Use of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of the risk classification of type 1 diabetes

Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis

AIMS/HYPOTHESIS

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.

METHODS

We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation.

RESULTS

The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics.

CONCLUSIONS/INTERPRETATION

Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk of (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb+; (2) when people who are IAb+ are initially identified there is a need for confirmation using a second sample; (3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care.

Early Metabolic Endpoints Identify Persistent Treatment Efficacy in Recent-Onset Type 1 Diabetes Immunotherapy Trials

OBJECTIVE

Mixed-meal tolerance test-stimulated area under the curve (AUC) C-peptide at 12-24 months represents the primary end point for nearly all intervention trials seeking to preserve β-cell function in recent-onset type 1 diabetes. We hypothesized that participant benefit might be detected earlier and predict outcomes at 12 months posttherapy. Such findings would support shorter trials to establish initial efficacy.

RESEARCH DESIGN AND METHODS

We examined data from six Type 1 Diabetes TrialNet immunotherapy randomized controlled trials in a post hoc analysis and included additional stimulated metabolic indices beyond C-peptide AUC. We partitioned the analysis into successful and unsuccessful trials and analyzed the data both in the aggregate as well as individually for each trial.

RESULTS

Among trials meeting their primary end point, we identified a treatment effect at 3 and 6 months when using C-peptide AUC (P = 0.030 and P < 0.001, respectively) as a dynamic measure (i.e., change from baseline). Importantly, no such difference was seen in the unsuccessful trials. The use of C-peptide AUC as a 6-month dynamic measure not only detected treatment efficacy but also suggested long-term C-peptide preservation (R2 for 12-month C-peptide AUC adjusted for age and baseline value was 0.80, P < 0.001), and this finding supported the concept of smaller trial sizes down to 54 participants.

CONCLUSIONS

Early dynamic measures can identify a treatment effect among successful immune therapies in type 1 diabetes trials with good long-term prediction and practical sample size over a 6-month period. While external validation of these findings is required, strong rationale and data exist in support of shortening early-phase clinical trials.

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.

Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study

BACKGROUND

Detection of two or more autoantibodies (Ab) in the blood might describe those individuals at increased risk of developing type 1 diabetes (T1D) during the following years. The aim of this exploratory study is to propose a high versus low T1D risk classifier using machine learning technology based on continuous glucose monitoring (CGM) home data.

METHODS

Forty-two healthy relatives of people with T1D with mean ± SD age of 23.8 ± 10.5 years, HbA1c (glycated hemoglobin) of 5.3% ± 0.3%, and BMI (body mass index) of 23.2 ± 5.2 kg/m2 with zero (low risk; N = 21), and ≥2 (high risk; N = 21) Ab, were enrolled in an NIH (National Institutes of Health)-funded TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic features were extracted from two-hour post-SLMM CGM traces, compared across groups, and used in four supervised machine learning Ab risk status classifiers. Recursive Feature Elimination (RFE) algorithm was used for feature selection; classifiers were evaluated through 10-fold cross-validation, using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.

RESULTS

The percent time of glucose >180 mg/dL (T180), glucose range, and glucose CV (coefficient of variation) were the only significant differences between the glycemic features in the two groups with P values of .040, .035, and .028 respectively. The linear SVM (Support Vector Machine) model with RFE features achieved the best performance of classifying low-risk versus high-risk individuals with AUC-ROC = 0.88.

CONCLUSIONS

A machine learning technology, combining a potentially self-administered one-week CGM home test, has the potential to reliably assess the T1D risk.

Longitudinal Assessment of Pancreas Volume by MRI Predicts Progression to Stage 3 Type 1 Diabetes

OBJECTIVE

This multicenter prospective cohort study compared pancreas volume as assessed by MRI, metabolic scores derived from oral glucose tolerance testing (OGTT), and a combination of pancreas volume and metabolic scores for predicting progression to stage 3 type 1 diabetes (T1D) in individuals with multiple diabetes-related autoantibodies.

RESEARCH DESIGN AND METHODS

Pancreas MRI was performed in 65 multiple autoantibody-positive participants enrolled in the Type 1 Diabetes TrialNet Pathway to Prevention study. Prediction of progression to stage 3 T1D was assessed using pancreas volume index (PVI), OGTT-derived Index60 score and Diabetes Prevention Trial-Type 1 Risk Score (DPTRS), and a combination of PVI and DPTRS.

RESULTS

PVI, Index60, and DPTRS were all significantly different at study entry in 11 individuals who subsequently experienced progression to stage 3 T1D compared with 54 participants who did not experience progression (P < 0.005). PVI did not correlate with metabolic testing across individual study participants. PVI declined longitudinally in the 11 individuals diagnosed with stage 3 T1D, whereas Index60 and DPTRS increased. The area under the receiver operating characteristic curve for predicting progression to stage 3 from measurements at study entry was 0.76 for PVI, 0.79 for Index60, 0.79 for DPTRS, and 0.91 for PVI plus DPTRS.

CONCLUSIONS

These findings suggest that measures of pancreas volume and metabolism reflect distinct components of risk for developing stage 3 type 1 diabetes and that a combination of these measures may provide superior prediction than either alone.

Continuous glucose monitoring has an increasing role in pre-symptomatic type 1 diabetes: advantages, limitations, and comparisons with laboratory-based testing

Type 1 diabetes (T1D) is well-recognised as a continuum heralded by the development of islet autoantibodies, progression to islet autoimmunity causing beta cell destruction, culminating in insulin deficiency and clinical disease. Abnormalities of glucose homeostasis are known to exist well before the onset of typical symptoms. Laboratory-based tests such as the oral glucose tolerance test (OGTT) and glycated haemoglobin (HbA1c) have been used to stage T1D and assess the risk of progression to clinical T1D. Continuous glucose monitoring (CGM) can detect early glycaemic abnormalities and can therefore be used to monitor for metabolic deterioration in pre-symptomatic, islet autoantibody positive, at-risk individuals. Early identification of these children can not only reduce the risk of presentation with diabetic ketoacidosis (DKA), but also determine eligibility for prevention trials, which aim to prevent or delay progression to clinical T1D. Here, we describe the current state with regard to the use of the OGTT, HbA1c, fructosamine and glycated albumin in pre-symptomatic T1D. Using illustrative cases, we present our clinical experience with the use of CGM, and advocate for an increased role of this diabetes technology, for monitoring metabolic deterioration and disease progression in children with pre-symptomatic T1D.

Quantifying beta cell function in the preclinical stages of type 1 diabetes

Clinically symptomatic type 1 diabetes (stage 3 type 1 diabetes) is preceded by a pre-symptomatic phase, characterised by progressive loss of functional beta cell mass after the onset of islet autoimmunity, with (stage 2) or without (stage 1) measurable changes in glucose profile during an OGTT. Identifying metabolic tests that can longitudinally track changes in beta cell function is of pivotal importance to track disease progression and measure the effect of disease-modifying interventions. In this review we describe the metabolic changes that occur in the early pre-symptomatic stages of type 1 diabetes with respect to both insulin secretion and insulin sensitivity, as well as the measurable outcomes that can be derived from the available tests. We also discuss the use of metabolic modelling to identify insulin secretion and sensitivity, and the measurable changes during dynamic tests such as the OGTT. Finally, we review the role of risk indices and minimally invasive measures such as those derived from the use of continuous glucose monitoring.

