Traveling Across Time Zones With Type 1 Diabetes: A Pilot Study Comparing Insulin Degludec With Insulin Glargine U100.

OBJECTIVE: For people with type 1 diabetes, there are limited evidence-based resources to support self-management when traveling across multiple time zones. Here, we compared glycemic control on insulin degludec versus glargine U100 as the basal insulin for adults using multiple daily injections (MDI) while traveling across multiple time zones. RESEARCH DESIGN AND METHODS: This randomized crossover pilot study compared insulin degludec versus glargine U100 for adults with type 1 diabetes using MDI insulin during long-haul travel to and from Hawaii to New York. Insulin degludec was administered daily at the same time regardless of time zone, and glargine was administered per travel algorithm. Primary end point was the percentage of time in range (TIR) between 70 and 140 mg/dL during the initial 24 h after each direction of travel. Secondary end points included standard continuous glucose monitoring metrics, jet lag, fatigue, and sleep. RESULTS: The study enrolled 25 participants (56% women, mean ± SD age of 35 ± 14.5 years, HbA1c of 7.4 ± 1.2% [57 ± 13.1 mmol/mol], and diabetes duration of 20.6 ± 15 years). There was no significant difference in glycemic outcomes between the two arms of the study, including TIR, hypoglycemia, or hyperglycemia. Neither group achieved >70% TIR 70-180 mg/dL during travel. Jet lag was greater on glargine U100 in eastward travel but not westward. Fatigue was greater after westward travel on glargine. Sleep was not significantly different between basal insulins. CONCLUSIONS: In adults with type 1 diabetes using MDI of insulin and traveling across multiple time zones, glycemic outcomes were similar comparing insulin degludec and glargine U100.

Oral anti-CD3 monoclonal antibody delays diabetes in non-obese diabetic (NOD) mice: effects on pregnancy and offspring--a preliminary report.

BACKGROUND: The objective was to observe the effect of oral anti-CD3 monoclonal antibody (mAb) on non-obese diabetic mice, pregnancy, and offspring. METHODS: Three protocols are classified as: (1) Twenty non-obese diabetic/ShiLtJ female mice were monitored for type 1 diabetes mellitus. If the blood glucose level was ≥ 250 mg/dL, the mice were treated for 8 days with either 50 or 100 µg oral anti-CD3 monoclonal antibody. If the diabetes resolved, the mice were bred. (2) F1 offspring were monitored for diabetes; 15 female mice became diabetic. Non-diabetic F1 female mice were divided into two groups [ten antibody (Ab) and ten control (C)] and bred. Ab female mice were given 100 µg monoclonal antibody before diabetes development and during pregnancy for 6 weeks. (3) Twenty-five F2 Ab and 23 F2 C mice were monitored. At 15-17 weeks, Ab mice, both female and male, were given 100 µg monoclonal antibody for 8 weeks. RESULTS: (1) The diabetes in four female mice treated with 50 µg did not resolve; in three of the six diabetic female mice treated with 100 µg, the diabetes resolved and the mice were bred. The remaining ten non-diabetic female mice were given 100 µg oral monoclonal antibody and then bred. No diabetes developed during pregnancy; 13 litters were born. (2) Three diabetic Ab female mice sustained their pregnancies versus one diabetic C female mouse (p ≤ 0.05). (3) At 30 weeks, six Ab female and three Ab male mice and seven C female and three C male mice developed diabetes. The number of diabetic Ab and C mice was not different; diagnosis age was significantly different-Ab 23.3 ± 5.1 and C 18.8 ± 3.7 weeks (p ≤ 0.05). CONCLUSIONS: In female non-obese diabetic mice, oral anti-CD3 monoclonal antibody was effective in reversing diabetes and allowing pregnancy and extending longevity, but it did not prevent diabetes in the offspring.

Insurance Status and Biological and Psychosocial Determinants of Cardiometabolic Risk Among Mexican-Origin U.S. Hispanic/Latino Adults with Type 2 Diabetes.

