RANKL/RANK is required for cytokine-induced beta cell death; osteoprotegerin, a RANKL inhibitor, reverses rodent type 1 diabetes

Treatment for type 1 diabetes (T1D) requires stimulation of functional β cell regeneration and survival under stress. Previously, we showed that inhibition of the RANKL/RANK [receptor activator of nuclear factor kappa Β (NF-κB) ligand] pathway, by osteoprotegerin and the anti-osteoporotic drug denosumab, induces rodent and human β cell proliferation. We demonstrate that the RANK pathway mediates cytokine-induced rodent and human β cell death through RANK-TRAF6 interaction and induction of NF-κB activation. Osteoprotegerin and denosumab protected β cells against this cytotoxicity. In human immune cells, osteoprotegerin and denosumab reduce proinflammatory cytokines in activated T-cells by inhibiting RANKL-induced activation of monocytes. In vivo, osteoprotegerin reversed recent-onset T1D in nonobese diabetic/Ltj mice, reduced insulitis, improved glucose homeostasis, and increased plasma insulin, β cell proliferation, and mass in these mice. Serum from T1D subjects induced human β cell death and dysfunction, but not α cell death. Osteoprotegerin and denosumab reduced T1D serum-induced β cell cytotoxicity and dysfunction. Inhibiting RANKL/RANK could have therapeutic potential.

Outcomes in Pump- and CGM-Baseline Use Subgroups in the International Diabetes Closed-Loop Trial

BACKGROUND

We investigated the potential benefits of automated insulin delivery (AID) among individuals with type 1 diabetes (T1D) in sub-populations of baseline device use determined by continuous glucose monitor (CGM) use status and insulin delivery via multiple daily injections (MDI) or insulin pump.

MATERIALS AND METHODS

In a six-month randomized, multicenter trial, 168 individuals were assigned to closed-loop control (CLC, Control-IQ, Tandem Diabetes Care), or sensor-augmented pump (SAP) therapy. The trial included a two- to eight-week run-in phase to train participants on study devices. The participants were stratified into four subgroups: insulin pump and CGM (pump+CGM), pump-only, MDI and CGM (MDI+CGM), and MDI users without CGM (MDI-only) users. We compared glycemic outcomes among four subgroups.

RESULTS

At baseline, 61% were pump+CGM users, 18% pump-only users, 10% MDI+CGM users, and 11% MDI-only users. Mean time in range 70-180 mg/dL (TIR) improved from baseline in the four subgroups using CLC: pump+CGM, 62% to 73%; pump-only, 61% to 70%; MDI+CGM, 54% to 68%; and MDI-only, 61% to 69%. The reduction in time below 70 mg/dL from baseline was comparable among the four subgroups. No interaction effect was detected with baseline device use for TIR (P = .67) or time below (P = .77). On the System Usability Questionnaire, scores were high at 26 weeks for all subgroups: pump+CGM: 87.2 ± 12.1, pump-only: 89.4 ± 8.2, MDI+CGM 87.2 ± 9.3, MDI: 78.1 ± 15.

CONCLUSIONS

There was a consistent benefit in patients with T1D when using CLC, regardless of baseline insulin delivery modality or CGM use. These data suggest that this CLC system can be considered across a wide range of patients.

LBSUN180 Preconception Counseling And Reproductive Education Program For People With Diabetes (PREPP'D) - An Educational Program For Underserved Women With Diabetes

Background

Unintended pregnancy in women with diabetes is associated with high public cost, delay in initiating prenatal care, and maternal and fetal complications. One third of the women with diabetes in our underserved prenatal clinic present with an HbA1C >7.5%. Due to limited data regarding preconception counseling for women with diabetes, we developed PREPP'D, a quality improvement initiative and educational program aiming to improve women's and providers’ knowledge of diabetes and pregnancy, increase awareness of contraception options, and implement standard protocols in our diabetes clinic for underserved populations. Design: A questionnaire was developed to measure the rate of contraception use and assess women's knowledge regarding pregnancy planning. The questionnaire has two sections: general questions about the woman's reproductive health, contraception use, and current pregnancy plan and specific questions about the woman's current knowledge about pregnancy and diabetes including glycemic targets and glucose monitoring and medication safety during pregnancy. The latter part of the questionnaire was scored with 6 points indicating all questions answered correctly. Women aged 18-50 with pre-gestational diabetes were identified and given an English or Spanish questionnaire prior to seeing the provider. The providers were given a prewritten SmartPhrase to guide counseling and explaining the importance of pre-pregnancy planning, glucose targets during pregnancy, and medication optimization. After the visit, women were given an educational flyer.

