The Pathological Evolution of Glucose Response Curves During the Progression to Type 1 Diabetes in the TrialNet Pathway to Prevention Study

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.

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.

Phenotypes Associated With Zones Defined by Area Under the Curve Glucose and C-peptide in a Population With Islet Autoantibodies

OBJECTIVE

Metabolic zones were developed to characterize heterogeneity of individuals with islet autoantibodies.

RESEARCH DESIGN AND METHODS

Baseline 2-h oral glucose tolerance test data from 6,620 TrialNet Pathway to Prevention Study (TNPTP) autoantibody-positive participants (relatives of individuals with type 1 diabetes) were used to form 25 zones from five area under the curve glucose (AUCGLU) rows and five area under the curve C-peptide (AUCPEP) columns. Zone phenotypes were developed from demographic, metabolic, autoantibody, HLA, and risk data.

RESULTS

As AUCGLU increased, changes of glucose and C-peptide response curves (from mean glucose and mean C-peptide values at 30, 60, 90, and 120 min) were similar within the five AUCPEP columns. Among the zones, 5-year risk for type 1 diabetes was highly correlated with islet antigen 2 antibody prevalence (r = 0.96, P < 0.001). Disease risk decreased markedly in the highest AUCGLU row as AUCPEP increased (0.88-0.41; P < 0.001 from lowest AUCPEP column to highest AUCPEP column). AUCGLU correlated appreciably less with Index60 (an indicator of insulin secretion) in the highest AUCPEP column (r = 0.33) than in other columns (r ≥ 0.78). AUCGLU was positively related to "fasting glucose × fasting insulin" and to "fasting glucose × fasting C-peptide" (indicators of insulin resistance) before and after adjustments for Index60 (P < 0.001).

CONCLUSIONS

Phenotypes of 25 zones formed from AUCGLU and AUCPEP were used to gain insights into type 1 diabetes heterogeneity. Zones were used to examine GCRC changes with increasing AUCGLU, associations between risk and autoantibody prevalence, the dependence of glucose as a predictor of risk according to C-peptide, and glucose heterogeneity from contributions of insulin secretion and insulin resistance.

The Transition From a Compensatory Increase to a Decrease in C-peptide During the Progression to Type 1 Diabetes and Its Relation to Risk

OBJECTIVE

To define the relationship between glucose and C-peptide during the progression to type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

We longitudinally studied glucose and C-peptide response curves (GCRCs), area under curve (AUC) for glucose, and AUC C-peptide from oral glucose tolerance tests (OGTTs), and Index60 (which integrates OGTT glucose and C-peptide values) in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 72) and TrialNet Pathway to Prevention Study (TNPTP) (n = 82) participants who had OGTTs at baseline and follow-up time points before diagnosis.

RESULTS

Similar evolutions of GCRC configurations were evident between DPT-1 and TNPTP from baseline to 0.5 years prediagnosis. Whereas AUC glucose increased throughout from baseline to 0.5 years prediagnosis, AUC C-peptide increased from baseline until 1.5 years prediagnosis (DPT-1, P = 0.004; TNPTP, P = 0.012) and then decreased from 1.5 to 0.5 years prediagnosis (DPT-1, P = 0.017; TNPTP, P = 0.093). This change was mostly attributable to change in the late AUC C-peptide response (i.e., 60- to 120-min AUC C-peptide). Median Index60 values of DPT-1 (1.44) and TNPTP (1.05) progressors to T1D 1.5 years prediagnosis (time of transition from increasing to decreasing AUC C-peptide) were used as thresholds to identify individuals at high risk for T1D in the full cohort at baseline (5-year risk of 0.75-0.88 for those above thresholds).

CONCLUSIONS

A transition from an increase to a decrease in AUC C-peptide ∼1.5 years prediagnosis was validated in two independent cohorts. The median Index60 value at that time point can be used as a pathophysiologic-based threshold for identifying individuals at high risk for T1D.

Mechanistic Insights Into the Heterogeneity of Glucose Response Classes in Youths With Obesity: A Latent Class Trajectory Approach

OBJECTIVE

In a large, multiethnic cohort of youths with obesity, we analyzed pathophysiological and genetic mechanisms underlying variations in plasma glucose responses to a 180 min oral glucose tolerance test (OGTT).

RESEARCH DESIGN AND METHODS

Latent class trajectory analysis was used to identify various glucose response profiles to a nine-point OGTT in 2,378 participants in the Yale Pathogenesis of Youth-Onset T2D study, of whom 1,190 had available TCF7L2 genotyping and 358 had multiple OGTTs over a 5 year follow-up. Insulin sensitivity, clearance, and β-cell function were estimated by glucose, insulin, and C-peptide modeling.

RESULTS

Four latent classes (1 to 4) were identified based on increasing areas under the curve for glucose. Participants in class 3 and 4 had the worst metabolic and genetic risk profiles, featuring impaired insulin sensitivity, clearance, and β-cell function. Model-predicted probability to be classified as class 1 and 4 increased across ages, while insulin sensitivity and clearance showed transient reductions and β-cell function progressively declined. Insulin sensitivity was the strongest determinant of class assignment at enrollment and of the longitudinal change from class 1 and 2 to higher classes. Transitions between classes 3 and 4 were explained only by changes in β-cell glucose sensitivity.

