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Background: Real-time continuous glucose monitoring (CGM) was studied in 140 adults with diabetes over a 12-week period of home use. Hemoglobin A(1c)(HbA1c) was measured on day 1 (baseline) and at weeks 6 and 12. Methods: On day 1, participants received the CGM device (STS(R) System, DexCom, Inc., San Diego, CA) and underwent training on proper use. Insertion of the first sensor was performed under staff supervision. Subjects inserted subsequent sensors on their own. After calibration, the device (a 3-day sensor, receiver, and transmitter) provided users with real-time glucose values updated at 5-min intervals, glucose trend graphs, configurable high/low alerts, and a hypoglycemia alarm (9.0%. Increased CGM use was associated with greater reductions in HbA1c. Conclusions: This observational study showed that home use of real-time GCM was safe and well tolerated and associated with a clinically and statistically significant reduction in HbA1c. Large-scale randomized, controlled outcome studies of CGM are indicated. [ABSTRACT FROM AUTHOR]

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m 2 ; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h-men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70-140 mg/dL). Average glycemia was also lower for females during exercise and sleep events ( p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Background: Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to post-acute sequelae of SARS-CoV-2 (PASC) that can persist for weeks to years following initial viral infection. Clinical manifestations of PASC are heterogeneous and often involve multiple organs. While many hypotheses have been made on the mechanisms of PASC and its associated symptoms, the acute biological drivers of PASC are still unknown., Methods: We enrolled 494 patients with COVID-19 at their initial presentation to a hospital or clinic and followed them longitudinally to determine their development of PASC. From 341 patients, we conducted multi-omic profiling on peripheral blood samples collected shortly after study enrollment to investigate early immune signatures associated with the development of PASC., Results: During the first week of COVID-19, we observed a large number of differences in the immune profile of individuals who were hospitalized for COVID-19 compared to those individuals with COVID-19 who were not hospitalized. Differences between individuals who did or did not later develop PASC were, in comparison, more limited, but included significant differences in autoantibodies and in epigenetic and transcriptional signatures in double-negative 1 B cells, in particular., Conclusions: We found that early immune indicators of incident PASC were nuanced, with significant molecular signals manifesting predominantly in double-negative B cells, compared with the robust differences associated with hospitalization during acute COVID-19. The emerging acute differences in B cell phenotypes, especially in double-negative 1 B cells, in PASC patients highlight a potentially important role of these cells in the development of PASC., Competing Interests: JL, MW, PK, CC, KD, GT, and CK maintain equity ownership and employment at Verily Life Sciences. NH, JA, CW, CP-K, and IM were employed at Oncimmune Limited. SP reports personal fees from Jazz Pharmaceuticals, Inc., and UpToDate, Inc., and grants from Philips, Inc., Sommetrics, Inc., and Regeneron. CD serves on advisory boards for Abbott Diagnostics, Ortho/Quidel Diagnostics, and Roche Diagnostics. JK receives research funding from Regeneron. JK has also provided consulting for GlaxoSmithKline, AstraZeneca, CereVu Medical, Propeller/ResMed, and BData, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Leung, Wu, Kheradpour, Chen, Drake, Tong, Ridaura, Zisser, Conrad, Hudson, Allen, Welberry, Parsy-Kowalska, Macdonald, Tapson, Moy, deFilippi, Rosas, Basit, Krishnan, Parthasarathy, Prabhakar, Salvatore and Kim.)

Using a large database of continuous glucose monitoring (CGM) data, this study aimed to gain insights into the association between pre-exercise food ingestion timing and reactive hypoglycemia. A group of 6,761 users self-reported 48,799 pre-exercise food ingestion events and logged minute-by-minute CGM, which was used to detect reactive hypoglycemia (<70 mg/dL) in the first 30 min of exercise. A linear and a non-linear binomial logistic regression model was used to investigate the association between food ingestion timing and the probability of experiencing reactive hypoglycemia. An analysis of variance was conducted to compare the predictive ability of the models. On average, reactive hypoglycemia was detected in 8.34 ± 3.04% of the total events, with <15% of individuals experiencing hypoglycemia in >20% of their events. The majority of the reactive hypoglycemia events were found with pre-exercise food timing between ∼30 and ∼90 min, with a peak at ∼60 min. The superior accuracy (62.05 vs 45.1%) and F-score (0.75 vs 0.59) of the non-linear vs the linear model were statistically superior ( P  < 0.0001). These results support the notion of an unfavourable 30-to-90 min pre-exercise food ingestion time window which can significantly impact the likelihood of reactive hypoglycemia in some individuals.

