Pharmacokinetics and Postprandial Glycemic Excursions following Insulin Lispro Delivered by Intradermal Microneedle or Subcutaneous Infusion

Background

Intradermal (ID) delivery has been shown to accelerate insulin pharmacokinetics (PK). We compared the PK and pharmacodynamic (PD) effects of insulin lispro administered before two daily standardized solid mixed meals (breakfast and lunch), using microneedle-based ID or traditional subcutaneous (SC) delivery.

Method

The study included 22 subjects with type 1 diabetes in an eight-arm full crossover block design. One arm established each subject’s optimal meal dose. In six additional arms, the optimal, higher, and lower doses (+30%, -30%) were each given ID and SC delivery, in random order. The final arm assessed earlier timing for the ID optimal dose (-12 versus -2 min). The PK/PD data were collected for 6 h following meals. Intravenous basal regular insulin was given throughout, and premeal blood glucose (BG) adjusted to 115 mg/dl.

Results

The primary end point, postprandial time in range (70–180 mg/dl), showed no route-based differences with a high level of overall BG control for both SC and ID delivery. Secondary insulin PK end points showed more rapid ID availability versus SC across doses and meals (∆Tmax -16 min, ∆T50rising -7 min, ∆T50falling -30 min, all p < .05). Both intrasubject and intersubject variability for ID Tmax were significantly lower. Intradermal delivery showed modest, statistically significant secondary PD differences across doses and meals, generally within 90–120 min postprandially (∆12 mg/dl BG at 90 min, ∆7 mg/dl BGmax, ∆7 mg/dl mean BG 0–2 h, all p < .05).

Conclusions

This study indicates that ID insulin delivery is superior to SC delivery in speed of systemic availability and PK consistency and may improve postprandial glucose control.     

Reduction of postprandial glycemic excursions in patients with type 1 diabetes: a novel human insulin formulation versus a rapid-acting insulin analog and regular human insulin

Background:

Evaluation of postprandial glycemic excursions in patients with type 1 diabetes with three prandial insulins: VIAject™ (Linjeta™), an ultra-fast insulin (UFI); insulin lispro (LIS); and regular human insulin (RHI).

Methods:

After stabilization of preprandial glycemia, 18 patients received a subcutaneous injection with an individualized insulin dose prior to a meal.

Results:

Injection of UFI resulted in a more rapid insulin absorption than with either LIS or RHI (time to half-maximal insulin levels: 13.1 ± 5.2 vs 25.4 ± 7.6 and 38.4 ± 19.5 min; p = .001 vs LIS and p < .001 vs RHI, LIS vs. RHI p < .001). Maximal postprandial glycemia was lower with UFI (0–180 min; 157 ± 30 mg/dl; p = .002 vs RHI) and LIS (170 ± 42 mg/dl; p = .668 vs RHI) than after RHI (191 ± 46 mg/dl; RHI vs LIS p = .008). The difference between maximum and minimum glycemia was smaller with UFI (70 ± 17 mg/dl) than with either RHI (91 ± 33 mg/dl; p = .007 vs UFI) or LIS (89 ± 18 mg/dl; p = .011 vs UFI). Also, the area under the blood glucose profile was lower with UFI than with RHI (0–180 min; 21.8 ± 5.8 vs 28.4 ± 7.6 g·min/dl; p < .001).

Conclusions:

The rapid absorption of UFI results in a reduction of postprandial glycemic excursions.     

Use of Continuous Glucose Monitoring to Estimate Insulin Requirements in Patients with Type 1 Diabetes Mellitus During a Short Course of Prednisone

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/m², 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.     

Effects of a structured self-monitoring of blood glucose method on patient self-management behavior and metabolic outcomes in type 2 diabetes mellitus

Background

The purpose of this study was to evaluate the effect of structured self-monitoring of blood glucose (SMBG) on patient self-management behavior and metabolic outcomes in patients with type 2 diabetes mellitus (T2DM).

Methods

From January to June 2009, 30 patients with basic diabetes education were followed for a period of 90 days. To provide assessment of glycemic control and frequency of dysglycemia, patients, underwent 3 consecutive days of seven-point SMBG during each month for 3 consecutive months, using the ACCU-CHEK 360° View tool. Glucose profiles of the first and third month were used for comparison.

