Increasing the accuracy of oral glucose tolerance testing and extending its application to individuals with normal glucose tolerance for the prediction of type 1 diabetes: the Diabetes Prevention Trial-Type 1

Discrimination and precision of Continuous Glucose Monitoring in staging children with presymptomatic type 1 diabetes

CONTEXT

Staging and monitoring of pre-symptomatic type 1 diabetes includes the assessment for dysglycemia.

OBJECTIVE

To assess the ability of Continuous Glucose Monitoring (CGM) to differentiate between islet autoantibody-negative controls and early-stage type 1 diabetes and explore whether CGM classifiers predict progression to clinical diabetes.

RESEARCH DESIGN AND METHODS

Children and adolescents participating in public health screening for islet autoantibodies in Bavaria, Germany were invited to undergo CGM with Dexcom G6. In total, 118 participated and valid data was obtained from 97 (57 female; median age 10 [range 3-17] years), including 46 with stage 1, 18 with stage 2, and 33 with no islet autoantibodies.

RESULTS

Mean glucose during CGM in islet autoantibody-negative controls was high (median, 115.3 mg/dl) and varied substantially (IQR, 106.8-124.4). Eleven (33%) of the controls had more than 10% of glucose values above 140 mg/dl (TA140). Using thresholds corresponding to 100% specificity in controls, differences between controls and stage 1 and stage 2 were obtained for glucose standard deviation, TA140, TA160 and TA180. Elevations in any two of these parameters identified 12 (67%) with stage 2 and 9 (82%) of 11 participants who developed clinical diabetes within one year. However, there was marked variation within groups for all parameters and poor consistency observed in a second CGM performed in 18 participants.

CONCLUSION

This study demonstrated the potential of integrating CGM into staging and monitoring of early-stage type 1 diabetes. However, substantial improvement in the precision of CGM is required for its application in routine monitoring practices.

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.

Barriers to Screening: An Analysis of Factors Impacting Screening for Type 1 Diabetes Prevention Trials

Context

Participants with stage 1 or 2 type 1 diabetes (T1D) qualify for prevention trials, but factors involved in screening for such trials are largely unknown.

Objective

To identify factors associated with screening for T1D prevention trials.

Methods

This study included TrialNet Pathway to Prevention participants who were eligible for a prevention trial: oral insulin (TN-07, TN-20), teplizumab (TN-10), abatacept (TN-18), and oral hydroxychloroquine (TN-22). Univariate and multivariate logistic regression models were used to examine participant, site, and study factors at the time of prevention trial accrual.

Results

Screening rates for trials were: 50% for TN-07 (584 screened/1172 eligible), 9% for TN-10 (106/1249), 24% for TN-18 (313/1285), 17% for TN-20 (113/667), and 28% for TN-22 (371/1336). Younger age and male sex were associated with higher screening rates for prevention trials overall and for oral therapies. Participants with an offspring with T1D showed lower rates of screening for all trials and oral drug trials compared with participants with other first-degree relatives as probands. Site factors, including larger monitoring volume and US site vs international site, were associated with higher prevention trial screening rates.

Conclusions

Clear differences exist between participants who screen for prevention trials and those who do not screen and between the research sites involved in prevention trial screening. Participant age, sex, and relationship to proband are significantly associated with prevention trial screening in addition to key site factors. Identifying these factors can facilitate strategic recruitment planning to support rapid and successful enrollment into prevention trials.

The utility of glucose area under the curve from the oral glucose tolerance test as a screening tool for cystic fibrosis-related diabetes

BACKGROUND

Consistently abnormal glucose levels on oral glucose tolerance test (OGTT) are the most effective screening tool for cystic fibrosis-related diabetes (CFRD). However, some cystic fibrosis (CF) patients demonstrate abnormal glucose profiles not reaching levels required for CFRD diagnosis and are, therefore, left untreated. Since CFRD is associated with disease deterioration, early diagnosis and treatment are desirable.

AIM

To explore the association between the area under the curve of glucose (G-AUC) obtained during a five-point 2-h standard OGTT and CF disease severity parameters.

METHODS

All CF patients referred for an annual routine OGTT at the Hadassah CF Center between 2002 and 2018, were included. Disease severity parameters were correlated with the G-AUC.

RESULTS

Two hundred forty-two OGTTs were performed in 81 patients (mean age 19.7 ± 9.0 years); 54% were normal, 14% showed impaired glucose tolerance (IGT), 5% had values in the indeterminate range (INDET), 11% had both IGT and INDET and 16% were diagnosed with CFRD. A gradual increase in mean G-AUC was observed among the groups. In multivariate regression models, G-AUC ≥ 295 mg h/dl was independently associated with an increased number of pulmonary exacerbations (PEx). Not all the patients having this value met the CFRD definition.

CONCLUSION

Patients who do not fulfill the criteria for CFRD may have abnormal glucose metabolism identifiable by abnormally high G-AUC values, which may be associated with more PEx. The potential advantage of treating these patients with insulin and the subsequent reduction in PEx needs further investigation.

CGM Metrics Predict Imminent Progression to Type 1 Diabetes: Autoimmunity Screening for Kids (ASK) Study

OBJECTIVE

Children identified with stage 1 type 1 diabetes are at high risk for progressing to stage 3 (clinical) diabetes and require accurate monitoring. Our aim was to establish continuous glucose monitoring (CGM) metrics that could predict imminent progression to diabetes.

RESEARCH DESIGN AND METHODS

In the Autoimmunity Screening for Kids study, 91 children who were persistently islet autoantibody positive (median age 11.5 years; 48% non-Hispanic White; 57% female) with a baseline CGM were followed for development of diabetes for a median of 6 (range 0.2-34) months. Of these, 16 (18%) progressed to clinical diabetes in a median of 4.5 (range 0.4-29) months.

RESULTS

Compared with children who did not progress to clinical diabetes (nonprogressors), those who did (progressors) had significantly higher average sensor glucose levels (119 vs. 105 mg/dL, P < 0.001) and increased glycemic variability (SD 27 vs. 16, coefficient of variation, 21 vs. 15, mean of daily differences 24 vs. 16, and mean amplitude of glycemic excursions 43 vs. 26, all P < 0.001). For progressors, 21% of the time was spent with glucose levels >140 mg/dL (TA140) and 8% of time >160 mg/dL, compared with 3% and 1%, respectively, for nonprogressors. In survival analyses, the risk of progression to diabetes in 1 year was 80% in those with TA140 >10%; in contrast, it was only 5% in the other participants. Performance of prediction by receiver operating curve analyses showed area under the curve of ≥0.89 for both individual and combined CGM metric models.

CONCLUSIONS

TA140 >10% is associated with a high risk of progression to clinical diabetes within the next year in autoantibody-positive children. CGM should be included in the ongoing monitoring of high-risk children and could be used as potential entry criterion for prevention trials.

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.

Impaired glucose tolerance and indeterminate glycemia in cystic fibrosis

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Oral glucose tolerance testing is used for screening, diagnosis, and risk stratification of cystic fibrosis related diabetes.

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Abnormal glucose tolerance in cystic fibrosis has prognostic utility with regards to progression towards overt diabetes, pulmonary function, weight loss, and mortality.

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Further research is needed to delineate the significance of impaired glucose tolerance and indeterminate glycemia within the CF population.

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Lower thresholds for indeterminate glycemia may be needed within the cystic fibrosis population.