Screening and Prevention of Type 1 Diabetes: Where Are We?

A diagnosis of type 1 diabetes (T1D) and the subsequent requirement for exogenous insulin treatment is associated with considerable acute and chronic morbidity and a substantial effect on patient quality of life. Importantly, a large body of work suggests that early identification of presymptomatic T1D can accurately predict clinical disease, and when paired with education and monitoring, can yield improved health outcomes. Furthermore, a growing cadre of effective disease-modifying therapies provides the potential to alter the natural history of early stages of T1D. In this mini review, we highlight prior work that has led to the current landscape of T1D screening and prevention, as well as challenges and next steps moving into the future of these rapidly evolving areas of patient care.

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis

Background

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk.

Methods

We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation.

Findings

The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics.

Interpretation

Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Progression likelihood score identifies substages of presymptomatic type 1 diabetes in childhood public health screening

AIMS/HYPOTHESIS

The aim of this study was to develop strategies that identify children from the general population who have late-stage presymptomatic type 1 diabetes and may, therefore, benefit from immune intervention.

METHODS

We tested children from Bavaria, Germany, aged 1.75-10 years, enrolled in the Fr1da public health screening programme for islet autoantibodies (n=154,462). OGTT and HbA_1c were assessed in children with multiple islet autoantibodies for diagnosis of presymptomatic stage 1 (normoglycaemia) or stage 2 (dysglycaemia) type 1 diabetes. Cox proportional hazards and penalised logistic regression of autoantibody, genetic, metabolic and demographic information were used to develop a progression likelihood score to identify children with stage 1 type 1 diabetes who progressed to stage 3 (clinical) type 1 diabetes within 2 years.

RESULTS

Of 447 children with multiple islet autoantibodies, 364 (81.4%) were staged. Undiagnosed stage 3 type 1 diabetes, presymptomatic stage 2, and stage 1 type 1 diabetes were detected in 41 (0.027% of screened children), 30 (0.019%) and 293 (0.19%) children, respectively. The 2 year risk for progression to stage 3 type 1 diabetes was 48% (95% CI 34, 58) in children with stage 2 type 1 diabetes (annualised risk, 28%). HbA_1c, islet antigen-2 autoantibody positivity and titre, and the 90 min OGTT value were predictors of progression in children with stage 1 type 1 diabetes. The derived progression likelihood score identified substages corresponding to ≤90th centile (stage 1a, n=258) and >90th centile (stage 1b, n=29; 0.019%) of stage 1 children with a 4.1% (95% CI 1.4, 6.7) and 46% (95% CI 21, 63) 2 year risk of progressing to stage 3 type 1 diabetes, respectively.

CONCLUSIONS/INTERPRETATION

Public health screening for islet autoantibodies found 0.027% of children to have undiagnosed clinical type 1 diabetes and 0.038% to have undiagnosed presymptomatic stage 2 or stage 1b type 1 diabetes, with 50% risk to develop clinical type 1 diabetes within 2 years.

Persistence of β-Cell Responsiveness for Over Two Years in Autoantibody-Positive Children With Marked Metabolic Impairment at Screening

OBJECTIVE

We studied longitudinal differences between progressors and nonprogressors to type 1 diabetes with similar and substantial baseline risk.

RESEARCH DESIGN AND METHODS

Changes in 2-h oral glucose tolerance test indices were used to examine variability in diabetes progression in the Diabetes Prevention Trial-Type 1 (DPT-1) study (n = 246) and Type 1 Diabetes TrialNet Pathway to Prevention study (TNPTP) (n = 503) among autoantibody (Ab)+ children (aged <18.0 years) with similar baseline metabolic impairment (DPT-1 Risk Score [DPTRS] of 6.5-7.5), as well as in TNPTP Ab- children (n = 94).

RESULTS

Longitudinal analyses revealed annualized area under the curve (AUC) of C-peptide increases in nonprogressors versus decreases in progressors (P ≤ 0.026 for DPT-1 and TNPTP). Vector indices for AUC glucose and AUC C-peptide changes (on a two-dimensional grid) also differed significantly (P < 0.001). Despite marked baseline metabolic impairment of nonprogressors, changes in AUC C-peptide, AUC glucose, AUC C-peptide-to-AUC glucose ratio (AUC ratio), and Index60 did not differ from Ab- relatives during follow-up. Divergence between nonprogressors and progressors occurred by 6 months from baseline in both cohorts (AUC glucose, P ≤ 0.007; AUC ratio, P ≤ 0.034; Index60, P < 0.001; vector indices of change, P < 0.001). Differences in 6-month change were positively associated with greater diabetes risk (respectively, P < 0.001, P ≤ 0.019, P < 0.001, and P < 0.001) in DPT-1 and TNPTP, except AUC ratio, which was inversely associated with risk (P < 0.001).

CONCLUSIONS

Novel findings show that even with similarly abnormal baseline risk, progressors had appreciably more metabolic impairment than nonprogressors within 6 months and that the measures showing impairment were predictive of type 1 diabetes. Longitudinal metabolic patterns did not differ between nonprogressors and Ab- relatives, suggesting persistent β-cell responsiveness in nonprogressors.

Index60 Identifies Individuals at Appreciable Risk for Stage 3 Among an Autoantibody-Positive Population With Normal 2-Hour Glucose Levels: Implications for Current Staging Criteria of Type 1 Diabetes

OBJECTIVE

We assessed whether Index60, a composite measure of fasting C-peptide, 60-min C-peptide, and 60-min glucose, could improve the metabolic staging of type 1 diabetes for progression to clinical disease (stage 3) among autoantibody-positive (Ab+) individuals with normal 2-h glucose values (<140 mg/dL).

RESEARCH DESIGN AND METHODS

We analyzed 3,058 Type 1 Diabetes TrialNet Pathway to Prevention participants with 2-h glucose <140 mg/dL and Index60 <1.00 values from baseline oral glucose tolerance tests. Characteristics associated with type 1 diabetes (younger age, greater Ab+, higher HLA DR3-DQ2/DR4-DQ8 prevalence, and lower C-peptide) were compared among four mutually exclusive groups: top 2-h glucose quartile only (HI-2HGLU), top Index60 quartile only (HI-IND60), both top quartiles (HI-BOTH), and neither top quartile (LO-BOTH). Additionally, within the 2-h glucose distribution of <140 mg/dL and separately within the Index60 <1.00 distribution, comparisons were made between those above or below the medians.

RESULTS

HI-IND60 and HI-BOTH were younger, with greater frequency of more than two Ab+, and lower C-peptide levels, than either HI-2HGLU or LO-BOTH (all P < 0.001). The cumulative incidence for stage 3 was greater for HI-IND60 and HI-BOTH than for either HI-2HGLU or LO-BOTH (all P < 0.001). Those with Index60 values above the median were younger and had higher frequency of two or more Ab+ (P < 0.001) and DR3-DQ2/DR4-DQ8 prevalence (P < 0.001) and lower area under the curve (AUC) C-peptide levels (P < 0.001) than those below. Those above the 2-h glucose median had higher AUC C-peptide levels (P < 0.001), but otherwise did not differ from those below.

CONCLUSIONS

Index60 identifies individuals with characteristics of type 1 diabetes at appreciable risk for progression who would otherwise be missed by 2-h glucose staging criteria.