Background: The understanding of Post-acute sequelae of SARS-CoV-2 infection (PASC) can be improved by longitudinal assessment of symptoms encompassing the acute illness period. To gain insight into the various disease trajectories of PASC, we assessed symptom evolution and clinical factors associated with the development of PASC over 3 months, starting with the acute illness period., Methods: We conducted a prospective cohort study to identify parameters associated with PASC. We performed cluster and case control analyses of clinical data, including symptomatology collected over 3 months following infection., Results: We identified three phenotypic clusters associated with PASC that could be characterized as remittent, persistent, or incident based on the 3-month change in symptom number compared to study entry: remittent (median; min, max: -4; -17, 3), persistent (-2; -14, 7), or incident (4.5; -5, 17) ( p = 0.041 remittent vs. persistent, p < 0.001 remittent vs. incident, p < 0.001 persistent vs. incident). Despite younger age and lower hospitalization rates, the incident phenotype had a greater number of symptoms (15; 8, 24) and a higher proportion of participants with PASC (63.2%) than the persistent (6; 2, 9 and 52.2%) or remittent clusters (1; 0, 6 and 18.7%). Systemic corticosteroid administration during acute infection was also associated with PASC at 3 months [OR (95% CI): 2.23 (1.14, 4.36)]., Conclusion: An incident disease phenotype characterized by symptoms that were absent during acute illness and the observed association with high dose steroids during acute illness have potential critical implications for preventing PASC., Competing Interests: CC, JL, MW, KD, and CK maintain equity ownership and employment at Verily Life Sciences. SP reports personal fees from Jazz Pharmaceuticals, Inc., and UpToDate, Inc., and grants from Philips, Inc., Sommetrics, Inc., and Regeneron. CRd serves on advisory boards for Abbott Diagnostics, Ortho/Quidel Diagnostics, and Roche Diagnostics. JK receives research funding from Regeneron. JK has also provided consulting for GlaxoSmithKline, AstraZeneca, CereVu Medical, Propeller/ResMed, and BData, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer RP declared a past co-authorship with the author SP to the handling editor., (Copyright © 2023 Chen, Parthasarathy, Leung, Wu, Drake, Ridaura, Zisser, Conrad, Tapson, Moy, deFilippi, Rosas, Prabhakar, Basit, Salvatore, Krishnan and Kim.)

Background: Intraperitoneal insulin delivery has proven to safely overcome a major limit of subcutaneous delivery-meal announcement-and has been able to optimize glycemic control in adults under controlled experimental conditions. In addition, intraperitoneal delivery avoids peripheral hyperinsulinemia resulting from the subcutaneous route and restores a physiological liver gradient., Methods: Relying on a unique data set of intraperitoneal closed-loop insulin delivery obtained with a Model Predictive Controller (MPC), we develop a compartmental model of intraperitoneal insulin kinetics, which, once included in the UVa/Padova T1D simulator, will facilitate the investigation of various control strategies, for example, the simpler Proportional Integral Derivative controller versus MPC., Results: Intraperitoneal insulin kinetics can be described with a 2-compartment model including liver and plasma., Conclusion: Intraperitoneal insulin transit is fast enough to render irrelevant the addition of a peritoneal compartment, proving the peritoneum being a virtual-not actual-transit space for insulin delivery.

Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3±1.5% vs. 90.1±5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems., Competing Interests: Competing Financial Interests PL is CEO of PhysioLogic Devices, Inc. BS and DC are consultants for PhysioLogic Devices, Inc. Other authors declare no competing financial interests.

Background: The objective of this study was to identify the minimum basal insulin infusion rates and bolus insulin doses that would result in clinically relevant changes in blood glucose levels in the most insulin sensitive subjects with type 1 diabetes., Methods: The UVA/PADOVA Type 1 Diabetes Simulator in silico population of children, adolescents, and adults was administered a basal insulin infusion rate to maintain blood glucose concentrations at 120 mg/dL (6.7 mmol/L). Two scenarios were modeled independently after 1 hour of simulated time: (1) basal insulin infusion rates in increments of 0.01 U/h were administered and (2) bolus doses in increments of 0.01 U were injected. Subjects were observed for 4 hours to determine insulin delivery required to change blood glucose by 12.5 mg/dL (0.7 mmol/L) and 25 mg/dL (1.4 mmol/L) in only 5% of the in silico population., Results: The basal insulin infusion rates required to change blood glucose by 12.5 mg/dL and 25 mg/dL in 5% of children, adolescents, and adults were 0.03, 0.11, and 0.10 U/h and 0.06, 0.21, and 0.19 U/h, respectively. The bolus insulin doses required to change blood glucose by the target amounts in the respective populations were 0.10, 0.28, and 0.30 U and 0.19, 0.55, and 0.60 U., Conclusions: In silico modeling suggests that only a very small percentage of individuals with type 1 diabetes, corresponding to children with high insulin sensitivity and low body weight, will exhibit a clinically relevant change in blood glucose with very low basal insulin rate changes or bolus doses.

