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Background: This pilot trial was designed to determine if an optimal dose of Technosphere(®) insulin (TI) inhalation powder (MannKind Corp., Valencia, CA) could be used regardless of variation in meal carbohydrate (CHO) content., Subjects and Methods: In total, eight subjects (seven men, one woman) with type 2 diabetes were enrolled. Subjects underwent dose optimization meal challenge (MC) visits (100% CHO) and MCs with varied CHO meal contents (50%, 200%, and 0% calculated CHOs). Primary end point was change in postprandial glucose (PPG) excursions. Baseline demographics were 60±7 years of age, diabetes duration of 12.3±4.27 years, hemoglobin A1c (A1C) of 7.82±1.04%, and body mass index of 31.3±5.48 kg/m(2)., Results: Maximum mean PPG excursions for the nominal 100% CHO meals were -13±15 mg/dL for breakfast (B) and -14±15 mg/dL for lunch (L), similar to those after 50% CHO meals (B, -17±16 mg/dL; L, +14±10 mg/dL). The largest excursions occurred during 200% CHO meals and remained below American Diabetes Association targets (B, +19±16 mg/dL; L, +32±29 mg/dL). During 15 of the MCs, subjects took their usual TI dose and then had no meal (0% CHO). For the 0% CHO MCs, the largest mean PPG excursion were -33±9 mg/dL at 60 min (B) and -31±10 mg/dL at 60 and 90 min (L). Mean A1C dropped from 7.82±1.04% at the Week 1 visit to 6.18±0.46% (P=0.00091) at the Week 19 visit., Conclusions: Results in eight patients suggest that once an optimal dose of TI is determined, type 2 diabetes patients can ingest meals with a wide range of CHO content or even skip meals without severe hypoglycemia. During this pilot study TI therapy improved A1C by -1.63% (P=0.00091) during 19 weeks of treatment.

Background: Emerging technology, such as an artificial pancreatic beta-cell, is not likely to be affordable to people who live in developing nations in the next 20-30 years. However, multiple-daily injection (MDI) therapy can be improved using similar advanced control algorithms designed for continuous glucose monitoring and continuous insulin infusion pumps., Methods: A simulation study of run-to-run control was developed for MDI therapy. Rapid- and slow-acting insulins were used in the protocol, which uses pre- and postprandial glucose measurements. The key information for the synthesis of the control algorithm is the subject insulin sensitivity that is calculated for two cases: (a) when the subject's glycemia and insulin dosing information is known (sensitivity response) and (b) when there is no previous information about the subject's response to the insulin protocol. In the latter case, this information needs to be estimated recursively using online data. After the sensitivity is recalculated, the run-to-run correction scheme is updated, obtaining an adaptive MDI therapy. The robustness of the advisory algorithm was evaluated by constant random parameter variations and superimposing sinusoidal oscillations on glucose-insulin model parameters to simulate intra-individual variability of the glucoregulatory system., Results: Optimal glycemic control has been achieved for both cases (a and b) despite variable meals (15% variation in carbohydrate content and 15-min variation in timing) and parametric variations in the glucose-insulin model. In Case (b), no profound hypoglycemic (<60 mg/dL) or hyperglycemic (>180 mg/dL) events were observed on average during all evaluations., Conclusions: This work shows that the run-to-run framework for insulin updating can be successfully extended to an adaptive MDI protocol. These results motivate the practical implementation of this scheme in portable units such as personal digital assistants or smartphones.

The prevalence of diabetes in pregnancy has continued to increase, both as obesity drives up the rate of glucose intolerance itself and as improvements in diabetes and infertility treatments allow more women with diabetes to become and remain pregnant into the third trimester. With this increase has come a concomitant increase in the number of pregnant women using insulin to control their blood glucose in pregnancy. This review seeks to identify advantages and disadvantages of insulin pump use in pregnancy, as compared to a more traditional multiple daily injection (MDI) insulin regimen. Insulin pumps have not yet been shown to offer superior glucose control compared to MDI insulin, and thus many healthcare practitioners and health insurance companies are hesitant to adopt such a practice; however, insulin pumps often facilitate ease of usage of insulin and promote postpartum insulin use when indicated. Although only a small percentage of pregnant women with diabetes in the United States currently use insulin pumps, we believe that insulin pumps may represent a superior mode of insulin delivery for many women with diabetes in pregnancy.