Results

Hemoglobin A1c (HbA1c) improved significantly during the 90-day period in all patients [confidence interval (CI) 95%, 0.32–1.64%, p < .05] and those with poor metabolic control (group B; CI 95%, 0.86–2.64%, p < .05). Mean blood glucose (MBG) values decreased significantly in group B (CI 95%, 0.56–24.78 mg/dl, p < .05) and all cases (CI 95%, 1.61–19.73 mg/dl, p < .05). Meanwhile, there was an average decrease of 15.7 mg/dl in fasting blood sugar (FBS) levels in the whole subjects. Mean postprandial blood glucose levels (MPP) decreased by 19.3 and 11.3 mg/dl in group B and in all cases, respectively. However, there were no significant changes in HbA1c, MBG, FBS, and MPP in people with good metabolic control.

Conclusion

A structured SMBG program improves HbA1c, FBS, MPP, and MBG in people with poorly controlled diabetes. This improvement shows the importance of patient self-management behavior on metabolic outcomes in T2DM.     

Alarms for Continuous Glucose Monitors: Methods of Evaluating the Utility of Continuous Glucose Monitor Alerts

Background

The evaluation of continuous glucose monitor (CGM) alert performance should reflect patient use in real time. By evaluating alerts as real-time events, their ability to both detect and predict low and high blood glucose (BG) events can be examined.

Method

True alerts (TA) were defined as a CGM alert occurring within ± 30 minutes from the beginning of a low or a high BG event. The TA time to detection was calculated as [time of CGM alert] – [beginning of event]. False alerts (FA) were defined as a BG event outside of the alert zone within ± 30 minutes from a CGM alert. Analysis was performed comparing DexCom™ SEVEN^® PLUS CGM data to BG measured with a laboratory analyzer.

Results

Of 49 low glucose events (BG ≤70 mg/dl), with the CGM alert set to 90 mg/dl, the TA rate was 91.8%. For 50% of TAs, the CGM alert preceded the event by at least 21 minutes. The FA rate was 25.0%. Similar results were found for high alerts.

Conclusion

Continuous glucose monitor alerts are capable of both detecting and predicting low and high BG events. The setting of alerts entails a trade-off between predictive ability and FA rate. Realistic analysis of this trade-off will guide patients in the effective utilization of CGM.     

Determinants of the accuracy of continuous glucose monitoring in non-critically ill patients with heart failure or severe hyperglycemia

Background

The accuracy of continuous glucose monitoring (CGM) in non-critically ill hospitalized patients with heart failure or severe hyperglycemia (SH) is unknown.

Methods

Hospitalized patients with congestive heart failure (CHF) exacerbation (receiving IV or subcutaneous insulin) or SH requiring insulin infusion were compared to outpatients referred for retrospective CGM.

Results

Forty-three patients with CHF, 15 patients with SH, and 88 outpatients yielded 470, 164, and 2150 meter–sensor pairs, respectively. Admission glucose differed (188 versus 509 mg/dl in CHF and SH, p < .001) but not the first sensor glucose (p = .35). In continuous glucose error grid analysis, 67–78% of pairs during hypoglycemia were in zones A+B (p = .63), compared with 98–100% in euglycemia (p < .001) and 98%, 92%, and 99% (p = .001) during hyperglycemia for the CHF, SH, and outpatient groups, respectively. Mean absolute relative difference (MARD) was lower in the CHF versus the SH group in glucose strata above 100 mg/dl, but there was no difference between the CHF and outpatient groups. Linear regression models showed that CHF versus outpatient, SH versus CHF, and coefficient of variation were significant predictors of higher MARD. Among subjects with CHF, MARD was not associated with brain natriuretic peptide or change in plasma volume, but it was significantly higher in subjects randomized to IV insulin (p = .04).

Conclusions

The results suggest that SH and glycemic variability are more important determinants of CGM accuracy than known CHF status alone in hospitalized patients.     

The identifiable virtual patient model: comparison of simulation and clinical closed-loop study results

Background

Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be obtained from a clinical study evaluating the same modification. We evaluated the ability of a low-order identifiable virtual patient (IVP) model to achieve this goal.

Methods

Ten adult subjects (42.5 ± 11.5 years of age; 18.0 ± 13.5 years diabetes; 6.9 ± 0.8% hemoglobin A1c) previously characterized with the IVP model were studied following the procedures independently reported in a pediatric study assessing proportional–integral–derivative control with and without a 50% meal insulin bolus. Peak postprandial glucose levels with and without the meal bolus and use of supplemental carbohydrate to treat hypoglycemia were compared using two-way analysis of variance and chi-square tests, respectively.

