TrialNet subjects completed 549 tests with up to 2 MMTT and 2 GST tests on separate days, and 118 ECPT subjects completed 348 tests (up to 3 each) with either two MMTTs or two GSTs. RESULTS:--Among individuals with up to 4 years' duration of type 1 diabetes, >85% had measurable stimulated C-peptide values. The MMTT stimulus produced significantly higher concentrations of C-peptide than the GST. Whereas both tests were highly reproducible, the MMTT was significantly more so (R² = 0.96 for peak C-peptide response). Overall, the majority of subjects preferred the MMTT, and there were few adverse events. Some older subjects preferred the shorter duration of the GST. Nausea was reported in the majority of GST studies, particularly in the young age-group. CONCLUSIONS:--The MMTT is preferred for the assessment of β-cell function in therapeutic trials in type 1 diabetes.
It is generally accepted that complete β-cell destruction eventually occurs in individuals with type 1 diabetes, which has implications for treatment approaches and insurance coverage. The frequency ...of residual insulin secretion in a large cohort of individuals at varying ages of diagnosis and type 1 diabetes duration is unknown.
The frequency of residual insulin secretion was determined by measurement of nonfasting serum C-peptide concentration in 919 individuals with type 1 diabetes according to prespecified groups based on age at diagnosis and duration of disease (from 3 to 81 years' duration). Stimulated C-peptide was measured in those with detectable nonfasting values and a group of those with undetectable values as control.
The overall frequency of detectable nonfasting C-peptide was 29%, decreasing with time from diagnosis regardless of age at diagnosis. In all duration groups, the frequency of C-peptide was higher with diagnosis age >18 years compared with ≤18 years. Nineteen percent of those with undetectable nonfasting C-peptide were C-peptide positive upon stimulation testing.
The American Diabetes Association's definition of type 1 diabetes as "usually leading to absolute insulin deficiency" results in clinicians often considering the presence of residual insulin secretion as unexpected in this population. However, our data suggest that residual secretion is present in almost one out of three individuals 3 or more years from type 1 diabetes diagnosis. The frequency of residual C-peptide decreases with time from diagnosis regardless of age at diagnosis, yet at all durations of disease, diagnosis during adulthood is associated with greater frequency and higher values of C-peptide.
The goal of personalized medicine is to match the right drugs to the right patients at the right time. Personalized medicine has been most successful in cases where there is a clear genetic linkage ...between a disease and a therapy. This is not the case with type 1 diabetes (T1D), a genetically complex immune-mediated disease of β-cell destruction. Researchers over decades have traced the natural history of disease sufficiently to use autoantibodies as predictive biomarkers for disease risk and to conduct successful clinical trials of disease-modifying therapy. Recent studies, however, have highlighted heterogeneity associated with progression, with nonuniform rate of insulin loss and distinct features of the peri-diagnostic period. Likewise, there is heterogeneity in immune profiles and outcomes in response to therapy. Unexpectedly, from these studies demonstrating perplexing complexity in progression and response to therapy, new biomarker-based principles are emerging for how to achieve personalized therapies for T1D. These include therapy timed to periods of disease activity, use of patient stratification biomarkers to align therapeutic target with disease endotype, pharmacodynamic biomarkers to achieve personalized dosing and appropriate combination therapies, and efficacy biomarkers for "treat-to-target" strategies. These principles provide a template for application of personalized medicine to complex diseases.
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