The Deterrence of Rapid Metabolic Decline Within 3 Months After Teplizumab Treatment in Individuals at High Risk for Type 1 Diabetes

End points that provide an early identification of treatment effects are needed to implement type 1 diabetes prevention trials more efficiently. To this end, we assessed whether metabolic end points can be used to detect a teplizumab effect on rapid β-cell decline within 3 months after treatment in high-risk individuals in the TrialNet teplizumab trial. Glucose and C-peptide response curves (GCRCs) were constructed by plotting mean glucose and C-peptide values from 2-h oral glucose tolerance tests on a two-dimensional grid. Groups were compared visually for changes in GCRC shape and movement. GCRC changes reflected marked metabolic deterioration in the placebo group within 3 months of randomization. By 6 months, GCRCs resembled typical GCRCs at diagnosis. In contrast, GCRC changes in the teplizumab group suggested metabolic improvement. Quantitative comparisons, including two novel metabolic end points that indicate GCRC changes, the within-quadrant end point and the ordinal directional end point, were consistent with visual impressions of an appreciable treatment effect at the 3- and 6-month time points. In conclusion, an analytic approach combining visual evidence with novel end points demonstrated that teplizumab delays rapid metabolic decline and improves the metabolic state within 3 months after treatment; this effect extends for at least 6 months.

Simplifying prediction of disease progression in pre-symptomatic type 1 diabetes using a single blood sample

AIMS/HYPOTHESIS

Accurate prediction of disease progression in individuals with pre-symptomatic type 1 diabetes has potential to prevent ketoacidosis and accelerate development of disease-modifying therapies. Current tools for predicting risk require multiple blood samples taken during an OGTT. Our aim was to develop and validate a simpler tool based on a single blood draw.

METHODS

Models to predict disease progression using a single OGTT time point (0, 30, 60, 90 or 120 min) were developed using TrialNet data collected from relatives with type 1 diabetes and validated in independent populations at high genetic risk of type 1 diabetes (TrialNet, Diabetes Prevention Trial-Type 1, The Environmental Determinants of Diabetes in the Young [1]) and in a general population of Bavarian children who participated in Fr1da.

RESULTS

Cox proportional hazards models combining plasma glucose, C-peptide, sex, age, BMI, HbA_1c and insulinoma antigen-2 autoantibody status predicted disease progression in all populations. In TrialNet, the AUC for receiver operating characteristic curves for models named M₆₀, M₉₀ and M₁₂₀, based on sampling at 60, 90 and 120 min, was 0.760, 0.761 and 0.745, respectively. These were not significantly different from the AUC of 0.760 for the gold standard Diabetes Prevention Trial Risk Score, which requires five OGTT blood samples. In TEDDY, where only 120 min blood sampling had been performed, the M₁₂₀ AUC was 0.865. In Fr1da, the M₁₂₀ AUC of 0.742 was significantly greater than the M₆₀ AUC of 0.615.

CONCLUSIONS/INTERPRETATION

Prediction models based on a single OGTT blood draw accurately predict disease progression from stage 1 or 2 to stage 3 type 1 diabetes. The operational simplicity of M₁₂₀, its validity across different at-risk populations and the requirement for 120 min sampling to stage type 1 diabetes suggest M₁₂₀ could be readily applied to decrease the cost and complexity of risk stratification.

Insulin immunotherapy for pretype 1 diabetes

PURPOSE OF REVIEW

Loss of tolerance to insulin likely contributes to the immunopathogenesis of type 1 diabetes (T1D). Several large clinical trials and smaller mechanistic studies have failed to demonstrate the efficacy of insulin antigen therapy. The growing awareness of the heterogeneity of T1D likely affects the response to various immune therapies including insulin. Identification of biomarkers of clinical response will provide further insight into mechanisms leading to the disease and classify responders in the quest for personalized therapy.

RECENT FINDINGS

Several biomarkers have identified subpopulations in posthoc analyses that showed benefit from oral insulin even though the placebo-controlled study was as a whole unsuccessful. High insulin autoantibody titer, low first phase insulin response, and high Diabetes Prevention Trial-Type 1 Risk Score identify at-risk relatives more likely to benefit from oral insulin. Future incorporation of human leukocyte antigen and the variable number of tandem repeats polymorphism located in the insulin gene promoter (INS VNTR) is of interest for both primary and secondary prevention studies.

SUMMARY

Although primary and secondary prevention trials using oral insulin are ongoing, those completed have been largely unsuccessful. However, we believe that oral insulin should be considered in future trials as part of combination therapies as prerandomization biomarker testing is refined.

The Effect of Immunosuppressive Adjuvant Kynurenine on Type 1 Diabetes Vaccine

Inducing antigen-specific tolerance is a promising treatment for preventing or reversing Type 1 diabetes (T1D). In contrast to a vaccine that induces immune responses against pathogens, a tolerogenic vaccine can suppress immunity against antigens causing diseases by administrating a mixture of self-antigens with an adjuvant that decreases the strength of antigen-specific response. Kynurenine (Kyn) is an endogenous substance that can inhibit the natural killer cell and T cell proliferation and promote the differentiation of naïve T cells into regulatory T cells (T_regs). In this study, we evaluated the efficacy of Kyn as a novel suppressive adjuvant. Kyn was co-immunized with GAD65 phage vaccine to induce T_reg cells and tolerogenic responses for the prevention of T1D in NOD mouse model. Mice were subcutaneously immunized two times with 10¹¹ Pfu (100μL,10¹² Pfu/ml) GAD65 phage vaccine doses mixed with 200 μg of Kyn. Serum antibodies and cytokines were detected by ELISA and electrochemiluminescence, respectively. Flow cytometry assay was used to analyze DC and Treg. MTS was used for the analysis of spleen lymphocyte proliferation. RNA sequencing was used to investigate mRNA and miRNA expression profiles in spleen lymphocytes. Compared to GAD65 phage vaccine alone, co-immunization of Kyn and GAD65 phage vaccine resulted in the prevention of hyperglycemia in 60% of mice for at least one month. Further, Kyn enhances GAD65-specific Th2-mediated immune responses; regulates the Th1/Th2 imbalance and increases the secretion of Th2 cytokines and the number of CD4⁺CD25⁺Foxp3⁺T cells; suppresses DC maturation and GAD65-specific T lymphocyte proliferation. Moreover, we integrated Kyn related miRNA and mRNA expression profiles obtained from the spleen lymphocyte RNA-sequencing which was stimulated by Kyn in vitro. These data provide an important basis for understanding the mechanisms underlying Kyn as an immunosuppressive adjuvant which regulated the immune response. These findings suggest that Kyn can serve as an effective suppressive adjuvant candidate for Type 1 diabetes vaccines.

The 1-Hour Plasma Glucose: Common Link Across the Glycemic Spectrum

Evidence from populations at risk for type 1 diabetes, type 2 diabetes or gestational diabetes substantiates the 1-hour plasma glucose as a sensitive alternative marker for identifying high-risk individuals when ß-cell function is relatively more functional. An elevated 1-hour plasma glucose could therefore diagnose dysglycemia and risk for complications across the glycemic spectrum. Reducing the 2-hour oral glucose tolerance test to 1-hour would reduce the burden on patients, likely reduce costs, and enhance its accessibility in practice.

