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

IA-2A positivity increases risk of progression within and across established stages of type 1 diabetes

AIMS/HYPOTHESIS

Accurate understanding of type 1 diabetes risk is critical for optimisation of counselling, monitoring and interventions, yet even within established staging classifications, individual time to clinical disease varies. Previous work has associated IA-2A positivity with increased type 1 diabetes progression but a comprehensive assessment of the impact of screening for IA-2A positivity across the natural history of autoantibody positivity has not been performed. We asked whether IA-2A would consistently be associated with higher risk of progression within and across established stages of type 1 diabetes in a large natural history study.

METHODS

Genetic, autoantibody and metabolic data from adult and paediatric autoantibody-negative (n=192) and autoantibody-positive (n=4577) relatives of individuals with type 1 diabetes followed longitudinally in the Type 1 Diabetes TrialNet Pathway to Prevention Study were analysed. Cox regression was used to compare cumulative incidences of clinical diabetes by autoantibody profiles and disease stages.

RESULTS

Compared with IA-2A- individuals, IA-2A+ individuals had higher genetic risk scores and clinical progression risk within single-autoantibody-positive (5.3-fold increased 5 year risk), stage 1 (2.2-fold increased 5 year risk) and stage 2 (1.3-fold increased 5 year risk) type 1 diabetes categories. Individuals with single-autoantibody positivity for IA-2A showed increased metabolic dysfunction and diabetes progression compared with people who were autoantibody negative, those positive for another single autoantibody, and IA-2A- stage 1 individuals. Individuals at highest risk within the single-IA-2A+ category included children (HR 14.2 [95% CI 1.9, 103.1], p=0.009), individuals with IA-2A titres above the median (HR 3.5 [95% CI 1.9, 6.6], p<0.001), individuals with high genetic risk scores (HR 1.4 [95% CI 1.2,1.6], p<0.001) and individuals with HLA DR4-positive status (HR 3.7 [95% CI 1.6, 8.3], p=0.002). When considering all autoantibody-positive individuals, progression risk was similar for euglycaemic IA-2A+ individuals and dysglycaemic IA-2A- individuals.

CONCLUSIONS/INTERPRETATION

IA-2A positivity is consistently associated with increased progression risk throughout the natural history of type 1 diabetes development. Individuals with single-autoantibody positivity for IA-2A have a greater risk of disease progression than those who meet stage 1 criteria but who are IA-2A-. Approaches to incorporate IA-2A+ status into monitoring strategies for autoantibody-positive individuals should be considered.

Trajectory of beta cell function and insulin clearance in stage 2 type 1 diabetes: natural history and response to teplizumab

AIMS/HYPOTHESIS

We aimed to analyse TrialNet Anti-CD3 Prevention (TN10) data using oral minimal model (OMM)-derived indices to characterise the natural history of stage 2 type 1 diabetes in placebo-treated individuals, to describe early metabolic responses to teplizumab and to explore the predictive capacity of OMM measures for disease-free survival rate.

METHODS

OMM-estimated insulin secretion, sensitivity and clearance and the disposition index were evaluated at baseline and at 3, 6 and 12 months post randomisation in placebo- and teplizumab-treated groups, and, within each group, in slow- and rapid-progressors (time to stage 3 disease >2 or ≤ 2 years). OMM metrics were also compared with the standard AUC C-peptide. Percentage changes in CD8+ T memory cell and programmed death-1 (PD-1) expression were evaluated in each group.