Purpose: Hispanics/Latinos in the United States bear higher burden of type 2 diabetes (T2D) and associated complications compared with the general population. Health insurance coverage is also lower in this population. We examined the association of health insurance with biological and psychosocial determinants of cardiometabolic risk among U.S. Mexican-origin Hispanic/Latino adults with T2D. Methods: Participants were self-reported Hispanic/Latino adults with T2D diagnosis. Trained bilingual community health workers collected cross-sectional information on biological and psychosocial factors using clinical examinations, laboratory tests, validated questionnaires, and wearable activity monitors. Results: One hundred and seven Hispanic/Latino adults (54±12 years, 65% female, 36% prescribed insulin, 60% uninsured) with T2D were enrolled. While 93% had low language-based acculturation, 88% had high health literacy in Spanish. Forty percent were food insecure and 47% expressed at least one social need. Overall, 35% had an HbA1c <7.0% (indicating good control) and 31% had an HbA1c >9.0%. Sixty-three percent had blood pressure within target (<130/80 mmHg), and overall participants were moderately physically active. However, 53% were obese (body mass index ≥30 kg/m2) and 76% had a waist measurement defined as high risk (>88 cm for women and >102 cm for men). Participants without health insurance were younger (51.9±10.4 vs. 58.8±10.5 years mean±standard deviation, p=0.0008) but had higher HbA1c (8.4±2.2% vs. 7.6±1.6, p=0.031) and fasting glucose (184.9±86.5 vs. 148.6±61.2 mg/dl, p=0.008) levels. Conclusions: Health insurance status appears to influence achieved glycemic control for U.S. Hispanic/Latino adults with T2D. However, various psychosocial factors potentially influencing cardiometabolic risk independently of health insurance status may also be implicated in the inequitable burden of T2D. ClinicalTrials.gov Identifier: NCT03736486.

Investigation of the Accuracy of 18 Marketed Blood Glucose Monitors.

OBJECTIVE: Cleared blood glucose monitors (BGMs) for personal use may not always deliver levels of accuracy currently specified by international and U.S. regulatory bodies. This study's objective was to assess the accuracy of 18 such systems cleared by the U.S. Food and Drug Administration representing approximately 90% of commercially available systems used from 2013 to 2015. RESEARCH DESIGN AND METHODS: A total of 1,035 subjects were recruited to have a capillary blood glucose (BG) level measured on six different systems and a reference capillary sample prepared for plasma testing at a reference laboratory. Products were obtained from consumer outlets and tested in three triple-blinded studies. Each of the three participating clinical sites tested a different set of six systems for each of the three studies in a round-robin. In each study, on average, a BGM was tested on 115 subjects. A compliant BG result was defined as within 15% of a reference plasma value (for BG ≥100 mg/dL [5.55 mmol/L]) or within 15 mg/dL (0.83 mmol/L) (for BG <100 mg/dL [5.55 mmol/L]). The proportion of compliant readings in each study was compared against a predetermined accuracy standard similar to, but more lenient than, current regulatory standards. Other metrics of accuracy included the overall compliance proportion; the proportion of extreme outlier readings differing from the reference value by >20%; modified Bland-Altman analysis including average bias, coefficient of variation, and 95% limits of agreement; and proportion of readings with no clinical risk as determined by the Surveillance Error Grid. RESULTS: The different accuracy metrics produced almost identical BGM rankings. Six of the 18 systems met the predetermined accuracy standard in all three studies, 5 systems met it in two studies, and 3 met it in one study. Four BGMs did not meet the accuracy standard in any of the three studies. CONCLUSIONS: Cleared BGMs do not always meet the level of analytical accuracy currently required for regulatory clearance. This information could assist patients, professionals, and payers in choosing products and regulators in evaluating postclearance performance.

Restoring euglycemia in the basal state using continuous glucose monitoring in subjects with type 1 diabetes mellitus.

