The Rare and Atypical Diabetes Network (RADIANT) Study: Design and Early Results

OBJECTIVE

The Rare and Atypical Diabetes Network (RADIANT) will perform a study of individuals and, if deemed informative, a study of their family members with uncharacterized forms of diabetes.

RESEARCH DESIGN AND METHODS

The protocol includes genomic (whole-genome [WGS], RNA, and mitochondrial sequencing), phenotypic (vital signs, biometric measurements, questionnaires, and photography), metabolomics, and metabolic assessments.

RESULTS

Among 122 with WGS results of 878 enrolled individuals, a likely pathogenic variant in a known diabetes monogenic gene was found in 3 (2.5%), and six new monogenic variants have been identified in the SMAD5, PTPMT1, INS, NFKB1, IGF1R, and PAX6 genes. Frequent phenotypic clusters are lean type 2 diabetes, autoantibody-negative and insulin-deficient diabetes, lipodystrophic diabetes, and new forms of possible monogenic or oligogenic diabetes.

CONCLUSIONS

The analyses will lead to improved means of atypical diabetes identification. Genetic sequencing can identify new variants, and metabolomics and transcriptomics analysis can identify novel mechanisms and biomarkers for atypical disease.

Improvements in Glycemic Control Achieved by Altering the tmax Setting in the iLet® Bionic Pancreas When Using Fast-Acting Insulin Aspart: A Randomized Trial

INTRODUCTION

We investigated the safety of, and glucose control by, the insulin-only configuration of the iLet^® bionic pancreas delivering fast-acting insulin aspart (faster aspart), using the same insulin-dosing algorithm but different time to maximal serum drug concentration (t_max) settings, in adults with type 1 diabetes.

METHODS

We performed a single-center, single-blinded, crossover (two 7-day treatment periods) escalation trial over three sequential cohorts. Participants from each cohort were randomized to a default t_max setting (t₆₅ [t_max = 65 min]) followed by a non-default t_max setting (t₅₀ [t_max = 50 min; cohort 1], t₄₀ [t_max = 40 min; cohort 2], t₃₀ [t_max = 30 min; cohort 3]), or vice versa, all with faster aspart. Each cohort randomized eight new participants if escalation-stopping criteria were not met in the previous cohort.

RESULTS

Overall, 24 participants were randomized into three cohorts. Two participants discontinued treatment, one due to reported 'low blood glucose' during the first treatment period of cohort 3 (t₃₀). Mean time in low sensor glucose (< 54 mg/dl, primary endpoint) was < 1.0% for all t_max settings. Mean sensor glucose in cohorts 1 and 2 was significantly lower at non-default versus default t_max settings, with comparable insulin dosing. The mean time sensor glucose was in range (70-180 mg/dl) was > 70% for all cohorts, except the default t_max setting in cohort 1. No severe hypoglycemic episodes were reported. Furthermore, there were no clinically significant differences in adverse events between the groups.

CONCLUSION

There were no safety concerns with faster aspart in the iLet at non-default t_max settings. Improvements were observed in mean sensor glucose without increases in low sensor glucose at non-default t_max settings.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03816761.

Algorithmic identification of atypical diabetes in electronic health record (EHR) systems

AIMS

Understanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review.

METHODS

Patients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. "Typical" T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional "branch algorithms," relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information.

RESULTS

Of 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P<0.05). Compared to the ML T1D group, the AD group had a significantly higher T2D polygenic score (P<0.01) and lower hemoglobin A1c (P<0.01).

CONCLUSION

Our EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

Randomized Trial of the Insulin-Only iLet Bionic Pancreas for the Treatment of Cystic Fibrosis- Related Diabetes

OBJECTIVE

Cystic fibrosis-related diabetes (CFRD) affects up to 50% of adults with cystic fibrosis and adds significant morbidity and treatment burden. We evaluated the safety and efficacy of automated insulin delivery with the iLet bionic pancreas (BP) in adults with CFRD in a single-center, open-label, random-order, crossover trial.

RESEARCH DESIGN AND METHODS

Twenty participants with CFRD were assigned in random order to 14 days each on the BP or their usual care (UC). No restrictions were placed on diet or activity. The primary outcome was the percent time sensor-measured glucose was in target range 70-180 mg/dL (time in range [TIR]) on days 3-14 of each arm, and key secondary outcomes included mean continuous glucose monitoring (CGM) glucose and the percent time sensor-measured glucose was in hypoglycemic range <54 mg/dL.

RESULTS

TIR was significantly higher in the BP arm than the UC arm (75 ± 11% vs. 62 ± 22%, P = 0.001). Mean CGM glucose was lower in the BP arm than in the UC arm (150 ± 19 vs. 171 ± 45 mg/dL, P = 0.007). There was no significant difference in percent time with sensor-measured glucose <54 mg/dL (0.27% vs. 0.36%, P = 1.0), although self-reported symptomatic hypoglycemia episodes were higher during the BP arm than the UC arm (0.7 vs. 0.4 median episodes per day, P = 0.01). No episodes of diabetic ketoacidosis or severe hypoglycemia occurred in either arm.

CONCLUSIONS

Adults with CFRD had improved glucose control without an increase in CGM-measured hypoglycemia with the BP compared with their UC, suggesting that this may be an important therapeutic option for this patient population.

Accuracy of continuous glucose monitoring in the hospital setting: an observational study

AIMS/HYPOTHESIS

Continuous glucose monitoring (CGM) improves glycaemic outcomes in the outpatient setting; however, there are limited data regarding CGM accuracy in hospital.

METHODS

We conducted a prospective, observational study comparing CGM data from blinded Dexcom G6 Pro sensors with reference point of care and laboratory glucose measurements during participants' hospitalisations. Key accuracy metrics included the proportion of CGM values within ±20% of reference glucose values >5.6 mmol/l or within ±1.1 mmol/l of reference glucose values ≤5.6 mmol/l (%20/20), the mean and median absolute relative difference between CGM and reference value (MARD and median ARD, respectively) and Clarke error grid analysis (CEGA). A retrospective calibration scheme was used to determine whether calibration improved sensor accuracy. Multivariable regression models and subgroup analyses were used to determine the impact of clinical characteristics on accuracy assessments.

RESULTS

A total of 326 adults hospitalised on 19 medical or surgical non-intensive care hospital floors were enrolled, providing 6648 matched glucose pairs. The %20/20 was 59.5%, the MARD was 19.2% and the median ARD was 16.8%. CEGA showed that 98.2% of values were in zone A (clinically accurate) and zone B (benign). Subgroups with lower accuracy metrics included those with severe anaemia, renal dysfunction and oedema. Application of a once-daily morning calibration schedule improved accuracy (MARD 11.4%).

CONCLUSIONS/INTERPRETATION

The CGM accuracy when used in hospital may be lower than that reported in the outpatient setting, but this may be improved with appropriate patient selection and daily calibration. Further research is needed to understand the role of CGM in inpatient settings.