Journal of Diabetes Science and Technology

Continuous glucose monitoring (CGM) sensors measure glucose concentration in the interstitial fluid (ISF). Equilibration between plasma and ISF glucose is not instantaneous. Therefore, ISF and plasma glucose concentrations exhibit different dynamic patterns, particularly during rapid changes. The purpose of this work was to investigate how well plasma glucose can be reconstructed from ISF CGM data.

Six diabetic volunteers were monitored for 2 days using the TheraSense FreeStyle Navigator (Abbott Diabetes Care, Alameda, CA), a minimally invasive device that, on the basis of an initial calibration procedure (hereafter referred to as standard calibration), returns ISF glucose concentration. Simultaneously, plasma glucose concentration was also measured every 15 minutes. First we identified, in each subject, the linear time-invariant (LTI) two-compartment model of plasma-interstitium kinetics. Then, a nonparametric regularization deconvolution method was used to reconstruct plasma from ISF glucose.

Deconvoluted profiles were always closer to plasma glucose than ISF ones. However, the quality of the reconstruction is unsatisfactory. Some visible discrepancies between average plasma and ISF time series suggest problems either in the applicability of the LTI model of plasma-interstitium kinetics to normal life conditions or in the standard calibration with which ISF glucose is determined from the sensor internal readings. Assuming that the LTI model of plasma-interstitium kinetics is correct, we focused on the influence of calibration and we employed a recently proposed method to recalibrate ISF data.

After the recalibration step, the relative error in reconstructing plasma glucose was reduced significantly. Results also demonstrate that further margins of improvement of plasma glucose reconstruction are possible by developing more sophisticated recalibration procedures.

The fear of hypoglycemia remains an important limiting factor in the ability of an individual with type 1 diabetes to tightly regulate glycemia. Continuous glucose monitors provide important feedback to improve glycemic control, but there remains a need for these devices to better alarm of possible impending hypoglycemia, particularly overnight or other periods when the individual is engaged in activities that take their focus away from glucose monitoring.

We have previously proposed an algorithm, based on the use of real-time glucose sensor signals and optimal estimation theory (Kalman filtering), to predict hypoglycemia; the algorithm was validated in simulation-based studies. In this article we further refine and validate the prediction algorithm based on the analysis of clinical hypoglycemic clamp data from 13 subjects. The sensitivity and specificity of the predictions are calculated with respect to reference blood glucose values obtained at the same sampling rate of the sensor.

For a 30-minute prediction horizon and alarm threshold of 70 mg/dl, the sensitivity and specificity were 90 and 79%, respectively, indicating that a 21% false alarm rate must be tolerated to predict 90% of the hypoglycemic events 30 minutes ahead of time. Shorter prediction horizons yield a significant improvement in sensitivity and specificity.

Sensitivity and specificity data as a function of prediction horizon and alarm threshold enable an individual to adjust the alarm to best meet their needs. Such decisions can be made depending on the subject's risk for hypoglycemia, for example.

The introduction of continuous glucose monitoring (CGM) devices has dramatically increased the amount of information available about each patient. While CGM has become a useful diagnostic tool for the individual patient, interpretive issues including noise reduction remain and further analytical work is needed to fully utilize the data richness.

We applied discrete Fourier transform methodology to CGM data to obtain an overall statistical model providing the dimension reduction necessary for insightful analyses of the whole function and explored some properties and possible applications of this technology.

The following example applications are shown. Discrete Fourier transform allows reduction of noise using an objective statistical criterion and may, as a first step, possibly enhance the value of various measures of variability through this noise reduction. Average functions of groups in a prospective randomized clinical are demonstrated and the aggregate function is readily visualized. Second and third harmonic amplitudes at baseline correlate with hemoglobin A1c after a 6-month treatment period. The time points of most rapid glucose decreases are identified easily with the functional through the second derivative, and its correlation with subsequent reported symptomatic hypoglycemia is shown.

Discrete Fourier transform offers an attractive analytical methodology for CGM data given the achievable dimension reduction without loss of essential information as well as its ability to eliminate noise.

