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

Early Dysglycemia Is Detectable Using Continuous Glucose Monitoring in Very Young Children at Risk of Type 1 Diabetes

OBJECTIVE

Continuous glucose monitoring (CGM) can detect early dysglycemia in older children and adults with presymptomatic type 1 diabetes (T1D) and predict risk of progression to clinical onset. However, CGM data for very young children at greatest risk of disease progression are lacking. This study aimed to investigate the use of CGM data measured in children being longitudinally observed in the Australian Environmental Determinants of Islet Autoimmunity (ENDIA) study from birth to age 10 years.

RESEARCH DESIGN AND METHODS

Between January 2021 and June 2023, 31 ENDIA children with persistent multiple islet autoimmunity (PM Ab+) and 24 age-matched control children underwent CGM assessment alongside standard clinical monitoring. The CGM metrics of glucose SD (SDSGL), coefficient of variation (CEV), mean sensor glucose (SGL), and percentage of time >7.8 mmol/L (>140 mg/dL) were determined and examined for between-group differences.

RESULTS

The mean (SD) ages of PM Ab+ and Ab- children were 4.4 (1.8) and 4.7 (1.9) years, respectively. Eighty-six percent of eligible PM Ab+ children consented to CGM wear, achieving a median (quartile 1 [Q1], Q3) sensor wear period of 12.5 (9.0, 15.0) days. PM Ab+ children had higher median (Q1, Q3) SDSGL (1.1 [0.9, 1.3] vs. 0.9 [0.8, 1.0] mmol/L; P < 0.001) and CEV (17.3% [16.0, 20.9] vs. 14.7% [12.9, 16.6]; P < 0.001). Percentage of time >7.8 mmol/L was greater in PM Ab+ children (median [Q1, Q3] 8.0% [4.4, 13.0] compared with 3.3% [1.4, 5.3] in Ab- children; P = 0.005). Mean SGL did not differ significantly between groups (P = 0.10).

CONCLUSIONS

CGM is feasible and well tolerated in very young children at risk of T1D. Very young PM Ab+ children have increased SDSGL, CEV, and percentage of time >7.8 mmol/L, consistent with prior studies involving older participants.

Design, rationale and protocol for Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs): an observational cohort study

INTRODUCTION

Given the increasing prevalence of both obesity and pre-diabetes in pregnant adults, there is growing interest in identifying hyperglycaemia in early pregnancy to optimise maternal and perinatal outcomes. Multiple organisations recommend first-trimester diabetes screening for individuals with risk factors; however, the benefits and drawbacks of detecting glucose abnormalities more mild than overt diabetes in early gestation and the best screening method to detect such abnormalities remain unclear.

METHODS AND ANALYSIS

The goal of the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study (GO MOMs) is to evaluate how early pregnancy glycaemia, measured using continuous glucose monitoring and oral glucose tolerance testing, relates to the diagnosis of gestational diabetes (GDM) at 24-28 weeks' gestation (maternal primary outcome) and large-for-gestational-age birth weight (newborn primary outcome). Secondary objectives include relating early pregnancy glycaemia to other adverse pregnancy outcomes and comprehensively detailing longitudinal changes in glucose over the course of pregnancy. GO MOMs enrolment began in April 2021 and will continue for 3.5 years with a target sample size of 2150 participants.

ETHICS AND DISSEMINATION

GO MOMs is centrally overseen by Vanderbilt University's Institutional Review Board and an Observational Study Monitoring Board appointed by National Institute of Diabetes and Digestive and Kidney Diseases. GO MOMs has potential to yield data that will improve understanding of hyperglycaemia in pregnancy, elucidate better approaches for early pregnancy GDM screening, and inform future clinical trials of early GDM treatment.

TRIAL REGISTRATION NUMBER

NCT04860336.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

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

Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning

Background: Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Methods: Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m2 with zero (n = 21), one (n = 18), and ≥2 (n = 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00-06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly (P = 0.04), overnight CGM incremental AUC (P = 0.005), and T180 for 75 min post-SLMM CGM traces (P = 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81. Conclusion: A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory. Trial registration: ClinicalTrials.gov registration no. NCT02663661.

CGM Metrics Identify Dysglycemic States in Participants From the TrialNet Pathway to Prevention Study

OBJECTIVE

Continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes. We aimed to determine whether CGM metrics provide additional insights into progression to clinical stage 3 type 1 diabetes.

RESEARCH DESIGN AND METHODS

One hundred five relatives of individuals in type 1 diabetes probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) from the TrialNet Pathway to Prevention study underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The baseline data are reported here. Three groups were evaluated: individuals with 1) stage 2 type 1 diabetes (n = 42) with two or more diabetes-related autoantibodies and abnormal OGTT; 2) stage 1 type 1 diabetes (n = 53) with two or more diabetes-related autoantibodies and normal OGTT; and 3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10).