Objective: We compared changes in response to unmasking of continuous glucose monitoring (CGM) in subjects with type 1 diabetes who use multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII)., Research Design and Methods: Use of real-time CGM (DexCom [San Diego, CA] SEVEN was studied in 38 subjects using CSII and 26 using MDI. CGM output was masked during Week 1 and unmasked during Weeks 2 and 3. We evaluated changes in 16 criteria for quality of glycemic control and eight criteria for glycemic variability., Results: All 24 criteria showed highly statistically significant improvement when considered simultaneously (P < 0.000001). For subjects using CSII, 18 of 24 criteria improved significantly (nominal P < 0.05); for subjects using MDI, 16 of 24 criteria improved significantly (P < 0.05). Twelve of the comparisons remained significant (P < 0.05) after applying the overconservative Bonferroni correction for multiple comparisons. The percentage of glucose values within the range 80-140 mg/dL increased by 19% and 17% relative to their control values (Week 1) for subjects using MDI and CSII, respectively. Mean glucose, overall SD (SD(T)), SD between daily means (SD(dm)), mean amplitude of glycemic excursion (MAGE), and mean of daily differences (MODD) improved significantly. Responses to CGM display were not significantly different between the MDI and CSII subject groups for any of the 24 criteria considered individually or in groups of eight, 16, or 24., Conclusion: CGM has similar effectiveness in subjects with type 1 diabetes using either CSII or MDI.

Objective: We evaluated effects of unmasking of continuous display of continuous glucose monitoring (CGM) on quality of glycemic control and glycemic variability., Methods: We reanalyzed CGM data from 85 patients using a 7-day glucose sensor. Glucose values were "masked" during the first week but "unmasked" during the next 2 weeks. We evaluated 48 criteria for quality of glycemic control, including mean glucose, SD, percentage of values within-, above- or below- specified ranges, Schlichtkrull's M(100) index, mean amplitude of glycemic excursion (MAGE), mean of daily differences (MODD), the J index, "Index of Glycemic Control" (IGC), Hyperglycemia Index, Hypoglycemia Index, High Blood Glucose Index (HBGI), Low Blood Glucose Index (LBGI), average daily risk range (ADRR), GRADE scores, and CONGA(n). We calculated SD values between daily means, between days-within time points, within days, between time points (for the average glucose profile for several days), and within series for time segments of arbitrary length., Results: Unmasking CGM displays resulted in rapid, highly statistically significant improvement in 29 indices, including percentage within, percentage above, and percentage below target range, mean glucose, SD, SD of daily means, MODD, M(100), IGC, GRADE, HBGI, and J index. Both hyperglycemia and hypoglycemia improved during the first week after unmasking; further improvement in hypoglycemia was seen during the following week. Results obtained using multiple criteria were consistent and highly correlated., Conclusions: Continuous access to display of CGM sensors dramatically improved 29 indices of glycemic control and glycemic variability.

Diabetes throughout gestation presents challenges that greatly influence maternal and fetal outcomes. Since the degree of maternal hyperglycemia is the most deterministic of these risks, glucose monitoring has become a fundamental tool in the management of diabetes in pregnancy. While most patients with diabetes are in need of improved glucose control, certain circumstances beg for more detailed glucose information than is available by conventional methods alone. In this article, we will review the state of diabetes during pregnancy and the serious outcomes that persist despite the overall improvement in glycemic control today. We will discuss the advantages of incorporating continuous glucose monitoring (CGM), specifically in the treatment of pregnancies complicated by diabetes. In addition to its clinical utility, CGM can advance our understanding and classification of normoglycemia during pregnancy. Demarcation of the normal glucose profile that is present during gestation better defines the therapeutic goals for women with diabetes and ultimately promotes success in pregnancy.

Background: A critical step in algorithm development for an artificial beta-cell is extensive in silico testing. Computer simulations usually involve only the controller software, leaving untested the hardware elements, including the critical communication interface between the controller and the glucose sensor and insulin pump., Methods: An in silico simulation platform has been developed that uses all of the components of the clinical system. At the core is a comprehensive in silico population model that covers the variability of principal metabolic parameters observed in vivo, to replace the human subject, with the ability to use historical clinical data. A continuous glucose monitor, in this case either the Abbott Diabetes Care (Alameda, CA) FreeStyle Navigator or the DexCom (San Diego, CA) STS7, is supplied with a glucose signal provided by the simulator. The Insulet (Bedford, MA) OmniPod insulin pump is also interfaced with the simulator to provide insulin delivery data. These hardware elements are an integral part of the system under testing, which also includes the algorithm components., Results: The system is unique in that it uses the same hardware components for simulations as are required in clinical trials, allowing for full-system level verification and validation. With a detailed mathematical model, a suite of patients can be simulated to reflect various conditions. Because all hardware is used, their related limitations are automatically included., Conclusions: A complete artificial beta-cell evaluation platform was realized with the flexibility to interface various algorithms and patient models, allowing for the systematic analysis of monitoring and control algorithms. The system facilitates a variety of tests and challenges to the software and the component devices, streamlining preclinical validation trials.

