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Artificial pancreas (AP) is a useful tool for maintaining the blood glucose (BG) of patients with type 1 diabetes (T1D) within the euglycemic range. An intelligent controller has been developed based on general predictive control (GPC) for AP. This controller exhibits good performance with the UVA/Padova T1D mellitus simulator approved by the US Food and Drug Administration. In this work, the GPC controller was further evaluated under strict conditions, including a pump with noise and error, a CGM sensor with noise and error, a high carbohydrate intake, and a large population of 100 in-silico subjects. Test results showed that the subjects are in high risk for hypoglycemia. Thus, an insulin on board (IOB) calculator, as well as an adaptive control weighting parameter (AW) strategy, was introduced. The percentage of time spent in euglycemic range of the in-silico subjects was 86.0% ± 5.8%, and the patient group had a low risk of hypoglycemia with the GPC+IOB+AW controller. Moreover, the proposed AW strategy is more effective in hypoglycemia prevention and does not require any personalized data compared with the IOB calculator. Thus, the proposed controller realized an automatic control of the BG of patients with T1D without meal announcements and complex user interaction.

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Hypoglycemia is a major complication associated with insulin therapy in people with diabetes that could cause life-threatening conditions if untreated. Glucagon, a counter-acting hormone, is thus administered for rescue of severe hypoglycemia. However, due to the instability of glucagon, only limited medications are available for emergency use, which are unsuitable for patients with hypoglycemia unawareness or with the inability to self-administer, especially during sleep (namely, nocturnal hypoglycemia). To prevent unattended and extended hypoglycemia, we designed a "smart" composite microneedle (cMN) patch capable of stabilizing glucagon, sensing hypoglycemia, and delivering glucagon automatically on demand. In this design, native glucagon was encapsulated in glucose-responsive microgels containing a glucagon-stabilizing component rationally selected by molecular dynamics (MD) simulation. A cMN patch was then prepared by incorporating the glucagon microgels with poly(methyl vinyl ether-alt-maleic anhydride) (PMVE-MAH) and poly(ethylene glycol) (PEG) followed by thermal cross-linking. The rationally designed zwitterionic polymer-based microgels preserved the native structure of glucagon and prevented heat-induced fibrillation evidenced by RP-HPLC, circular dichroism, and transmission electron microscopy. MD simulations suggested that the polymeric microgels stabilized glucagon by inhibition of oligomer formation via peptide-polymer noncovalent interactions. The polymer formed multiple hydrogen bonds with the polar and charged amino acid residues of the glucagon molecule, shielding the peptide surface from aggregation. In vivo efficacy studies using streptozotocin-induced type 1 diabetic (T1D) rats demonstrated that the glucagon-loaded cMN patch could prevent hypoglycemia induced by insulin overdose during a 12 h period. The results suggest that this new glucagon "smart" patch may be a promising system for improving the quality of life of those suffering from nocturnal hypoglycemia and hypoglycemia unawareness.

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Hypoglycemia is a serious and potentially fatal complication experienced by people with insulin-dependent diabetes. The complication is usually caused by insulin overdose, skipping meals, and/or excessive physical activities. In type 1 diabetes (T1D), on top of impaired pancreatic α-cells, excessive levels of somatostatin from δ-cells further inhibit glucagon secretion to counteract overdosed insulin. Herein, we aimed to develop a microneedle (MN) patch for transdermal delivery of a peptide (PRL-2903) that antagonizes somatostatin receptor type 2 (SSTR2) in α-cells. First, we investigated the efficacy of subcutaneously administered PRL-2903 and identified the optimal dose (i.e., the minimum effective dose) and treatment scheduling (i.e., the best administration time for hypoglycemia prevention) in a T1D rat model. We then designed an MN patch using a hyaluronic acid (HA)-based polymer. The possible effect of the polymer on stabilizing the native structure of PRL-2903 was studied by molecular dynamics (MD) simulations. The results showed that the HA-based polymer could stabilize the PRL-2903 structure by restricting water molecules, promoting intra-molecular H-bonding, and constraining torsional angles of important bonds. In vivo studies with an overdose insulin challenge revealed that the PRL-2903-loaded MN patch effectively increased the plasma glucagon level, restored the counter-regulation of blood glucose concentration, and prevented hypoglycemia. The proposed MN patch is the first demonstration of a transdermal microneedle patch designed to deliver an SSTR2 antagonist for the prevention of hypoglycemia. This counter-regulatory peptide delivery system may be applied alongside with insulin delivery systems to provide a more effective and safer treatment for people with insulin-dependent diabetes.