RESULTS

Multiple CGM metrics were associated with progression to stage 3 type 1 diabetes. Specifically, spending ≥5% time with glucose levels ≥140 mg/dL (P = 0.01), ≥8% time with glucose levels ≥140 mg/dL (P = 0.02), ≥5% time with glucose levels ≥160 mg/dL (P = 0.0001), and ≥8% time with glucose levels ≥160 mg/dL (P = 0.02) were all associated with progression to stage 3 disease. Stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; spent more time with glucose values over 120, 140, and 160 mg/dL; and had greater variability.

CONCLUSIONS

CGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 type 1 diabetes.

Methylglyoxal products in pre-symptomatic type 1 diabetes

INTRODUCTION

Progression to type 1 diabetes has emerged as a complex process with metabolic alterations proposed to be a significant driver of disease. Monitoring products of altered metabolism is a promising tool for determining the risk of type 1 diabetes progression and to supplement existing predictive biomarkers. Methylglyoxal (MG) is a reactive product produced from protein, lipid, and sugar metabolism, providing a more comprehensive measure of metabolic changes compared to hyperglycemia alone. MG forms covalent adducts on nucleic and amino acids, termed MG-advanced glycation end products (AGEs) that associate with type 1 diabetes.

METHODS

We tested their ability to predict risk of disease and discriminate which individuals with autoimmunity will progress to type 1 diabetes. We measured serum MG-AGEs from 141 individuals without type 1 diabetes and 271 individuals with type 1 diabetes enrolled in the Fr1da cohort. Individuals with type 1 diabetes were at stages 1, 2, and 3.

RESULTS

We examined the association of MG-AGEs with type 1 diabetes. MG-AGEs did not correlate with HbA1c or differ between stages 1, 2, and 3 type 1 diabetes. Yet, RNA MG-AGEs were significantly associated with the rate of progression to stage 3 type 1 diabetes, with lower serum levels increasing risk of progression.

DISCUSSION

MG-AGEs were able to discriminate which individuals with autoantibodies would progress at a faster rate to stage 3 type 1 diabetes providing a potential new clinical biomarker for determining rate of disease progression and pointing to contributing metabolic pathways.

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.

Time to Peak Glucose and Peak C-Peptide During the Progression to Type 1 Diabetes in the Diabetes Prevention Trial and TrialNet Cohorts

OBJECTIVE

To assess the progression of type 1 diabetes using time to peak glucose or C-peptide during oral glucose tolerance tests (OGTTs) in autoantibody-positive relatives of people with type 1 diabetes.

RESEARCH DESIGN AND METHODS

We examined 2-h OGTTs of participants in the Diabetes Prevention Trial Type 1 (DPT-1) and TrialNet Pathway to Prevention (PTP) studies. We included 706 DPT-1 participants (mean ± SD age, 13.84 ± 9.53 years; BMI Z-score, 0.33 ± 1.07; 56.1% male) and 3,720 PTP participants (age, 16.01 ± 12.33 years; BMI Z-score, 0.66 ± 1.3; 49.7% male). Log-rank testing and Cox regression analyses with adjustments (age, sex, race, BMI Z-score, HOMA-insulin resistance, and peak glucose/C-peptide levels, respectively) were performed.

RESULTS

In each of DPT-1 and PTP, higher 5-year diabetes progression risk was seen in those with time to peak glucose >30 min and time to peak C-peptide >60 min (P < 0.001 for all groups), before and after adjustments. In models examining strength of association with diabetes development, associations were greater for time to peak C-peptide versus peak C-peptide value (DPT-1: χ2 = 25.76 vs. χ2 = 8.62; PTP: χ2 = 149.19 vs. χ2 = 79.98; all P < 0.001). Changes in the percentage of individuals with delayed glucose and/or C-peptide peaks were noted over time.

CONCLUSIONS

In two independent at-risk populations, we show that those with delayed OGTT peak times for glucose or C-peptide are at higher risk of diabetes development within 5 years, independent of peak levels. Moreover, time to peak C-peptide appears more predictive than the peak level, suggesting its potential use as a specific biomarker for diabetes progression.

Stress hyperglycemia as first sign of asymptomatic type 1 diabetes: an instructive case

Background

Stress hyperglycemia (SH) is considered a transient manifestation and routine diagnostic evaluation was thought to be unnecessary due to the lack of definite correlation with diabetes mellitus (DM). Although SH was usually benign and long-term treatment was superfluous, it might be the first sign of insulinopenic status such as type 1 DM (T1DM).