Oral glucose tolerance testing (OGTT) is the primary method to screen for and diagnose cystic fibrosis-related diabetes (CFRD). Diagnostic thresholds as currently defined are based on microvascular complications seen in type 2 diabetes. Abnormal glucose tolerance (AGT) refers to OGTT glucose elevations outside the normal range and encompasses both impaired and indeterminate glucose tolerance. Current guidelines define impaired glucose tolerance (IGT) as a 2-hour glucose of 140–199 mg/dL (7.8–11 mmol/L) and indeterminate glucose tolerance (INDET) as any mid-OGTT glucose ≥ 200 mg/dL (11.1 mmol/L) with a normal fasting and 2 h glucose. There is growing evidence that AGT also has associations with CF-centered outcomes including pulmonary decline, hospitalizations, and weight loss. Here we aim to review the historical emergence of glucose tolerance testing, review relevance to risk stratification for CFRD, discuss alternate cutoffs for identifying AGT earlier, and highlight the need for larger, future studies to inform our understanding of the implications of IGT and INDET on CF health.

Continuous Glucose Monitoring Predicts Progression to Diabetes in Autoantibody Positive Children

CONTEXT

Accurate measures are needed for the prediction and diagnosis of type 1 diabetes (T1D) in at-risk persons.

OBJECTIVE

The purpose of this study was to explore the value of continuous glucose monitoring (CGM) in predicting T1D onset.

DESIGN AND SETTING

The Diabetes Autoimmunity Study in the Young (DAISY) prospectively follows children at increased risk for development of islet autoantibodies (islet autoantibody positive; Ab+) and T1D.

PARTICIPANTS

We analyzed 23 Ab+ participants with available longitudinal CGM data.

MAIN OUTCOME MEASURE

CGM metrics as glycemic predictors of progression to T1D.

RESULTS

Of 23 Ab+ participants with a baseline CGM, 8 progressed to diabetes at a median age of 13.8 years during a median follow-up of 17.7 years (interquartile range, 14.6 to 22.0 years). Compared with nonprogressors, participants who progressed to diabetes had significantly increased baseline glycemic variability (SD, 29 vs 21 mg/dL; P = 0.047), daytime sensor average (122 vs 106 mg/dL; P = 0.02), and daytime sensor area under the curve (AUC, 470,370 vs 415,465; P = 0.047). They spent 24% of time at >140 mg/dL and 12% at >160 mg/dL compared with, respectively, 8% and 3% for nonprogressors (both P = 0.005). A receiver-operating characteristic curve analysis showed an AUC of 0.85 for percentage of time spent at >140 or 160 mg/dL. The cutoff of 18% time spent at >140 mg/dL had 75% sensitivity, 100% specificity, and a 100% positive predictive value for diabetes prediction, although these values could change because some nonprogressors may develop diabetes with longer follow-up.

CONCLUSIONS

Eighteen percent or greater CGM time spent at >140 mg/dL predicts progression to diabetes in Ab+ children.

Predicting progression to diabetes in islet autoantibody positive children

While full oral glucose tolerance test (OGTT) helps improve prediction, it requires intravenous access with 6 sample collections for glucose and C-peptide. The objective of this study was to explore less costly and less time-consuming options. All children being prospectively followed by the Diabetes Autoimmunity Study in the Young (DAISY) who had a complete baseline OGTT and at least one confirmed islet autoantibody (Ab+) were included in this study (n = 68). Of 68 Ab+ subjects with a baseline OGTT, 25 developed diabetes after a mean follow-up 5.7 yrs, at a mean age of 12.4 yrs. Univariate proportional hazards (PH) models suggested that age at seroconversion, number of Ab+, IA-2A levels, HbA1c and metabolic variables from the OGTT predicted progression to diabetes, while HLA DR3/4, BMI, levels of IAA or GADA did not. Five multivariate PH predictive models were similar (p = 0.32). All five models included age at seroconversion, number of Ab+, IA-2A levels and HbA1c, and in addition included: model 1 - 1 h glucose and 1 h C-peptide; model 2 - 2 h glucose and 2 h C-peptide; model 3 - glucose sum and C-peptide sum; model 4 - glucose AUC and C-peptide AUC; and model 5: index 60. A model containing age at seroconversion, number of Ab+, IA-2A levels, HbA1c, 1 h glucose and 1 h C-peptide was as predictive for type 1 diabetes progression as models including all sum or AUC values for glucose and C-peptide from full OGTT. The performance of this model should be confirmed in an independent population of Ab+ children.

Differentiation of Diabetes by Pathophysiology, Natural History, and Prognosis

The American Diabetes Association, JDRF, the European Association for the Study of Diabetes, and the American Association of Clinical Endocrinologists convened a research symposium, "The Differentiation of Diabetes by Pathophysiology, Natural History and Prognosis" on 10-12 October 2015. International experts in genetics, immunology, metabolism, endocrinology, and systems biology discussed genetic and environmental determinants of type 1 and type 2 diabetes risk and progression, as well as complications. The participants debated how to determine appropriate therapeutic approaches based on disease pathophysiology and stage and defined remaining research gaps hindering a personalized medical approach for diabetes to drive the field to address these gaps. The authors recommend a structure for data stratification to define the phenotypes and genotypes of subtypes of diabetes that will facilitate individualized treatment.

Type I Interferon Is a Catastrophic Feature of the Diabetic Islet Microenvironment

A detailed understanding of the molecular pathways and cellular interactions that result in islet beta cell (β cell) destruction is essential for the development and implementation of effective therapies for prevention or reversal of type 1 diabetes (T1D). However, events that define the pathogenesis of human T1D have remained elusive. This gap in our knowledge results from the complex interaction between genetics, the immune system, and environmental factors that precipitate T1D in humans. A link between genetics, the immune system, and environmental factors are type 1 interferons (T1-IFNs). These cytokines are well known for inducing antiviral factors that limit infection by regulating innate and adaptive immune responses. Further, several T1D genetic risk loci are within genes that link innate and adaptive immune cell responses to T1-IFN. An additional clue that links T1-IFN to T1D is that these cytokines are a known constituent of the autoinflammatory milieu within the pancreas of patients with T1D. The presence of IFNα/β is correlated with characteristic MHC class I (MHC-I) hyperexpression found in the islets of patients with T1D, suggesting that T1-IFNs modulate the cross-talk between autoreactive cytotoxic CD8⁺ T lymphocytes and insulin-producing pancreatic β cells. Here, we review the evidence supporting the diabetogenic potential of T1-IFN in the islet microenvironment.

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.

Prognostic Classification Factors Associated With Development of Multiple Autoantibodies, Dysglycemia, and Type 1 Diabetes-A Recursive Partitioning Analysis

OBJECTIVE

To define prognostic classification factors associated with the progression from single to multiple autoantibodies, multiple autoantibodies to dysglycemia, and dysglycemia to type 1 diabetes onset in relatives of individuals with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Three distinct cohorts of subjects from the Type 1 Diabetes TrialNet Pathway to Prevention Study were investigated separately. A recursive partitioning analysis (RPA) was used to determine the risk classes. Clinical characteristics, including genotype, antibody titers, and metabolic markers were analyzed.