RESULTS

Baseline metabolic characteristics were similar between 28 placebo- and 39 teplizumab-treated participants. Over 12 months, insulin secretion declined in placebo-treated and rose in teplizumab-treated participants. Within groups, placebo slow-progressors (n=14) maintained insulin secretion and sensitivity, while both declined in placebo rapid-progressors (n=14). Teplizumab slow-progressors (n=28) maintained elevated insulin secretion, while teplizumab rapid-progressors (n=11) experienced mild metabolic decline. Compared with rapid-progressor groups, insulin clearance significantly decreased between baseline and 3, 6 and 12 months in the slow-progressor groups in both treatment arms. In aggregate, both higher baseline insulin secretion (p=0.027) and reduced 12 month insulin clearance (p=0.045) predicted slower progression. A >25% loss of insulin secretion at 3 months had specificity of 0.95 (95% CI 0.86, 1.00) to identify rapid-progressors and correctly classified the 2 year risk for progression in 92% of participants, with a sensitivity of 0.19 (95% CI 0.08, 0.30). OMM-estimated insulin secretion outperformed AUC C-peptide to differentiate groups by treatment or to predict progression. Metabolic changes were paralleled by relative frequency of change in PD-1+ CD8+ T effector memory cells.

CONCLUSIONS/INTERPRETATION

OMM measures characterise the metabolic heterogeneity in stage 2 diabetes, identifying differences between rapid- and slow-progressors, and heterogeneous impacts of immunotherapy, suggesting the need to account for these differences when designing and interpreting clinical trials.

A Glucose Fraction Independent of Insulin Secretion: Implications for Type 1 Diabetes Progression in Autoantibody-Positive Cohorts

Objective: We assessed whether there is an impactful glucose fraction independent of insulin secretion in autoantibody-positive individuals. Research Design and Methods: Baseline 2-h oral glucose tolerance test data from the TrialNet Pathway to Prevention (TNPTP; n = 6190) and Diabetes Prevention Trial-Type 1 (DPT-1; n = 705) studies were used. Linear regression of area under the curve (AUC) glucose versus Index60 was performed to identify two fractions: dependent (dAUCGLU) or independent (iAUCGLU) of insulin secretion. Results: The lack of correlation (r = 0.06) of iAUCGLU and the inverse correlation of dAUCGLU (r = -0.59) with the first-phase insulin response from DPT-1 were consistent with the independent and dependent designations of the glucose fractions. Correlations of AUC C-peptide were inverse with dAUCGLU and positive with iAUCGLU (TNPTP: r = -0.72, r = 0.57; DPT-1: r = -0.56, r = 0.60). The explained variance of AUC C-peptide increased markedly after separating AUC glucose into its fractions (from 4% to 85% in TNPTP; from 1% to 67% in DPT-1). The independent fraction contributed more to the increased glycemia of impaired glucose tolerance (IGT) than did the dependent fraction. Both dAUCGLU and iAUCGLU predicted IGT and type 1 diabetes (T1D) (P < 0.0001 for all). However, whereas dAUCGLU was more predictive of T1D (chi-square: 849 vs. 249), iAUCGLU was more predictive of IGT (chi-square: 451 vs. 176). Conclusions: A glucose fraction independent of insulin secretion was identified that was appreciable in autoantibody-positive individuals. It provides insight into the relation between glucose and C-peptide, contributes substantially to the glycemia of IGT, and predicts both T1D and IGT, particularly the latter.

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing. Here, we review emerging approaches for repurposing agents used to treat diseases that share pathogenic pathways with type 1 diabetes and selecting synergistic combinations of drugs to maximise therapeutic efficacy. We discuss how emerging multi-omics technologies, including analysis of antigen processing and presentation to adaptive immune cells, may lead to the discovery of novel biomarkers and subsequent translation into antigen-specific immunotherapies. We also discuss the potential for using artificial intelligence to create 'digital twin' models that enable rapid in silico testing of personalised agents as well as dose determination. To conclude, we discuss some limitations of artificial intelligence and machine learning, including issues pertaining to model interpretability and bias, as well as the continued need for validation studies via confirmatory intervention trials.

