Jordan E. Pinsker

Multinational Home Use of Closed-Loop Control Is Safe and Effective

OBJECTIVE To evaluate the efficacy of a portable, wearable, wireless artificial pancreas system (the Diabetes Assistant [DiAs] running the Unified Safety System) on glucose control at home in overnight-only and 24/7 closed-loop control (CLC) modes in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS At six clinical centers in four countries, 30 participants 18–66 years old with type 1 diabetes (43% female, 96% non-Hispanic white, median type 1 diabetes duration 19 years, median A1C 7.3%) completed the study. The protocol included a 2-week baseline sensor-augmented pump (SAP) period followed by 2 weeks of overnight-only CLC and 2 weeks of 24/7 CLC at home. Glucose control during CLC was compared with the baseline SAP. RESULTS Glycemic control parameters for overnight-only CLC were improved during the nighttime period compared with baseline for hypoglycemia (time <70 mg/dL, primary end point median 1.1% vs. 3.0%; P < 0.001), time in target (70–180 mg/dL: 75% vs. 61%; P < 0.001), and glucose variability (coefficient of variation: 30% vs. 36%; P < 0.001). Similar improvements for day/night combined were observed with 24/7 CLC compared with baseline: 1.7% vs. 4.1%, P < 0.001; 73% vs. 65%, P < 0.001; and 34% vs. 38%, P < 0.001, respectively. CONCLUSIONS CLC running on a smartphone (DiAs) in the home environment was safe and effective. Overnight-only CLC reduced hypoglycemia and increased time in range overnight and increased time in range during the day; 24/7 CLC reduced hypoglycemia and increased time in range both overnight and during the day. Compared with overnight-only CLC, 24/7 CLC provided additional hypoglycemia protection during the day.

Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial

CONTEXT Closed-loop control (CLC) relies on an individuals open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort. OBJECTIVE The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC. DESIGN This study reports a multicenter, outpatient, randomized, crossover clinical trial. PATIENTS Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites. INTERVENTIONS Each subjects insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subjects followed their usual meal-plan and had an unannounced exercise session. MAIN OUTCOMES AND MEASURES Time in the glucose range was 80-140 mg/dL, compared between both arms. RESULTS Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80-140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL. CONCLUSIONS A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm.

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.

OBJECTIVE To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions. RESEARCH DESIGN AND METHODS After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70–180 mg/dL. RESULTS Mean time in range 70–180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period. CONCLUSIONS This first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.

Early Detection of Physical Activity for People With Type 1 Diabetes Mellitus.

Background: Early detection of exercise in individuals with type 1 diabetes mellitus (T1DM) may allow changes in therapy to prevent hypoglycemia. Currently there is limited experience with automated methods that detect the onset and end of exercise in this population. We sought to develop a novel method to quickly and reliably detect the onset and end of exercise in these individuals before significant changes in blood glucose (BG) occur. Methods: Sixteen adults with T1DM were studied as outpatients using a diary, accelerometer, heart rate monitor, and continuous glucose monitor for 2 days. These data were used to develop a principal component analysis based exercise detection method. Subjects also performed 60 and 30 minute exercise sessions at 30% and 50% predicted heart rate reserve (HRR), respectively. The detection method was applied to the exercise sessions to determine how quickly the detection of start and end of exercise occurred relative to change in BG. Results: Mild 30% HRR and moderate 50% HRR exercise onset was identified in 6 ± 3 and 5 ± 2 (mean ± SD) minutes, while completion was detected in 3 ± 8 and 6 ± 5 minutes, respectively. BG change from start of exercise to detection time was 1 ± 6 and −1 ± 3 mg/dL, and, from the end of exercise to detection time was 6 ± 4 and −17 ± 13 mg/dL, respectively, for the 2 exercise sessions. False positive and negative ratios were 4 ± 2% and 21 ± 22%. Conclusions: The novel method for exercise detection identified the onset and end of exercise in approximately 5 minutes, with an average BG change of only −6 mg/dL.

Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial

OBJECTIVE As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)–based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively. RESEARCH DESIGN AND METHODS A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms. RESULTS AP improved percent time 70–140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70–180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time <70 mg/dL (1.3 vs. 2.7%; P = 0.001). AP also improved overnight control, as measured by mean glucose at 0600 h (140 vs. 158 mg/dL; P = 0.02). IIS failures (1.26 ± 1.44 vs. 0.78 ± 0.78 events; P = 0.13) and sensor failures (0.84 ± 0.6 vs. 1.1 ± 0.73 events; P = 0.25) were similar between AP and SAP arms. Higher percent time in closed loop was associated with better glycemic outcomes. CONCLUSIONS Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.