Results

Thirty-six women aged 21-49 years with type 1 (n=16), type 2 (n=19), or steroid-induced (n=1) diabetes are included in this analysis. Women were from various backgrounds: Black/African American (n = 4), non-Hispanic White (n = 11), Hispanic (n =18), and other (n=3). Average diabetes duration was 10.2 (range 1-32) years. Mean HbA1C was 8.7% (range 5.3-14.7%). Only seven women met the HbA1C target goal for pregnancy (<6.5%). Complications of diabetes were reported in 23%, and one woman reported prior fetal cardiac anomalies. Of the three women planning pregnancies within the next year, only one had an HbA1c <6.5%. The mean questionnaire score was 1.6/6 points (±1.3, range 0-5) with nine women scoring 0 points. Documentation of pregnancy discussion was noted in 83% of records (compared to 38% prior to intervention).

Conclusions

Knowledge of pregnancy is limited for many women with diabetes. PREPP'D is a novel approach to standardize preconception counseling in the diabetes clinic. It also provides an opportunity to raise women's and providers’ awareness about diabetes and pregnancy. The next step of this project is to conduct follow-up surveys for both women and providers 3 to 6 months post initial survey to assess impact and improve future interventions including expansion to adolescents.

Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4

Background: Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings. Methods: Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome. Results: Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%], P = 0.22). Median time <70 mg/dL improved from 3.0% with SAP to 1.6% with AID (-1.5% [-2.4% to -0.5%], P = 0.002). The adaptation system decreased initial basal rates by a median of 4% (-8%, 16%) and increased initial carbohydrate ratios by a median of 45% (32%, 59%) after 13 weeks. Conclusions: Automated adaptation of insulin delivery settings with AID use did not significantly improve time-in-range in this very well-controlled population. Additional study and further refinement of the adaptation system are needed, especially in populations with differing degrees of baseline glycemic control, who may show larger benefits from adaptation.

Hypoglycemia in Prospective Multicenter Study of Pregnancies with Pre-Existing Type 1 Diabetes on Sensor-Augmented Pump Therapy: The LOIS-P Study

Background: Pregnancies in type 1 diabetes are high risk, and data in the United States are limited regarding continuous glucose monitoring (CGM)-based hypoglycemia throughout pregnancy while on sensor-augmented insulin pump therapy. Materials and Methods: Pregnant women with type 1 diabetes in the LOIS-P Study (Longitudinal Observation of Insulin use and glucose Sensor metrics in Pregnant women with type 1 diabetes using continuous glucose monitors and insulin pumps) were enrolled before 17 weeks gestation at three U.S. centers and we used their personal insulin pump and a study Dexcom G6 CGM. We analyzed data of 25 pregnant women for CGM hypoglycemia based on international consensus guidelines for percentage time <63 and 54 mg/dL, hypoglycemic events and prolonged hypoglycemia events for 24-h, daytime, and overnight periods, and severe hypoglycemia (SH) episodes. Results: For a 24-h period, biweekly median percentage of time <63 mg/dL ranged from 0.8% at biweek 4-5 to 3.7% at biweek 14-15 with high variability throughout pregnancy. Median percentage of time <63 and 54 mg/dL was higher overnight than daytime (P < 0.01). Hypoglycemic events occurred throughout the pregnancy, ranged 1-4 events per 2 weeks, significantly decreased after the 20th week, and occurred predominantly during daytime (P < 0.01). For overnight period, hypoglycemia and events were more concentrated from 12 to 3 am. Seven prolonged hypoglycemia events without any associated SH occurred in four participants (16%), primarily overnight. Three participants experienced a single episode of SH. Conclusions: Our results suggest a higher overall risk of hypoglycemia throughout pregnancy during the overnight period with continued daytime risk of hypoglycemic events in pregnancies complicated by type 1 diabetes.