Aims: To compare intraperitoneal (IP) to subcutaneous (SC) insulin delivery in an artificial pancreas (AP)., Research Design and Methods: Ten adults with type 1 diabetes participated in a non-randomized, non-blinded sequential AP study using the same SC glucose sensing and Zone Model Predictive Control (ZMPC) algorithm adjusted for insulin clearance. On first admission, subjects underwent closed-loop control with SC delivery of a fast-acting insulin analogue for 24 hours. Following implantation of a DiaPort IP insulin delivery system, the identical 24-hour trial was performed with IP regular insulin delivery. The clinical protocol included 3 unannounced meals with 70, 40 and 70 g carbohydrate, respectively. Primary endpoint was time spent with blood glucose (BG) in the range of 80 to 140 mg/dL (4.4-7.7 mmol/L)., Results: Percent of time spent within the 80 to 140 mg/dL range was significantly higher for IP delivery than for SC delivery: 39.8 ± 7.6 vs 25.6 ± 13.1 ( P = .03). Mean BG (mg/dL) and percent of time spent within the broader 70 to 180 mg/dL range were also significantly better for IP insulin: 151.0 ± 11.0 vs 190.0 ± 31.0 ( P = .004) and 65.7 ± 9.2 vs 43.9 ± 14.7 ( P = .001), respectively. Superiority of glucose control with IP insulin came from the reduced time spent in hyperglycaemia (>180 mg/dL: 32.4 ± 8.9 vs 53.5 ± 17.4, P = .014; >250 mg/dL: 5.9 ± 5.6 vs 23.0 ± 11.3, P = .0004). Higher daily doses of insulin (IU) were delivered with the IP route (43.7 ± 0.1 vs 32.3 ± 0.1, P < .001) with no increased percent time spent <70 mg/dL (IP: 2.5 ± 2.9 vs SC: 4.1 ± 5.3, P = .42)., Conclusions: Glycaemic regulation with fully-automated AP delivering IP insulin was superior to that with SC insulin delivery. This pilot study provides proof-of-concept for an AP system combining a ZMPC algorithm with IP insulin delivery., (© 2017 John Wiley & Sons Ltd.)

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., (© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Background: The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings., Methods: A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses., Results: Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices., Conclusions: These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: LAGF has been a consultant for, received research funding/support from, and/or served on advisory boards for Abbot Laboratories, AstraZeneca plc, Dexcom Inc, Johnson & Johnson Services Inc, and/or Merck & Co Inc. BK, SAB, and CC have received research support/funding from Dexcom Inc and Roche Diagnostics. BK and SP report patents/patent applications related to glycemic control in diabetes managed by the University of Virginia Licensing and Ventures group and holding shares in TypeZero Technologies. ED and FJD report grants from JDRF and NIH during conduct of the study; algorithms licensing by Animas Corporation and mod AGC. BK reports additional research support/funding from Sanofi-Aventis, BD, and Tandem Diabetes Care; advisory panel/consulting for AstraZeneca plc, BD, and Inspark Technologies. SAB has received additional research support/funding from LifeScan, Animas, and ConAgra. SP has received research funding/support from Eli Lilly. HCZ is currently an employee of Insulet Corporation., (© 2016 Diabetes Technology Society.)

Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an artificial pancreas (AP). In this work, we outline the design of a fully implantable AP using intraperitoneal (IP) insulin delivery and glucose sensing. The design process utilizes the rapid glucose sensing and insulin action offered by the IP space to tune a PID controller with insulin feedback to provide safe and effective insulin delivery. The controller was tuned to meet robust performance and stability specifications. An anti-reset windup strategy was introduced to prevent dangerous undershoot toward hypoglycemia after a large meal disturbance. The final controller design achieved 78% of time within the tight glycemic range of 80-140 mg/dL, with no time spent in hypoglycemia. The next step is to test this controller design in an animal model to evaluate the in vivo performance.

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.

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.

The paramount goal in the treatment of type 1 diabetes is the maintenance of normoglycemia. Continuous glucose monitoring (CGM) technologies enable frequent sensing of glucose to inform exogenous insulin delivery timing and dosages. The most commonly available CGMs are limited by the physiology of the subcutaneous space in which they reside. The very same advantages of this minimally invasive approach are disadvantages with respect to speed. Because subcutaneous blood flow is sensitive to local fluctuations (e.g., temperature, mechanical pressure), subcutaneous sensing can be slow and variable. We propose the use of a more central, physiologically stable body space for CGM: the intraperitoneal space. We compared the temporal response characteristics of simultaneously placed subcutaneous and intraperitoneal sensors during intravenous glucose tolerance tests in eight swine. Using compartmental modeling based on simultaneous intravenous sensing, blood draws, and intraarterial sensing, we found that intraperitoneal kinetics were more than twice as fast as subcutaneous kinetics (mean time constant of 5.6 min for intraperitoneal vs. 12.4 min for subcutaneous). Combined with the known faster kinetics of intraperitoneal insulin delivery over subcutaneous delivery, our findings suggest that artificial pancreas technologies may be optimized by sensing glucose and delivering insulin in the intraperitoneal space., (© 2014 by the American Diabetes Association.)

