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

Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development

AIMS/HYPOTHESIS

We aimed to assess whether continuous glucose monitor (CGM) metrics can accurately predict stage 3 type 1 diabetes diagnosis in those with islet autoantibodies (AAb).

METHODS

Baseline CGM data were collected from participants with ≥1 positive AAb type from five studies: ASK (n=79), BDR (n=22), DAISY (n=18), DIPP (n=8) and TrialNet Pathway to Prevention (n=91). Median follow-up time was 2.6 years (quartiles: 1.5 to 3.6 years). A participant characteristics-only model, a CGM metrics-only model and a full model combining characteristics and CGM metrics were compared.

RESULTS

The full model achieved a numerically higher performance predictor estimate (C statistic=0.74; 95% CI 0.66, 0.81) for predicting stage 3 type 1 diabetes diagnosis compared with the characteristics-only model (C statistic=0.69; 95% CI 0.60, 0.77) and the CGM-only model (C statistic=0.68; 95% CI 0.61, 0.75). Greater percentage of time >7.8 mmol/l (p<0.001), HbA1c (p=0.02), having a first-degree relative with type 1 diabetes (p=0.02) and testing positive for IA-2 AAb (p<0.001) were associated with greater risk of type 1 diabetes diagnosis. Additionally, being male (p=0.06) and having a negative GAD AAb (p=0.09) were selected but not found to be significant. Participants classified as having low (n=79), medium (n=98) or high (n=41) risk of stage 3 type 1 diabetes diagnosis using the full model had a probability of developing symptomatic disease by 2 years of 5%, 13% and 48%, respectively.

CONCLUSIONS/INTERPRETATION

CGM metrics can help predict disease progression and classify an individual's risk of type 1 diabetes diagnosis in conjunction with other factors. CGM can also be used to better assess the risk of type 1 diabetes progression and define eligibility for potential prevention trials.

Enhancing Type 1 Diabetes Immunological Risk Prediction with Continuous Glucose Monitoring and Genetic Profiling

Background: Early identification of individuals at high risk for type 1 diabetes (T1D) is essential for timely intervention. Islet autoantibodies (AB) and continuous glucose monitoring (CGM) reveal early signs of glycemic dysregulation, while T1D genetic risk scores (GRS) further improve disease prediction. We use CGM data and T1D GRS to develop an AB classifier (1 AB vs. ≥2 AB) and predict early T1D risk. Methods: Thirty-nine AB-positive (18 with 1 and 21 with ≥2 AB) healthy relatives of T1D (mean age 22.1 ± 11.1 years, HbA1c 5.3 ± 0.3%, body mass index 24.1 ± 5.8 kg/m2) were enrolled in a National Institutes of Health's (NIH) TrialNet ancillary study. Participants wore CGMs for a week and consumed three standardized liquid mixed meals (SLMM). Post-SLMM CGM glycemic features and T1D GRS were used in a linear support vector machine (SVM) model with recursive feature elimination (RFE) for AB classification, evaluated via fivefold cross-validation using the receiver operating characteristic and precision-recall area under the curve (AUC-ROC/PR). Results: Significant differences between the AB groups were observed in the post-SLMM percent time of glucose >180 mg/dL and GRS (P = 0.020 and P = 0.001, respectively). An SVM model with two RFE-selected features (T1D GRS and incremental AUC) achieved the best performance, classifying 1 versus ≥2 AB individuals with an AUC-ROC of 0.93 (95% confidence interval [CI]: 0.83-1.00) and AUC-PR of 0.89 (95% CI: 0.71-0.99), compared with AUC-ROC of 0.80 (95% CI: 0.46-1.00) and AUC-PR of 0.82 (95% CI: 0.71-0.93) using all features. Conclusions: A machine learning approach combining a 1-week CGM home test and T1D GRS reliably assesses T1D immunological risk, enabling early intervention.

Repeated OGTT Versus Continuous Glucose Monitoring for Predicting Development of Stage 3 Type 1 Diabetes: A Longitudinal Analysis

OBJECTIVE

Evidence for using continuous glucose monitoring (CGM) as an alternative to oral glucose tolerance tests (OGTTs) in presymptomatic type 1 diabetes is primarily cross-sectional. We used longitudinal data to compare the diagnostic performance of repeated CGM, HbA1c, and OGTT metrics to predict progression to stage 3 type 1 diabetes.

RESEARCH DESIGN AND METHODS

Thirty-four multiple autoantibody-positive first-degree relatives (FDRs) (BMI SD score [SDS] <2) were followed in a multicenter study with semiannual 5-day CGM recordings, HbA1c, and OGTT for a median of 3.5 (interquartile range [IQR] 2.0-7.5) years. Longitudinal patterns were compared based on progression status. Prediction of rapid (<3 years) and overall progression to stage 3 was assessed using receiver operating characteristic (ROC) areas under the curve (AUCs), Kaplan-Meier method, baseline Cox proportional hazards models (concordance), and extended Cox proportional hazards models with time-varying covariates in multiple record data (n = 197 OGTTs and concomitant CGM recordings), adjusted for intraindividual correlations (corrected Akaike information criterion [AICc]).