Since the advent of subcutaneous glucose sensors, there has been intense focus on characterizing the delay in the interstitial fluid (ISF) glucose response and the effect of insulin to alter the plasma-to-ISF glucose gradient. The Medtronic MiniMed continuous glucose monitoring system (CGMS) has often been used for this purpose; however, many of the studies have used experimental conditions that fall outside its intended use, for example, studies that have assessed the delay during rapid glucose excursions brought about by intravenous infusion of glucose or insulin. Under these conditions, it is possible that the rate of glucose change may exceed that allowed by CGMS filtering routines. If so, the estimated delay may be because of the filter rather than the ISF. Also, sensor characteristics, such as nonspecific offset current or stability, may have been inadvertently attributed to changes in the plasma-to-ISF gradient. The potential for these issues to have confounded the understanding of ISF glucose delay and gradient is investigated.

An in vitro preparation in which no delay or gradient exists between sensor and measurement solution was used to recreate a rapidly changing glucose profile from a previously published in vivo study. The CGMS system (N = 6 sensors) was then used to estimate any artifactual delay and gradient introduced by the system per se.

One-point calibration resulted in an apparent change in gradient as glucose was lowered from ∼100 to 50 mg/dl. After a two-point calibration, sensor glucose followed the glucose profile as it was decreased slowly from ∼100 to ∼60 mg/dl; however, when the glucose level was subsequently increased rapidly to ∼150 mg/dl, CGMS filtering routines limited the rate of change of sensor glucose and introduced a delay similar to that previously attributed to ISF glucose equilibration delay.

Studies that have previously used the Medtronic MiniMed CGMS system to assess changes in the plasma-to-ISF glucose gradient may need to be reassessed to ensure that the offset current was estimated accurately. Studies that have used the system to assess ISF glucose delay during rapid, unphysiologic changes in glucose and did not remove the CGMS smoothing filters may have attributed CGMS filter delay to ISF glucose equilibration.

The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data.

We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference.

Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges.

For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.

There has been considerable debate on what constitutes a good hypoglycemia (Hypo) detector and what is the accuracy required from the continuous monitoring sensor to meet the requirements of such a detector. The performance of most continuous monitoring sensors today is characterized by the mean absolute relative difference (MARD), whereas Hypo detectors are characterized by the number of false positive and false negative alarms, which are more relevant to the performance of a Hypo detector. This article shows that the overall accuracy of the system and not just the sensor plays a key role.

A mathematical model has been developed to investigate the relationship between the accuracy of the continuous monitoring system as described by the MARD, and the number of false negatives and false positives as a function of blood glucose rate change is established. A simulation method with N = 10,000 patients is used in developing the model and generating the results.

Based on simulation for different scenarios for rate of change (0.5, 1.0, and 5.0 mg/dl per minute), sampling rate (from 1, 2.5, 5, and 10 minutes), and MARD (5, 7.5, 10, 12.5, and 15%), the false positive and false negative ratios are computed. The following key results are from these computations.

Based on the model, assumptions in the model, and the simulation on N = 10,000 patients for different scenarios for rate of glucose change, sampling rate, and MARD, it is concluded that the false negative and false positive ratio will vary depending on the alarm Hypo threshold set by the patient and the MARD value. Also, to achieve a false negative ratio <5% and a false positive ratio <10% would require continuous glucose monitoring to have an MARD ≤7.5%.

The clinical role and the potential benefit of self-measurement of blood glucose (SMBG) for patients with type 2 diabetes are still under discussion. Even less information is available on the cost-effectiveness of performing SMBG by this patient group. The goal of this study was to establish cost-effectiveness ratios of performing SMBG by patients afflicted by this disease.

We assessed the benefit and cost-effectiveness of SMBG in type 2 diabetes from a third-party payer perspective based on results of both a large epidemiologic cohort study reflecting the reality of care, and a Markov model calculation.