One-Hour Oral Glucose Tolerance Tests for the Prediction and Diagnostic Surveillance of Type 1 Diabetes

CONTEXT

Once islet autoantibody-positive individuals are identified, predicting which individuals are at highest risk for type 1 diabetes (T1D) is important. A metabolic risk score derived from 2-hour oral glucose tolerance test (OGTT) data, the Diabetes Prevention Trial-Type 1 risk score (DPTRS), can accurately predict T1D. However, 2-hour OGTTs are time-consuming and costly.

OBJECTIVE

We aimed to determine whether a risk score derived from 1-hour OGTT data can predict T1D as accurately as the DPTRS. Secondarily, we evaluated whether a 1-hour glucose value can be used for diagnostic surveillance.

METHODS

The DPTRS was modified to derive a 1-hour OGTT risk score (DPTRS60) using fasting C-peptide, 1-hour glucose and C-peptide, age, and body mass index. Areas under receiver operating curves (ROCAUCs) were used to compare prediction accuracies of DPTRS60 with DPTRS in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 654) and TrialNet Pathway to Prevention (TNPTP) (n = 4610) participants. Negative predictive values (NPV) for T1D diagnosis were derived for 1-hour glucose thresholds.

RESULTS

ROCAUCs for T1D prediction 5 years from baseline were similar between DPTRS60 and DPTRS (DPT-1: 0.805 and 0.794; TNPTP: 0.832 and 0.847, respectively). DPTRS60 predicted T1D significantly better than 2-hour glucose (P < .001 in both cohorts). A 1-hour glucose of less than 180 mg/dL had a similar NPV, positive predictive value, and specificity for T1D development before the next 6-month visit as the standard 2-hour threshold of less than 140 mg/dL (both ≥ 98.5%).

CONCLUSION

A 1-hour OGTT can predict T1D as accurately as a 2-hour OGTT with minimal risk of missing a T1D diagnosis before the next visit.

Mechanisms of diabetic cardiomyopathy and potential therapeutic strategies: preclinical and clinical evidence

The pathogenesis and clinical features of diabetic cardiomyopathy have been well-studied in the past decade, but effective approaches to prevent and treat this disease are limited. Diabetic cardiomyopathy occurs as a result of the dysregulated glucose and lipid metabolism associated with diabetes mellitus, which leads to increased oxidative stress and the activation of multiple inflammatory pathways that mediate cellular and extracellular injury, pathological cardiac remodelling, and diastolic and systolic dysfunction. Preclinical studies in animal models of diabetes have identified multiple intracellular pathways involved in the pathogenesis of diabetic cardiomyopathy and potential cardioprotective strategies to prevent and treat the disease, including antifibrotic agents, anti-inflammatory agents and antioxidants. Some of these interventions have been tested in clinical trials and have shown favourable initial results. In this Review, we discuss the mechanisms underlying the development of diabetic cardiomyopathy and heart failure in type 1 and type 2 diabetes mellitus, and we summarize the evidence from preclinical and clinical studies that might provide guidance for the development of targeted strategies. We also highlight some of the novel pharmacological therapeutic strategies for the treatment and prevention of diabetic cardiomyopathy.

Slowed Metabolic Decline After 1 Year of Oral Insulin Treatment Among Individuals at High Risk for Type 1 Diabetes in the Diabetes Prevention Trial-Type 1 (DPT-1) and TrialNet Oral Insulin Prevention Trials

We assessed whether oral insulin slowed metabolic decline after 1 year of treatment in individuals at high risk for type 1 diabetes. Two oral insulin trials that did not show efficacy overall and had type 1 diabetes as the primary end point were analyzed: the Diabetes Prevention Trial-Type 1 (DPT-1) and the TrialNet oral insulin trials. Oral glucose tolerance tests at baseline and after 1 year of treatment were analyzed. Among those at high risk (with a Diabetes Prevention Trial-Type 1 Risk Score [DPTRS] ≥6.75), the area under the curve (AUC) C-peptide increased significantly from baseline to 1 year in each oral insulin group, whereas the AUC glucose increased significantly in each placebo group. At 1 year, the AUC C-peptide/AUC glucose (AUC Ratio) was significantly higher in the oral insulin group than in the placebo group in each trial (P < 0.05; P = 0.057 when adjusted for age in the TrialNet trial) and in both trials combined (P < 0.01 with or without adjustment for age). For a DPTRS <6.75, oral insulin groups did not differ from placebo groups in the AUC Ratio. The findings suggest that 1 year of treatment with oral insulin slows metabolic deterioration in individuals at high risk for type 1 diabetes. Moreover, the findings further suggest that metabolic end points can be useful adjuncts to the diagnostic end point in assessments of preventive treatments for the disorder.

Cause or effect? A review of clinical data demonstrating beta cell dysfunction prior to the clinical onset of type 1 diabetes

Background

Limited successes of conventional approaches to type 1 diabetes (T1D) prevention and treatment have highlighted the need for improved understanding of risk factors contributing to or hastening progression to clinical diagnosis.

Scope of review

This review summarizes beta cell function metabolic phenotyping data from clinical studies conducted in at-risk individuals before T1D onset and healthy controls. Data are drawn from studies comparing at-risk individuals who progress to T1D to at-risk individuals who do not progress to T1D, as well as from studies comparing at-risk individuals to controls without a T1D family history.

Major conclusions

Rapid loss of beta cell insulin secretion occurs in the months immediately preceding clinical onset. However, evidence of beta cell dysfunction is present even years earlier. Comparisons to controls without a family history suggest that many individuals in families impacted by T1D have evidence of beta cell dysfunction, even individuals who are unlikely to develop clinical disease. These findings may mean that underlying metabolic beta cell dysfunction contributes to T1D development and may explain some of the heterogeneity observed in the disease.

The HD-OCT Study May Be Useful in Searching for Markers of Preclinical Stage of Diabetic Retinopathy in Patients with Type 1 Diabetes

The aim of the study was to analyze the thickness of individual retinal layers in patients with type 1 diabetes (T1D) in comparison to the control group and in relation to markers of diabetes metabolic control. The study group consisted of 111 patients with an average of 6-years of T1D duration. The control group included 36 gender- and age-matched individuals. In all patients optical coherence tomography (OCT) study was performed using HD-OCT Cirrus 5000 with evaluation of optic nerve head (ONH) parameters, thickness of retinal nerve fiber layer (RNFL) with its quadrants, macular full-thickness parameters, ganglion cells with inner plexus layer (GCIPL) and choroidal thickness (CT). Lower disc area value was observed in the study group as compared to controls (p = 0.0215). Negative correlations were found both between age at examination and rim area (R = −0.28, p = 0.0007) and between superior RNFL thickness and duration of diabetes (R = −0.20, p = 0.0336). Positive correlation between center thickness and SD for average glycemia (R = 0.30, p = 0.0071) was noted. Temporal CT correlated positively with age at examination (R = 0.21, p = 0.0127). The selected parameters the HD-OCT study may in the future serve as potential markers of preclinical phase of DR in patients with T1D.