Feasibility of Closed-Loop Insulin Delivery with a Pregnancy-Specific Zone Model Predictive Control Algorithm

Objective: Evaluating the feasibility of closed-loop insulin delivery with a zone model predictive control (zone-MPC) algorithm designed for pregnancy complicated by type 1 diabetes (T1D). Research Design and Methods: Pregnant women with T1D from 14 to 32 weeks gestation already using continuous glucose monitor (CGM) augmented pump therapy were enrolled in a 2-day multicenter supervised outpatient study evaluating pregnancy-specific zone-MPC based closed-loop control (CLC) with the interoperable artificial pancreas system (iAPS) running on an unlocked smartphone. Meals and activities were unrestricted. The primary outcome was the CGM percentage of time between 63 and 140 mg/dL compared with participants' 1-week run-in period. Early (2-h) postprandial glucose control was also evaluated. Results: Eleven participants completed the study (age: 30.6 ± 4.1 years; gestational age: 20.7 ± 3.5 weeks; weight: 76.5 ± 15.3 kg; hemoglobin A1c: 5.6% ± 0.5% at enrollment). No serious adverse events occurred. Compared with the 1-week run-in, there was an increased percentage of time in 63-140 mg/dL during supervised CLC (CLC: 81.5%, run-in: 64%, P = 0.007) with less time >140 mg/dL (CLC: 16.5%, run-in: 30.8%, P = 0.029) and time <63 mg/dL (CLC: 2.0%, run-in:5.2%, P = 0.039). There was also less time <54 mg/dL (CLC: 0.7%, run-in:1.6%, P = 0.030) and >180 mg/dL (CLC: 4.9%, run-in: 13.1%, P = 0.032). Overnight glucose control was comparable, except for less time >250 mg/dL (CLC: 0%, run-in:3.9%, P = 0.030) and lower glucose standard deviation (CLC: 23.8 mg/dL, run-in:42.8 mg/dL, P = 0.007) during CLC. Conclusion: In this pilot study, use of the pregnancy-specific zone-MPC was feasible in pregnant women with T1D. Although the duration of our study was short and the number of participants was small, our findings add to the limited data available on the use of CLC systems during pregnancy (NCT04492566).

Impact of a Novel Diabetes Support System on a Cohort of Individuals With Type 1 Diabetes Treated With Multiple Daily Injections: A Multicenter Randomized Study

OBJECTIVE

Achieving optimal glycemic control for many individuals with type 1 diabetes (T1D) remains challenging, even with the advent of newer management tools, including continuous glucose monitoring (CGM). Modern management of T1D generates a wealth of data; however, use of these data to optimize glycemic control remains limited. We evaluated the impact of a CGM-based decision support system (DSS) in patients with T1D using multiple daily injections (MDI).

RESEARCH DESIGN AND METHODS

The studied DSS included real-time dosing advice and retrospective therapy optimization. Adults and adolescents (age >15 years) with T1D using MDI were enrolled at three sites in a 14-week randomized controlled trial of MDI + CGM + DSS versus MDI + CGM. All participants (N = 80) used degludec basal insulin and Dexcom G5 CGM. CGM-based and patient-reported outcomes were analyzed. Within the DSS group, ad hoc analysis further contrasted active versus nonactive DSS users.

RESULTS

No significant differences were detected between experimental and control groups (e.g., time in range [TIR] +3.3% with CGM vs. +4.4% with DSS). Participants in both groups reported lower HbA1c (-0.3%; P = 0.001) with respect to baseline. While TIR may have improved in both groups, it was statistically significant only for DSS; the same was apparent for time spent <60 mg/dL. Active versus nonactive DSS users showed lower risk of and exposure to hypoglycemia with system use.

CONCLUSIONS

Our DSS seems to be a feasible option for individuals using MDI, although the glycemic benefits associated with use need to be further investigated. System design, therapy requirements, and target population should be further refined prior to use in clinical care.

Longitudinal Observation of Insulin Use and Glucose Sensor Metrics in Pregnant Women with Type 1 Diabetes Using Continuous Glucose Monitors and Insulin Pumps: The LOIS-P Study

Background: Suboptimal glycemic control is associated with maternal and neonatal morbidity and mortality in pregnancy complicated by type 1 diabetes (T1D). Prospective analysis of continuous glucose monitoring (CGM) metrics, insulin pump settings, and insulin delivery can better characterize the changes in glycemic levels and insulin use throughout pregnancy with T1D. Materials and Methods: Prescribed parameters, insulin delivery, carbohydrate intake, and CGM data for 25 pregnant women with T1D from three U.S. sites were collected. Participants enrolled before 17 weeks gestation and used personal insulin pumps and study CGM. Mean daily total, basal, and bolus insulin doses (units/kg), CGM time in range (TIR: 63-140 mg/dL), and pump-entered carbohydrates were analyzed for every 2-week gestational interval. Linear mixed-effects regression models were used to evaluate changes across gestational ages compared to 12-14 weeks. Results: Basal insulin was higher during weeks 6-12 and 24-40. Daily bolus and total insulin were higher during weeks 20-40. Pump parameters were adjusted to intensify insulin therapy from 22 weeks onward. Average TIR across pregnancy was 59% ± 14%. Between 18 and 30 weeks, TIR was significantly lower, and time above range was significantly higher compared to the reference biweek. Time below target was lower between 22 and 34 weeks. Seven participants achieved >70% recommended TIR for pregnancy. Participants with maternal complications or infant neonatal intensive care unit admissions had lower TIR. Conclusion: While insulin dosing changed significantly with advancing gestation, most participants did not achieve >70% TIR. Customized anticipatory pump setting adjustments and automated systems aimed toward the designated TIR are needed to improve outcomes for this population. NCT03761615.