BACKGROUND: Our objective was to use continuous glucose monitoring to derive the optimal basal insulin infusion rates in adults with type 1 diabetes and using continuous subcutaneous insulin infusion (CSII) pumps. METHODS: In an effort to mimic euglycemia during the basal state, we used a standard protocol to adjust basal insulin infusion rates in 16 subjects with type 1 diabetes mellitus who were using CSII pumps. All subjects wore Continuous Glucose Monitoring System sensors (CGMS), Medtronic Minimed, Northridge, CA) in order to obtain around-the-clock tracings of their glucose measurements. Subjects were asked to skip meals periodically in order to optimize basal insulin infusion rates, defined as the basal infusion rates that maintained glucose levels in the range of 65-120 mg/dL during the fasting state or between meals. RESULTS: In order to demonstrate improved glycemic control, with blunting of glucose excursion, we compared the baseline CGMS area under the curve (AUC) to the AUC obtained after optimizing the basal insulin dosages. We analyzed the curves by determining the AUC for glucose excursions above 110 mg/dL. The AUC for glucose excursions above 110 mg/dL was significantly smaller after optimization (19 +/- 13 mg/dL.day) compared to the baseline AUC (50 +/- 31 mg/dL.day) (P < 0.001). CONCLUSIONS: Using both a standard protocol for initial basal insulin infusion rates and CGMS curves to optimize basal infusion rates, one can improve glycemia in subjects with type 1 diabetes using CSII.

Practical issues in the identification of empirical models from simulated type 1 diabetes data.

BACKGROUND: A model-based controller for an artificial beta-cell automatically regulates blood glucose levels based on available glucose measurements, insulin infusion and meal information, and model predictions of future glucose trends. Thus, the identification of simple, accurate models plays an important role in the development of an artificial beta-cell. METHODS: Glucose data simulated from a nonlinear physiological model of type 1 diabetes are used to identify linear dynamic models of two types: autoregressive exogenous input (ARX) and output-error (OE) models. The model inputs are meal carbohydrates and exogenous insulin, which in practice are often administered simultaneously and in the same ratio, i.e., the insulin-to-carbohydrate ratio. The effect of modeling these inputs as impulses versus time-smoothed profiles ("transformed inputs") is explored in depth. The models are evaluated based on their ability to describe the data from which they were identified (i.e., calibration data) as well as independent data (i.e., validation data). RESULTS: In general, the best models described their calibration data more accurately using transformed inputs (R(Cal) (2) = 71% for the ARX models and R (Cal) (2) = 78% for the OE models) than using impulse inputs (R (Cal) (2) = 14% for the ARX models and R (Cal) (2) = 70% for the OE models). The only model/input combination that resulted in consistently accurate validation fits was the ARX models using transformed inputs (39%

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.

Feasibility of Long-Term Closed-Loop Control: A Multicenter 6-Month Trial of 24/7 Automated Insulin Delivery.

BACKGROUND: In the past few years, the artificial pancreas-the commonly accepted term for closed-loop control (CLC) of blood glucose in diabetes-has become a hot topic in research and technology development. In the summer of 2014, we initiated a 6-month trial evaluating the safety of 24/7 CLC during free-living conditions. RESEARCH DESIGN AND METHODS: Following an initial 1-month Phase 1, 14 individuals (10 males/4 females) with type 1 diabetes at three clinical centers in the United States and one in Italy continued with a 5-month Phase 2, which included 24/7 CLC using the wireless portable Diabetes Assistant (DiAs) developed at the University of Virginia Center for Diabetes Technology. Median subject characteristics were age 45 years, duration of diabetes 27 years, total daily insulin 0.53 U/kg/day, and baseline HbA1c 7.2% (55 mmol/mol). RESULTS: Compared with the baseline observation period, the frequency of hypoglycemia below 3.9 mmol/L during the last 3 months of CLC was lower: 4.1% versus 1.3%, P < 0.001. This was accompanied by a downward trend in HbA1c from 7.2% (55 mmol/mol) to 7.0% (53 mmol/mol) at 6 months. HbA1c improvement was correlated with system use (Spearman r = 0.55). The user experience was favorable with identified benefit particularly at night and overall trust in the system. There were no serious adverse events, severe hypoglycemia, or diabetic ketoacidosis. CONCLUSION: We conclude that CLC technology has matured and is safe for prolonged use in patients' natural environment. Based on these promising results, a large randomized trial is warranted to assess long-term CLC efficacy and safety.