Abstract The use of continuous subcutaneous insulin infusion (CSII) pumps has been gaining popularity since 1979, when the first research report on insulin pumps was published. Insulin pumps-small medical devices that are programmed to infuse insulin through a catheter placed under the skin-are a replacement for multiple daily injections of insulin. They are currently being used by 375,000 people with type 1 diabetes, many of whom prefer CSII to multiple daily injections because of the increased flexibility of diet and exercise, increased convenience and precision when dosing, and better predictability of blood glucose levels that insulin pumps can provide when used correctly. Recent pump manufacturers have engineered a new feature called a bolus calculator, which calculates bolus insulin doses based on input from the pump wearer, which functions to help patients obtain optimum control over blood glucose levels. The bolus calculator takes into account the patient's current blood glucose, target blood glucose, amount of carbohydrate consumed, and other factors such as insulin sensitivity and insulin-to-carbohydrate ratio as well as duration of insulin action ("insulin on board"). Each pump company calculates insulin doses in a slightly different way. This article will review differences in bolus calculator recommendations between four insulin pumps, as well as errors that may occur when using bolus calculators. It will also include an in silico simulation of a meal followed by a snack using multiple insulin decay curves.

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.

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%

Background: This study was designed to determine if it was feasible to use a run-to-run algorithm to improve postprandial glucose concentrations in individuals with type 1 diabetes mellitus (T1DM)., Methods: Fourteen subjects were recruited for this 10-week study. During the initial phases of the study, the following information was derived for each subject: basal insulin infusion rates, insulin-to-carbohydrate ratios, insulin correction factors for hyperglycemia, and insulin sensitivities. During the final phases, the algorithm was used to suggest preprandial insulin doses, with a goal of bringing the postprandial glucose into a predetermined target range within 3-7 days., Results: In the single-meal phase (phase 5), 33% of the subject-meal responses were convergent in 3-4 days to a clinically acceptable range, 33% always stayed in range, and 33% had divergent responses, incorrect sensitivities, and/or other mitigating circumstances. In the three-meal phase (phase 6), 41% of the subject-meal responses were convergent in 3-4 days to a clinically acceptable range, 26% were always in range, and 33% had divergent responses, incorrect sensitivities, and/or other mitigating circumstances., Conclusions: Overall, we were able to safely demonstrate that run-to-run control can be used to manage meal-related insulin in subjects with T1DM.

1,5-Anhydroglucitol (1,5-AG), the 1-deoxy form of glucose, has been measured and used clinically in Japan for over a decade to monitor short-term glycemic control. Evaluation of glucose control otherwise requires measuring plasma glucose or glycated proteins whose levels reflect average glucose concentration over the half-life of the protein analyzed. Hemoglobin A1c measurements reflect blood glucose levels over that past 2-3 months, while fructosamine can be used to evaluate glycemic control over 10-14 days. In contrast, 1,5-AG levels in blood respond within 24 h as a result of glucose's competitive inhibition of 1,5-AG reabsorption in the kidney tubule. When glucose levels rise, even transiently, urinary loss of 1,5-AG occurs, and circulating levels fall. Because of changes in renal hemodynamics in normal pregnancies, 1,5-AG appears of limited usefulness in evaluation of gestational diabetes. However, the characteristics of 1,5-AG levels in patients with moderate to near-normal glycemic control suggest that it may be a valuable complement to frequent self-monitoring or continuous monitoring of plasma glucose to confirm stable glycemic control. Measurements performed daily or weekly in a given patient would suggest that overall glycemic control has been stable or improved if 1,5-AG levels are stable or increasing. If 1,5-AG levels fall, greater attention to glucose monitoring and both lifestyle and medical management could be prescribed to correct the glycemic excursions that would underlie such changes. The behavior of this analyte is different from all others used in the management of diabetes, creating potential opportunities for its use in clinical practice.