Performance of the Dexcom G6 Continuous Glucose Monitoring System in Pregnant Women with Diabetes

Background: The aim of this study was to determine the performance of the Dexcom G6 continuous glucose monitoring (CGM) system across three sensor wear sites in pregnant women with diabetes in the second or third trimesters. Methods: Participants with type 1 (T1D), type 2 (T2D), or gestational (GDM) diabetes mellitus were enrolled at three sites. Each wore two G6 sensors on the abdomen, upper buttock, and/or posterior upper arm for 10 days and underwent a 6-h clinic session between days 3 and 7 of sensor wear, during which YSI reference blood glucose values were obtained every 30 min. No intentional glucose manipulations were performed. Accuracy metrics included the proportion of CGM values that were within ±20% of paired reference values >100 mg/dL or ±20 mg/dL of YSI values ≤100 mg/dL (hereafter referred to as %20/20), as well as the analogous %15/15, %30/30, and %40/40. The mean absolute relative difference (MARD) between CGM-YSI pairs was also calculated. Results: Thirty-two participants with T1D (n = 20), T2D (n = 3), or GDM (n = 9) were enrolled: 19 were in the second trimester and 13 were in the third trimester of pregnancy. Compared with the reference, 92.5% of CGM values were within ±20%/20 mg/dL. The overall MARD and that of sensors worn on the abdomen, upper buttock, and posterior upper arm was 10.3%, 11.5%, 11.2%, and 8.7%, respectively. There were no device-related adverse events. Skin reactions at the insertion sites were absent or minor. Conclusions: The Dexcom G6 CGM system is accurate and safe in pregnant women with diabetes.

Glycemic Outcomes of Use of CLC Versus PLGS in Type 1 Diabetes: A Randomized Controlled Trial

OBJECTIVE

Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system.

RESEARCH DESIGN AND METHODS

After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14-72 years; mean HbA_1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70-180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study.

RESULTS

All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = -6.0%; 95% CI -8.4%, -3.7%; P < 0.001) while time <54 mg/dL was similar (0.04%; 95% CI -0.05%, 0.13%; P = 0.41). HbA_1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference -0.34% [-3.7 mmol/mol]; 95% CI -0.57% [-6.2 mmol/mol], -0.11% [1.2 mmol/mol]; P = 0.0035).

CONCLUSIONS

Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA_1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.

A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

OBJECTIVE

We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals.

RESEARCH DESIGN AND METHODS

We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables.

RESULTS

Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002).

CONCLUSIONS

The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.

Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting

BACKGROUND

Initial Food and Drug Administration-approved artificial pancreas (AP) systems will be hybrid closed-loop systems that require prandial meal announcements and will not eliminate the burden of premeal insulin dosing. Multiple model probabilistic predictive control (MMPPC) is a fully closed-loop system that uses probabilistic estimation of meals to allow for automated meal detection. In this study, we describe the safety and performance of the MMPPC system with announced and unannounced meals in a supervised hotel setting.

RESEARCH DESIGN AND METHODS

The Android phone-based AP system with remote monitoring was tested for 72 h in six adults and four adolescents across three clinical sites with daily exercise and meal challenges involving both three announced (manual bolus by patient) and six unannounced (no bolus by patient) meals. Safety criteria were predefined. Controller aggressiveness was adapted daily based on prior hypoglycemic events.