Analysis of cohort study data revealed that total costs cumulated over the observation period of 8 years were lower in the SMBG group than in the non-SMBG group according to savings of € 1'714 [oral antidiabetic drugs (OAD) only] and € 13'815 (OAD + insulin) per patient. Several scenarios were considered in the model-based calculation. The cost-effectiveness ratio varied from € 20'768/life year gained to domination of SMBG use compared to nonusers in OAD treated patients and from € 59'057/life year gained to domination of SMBG use compared to nonusers in OAD + insulin treated patients.

Results indicate that SMBG in type 2 diabetes offers an excellent opportunity to get a high investment-outcome ratio in the treatment of this pandemic disease.

A 5-day in-patient study designed to assess the accuracy of the FreeStyle Navigator® Continuous Glucose Monitoring System revealed that the level of accuracy of the continuous sensor measurements was dependent on the rate of glucose change. When the absolute rate of change was less than 1 mg·dl⁻¹·min⁻¹ (75% of the time), the median absolute relative difference (ARD) was 8.5%, with 85% of all points falling within the A zone of the Clarke error grid. When the absolute rate of change was greater than 2 mg·dl⁻¹·min⁻¹ (8% of the time), the median ARD was 17.5%, with 59% of all points falling within the Clarke A zone.

Numerical simulations were performed to investigate effects of the rate of change of glucose on sensor measurement error. This approach enabled physiologically relevant distributions of glucose values to be reordered to explore the effect of different glucose rate-of-change distributions on apparent sensor accuracy.

The physiological lag between blood and interstitial fluid glucose levels is sufficient to account for the observed difference in sensor accuracy between periods of stable glucose and periods of rapidly changing glucose.

The role of physiological lag on the apparent decrease in sensor accuracy at high glucose rates of change has implications for clinical study design, regulatory review of continuous glucose sensors, and development of performance standards for this new technology. This work demonstrates the difficulty in comparing accuracy measures between different clinical studies and highlights the need for studies to include both relevant glucose distributions and relevant glucose rate-of-change distributions.

This study investigated continuous glucose profiles in nondiabetic subjects.

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.

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.

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.

Basal continuous subcutaneous insulin infusion (CSII) therapy at a fixed rate may effectively improve glycemic control in patients with type 2 diabetes when oral antidiabetic treatment fails. Regimens of simple constant subcutaneous delivery of insulin may provide theoretical advantages in type 2 diabetes.

Ten subjects with type 2 diabetes who obtained insufficient glycemic control on oral antidiabetic drugs were included. Following an initial control day, two periods of 3 days with CSII of a rapid-acting insulin analogue, 1.5 IU/h (dose obtained from a preceding study), for 8 hours overnight and for 24 hours, respectively, were carried out in random order. Profiles of serum insulin aspart, serum endogenous insulin, and plasma glucose were recorded.

Compared to the control day, an 8-hour overnight insulin infusion during a 3-day period improved fasting plasma glucose (FPG) (mean differences ± SEM; Δ59.0 ± 10.1 mg/dl; p < 0.01) and 2-hour postprandial plasma glucose (PPPG) (Δ57.8 ± 10.6 mg/dl; p < 0.01) after breakfast. Compared to an 8-hour overnight infusion, a 24-hour infusion further improved all three PPPG values after breakfast, lunch, and dinner (Δ28.8 ± 8.1 mg/dl, Δ30.6 ± 8.1 mg/dl, and Δ35.1 ± 7.9 mg/dl; p < 0.01). During insulin infusion, only one hypoglycemic episode with PG <55.8 mg/dl and mild symptoms was recorded.

Continuous subcutaneous insulin infusion with a rapid-acting insulin analogue at a fixed rate of 1.5 IU/h, either overnight or for 24 hours, improved glycemic control without safety concerns in patients with type 2 diabetes who had secondary failure to oral antidiabetic drugs. The effect on FPG was similar for both treatments, whereas the effect on PPPG was superior when insulin was infused during the entire 24 hours.

Diabetes mellitus is a leading cause of illness and death across the world and is responsible for a growing proportion of global health care expenditures. The present study was designed to observe the effect of diabetes mellitus on lung function in patients with diabetes belonging to a specific ethnic group, namely Saudis.