RESULTS

Age and GAD65 autoantibody (GAD65Ab) titers defined three risk classes for progression from single to multiple autoantibodies. The 5-year risk was 11% for those subjects >16 years of age with low GAD65Ab titers, 29% for those ≤16 years of age with low GAD65Ab titers, and 45% for those subjects with high GAD65Ab titers regardless of age. Progression to dysglycemia was associated with islet antigen 2 Ab titers, and 2-h glucose and fasting C-peptide levels. The 5-year risk is 28%, 39%, and 51% for respective risk classes defined by the three predictors. Progression to type 1 diabetes was associated with the number of positive autoantibodies, peak C-peptide level, HbA1c level, and age. Four risk classes defined by RPA had a 5-year risk of 9%, 33%, 62%, and 80%, respectively.

CONCLUSIONS

The use of RPA offered a new classification approach that could predict the timing of transitions from one preclinical stage to the next in the development of type 1 diabetes. Using these RPA classes, new prevention techniques can be tailored based on the individual prognostic risk characteristics at different preclinical stages.

Glucose area under the curve during oral glucose tolerance test as an index of glucose intolerance

HbA1c and fasting plasma glucose (FPG) levels are commonly recognized as diagnostic indices for diabetes and glucose intolerance. However, they are not sufficient for clear detection of glucose intolerance in the early stage unless an oral glucose tolerance test (OGTT) is performed. Moreover, even in case of an OGTT, 2-h postprandial plasma glucose (PG) levels, a criterion for glucose intolerance in OGTTs, may not provide complete information regarding glucose tolerance. Whole glucose excursion after OGTT is considered to represent glucose tolerance well, and the glucose area under the curve (AUC) can be an index of glucose excursion. However, few studies have investigated measurement of the glucose AUC in glucose intolerance screening. In the present study, data from 520 OGTTs were analyzed to define the cutoff value for the glucose AUC for glucose intolerance screening. Our results showed that a cutoff value of 290 mg h/dl for the glucose AUC was highly sensitive and specific (90 and 93 %, respectively) for detecting diabetes, impaired glucose tolerance (IGT), and group at increased risk of diabetes (normal glucose tolerance with 1-h PG levels of ≥180 mg/dl after glucose load) and showed a better concordance rate than the use of HbA1c, FPG, or 2-h PG levels. Moreover, the cutoff value for the glucose AUC calculated using the diagnostic criteria in the OGTT (305 mg h/dl) was consistent with the value determined from OGTT analysis. These data suggest a possibility that glucose intolerance screening using a glucose AUC cutoff value of 290 mg h/dl could be useful.

DCIR2+ cDC2 DCs and Zbtb32 Restore CD4+ T-Cell Tolerance and Inhibit Diabetes

During autoimmunity, the normal ability of dendritic cells (DCs) to induce T-cell tolerance is disrupted; therefore, autoimmune disease therapies based on cell types and molecular pathways that elicit tolerance in the steady state may not be effective. To determine which DC subsets induce tolerance in the context of chronic autoimmunity, we used chimeric antibodies specific for DC inhibitory receptor 2 (DCIR2) or DEC-205 to target self-antigen to CD11b(+) (cDC2) DCs and CD8(+) (cDC1) DCs, respectively, in autoimmune-prone nonobese diabetic (NOD) mice. Antigen presentation by DCIR2(+) DCs but not DEC-205(+) DCs elicited tolerogenic CD4(+) T-cell responses in NOD mice. β-Cell antigen delivered to DCIR2(+) DCs delayed diabetes induction and induced increased T-cell apoptosis without interferon-γ (IFN-γ) or sustained expansion of autoreactive CD4(+) T cells. These divergent responses were preceded by differential gene expression in T cells early after in vivo stimulation. Zbtb32 was higher in T cells stimulated with DCIR2(+) DCs, and overexpression of Zbtb32 in T cells inhibited diabetes development, T-cell expansion, and IFN-γ production. Therefore, we have identified DCIR2(+) DCs as capable of inducing antigen-specific tolerance in the face of ongoing autoimmunity and have also identified Zbtb32 as a suppressive transcription factor that controls T cell-mediated autoimmunity.

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.

Predictors of Progression From the Appearance of Islet Autoantibodies to Early Childhood Diabetes: The Environmental Determinants of Diabetes in the Young (TEDDY)

OBJECTIVE

While it is known that there is progression to diabetes in <10 years in 70% of children with two or more islet autoantibodies, predictors of the progression to diabetes are only partially defined.

RESEARCH DESIGN AND METHODS

The Environmental Determinants of Diabetes in the Young (TEDDY) study has observed 8,503 children who were at increased genetic risk for autoimmune diabetes. Insulin autoantibodies (IAAs), GAD65 autoantibodies (GADAs), and insulinoma-associated protein 2 autoantibodies (IA-2As) were measured every 3 months until 4 years of age and every 6 months thereafter; if results were positive, the autoantibodies were measured every 3 months.

RESULTS

Life table analysis revealed that the cumulative incidence of diabetes by 5 years since the appearance of the first autoantibody differed significantly by the number of positive autoantibodies (47%, 36%, and 11%, respectively, in those with three autoantibodies, two autoantibodies, and one autoantibody, P < 0.001). In time-varying survival models adjusted for first-degree relative status, number of autoantibodies, age at first persistent confirmed autoantibodies, and HLA genotypes, higher mean IAA and IA-2A levels were associated with an increased risk of type 1 diabetes in children who were persistently autoantibody positive (IAAs: hazard ratio [HR] 8.1 [95% CI 4.6-14.2]; IA-2A: HR 7.4 [95% CI 4.3-12.6]; P < 0.0001]). The mean GADA level did not significantly affect the risk of diabetes.

CONCLUSIONS

In the TEDDY study, children who have progressed to diabetes usually expressed two or more autoantibodies. Higher IAA and IA-2A levels, but not GADA levels, increased the risk of diabetes in those children who were persistently autoantibody positive.

β cell death and dysfunction during type 1 diabetes development in at-risk individuals

Role of the funding source: Funding from the NIH was used for support of the participating clinical centers and the coordinating center. The funding source did not participate in the collection or the analysis of the data.

BACKGROUND

The β cell killing that characterizes type 1 diabetes (T1D) is thought to begin years before patients present clinically with metabolic decompensation; however, this primary pathologic process of the disease has not been measured.

METHODS

Here, we measured β cell death with an assay that detects β cell-derived unmethylated insulin (INS) DNA. Using this assay, we performed an observational study of 50 participants from 2 cohorts at risk for developing T1D from the TrialNet Pathway to Prevention study and of 4 subjects who received islet autotransplants.

RESULTS

In at-risk subjects, those who progressed to T1D had average levels of unmethylated INS DNA that were elevated modestly compared with those of healthy control subjects. In at-risk individuals that progressed to T1D, the observed increases in unmethylated INS DNA were associated with decreases in insulin secretion, indicating that the changes in unmethylated INS DNA are indicative of β cell killing. Subjects at high risk for T1D had levels of unmethylated INS DNA that were higher than those of healthy controls and higher than the levels of unmethylated INS DNA in the at-risk progressor and at-risk nonprogressor groups followed for 4 years. Evaluation of insulin secretory kinetics also distinguished high-risk subjects who progressed to overt disease from those who did not.

CONCLUSION

We conclude that a blood test that measures unmethylated INS DNA serves as a marker of active β cell killing as the result of T1D-associated autoimmunity. Together, the data support the concept that β cell killing occurs sporadically during the years prior to diagnosis of T1D and is more intense in the peridiagnosis period.