Objective: We estimate the effect size of hypoglycemia risk reduction on closed-loop control (CLC) versus open-loop (OL) sensor-augmented insulin pump therapy in supervised outpatient setting., Research Design and Methods: Twenty patients with type 1 diabetes initiated the study at the Universities of Virginia, Padova, and Montpellier and Sansum Diabetes Research Institute; 18 completed the entire protocol. Each patient participated in two 40-h outpatient sessions, CLC versus OL, in randomized order. Sensor (Dexcom G4) and insulin pump (Tandem t:slim) were connected to Diabetes Assistant (DiAs)-a smartphone artificial pancreas platform. The patient operated the system through the DiAs user interface during both CLC and OL; study personnel supervised on site and monitored DiAs remotely. There were no dietary restrictions; 45-min walks in town and restaurant dinners were included in both CLC and OL; alcohol was permitted., Results: The primary outcome-reduction in risk for hypoglycemia as measured by the low blood glucose (BG) index (LGBI)-resulted in an effect size of 0.64, P = 0.003, with a twofold reduction of hypoglycemia requiring carbohydrate treatment: 1.2 vs. 2.4 episodes/session on CLC versus OL (P = 0.02). This was accompanied by a slight decrease in percentage of time in the target range of 3.9-10 mmol/L (66.1 vs. 70.7%) and increase in mean BG (8.9 vs. 8.4 mmol/L; P = 0.04) on CLC versus OL., Conclusions: CLC running on a smartphone (DiAs) in outpatient conditions reduced hypoglycemia and hypoglycemia treatments when compared with sensor-augmented pump therapy. This was accompanied by marginal increase in average glycemia resulting from a possible overemphasis on hypoglycemia safety., (© 2014 by the American Diabetes Association.)

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.

In this two-part Bench to Clinic narrative, recent advances in both the preclinical and clinical aspects of artificial pancreas (AP) development are described. In the preceding Bench narrative, Kudva and colleagues provide an in-depth understanding of the modified glucoregulatory physiology of type 1 diabetes that will help refine future AP algorithms. In the Clinic narrative presented here, we compare and evaluate AP technology to gain further momentum toward outpatient trials and eventual approval for widespread use. We enumerate the design objectives, variables, and challenges involved in AP development, concluding with a discussion of recent clinical advancements. Thanks to the effective integration of engineering and medicine, the dream of automated glucose regulation is nearing reality. Consistent and methodical presentation of results will accelerate this success, allowing head-to-head comparisons that will facilitate adoption of the AP as a standard therapy for type 1 diabetes.

Background: The objective of this research is an artificial pancreas (AP) that performs automatic regulation of blood glucose levels in people with type 1 diabetes mellitus. This article describes a control strategy that performs algorithmic insulin dosing for maintaining safe blood glucose levels over prolonged, overnight periods of time and furthermore was designed with outpatient, multiday deployment in mind. Of particular concern is the prevention of nocturnal hypoglycemia, because during sleep, subjects cannot monitor themselves and may not respond to alarms. An AP intended for prolonged and unsupervised outpatient deployment must strategically reduce the risk of hypoglycemia during times of sleep, without requiring user interaction., Methods: A diurnal insulin delivery strategy based on predictive control methods is proposed. The so-called "periodic-zone model predictive control" (PZMPC) strategy employs periodically time-dependent blood glucose output target zones and furthermore enforces periodically time-dependent insulin input constraints to modulate its behavior based on the time of day., Results: The proposed strategy was evaluated through an extensive simulation-based study and a preliminary clinical trial. Results indicate that the proposed method delivers insulin more conservatively during nighttime than during daytime while maintaining safe blood glucose levels at all times. In clinical trials, the proposed strategy delivered 77% of the amount of insulin delivered by a time-invariant control strategy; specifically, it delivered on average 1.23 U below, compared with 0.31 U above, the nominal basal rate overnight while maintaining comparable, and safe, blood glucose values., Conclusions: The proposed PZMPC algorithm strategically prevents nocturnal hypoglycemia and is considered a significant step toward deploying APs into outpatient environments for extended periods of time in full closed-loop operation., (© 2013 Diabetes Technology Society.)

