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.

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).

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 (tmax) 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 tmax setting (t65 [tmax = 65 min]) followed by a non-default tmax setting (t50 [tmax = 50 min; cohort 1], t40 [tmax = 40 min; cohort 2], t30 [tmax = 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 tmax settings. Mean sensor glucose in cohorts 1 and 2 was significantly lower at non-default versus default tmax 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 tmax 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 tmax settings. Improvements were observed in mean sensor glucose without increases in low sensor glucose at non-default tmax settings. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03816761.

One way to give insulin is to use an insulin delivery system. The iLet^® is a new type of insulin delivery system that works together with a continuous sugar monitoring tool (CGM). The CGM shows the blood sugar level in the body throughout the day. Based on this, the iLet automatically gives the insulin that is needed to control the blood sugar. Fast-acting insulin aspart (faster aspart) is a type of insulin that doctors can prescribe for use with insulin pens and insulin pumps. The researchers wanted to test the safety of faster aspart when given to people at different delivery settings in the iLet. Twenty-four men and women with type 1 diabetes from the USA took part. The different insulin delivery settings were the standard setting (t_max65 = 65 min) and new settings (t_max50 = 50 min; t_max40 = 40 min; t_max30 = 30 min). The shorter the t_max setting, the faster the insulin was assumed to be absorbed into the body by the iLet. People had good blood sugar control with faster aspart delivered using the iLet. The time with low blood sugar (i.e., < 54 mg/dl) was low for both the standard setting and the new settings. The average blood sugar was lower with the shorter, non-standard t_max settings. No people had serious side effects. No severe hypoglycemic episodes were reported. In this study, researchers found that it was safe to use faster aspart with the different settings in the iLet.

Patient perceived value of teleophthalmology in an urban, low income US population with diabetes.

Dilated eye exams are the standard of care to detect advancing, vision threatening, but often asymptomatic retinopathy in a timely fashion, allowing for vision preserving treatments. Annual exam rates are suboptimal, especially in underserved populations. Although teleophthalmology programs tremendously improve annual exam rates in low income/under resourced settings, widespread adoption is limited. Using a mixed methods approach, three focus groups and individual interviews were conducted for patients with type 2 diabetes (N = 23) who had a teleophthalmology exam or a dilated eye exam. A survey and discussion assessed patients' perspectives and value of teleophthalmology, including willingness to pay (WTP). Financial, transportation, and motivational barriers to obtaining an annual dilated eye exam were identified. Patients greatly valued having primary care (PC) based teleophthalmology for its convenience and ability to detect disease to allow for timely treatment and would recommend such a service. Although their WTP was at least the amount of their usual copay, cost was universally cited as a concern. Having a conveniently offered PC based teleophthalmology exam was valued. Educating patients on the value and costs of having such exams may be helpful to encourage informed discussions on eye care, especially in low income, underserved populations. Our study is among the few to provide insight on the value and perceptions of teleophthalmology in US low income, urban minority populations needed to help increase uptake of this innovation. Using surveys followed by facilitated discussion allowed for richer and more varied responses.