TRIAL REGISTRATION

Clinicaltrials.gov NCT00097292.

FUNDING

Funding was from the NIH, the Juvenile Diabetes Research Foundation, and the American Diabetes Association.

Hyperglycemic clamp and oral glucose tolerance test for 3-year prediction of clinical onset in persistently autoantibody-positive offspring and siblings of type 1 diabetic patients

CONTEXT AND OBJECTIVE

In preparation of future prevention trials, we aimed to identify predictors of 3-year diabetes onset among oral glucose tolerance test (OGTT)- and hyperglycemic clamp-derived metabolic markers in persistently islet autoantibody positive (autoAb(+)) offspring and siblings of patients with type 1 diabetes (T1D).

DESIGN

The design is a registry-based study.

SETTING

Functional tests were performed in a hospital setting.

PARTICIPANTS

Persistently autoAb(+) first-degree relatives of patients with T1D (n = 81; age 5-39 years).

MAIN OUTCOME MEASURES

We assessed 3-year predictive ability of OGTT- and clamp-derived markers using receiver operating characteristics (ROC) and Cox regression analysis. Area under the curve of clamp-derived first-phase C-peptide release (AUC(5-10 min); min 5-10) was determined in all relatives and second-phase release (AUC(120-150 min); min 120-150) in those aged 12-39 years (n = 62).

RESULTS

Overall, the predictive ability of AUC(5-10 min) was better than that of peak C-peptide, the best predictor among OGTT-derived parameters (ROC-AUC [95%CI]: 0.89 [0.80-0.98] vs 0.81 [0.70-0.93]). Fasting blood glucose (FBG) and AUC(5-10 min) provided the best combination of markers for prediction of diabetes within 3 years; (ROC-AUC [95%CI]: 0.92 [0.84-1.00]). In multivariate Cox regression analysis, AUC(5-10 min)) (P = .001) was the strongest independent predictor and interacted significantly with all tested OGTT-derived parameters. AUC(5-10 min) below percentile 10 of controls was associated with 50-70% progression to T1D regardless of age. Similar results were obtained for AUC(120-150 min).

CONCLUSIONS

Clamp-derived first-phase C-peptide release can be used as an efficient and simple screening strategy in persistently autoAb(+) offspring and siblings of T1D patients to predict impending diabetes.

Network-Based Methods to Identify Highly Discriminating Subsets of Biomarkers

Complex diseases such as various types of cancer and diabetes are conjectured to be triggered and influenced by a combination of genetic and environmental factors. To integrate potential effects from interplay among underlying candidate factors, we propose a new network-based framework to identify effective biomarkers by searching for groups of synergistic risk factors with high predictive power to disease outcome. An interaction network is constructed with node weights representing individual predictive power of candidate factors and edge weights capturing pairwise synergistic interactions among factors. We then formulate this network-based biomarker identification problem as a novel graph optimization model to search for multiple cliques with maximum overall weight, which we denote as the Maximum Weighted Multiple Clique Problem (MWMCP). To achieve optimal or near optimal solutions, both an analytical algorithm based on column generation method and a fast heuristic for large-scale networks have been derived. Our algorithms for MWMCP have been implemented to analyze two biomedical data sets: a Type 1 Diabetes (T1D) data set from the Diabetes Prevention Trial-Type 1 (DPT-1) study, and a breast cancer genomics data set for metastasis prognosis. The results demonstrate that our network-based methods can identify important biomarkers with better prediction accuracy compared to the conventional feature selection that only considers individual effects.

Early hyperglycemia detected by continuous glucose monitoring in children at risk for type 1 diabetes

OBJECTIVE

We explore continuous glucose monitoring (CGM) as a new approach to defining early hyperglycemia and diagnosing type 1 diabetes in children with positive islet autoantibodies (Ab+).

RESEARCH DESIGN AND METHODS

Fourteen Ab+ children, free of signs or symptoms of diabetes, and nine antibody-negative (Ab-) subjects, followed by the Diabetes Autoimmunity Study in the Young, were asked to wear a Dexcom SEVEN CGM.

RESULTS

The Ab+ subjects showed more hyperglycemia, with 18% time spent above 140 mg/dL, compared with 9% in Ab- subjects (P = 0.04). Their average maximum daytime glucose value was higher, and they had increased glycemic variability. The mean HbA1c in the Ab+ subjects was 5.5% (37 mmol/mol). Among Ab+ subjects, ≥18-20% CGM time spent above 140 mg/dL seems to predict progression to diabetes.

CONCLUSIONS

CGM can detect early hyperglycemia in Ab+ children who are at high risk for progression to diabetes. Proposed CGM predictors of progression to diabetes require further validation.

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

OBJECTIVE We studied the utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of type 1 diabetes (T1D) risk classification in TrialNet Natural History Study (TNNHS) participants. RESEARCH DESIGN AND METHODS The cumulative incidence of T1D was compared between normoglycemic individuals with DPTRS values >7.00 and dysglycemic individuals in the TNNHS (n = 991). It was also compared between individuals with DPTRS values <7.00 or >7.00 among those with dysglycemia and those with multiple autoantibodies in the TNNHS. DPTRS values >7.00 were compared with dysglycemia for characterizing risk in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 670) and TNNHS participants. The reliability of DPTRS values >7.00 was compared with dysglycemia in the TNNHS. RESULTS The cumulative incidence of T1D for normoglycemic TNNHS participants with DPTRS values >7.00 was comparable to those with dysglycemia. Among those with dysglycemia, the cumulative incidence was much higher (P < 0.001) for those with DPTRS values >7.00 than for those with values <7.00 (3-year risks: 0.16 for <7.00 and 0.46 for >7.00). Dysglycemic individuals in DPT-1 were at much higher risk for T1D than those with dysglycemia in the TNNHS (P < 0.001); there was no significant difference in risk between the studies among those with DPTRS values >7.00. The proportion in the TNNHS reverting from dysglycemia to normoglycemia at the next visit was higher than the proportion reverting from DPTRS values >7.00 to values <7.00 (36 vs. 23%). CONCLUSIONS DPTRS thresholds can improve T1D risk classification accuracy by identifying high-risk normoglycemic and low-risk dysglycemic individuals. The 7.00 DPTRS threshold characterizes risk more consistently between populations and has greater reliability than dysglycemia.

Feature ranking based on synergy networks to identify prognostic markers in DPT-1

Interaction among different risk factors plays an important role in the development and progress of complex disease, such as diabetes. However, traditional epidemiological methods often focus on analyzing individual or a few ‘essential’ risk factors, hopefully to obtain some insights into the etiology of complex disease. In this paper, we propose a systematic framework for risk factor analysis based on a synergy network, which enables better identification of potential risk factors that may serve as prognostic markers for complex disease. A spectral approximate algorithm is derived to solve this network optimization problem, which leads to a new network-based feature ranking method that improves the traditional feature ranking by taking into account the pairwise synergistic interactions among risk factors in addition to their individual predictive power. We first evaluate the performance of our method based on simulated datasets, and then, we use our method to study immunologic and metabolic indices based on the Diabetes Prevention Trial-Type 1 (DPT-1) study that may provide prognostic and diagnostic information regarding the development of type 1 diabetes. The performance comparison based on both simulated and DPT-1 datasets demonstrates that our network-based ranking method provides prognostic markers with higher predictive power than traditional analysis based on individual factors.