Objective: To evaluate the feasibility of a wearable artificial pancreas system, the Diabetes Assistant (DiAs), which uses a smart phone as a closed-loop control platform., Research Design and Methods: Twenty patients with type 1 diabetes were enrolled at the Universities of Padova, Montpellier, and Virginia and at Sansum Diabetes Research Institute. Each trial continued for 42 h. The United States studies were conducted entirely in outpatient setting (e.g., hotel or guest house); studies in Italy and France were hybrid hospital-hotel admissions. A continuous glucose monitoring/pump system (Dexcom Seven Plus/Omnipod) was placed on the subject and was connected to DiAs. The patient operated the system via the DiAs user interface in open-loop mode (first 14 h of study), switching to closed-loop for the remaining 28 h. Study personnel monitored remotely via 3G or WiFi connection to DiAs and were available on site for assistance., Results: The total duration of proper system communication functioning was 807.5 h (274 h in open-loop and 533.5 h in closed-loop), which represented 97.7% of the total possible time from admission to discharge. This exceeded the predetermined primary end point of 80% system functionality., Conclusions: This study demonstrated that a contemporary smart phone is capable of running outpatient closed-loop control and introduced a prototype system (DiAs) for further investigation. Following this proof of concept, future steps should include equipping insulin pumps and sensors with wireless capabilities, as well as studies focusing on control efficacy and patient-oriented clinical outcomes.

One of the difficulties in the development of a reliable artificial pancreas for people with type 1 diabetes mellitus (T1DM) is the lack of accurate models of an individual's response to insulin. Most control algorithms proposed to control the glucose level in subjects with T1DM are model-based. Avoiding postprandial hypoglycemia ( 60 mg/dl) while minimizing prandial hyperglycemia ( > 180 mg/dl) has shown to be difficult in a closed-loop setting due to the patient-model mismatch. In this paper, control-relevant models are developed for T1DM, as opposed to models that minimize a prediction error. The parameters of these models are chosen conservatively to minimize the likelihood of hypoglycemia events. To limit the conservatism due to large intersubject variability, the models are personalized using a priori patient characteristics. The models are implemented in a zone model predictive control algorithm. The robustness of these controllers is evaluated in silico, where hypoglycemia is completely avoided even after large meal disturbances. The proposed control approach is simple and the controller can be set up by a physician without the need for control expertise.

Background: Accurate prediction of future glucose concentration for type 1 diabetes mellitus (T1DM) is needed to improve glycemic control and to facilitate proactive management before glucose concentrations reach undesirable concentrations. The availability of frequent glucose measurements, insulin infusion rates, and meal carbohydrate estimates can be used to good advantage to capture important information concerning glucose dynamics., Methods: This article evaluates the feasibility of using a latent variable (LV)-based statistical method to model glucose dynamics and to forecast future glucose concentrations for T1DM applications. The prediction models are developed using a proposed LV-based approach and are evaluated for retrospective clinical data from seven individuals with T1DM and for In silico simulations using the Food and Drug Administration-accepted University of Virginia/University of Padova metabolic simulator. This article provides comparisons of the prediction accuracy of the LV-based method with that of a standard modeling alternative. The influence of key design parameters on the performance of the LV-based method is also illustrated., Results: In general, the LV-based method provided improved prediction accuracy in comparison with conventional autoregressive (AR) models and autoregressive with exogenous input (ARX) models. For larger prediction horizons (≥30 min), the LV-based model with exogenous inputs achieved the best prediction performance based on a paired t-test (α = 0.05)., Conclusions: The LV-based method resulted in models whose glucose prediction accuracy was as least as good as the accuracies of standard AR/ARX models and a simple model-free approach. Furthermore, the new approach is less sensitive to changing conditions and the effect of key design parameters., (© 2012 Diabetes Technology Society.)

Background: Users of continuous glucose monitoring are concerned with product accuracy and choice of insertion site. The Medtronic NexSensor™ was evaluated for accuracy during 6 days of wear when inserted in the abdomen and buttocks areas., Methods: Adults (ages 18-75) with type 1 diabetes wore two sensors simultaneously for 6 days, one each inserted in the abdomen and buttocks. Subjects underwent a frequent blood sampling study for 12 hours, during which time reference blood glucose values were obtained every 15 minutes and compared to sensor values., Results: Sixty-three subjects were enrolled, and 61 subjects completed the study. The mean agreement rate between sensor and blood glucose values was 75.5% [95% confidence interval (CI), 69.5, 81.4] at the abdomen site, 73.8% (95% CI, 68.8, 78.8) at the buttocks site, and 75.6% (95% CI, 70.8, 80.4) when sensor and reference data were combined between sites. Over 90% of paired sensor-reference values on Clarke error grids were within the A and B ranges. The mean absolute relative differences were 17.1% at the abdomen site, 16.5% at the buttocks site, and 16.8% when sites were combined., Conclusion: The NexSensor was accurate for inpatient, frequent-sample testing for 6 days when inserted into the abdomen and buttocks. The results of this study also provide evidence that both the abdomen and buttocks are suitable as sensor insertion sites., (© 2011 Diabetes Technology Society.)