Results

The meal bolus decreased the peak postprandial glucose levels in both the adult-simulation and pediatric-clinical study (231 ± 38 standard deviation to 205 ± 33 mg/dl and 226 ± 51 to 194 ± 47 mg/dl, respectively; p = .0472). No differences were observed between the peak postprandial levels obtained in the two studies (clinical and simulation study not different, p = .57; interaction p = .83) or in the use of supplemental carbohydrate (3 occurrences in 17 patient days of closed-loop control in the clinical-pediatric study; 7 occurrences over 20 patient days in the adult-simulation study, p = .29).

Conclusions

Closed-loop simulations using an IVP model can predict clinical study outcomes in patients studied independently from those used to develop the model.     

Continuous Glucose Profiles in Healthy Subjects under Everyday Life Conditions and after Different Meals

Background

This study investigated continuous glucose profiles in nondiabetic subjects.

Methods

Continuous interstitial glucose measurement was performed under everyday life conditions (2 days) and after ingestion of four meals with standardized carbohydrate content (50 grams), but with different types of carbohydrates and variable protein and fat content. Twenty-four healthy volunteers (12 female, 12 male, age 27.1 ± 3.6 years) participated in the study. Each subject wore two microdialysis devices (SCGM1, Roche Diagnostics) simultaneously.

Results

The mean 24-hour interstitial glucose concentration under everyday life conditions was 89.3 ± 6.2 mg/dl (mean ± SD, n = 21), and mean interstitial glucose concentrations at daytime and during the night were 93.0 ± 7.0 and 81.8 ± 6.3 mg/dl, respectively. The highest postprandial glucose concentrations were observed after breakfast: 132.3 ± 16.7 mg/dl (range 101–168 mg/dl); peak concentrations after lunch and dinner were 118.2 ± 13.4 and 123.0 ± 16.9 mg/dl, respectively. Mean time to peak glucose concentration was between 46 and 50 minutes. After ingestion of standardized meals with fast absorption characteristics, peak interstitial glucose concentrations were 133.2 ± 14.4 and 137.2 ± 21.1 mg/dl, respectively. Meals with a higher fiber, protein, and fat content induced a smaller increase and a slower decrease of postprandial glucose concentrations with peak values of 99.2 ± 10.5 and 122.1 ± 20.4 mg/dl, respectively.

Conclusions

This study provided continuous glucose profiles in nondiabetic subjects and demonstrated that differences in meal composition are reflected in postprandial interstitial glucose concentrations. Regarding the increasing application of continuous glucose monitoring in diabetic patients, these data suggest that detailed information about the ingested meals is important for adequate interpretation of postprandial glucose profiles.     

Interindividual and intraindividual variations in postprandial glycemia peak time complicate precise recommendations for self-monitoring of glucose in persons with type 1 diabetes mellitus

Background

In glycemic control, postprandial glycemia may be important to monitor and optimize as it reveals glycemic control quality, and postprandial hyperglycemia partly predicts late diabetic complications. Self-monitoring of blood glucose (SMBG) may be an appropriate technology to use, but recommendations on measurement time are crucial.

Method

We retrospectively analyzed interindividual and intraindividual variations in postprandial glycemic peak time. Continuous glucose monitoring (CGM) and carbohydrate intake were collected in 22 patients with type 1 diabetes mellitus. Meals were identified from carbohydrate intake data. For each meal, peak time was identified as time from meal to CGM zenith within 40–150 min after meal start. Interindividual (one-way Anova) and intraindividual (intraclass correlation coefficient) variation was calculated.

Results

Nineteen patients were included with sufficient meal data quality. Mean peak time was 87 ± 29 min. Mean peak time differed significantly between patients (p = 0.02). Intraclass correlation coefficient was 0.29.

Conclusions

Significant interindividual and intraindividual variations exist in postprandial glycemia peak time, thus hindering simple and general advice regarding postprandial SMBG for detection of maximum values.     

Prediction of the risk to develop diabetes-related late complications by means of the glucose pentagon model: analysis of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring study

Background

By taking parameters into account that describe the variability of continuously monitored glucose and long-term metabolic control [hemoglobin A1c (HbA1c)], the glucose pentagon model (GPM) allows characterization of the glucose profile of individual patients with diabetes in a graphical format. A glycemic risk parameter (GRP) derived from this model might allow a better prognosis of the risk to develop diabetes-related complications than the HbA1c.