Case presentation

We reported a boy with acute asthma attack presented incidentally with high blood glucose levels exceeding 300 mg/dL and obvious glycemic variability. A prolonged hyperglycemic duration of more than 48 h was also noticed. To elucidate his unique situation, glucagon test and insulin autoantibody survey were done which showed insulinopenia with positive anti-insulin antibody and glutamic acid decarboxylase antibody despite the absence of overt DM symptoms and signs.

Conclusions

This case highlights that SH might be a prodromal presentation in T1DM children, especially when accompanied simultaneously with extreme hyperglycemia, apparent glucose variability, as well as prolonged hyperglycemic duration.

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

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.

Continuous Glucose Monitoring in Healthy Children Aged 2-8 Years

BACKGROUND

The purpose of this study was to add the missing information on glycemic levels and patterns as measured by continuous glucose monitoring (CGM) in the daily life of healthy children aged 2-8 years. These data are needed when studying glycemic patterns and treatment outcome in children aged 2-8 years with diabetes.

METHODS

Each of the 15 healthy children aged 2-7.99 years used a CGM device (Dexcom G4 Platinum) for 7 days.

RESULTS

A total of 15 children (10 girls) aged 5.4 ± 1.6 years registered a mean of 1976 ± 15 counts. Mean sensor glucose was 5.3 ± 1.0 mmol/L (95 ± 18 mg/dL) and 89% of values were in the range 4-7.8 mmol/L (72-140 mg/dL), 9% of sensor glucose values were <4.0 mmol/L (72 mg/dL), and 2% of sensor glucose values were >7.8 mmol/L (140 mg/dL).

CONCLUSION

We present glycemic data as measured by CGM in healthy children aged 2-8 years.

Type 1 Diabetes: Disease Stratification

Type 1 diabetes, a disorder characterized by immune-mediated loss of functional pancreatic beta cells, is a disease continuum with specific presymptomatic stages with defined risk of progression to symptomatic disease. Prognostic biomarkers have been developed for disease staging and for stratification of subjects that address the heterogeneity in rate of disease progression. Using biomarkers for stratification of subjects at different stages of type 1 diabetes will enable smaller and shorter intervention clinical trials with greater effect size. Addressing the heterogeneity of the disease will allow precision medicine-based approaches to prevention and interception of presymptomatic stages of disease and treatment and cure of symptomatic disease.

Continuous glucose monitoring and HbA1c in the evaluation of glucose metabolism in children at high risk for type 1 diabetes mellitus

AIMS

Continuous glucose monitoring (CGM) parameters, self-monitored blood glucose (SMBG), HbA1c and oral glucose tolerance test (OGTT) were studied during preclinical type 1 diabetes mellitus.

METHODS

Ten asymptomatic children with multiple (⩾2) islet autoantibodies (cases) and 10 age and sex-matched autoantibody-negative controls from the Type 1 Diabetes Prediction and Prevention (DIPP) Study were invited to 7-day CGM with Dexcom G4 Platinum Sensor. HbA1c and two daily SMBG values (morning and evening) were analyzed. Five-point OGTTs were performed and carbohydrate intake was assessed by food records. The matched pairs were compared with the paired sample t-test.

RESULTS

The cases showed higher mean values and higher variation in glucose levels during CGM compared to the controls. The time spent ⩾7.8mmol/l was 5.8% in the cases compared to 0.4% in the controls (p=0.040). Postprandial CGM values were similar except after the dinner (6.6mmol/l in cases vs. 6.1mmol/l in controls; p=0.023). When analyzing the SMBG values higher mean level, higher evening levels, as well as higher variation were observed in the cases when compared to the controls. HbA1c was significantly higher in the cases [5.7% (39mmol/mol) vs. 5.3% (34mmol/mol); p=0.045]. No differences were observed in glucose or C-peptide levels during OGTT. Daily carbohydrate intake was slightly higher in the cases (254.2g vs. 217.7g; p=0.034).

CONCLUSIONS

Glucose levels measured by CGM and SMBG are useful indicators of dysglycemia during preclinical type 1 diabetes mellitus. Increased evening glucose values seem to be common in children with preclinical type 1 diabetes mellitus.