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OBJECTIVE: To examine the use of multiple daily injections (MDI), insulin pumps, self-measured blood glucose (SMBG), and continuous glucose monitoring (CGM) systems, and their association with glycated hemoglobin (HbA1c), diabetic ketoacidosis (DKA), and severe hypoglycemia. METHODS: In a pediatric population-based nationwide cross-sectional study, we analyzed data from 2623 participants up to 18 years of age with type 1 diabetes, using 2017 annual data from the Norwegian Childhood Diabetes Registry. HbA1c was adjusted for age, gender, and diabetes duration. Using a linear mixed-effects model, we assessed HbA1c and the incidence of DKA and severe hypoglycemia according to the use of MDI, insulin pumps, SMBG, and CGM. RESULTS: We observed that 74.7% of participants were using an insulin pump and 52.6% were using a CGM system. Mean HbA1c was 7.8% (62 mmol/mol). The HbA1c of pump users was 0.14 percentage points (pp) higher than that of MDI users. Fewer pump users than MDI users achieved an HbA1c of < 7.5% (38.3 vs. 41.6%). CGM users had a 0.18 pp lower HbA1c than SMBG users, with 40.5 and 38.0%, respectively, achieving an HbA1c of < 7.5%. The incidence of severe hypoglycemia or hospitalization due to DKA was not different in pump and CGM users compared with nonusers. Compared with other insulin pumps, patch pump use was associated with a significantly lower odds ratio for DKA. CONCLUSIONS: Despite the broad use of diabetes technology, as many as 61% of our pediatric cohort did not reach the HbA1c target recommended by the International Society for Pediatric and Adolescent Diabetes (ISPAD). Lower HbA1c was associated with CGM use but not with insulin pump use. Acute complications were not less frequent in the groups using insulin pumps or CGM compared with those using MDI and SMBG. Further research is required to explore the lower incidence of DKA among patch pump users. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT04201171.

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OBJECTIVE: To evaluate the safety and performance of a new multivariable closed-loop (MCL) glucose controller with automatic carbohydrate recommendation during and after unannounced and announced exercise in adults with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: A randomized, 3-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-minute sets on a cycle ergometer with 5 minutes rest in between. In a randomly determined order, we compared MCL control with unannounced (CLNA) and announced (CLA) exercise to open-loop therapy (OL). Adults with T1D, insulin pump users, and those with hemoglobin (Hb)A1c between 6.0% and 8.5% were eligible. We investigated glucose control during and 3 hours after exercise. RESULTS: Ten participants (aged 40.8 ± 7.0 years; HbA1c of 7.3 ± 0.8%) participated. The use of the MCL in both closed-loop arms decreased the time spent <70 mg/dL of sensor glucose (0.0%, [0.0-16.8] and 0.0%, [0.0-19.2] vs 16.2%, [0.0-26.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.047, P = 0.063) and the number of hypoglycemic events when compared with OL (CLNA 4 and CLA 3 vs OL 8; P = 0.218, P = 0.250). The use of the MCL system increased the proportion of time within 70 to 180 mg/dL (87.8%, [51.1-100] and 91.9%, [58.7-100] vs 81.1%, [65.4-87.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.227, P = 0.039). This was achieved with the administration of similar doses of insulin and a reduced amount of carbohydrates. CONCLUSIONS: The MCL with automatic carbohydrate recommendation performed well and was safe during and after both unannounced and announced exercise, maintaining glucose mostly within the target range and reducing the risk of hypoglycemia despite a reduced amount of carbohydrate intake.Register Clinicaltrials.gov: NCT03577158.

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Hypoglycemia in inpatients with diabetes remains the most common complication of diabetes therapies. Hypoglycemia is independently associated with increased morbidity and mortality, increased length of stay, increased readmission rate, and increased cost. This review describes the importance of reporting and addressing inpatient hypoglycemia; it further summarizes eight strategies that aid clinicians in the prevention of inpatient hypoglycemia: auditing the electronic medical record, formulary restrictions and dose-limiting strategies, hyperkalemia order sets, electronic glucose management systems, prediction tools, diabetes self-management, remote surveillance, and noninsulin medications.