Methods

To evaluate this hypothesis, we analyzed a subset of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring (CGM) study. The values of the different parameters that are integrated in the GPM were extracted automatically from CGM profiles registered before and after 6 months by means of the Medtronic CGM system in 108 patients.

Results

In these patients, the significant reduction in HbA1c from 7.4% to 7.0% was accompanied by a reduction in glycemia from 164 to 156 mg/dl, standard deviation from 61 to 57 mg/dl, area under the curve >160 mg/dl 29.2 to 23.1, and time per day >160 mg/dl 634 to 576 min. This led to a subsequent reduction in GRP from 3.3 to 2.7; this decrease by 18.2% was significantly larger than that in HbA1c by 8.6% (p < .001). Changes in individual GPMs/GRPs support this observation. They also show the impact of high glycemic variability on GPM/GRP.

Conclusions

Our analysis of data of a study with a considerable sample size and study duration showed that the GPM is not only helpful for rapid assessment of individual glycemic profiles and how therapeutic interventions influence these, but also appears to provide a better prognosis of the risk to develop late complications than the HbA1c per se. However, it is also clear that a true validation of such a model requires performance of a long-term study in a large number of patients with diabetes.     

Alarms for Continuous Glucose Monitors: Alarm Characterization for Continuous Glucose Monitors Used as Adjuncts to Self-Monitoring of Blood Glucose

Background

Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG). Alarm evaluation in the Clinical and Laboratory Standards Institute (CLSI) guideline for CGM does not specifically address devices that employ both CGM and SMBG. In this report, an alarm evaluation method is proposed for these devices.

Method

The proposed method builds on the CLSI method using data from an in-clinic study of subjects with type 1 diabetes. CGM was used to detect glycemic events, and SMBG was used to determine treatment. To optimize detection of a single glucose level, such as 70 mg/dl, a range of alarm threshold settings was evaluated. The alarm characterization provides a choice of alarm settings that trade off detection and false alarms. Detection of a range of high glucose levels was similarly evaluated.

Results

Using low glucose alarms, detection of 70 mg/dl within 30 minutes increased from 64 to 97% as alarm settings increased from 70 to100 mg/dl, and alarms that did not require treatment (SMBG >85 mg/dl) increased from 18 to 52%. Using high glucose alarms, detection of 180 mg/dl within 30 minutes increased from 87 to 96% as alarm settings decreased from 180 to 165 mg/dl, and alarms that did not require treatment (SMBG <180 mg/dl) increased from 24 to 42%.

Conclusion

The proposed alarm evaluation method provides information for choosing appropriate alarm thresholds and reflects the clinical utility of CGM alarms.     

A 16-week open-label, multicenter pilot study assessing insulin pump therapy in patients with type 2 diabetes suboptimally controlled with multiple daily injections

Background

We assessed the efficacy, safety, and patient-reported outcomes (PROs) of insulin pump therapy in patients with type 2 diabetes mellitus (T2DM) who were suboptimally controlled with a multiple daily injection (MDI) regimen.

Methods

In this subanalysis of a 16-week multicenter study, 21 insulin-pump-naïve patients [age 57 ± 13 years, hemoglobin A1c (A1C) 8.4 ± 1.0%, body weight 98 ± 20 kg, total daily insulin dose 99 ± 65 U, mean ± standard deviation] treated at baseline with MDI therapy with or without oral antidiabetic agents discontinued all diabetes medications except metformin and initiated insulin pump therapy. Insulin was titrated to achieve the best possible glycemic control with the simplest possible dosing regimen. Outcome measures included A1C, fasting and postprandial glucose, body weight, incidence of hypoglycemia, and PROs.

Results

Glycemic control improved significantly after 16 weeks: A1C 7.3 ± 1.0% (−1.1 ± 1.2%, p < .001), fasting glucose 133 ± 33mg/dl (−32 ± 74 mg/dl, p < .005), and postprandial glucose 153 ± 35 mg/dl (−38 ± 46 mg/dl, p < .001). At week 16, the mean daily basal, bolus, and total insulin doses were 66 ± 36, 56 ± 40, and 122 ± 72 U (1.2 U/kg), respectively, and 90% of patients were treated with two or fewer daily basal rates. Body weight increased by 2.8 ± 2.6 kg (p < .001). Mild hypoglycemia was experienced by 81% of patients at least once during the course of the study with no episodes of severe hypoglycemia. There were significant improvements in PRO measures.