The prediction of type 1 diabetes by multiple autoantibody levels and their incorporation into an autoantibody risk score in relatives of type 1 diabetic patients

OBJECTIVE

We assessed whether a risk score that incorporates levels of multiple islet autoantibodies could enhance the prediction of type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

TrialNet Natural History Study participants (n = 784) were tested for three autoantibodies (GADA, IA-2A, and mIAA) at their initial screening. Samples from those positive for at least one autoantibody were subsequently tested for ICA and ZnT8A. An autoantibody risk score (ABRS) was developed from a proportional hazards model that combined autoantibody levels from each autoantibody along with their designations of positivity and negativity.

RESULTS

The ABRS was strongly predictive of T1D (hazard ratio [with 95% CI] 2.72 [2.23-3.31], P < 0.001). Receiver operating characteristic curve areas (with 95% CI) for the ABRS revealed good predictability (0.84 [0.78-0.90] at 2 years, 0.81 [0.74-0.89] at 3 years, P < 0.001 for both). The composite of levels from the five autoantibodies was predictive of T1D before and after an adjustment for the positivity or negativity of autoantibodies (P < 0.001). The findings were almost identical when ICA was excluded from the risk score model. The combination of the ABRS and the previously validated Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) predicted T1D more accurately (0.93 [0.88-0.98] at 2 years, 0.91 [0.83-0.99] at 3 years) than either the DPTRS or the ABRS alone (P ≤ 0.01 for all comparisons).

CONCLUSIONS

These findings show the importance of considering autoantibody levels in assessing the risk of T1D. Moreover, levels of multiple autoantibodies can be incorporated into an ABRS that accurately predicts T1D.

Shape information from glucose curves: functional data analysis compared with traditional summary measures

Background

Plasma glucose levels are important measures in medical care and research, and are often obtained from oral glucose tolerance tests (OGTT) with repeated measurements over 2–3 hours. It is common practice to use simple summary measures of OGTT curves. However, different OGTT curves can yield similar summary measures, and information of physiological or clinical interest may be lost. Our mean aim was to extract information inherent in the shape of OGTT glucose curves, compare it with the information from simple summary measures, and explore the clinical usefulness of such information.

Methods

OGTTs with five glucose measurements over two hours were recorded for 974 healthy pregnant women in their first trimester. For each woman, the five measurements were transformed into smooth OGTT glucose curves by functional data analysis (FDA), a collection of statistical methods developed specifically to analyse curve data. The essential modes of temporal variation between OGTT glucose curves were extracted by functional principal component analysis. The resultant functional principal component (FPC) scores were compared with commonly used simple summary measures: fasting and two-hour (2-h) values, area under the curve (AUC) and simple shape index (2-h minus 90-min values, or 90-min minus 60-min values). Clinical usefulness of FDA was explored by regression analyses of glucose tolerance later in pregnancy.

Results

Over 99% of the variation between individually fitted curves was expressed in the first three FPCs, interpreted physiologically as “general level” (FPC1), “time to peak” (FPC2) and “oscillations” (FPC3). FPC1 scores correlated strongly with AUC (r=0.999), but less with the other simple summary measures (−0.42≤r≤0.79). FPC2 scores gave shape information not captured by simple summary measures (−0.12≤r≤0.40). FPC2 scores, but not FPC1 nor the simple summary measures, discriminated between women who did and did not develop gestational diabetes later in pregnancy.

Conclusions

FDA of OGTT glucose curves in early pregnancy extracted shape information that was not identified by commonly used simple summary measures. This information discriminated between women with and without gestational diabetes later in pregnancy.

Prognostic accuracy of immunologic and metabolic markers for type 1 diabetes in a high-risk population: receiver operating characteristic analysis

OBJECTIVE

To establish and compare the prognostic accuracy of immunologic and metabolic markers in predicting onset of type 1 diabetes in those with high risk in a prospective study.

RESEARCH DESIGN AND METHODS

A total of 339 subjects from the Diabetes Prevention Trial-Type 1 (DPT-1) parenteral study, who were islet cell antibody (ICA)-positive, with low first-phase insulin response (FPIR) and/or abnormal glucose tolerance at baseline, were followed until clinical diabetes onset or study end (5-year follow-up). The prognostic performance of biomarkers was estimated using receiver operating characteristic (ROC) curve analysis and compared with nonparametric testing of ROC curve areas. Pearson correlation was used to assess the relationship between the markers.

RESULTS

Individually, insulin autoantibody titer, ICA512A titer, peak C-peptide, 2-h glucose, FPIR, and FPIR/homeostasis model assessment of insulin resistance provided modest but significant prognostic values for 5-year risk with a similar level of area under ROC curve ranging between 0.61 and 0.67. The combination of 2-h glucose, peak C-peptide, and area under the curve C-peptide significantly improved the prognostic accuracy compared with any solitary index (P < 0.05) with an area under ROC curve of 0.76 (95% CI 0.70-0.81). The addition of antibody titers and/or intravenous glucose tolerance test (IVGTT) markers did not increase the prognostic accuracy further (P = 0.46 and P = 0.66, respectively).

CONCLUSIONS

The combination of metabolic markers derived from the oral glucose tolerance test improved accuracy in predicting progression to type 1 diabetes in a population with ICA positivity and abnormal metabolism. The results indicate that the autoimmune activity may not alter the risk of type 1 diabetes after metabolic function has deteriorated. Future intervention trials may consider eliminating IVGTT measurements as an effective cost-reduction strategy for prognostic purposes.

The application of the diabetes prevention trial-type 1 risk score for identifying a preclinical state of type 1 diabetes

OBJECTIVE

We assessed the utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for identifying individuals who are highly likely to progress to type 1 diabetes (T1D) within 2 years.

RESEARCH DESIGN AND METHODS

The DPTRS was previously developed from Diabetes Prevention Trial-Type 1 (DPT-1) data and was subsequently validated in the TrialNet Natural History Study (TNNHS). DPTRS components included C-peptide and glucose indexes from oral glucose tolerance testing, along with age and BMI. The cumulative incidence of T1D was determined after DPTRS thresholds were first exceeded and after the first occurrences of glucose abnormalities.

RESULTS

The 2-year risks after the 9.00 DPTRS threshold was exceeded were 0.88 and 0.77 in DPT-1 (n = 90) and the TNNHS (n = 69), respectively. In DPT-1, the 2-year risks were much lower after dysglycemia first occurred (0.37; n = 306) and after a 2-h glucose value between 190 and 199 mg/dL was first reached (0.64; n = 59). Among those who developed T1D in DPT-1, the 9.00 threshold was exceeded 0.81 ± 0.53 years prior to the conventional diagnosis. Postchallenge C-peptide levels were substantially higher (P = 0.001 for 30 min; P < 0.001 for other time points) when the 9.00 threshold was first exceeded compared with the levels at diagnosis.

CONCLUSIONS

A DPTRS threshold of 9.00 identifies individuals who are very highly likely to progress to the conventional diagnosis of T1D within 2 years and, thus, are essentially in a preclinical diabetic state. The 9.00 threshold is exceeded well before diagnosis, when stimulated C-peptide levels are substantially higher.