Time to reframe the disease staging system for type 1 diabetes

In 2015, introduction of a disease staging system offered a framework for benchmarking progression to clinical type 1 diabetes. This model, based on islet autoantibodies (stage 1) and dysglycaemia (stage 2) before type 1 diabetes diagnosis (stage 3), has facilitated screening and identification of people at risk. Yet, there are many limitations to this model as the stages combine a very heterogeneous group of individuals; do not have high specificity for type 1 diabetes; can occur without persistence (ie, reversion to an earlier risk stage); and exclude age and other influential risk factors. The current staging system also infers that individuals at risk of type 1 diabetes progress linearly from stage 1 to stage 2 and subsequently stage 3, whereas such movements are often more complex. With the approval of teplizumab by the US Food and Drug Administration in 2022 to delay type 1 diabetes in people at stage 2, there is a need to refine the definition and accuracy of type 1 diabetes staging. Theoretically, we propose that a type 1 diabetes risk calculator should incorporate any available demographic, genetic, autoantibody, metabolic, and immune data that could be continuously updated. Additionally, we call to action for the field to increase the breadth of knowledge regarding type 1 diabetes risk in non-relatives, adults, and individuals from minority populations.

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 autoantibodies (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, hemoglobin A1c, 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 type 1 diabetes. 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; a lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of the rate of disease progression within stage 2.

CONCLUSION

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.

Evolving Concepts in Pathophysiology, Screening, and Prevention of Type 1 Diabetes: Report of Diabetes Mellitus Interagency Coordinating Committee Workshop

The approval of teplizumab to delay the onset of type 1 diabetes is an important inflection point in the decades-long pursuit to treat the cause of the disease rather than its symptoms. The National Institute of Diabetes and Digestive and Kidney Diseases convened a workshop of the Diabetes Mellitus Interagency Coordinating Committee titled "Evolving Concepts in Pathophysiology, Screening, and Prevention of Type 1 Diabetes" to review this accomplishment and identify future goals. Speakers representing Type 1 Diabetes TrialNet (TrialNet) and the Immune Tolerance Network emphasized that the ability to robustly identify individuals destined to develop type 1 diabetes was essential for clinical trials. The presenter from the U.S. Food and Drug Administration described how regulatory approval relied on data from the single clinical trial of TrialNet with testing of teplizumab for delay of clinical diagnosis, along with confirmatory evidence from studies in patients after diagnosis. The workshop reviewed the etiology of type 1 diabetes as a disease involving multiple immune pathways, highlighting the current understanding of prognostic markers and proposing potential strategies to improve the therapeutic response of disease-modifying therapies based on the mechanism of action. While celebrating these achievements funded by the congressionally appropriated Special Diabetes Program, panelists from professional organizations, nonprofit advocacy/funding groups, and industry also identified significant hurdles in translating this research into clinical care.

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.

Comparisons of Metabolic Measures to Predict T1D vs Detect a Preventive Treatment Effect in High-Risk Individuals

CONTEXT

Metabolic measures are frequently used to predict type 1 diabetes (T1D) and to understand effects of disease-modifying therapies.

OBJECTIVE

Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D.

METHODS

Six-month changes in metabolic endpoints were assessed for (1) detecting treatment effects by comparing placebo and treatment arms from the randomized controlled teplizumab prevention trial, a multicenter clinical trial investigating 14-day intravenous teplizumab infusion and (2) predicting T1D in the TrialNet Pathway to Prevention natural history study. For each metabolic measure, t-Values from t tests for detecting a treatment effect were compared with chi-square values from proportional hazards regression for predicting T1D. Participants in the teplizumab prevention trial and participants in the Pathway to Prevention study selected with the same inclusion criteria used for the teplizumab trial were studied.

RESULTS

Six-month changes in glucose-based endpoints predicted diabetes better than C-peptide-based endpoints, yet the latter were better at detecting a teplizumab effect. Combined measures of glucose and C-peptide were more balanced than measures of glucose alone or C-peptide alone for predicting diabetes and detecting a teplizumab effect.