Background: Hypoglycemia and hyperglycemia can pose a number of serious risks to pregnant mothers with diabetes, but these risks are not always related to glucose concentrations directly. Previous studies have shown the utility of using mathematical transformation functions to create patient risk profiles that can then be used to analyze and predict adverse outcomes in individuals with diabetes. We propose a novel use of these functions to analyze the risks posed to the fetus in pregnancies complicated by diabetes., Methods: We retrospectively analyzed 71 h continuous glucose monitoring system (CGMS Gold, Medtronic Northridge, CA) third trimester tracings obtained during a normal pregnancy and in those complicated by gestational diabetes mellitus (GDM), type 2 diabetes mellitus (T2DM), and type 1 diabetes mellitus (T1DM). We then used a transformation function to calculate fetal and maternal risk in each case., Results: In the normal pregnancy (0.93), the risk was at a minimum. Along with mean glucose values, the risk increased in those cases where gestation was complicated by GDM (3.12), T2DM (7.85), and T1DM (16.94). In contrast, the original patient risk profile yielded a minimal value for the GDM tracings., Conclusions: Total fetal risk increases from normal to GDM to T2DM to T1DM pregnancies. This new risk assignment better distinguishes the stages of fetal risk than the original method and therefore may be useful in future clinical trials and applications to predict risk for adverse outcomes in pregnancies complicated by diabetes., (© 2010 Diabetes Technology Society.)

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., (© 2010 Diabetes Technology Society.)

This review of insulin pump therapy focuses on the OmniPod® Insulin Management System (Insulet Corp., Bedford, MA, USA). The OmniPod System is the first commercially available "patch pump." It is a fully integrated wearable pump, controlled wirelessly through a handheld device containing a built-in blood glucose meter. This is an evaluation of the OmniPod System, with the aim of providing an educational tool for physicians who are considering recommending this product to their patients. The review includes a discussion of the traditional insulin pump configuration and its limitations, a detailed overview of the OmniPod System, references to clinical study data, planned product enhancements, its use as an insulin delivery system in the Juvenile Diabetes Research Foundation's Artificial Pancreas Project, and its use to deliver additional compounds.

Background: Development of an artificial pancreas based on an automatic closed-loop algorithm that uses a subcutaneous insulin pump and continuous glucose sensor is a goal for biomedical engineering research. However, closing the loop for the artificial pancreas still presents many challenges, including model identification and design of a control algorithm that will keep the type 1 diabetes mellitus subject in normoglycemia for the longest duration and under maximal safety considerations., Method: An artificial pancreatic beta-cell based on zone model predictive control (zone-MPC) that is tuned automatically has been evaluated on the University of Virginia/University of Padova Food and Drug Administration-accepted metabolic simulator. Zone-MPC is applied when a fixed set point is not defined and the control variable objective can be expressed as a zone. Because euglycemia is usually defined as a range, zone-MPC is a natural control strategy for the artificial pancreatic beta-cell. Clinical data usually include discrete information about insulin delivery and meals, which can be used to generate personalized models. It is argued that mapping clinical insulin administration and meal history through two different second-order transfer functions improves the identification accuracy of these models. Moreover, using mapped insulin as an additional state in zone-MPC enriches information about past control moves, thereby reducing the probability of overdosing. In this study, zone-MPC is tested in three different modes using unannounced and announced meals at their nominal value and with 40% uncertainty. Ten adult in silico subjects were evaluated following a scenario of mixed meals with 75, 75, and 50 grams of carbohydrates (CHOs) consumed at 7 am, 1 pm, and 8 pm, respectively. Zone-MPC results are compared to those of the "optimal" open-loop preadjusted treatment., Results: Zone-MPC succeeds in maintaining glycemic responses closer to euglycemia compared to the "optimal" open-loop treatment in te three different modes with and without meal announcement. In the face of meal uncertainty, announced zone-MPC presented only marginally improved results over unannounced zone-MPC. When considering user error in CHO estimation and the need to interact with the system, unannounced zone-MPC is an appealing alternative., Conclusions: Zone-MPC reduces the variability of control moves over fixed set point control without the need to detune the controller. This strategy gives zone-MPC the ability to act quickly when needed and reduce unnecessary control moves in the euglycemic range., (2010 Diabetes Technology Society.)