The Influence of Type 2 Diabetes-Associated Factors on Type 1 Diabetes

Current efforts to prevent progression from islet autoimmunity to type 1 diabetes largely focus on immunomodulatory approaches. However, emerging data suggest that the development of diabetes in islet autoantibody-positive individuals may also involve factors such as obesity and genetic variants associated with type 2 diabetes, and the influence of these factors increases with age at diagnosis. Although these factors have been linked with metabolic outcomes, particularly through their impact on β-cell function and insulin sensitivity, growing evidence suggests that they might also interact with the immune system to amplify the autoimmune response. The presence of factors shared by both forms of diabetes contributes to disease heterogeneity and thus has important implications. Characteristics that are typically considered to be nonimmune should be incorporated into predictive algorithms that seek to identify at-risk individuals and into the designs of trials for disease prevention. The heterogeneity of diabetes also poses a challenge in diagnostic classification. Finally, after clinically diagnosing type 1 diabetes, addressing nonimmune elements may help to prevent further deterioration of β-cell function and thus improve clinical outcomes. This Perspectives in Care article highlights the role of type 2 diabetes-associated genetic factors (e.g., gene variants at transcription factor 7-like 2 [TCF7L2]) and obesity (via insulin resistance, inflammation, β-cell stress, or all three) in the pathogenesis of type 1 diabetes and their impacts on age at diagnosis. Recognizing that type 1 diabetes might result from the sum of effects from islet autoimmunity and type 2 diabetes-associated factors, their interactions, or both affects disease prediction, prevention, diagnosis, and treatment.

Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children

OBJECTIVE

The capacity to precisely predict progression to type 1 diabetes (T1D) in young children over a short time span is an unmet need. We sought to develop a risk algorithm to predict progression in children with high-risk human leukocyte antigen (HLA) genes followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study.

METHODS

Logistic regression and 4-fold cross-validation examined 38 candidate predictors of risk from clinical, immunologic, metabolic, and genetic data. TEDDY subjects with at least one persistent, confirmed autoantibody at age 3 were analyzed with progression to T1D by age 6 serving as the primary endpoint. The logistic regression prediction model was compared to two non-statistical predictors, multiple autoantibody status, and presence of insulinoma-associated-2 autoantibodies (IA-2A).

RESULTS

A total of 363 subjects had at least one autoantibody at age 3. Twenty-one percent of subjects developed T1D by age 6. Logistic regression modeling identified 5 significant predictors - IA-2A status, hemoglobin A1c, body mass index Z-score, single-nucleotide polymorphism rs12708716_G, and a combination marker of autoantibody number plus fasting insulin level. The logistic model yielded a receiver operating characteristic area under the curve (AUC) of 0.80, higher than the two other predictors; however, the differences in AUC, sensitivity, and specificity were small across models.

CONCLUSIONS

This study highlights the application of precision medicine techniques to predict progression to diabetes over a 3-year window in TEDDY subjects. This multifaceted model provides preliminary improvement in prediction over simpler prediction tools. Additional tools are needed to maximize the predictive value of these approaches.

Advances in immunotherapy of type I diabetes

Type 1 diabetes mellitus (T1DM) is an autoimmune disease affecting 3 million individuals in the U.S. The pathogenesis of T1DM is driven by immune-mediated destruction of pancreatic β cells, the source of glucose regulator insulin. While T1DM can be successfully managed with insulin replacement therapy, approaches that can modify the underlying immuno-pathology of β cell destruction has been long sought after. Immunotherapy can attenuate T cell responses against β cell antigens. Given the detailed cellular and molecular definitions of T1DM immune responses, rational immunomodulation can be and have been developed in mouse models, and in some instances, tested in humans. The possibility of identifying individuals who are predisposed to T1DM through genotyping lend to the possibility of preventive vaccines. While much has been accomplished in delineating the mechanisms of immunotherapies, some of which are being tested in humans, long-term preservation of β cells and insulin independency has not been achieved. In this regard, the drug delivery field has much to offer in maximizing the benefits of immune modulators by optimizing spatiotemporal presentation of antigens and costimulatory signals. In this review, we attempt to capture the current state of T1DM immunotherapy by highlighting representative studies.