CONCLUSION

The capacity of a metabolic endpoint to detect a treatment effect does not necessarily correspond to its accuracy for predicting T1D. However, combined glucose and C-peptide endpoints appear to be effective for both predicting diabetes and detecting a response to immunotherapy. These findings suggest that combined glucose and C-peptide endpoints should be incorporated into the design of future T1D prevention trials.

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.

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.

HOMA-IR and the Matsuda Index as predictors of progression to type 1 diabetes in autoantibody-positive relatives

AIM/HYPOTHESIS

We assessed whether HOMA-IR and the Matsuda Index are associated with transitions through stages of type 1 diabetes.

METHODS

Autoantibody (AAb)-positive relatives of individuals with type 1 diabetes (n=6256) from the TrialNet Pathway to Prevention were studied. Associations of indicators of insulin resistance (HOMA-IR) and insulin sensitivity (Matsuda Index) with BMI percentile (BMIp) and age were assessed with adjustments for measures of insulin secretion, Index60 and insulinogenic index (IGI). Cox regression was used to determine if tertiles of HOMA-IR and Matsuda Index predicted transitions from Not Staged (<2 AAbs) to Stage 1 (≥2 AAbs and normoglycaemia), from Stage 1 to Stage 2 (≥2 AAbs with dysglycaemia), and progression to Stage 3 (diabetes as defined by WHO/ADA criteria).

RESULTS

There were strong associations of HOMA-IR (positive) and Matsuda Index (inverse) with baseline age and BMIp (p<0.0001). After adjustments for Index60, transitioning from Stage 1 to Stage 2 was associated with higher HOMA-IR and lower Matsuda Index (HOMA-IR: HR=1.71, p<0.0001; Matsuda Index, HR=0.40, p<0.0001), as with progressing from Stages 1 or 2 to Stage 3 (HOMA-IR: HR=1.98, p<0.0001; Matsuda Index: HR=0.46, p<0.0001). Without adjustments, associations of progression to Stage 3 were inverse for HOMA-IR and positive for Matsuda Index, opposite in directionality with adjustments. When IGI was used in place of Index60, the findings were similar.

CONCLUSIONS/INTERPRETATION

Progression to Stages 2 and 3 of type 1 diabetes increases with HOMA-IR and decreases with the Matsuda Index after adjustments for insulin secretion. Indicators of insulin secretion appear helpful for interpreting associations of progression to type 1 diabetes with HOMA-IR or the Matsuda Index in AAb-positive relatives.

Unveiling Diabetes: Categories, Genetics, Diagnostics, Treatments, and Future Horizons

Diabetes mellitus is a global epidemic affecting millions of individuals worldwide. This comprehensive review aims to provide a thorough understanding of the categorization, disease identity, genetic architecture, diagnosis, and treatment of diabetes. The categorization of diabetes is discussed, with a focus on type 1 and type 2 diabetes, as well as the lesser-known types, type 3 and type 4 diabetes. The geographical variation, age, gender, and ethnic differences in the prevalence of type 1 and type 2 diabetes are explored. The impact of disease identity on disease management and the role of autoimmunity in diabetes are examined. The genetic architecture of diabetes, including the interplay between genotype and phenotype, is discussed to enhance our understanding of the underlying mechanisms. The importance of insulin injection sites and the insulin signalling pathway in diabetes management are highlighted. The diagnostic techniques for diabetes are reviewed, along with advancements for improved differentiation between types. Treatment and management approaches, including medications used in diabetes management are presented. Finally, future perspectives are discussed, emphasizing the need for further research and interventions to address the global burden of diabetes. This review serves as a valuable resource for healthcare professionals, researchers, and policymakers, providing insights to develop targeted strategies for the prevention, diagnosis, and management of this complex disease.

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.

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.

β-Cell Function and Insulin Sensitivity in Youth With Early Type 1 Diabetes From a 2-Hour 7-Sample OGTT

CONTEXT

The oral minimal model is a widely accepted noninvasive tool to quantify both β-cell responsiveness and insulin sensitivity (SI) from glucose, C-peptide, and insulin concentrations during a 3-hour 9-point oral glucose tolerance test (OGTT).