Background: The objective was to quantify hydrostatic effects on continuous subcutaneous insulin infusion (CSII) pumps during basal and bolus insulin delivery., Methods: We tested CSII pumps from Medtronic Diabetes (MiniMed 512 and 515), Smiths Medical (Deltec Cozmo 1700), and Insulet (OmniPod) using insulin aspart (Novolog, Novo Nordisk). Pumps were filled and primed per manufacturer's instructions. The fluid level change was measured using an inline graduated glass pipette (100 microl) when the pipette was moved in relation to the pump (80 cm Cosmo and 110 cm Medtronics) and when level. Pumps were compared during 1 and 5 U boluses and basal insulin delivery of 1.0 and 1.5 U/h., Results: Pronounced differences were seen during basal delivery in pumps using 80-100 cm tubing. For the 1 U/h rate, differences ranged from 74.5% of the expected delivery when the pumps were below the pipettes and pumping upward to 123.3% when the pumps were above the pipettes and pumping downward. For the 1.5 U/h rate, differences ranged from 86.7% to 117.0% when the pumps were below or above the pipettes, respectively. Compared to pumps with tubing, OmniPod performed with significantly less variation in insulin delivery., Conclusions: Changing position of a conventional CSII pump in relation to its tubing results in significant changes in insulin delivery. The siphon effect in the tubing may affect the accuracy of insulin delivery, especially during low basal rates. This effect has been reported when syringe pumps were moved in relation to infusion sites but has not been reported with CSII pumps., (2010 Diabetes Technology Society.)

Intensive insulin therapy has an inherent risk of hypoglycemia that can lead to loss of consciousness, cardiac arrhythmia, seizure, and death ("dead-in-bed syndrome"). This risk of hypoglycemia is a major concern for patients, families, and physicians. The need for an automated system that can alert in the event of severe hypoglycemia is evident. In engineering systems, where there is a risk of malfunction of the primary control system, alert and safety mechanisms are implemented in layers of protection. This concept has been adopted in the proposed system that integrates a hypoglycemia prediction algorithm with a global position system (GPS) locator and short message service such that the current glucose value with the rate of change (ROC) and the location of the subject can be communicated to a predefined list. Furthermore, if the system is linked to the insulin pump, it can suspend the pump or decrease the basal insulin infusion rate to prevent the pending event. The system was evaluated on clinical datasets of glucose tracings from the DexCom Seven system. Glucose tracings were analyzed for hypoglycemia events and then a text message was broadcast to a predefined list of people who were notified with the glucose value, ROC, GPS coordinates, and a Google map of the location. In addition to providing a safety layer to a future artificial pancreas, this system also can be easily implemented in current continuous glucose monitors to help provide information and alerts to people with diabetes.

Background: Modern insulin pump therapy for type 1 diabetes mellitus offers the freedom to program several basal profiles that may accommodate diurnal ariability in insulin sensitivity and activity level. However, these basal profiles do not change even if a pending hypoglycemic or hyperglycemic event is foreseen. New insulin pumps could receive a direct feed of glucose values from a continuous glucose monitoring (CGM) system and could enable dynamic basal adaptation to improve glycemic control., Method: The proposed method is a two-step procedure. After the design of an initial basal profile, an adaptation of the basal rate is suggested as a gain multiplier based on the current CGM glucose value and its rate of change (ROC). Taking the glucose value and its ROC as axes, a two-dimensional plane is divided into a nine-zone mosaic, where each zone is given a predefined basal multiplier; for example, a basal multiplier of zero indicates a recommendation to shut off the pump., Results: The proposed therapy was evaluated on 20 in silico subjects (ten adults and ten adolescents) in the Food and Drug Administration-approved UVa/Padova simulator. Compared with conventional basal therapy, the proposed basal adjustment improved the percentage of glucose levels that stayed in the range of 60-180 mg/dl for all 20 subjects. In addition, the adaptive basal therapy reduced the average blood glucose index values., Conclusions: The proposed therapy provides the flexibility to account for insulin sensitivity variations that may result from stress and/or physical activities. Because of its simplicity, the proposed method could be embedded in a chip in a future artificial pancreatic beta cell or used in a "smart" insulin pump., (2009 Diabetes Technology Society.)

Background: The purpose of this study was to compare the accuracy of measurements obtained from the DexCom SEVEN system with Yellow Springs Instrument (YSI) laboratory measurements of venous blood glucose., Methods: Seventy-two subjects with insulin-requiring diabetes, aged 18-71, were enrolled in a multicenter, prospective single-arm study. All participants wore the SEVEN continuous glucose monitoring (CGM) system for one, 7-day wear period. Calibration with capillary finger stick measurements was performed 2 hours after sensor insertion and once every 12 hours thereafter. A subset of subjects (28) wore two systems simultaneously to assess precision. All subjects participated in one, 10-hour in-clinic session on day 1, 4, or 7 of the study to compare CGM measurements against a laboratory method (YSI analyzer) using venous measurements taken once every 20 minutes. Carbohydrate consumption and insulin dosing were adjusted in order to obtain a broad range of glucose values., Results: Comparison of CGM measurements with the laboratory reference method (n = 2318) gave mean and median absolute relative differences (ARDs) of 16.7 and 13.2%, respectively. The percentage was 70.4% in the clinically accurate Clarke error grid A zone and 27.5% in the benign error B zone. Performance of the SEVEN system was consistent over time with mean and median ARD lowest on day 7 as compared to YSI (13.3 and 10.2%, respectively). Average sensor time lag was 5 minutes., Conclusions: Measurements of the DexCom SEVEN system were found to be consistent and accurate compared with venous measurements made using a laboratory reference method over 7 days of wear., (2009 Diabetes Technology Society.)