Marie M, Stickney K, VanBuecken D, Vellek B, Webber C, Allen L, Bollyk J, Hilderman N, Ismail H, Lamola S, Sanda S, Vendettuoli H, Tridgell D, Monzavi R, Bock M, Fisher L, Halvorson M, Jeandron D, Kim M, Wood J, Geffner M, Kaufman F, Parkman R, Salazar C, Goland R, Clynes R, Cook S, Freeby M, Gallagher MP, Gandica R, Greenberg E, Kurland A, Pollak S, Wolk A, Chan M, Koplimae L, Levine E, Smith K, Trast J, DiMeglio L, Blum J, Evans-Molina C, Hufferd R, Jagielo B, Kruse C, Patrick V, Rigby M, Spall M, Swinney K, Terrell J, Christner L, Ford L, Lynch S, Menendez M, Merrill P, Pescovitz M, Rodriguez H, Alleyn C, Baidal D, Fay S, Gaglia J, Resnick B, Szubowicz S, Weir G, Benjamin R, Conboy D, deManbey A, Jackson R, Jalahej H, Orban T, Ricker A, Wolfsdorf J, Zhang HH, Wilson D, Aye T, Baker B, Barahona K, Buckingham B, Esrey K, Esrey T, Fathman G, Snyder R, Aneja B, Chatav M, Espinoza O, Frank E, Liu J, Perry J, Pyle R, Rigby A, Riley K, Soto A, Gitelman S, Adi S, Anderson M, Berhel A, Breen K, Fraser K, Gerard-Gonzalez A, Jossan P, Lustig R, Moassesfar S, Mugg A, Ng D, Prahalod P, Rangel-Lugo M, Sanda S, Tarkoff J, Torok C, Wesch R, Aslan I, Buchanan J, Cordier J, Hamilton C, Hawkins L, Ho T, Jain A, Ko K, Lee T, Phelps S, Rosenthal S, Sahakitrungruang T, Stehl L, Taylor L, Wertz M, Wong J, Philipson L, Briars R, Devine N, Littlejohn E, Grant T, Gottlieb P, Klingensmith G, Steck A, Alkanani A, Bautista K, Bedoy R, Blau A, Burke B, Cory L, Dang M, Fitzgerald-Miller L, Fouts A, Gage V, Garg S, Gesauldo P, Gutin R, Hayes C, Hoffman M, Ketchum K, Logsden-Sackett N, Maahs D, Messer L, Meyers L, Michels A, Peacock S, Rewers M, Rodriguez P, Sepulbeda F, Sippl R, Steck A, Taki I, Tran BK, Tran T, Wadwa RP, Zeitler P, Barker J, Barry S, Birks L, Bomsburger L, Bookert T, Briggs L, Burdick P, Cabrera R, Chase P, Cobry E, Conley A, Cook G, Daniels J, DiDomenico D, Eckert J, Ehler A, Eisenbarth G, Fain P, Fiallo-Scharer R, Frank N, Goettle H, Haarhues M, Harris S, Horton L, Hutton J, Jeffrrey J, Jenison R, Jones K, Kastelic W, King MA, Lehr D, Lungaro J, Mason K, Maurer H, Nguyen L, Proto A, Realsen J, Schmitt K, Schwartz M, Skovgaard S, Smith J, Vanderwel B, Voelmle M, Wagner R, Wallace A, Walravens P, Weiner L, Westerhoff B, Westfall E, Widmer K, Wright H, Schatz D, Abraham A, Atkinson M, Cintron M, Clare-Salzler M, Ferguson J, Haller M, Hosford J, Mancini D, Rohrs H, Silverstein J, Thomas J, Winter W, Cole G, Cook R, Coy R, Hicks E, Lewis N, Marks J, Pugliese A, Blaschke C, Matheson D, Sanders-Branca N, Sosenko J, Arazo L, Arce R, Cisneros M, Sabbag S, Moran A, Gibson C, Fife B, Hering B, Kwong C, Leschyshyn J, Nathan B, Pappenfus B, Street A, Boes MA, Eck SP, Finney L, Fischer TA, Martin A, Muzamhindo CJ, Rhodes M, Smith J, Wagner J, Wood B, Becker D, Delallo K, Diaz A, Elnyczky B, Libman I, Pasek B, Riley K, Trucco M, Copemen B, Gwynn D, Toledo F, Rodriguez H, Bollepalli S, Diamond F, Eyth E, Henson D, Lenz A, Shulman D, Raskin P, Adhikari S, Dickson B, Dunnigan E, Lingvay I, Pruneda L, Ramos-Roman M, Raskin P, Rhee C, Richard J, Siegelman M, Sturges D, Sumpter K, White P, Alford M, Arthur J, Aviles-Santa ML, Cordova E, Davis R, Fernandez S, Fordan S, Hardin T, Jacobs A, Kaloyanova P, Lukacova-Zib I, Mirfakhraee S, Mohan A, Noto H, Smith O, Torres N, Wherrett D, Balmer D, Eisel L, Kovalakovska R, Mehan M, Sultan F, Ahenkorah B, Cevallos J, Razack N, Ricci MJ, Rhode A, Srikandarajah M, Steger R, Russell WE, Black M, Brendle F, Brown A, Moore D, Pittel E, Robertson A, Shannon A, Thomas JW, Herold K, Feldman L, Sherwin R, Tamborlane W, Weinzimer S, Toppari J, Kallio T, Kärkkäinen M, Mäntymäki E, Niininen T, Nurmi B, Rajala P, Romo M, Suomenrinne S, Näntö-Salonen K, Simell O, Simell T, Bosi E, Battaglia M, Bianconi E, Bonfanti R, Grogan P, Laurenzi A, Martinenghi S, Meschi F, Pastore M, Falqui L, Muscato MT, Viscardi M, Castleden H, Farthing N, Loud S, Matthews C, McGhee J, Morgan A, Pollitt J, Elliot-Jones R, Wheaton C, Knip M, Siljander H, Suomalainen H, Colman P, Healy F, Mesfin S, Redl L, Wentworth J, Willis J, Farley M, Harrison L, Perry C, Williams F, Mayo A, Paxton J, Thompson V, Volin L, Fenton C, Carr L, Lemon E, Swank M, Luidens M, Salgam M, Sharma V, Schade D, King C, Carano R, Heiden J, Means N, Holman L, Thomas I, Madrigal D, Muth T, Martin C, Plunkett C, Ramm C, Auchus R, Lane W, Avots E, Buford M, Hale C, Hoyle J, Lane B, Muir A, Shuler S, Raviele N, Ivie E, Jenkins M, Lindsley K, Hansen I, Fadoju D, Felner E, Bode B, Hosey R, Sax J, Jefferies C, Mannering S, Prentis R, She J, Stachura M, Hopkins D, Williams J, Steed L, Asatapova E, Nunez S, Knight S, Dixon P, Ching J, Donner T, Longnecker S, Abel K, Arcara K, Blackman S, Clark L, Cooke D, Plotnick L, Levin P, Bromberger L, Klein K, Sadurska K, Allen C, Michaud D, Snodgrass H, Burghen G, Chatha S, Clark C, Silverberg J, Wittmer C, Gardner J, LeBoeuf C, Bell P, McGlore O, Tennet H, Alba N, Carroll M, Baert L, Beaton H, Cordell E, Haynes A, Reed C, Lichter K, McCarthy P, McCarthy S, Monchamp T, Roach J, Manies S, Gunville F, Marosok L, Nelson T, Ackerman K, Rudolph J, Stewart M, McCormick K, May S, Falls T, Barrett T, Dale K, Makusha L, McTernana C, Penny-Thomas K, Sullivan K, Narendran P, Robbie J, Smith D, Christensen R, Koehler B, Royal C, Arthur T, Houser H, Renaldi J, Watsen S, Wu P, Lyons L, House B, Yu J, Holt H, Nation M, Vickers C, Watling R, Heptulla R, Trast J, Agarwal C, Newell D, Katikaneni R, Gardner C, Del Rio A, Logan A, Collier H, Rishton C, Whalley G, Ali A, Ramtoola S, Quattrin T, Mastrandea L, House A, Ecker M, Huang C, Gougeon C, Ho J, Pacuad D, Dunger D, May J, O’Brien C, Acerini C, Salgin B, Thankamony A, Williams R, Buse J, Fuller G, Duclos M, Tricome J, Brown H, Pittard D, Bowlby D, Blue A, Headley T, Bendre S, Lewis K, Sutphin K, Soloranzo C, Puskaric J, Madison H, Rincon M, Carlucci M, Shridharani R, Rusk B, Tessman E, Huffman D, Abrams H, Biederman B, Jones M, Leathers V, Brickman W, Petrie P, Zimmerman D, Howard J, Miller L, Alemzadeh R, Mihailescu D, Melgozza-Walker R, Abdulla N, Boucher-Berry C, Ize-Ludlow D, Levy R, Swenson Brousell C, Scott R, Heenan H, Lunt H, Kendall D, Willis J, Darlow B, Crimmins N, Edler D, Weis T, Schultz C, Rogers D, Latham D, Mawhorter C, Switzer C, Spencer W, Konstantnopoulus P, Broder S, Klein J, Bachrach B, Gardner M, Eichelberger D, Knight L, Szadek L, Welnick G, Thompson B, Hoffman R, Revell A, Cherko J, Carter K, Gilson E, Haines J, Arthur G, Bowen B, Zipf W, Graves P, Lozano R, Seiple D, Spicer K, Chang A, Fregosi J, Harbinson J, Paulson C, Stalters S, Wright P, Zlock D, Freeth A, Victory J, Maheshwari H, Maheshwari A, Holmstrom T, Bueno J, Arguello R, Ahern J, Noreika L, Watson V, Hourse S, Breyer P, Kissel C, Nicholson Y, Pfeifer M, Almazan S, Bajaj J, Quinn M, Funk K, McCance J, Moreno E, Veintimilla R, Wells A, Cook J, Trunnel S, Transue D, Surhigh J, Bezzaire D, Moltz K, Zacharski E, Henske J, Desai S, Frizelis K, Khan F, Sjoberg R, Allen K, Manning P, Hendry G, Taylor B, Jones S, Couch R, Danchak R, Lieberman D, Strader W, Bencomo M, Bailey T, Bedolla L, Roldan C, Moudiotis C, Vaidya B, Anning C, Bunce S, Estcourt S, Folland E, Gordon E, Harrill C, Ireland J, Piper J, Scaife L, Sutton K, Wilkins S, Costelloe M, Palmer J, Casas L, Miller C, Burgard M, Erickson C, Hallanger-Johnson J, Clark P, Taylor W, Galgani J, Banerjee S, Banda C, McEowen D, Kinman R, Lafferty A, Gillett S, Nolan C, Pathak M, Sondrol L, Hjelle T, Hafner S, Kotrba J, Hendrickson R, Cemeroglu A, Symington T, Daniel M, Appiagyei-Dankah Y, Postellon D, Racine M, Kleis L, Barnes K, Godwin S, McCullough H, Shaheen K, Buck G, Noel L, Warren M, Weber S, Parker S, Gillespie I, Nelson B, Frost C, Amrhein J, Moreland E, Hayes A, Peggram J, Aisenberg J, Riordan M, Zasa J, Cummings E, Scott K, Pinto T, Mokashi A, McAssey K, Helden E, Hammond P, Dinning L, Rahman S, Ray S, Dimicri C, Guppy S, Nielsen H, Vogel C, Ariza C, Morales L, Chang Y, Gabbay R, Ambrocio L, Manley L, Nemery R, Charlton W, Smith P, Kerr L, Steindel-Kopp B, Alamaguer M, Tabisola-Nuesca E, Pendersen A, Larson N, Cooper-Olviver H, Chan D, Fitz-Patrick D, Carreira T, Park Y, Ruhaak R, Liljenquist D. A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