RESULTS

After a median of 40 (IQR 20-91) months, 17 of 34 FDRs (baseline median age 16.6 years) developed stage 3 type 1 diabetes. CGM metrics increased close to onset, paralleling changes in OGTT, both with substantial intra- and interindividual variability. Cross-sectionally, the best OGTT and CGM metrics similarly predicted rapid (ROC AUC = 0.86-0.92) and overall progression (concordance = 0.73-0.78). In longitudinal models, OGTT-derived AUC glucose (AICc = 71) outperformed the best CGM metric (AICc = 75) and HbA1c (AICc = 80) (all P < 0.001). HbA1c complemented repeated CGM metrics (AICc = 68), though OGTT-based multivariable models remained superior (AICc = 59).

CONCLUSIONS

In longitudinal models, repeated CGM and HbA1c were nearly as effective as OGTT in predicting stage 3 type 1 diabetes and may be more convenient for long-term clinical monitoring.

The Current Landscape for Screening and Monitoring of Early-Stage Type 1 Diabetes

Type 1 diabetes (T1D) has two pre-symptomatic phases (stages 1 and 2) with progressive destruction of beta cells which have been identified through longitudinal cohort studies in recent decades. The definition of T1D, with hyperglycaemia that may or may not be symptomatic, is now defined as stage 3. There is growing evidence that screening for stages 1 and 2 reduces rates of diabetic ketoacidosis and prevents long-term complications. These stages can be defined by the presence of islet autoantibodies which are markers of autoimmune beta cell damage. Furthermore, genetic risk scores, which combine a variety of single nucleotide polymorphisms, identify people at high genetic risk of future T1D. Thus, they provide an opportunity to select high-risk individuals for islet autoantibody testing. Individuals identified as having stage 1 or 2 T1D require ongoing monitoring to detect hyperglycaemia and the need for insulin replacement. These individuals may also be eligible for emerging immunotherapies in future to delay progression to stage 3. This review article explores the current evidence for screening and summarises the recommended clinical care for early-stage T1D.

International Society for Pediatric and Adolescent Diabetes Clinical Practice Consensus Guidelines 2024 Diabetes Technologies: Glucose Monitoring

The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This chapter builds on the 2022 ISPAD guidelines, and summarizes recent advances in the technology behind glucose monitoring, and its role in glucose-responsive integrated technology that is feasible with the use of automated insulin delivery (AID) systems in children and adolescents. The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This chapter builds on the 2022 ISPAD guidelines, and summarizes recent advances in the technology behind glucose monitoring, and its role in glucose-responsive integrated technology that is feasible with the use of automated insulin delivery (AID) systems in children and adolescents.

Pre-Type 1 Diabetes in Adolescents and Teens: Screening, Nutritional Interventions, Beta-Cell Preservation, and Psychosocial Impacts

Type 1 Diabetes (T1D) is a progressive autoimmune disease often identified in childhood or adolescence, with early stages detectable through pre-diabetic markers such as autoantibodies and subclinical beta-cell dysfunction. The identification of the pre-T1D stage is critical for preventing complications, such as diabetic ketoacidosis, and for enabling timely interventions that may alter disease progression. This review examines the multifaceted approach to managing T1D risk in adolescents and teens, emphasizing early detection, nutritional interventions, beta-cell preservation strategies, and psychosocial support. Screening for T1D-associated autoantibodies offers predictive insight into disease risk, particularly when combined with education and family resources that promote lifestyle adjustments. Although nutritional interventions alone are not capable of preventing T1D, certain lifestyle interventions, such as weight management and specific nutritional choices, have shown the potential to preserve insulin sensitivity, reduce inflammation, and mitigate metabolic strain. Pharmacological strategies, including immune-modulating drugs like teplizumab, alongside emerging regenerative and cell-based therapies, offer the potential to delay disease onset by protecting beta-cell function. The social and psychological impacts of a T1D risk diagnosis are also significant, affecting adolescents' quality of life, family dynamics, and mental health. Supportive interventions, including counseling, cognitive-behavioral therapy (CBT), and group support, are recommended for managing the emotional burden of pre-diabetes. Future directions call for integrating universal or targeted screening programs within schools or primary care, advancing research into nutrition and psychosocial support, and promoting policies that enhance access to preventive resources. Advocacy for the insurance coverage of screening, nutritional counseling, and mental health services is also crucial to support families in managing T1D risk. By addressing these areas, healthcare systems can promote early intervention, improve beta-cell preservation, and support the overall well-being of adolescents at risk of T1D.

Screening and care for preclinical stage 1-2 type 1 diabetes in first-degree relatives: French expert position statement

The natural history of type 1 diabetes (T1D) evolves from stage 1 (islet autoimmunity with normoglycemia; ICD-10 diagnostic code E10.A1) to stage 2 (autoimmunity with dysglycemia; E10.A2) and subsequent clinical stage 3 (overt hyperglycemia), which is commonly the first time of referral. Autoantibody testing can diagnose T1D at its preclinical stages 1-2 and lead to earlier initiation of care, particularly for first-degree relatives of people living with T1D, who are at higher genetic risk. Preclinical T1D screening and monitoring aims to avoid inaugural ketoacidosis and prolong preservation of endogenous insulin secretion, thereby improving glycemic control and reducing long-term morbidity. Moreover, early management can help coping with T1D and correct modifiable risk factors (obesity, sedentary lifestyle). New treatments currently under clinical deployment or trials also offer the possibility of delaying clinical progression. All these arguments lead to the proposition of a national screening and care pathway open to interested first-degree relatives. This pathway represents a new expertise to acquire for healthcare professionals. By adapting international consensus guidance to the French specificities, the proposed screening strategy involves testing for ≥ 2 autoantibodies (among IAA, anti-GAD, anti-IA-2) in relatives aged 2-45 years. Negative screening (∼95 % of cases) should be repeated every 4 years until the age of 12. A management workflow is proposed for relatives screening positive (∼5 % of cases), with immuno-metabolic monitoring by autoantibody testing, OGTT, glycemia and/or HbA1c of variable frequency, depending on T1D stage, age, patient preference and available resources, as well as the definition of expert centers for preclinical T1D.

Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning

The classification of type 2 diabetes and prediabetes does not consider heterogeneity in the pathophysiology of glucose dysregulation. Here we show that prediabetes is characterized by metabolic heterogeneity, and that metabolic subphenotypes can be predicted by the shape of the glucose curve measured via a continuous glucose monitor (CGM) during standardized oral glucose-tolerance tests (OGTTs) performed in at-home settings. Gold-standard metabolic tests in 32 individuals with early glucose dysregulation revealed dominant or co-dominant subphenotypes (muscle or hepatic insulin-resistance phenotypes in 34% of the individuals, and β-cell-dysfunction or impaired-incretin-action phenotypes in 40% of them). Machine-learning models trained with glucose time series from OGTTs from the 32 individuals predicted the subphenotypes with areas under the curve (AUCs) of 95% for muscle insulin resistance, 89% for β-cell deficiency and 88% for impaired incretin action. With CGM-generated glucose curves obtained during at-home OGTTs, the models predicted the muscle-insulin-resistance and β-cell-deficiency subphenotypes of 29 individuals with AUCs of 88% and 84%, respectively. At-home identification of metabolic subphenotypes via a CGM may aid the risk stratification of individuals with early glucose dysregulation.

Lower Prevalence of Diabetic Ketoacidosis at Diagnosis in Research Participants Monitored for Hyperglycemia

CONTEXT

In Colorado children, the prevalence of diabetic ketoacidosis (DKA) at diagnosis of type 1 diabetes has been increasing over time.

OBJECTIVE

To evaluate the prevalence of and factors involved in DKA at type 1 diabetes diagnosis among participants followed in monitoring research studies before diagnosis compared to patients from the community.

METHODS

We studied patients < 18 years diagnosed with type 1 diabetes between 2005 and 2021 at the Barbara Davis Center for Diabetes and compared the prevalence of and factors associated with DKA at diagnosis among participants in preclinical monitoring studies vs those diagnosed in the community.

RESULTS

Of 5049 subjects, 164 were active study participants, 42 inactive study participants, and 4843 were community patients. Active study participants, compared to community patients, had lower HbA1c (7.3% vs 11.9%; P < .001) and less frequently experienced DKA (4.9% vs 48.5%; P < .001), including severe DKA (1.2% vs 16.2%; P < .001). Inactive study participants had intermediate levels for both prevalence and severity of DKA. DKA prevalence increased in community patients, from 44.0% to 55%, with less evidence for a temporal trend in study participants. DKA prevalence was highest in children < 2 years (13% in active study participants vs 83% in community patients). In community patients, younger age (P = .0038), public insurance (P < .0001), rural residence (P < .0076), higher HbA1c (P < .0001), and ethnicity minority status (P < .0001) were associated with DKA at diagnosis.

CONCLUSION

While DKA prevalence increases in community patients over time, it stayed < 5% in active research participants, who have a 10 times lower prevalence of DKA at diagnosis, including among minorities.

Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics

CONTEXT

Static measures of continuous glucose monitoring (CGM) data, such as time spent in specific glucose ranges (70-180 mg/dL or 70-140 mg/dL), do not fully capture the dynamic nature of blood glucose, particularly the subtle gradual deterioration of glycemic control over time in individuals with early-stage type 1 diabetes.

OBJECTIVE

Develop a diabetes diagnostic tool based on 2 markers of CGM dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S).

METHODS

A total of 5754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute 2 individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between 8 glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden index was used to determine "optimal" cut-points for ER and S for health vs diabetes (case 1); type 1 vs type 2 (case 2); and low vs high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC).

RESULTS

Optimal cutoff points were determined for ER and S for each of the 3 cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI, 0.97-0.99) and AUC = 0.99 (95% CI, 0.99-1.00), respectively (cutoffs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.72 (95% CI, 0.58-0.86), and AUC = 0.66 (95% CI, 0.47-0.86), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2).

CONCLUSION

CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing to identify individuals at higher immunological risk of progressing to type 1 diabetes.

ISPAD Clinical Practice Consensus Guidelines 2024: Screening, Staging, and Strategies to Preserve Beta-Cell Function in Children and Adolescents with Type 1 Diabetes

The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This guideline serves as an update to the 2022 ISPAD consensus guideline on staging for type 1 diabetes (T1D). Key additions include an evidence-based summary of recommendations for screening for risk of T1D and monitoring those with early-stage T1D. In addition, a review of clinical trials designed to delay progression to Stage 3 T1D and efforts seeking to preserve beta-cell function in those with Stage 3 T1D are included. Lastly, opportunities and challenges associated with the recent US Food and Drug Administration (FDA) approval of teplizumab as an immunotherapy to delay progression are discussed. The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This guideline serves as an update to the 2022 ISPAD consensus guideline on staging for type 1 diabetes (T1D). Key additions include an evidence-based summary of recommendations for screening for risk of T1D and monitoring those with early-stage T1D. In addition, a review of clinical trials designed to delay progression to Stage 3 T1D and efforts seeking to preserve beta-cell function in those with Stage 3 T1D are included. Lastly, opportunities and challenges associated with the recent US Food and Drug Administration (FDA) approval of teplizumab as an immunotherapy to delay progression are discussed.