Conclusions

Insulin pump therapy using a relatively simple dosing regimen safely improved glucose control and PROs in patients with T2DM who were unable to achieve glycemic targets with MDI therapy. Controlled trials are needed to further assess the clinical benefits and cost-effectiveness of insulin pumps in this patient population.     

Artificial Pancreas Systems: Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience

Background

Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented.

Methods

Eight adults with type 1 diabetes were studied twice, once using their personal open-loop systems to control BG overnight and for 4 h following a standardized meal and once using a closed-loop system that utilizes the MPC algorithm to control BG overnight and for 4 h following a standardized meal. Average BG levels, percentage of time within BG target of 70–140 mg/dl, number of hypoglycemia episodes, and postprandial BG excursions during both study periods were compared.

Results

With closed-loop control, once BG levels achieved the target range (70–140 mg/dl), they remained within that range throughout the night in seven of the eight subjects. One subject developed a BG level of 65 mg/dl, which was signaled by the CGM trend analysis, and the MPC algorithm directed the discontinuance of the insulin infusion. The number of overnight hypoglycemic events was significantly reduced (p = .011) with closed-loop control. Postprandial BG excursions were similar during closed-loop and open-loop control

Conclusion

Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This “artificial pancreas” controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.     

Precision, accuracy, and user acceptance of the OneTouch SelectSimple blood glucose monitoring system

Background

The OneTouch® SelectSimple™ blood glucose monitoring system (BGMS) is a device for self-monitoring of blood glucose designed for ease of use. Alarms alert subjects to low [20–69 mg/dl (1.1–3.8 mmol/liter)], high [180–239 mg/dl (9.9–13.2 mmol/liter)], and very high [240–600 mg/dl (13.3–33.1 mmol/liter)] blood glucose readings.

Methods

Repeatability in blood and intermediate precision with aqueous controls were examined using blood from one donor adjusted to different glucose concentrations, and tested with 10 meters and 1 test-strip lot. System accuracy was evaluated with blood samples from 100 diabetes patients tested on 3 test-strip lots, compared with a reference system (YSI 2300 STAT). To test user accuracy, patients (n = 156) and health care professionals (HCPs) tested subject blood with the SelectSimple twice. Health care professionals evaluated subject BGMS technique after a 3–5 day home-testing period. Users evaluated the instructions for use and responded to a user acceptance questionnaire.

Results

In repeatability and intermediate precision testing, the SelectSimple BGMS had a coefficient of variation of ≤5% or standard deviation of ≤5 mg/dl. In the clinical accuracy study, 100% of measurements <75 mg/dl (4.2 mmol/liter) were within ±15 mg/dl (0.8 mmol/liter) of reference value, and 99.6% of measurements ≥75 mg/dl (4.2 mmol/liter) were within ±20%. Patients were able to use the BGMS appropriately and evaluated it as easy to use. Acceptance of the SelectSimple BGMS was within predefined limits.

Conclusions

In these studies, the SelectSimple BGMS met all criteria for precision, system, and user accuracy, was easy to use, and was well accepted by patients.     

Clinical performance of a device that applies local heat to the insulin infusion site: a crossover study

Background

Fast-acting insulin analogs have been available since 1996. The absorption rate of these insulins is still too slow to mimic the physiological insulin action in healthy subjects. This study investigates the clinical performance of InsuPatch™, a local skin-heating device, on postprandial glucose excursion.

Methods

Twenty-four type 1 diabetes mellitus subjects on continuous subcutaneous insulin infusion were included in this crossover study [10 male, 14 female, age: 43.5 ± 11.3 years, diabetes duration: 18.3 ± 10.5 years, glycosylated hemoglobin: 7.4 ± 0.8%, body mass index: 25.0 ± 3.0 kg/m² (mean ± standard deviation)]. The impact of local skin heating was measured by dividing the two-hour area under the curve by integration time (AUC/t₁₂₀) for blood glucose (BG) above baseline after two standardized breakfast and dinner meal pairs (with and without heating) per subject. For the first breakfast pair, venous insulin concentration was also measured.