OBJECTIVE

We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals.

RESEARCH DESIGN AND METHODS

We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables.

RESULTS

Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002).

CONCLUSIONS

The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.

The Role of Age and Excess Body Mass Index in Progression to Type 1 Diabetes in At-Risk Adults

BACKGROUND

Given the global rise in both type 1 diabetes incidence and obesity, the role of body mass index (BMI) on type 1 diabetes pathophysiology has gained great interest. Sustained excess BMI in pediatric participants of the TrialNet Pathway to Prevention (PTP) cohort increased risk for progression to type 1 diabetes, but the effects of age and obesity in adults remain largely unknown.

OBJECTIVE

To determine the effect of age and sustained obesity on the risk for type 1 diabetes in adult participants in the TrialNet PTP cohort (i.e., nondiabetic autoantibody-positive relatives of patients with type 1 diabetes).

RESEARCH DESIGN AND METHODS

Longitudinally accumulated BMI >25 kg/m2 was calculated to generate a cumulative excess BMI (ceBMI) for each participant, with ceBMI values ≥0 kg/m2 and ≥5 kg/m2 representing sustained overweight or obese status, respectively. Recursive partitioning analysis yielded sex- and age-specific thresholds for ceBMI that confer the greatest risk for type 1 diabetes progression.

RESULTS

In this cohort of 665 adults (age 20 to 50 years; median follow-up, 3.9 years), 49 participants developed type 1 diabetes. Age was an independent protective factor for type 1 diabetes progression (hazard ratio, 0.95; P = 0.008), with a threshold of >35 years that reduced risk for type 1 diabetes. In men age >35 years and women age <35 years, sustained obesity (ceBMI ≥5 kg/m2) increased the risk for type 1 diabetes.

CONCLUSIONS

Age is an important factor for type 1 diabetes progression in adults and influences the impact of elevated BMI, indicating an interplay of excess weight, age, and sex in adult type 1 diabetes pathophysiology.

Elevations in the Fasting Serum Proinsulin-to-C-Peptide Ratio Precede the Onset of Type 1 Diabetes

OBJECTIVE

We tested whether an elevation in the serum proinsulin-to-C-peptide ratio (PI:C), a biomarker of β-cell endoplasmic reticulum (ER) dysfunction, was associated with progression to type 1 diabetes.

RESEARCH DESIGN AND METHODS

Fasting total PI and C levels were measured in banked serum samples obtained from TrialNet Pathway to Prevention (PTP) participants, a cohort of autoantibody-positive relatives without diabetes of individuals with type 1 diabetes. Samples were obtained ∼12 months before diabetes onset from PTP progressors in whom diabetes developed (n = 60), and were compared with age-, sex-, and BMI-matched nonprogressors who remained normoglycemic (n = 58). PI:C ratios were calculated as molar ratios and were multiplied by 100% to obtain PI levels as a percentage of C levels.

RESULTS

Although absolute PI levels did not differ between groups, PI:C ratios were significantly increased in antibody-positive subjects in whom there was progression to diabetes compared with nonprogressors (median 1.81% vs. 1.17%, P = 0.03). The difference between groups was most pronounced in subjects who were ≤10 years old, where the median progressor PI:C ratio was nearly triple that of nonprogressors; 90.0% of subjects in this age group within the upper PI:C quartile progressed to the development of diabetes. Logistic regression analysis, adjusted for age and BMI, demonstrated increased odds of progression for higher natural log PI:C ratio values (odds ratio 1.44, 95% CI 1.02, 2.05).

CONCLUSIONS

These data suggest that β-cell ER dysfunction precedes type 1 diabetes onset, especially in younger children. Elevations in the serum PI:C ratio may have utility in predicting the onset of type 1 diabetes in the presymptomatic phase.

Staging the progression to type 1 diabetes with prediagnostic markers

PURPOSE OF REVIEW

There are an increasing number of markers that are used to predict the occurrence of type 1 diabetes (T1D), and to study the progression of pathologic changes prior to diagnosis. This review discusses some of those markers, particularly markers for which data are available that pertain to the progression to T1D.

RECENT FINDINGS

A study of birth cohorts showed that young children who develop multiple autoantibodies are at a particularly high risk for developing T1D, and that there appears to be a typical sequence for autoantibody development. The measurement of autoantibodies by electrochemiluminescence can increase the prediction accuracy for T1D. A new marker of changes in glucose over 6 months (PS6 M) has potential utility as an endpoint in short-term prevention trials. Markers which combine C-peptide and glucose, such as the Diabetes Prevention Trial-Type 1 Risk Score and the Index60, can increase the accuracy of prediction, and can potentially be utilized as prediagnostic endpoints. β-cell death measurements could have substantial utility in future T1D research.

SUMMARY

Markers are highly useful for studying the prediction of and progression to T1D. Moreover, markers can possibly be utilized to diagnose T1D at an earlier stage of disease.

The relationship between BMI and insulin resistance and progression from single to multiple autoantibody positivity and type 1 diabetes among TrialNet Pathway to Prevention participants

AIMS/HYPOTHESIS

The incidence of type 1 diabetes is increasing at a rate of 3-5% per year. Genetics cannot fully account for this trend, suggesting an influence of environmental factors. The accelerator hypothesis proposes an effect of metabolic factors on type 1 diabetes risk. To test this in the TrialNet Pathway to Prevention (PTP) cohort, we analysed the influence of BMI, weight status and insulin resistance on progression from single to multiple islet autoantibodies (Aab) and progression from normoglycaemia to diabetes.

METHODS

HOMA1-IR was used to estimate insulin resistance in Aab-positive PTP participants. Cox proportional hazards models were used to evaluate the effects of BMI, BMI percentile (BMI%), weight status and HOMA1-IR on the progression of autoimmunity or the development of diabetes.