In this study, a group of 47 apparently healthy volunteer male Saudi patients with diabetes was randomly selected. Their ages ranged from 20 to 70 years. The patients were matched with another group of 50 healthy male control subjects in terms of age, height, weight, ethnicity, and socioeconomic status. Both groups met exclusion criteria as per standard. Spirometry was performed with an electronic spirometer (Schiller AT-2 Plus, Switzerland), and results were compared by a Student's t test.

Subjects with diabetes showed a significant reduction in forced vital capacity (FVC) and forced expiratory volume in the first second (FEV₁) relative to their matched controls. However, there were no significant differences in the forced expiratory ratio (FE₁/FVC%) and the middle half of the FVC (FEF_25–75%) between the groups. We observed a significantly negative correlation between duration of disease and pulmonary function, as measured by FEV₁ (r = 0.258, p = 0.04), FVC (r = 0.282, p = 0.28), and the middle half of the FVC (FEF_25–75%) (r = 0.321, p = 0.014).

Pulmonary function in a specific ethnic group of patients with diabetes was impaired as evidenced by a decrease in FVC and FEV₁ compared to pulmonary function in matched controls. Stratification of results by years of disease revealed a significant correlation between duration of disease and a decline in pulmonary function.

How smoothly insulin is injected is one of the major concerns when patients commence insulin injection therapy. Improving its usability may be important in initiation therapy and adherence, resulting in clinical

In a single-center, open-label and randomized two-period crossover trial, the effect of the tapered needle of NanoPass® (33 gauge, 5 mm) on usability in comparison with the standard needle of Micro Fine Plus® (31 gauge, 5 mm) was examined using a questionnaire. Patients with insulin-dependent diabetes (n = 40, self-injecting insulin four times daily for more than 3 months) were randomized to use NanoPass or Micro Fine Plus needles for 1 week and then use the alternative for 1 week. Patients completed the questionnaire before and after each test week. Each evaluation was scored from −100 (worst) to +100 (best) by a visual analogue scale. A higher score indicated a more favorable outcome compared with the other needle.

The NanoPass needles. Meanwhile, the NanoPass needle, which had less resistance in insertion with a new lubricant coating P < 0.001) overall patient's satisfaction score, including less frightening use, less bleeding, and less dribbling of injected insulin in comparison with the former evaluation.

For overall patient satisfaction in using an insulin needle, developing a thinner needle and improving other factors, such as lubricity coating the needle, are important.

Diabetes mellitus is one of the chronic diseases exploiting the largest number of telemedicine systems. Our research group has been involved since 1996 in two projects funded by the European Union proposing innovative architectures and services according to the best current medical practices and advances in the information technology area.

We propose an enhanced architecture for telemedicine giving rise to a multitier application. The lower tier is represented by a mobile phone hosting the patient unit able to acquire data and provide first-level advice to the patient. The patient unit also facilitates interaction with the health care center, representing the higher tier, by automatically uploading data and receiving back any therapeutic plan supplied by the physician. On the patient's side the mobile phone exploits Bluetooth technology and therefore acts as a hub for a wireless network, possibly including several devices in addition to the glucometer.

A new system architecture based on mobile technology is being used to implement several prototypes for assessing its functionality. A subsequent effort will be undertaken to exploit the new system within a pilot study for the follow-up of patients cared at a major hospital located in northern Italy.

We expect that the new architecture will enhance the interaction between patient and caring physician, simplifying and improving metabolic control. In addition to sending glycemic data to the caring center, we also plan to automatically download the therapeutic protocols provided by the physician to the insulin pump and collect data from multiple sensors.

Overall obesity and, as it is increasingly appreciated, body fat distribution and ectopic fat deposition in liver and skeletal muscle, determine insulin resistance in humans. However, little is known about the independence of these relationships. Therefore, we determined the impact of different fat depots as well as fat accumulation in ectopic tissues such as liver and skeletal muscle in the prediction of insulin resistance in healthy humans.