Results

A significant reduction was found for the AUC/t₁₂₀ after breakfast and after dinner meals (42 breakfast meal pairs, AUC/t₁₂₀ not heated 66.4 ± 32.8 mg/dl vs heated 56.8 ± 34.0 mg/dl, p = .017; 38 dinner meal pairs, AUC/t₁₂₀ not heated 30.8 ± 31.0 mg/dl vs heated 18.4 ± 23.9 mg/dl, p = .0028). The maximum venous insulin concentration with heating was 27% higher than without heating (n = 23). The number of hypoglycemic events on days with heating (n = 9) was similar to the number of days without heating (n = 13).

Conclusions

Local heating of the skin around the infusion site significantly reduced postprandial BG by enhancing insulin absorption. The heating device was well tolerated, and it could facilitate development of closed-loop systems.     

Effect of diabetes mellitus on outcomes of hyperglycemia in a mixed medical surgical intensive care unit

Background:

Intensive insulin therapy and degree of glycemic control in critically ill patients remains controversial, particularly in patients with diabetes mellitus. We hypothesized that diabetic patients who achieved tight glucose control with continuous insulin therapy would have less morbidity and lower mortality than diabetic patients with uncontrolled blood glucose.

Method:

A retrospective chart review was performed on 395 intensive care unit (ICU) patients that included 235 diabetic patients. All patients received an intravenous insulin protocol targeted to a blood glucose (BG) level of 80–140mg/dl. Outcomes were compared between (a) nondiabetic and diabetic patients, (b) diabetic patients with controlled BG levels (80–140mg/dl) versus uncontrolled levels (>140 mg/dl), and (c) diabetic survivors and nonsurvivors.

Results:

Diabetic patients had a shorter ICU stay compared to nondiabetic patients (10 ± 0.7 vs 13 ± 1.1, p = .01). The mean BG of the diabetic patients was 25% higher on average in the uncontrolled group than in the controlled (166 ± 26 vs 130 ± 9.4 mg/dl, p < .01). There was no difference in ICU and hospital length of stay (LOS) between diabetic patients who were well controlled compared to those who were uncontrolled. Diabetic nonsurvivors had a significantly higher incidence of hypoglycemia (BG <60 mg/dl) compared to diabetic survivors.

Conclusion:

The results showed that a diagnosis of diabetes was not an independent predictor of mortality, and that diabetic patients who were uncontrolled did not have worse outcomes. Diabetic nonsurvivors were associated with a greater amount of hypoglycemic episodes, suggesting these patients may benefit from a more lenient blood glucose protocol.     

An Evaluation of “I,Pancreas” Algorithm Performance In Silico

Background

The objective of this study was to investigate the performance of a newly proposed insulin titrating algorithm to achieve tight glycemic control in the critically ill.

Methods

A simulation environment with 10 critically ill virtual subjects was employed to evaluate the “I, Pancreas” algorithm proposed by Braithwaite et al. and described in an article in this issue of Journal of Diabetes Science and Technology. The algorithm was coded in MATLAB^® and was “plugged in” to a simulation environment to provide glucose control in a 48-hour-long simulated study.

Results

Mean blood glucose was 6.5 ± 0.4 mmol/liter (118 ± 7.8 mg/dl), percentage of time spent in the target glucose range was 38% (32–44%), and the hyperglycemic index was 0.6 (0.4 –1.0) mmol/liter [11.1 (7.7–18.1) mg/dl]. A single episode of mild hypoglycemia at 3.8 mmol/liter (69 mg/dl) was observed during 480 hours of glucose control.

Conclusion

In this initial in silico evaluation, the “I, Pancreas” algorithm provided a safe control of glucose in the simulated study and achieved tight glycemic control 38% of the time.     

Postprandial glucose monitoring further improved glycemia, lipids, and weight in persons with type 2 diabetes mellitus who had already reached hemoglobin A1c goal

Background

Postprandial hyperglycemia contributes to poor glucose control and is associated with increased cardiovascular risk in type 2 diabetes mellitus (T2DM). The objective of the study was to determine the effect of postprandial self-monitoring of blood glucose (pp-SMBG) on glucose control, lipids, body weight, and cardiovascular events.

Method

Subjects with T2DM hemoglobin A1c (A1C) between 6.5 to 7.0% were randomized into the study group (at least two pp-SMBG a day and dietary modification based on glucose readings) and control group (dietary modification based on glucose readings but no mandatory pp-SMBG) for a 6-month, observational study. Oral antidiabetic drugs or insulin regimen was unchanged in either group if A1C remained less than 7.0% during the study. End points included A1C, lipids, body weight, and cardiovascular events.

