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

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.