What is the Relationship Between Time in Range, Time in Tight Range, and HbA1c in Youth and Young Adults With Type 1 Diabetes? Results From the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-Up Registry

OBJECTIVES

Time in range (TIR, 70-180 mg/dL) is an established marker of glycemic control. More recently, time in tight range (TTR, 70-140 mg/dL) has been proposed as well. The aim of this study was to examine the relationship between TIR, TTR, and HbA1c in youth and young adults with type 1 diabetes (T1D) in the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-up (DPV) registry.

METHODS

Data of youth and young adults aged ≤25 years with T1D for >3 months, documented in the DPV registry between 2019 and 2022 were analyzed. The most recent available HbA1c and corresponding continuous glucose monitoring (CGM) profiles in the 12 preceding weeks with at least 80% completeness were included. Associations were investigated using correlation and adjusted regression models.

RESULTS

1901 individuals (median age 14.0 years [IQR 10.4-16.9]) were included in the analysis. TIR and TTR correlated strongly, r = 0.965 (95% CI [0.962, 0.968]), P < .001. TTR estimates predicted from TIR were significantly higher in the group with high coefficient of variation (CV group ≥ 36%), P < .001. Correlations between TIR or TTR and HbA1c were both strong, r = -0.764 (95% CI [-0.782, -0.745]) and r = -0.777 (95% CI [-0.795, -0.759]), both P < .001, with no significant difference (P = .312) However, adjusted regression models indicated a slightly better fit for the prediction of HbA1c from TIR compared with TTR.

CONCLUSIONS

Based on large, real-world data from a multinational registry, TIR and TTR correlated strongly, and both showed a good prediction of HbA1c. TTR estimates predicted from TIR were significantly higher in people with high glucose variability (CV).

Parental experiences of using continuous glucose monitoring in their young children with early-stage type 1 diabetes: a qualitative interview study

Aim

To explore parents' experiences of using continuous glucose monitoring (CGM) in their young children with early-stage type 1 diabetes, being followed in the Australian Environmental Determinants of Islet Autoimmunity (ENDIA) study.

Methods

Parents of children with persistent islet autoimmunity who enrolled in the ENDIA CGM sub-study were invited to participate in an optional interview. Semi-structured phone interviews were conducted by a single researcher using an interview guide developed by a multi-disciplinary team. Interviews were conducted following a single CGM monitoring period and prior to parents receiving feedback on their child's glycemic status. Following transcription, thematic analysis was conducted to determine common themes.

Results

Nine parents (8 mothers, 1 father) were interviewed corresponding to ten children, with a mean (SD) age of 5.6 (2.2) years, who wore CGM for 97 (0.1)% of the time during their monitoring period. Three main themes were identified: (1) Information empowers and helps to reduce uncertainty; (2) Families' acceptance of using CGM; and (3) Involvement in research provides support and preparation for the unknown.

Conclusions

Parents reported a positive experience of their young child wearing blinded CGM, and the children tolerated wearing CGM very well. Parents were empowered by knowing they would receive information on their child's glucose levels and patterns and felt well supported. This study provides novel insights into parents' experiences of using CGM in very young children with early-stage type 1 diabetes.

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.

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.

Predicting Type 2 Diabetes Metabolic Phenotypes Using Continuous Glucose Monitoring and a Machine Learning Framework

Type 2 diabetes (T2D) and prediabetes are classically defined by the level of fasting glucose or surrogates such as hemoglobin HbA1c. This classification does not take into account the heterogeneity in the pathophysiology of glucose dysregulation, the identification of which could inform targeted approaches to diabetes treatment and prevention and/or predict clinical outcomes. We performed gold-standard metabolic tests in a cohort of individuals with early glucose dysregulation and quantified four distinct metabolic subphenotypes known to contribute to glucose dysregulation and T2D: muscle insulin resistance, β-cell dysfunction, impaired incretin action, and hepatic insulin resistance. We revealed substantial inter-individual heterogeneity, with 34% of individuals exhibiting dominance or co-dominance in muscle and/or liver IR, and 40% exhibiting dominance or co-dominance in β-cell and/or incretin deficiency. Further, with a frequently-sampled oral glucose tolerance test (OGTT), we developed a novel machine learning framework to predict metabolic subphenotypes using features from the dynamic patterns of the glucose time-series ("shape of the glucose curve"). The glucose time-series features identified insulin resistance, β-cell deficiency, and incretin defect with auROCs of 95%, 89%, and 88%, respectively. These figures are superior to currently-used estimates. The prediction of muscle insulin resistance and β-cell deficiency were validated using an independent cohort. We then tested the ability of glucose curves generated by a continuous glucose monitor (CGM) worn during at-home OGTTs to predict insulin resistance and β-cell deficiency, yielding auROC of 88% and 84%, respectively. We thus demonstrate that the prediabetic state is characterized by metabolic heterogeneity, which can be defined by the shape of the glucose curve during standardized OGTT, performed in a clinical research unit or at-home setting using CGM. The use of at-home CGM to identify muscle insulin resistance and β-cell deficiency constitutes a practical and scalable method by which to risk stratify individuals with early glucose dysregulation and inform targeted treatment to prevent T2D.

Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk of (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb+; (2) when people who are IAb+ are initially identified there is a need for confirmation using a second sample; (3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care.

Predicting autoimmune diseases: A comprehensive review of classic biomarkers and advances in artificial intelligence

Autoimmune diseases comprise a spectrum of disorders characterized by the dysregulation of immune tolerance, resulting in tissue or organ damage and inflammation. Their prevalence has been on the rise, significantly impacting patients' quality of life and escalating healthcare costs. Consequently, the prediction of autoimmune diseases has recently garnered substantial interest among researchers. Despite their wide heterogeneity, many autoimmune diseases exhibit a consistent pattern of paraclinical findings that hold predictive value. From serum biomarkers to various machine learning approaches, the array of available methods has been continuously expanding. The emergence of artificial intelligence (AI) presents an exciting new range of possibilities, with notable advancements already underway. The ultimate objective should revolve around disease prevention across all levels. This review provides a comprehensive summary of the most recent data pertaining to the prediction of diverse autoimmune diseases and encompasses both traditional biomarkers and the latest innovations in AI.

Exploring the Continuous Glucose Monitoring in Pediatric Diabetes: Current Practices, Innovative Metrics, and Future Implications

Continuous glucose monitoring (CGM) systems, including real-time CGM and intermittently scanned CGM, have revolutionized diabetes management, particularly in children and adolescents with type 1 diabetes (T1D). These systems provide detailed insights into glucose variability and detect asymptomatic and nocturnal hypoglycemia, addressing limitations of traditional self-monitoring blood glucose methods. CGM devices measure interstitial glucose concentrations constantly, enabling proactive therapeutic decisions and optimization of glycemic control through stored data analysis. CGM metrics such as time in range, time below range, and coefficient of variation are crucial for managing T1D, with emerging metrics like time in tight range and glycemia risk index showing potential for enhanced glycemic assessment. Recent advancements suggest the utility of CGM systems in monitoring the early stages of T1D and individuals with obesity complicated by pre-diabetes, highlighting its therapeutic versatility. This review discusses the current CGM systems for T1D during the pediatric age, established and emerging metrics, and future applications, emphasizing the critical role of CGM devices in improving glycemic control and clinical outcomes in children and adolescents with diabetes.

Recommendations for Screening and Monitoring the Stages of Type 1 Diabetes in the Immune Therapy Era

Type 1 diabetes (T1D) is a complex, chronic autoimmune disease that affects over 1.6 million people in the United States. It is now understood that T1D may be undetected for many years while the disease progresses quietly without producing symptoms. T1D can be identified through diabetes-related autoantibody screening and staged accordingly, enabling healthcare providers to identify high-risk individuals in the early stages of the disease and either provide a stage-specific intervention or offer clinical trial opportunities to preserve beta cell function and anticipate the onset of clinical T1D. Evidence-based clinical practice guidelines currently do not exist for routine diabetes-related autoantibody screening of individuals at risk of developing T1D or of the general population. The purpose of this article is to help clinicians acquire an understanding of the rationale and protocols recommended for identifying patients at risk of developing T1D and monitoring such patients for autoimmune markers and progression of disease from Stage 1 to Stage 3 (clinical disease).

Pediatric Endocrine Society Statement on Considerations for Use of Teplizumab (Tzield™) in Clinical Practice

Teplizumab (TzieldTM, Provention Bio), a monoclonal antibody directed at T-cell marker CD3, is the first medication approved by the FDA to delay progression from stage 2 to stage 3 type 1 diabetes. To date, the overwhelming majority of pediatric endocrinologists do not have experience using immunotherapeutics and seek guidance on the use of teplizumab in clinical practice. To address this need, the Pediatric Endocrine Society (PES) Diabetes Special Interest Group (Diabetes SIG) and Drug and Therapeutics Committee assembled a task force to review clinical trial data and solicit expert recommendations on the approach to teplizumab infusions. We present considerations on all aspects of teplizumab administration, utilizing evidence where possible and providing a spectrum of expert opinions on unknown aspects. We discuss patient selection and prescreening, highlighting the safety and considerations for monitoring and treatment of side effects. We propose a schedule of events, a protocol for administration, and discuss practice management aspects. We advocate for the need for further long-term systematic surveillance studies to continue evaluating the efficacy and safety of teplizumab.

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.

Type 1 Diabetes Screening and Diagnosis

Those with concerning signs or symptoms should be evaluated for type 1 diabetes (T1D). Those with first-degree relatives with T1D or based on the presence of high-risk genes are at increased risk and benefit from screening. Universal screening should be considered in light of new potential therapies to delay disease progression. Although oral glucose tolerance test is the gold standard for T1D staging, there are multiple tools available when oral glucose tolerance test is not feasible. Risk score calculations increase the ability to predict disease progression. Testing should be repeated when symptoms of overt diabetes mellitus are not present.

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.