Polymeric nanomaterials for islet targeting and immunotherapeutic delivery

Here we report a proof-of-concept for development of pancreatic islet-targeting nanoparticles for immunomodulatory therapy of autoimmune type 1 diabetes. Modified with a unique islet-homing peptide, these polymeric nanomaterials exhibit 3-fold greater binding to islet endothelial cells and a 200-fold greater anti-inflammatory effect through targeted islet endothelial cell delivery of an immunosuppressant drug. Our findings also underscore the need to carefully tailor drug loading and nanoparticle dosage to achieve maximal vascular targeting and immunosuppression.

Detection of β cell death in diabetes using differentially methylated circulating DNA

In diabetes mellitus, β cell destruction is largely silent and can be detected only after significant loss of insulin secretion capacity. We have developed a method for detecting β cell death in vivo by amplifying and measuring the proportion of insulin 1 DNA from β cells in the serum. By using primers that are specific for DNA methylation patterns in β cells, we have detected circulating copies of β cell-derived demethylated DNA in serum of mice by quantitative PCR. Accordingly, we have identified a relative increase of β cell-derived DNA after induction of diabetes with streptozotocin and during development of diabetes in nonobese diabetic mice. We have extended the use of this assay to measure β cell-derived insulin DNA in human tissues and serum. We found increased levels of demethylated insulin DNA in subjects with new-onset type 1 diabetes compared with age-matched control subjects. Our method provides a noninvasive approach for detecting β cell death in vivo that may be used to track the progression of diabetes and guide its treatment.

Validation of the Diabetes Prevention Trial-Type 1 Risk Score in the TrialNet Natural History Study

OBJECTIVE

We assessed the accuracy of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS), developed from the Diabetes Prevention Trial-Type 1 (DPT-1), in the TrialNet Natural History Study (TNNHS).

RESEARCH DESIGN AND METHODS

Prediction accuracy of the DPTRS was assessed with receiver-operating characteristic curve areas. The type 1 diabetes cumulative incidence within the DPTRS intervals was compared between the TNNHS and DPT-1 cohorts.

RESULTS

Receiver-operating characteristic curve areas for the DPTRS were substantial in the TNNHS (P < 0.001 at both 2 and 3 years). The type 1 diabetes cumulative incidence did not differ significantly between the TNNHS and DPT-1 cohorts within DPTRS intervals. In the TNNHS, 2-year and 3-year risks were low for DPTRS intervals <6.50 (<0.10 and <0.20, respectively). Thresholds ≥7.50 were indicative of high risk in both cohorts (2-year risks: 0.49 in the TNNHS and 0.51 in DPT-1).

CONCLUSIONS

The DPTRS is an accurate and robust predictor of type 1 diabetes in autoantibody-positive populations.

Predictors of microvascular complications in type 1 diabetic patients at onset: the role of metabolic memory

BACKGROUND

Several epidemiological studies showed a close association between metabolic control and microvascular complications in type 1 Diabetes Mellitus (T1DM). The aim of our longitudinal observational study was to evaluate the predictive role of the main clinical and biochemical parameters in determining microvascular complications.

METHODS

376 T1DM patients, hospitalized in our division from 1991 to 2005 (mean follow-up=10.93±4.26 years) were studied. Stepwise Cox regression analysis was used to identify the influence of residual ß-cell function, ß-cell autoimmunity, HbA1c levels and other clinical and laboratory parameters in the development of microalbuminuria and retinopathy.

RESULTS

The probability of developing microalbuminuria was higher in males than in females (HR 1.82; 95% CI 1.01-3.28; p=0.044), in patients with higher mean HbA1c values (HR 2.80; 95% CI 1.63-4.83; p<0.001), longer duration of disease (HR 1.98; 95% CI 1.10-3.57; p=0.022) and younger age of diabetes onset (HR 0.53; 95% CI 0.03-0.92; p=0.026). An increased probability of developing retinopathy was found in patients with higher mean HbA1c levels during follow-up (HR 2.35; 95% CI 1.34-4.12, p=0.003), as well as at onset (HR 1.85; 95% CI 1.06-3.24; p=0.030).

CONCLUSIONS

Our study suggests that among the clinical, metabolic, immunological and biochemical factors evaluated at onset, only HbA1c is predictive for the microangiopathy development in T1DM.

Genistein reduces hyperglycemia and islet cell loss in a high-dosage manner in rats with alloxan-induced pancreatic damage

OBJECTIVES

Elucidate whether genistein (a soy-derived isoflavone) possesses the capacity to alleviate hyperglycemia and minimize islet cell loss after the onset of diabetes and whether the beneficial effect of genistein is dosage dependent.

METHODS

Alloxan-induced diabetic male Sprague-Dawley rats were randomly divided into 5 groups (10 rats per group) and treated with saline, vehicle, and 3 different dosages of genistein by daily gavage. Blood glucose and insulin levels, body weight, and oral glucose tolerance test were assessed; histological changes in pancreatic islets were quantified. In addition, rat islets were isolated, cultured, and exposed to alloxan in the presence or absence of genistein. The survival and the proliferation of islet cells were assessed, and insulin levels in the culture supernatant were measured.

RESULTS

In vivo high-dose (30 mg/kg per day) but not low-dose genistein significantly decreases weight loss, hyperglycemia, and islet cell loss in alloxan-induced diabetic rats, while increasing blood insulin levels and glucose tolerance. In vitro experiments reveal that genistein improves islet cell survival and proliferation and facilitates insulin production after alloxan injury.

CONCLUSIONS

Genistein possesses the capacity to reduce hyperglycemia via minimization of islet cell loss in a dosage-dependent manner (estimating >5-fold than physical intakes) after the onset of diabetes.

Prognostic performance of metabolic indexes in predicting onset of type 1 diabetes

OBJECTIVE

In this investigation we evaluated nine metabolic indexes from intravenous glucose tolerance tests (IVGTTs) and oral glucose tolerance tests (OGTTs) in an effort to determine their prognostic performance in predicting the development of type 1 diabetes in those with moderate risk, as defined by familial relation to a type 1 diabetic individual, a positive test for islet cell antibodies and insulin autoantibody, but normal glucose tolerance.

RESEARCH DESIGN AND METHODS

Subjects (n = 186) who had a projected risk of 25-50% for developing type 1 diabetes within 5 years were followed until clinical diabetes onset or the end of the study as part of the Diabetes Prevention Trial-Type 1. Prognostic performance of the metabolic indexes was determined using receiver operating characteristic (ROC) curve and survival analyses.

RESULTS

Two-hour glucose from an OGTT most accurately predicted progression to disease compared with all other metabolic indicators with an area under the ROC curve of 0.67 (95% CI 0.59-0.76), closely followed by the ratio of first-phase insulin response (FPIR) to homeostasis model assessment of insulin resistance (HOMA-IR) with an area under the curve value of 0.66. The optimal cutoff value for 2-h glucose (114 mg/dl) maintained sensitivity and specificity values >0.60. The hazard ratio for those with 2-h glucose ≥ 114 mg/dl compared with those with 2-h glucose <114 mg/dl was 2.96 (1.67-5.22).

CONCLUSIONS

The ratio of FPIR to HOMA-IR from an IVGTT provided accuracy in predicting the development of type 1 diabetes similar to that of 2-h glucose from an OGTT, which, because of its lower cost, is preferred. The optimal cutoff value determined for 2-h glucose provides additional guidance for clinicians to identify subjects for potential prevention treatments before the onset of impaired glucose tolerance.