Results

One hundred sixty-nine subjects, mean age 63 years, and body weight 88 kg were recruited. Hemoglobin A1c, weight, low-density lipoprotein (LDL), and triglycerides (TGs) were similar in the groups at baseline. By the end of 6 months, A1C (6.7 ± 0.1 to 6.4 ± 0.1%, p < .05), body weight (88.5 ± 7.3 to 85.2 ± 6.3 kg, p < .05), LDL (92.3 ± 2 8.4 to 81.1 ± 22.6 mg/dl, p < .05), and TGs (141 ± 21 to 96 ± 17 mg/dl, p < .05) decreased in the study group, but did not change in the control group. No cardiovascular events were observed in either group during the 6-month study period.

Conclusions

In T2DM subjects who had already reached their A1C goal, pp-SMBG at least twice a day was associated with further improvement in glycemia, lipids, and weight, as well as exercise and dietary habit. We assume that lifestyle modification promoted by postprandial hyperglycemia awareness may underlie these findings. These results substantiate the importance of implementing pp-SMBG into lifestyle modification, and emphasize that pp-SMBG is critical in the control of T2DM.     

Continuous glucose monitoring considerations for the development of a closed-loop artificial pancreas system

Background

Commercialization of a closed-loop artificial pancreas system that employs continuous subcutaneous insulin infusion and interstitial fluid glucose sensing has been encumbered by state-of-the-art technology. Continuous glucose monitoring (CGM) devices with improved accuracy could significantly advance development efforts. However, the current accuracy of CGM devices might be adequate for closed-loop control.

Methods

The influence that known CGM limitations have on closed-loop control was investigated by integrating sources of sensor inaccuracy with the University of Virginia Padova Diabetes simulator. Non-glucose interference, physiological time lag and sensor error measurements, selected from 83 Enlite™ glucose sensor recordings with the Guardian® REAL-Time system, were used to modulate simulated plasma glucose signals. The effect of sensor accuracy on closed-loop controller performance was evaluated in silico, and contrasted with closed-loop clinical studies during the nocturnal control period.

Results

Based on n = 2472 reference points, a mean sensor error of 14% with physiological time lags of 3.28 ± 4.62 min (max 13.2 min) was calculated for simulation. Sensor bias reduced time in target for both simulation and clinical experiments. In simulation, additive error increased time <70 mg/dl and >180 mg/dl by 0.2% and 5.6%, respectively. In-clinic, the greatest low blood glucose index values (max = 5.9) corresponded to sensor performance.

Conclusion

Sensors have sufficient accuracy for closed-loop control, however, algorithms are necessary to effectively calibrate and detect erroneous calibrations and failing sensors. Clinical closed-loop data suggest that control with a higher target of 140 mg/dl during the nocturnal period could significantly reduce the risk for hypoglycemia.     

Clinical Update on Optimal Prandial Insulin Dosing Using a Refined Run-to-Run Control Algorithm

Background

This article provides a clinical update using a novel run-to-run algorithm to optimize prandial insulin dosing based on sparse glucose measurements from the previous day''s meals. The objective was to use a refined run-to-run algorithm to calculate prandial insulin-to-carbohydrate ratios (I:CHO) for meals of variable carbohydrate content in subjects with type 1 diabetes (T1DM).

Method

The open-labeled, nonrandomized study took place over a 6-week period in a nonprofit research center. Nine subjects with T1DM using continuous subcutaneous insulin infusion participated. Basal insulin rates were optimized using continuous glucose monitoring, with a target fasting blood glucose of 90 mg/dl. Subjects monitored blood glucose concentration at the beginning of the meal and at 60 and 120 minutes after the start of the meal. They were instructed to start meals with blood glucose levels between 70 and 130 mg/dl. Subjects were contacted daily to collect data for the previous 24-hour period and to give them the physicianapproved, algorithm-derived I:CHO ratios for the next 24 hours. Subjects calculated the amount of the insulin bolus for each meal based on the corresponding I:CHO and their estimate of the meal''s carbohydrate content. One- and 2-hour postprandial glucose concentrations served as the main outcome measures.

Results

The mean 1-hour postprandial blood glucose level was 104 ± 19 mg/dl. The 2-hour postprandial levels (96.5 ± 18 mg/dl) approached the preprandial levels (90.1 ± 13 mg/dl).

Conclusions

Run-to-run algorithms are able to improve postprandial blood glucose levels in subjects with T1DM.