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BACKGROUND: The objective of this research is to show the effectiveness of individualized hypoglycemia predictive alerts (IHPAs) based on patient-tailored glucose-insulin models (PTMs) for different subjects. Interpatient variability calls for PTMs that have been identified from data collected in free-living conditions during a one-month trial. METHODS: A new impulse-response (IR) identification technique has been applied to free-living data in order to identify PTMs that are able to predict the future glucose trends and prevent hypoglycemia events. Impulse response has been applied to seven patients with type 1 diabetes (T1D) of the University of Amsterdam Medical Centre. Individualized hypoglycemia predictive alert has been designed for each patient thanks to the good prediction capabilities of PTMs. RESULTS: The PTMs performance is evaluated in terms of index of fitting (FIT), coefficient of determination, and Pearson's correlation coefficient with a population FIT of 63.74%. The IHPAs are evaluated on seven patients with T1D with the aim of predicting in advance (between 45 and 10 minutes) the unavoidable hypoglycemia events; these systems show better performance in terms of sensitivity, precision, and accuracy with respect to previously published results. CONCLUSION: The proposed work shows the successful results obtained applying the IR to an entire set of patients, participants of a one-month trial. Individualized hypoglycemia predictive alerts are evaluated in terms of hypoglycemia prevention: the use of a PTM allows to detect 84.67% of the hypoglycemia events occurred during a one-month trial on average with less than 0.4% of false alarms. The promising prediction capabilities of PTMs can be a key ingredient for new generations of individualized model predictive control for artificial pancreas.

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Background: The standard treatment for hypoglycemia recommended by the American Diabetes Association (ADA) suggests patients with diabetes to take small amounts of carbohydrates, the so-called hypotreatments (HTs), as soon as blood glucose concentration goes below 70 mg/dL. However, prevention, or at least mitigation, of hypoglycemic events could be achieved by triggering HTs ahead of time thanks to the use of the predictive capabilities of suitable real-time algorithms fed by continuous glucose monitoring (CGM) sensor data. Materials and Methods: The algorithm proposed in this article to trigger HTs for preventing forthcoming hypoglycemic events is based on the computation of the "dynamic risk", there is a nonlinear function combining current glycemia with its rate-of-change, both provided by CGM. A comparison of performance of the proposed algorithm against the ADA guidelines is made, in silico, on datasets of 100 virtual patients undergoing a single-meal experiment, with induced postmeal hypoglycemia, generated by the UVA/Padova type 1 diabetes simulator. Results: On noise-free CGM data, the proposed algorithm reduces the time spent in hypoglycemia, on median [25th-75th percentiles] from 36 [29-43] to 0 [0-11] min (P < 0.0001), with a concomitant decrease of the post-treatment rebound (PTR) in glucose concentration, on median [25th-75th percentiles] from 136 [121-148] to 121 [116-127] mg/dL (P < 0.0001). On noisy CGM data, there is still a reduction of both time spent in hypoglycemia from 41 [28-49] min to 25 [0-41] min (P < 0.0001) and PTR from 174 [146-189] mg/dL to 137 [123-151] mg/dL (P < 0.0001). Conclusions: The potentiality of the new algorithm in generating preventive HTs, which can allow significant reduction of hypoglycemia without concomitant increase of hyperglycemia, suggests its further development and test in silico, for example, simulating both insulin pump and multiple-daily-injection therapies.

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Insulin-dependent patients with diabetes mellitus require multiple daily injections of exogenous insulin to combat hyperglycemia. However, administration of excess insulin can lead to hypoglycemia, a life-threatening condition characterized by abnormally low blood glucose levels (BGLs). To prevent hypoglycemia associated with intensive insulin therapy, a "smart" composite microneedle (cMN) patch is developed, which releases native glucagon at low glucose levels. The cMN patch is composed of a photo-crosslinked methacrylated hyaluronic acid (MeHA) microneedle array with embedded multifunctional microgels. The microgels incorporate zwitterionic moieties that stabilize loaded glucagon and phenylboronic acid moieties that provide glucose-dependent volume change to facilitate glucagon release. Hypoglycemia-triggered release of structurally unchanged glucagon from the cMN patch is demonstrated in vitro and in a rat model of type 1 diabetes (T1D). Transdermal application of the patch prevented insulin-induced hypoglycemia in the diabetic rats. This work is the first demonstration of a glucose-responsive glucagon-delivery MN patch for the prevention of hypoglycemia, which has a tremendous potential to reduce the dangers of intensive insulin therapy and improve the quality of life of patients with diabetes and their caregivers.