Dysregulation of glucose metabolism in preclinical type 1 diabetes

Long-term prospective studies have provided valuable information about preclinical type 1 diabetes (T1D). Children who have seroconverted to positive for islet autoantibodies have also, in follow-up, had metabolic tests to understand the timing and development of abnormal glucose tolerance and declining insulin secretion before the clinical diagnosis of T1D. First phase insulin response (FPIR) in the intravenous glucose tolerance test (IVGTT) is lower in the progressors positive for multiple islet autoantibodies in all age groups and as early as 4-6 years before the diagnosis when compared with the non-progressors positive for only islet cell antibodies (ICA). An accelerated decline in FPIR is seen in the progressors during the last 1.5 years before the diagnosis. These results indicate that the progressors may have an early intrinsic defect in beta cell development or function. In the oral glucose tolerance test (OGTT) the peak C-peptide response is delayed in the progressors at least 2 years before diagnosis. Glucose levels and HbA1c are increasing about 2 years before clinical diagnosis. An increase in HbA1c and detection of abnormal glucose tolerance in OGTT are useful in the prediction of the timing of clinical onset of T1D. Continuous glucose monitoring (CGM) may be useful in the prediction of T1D as an early indicator of increased glycemic variability but more data from larger series are needed for confirmation. Children followed in the prospective studies are diagnosed earlier and have a decreased frequency of ketoacidosis at the diagnosis of T1D when compared with age-matched cases from the population.

Glycemic variability in patients with Wolfram syndrome is lower than in type 1 diabetes

AIMS

Wolfram syndrome (WFS) is diagnosed as coexistence of diabetes mellitus and optic atrophy, where pancreatic beta cell destruction is associated with neurodegeneration. Typically, WFS necessitates insulin treatment similar to type 1 diabetes (T1D), but the mechanism of beta cell mass reduction leading to hyperglycemia is different.

METHODS

The aim of the study was to assess glycemic variability using the continuous glucose monitoring (CGM) system in seven pediatric patients with genetically confirmed WFS and compare the results with data obtained from 21 propensity score-matched patients with T1D. The "GlyCulator" application was used for the calculation of glycemic variability indices.

RESULTS

CGM recordings showed similarities in glycemic variability among WFS patients, but differing from those of the T1D group. Coefficient of variation (%CV), CONGA4h, and GONGA6h were significantly (p < 0.05) lower in WFS patients (28.08 ± 7.37, 54.96 ± 11.92, and 55.99 ± 10.58) than in T1D patients (37.87 ± 14.24, 74.12 ± 28.74, p = 0.02, and 80.26 ± 35.05, respectively). In WFS patients, the percentage of values above 126 mg/dL was 69.79 (52.08-77.43), whereas in patients with T1D, the percentage was significantly lower-47.22 (35.07-62.85, p = 0.018). Curiously, a tendency toward a lower percentage of measurements below 70 mg/dL was noted in the WFS group [0 (0-7.29)] in comparison with the T1D group [6.25 (0-18.06), p = 0.122]. WFS patients had a significantly higher C-peptide level (0.31 ± 0.2 ng/mL) than T1D patients (0.04 ± 0.04 ng/mL; p = 0.006).

CONCLUSIONS

Patients with WFS show smaller glycemic variability than individuals with T1D, and this may be associated with persistent residual insulin secretion.

Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes

AIMS/HYPOTHESIS

We examined whether measures of glycaemic variability (GV), assessed by continuous glucose monitoring (CGM) and self-monitoring of blood glucose (SMBG), can complement or replace measures of beta cell function and insulin action in detecting the progression of preclinical disease to type 1 diabetes.

METHODS

Twenty-two autoantibody-positive (autoAb(+)) first-degree relatives (FDRs) of patients with type 1 diabetes who were themselves at high 5-year risk (50%) for type 1 diabetes underwent CGM, a hyperglycaemic clamp test and OGTT, and were followed for up to 31 months. Clamp variables were used to estimate beta cell function (first-phase [AUC5-10 min] and second-phase [AUC120-150 min] C-peptide release) combined with insulin resistance (glucose disposal rate; M 120-150 min). Age-matched healthy volunteers (n = 20) and individuals with recent-onset type 1 diabetes (n = 9) served as control groups.

RESULTS

In autoAb(+) FDRs, M 120-150 min below the 10th percentile (P10) of controls achieved 86% diagnostic efficiency in discriminating between normoglycaemic FDRs and individuals with (impending) dysglycaemia. M 120-150 min outperformed AUC5-10 min and AUC120-150 min C-peptide below P10 of controls, which were only 59-68% effective. Among GV variables, CGM above the reference range was better at detecting (impending) dysglycaemia than elevated SMBG (77-82% vs 73% efficiency). Combined CGM measures were equally efficient as M 120-150 min (86%). Daytime GV variables were inversely correlated with clamp variables, and more strongly with M 120-150 min than with AUC5-10 min or AUC120-150 min C-peptide.

CONCLUSIONS/INTERPRETATION

CGM-derived GV and the glucose disposal rate, reflecting both insulin secretion and action, outperformed SMBG and first- or second-phase AUC C-peptide in identifying FDRs with (impending) dysglycaemia or diabetes. Our results indicate the feasibility of developing minimally invasive CGM-based criteria for close metabolic monitoring and as outcome measures in trials.