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.

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.

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.

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 individual's 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 subject's 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. Subject's 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.

Artificial pancreas (AP) clinical trial participants' acceptance of future AP technology.

BACKGROUND: Artificial pancreas (AP) systems are currently an active field of diabetes research. This pilot study examined the attitudes of AP clinical trial participants toward future acceptance of the technology, having gained firsthand experience. SUBJECTS AND METHODS: After possible influencers of AP technology adoption were considered, a 34-question questionnaire was developed. The survey assessed current treatment satisfaction, dimensions of clinical trial participant motivation, and variables of the technology acceptance model (TAM). Forty-seven subjects were contacted to complete the survey. The reliability of the survey scales was tested using Cronbach's α. The relationship of the factors to the likelihood of AP technology adoption was explored using regression analysis. RESULTS: Thirty-six subjects (76.6%) completed the survey. Of the respondents, 86.1% were either highly likely or likely to adopt the technology once available. Reliability analysis of the survey dimensions revealed good internal consistency, with scores of >0.7 for current treatment satisfaction, convenience (motivation), personal health benefit (motivation), perceived ease of use (TAM), and perceived usefulness (TAM). Linear modeling showed that future acceptance of the AP was significantly associated with TAM and the motivation variables of convenience plus the individual item benefit to others (R(2)=0.26, P=0.05). When insulin pump and continuous glucose monitor use were added, the model significance improved (R(2)=0.37, P=0.02). CONCLUSIONS: This pilot study demonstrated that individuals with direct AP technology experience expressed high likelihood of future acceptance. Results support the factors of personal benefit, convenience, perceived usefulness, and perceived ease of use as reliable scales that suggest system adoption in this highly motivated patient population.

Clinical evaluation of an automated artificial pancreas using zone-model predictive control and health monitoring system.

BACKGROUND: This study was performed to evaluate the safety and efficacy of a fully automated artificial pancreas using zone-model predictive control (zone-MPC) with the health monitoring system (HMS) during unannounced meals and overnight and exercise periods. SUBJECTS AND METHODS: A fully automated closed-loop artificial pancreas was evaluated in 12 subjects (eight women, four men) with type 1 diabetes (mean±SD age, 49.4±10.4 years; diabetes duration, 32.7±16.0 years; glycosylated hemoglobin, 7.3±1.2%). The zone-MPC controller used an a priori model that was initialized using the subject's total daily insulin. The controller was designed to keep glucose levels between 80 and 140 mg/dL. A hypoglycemia prediction algorithm, a module of the HMS, was used in conjunction with the zone controller to alert the user to consume carbohydrates if the glucose level was predicted to fall below 70 mg/dL in the next 15 min. RESULTS: The average time spent in the 70-180 mg/dL range, measured by the YSI glucose and lactate analyzer (Yellow Springs Instruments, Yellow Springs, OH), was 80% for the entire session, 92% overnight from 12 a.m. to 7 a.m., and 69% and 61% for the 5-h period after dinner and breakfast, respectively. The time spent < 60 mg/dL for the entire session by YSI was 0%, with no safety events. The HMS sent appropriate warnings to prevent hypoglycemia via short and multimedia message services, at an average of 3.8 treatments per subject. CONCLUSIONS: The combination of the zone-MPC controller and the HMS hypoglycemia prevention algorithm was able to safely regulate glucose in a tight range with no adverse events despite the challenges of unannounced meals and moderate exercise.