OGTT Metrics Surpass Continuous Glucose Monitoring Data for T1D Prediction in Multiple-Autoantibody-Positive Individuals

CONTEXT

The value of continuous glucose monitoring (CGM) for monitoring autoantibody (AAB)-positive individuals in clinical trials for progression of type 1 diabetes (T1D) is unknown.

OBJECTIVE

Compare CGM with oral glucose tolerance test (OGTT)-based metrics in prediction of T1D.

METHODS

At academic centers, OGTT and CGM data from multiple-AAB relatives were evaluated for associations with T1D diagnosis. Participants were multiple-AAB-positive individuals in a TrialNet Pathway to Prevention (TN01) CGM ancillary study (n = 93). The intervention was CGM for 1 week at baseline, 6 months, and 12 months. Receiver operating characteristic (ROC) curves of CGM and OGTT metrics for prediction of T1D were analyzed.

RESULTS

Five of 7 OGTT metrics and 29/48 CGM metrics but not HbA1c differed between those who subsequently did or did not develop T1D. ROC area under the curve (AUC) of individual CGM values ranged from 50% to 69% and increased when adjusted for age and AABs. However, the highest-ranking metrics were derived from OGTT: 4/7 with AUC ∼80%. Compared with adjusted multivariable models using CGM data, OGTT-derived variables, Index60 and DPTRS (Diabetes Prevention Trial-Type 1 Risk Score), had higher discriminative ability (higher ROC AUC and positive predictive value with similar negative predictive value).

CONCLUSION

Every 6-month CGM measures in multiple-AAB-positive individuals are predictive of subsequent T1D, but less so than OGTT-derived variables. CGM may have feasibility advantages and be useful in some settings. However, our data suggest there is insufficient evidence to replace OGTT measures with CGM in the context of clinical trials.

Baricitinib and β-Cell Function in Patients with New-Onset Type 1 Diabetes

BACKGROUND

Janus kinase (JAK) inhibitors, including baricitinib, block cytokine signaling and are effective disease-modifying treatments for several autoimmune diseases. Whether baricitinib preserves β-cell function in type 1 diabetes is unclear.

METHODS

In this phase 2, double-blind, randomized, placebo-controlled trial, we assigned patients with type 1 diabetes diagnosed during the previous 100 days to receive baricitinib (4 mg once per day) or matched placebo orally for 48 weeks. The primary outcome was the mean C-peptide level, determined from the area under the concentration-time curve, during a 2-hour mixed-meal tolerance test at week 48. Secondary outcomes included the change from baseline in the glycated hemoglobin level, the daily insulin dose, and measures of glycemic control assessed with the use of continuous glucose monitoring.

RESULTS

A total of 91 patients received baricitinib (60 patients) or placebo (31 patients). The median of the mixed-meal-stimulated mean C-peptide level at week 48 was 0.65 nmol per liter per minute (interquartile range, 0.31 to 0.82) in the baricitinib group and 0.43 nmol per liter per minute (interquartile range, 0.13 to 0.63) in the placebo group (P = 0.001). The mean daily insulin dose at 48 weeks was 0.41 U per kilogram of body weight per day (95% confidence interval [CI], 0.35 to 0.48) in the baricitinib group and 0.52 U per kilogram per day (95% CI, 0.44 to 0.60) in the placebo group. The levels of glycated hemoglobin were similar in the two trial groups. However, the mean coefficient of variation of the glucose level at 48 weeks, as measured by continuous glucose monitoring, was 29.6% (95% CI, 27.8 to 31.3) in the baricitinib group and 33.8% (95% CI, 31.5 to 36.2) in the placebo group. The frequency and severity of adverse events were similar in the two trial groups, and no serious adverse events were attributed to baricitinib or placebo.

CONCLUSIONS

In patients with type 1 diabetes of recent onset, daily treatment with baricitinib over 48 weeks appeared to preserve β-cell function as estimated by the mixed-meal-stimulated mean C-peptide level. (Funded by JDRF International and others; BANDIT Australian New Zealand Clinical Trials Registry number, ACTRN12620000239965.).

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.

SAI-CH-6: Development of a Short Form of the State Anxiety Inventory for Children At-Risk for Type 1 Diabetes

OBJECTIVE

To develop a reliable and valid short form of the State Anxiety Subscale of the State-Trait Anxiety Inventory for Children (STAI-CH) in the Environmental Determinants of Diabetes in the Young (TEDDY) study.

METHODS

A Development Sample of 842 10-year-old TEDDY children completed the STAI-CH State Subscale about their type 1 diabetes (T1D) risk. The best 6 items (three anxiety-present and three anxiety-absent) for use in a short form (SAI-CH-6) were identified via item-total correlations. SAI-CH-6 reliability was examined in a Validation Sample (n = 257) of children who completed the full 20-item STAI-CH State Subscale and then again in an Application Sample (n = 2,710) who completed only the SAI-CH-6. Expected associations between the children's SAI-CH-6 scores and country of residence, sex, T1D family history, accuracy of T1D risk perception, worry about getting T1D, and their parents' anxiety scores were examined.

RESULTS

The SAI-CH-6 was reliable (α = 0.81-0.87) and highly correlated with the full 20-item STAI-CH State Subscale (Development Sample: r = 0.94; Validation Sample: r = 0.92). SAI-CH-6 scores detected significant differences in state anxiety symptoms associated with T1D risk by country, T1D family history, accuracy of T1D risk perception, and worry about getting T1D and were correlated with the child's parent's anxiety.