Progression to diabetes in relatives of type 1 diabetic patients: mechanisms and mode of onset

OBJECTIVE

Relatives of type 1 diabetic patients are at enhanced risk of developing diabetes. We investigated the mode of onset of hyperglycemia and how insulin sensitivity and beta-cell function contribute to the progression to the disease.

RESEARCH DESIGN AND METHODS

In 328 islet cell autoantibody-positive, nondiabetic relatives from the observational arms of the Diabetes Prevention Trial-1 Study (median age 11 years [interquartile range 8], sequential OGTTs (2,143 in total) were performed at baseline, every 6 months, and 2.7 years [2.7] later, when 115 subjects became diabetic. Beta-cell glucose sensitivity (slope of the insulin-secretion/plasma glucose dose-response function) and insulin sensitivity were obtained by mathematical modeling of the OGTT glucose/C-peptide responses.

RESULTS

In progressors, baseline insulin sensitivity, fasting insulin secretion, and total postglucose insulin output were similar to those of nonprogressors, whereas beta-cell glucose sensitivity was impaired (median 48 pmol/min per m2 per mmol/l [interquartile range 36] vs. 87 pmol/min per m2 per mmol/l [67]; P < 0.0001) and predicted incident diabetes (P < 0.0001) independently of sex, age, BMI, and clinical risk. In progressors, 2-h glucose levels changed little until 0.78 years before diagnosis, when they started to rise rapidly (approximately 13 mmol x l(-1) x year(-1)); glucose sensitivity began to decline significantly (P < 0.0001) earlier (1.45 years before diagnosis) than the plasma glucose surge. During this anticipation phase, both insulin secretion and insulin sensitivity were essentially stable.

CONCLUSIONS

In high-risk relatives, beta-cell glucose sensitivity is impaired and is a strong predictor of diabetes progression. The time trajectories of plasma glucose are frequently biphasic, with a slow linear increase followed by a rapid surge, and are anticipated by a further deterioration of beta-cell glucose sensitivity.

Trends of earlier and later responses of C-peptide to oral glucose challenges with progression to type 1 diabetes in diabetes prevention trial-type 1 participants

OBJECTIVE We studied the C-peptide response to oral glucose with progression to type 1 diabetes in Diabetes Prevention Trial-Type 1 (DPT-1) participants. RESEARCH DESIGN AND METHODS Among 504 DPT-1 participants <15 years of age, longitudinal analyses were performed in 36 progressors and 80 nonprogressors. Progressors had oral glucose tolerance tests (OGTTs) at baseline and every 6 months from 2.0 to 0.5 years before diagnosis; nonprogressors had OGTTs over similar intervals before their last visit. Sixty-six progressors and 192 nonprogressors were also studied proximal to and at diagnosis. RESULTS The 30-0 min C-peptide difference from OGTTs performed 2.0 years before diagnosis in progressors was lower than the 30-0 min C-peptide difference from OGTTs performed 2.0 years before the last visit in nonprogressors (P < 0.01) and remained lower over time. The 90-60 min C-peptide difference was positive at every OGTT before diagnosis in progressors, whereas it was negative at every OGTT before the last visit in nonprogressors (P < 0.01 at 2.0 years). The percentage whose peak C-peptide occurred at 120 min was higher in progressors at 2.0 years (P < 0.05); this persisted over time (P < 0.001 at 0.5 years). However, the peak C-peptide levels were only significantly lower at 0.5 years in progressors (P < 0.01). The timing of the peak C-peptide predicted type 1 diabetes (P < 0.001); peak C-peptide levels were less predictive (P < 0.05). CONCLUSIONS A decreased early C-peptide response to oral glucose and an increased later response occur at least 2 years before the diagnosis of type 1 diabetes.

Incident dysglycemia and progression to type 1 diabetes among participants in the Diabetes Prevention Trial-Type 1

OBJECTIVE

We studied the incidence of dysglycemia and its prediction of the development of type 1 diabetes in islet cell autoantibody (ICA)-positive individuals. In addition, we assessed whether dysglycemia was sustained.

RESEARCH DESIGN AND METHODS

Participants (n = 515) in the Diabetes Prevention Trial-Type 1 (DPT-1) with normal glucose tolerance who underwent periodic oral glucose tolerance tests (OGTTs) were followed for incident dysglycemia (impaired fasting glucose, impaired glucose tolerance, and/or high glucose levels at intermediate time points of OGTTs). Incident dysglycemia at the 6-month visit was assessed for type 1 diabetes prediction.

RESULTS

Of 515 participants with a normal baseline OGTT, 310 (60%) had at least one episode of dysglycemia over a maximum follow-up of 7 years. Dysglycemia at the 6-month visit was highly predictive of the development of type 1 diabetes, both in those aged <13 years (P < 0.001) and those aged > or =13 years (P < 0.01). Those aged <13 years with dysglycemia at the 6-month visit had a high cumulative incidence (94% estimate by 5 years). Among those who developed type 1 diabetes after a dysglycemic OGTT and who had at least two OGTTs after the dysglycemic OGTT, 33 of 64 (52%) reverted back to a normal OGTT. However, 26 (79%) of the 33 then had another dysglycemic OGTT before diagnosis.

CONCLUSIONS

ICA-positive individuals with normal glucose tolerance had a high incidence of dysglycemia. Incident dysglycemia in those who are ICA positive is strongly predictive of type 1 diabetes. Children with incident dysglycemia have an especially high risk. Fluctuations in and out of the dysglycemic state are not uncommon before the onset of type 1 diabetes.

Early metabolic markers of islet allograft dysfunction

BACKGROUND

Islet transplantation can restore normoglycemia to patients with unstable type 1 diabetes mellitus, but long-term insulin independence is usually not sustained. Identification of predictor(s) of islet allograft dysfunction (IGD) might allow for early intervention(s) to preserve functional islet mass.

METHODS

Fourteen islet transplantation recipients with long-term history of type 1 diabetes mellitus underwent metabolic testing by mixed meal tolerance test, intravenous glucose tolerance test, and arginine stimulation test every 3 months postislet transplant completion. Metabolic responses were compared between subjects who maintained insulin independence at 18 months (group 1; n=5) and those who restarted insulin within 18 months (group 2; n=9). Data were analyzed before development of islet graft dysfunction and while insulin independent.

RESULTS

The 90-min glucose, time-to-peak C-peptide, and area under the curve for glucose were consistently higher in group 2 and increased as a function of time. At 12 months, acute insulin release to glucose in group 2 was markedly reduced as compared with baseline (5.62+/-1.21 microIU/mL, n=4 vs. 16.14+/-3.69 microIU/mL, n=8), whereas it remained stable in group 1 (22.36+/-4.98 microIU/mL, n=5 vs. 27.70+/-2.83 microIU/mL, n=5). Acute insulin release to glucose, acute C-peptide release to glucose (ACpRg), and mixed meal stimulation index were significantly decreased and time-to-peak C-peptide, 90-min glucose, and area under the curve for glucose were significantly increased when measured at time points preceding intervals where IGD occurred compared with intervals where there was no IGD.

CONCLUSIONS

The intravenous glucose tolerance test and mixed meal tolerance test may be useful in the prediction of IGD and should be essential components of the metabolic testing of islet transplant recipients.

Beta-cell mass and type 1 diabetes: going, going, gone?