Automatic Detection of Stress States in Type 1 Diabetes Subjects in Ambulatory Conditions.

Two levels of control are crucial to the robustness of an artificial ß-cell, a medical device that would automatically regulate blood glucose levels in patients with type 1 diabetes. A low-level component would attempt to regulate blood glucose continuously, while a supervisory-level, or monitoring, component would detect underlying changes in the subject's glucose-insulin dynamics and take corrective actions accordingly. These underlying changes, or "faults," can include changes in insulin sensitivity, sensor problems, and insulin delivery problems, to name a few. A multivariate statistical monitoring technique, principal component analysis (PCA), has been applied to both simulated and experimental type 1 diabetes data. The objective of this study was to determine if PCA could be used to distinguish between normal patient data, and data for abnormal conditions that included a variety of "faults." The PCA results showed a high degree of accuracy; for data from nine type 1 diabetes subjects in ambulatory conditions, 33 of 37 total test days (89%), including fault days and normal days, were classified correctly. Thus, the proposed monitoring technique shows considerable promise for incorporation into an artificial ß-cell.

Modeling the effects of subcutaneous insulin administration and carbohydrate consumption on blood glucose.

BACKGROUND: Estimation of the magnitude and duration of effects of carbohydrate (CHO) and subcutaneously administered insulin on blood glucose (BG) is required for improved BG regulation in people with type 1 diabetes mellitus (T1DM). The goal of this study was to quantify these effects in people with T1DM using a novel protocol. METHODS: The protocol duration was 8 hours: a 1-3 U subcutaneous (SC) insulin bolus was administered and a 25-g CHO meal was consumed, with these inputs separated by 3-5 hours. The DexCom SEVEN® PLUS continuous glucose monitor was used to obtain SC glucose measurements every 5 minutes and YSI 2300 Stat Plus was used to obtain intravenous glucose measurements every 15 minutes. RESULTS: The protocol was tested on 11 subjects at Sansum Diabetes Research Institute. The intersubject parameter coefficient of variation for the best identification method was 170%. The mean percentages of output variation explained by the bolus insulin and meal models were 68 and 69%, respectively, with root mean square error of 14 and 10 mg/dl, respectively. Relationships between the model parameters and clinical parameters were observed. CONCLUSION: Separation of insulin boluses and meals in time allowed unique identification of model parameters. The wide intersubject variation in parameters supports the notion that glucose-insulin models and thus insulin delivery algorithms for people with T1DM should be personalized. This experimental protocol could be used to refine estimates of the correction factor and the insulin-to-carbohydrate ratio used by people with T1DM.

Clinical update on optimal prandial insulin dosing using a refined run-to-run control algorithm.

BACKGROUND: This article provides a clinical update using a novel run-to-run algorithm to optimize prandial insulin dosing based on sparse glucose measurements from the previous day's meals. The objective was to use a refined run-to-run algorithm to calculate prandial insulin-to-carbohydrate ratios (I:CHO) for meals of variable carbohydrate content in subjects with type 1 diabetes (T1DM). METHOD: The open-labeled, nonrandomized study took place over a 6-week period in a nonprofit research center. Nine subjects with T1DM using continuous subcutaneous insulin infusion participated. Basal insulin rates were optimized using continuous glucose monitoring, with a target fasting blood glucose of 90 mg/dl. Subjects monitored blood glucose concentration at the beginning of the meal and at 60 and 120 minutes after the start of the meal. They were instructed to start meals with blood glucose levels between 70 and 130 mg/dl. Subjects were contacted daily to collect data for the previous 24-hour period and to give them the physician-approved, algorithm-derived I:CHO ratios for the next 24 hours. Subjects calculated the amount of the insulin bolus for each meal based on the corresponding I:CHO and their estimate of the meal's carbohydrate content. One- and 2-hour postprandial glucose concentrations served as the main outcome measures. RESULTS: The mean 1-hour postprandial blood glucose level was 104 +/- 19 mg/dl. The 2-hour postprandial levels (96.5 +/- 18 mg/dl) approached the preprandial levels (90.1 +/- 13 mg/dl). CONCLUSIONS: Run-to-run algorithms are able to improve postprandial blood glucose levels in subjects with T1DM.