CONCLUSIONS

We identified four glucose response classes in youths with obesity with different genetic risk profiles and progressive impairment in insulin kinetics and action. Insulin sensitivity was the main determinant in the transition between lower and higher glucose classes across ages. In contrast, transitions between the two worst glucose classes were driven only by β-cell glucose sensitivity.

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.

Shape of the OGTT glucose response curve: relationship with β-cell function and differences by sex, race, and BMI in adults with early type 2 diabetes treated with metformin

INTRODUCTION

The shape of the glucose curve during an oral glucose tolerance test (OGTT) reflects β-cell function in populations without diabetes but has not been as well studied in those with diabetes. A monophasic shape has been associated with higher risk of diabetes, while a biphasic pattern has been associated with lower risk. We sought to determine if phenotypic or metabolic characteristics were associated with glucose response curve shape in adults with type 2 diabetes treated with metformin alone.

RESEARCH DESIGN AND METHODS

This is a cross-sectional analysis of 3108 metformin-treated adults with type 2 diabetes diagnosed <10 years who underwent 2-hour 75 g OGTT at baseline as part of the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE). Insulin sensitivity (homeostasis model of insulin sensitivity, HOMA2-S) and β-cell function (early, late, and total incremental insulin and C peptide responses adjusted for HOMA2-S) were calculated. Glucose curve shape was classified as monophasic, biphasic, or continuous rise.

RESULTS

The monophasic profile was the most common (67.8% monophasic, 5.5% biphasic, 26.7% continuous rise). The monophasic subgroup was younger, more likely male and white, and had higher body mass index (BMI), while the continuous rise subgroup was more likely female and African American/black. HOMA2-S and fasting glucose did not differ among the subgroups. The biphasic subgroup had the highest early, late, and total insulin and C peptide responses (all p<0.05 vs monophasic and continuous rise). Compared with the monophasic subgroup, the continuous rise subgroup had similar early insulin (p=0.3) and C peptide (p=0.6) responses but lower late insulin (p<0.001) and total insulin (p=0.008) and C peptide (p<0.001) responses.

CONCLUSIONS

Based on the large multiethnic GRADE cohort, sex, race, age, and BMI were found to be important determinants of the shape of the glucose response curve. A pattern of a continuously rising glucose at 2 hours reflected reduced β-cell function and may portend increased glycemic failure rates.

TRIAL REGISTRATION NUMBER

NCT01794143.

Early Impairment of Insulin Sensitivity, β-Cell Responsiveness, and Insulin Clearance in Youth with Stage 1 Type 1 Diabetes

CONTEXT

Clinical onset of type 1 diabetes (Stage 3 T1D) is preceded by a presymptomatic phase characterized by multiple islet autoantibodies with normal glucose tolerance (Stage 1 T1D).

OBJECTIVE

The aim was to explore the metabolic phenotypes of β-cell function and insulin sensitivity and clearance in normoglycemic youth with Stage 1 T1D and compare them with healthy nonrelated peers during a 3-hour oral glucose tolerance test (OGTT).

METHODS

Twenty-eight lean youth, 14 with ≥2 islet autoantibodies (cases) and 14 healthy controls underwent a 3-hour 9-point OGTT with measurement of glucose, C-peptide, and insulin. The oral minimal model was used to quantitate β-cell responsiveness (φtotal) and insulin sensitivity (SI), allowing assessment of β-cell function by the disposition index (DI=φtotal×SI). Fasting insulin clearance (CL0) was calculated as the ratio between the fasting insulin secretion rate (ISR) and plasma insulin levels (ISR0/I0), while postload clearance (CL180) was estimated by the ratio of AUC of ISR over the plasma insulin AUC for the 3-hour OGTT (ISRAUC/IAUC). Participants with impaired fasting glucose, impaired glucose tolerance, or any OGTT glucose concentration ≥200 mg/dL were excluded.

RESULTS

Cases (10.5 years [8, 15]) exhibited reduced DI (P < .001) due to a simultaneous reduction in both φtotal (P < 0.001) and SI (P = .008) compared with controls (11.5 years [10.4, 14.9]). CL0 and CL180 were lower in cases than in controls (P = .005 and P = .019).

CONCLUSION

Presymptomatic Stage 1 T1D in youth is associated with reduced insulin sensitivity and lower β-cell responsiveness, and the presence of blunted insulin clearance.

Transforming type 1 diabetes: the next wave of innovation

The discovery of insulin in 1921 enabled pharmaceutical production of animal insulins for the treatment of people with type 1 diabetes by 1922. The last several decades have witnessed enormous scientific progress in the therapy of type 1 diabetes, yet some developments have been incremental, and insulin is not a cure. Herein, I highlight key scientific advances potentially poised to improve the quality of life and treatment outcomes in type 1 diabetes. These innovations range from newer insulin analogues to the development of smart insulins, oral and weekly insulins, glucose sensors and closed-loop insulin-delivery devices, as well as strategies for durable human beta cell replacement coupled with selective immune manipulation to preserve beta cell function. Finally, progress in the prediction and prevention of type 1 diabetes highlights the ongoing challenges and potential for altering the natural history of the disease or eliminating type 1 diabetes altogether.