This issue of Journal of Diabetes Science and Technology contains a collection of 12 original articles describing the latest advances in the development of algorithms for controlling insulin delivery in an artificial pancreas. Algorithms presented in this issue are affected by numerous quantifiable factors, including insulin pharmaco-kinetics, timing of meal carbohydrate appearance, meal size, amount of exercise, presence of stress, day-to-day variations in insulin sensitivity, insulin time-activity profiles, accuracy of glucose monitor calibration, metabolic profiles of both adults and neonates, and risks of hypoglycemia/hyperglycemia. These articles present theoretical advances in insulin delivery algorithms from modeled in silico patients, as well as clinical data from actual patients who have used closed loop systems. The novel approaches described in these articles are expected to bring us much closer to realization of a commercially available closed loop system for controlling glucose levels in patients with diabetes., (2009 Diabetes Technology Society.)

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., (2009 Diabetes Technology Society.)

The effects of diabetes in pregnancy were first noticed in the beginning of the 19(th) century. Today approximately seven percent of all pregnancies in the United States are affected by gestational diabetes. Since becoming more knowledgeable of the disease, the medical community has developed diagnostic criteria for detecting gestational diabetes and has created treatment options for lowering the risk of adverse fetal outcomes. A pregnancy affected by diabetes is associated with macrosomia, fetal malformations, spontaneous preterm delivery, and labor complications. These risks can be minimized by tight glycemic control through diet, insulin, and attentive monitoring of blood glucose levels. Although most pregnant diabetic women currently monitor their diabetes using self-monitoring blood glucose, the technology of continuous glucose monitoring (CGM) offers a myriad of benefits. This mini-review looks at the advantages of using CGM in pregnancy which includes decreasing the risks of poor fetal outcomes, improving a patient's overall glucose profile, helping start or adjust insulin treatment, adjusting current screening protocol and developing a normoglycemic target for gestational diabetic women to aim for during their pregnancy. With the use of CGM, the complications of diabetic pregnancies first seen nearly two centuries ago will become a problem of the past.

Background: Insulin requirements to maintain normoglycemia during glucocorticoid therapy and stress are often difficult to estimate. To simulate insulin resistance during stress, adults with type 1 diabetes mellitus (T1DM) were given a three-day course of prednisone., Methods: Ten patients (7 women, 3 men) using continuous subcutaneous insulin infusion pumps wore the Medtronic Minimed CGMS (Northridge, CA) device. Mean (standard deviation) age was 43.1 (14.9) years, body mass index 23.9 (4.7) kg/m(2), hemoglobin A1c 6.8% (1.2%), and duration of diabetes 18.7 (10.8) years. Each patient wore the CGMS for one baseline day (day 1), followed by three days of self-administered prednisone (60 mg/dl; days 2-4), and one post-prednisone day (day 5)., Results: Analysis using Wilcoxon signed rank test (values are median [25th percentile, 75th percentile]) indicated a significant difference between day 1 and the mean of days on prednisone (days 2-4) for average glucose level (110.0 [81.0, 158.0] mg/dl vs 149.2 [137.7, 168.0] mg/dl; p = .022), area under the glucose curve and above the upper limit of 180 mg/dl per day (0.5 [0, 8.0] mg/dl.d vs 14.0 [7.7, 24.7] mg/dl.d; p = .002), and total daily insulin dose (TDI) , (0.5 [0.4, 0.6] U/kg.d vs 0.9 [0.8, 1.0] U/kg.d; p = .002). In addition, the TDI was significantly different for day 1 vs day 5 (0.5 [0.4, 0.6] U/kg.d vs 0.6 [0.5, 0.8] U/kg.d; p = .002). Basal rates and insulin boluses were increased by an average of 69% (range: 30-100%) six hours after the first prednisone dose and returned to baseline amounts on the evening of day 4., Conclusions: For adults with T1DM, insulin requirements during prednisone induced insulin resistance may need to be increased by 70% or more to normalize blood glucose levels.

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.

Polycystic ovary syndrome (PCOS) usually arises during puberty and is marked by hyperinsulinemia and hyperandrogenism. Adolescents with PCOS are at an increased risk of developing health problems later on in life such as type 2 diabetes, cardiovascular disease, and infertility. Furthermore, the physical signs of PCOS can be detrimental to a teenage girl's self-image. Early diagnosis and treatment of PCOS in adolescents are essential in ensuring adulthood health and restoring self-esteem. Treatments for an adolescent with PCOS include diet and exercise, metformin, and oral contraceptive pills. Each of these options has been shown to be effective in improving certain aspects of PCOS, and probably the best treatment plan involves some combination of them.