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BACKGROUND: We aimed to compare closed-loop glucose control for people with type 1 diabetes undertaking high-intensity interval exercise (HIIE) versus moderate-intensity exercise (MIE). METHODS: Adults with type 1 diabetes established on insulin pumps undertook HIIE and MIE stages in random order during automated insulin delivery via a closed-loop system (Medtronic). Frequent venous sampling for glucose, lactate, ketones, insulin, catecholamines, cortisol, growth hormone, and glucagon levels was performed. The primary outcome was plasma glucose <4.0 mmol/L for ≥15 min, from exercise commencement to 120 min postexercise. Secondary outcomes included continuous glucose monitoring and biochemical parameters. RESULTS: Twelve adults (age mean ± standard deviation 40 ± 13 years) were recruited; all completed the study. Plasma glucose of one participant fell to 3.4 mmol/L following MIE completion; no glucose levels were <4.0 mmol/L for HIIE (primary outcome). There were no glucose excursions >15.0 mmol/L for either stage. Mean (±standard error) plasma glucose did not differ between stages pre-exercise; was higher during exercise in HIIE than MIE (11.3 ± 0.5 mmol/L vs. 9.7 ± 0.6 mmol/L, respectively; P < 0.001); and remained higher until 60 min postexercise. There were no differences in circulating free insulin before, during, or postexercise. During HIIE compared with MIE, there were greater increases in lactate (P < 0.001), catecholamines (all P < 0.05), and cortisol (P < 0.001). Ketones increased more with HIIE than MIE postexercise (P = 0.031). CONCLUSIONS: Preliminary findings suggest that closed-loop glucose control is safe for people undertaking HIIE and MIE. However, the management of the postexercise rise in ketones secondary to counter-regulatory hormone-induced insulin resistance observed with HIIE may represent a challenge for closed-loop systems.

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BACKGROUND: A sensor-augmented insulin pump (SAP) using the MiniMed® 640G system with SmartGuard™ technology allows an automatic stop of insulin delivery based on prediction of low glucose levels. Since pediatric patients are particularly prone to hypoglycemia, this device may offer additional protection beyond conventional sensor-augmented therapy. METHODS: This prospective, pediatric multicenter user evaluation assessed 6 weeks of SAP with SmartGuard (threshold setting for hypoglycemia: 70 mg/dL) compared to a preceding period of 2 weeks with SAP only. The primary outcome was the potential reduction in the frequency of hypoglycemic episodes and hypoglycemic intensity (area under the curve [AUC] and time <70 mg/dL). RESULTS: The study included 24 patients with at least 3 months of insulin pump use (average age: 11.6 ± 5.1 years, 15 female, average type 1 diabetes duration: 7.5 ± 4.2 years, mean ± SD) who had on average 3.2 ± 1.0 predictive suspensions/patient/day. The mean sensor glucose minimum during suspension was 78 ± 6 mg/dL and the average suspension time was 155 ± 47 min/day. Use of SmartGuard in patients treated as per the protocol (n = 18) reduced the number of instances in which the glucose level was <70 mg/dL (1.02 ± 0.52 to 0.72 ± 0.36; P = 0.027), as well as AUC <70 mg/dL (0.76 ± 0.73 to 0.38 ± 0.24; P = 0.027) and the time/day the level fell below 70 mg/dL (73 ± 56 to 31 ± 22 min). The reduction of hypoglycemia was not associated with a significant change in mean glucose concentration (171 ± 26 to 180 ± 19 mg/dL, P = 0.111) and HbA1c (7.5% ± 0.5% to 7.6% ± 0.7%, (P = 0.329). Manual resumption of insulin delivery followed by carbohydrate intake resulted in significantly higher glucose levels 1 h after suspension compared to SmartGuard suspensions with automatic resume (190.8 ± 26.5 vs. 138.7 ± 10.3 mg/dL; P < 0.001). CONCLUSIONS: SmartGuard technology significantly reduced the risk for hypoglycemia in pediatric type 1 diabetes patients without increasing HbA1c. Patients must be educated that when using combining predictive low-glucose insulin suspension technology, extra carbohydrate intake in response to an alarm combined with manual resumption is likely to cause rebound hyperglycemia. The best results were achieved when the user did not interfere with pump operation.

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