Experimental evaluation of a recursive model identification technique for type 1 diabetes.

BACKGROUND: A model-based controller for an artificial beta cell requires an accurate model of the glucose-insulin dynamics in type 1 diabetes subjects. To ensure the robustness of the controller for changing conditions (e.g., changes in insulin sensitivity due to illnesses, changes in exercise habits, or changes in stress levels), the model should be able to adapt to the new conditions by means of a recursive parameter estimation technique. Such an adaptive strategy will ensure that the most accurate model is used for the current conditions, and thus the most accurate model predictions are used in model-based control calculations. METHODS: In a retrospective analysis, empirical dynamic autoregressive exogenous input (ARX) models were identified from glucose-insulin data for nine type 1 diabetes subjects in ambulatory conditions. Data sets consisted of continuous (5-minute) glucose concentration measurements obtained from a continuous glucose monitor, basal insulin infusion rates and times and amounts of insulin boluses obtained from the subjects' insulin pumps, and subject-reported estimates of the times and carbohydrate content of meals. Two identification techniques were investigated: nonrecursive, or batch methods, and recursive methods. Batch models were identified from a set of training data, whereas recursively identified models were updated at each sampling instant. Both types of models were used to make predictions of new test data. For the purpose of comparison, model predictions were compared to zero-order hold (ZOH) predictions, which were made by simply holding the current glucose value constant for p steps into the future, where p is the prediction horizon. Thus, the ZOH predictions are model free and provide a base case for the prediction metrics used to quantify the accuracy of the model predictions. In theory, recursive identification techniques are needed only when there are changing conditions in the subject that require model adaptation. Thus, the identification and validation techniques were performed with both "normal" data and data collected during conditions of reduced insulin sensitivity. The latter were achieved by having the subjects self-administer a medication, prednisone, for 3 consecutive days. The recursive models were allowed to adapt to this condition of reduced insulin sensitivity, while the batch models were only identified from normal data. RESULTS: Data from nine type 1 diabetes subjects in ambulatory conditions were analyzed; six of these subjects also participated in the prednisone portion of the study. For normal test data, the batch ARX models produced 30-, 45-, and 60-minute-ahead predictions that had average root mean square error (RMSE) values of 26, 34, and 40 mg/dl, respectively. For test data characterized by reduced insulin sensitivity, the batch ARX models produced 30-, 60-, and 90-minute-ahead predictions with average RMSE values of 27, 46, and 59 mg/dl, respectively; the recursive ARX models demonstrated similar performance with corresponding values of 27, 45, and 61 mg/dl, respectively. The identified ARX models (batch and recursive) produced more accurate predictions than the model-free ZOH predictions, but only marginally. For test data characterized by reduced insulin sensitivity, RMSE values for the predictions of the batch ARX models were 9, 5, and 5% more accurate than the ZOH predictions for prediction horizons of 30, 60, and 90 minutes, respectively. In terms of RMSE values, the 30-, 60-, and 90-minute predictions of the recursive models were more accurate than the ZOH predictions, by 10, 5, and 2%, respectively. CONCLUSION: In this experimental study, the recursively identified ARX models resulted in predictions of test data that were similar, but not superior, to the batch models. Even for the test data characteristic of reduced insulin sensitivity, the batch and recursive models demonstrated similar prediction accuracy. The predictions of the identified ARX models were only marginally more accurate than the model-free ZOH predictions. Given the simplicity of the ARX models and the computational ease with which they are identified, however, even modest improvements may justify the use of these models in a model-based controller for an artificial beta cell.