RESULTS

Data from 1,310 single and 1,897 multiple Aab-positive PTP participants were included. We found no significant relationships between BMI, BMI%, weight status or HOMA1-IR and the progression from one to multiple Aabs. Similarly, among all Aab-positive participants, no significant relationships were found between BMI, weight status or HOMA1-IR and progression to diabetes. Diabetes risk was modestly increased with increasing BMI% among the entire cohort, in obese participants 13-20 years of age and with increasing HOMA1-IR in adult Aab-positive participants.

CONCLUSIONS/INTERPRETATION

Analysis of the accelerator hypothesis in the TrialNet PTP cohort does not suggest a broad influence of metabolic variables on diabetes risk. Efforts to identify other potentially modifiable environmental factors should continue.

Type 1 Diabetes--Reaping the Rewards of a Targeted Research Investment

The Diabetes Control and Complications Trial (DCCT) precipitated a major research effort to develop new approaches to achieve near-normal glycemic control in real-world settings in people with type 1 diabetes. Toward that end, a unique funding stream from the U.S. Congress-the Special Statutory Funding Program for Type 1 Diabetes Research-has provided nearly $2.5 billion for research into the prevention, cure, and treatment of type 1 diabetes since 1998. This funding generated a targeted, sustained investment in type 1 diabetes research with six specific goals: identifying new therapeutic targets through the understanding of disease etiology and pathogenesis, preventing or reversing the disease, developing cell replacement therapy, improving management and care, preventing or reducing the complications, and attracting new talent and applying new technologies to type 1 diabetes research. This Perspective describes exciting results that have emerged from the investment and further advances on the horizon, including artificial pancreas technologies, new therapies for diabetic retinopathy, and breakthroughs in laboratory production of β-cells. The recent program extension enables us to build on this foundation and pursue key new initiatives to harness emerging technologies and develop the next generation of type 1 diabetes researchers.

Disease modifying therapies in type 1 diabetes: Where have we been, and where are we going?

With more than four decades of clinical research and 25 years of clinical trials, much is known about the natural history of T1D before and after clinical diagnosis. We know that autoimmunity occurs early in life, that islet autoimmunity inevitably leads to clinically overt disease, and that some immune therapies can alter the disease course. In the future, we will likely conduct trials to more deeply explore mechanisms of disease and response to therapy, employ combinations of agents including those aimed at supporting beta cells, consider the use of chronic, intermittent therapy, focus studies on preventing progression from islet autoimmunity, and consider the potential benefits of studying children independently from adults. Much of this work will depend upon clinical trial networks such as Diabetes TrialNet. Such networks not only have the expertise to conduct studies but their sharing of data and samples also allows for discovery work by multiple investigators, laying the groundwork for the future. Working with patients, families, funders and industry, such collaborative networks can accelerate the translation of science to clinical practice to improve the lives of those living with T1D.

OGTT and random plasma glucose in the prediction of type 1 diabetes and time to diagnosis

AIMS/HYPOTHESIS

We assessed the utility of the OGTT and random plasma glucose concentrations in predicting the time to diagnosis of type 1 diabetes.

METHODS

A population-derived cohort of 14,876 newborns with HLA-conferred risk of type 1 diabetes were invited to regular follow-up for islet autoantibodies. When two or more autoantibodies were detected, an OGTT was performed once a year and random plasma glucose analysed twice a year. During follow-up, 567 children developed multiple autoantibodies, 255 (45%) of whom were diagnosed with type 1 diabetes, while 312 remained non-diabetic by December 2011.

RESULTS

Impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) were risk factors for type 1 diabetes (HR 3.2 [95% CI 1.5, 7.0] and 8.3 [95% CI 6.0, 11.5], respectively). When a random plasma glucose value ≥ 7.8 mmol/l was observed, the HR for diabetes was 6.0 (95% CI 4.3, 8.6). The median time to diagnosis after the detection of IFG was 5.2 years (interquartile range [IQR] 3.4, 6.3); after IGT, 0.7 years (IQR 0.3, 1.9); and, after a random plasma glucose ≥ 7.8 mmol/l, 1.0 years (IQR 0.3, 1.5). In a retrospective analysis, both OGTT-derived 2 h plasma glucose and random plasma glucose started to increase 1.5 years before diagnosis (p < 0.001 and p = 0.004, respectively).

CONCLUSIONS/INTERPRETATION

Dysglycaemia detected in an OGTT or based on random plasma glucose is a useful marker in the prediction of time to onset of type 1 diabetes in high-risk children. Random plasma glucose is a simple and low-cost measurement with comparable predictive characteristics to that of OGTT-derived 2 h glucose.

The development, validation, and utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS)

This report details the development, validation, and utility of the Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score (DPTRS) for type 1 diabetes (T1D). Proportional hazards regression was used to develop the DPTRS model which includes the glucose and C-peptide sums from oral glucose tolerance tests at 30, 60, 90, and 120 min, the log fasting C-peptide, age, and the log BMI. The DPTRS was externally validated in the TrialNet Natural History Study cohort (TNNHS). In a study of the application of the DPTRS, the findings showed that it could be used to identify normoglycemic individuals who were at a similar risk for T1D as those with dysglycemia. The DPTRS could also be used to identify lower risk dysglycemic individuals. Risk estimates of individuals deemed to be at higher risk according to DPTRS values did not differ significantly between the DPT-1 and the TNNHS; whereas, the risk estimates for those with dysglycemia were significantly higher in DPT-1. Individuals with very high DPTRS values were found to be at such marked risk for T1D that they could reasonably be considered to be in a pre-diabetic state. The findings indicate that the DPTRS has utility in T1D prevention trials and for identifying pre-diabetic individuals.

Care of diabetes in children and adolescents: controversies, changes, and consensus

Diabetes is one of the most common chronic medical disorders in children. The management of diabetes remains a substantial burden on children with diabetes and their families, despite improvements in treatment and rates of morbidity and mortality. Although most children with diabetes have type 1 diabetes, the increasing recognition of type 2 diabetes and genetic forms of diabetes in the paediatric population has important treatment implications. Diabetes therapy focuses strongly on targets for good metabolic control to reduce the risk of long-term complications. A parallel goal is to minimise short-term complications of hypoglycaemia and diabetic ketoacidosis. Technology offers opportunity for improvement in care, but has not yet fully lived up to its potential. New insights into the pathogenesis of diabetes and the development of new therapies have led to clinical trials aimed at the prevention of diabetes.

The rise, fall, and resurgence of immunotherapy in type 1 diabetes

Despite considerable effort to halt or delay destruction of β-cells in autoimmune type 1 diabetes (T1D), success remains elusive. Over the last decade, we have seen a proliferation of knowledge on the pathogenesis of T1D that emerged from studies performed in non-obese diabetic (NOD) mice. However, while results of these preclinical studies appeared to hold great promise and boosted patients' hopes, none of these approaches, once tested in clinical settings, induced remission of autoimmune diabetes in individuals with T1D. The primary obstacles to translation reside in the differences between the human and murine autoimmune responses and in the contribution of many environmental factors associated with the onset of disease. Moreover, inaccurate dosing as well as inappropriate timing and uncertain length of drug exposure have played a central role in the negative outcomes of such therapeutic interventions. In this review, we summarize the most important approaches tested thus far in T1D, beginning with the most successful preclinical studies in NOD mice and ending with the latest disappointing clinical trials in humans. Finally, we highlight recent stem cell-based trials, for which expectations in the scientific community and among individuals with T1D are high.