OBJECTIVE

Here, we aimed to validate a 2-hour 7-point protocol against the 3-hour OGTT and to test how variation in early sampling frequency impacts estimates of β-cell responsiveness and SI.

METHODS

We conducted a secondary analysis on 15 lean youth with stage 1 type 1 diabetes (T1D; ≥ 2 islet autoantibodies with no dysglycemia) who underwent a 3-hour 9-point OGTT. 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). Seven- and 5-point 2-hour OGTT protocols were tested against the 3-hour 9-point gold standard to determine agreement between estimates of φtotal and its dynamic and static components, SI, and DI across different sampling strategies.

RESULTS

The 2-hour estimates for the disposition index exhibited a strong correlation with 3-hour measures (r = 0.975; P < .001) with similar results for β-cell responsiveness and SI (r = 0.997 and r = 0.982; P < .001, respectively). The agreement of the 3 estimates between the 7-point 2-hour and 9-point 3-hour protocols fell within the 95% CI on the Bland-Altman grid with a median difference of 16.9% (-35.3 to 32.5), 0.2% (-0.6 to 1.3), and 14.9% (-1.4 to 28.3) for DI, φtotal, and SI. Conversely, the 5-point protocol did not provide reliable estimates of φ dynamic and static components.

CONCLUSION

The 2-hour 7-point OGTT is reliable in individuals with stage 1 T1D for assessment of β-cell responsiveness, SI, and DI. Incorporation of these analyses into current 2-hour diabetes staging and monitoring OGTTs offers the potential to more accurately quantify risk of progression in the early stages of T1D.

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.

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.

Index60 Is Superior to HbA1c for Identifying Individuals at High Risk for Type 1 Diabetes

CONTEXT

HbA1c from ≥ 5.7% to < 6.5% (39-46 mmol/mol) indicates prediabetes according to American Diabetes Association guidelines, yet its identification of prediabetes specific for type 1 diabetes has not been assessed. A composite glucose and C-peptide measure, Index60, identifies individuals at high risk for type 1 diabetes.

OBJECTIVE

We compared Index60 and HbA1c thresholds as markers for type 1 diabetes risk.

METHODS

TrialNet Pathway to Prevention study participants with ≥ 2 autoantibodies (GADA, IAA, IA-2A, or ZnT8A) who had oral glucose tolerance tests and HbA1c measurements underwent 1) predictive time-dependent modeling of type 1 diabetes risk (n = 2776); and 2) baseline comparisons between high-risk mutually exclusive groups: Index60 ≥ 2.04 (n = 268) vs HbA1c ≥ 5.7% (n = 268). The Index60 ≥ 2.04 threshold was commensurate in ordinal ranking with the standard prediabetes threshold of HbA1c ≥ 5.7%.

RESULTS

In mutually exclusive groups, individuals exceeding Index60 ≥ 2.04 had a higher cumulative incidence of type 1 diabetes than those exceeding HbA1c ≥ 5.7% (P < 0.0001). Appreciably more individuals with Index60 ≥ 2.04 were at stage 2, and among those at stage 2, the cumulative incidence was higher for those with Index60 ≥ 2.04 (P = 0.02). Those with Index60 ≥ 2.04 were younger, with lower BMI, greater autoantibody number, and lower C-peptide than those with HbA1c ≥ 5.7% (P < 0.0001 for all comparisons).

CONCLUSION

Individuals with Index60 ≥ 2.04 are at greater risk for type 1 diabetes with features more characteristic of the disorder than those with HbA1c ≥ 5.7%. Index60 ≥ 2.04 is superior to the standard HbA1c ≥ 5.7% threshold for identifying prediabetes in autoantibody-positive individuals. These findings appear to justify using Index60 ≥ 2.04 as a prediabetes criterion in this population.