CONCLUSION

The SAI-CH-6 appears useful for assessing children's state anxiety symptoms when burden and time limitations prohibit the use of the STAI-CH. The utility of the SAI-CH-6 in older children with and without chronic conditions needs to be assessed.

Continuous glucose monitoring in individuals undergoing gestational diabetes screening

BACKGROUND

Among guidelines on gestational diabetes mellitus, there is an incongruity about the threshold of maternal hyperglycemia to diagnose gestational diabetes mellitus.

OBJECTIVE

This study aimed to ascertain the association between continuous glucose monitoring metrics and adverse outcomes among individuals undergoing gestational diabetes mellitus screening.

STUDY DESIGN

This was a prospective study (from June 2020 to January 2022) of individuals who underwent 2-step gestational diabetes mellitus screening at ≤30 weeks of gestation. The participants wore a blinded continuous glucose monitoring device (Dexcom G6 Pro; Dexcom, Inc, San Diego, CA) for 10 days starting when they took the 50-g glucose challenge test. The primary outcome was a composite of adverse neonatal outcomes (large for gestational age, shoulder dystocia or neonatal injury, respiratory distress, need for intravenous glucose treatment for hypoglycemia, or fetal or neonatal death). The secondary neonatal outcomes included preterm birth, neonatal intensive care unit admission, hypoglycemia, mechanical ventilation or continuous positive airway pressure, hyperbilirubinemia, and hospital length of stay. The secondary maternal outcomes included weight gain during pregnancy, hypertensive disorders of pregnancy, induction of labor, cesarean delivery, and postpartum complications. Time within the target range (63-140 mg/dL), time above the target range (>140 mg/dL) expressed as a percentage of all continuous glucose monitoring readings, and mean glucose level were analyzed. The Youden index was used to choose the threshold of ≥10% for the time above the target range and association with adverse outcomes.

RESULTS

Of 136 participants recruited, data were available from 92 individuals (67.6%). The 2-step method diagnosed gestational diabetes mellitus in 2 individuals (2.2%). Continuous glucose monitoring indicated that 17 individuals (18.5%) had time above the target range of ≥10%. Individuals with time above the target range of ≥10% had a significantly higher likelihood of composite adverse neonatal outcomes than individuals with time above the target range of <10% (63% vs 18%; P=.001). Furthermore, compared with neonates born to individuals with time above the target range of <10%, neonates born to individuals with time above the target range of ≥10% had an increased likelihood for hypoglycemia (14.5% vs 47%; P=.009) and had a longer length of stay (2 vs 4 days; P=.03). No difference in maternal outcomes was noted between the groups.

CONCLUSION

In this prospective study of individuals undergoing gestational diabetes mellitus screening, a cutoff of the time above the target range of ≥10% using continuous glucose monitoring was associated with a higher rate of neonatal adverse outcomes. A randomized trial of continuous glucose monitoring vs 2-step screening for gestational diabetes mellitus to lower the rate of adverse outcomes is underway (identification number: NCT05430204).

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.

Previous SARS-CoV-2 Infection Is Not Associated With Increased Celiac Disease Autoimmunity in Children and Adolescents

INTRODUCTION

Recent reports suggest severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections may increase the risk of celiac disease autoimmunity. This study aims to evaluate potential associations between coronavirus disease 2019 infection and tissue transglutaminase autoantibodies (TGA) immunoglobulin A.

METHODS

From 2020 to 2021, cross-sectional screening for SARS-CoV-2 antibodies and TGA was offered to 4,717 children in Colorado through the Autoimmunity Screening for Kids study. Multivariable logistic regression assessed association between previous SARS-CoV-2 infection and TGA positivity.

RESULTS

Previous SARS-CoV-2 infection was not associated with TGA positivity (odds ratio 1.02, 95% confidence interval 0.63-1.59; P = 0.95).

DISCUSSION

In this large-scale analysis, previous SARS-CoV-2 infection was not associated with celiac disease autoimmunity in Colorado children.

Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases

Over the past decade there have been many advances in diabetes technologies, such as continuous glucose monitors (CGM s), insulin-delivery devices, and hybrid closed loop systems . Now most CGMs (Medtronic-Guardian, Dexcom-G6, and Abbott-Libre-2) have MARD values of < 10%, in contrast to two decades ago when the MARD used to be > 20%. In addition, the majority of the new CGMs do not require calibrations, and the latest CGMs last for 10-14 days. An implantable 6-months CGM by Eversense-3 is now approved in the USA and Europe. Recently, the FDA approved Libre 3 which provides real-time glucose values every minute. Even though it is approved as an iCGM it is not interoperable with automatic-insulin-delivery (AID) systems. The newer CGMs that are likely to be launched in the next few months in the USA include the 10-11 days Dexcom G7 (60% smaller than the existing G6), and the 7-days Medtronic Guardian 4. Most of the newer CGM have several features like automatic initialization, easy insertion, predictive alarms, and alerts. It has also been noticed that an arm insertion site might have better accuracy than abdomen or other sites, like the buttock for kids. Lag time between YSI and different sensors have been reported differently, sometimes it is down to 2-3 min; however, in many instances, it is still 15-20 min, especially when the rate of change of glucose is > 2 mg/min. We believe that in the next decade there will be a significant increase in the number of people who use CGM for their day-to-day diabetes care.