OBJECTIVE

Beta-cell regeneration is a fundamental but elusive goal for type 1 diabetes research. Our objective is to review newer human and animal studies of beta-cell destruction and regeneration and consider the implications for treatment of type 1 diabetes.

RESEARCH DESIGN AND METHODS

Recent human and animal studies of beta-cell destruction and regeneration in type 1 diabetes are reviewed.

RESULTS

The loss of beta-cells that characterizes type 1 diabetes reflects the net effects of destruction and regeneration. These processes have been examined in the nonobese diabetic (NOD) mouse; uncertainty remains about beta-cell dynamics in humans. Islet inflammation stimulates beta-cell replication that produces new insulin-positive cells. The regenerative process may tide the loss of overall beta-cell function, but it also may enhance the autoimmune attack on beta-cells by providing new epitopes. The highest rates of beta-cell replication are at the time of diagnosis of diabetes in NOD mice, and if autoimmunity and islet inflammation are arrested, new beta-cells are formed. However, the majority of beta-cells after treatment with immune modulators such as anti-CD3 monoclonal antibody, and most likely during the "honeymoon" in human disease, are recovered beta-cells that had been degranulated but present at the time of diagnosis of diabetes.

CONCLUSIONS

Residual beta-cells play a significant role for the design of therapeutic trials: they not only may respond to combination therapies that include stimulants of metabolic function but are also the potential source of new beta-cells.

VMAT2 quantitation by PET as a biomarker for beta-cell mass in health and disease

The common pathology underlying both type 1 and type 2 diabetes (T1DM and T2DM) is insufficient beta-cell mass (BCM) to meet metabolic demands. An important impediment to the more rapid evaluation of interventions for both T1DM and T2DM lack of biomarkers of pancreatic BCM. A reliable means of monitoring the mass and/or function of beta-cells would enable evaluation of the progression of diabetes as well as the monitoring of pharmacologic and other interventions. Recently, we identified a biomarker of BCM that is quantifiable by positron emission tomography (PET). PET is an imaging technique which allows for non-invasive measurements of radioligand uptake and clearance, is sensitive in the pico- to nanomolar range and of which the results can be deconvoluted into measurements of receptor concentration. For BCM estimates, we have identified VMAT2 (vesicular monoamine transporter type 2) as a biomarker and [(11)C] DTBZ (dihydrotetrabenazine) as the transporter's ligand. VMAT2 is highly expressed in beta-cells of the human pancreas relative to other cells of the endocrine and exocrine pancreas. Thus measurements of [(11)C] DTBZ in the pancreas provide an indirect measurement of BCM. Here we summarize our ongoing efforts to validate the clinical utility of this non-invasive approach to real-time BCM measurements.

Glucose and C-peptide changes in the perionset period of type 1 diabetes in the Diabetes Prevention Trial-Type 1

OBJECTIVE

We examined metabolic changes in the period immediately after the diagnosis of type 1 diabetes and in the period leading up to its diagnosis in Diabetes Prevention Trial-Type 1 (DPT-1) participants.

RESEARCH DESIGN AND METHODS

The study included oral insulin trial participants and parenteral insulin trial control subjects (n = 63) in whom diabetes was diagnosed by a 2-h diabetic oral glucose tolerance test (OGTT) that was confirmed by another diabetic OGTT within 3 months. Differences in glucose and C-peptide levels between the OGTTs were assessed.

RESULTS

Glucose levels increased at 90 (P = 0.006) and 120 min (P < 0.001) from the initial diabetic OGTT to the confirmatory diabetic OGTT (mean +/- SD interval 5.5 +/- 2.8 weeks). Peak C-peptide levels fell substantially between the OGTTs (median change -14.3%, P < 0.001). Among the 55 individuals whose last nondiabetic OGTT was approximately 6 months before the initial diabetic OGTT, peak C-peptide levels decreased between these two OGTTs (median change -14.0%, P = 0.052). Among those same individuals the median change in peak C-peptide levels from the last normal OGTT to the confirmatory OGTT (interval 7.5 +/- 1.3 months) was -23.8% (P < 0.001). Median rates of change in peak C-peptide levels were 0.00 ng x ml(-1) x month(-1) (P = 0.468, n = 36) from approximately 12 to 6 months before diagnosis, -0.10 ng x ml(-1) x month(-1) (P = 0.059, n = 55) from 6 months before diagnosis to diagnosis, and -0.43 ng x ml(-1) x month(-1) (P = 0.002, n = 63) from the initial diabetic OGTT to the confirmatory diabetic OGTT.

CONCLUSIONS

It seems that postchallenge C-peptide levels begin to decrease appreciably in the 6 months before diagnosis and decrease even more rapidly within 3 months after diagnosis.

Prevention of type 1 diabetes: the time has come

Improved understanding of the pathogenesis of type 1 diabetes mellitus has completely changed our view of this disease in the past 25 years-from an acute, fulminant disease, to a chronic, autoimmune process. Information on genetic and serologic markers has increased our ability to identify individuals at risk. Prospectively gathered data indicate that, with a combination of immunologic and metabolic studies, children with a 6-year risk of disease higher than 90% can be identified due to an ongoing immune process. They differ from children with overt disease only in the time it will take for glucose levels to rise above a diagnostic threshold. Therapies to change the progression of beta-cell loss have been tested in patients with newly diagnosed type 1 diabetes. With improved predictive capabilities and agents that can have longer-lasting effects than those tested more than 10 years ago, new prevention studies are underway. These studies are large and costly but the risks posed by such interventions compare favorably with those of developing hyperglycemia and of future complications portended by the diagnosis of diabetes. In this Review we discuss risk-stratification techniques and how they are applied, other diagnostic criteria, and outcomes from diabetes-prevention trials.

A risk score for type 1 diabetes derived from autoantibody-positive participants in the diabetes prevention trial-type 1

OBJECTIVE

The accurate prediction of type 1 diabetes is essential for appropriately identifying prevention trial participants. Thus, we have developed a risk score for the prediction of type 1 diabetes.

RESEARCH DESIGN AND METHODS

Diabetes Prevention Trial-Type 1 (DPT-1) participants, islet cell autoantibody (ICA)-positive relatives of type 1 diabetic patients (n = 670), were randomly divided into development and validation samples. Risk score values were calculated for the validation sample from development sample model coefficients obtained through forward stepwise proportional hazards regression.

RESULTS

A risk score based on a model including log-BMI, age, log-fasting C-peptide, and postchallenge glucose and C-peptide sums from 2-h oral glucose tolerance tests (OGTTs) was derived from the development sample. The baseline risk score strongly predicted type 1 diabetes in the validation sample (chi(2) = 82.3, P < 0.001). Its strength of prediction was almost the same (chi(2) = 83.3) as a risk score additionally dependent on a decreased first-phase insulin response variable from intravenous glucose tolerance tests (IVGTTs). Biochemical autoantibodies did not contribute significantly to the risk score model. A final type 1 diabetes risk score was then derived from all participants with the same variables as those in the development sample model. The change in the type 1 diabetes risk score from baseline to 1 year was in itself also highly predictive of type 1 diabetes (P < 0.001).

CONCLUSIONS

A risk score based on age, BMI, and OGTT indexes, without dependence on IVGTTs or additional autoantibodies, appears to accurately predict type 1 diabetes in ICA-positive relatives.