Response to Comment on Pinsker et al. Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas. Diabetes Care 2016;39:1135–1142

We thank Dr. Steil (1) for critically reviewing our study comparing model predictive control (MPC) and proportional integral derivative (PID) control for the artificial pancreas (2). We agree that MPC and PID both come in many variants and that there are many successful trials of PID control for automated insulin delivery. We acknowledged in our study that both controllers performed very well overall, even after the 65-g unannounced meal was accounted for, and did so with low rates of hypoglycemia. We recognize the value in comparing results across different studies and want to emphasize that we did not intend to dismiss studies with different designs. However, it is not possible to have an equitable comparison of MPC versus PID controllers through such meta-analyses. Our study was specifically designed for as fair and balanced a comparison as possible between the …

Twelve-Week 24/7 Ambulatory Artificial Pancreas With Weekly Adaptation of Insulin Delivery Settings: Effect on Hemoglobin A1c and Hypoglycemia

OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P < 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.

Minority groups and the artificial pancreas: who is (not) in line?

Technological advancements in diabetes management are underway, with industrial eff orts in place to design and assess artifi cial pancreas systems. Although it is sometimes diffi cult to separate hype from hope, the general consensus is that some form of closed-loop insulin delivery system will become commercially available as early as 2017. The present academic debate has focused on system design choices, such as whether to include glucagon and the level of system automation, but what is less clear is who is going to be fi rst in line to use the technology. At present, data show that type 1 diabetes is most prevalent in the non-Hispanic white population, although there is evidence of an increasing burden of this type of diabetes in racial minority groups as well as the non-Hispanic white population.Additionally, there are disparities in health outcomes in these patients according to ethnic, racial, and socioeconomic groups, even after accounting for deprivation and access to insulin pump therapy. For children with type 1 diabetes in particular, inequalities in access to modern treatments increases the risks of acute and long-term complications. This inequality creates an issue for study recruitment because many artifi cial pancreas trials require previous experience with insulin pumps and continuous glucose monitoring for enrolment. As a result, existing data might be limited to a specifi c cohort of patients, excluding those who do not prefer to use these technologies or cannot obtain access to them. The US National Institutes of Health (NIH) supports open enrolment so that women and ethnic and racial minorities are included in its funded clinical research trials. These demographics are reported annually to NIH as part of all NIH-funded projects, although these details are not always fully reported in publications. We surveyed the scientifi c literature for publications of artifi cial pancreas clinical studies. A total of 99 publications (with 59 supplementary fi les) were identifi ed, 29 of which were published in 2015 or 2016. Computerised text searches and manual screening for words related to race, ethnicity, gender, age, and socioeconomic status were done on the 158 fi les. Age was reported in 98% of the publications and gender in 87%. However, only six of 99 studies mentioned race or ethnicity, with only four studies reporting the number of trial participants belonging to diff erent racial or ethnic groups (fi gure). In these four studies, 43 of 46, 24 of 25, 11 of 15, and 11 of 12 participants were nonHispanic whites. There were no matches for the words “socioeconomic”, “literacy”, “numeracy”, or “income”. Our review of the scientifi c literature shows that it has been common practice in the published research on artifi cial pancreas technology to include data for age and gender, but often no additional information is given on race, ethnicity, socioeconomic status, and literacy and numeracy status in fi nal publications. From the currently published studies, it is impossible to determine the diversity of participants being served by artifi cial pancreas clinical research beyond age and gender, although it is likely that the overwhelming majority of participants are non-Hispanic white people. New technology for diabetes care might have limited value if some groups do not have access or the skills to leverage these new tools and if research has not explored potential cultural, social, and economic barriers to a particular technological innovation. There are already many economic barriers to obtaining the components of an artifi cial pancreas system, with little to no insurance coverage of continuous glucose monitors by some insurance carriers and national health systems. What is even less clear are the cultural and lifestyle barriers that manufacturers of artifi cial pancreas systems will need to overcome before such a system becomes mainstream, and whether race and ethnicity will aff ect access, uptake, and sustainability of an artifi cial pancreas system that reaches the market.

Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

Background: The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings. Methods: A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses. Results: Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r2 = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r2 = 2.33%) and decreased carbohydrate consumption (r = –.157, P = .031, r2 = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices. Conclusions: These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.

Comment on American Diabetes Association. Approaches to glycemic treatment. Sec. 7. In Standards of Medical Care in Diabetes-2015. Diabetes Care 2015;38(Suppl. 1):S41-S48.

One of the difficult challenges for individuals with type 1 diabetes is trying to determine the optimum time to inject a bolus of rapid-acting insulin for meals. Therefore, we were surprised that the updated 2015 American Diabetes Association (ADA) Standards of Medical Care in Diabetes lacks a specific recommendation on this topic (1), especially given the contribution of postprandial hyperglycemia to achieved hemoglobin A1c levels (2). Although several recent publications have concluded that premeal bolusing 15–20 min ahead of time with currently available rapid-acting insulin analogs is advantageous to reducing postprandial hyperglycemia in people with type 1 diabetes without …