RESULTS

Mean 24-h continuous glucose monitor (CGM) was 157.4 ± 14.4 mg/dL, with 63.6 ± 9.2% of readings between 70 and 180 mg/dL, 2.9 ± 2.3% of readings <70 mg/dL, and 9.0 ± 3.9% of readings >250 mg/dL. Moderate hyperglycemia was relatively common with 24.6 ± 6.2% of readings between 180 and 250 mg/dL, primarily within 3 h after a meal. Overnight mean CGM was 139.6 ± 27.6 mg/dL, with 77.9 ± 16.4% between 70 and 180 mg/dL, 3.0 ± 4.5% <70 mg/dL, 17.1 ± 14.9% between 180 and 250 mg/dL, and 2.0 ± 4.5%> 250 mg/dL. Postprandial hyperglycemia was more common for unannounced meals compared with announced meals (4-h postmeal CGM 197.8 ± 44.1 vs. 140.6 ± 35.0 mg/dL; P < 0.001). No participants met safety stopping criteria.

CONCLUSIONS

MMPPC was safe in a supervised setting despite meal and exercise challenges. Further studies are needed in a less supervised environment.

Overnight Closed-Loop Control Improves Glycemic Control in a Multicenter Study of Adults With Type 1 Diabetes

CONTEXT

Closed-loop control (CLC) for the management of type 1 diabetes (T1D) is a novel method for optimizing glucose control, and strategies for individualized implementation are being developed.

OBJECTIVE

To analyze glycemic control in an overnight CLC system designed to "reset" the patient to near-normal glycemic targets every morning.

DESIGN

Randomized, crossover, multicenter clinical trial.

PARTICIPANTS

Forty-four subjects with T1D requiring insulin pump therapy.

INTERVENTION

Sensor-augmented pump therapy (SAP) at home vs 5 nights of CLC (active from 23:00 to 07:00) in a supervised outpatient setting (research house or hotel), with a substudy of 5 nights of CLC subsequently at home.

MAIN OUTCOME MEASURE

The percentage of time spent in the target range (70 to 180 mg/dL measured using a continuous glucose monitor).

RESULTS

Forty subjects (age, 45.5 ± 9.5 years; hemoglobin A1c, 7.4% ± 0.8%) completed the study. The time in the target range (70 to 180 mg/dL) significantly improved in CLC vs SAP over 24 hours (78.3% vs 71.4%; P = 0.003) and overnight (85.7% vs 67.6%; P < 0.001). The time spent in a hypoglycemic range (<70 mg/dL) decreased significantly in the CLC vs SAP group over 24 hours (2.5% vs 4.3%; P = 0.002) and overnight (0.9% vs 3.2%; P < 0.001). The mean glucose level at 07:00 was lower with CLC than with SAP (123.7 vs 145.3 mg/dL; P < 0.001). The substudy at home, involving 10 T1D subjects, showed similar trends with an increased time in target (70 to 180 mg/dL) overnight (75.2% vs 62.2%; P = 0.07) and decreased time spent in the hypoglycemic range (<70 mg/dL) overnight in CLC vs SAP (0.6% vs 3.7%; P = 0.03).

CONCLUSION

Overnight-only CLC increased the time in the target range over 24 hours and decreased the time in hypoglycemic range over 24 hours in a supervised outpatient setting. A pilot extension study at home showed a similar nonsignificant trend.

Closed-Loop Control Without Meal Announcement in Type 1 Diabetes

OBJECTIVE

A fully closed-loop insulin-only system was developed to provide glucose control in patients with type 1 diabetes without requiring announcement of meals or activity. Our goal was to assess initial safety and efficacy of this system.

RESEARCH DESIGN AND METHODS

The multiple model probabilistic controller (MMPPC) anticipates meals when the patient is awake. The controller used the subject's basal rates and total daily insulin dose for initialization. The system was tested at two sites on 10 patients in a 30-h inpatient study, followed by 15 subjects at three sites in a 54-h supervised hotel study, where the controller was challenged by exercise and unannounced meals. The system was implemented on the UVA DiAs system using a Roche Spirit Combo Insulin Pump and a Dexcom G4 Continuous Glucose Monitor.

RESULTS

The mean overall (24-h basis) and nighttime (11 PM-7 AM) continuous glucose monitoring (CGM) values were 142 and 125 mg/dL during the inpatient study. The hotel study used a different daytime tuning and manual announcement, instead of automatic detection, of sleep and wake periods. This resulted in mean overall (24-h basis) and nighttime CGM values of 152 and 139 mg/dL for the hotel study and there was also a reduction in hypoglycemia events from 1.6 to 0.91 events/patient/day.

CONCLUSIONS

The MMPPC system achieved a mean glucose that would be particularly helpful for people with an elevated A1c as a result of frequent missed meal boluses. Current full closed loop has a higher risk for hypoglycemia when compared with algorithms using meal announcement.