Visceral and subcutaneous abdominal fat were determined by magnetic resonance (MR) tomography and liver fat and intramyocellular fat in the tibialis anterior muscle by ¹H-MR spectroscopy in 220 subjects. Insulin sensitivity was estimated from the oral glucose tolerance test (OGTT) and measured by a euglycemic hyperinsulinemic clamp in a subgroup (n = 157).

Insulin sensitivity estimated from the OGTT correlated negatively with total body fat (r = −0.27, p < 0.0001), subcutaneous abdominal fat (r = −0.35, p < 0.0001), and visceral fat (r = −0.43, p < 0.0001). Furthermore, insulin sensitivity correlated negatively with liver fat (r = −0.53, p < 0.0001) and intramyocellular fat (r = −0.26, p < 0.0001). In multivariate regression models, high liver and visceral fat emerged as the strongest predictors of low insulin sensitivity.

Among various fat compartments, high liver fat and high visceral fat are the strongest determinants of insulin sensitivity in humans.

Approved for treatment of treatment-resistant depression and for epilepsy, vagus nerve stimulation (VNS) therapy involves stimulation of the vagus nerve, affecting both mood and appetite regulating systems. VNS is associated with changes in food intake and weight loss in animals. Studies of its impact on food intake and weight with humans are limited. It is not known whether or how VNS influences emotional response to food, but vagus afferents project to regions in the insula involving satiety and taste.

Thirty-three participants were recruited for three groups: depressed patients undergoing VNS therapy, depressed patients not undergoing VNS therapy, and healthy controls. All participants viewed images of foods twice in random order. When applicable, VNS devices were turned on for one viewing and off for the other. Participants were instructed to rate immediately after the viewings how each picture made them feel on a visual analog on three dimensions (unhappy to happy, calm to aroused, and small/submissive to big/domineering).

Controlling for time since last meal, a significant main effect was found for arousal ratings in response to sweet food images. Post-hoc analyses indicated that the VNS group demonstrated significant changes in arousal ratings between paired food image viewings compared to controls. Sixty-four percent of VNS participants demonstrated increases and 36% demonstrated decreases in arousal. Higher body mass indexes and greater levels of self-reported sweet cravings were associated with increased arousal during VNS activation.

This study was the first to examine the effects of acute left cervical VNS on emotional ratings of food in adults with major depression. Results suggest that VNS device activation may be associated with acute alteration in arousal response to sweet foods among depressed patients. Future research is needed to replicate these findings and to assess how activation of the vagus nerve affects eating and weight.

The AIDA interactive educational diabetes simulator has been available without charge for over a decade via the Internet (see www.2aida.org). Part 1 of this report [J Diabetes Sci Technol. 2007;1(3):423–35] described the model components to be integrated to enhance the utility of the software, with the aim being to provide enhanced functionality and educational simulations of regimens utilizing insulin analogues, as well as insulin doses greater than 40 units. This report provides some preliminary subcutaneous insulin absorption bench testing results for the updated modeling prototype.

An analysis has been done of the spatial distribution of insulin in the region of the injection site for different classes of insulin preparations and times after the administration of a set insulin injection. Demonstrations of the proportion of residual insulin in depot versus time after a subcutaneous bolus have also been simulated for different insulin injection volumes and concentrations, as well as to show the proportions of hexameric, dimeric, and bound insulin over time after an injection.

Some early bench testing results are highlighted following subcutaneous injections of a rapidly acting insulin analogue (such as lispro/Humalog or aspart/NovoLog), a short-acting (regular) insulin preparation (e.g., Actrapid), intermediate-acting insulins (both Semilente and neutral protamine Hagedorn types), and a very long-acting insulin analogue (such as glargine/Lantus). The transformation, dissociation/association, and absorption processes by which insulin moves from the subcutaneous injection site to the plasma are also illustrated.

This report demonstrates how enhanced capabilities may be added to AIDA once a new model of subcutaneous insulin absorption is incorporated. The revised approach, once fully implemented, should permit the simulation of plasma insulin profiles for rapidly acting and very long-acting insulin analogues, as well as insulin injections greater than 40 units.