A perspective on treating type 1 diabetes mellitus before insulin is needed

Type 1 diabetes mellitus (T1DM) is a progressive autoimmune disease that starts long before a clinical diagnosis is made. The American Diabetes Association recognizes three stages: stage 1 (normoglycaemic and positive for autoantibodies to β-cell antigens); stage 2 (asymptomatic with dysglycaemia); and stage 3, which is defined by glucose levels consistent with the definition of diabetes mellitus. This Perspective focuses on the management of the proportion of individuals with early stage 3 T1DM who do not immediately require insulin; a stage we propose should be termed stage 3a. To date, this period of non-insulin-dependent T1DM has been largely unrecognized. Importantly, it represents a window of opportunity for intervention, as remaining at this stage might delay the need for insulin by months or years. Extending the insulin-free period and/or avoiding unnecessary insulin therapy are important goals, as there is no risk of hypoglycaemia during this period and the adherence burden on patients of glycaemic monitoring and daily adjustments for diet and exercise is substantially reduced. Recognizing the pressing need for guidance on adequate management of children and adults with stage 3a T1DM, we present our perspective on the subject, which needs to be tested in formal and adequately powered clinical trials.

Precision Medicine in Type 1 Diabetes

Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.

Prevalence of SARS-CoV-2 Antibodies Among Healthy Children From Colorado From 2020 to 2021: A Brief Report

There are few estimates of the seroprevalence of SARS-CoV-2 antibodies among children in the United States. We measured vaccine and infection induced seroprevalence among nearly 5000 healthy 1 to 17-year-old children in Colorado from 2020 to 2021. By December 2021, 89% of older children, ages 12 to 18, had antibodies detected. The increase was largely driven from vaccination rather than infection.

Progression likelihood score identifies substages of presymptomatic type 1 diabetes in childhood public health screening

AIMS/HYPOTHESIS

The aim of this study was to develop strategies that identify children from the general population who have late-stage presymptomatic type 1 diabetes and may, therefore, benefit from immune intervention.

METHODS

We tested children from Bavaria, Germany, aged 1.75-10 years, enrolled in the Fr1da public health screening programme for islet autoantibodies (n=154,462). OGTT and HbA_1c were assessed in children with multiple islet autoantibodies for diagnosis of presymptomatic stage 1 (normoglycaemia) or stage 2 (dysglycaemia) type 1 diabetes. Cox proportional hazards and penalised logistic regression of autoantibody, genetic, metabolic and demographic information were used to develop a progression likelihood score to identify children with stage 1 type 1 diabetes who progressed to stage 3 (clinical) type 1 diabetes within 2 years.

RESULTS

Of 447 children with multiple islet autoantibodies, 364 (81.4%) were staged. Undiagnosed stage 3 type 1 diabetes, presymptomatic stage 2, and stage 1 type 1 diabetes were detected in 41 (0.027% of screened children), 30 (0.019%) and 293 (0.19%) children, respectively. The 2 year risk for progression to stage 3 type 1 diabetes was 48% (95% CI 34, 58) in children with stage 2 type 1 diabetes (annualised risk, 28%). HbA_1c, islet antigen-2 autoantibody positivity and titre, and the 90 min OGTT value were predictors of progression in children with stage 1 type 1 diabetes. The derived progression likelihood score identified substages corresponding to ≤90th centile (stage 1a, n=258) and >90th centile (stage 1b, n=29; 0.019%) of stage 1 children with a 4.1% (95% CI 1.4, 6.7) and 46% (95% CI 21, 63) 2 year risk of progressing to stage 3 type 1 diabetes, respectively.

CONCLUSIONS/INTERPRETATION

Public health screening for islet autoantibodies found 0.027% of children to have undiagnosed clinical type 1 diabetes and 0.038% to have undiagnosed presymptomatic stage 2 or stage 1b type 1 diabetes, with 50% risk to develop clinical type 1 diabetes within 2 years.

Continuous Glucose Monitoring Profiles in Healthy, Nondiabetic Young Children

Context

Continuous glucose monitoring (CGM) is increasingly being used for both day-to-day management in patients with diabetes and in clinical research. While data on glycemic profiles of healthy, non-diabetic individuals exists, data on non-diabetic very young children are lacking.

Objective

To establish reference sensor glucose ranges in healthy, non-diabetic young children, using a current generation CGM sensor.

Design

Prospective observational study

Setting

Institutional practice

Participants

Healthy, non-diabetic children 1-6 years old; with normal body mass index

Intervention

A blinded Dexcom G6 Pro CGM was worn for approximately 10 days by each participant.

Main Outcome Measure

CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability

Results

39 participants were included in the analyses. Mean average glucose was 103 mg/dL (5.7 mmol/L). Median % time between 70-140 mg/dL (3.9-7.8 mmol/L) was 96% (IQR 92%-97%), mean within-individual coefficient of variation was 17±3%, median time spent with glucose levels &gt;140mg/dL was 3.4% (49 min/day), and median time &lt;70 mg/dL (3.9 mmol/L) was 0.4% (6 min/day).

Conclusions

Collecting normative sensor glucose data and describing glycemic measures for young children fills an important informational gap and will be useful as a benchmark for future clinical studies.