Interstitium versus Blood Equilibrium in Glucose Concentration and its Impact on Subcutaneous Continuous Glucose Monitoring Systems

The relationship between both interstitial and blood glucose remains a debated topic, on which there is still no consensus. The experimental evidence suggests that blood and interstitial fluid glucose levels are correlated by a kinetic equilibrium, which as a consequence has a time and magnitude gradient in glucose concentration between blood and interstitium. Furthermore, this equilibrium can be perturbed by several physiological effects (such as foreign body response, wound-healing effect, etc.), with a consequent reduction of interstitial fluid glucose versus blood glucose correlation. In the present study, the impact of operating in the interstitium on continuous glucose monitoring systems (CGMs) will be discussed in depth, both for the application of CGMs in the management of diabetes and in other critical areas, such as tight glycaemic control in critically ill patients.

Cell based metabolic barriers to glucose diffusion: macrophages and continuous glucose monitoring

It is assumed that MQ are central to glucose sensor bio-fouling and therefore have a major negative impact on continuous glucose monitoring (CGM) performance in vivo. However to our knowledge there is no data in the literature to directly support or refute this assumption. Since glucose and oxygen (O2) are key to glucose sensor function in vivo, understanding and controlling glucose and O2 metabolic activity of MQ is likely key to successful glucose sensor performance. We hypothesized that the accumulation of MQ at the glucose sensor-tissue interface will act as "Cell Based Metabolic Barriers" (CBMB) to glucose diffusing from the interstitial tissue compartment to the implanted glucose sensor and as such creating an artificially low sensor output, thereby compromising sensor function and CGM. Our studies demonstrated that 1) direct injections of MQ at in vivo sensor implantation sites dramatically decreased sensor output (measured in nA), 2) addition of MQ to glucose sensors in vitro resulted in a rapid and dramatic fall in sensor output and 3) lymphocytes did not affect sensor function in vitro or in vivo. These data support our hypothesis that MQ can act as metabolic barriers to glucose and O2 diffusion in vivo and in vitro.

Effect of repaglinide versus glimepiride on daily blood glucose variability and changes in blood inflammatory and oxidative stress markers

BACKGROUND

Hemoglobin A1c is the main treatment target for patients with type 2 diabetes. It has also been shown recently that postprandial glucose and daily glucose fluctuations affect the progression of diabetic complications and atherosclerotic damages.

METHODS

Continuous glucose monitoring was performed in patients with type 2 diabetes to evaluate the efficacy of repaglinide vs. glimepiride on postprandial glucose spikes and fluctuations. A total of 10 Japanese patients with type 2 diabetes treated with glimepiride monotherapy were enrolled. After observation period for 8 weeks, glimepiride was changed to repaglinide. Continuous glucose monitoring was performed whilst consuming calorie-restricted diets for two days at baseline and at the end of the 12-week trial. Blood and urine samples were collected for measurement of glucose control parameters and inflammatory and oxidative stress markers on the last day of taking either glimepiride or repaglinide.

RESULTS

Nine patients completed the trial. Although the glucose control parameters were not significantly different between glimepiride and repaglinide, the mean amplitude of glycemic excursions measured by continuous glucose monitoring was significantly reduced by changing treatment from glimepiride to repaglinide. The levels of plasminogen activator inhibitor-1, high sensitivity C-reactive protein, and urinary 8-hydoroxydeoxyguanosine were reduced significantly by repaglinide treatment.

CONCLUSION

These results suggest that repaglinide may decrease the risk of cardiovascular disease in type 2 diabetes by minimizing glucose fluctuations thereby reducing inflammation and oxidative stress.

The relationship between glycemic variability and diabetic peripheral neuropathy in type 2 diabetes with well-controlled HbA1c

BACKGROUND

Diabetic peripheral neuropathy (DPN) is one of the most common microvascular complications of diabetes. Glycemic variability could be an independent risk factor for diabetes complications in addition to average glucose. Type 2 diabetes with well-controlled glycosylated hemoglobin A1c (HbA1c) may have different terms of glycemic variability and vascular complication consequences. The aim of the study is to investigate the relationship between glycemic variability and DPN in type 2 diabetes with well-controlled HbA1c (HbA1c < 7.0%).

METHODS

45 type 2 diabetes with well-controlled HbA1c(HbA1c < 7.0%) and with DPN (DM/DPN group) were recruited in the study, and 45 type 2 diabetes with well-controlled HbA1c and without DPN (DM/-DPN group) were set as controls. The two groups were also matched for age and diabetic duration. Blood pressure, body mass index(BMI), insulin sensitivity index (Matsuda index, ISI), total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDLC), and low density lipoprotein cholesterol (LDLC) were tested in the two groups. And all patients were monitored using the continuous glucose monitoring (CGM) system for consecutive 72 hours. The multiple parameters of glycemic variability included the standard deviation of blood glucose (SDBG), mean of daily differences (MODD) and mean amplitude of glycemic excursions (MAGE).

RESULTS

The DM/DPN group had a greater SDBG, MODD and MAGE, when compared to the DM/-DPN group (p < 0.05). BMI, TC, and LDLC of DM/DPN group were lower than those of DM/-DPN group (p < 0.05). The patients with hypoglycemia were comparable between the two groups (p > 0.05). Univariate analysis showed DPN was closely associated with BMI (OR 0.82, CI 0.72-0.94, p = 0.005), TC (OR 0.63, CI 0.42-0.93, p = 0.02), LDLC (OR 0.4, CI 0.20-0.80, p = 0.009), SDBG (OR 2.95, CI 1.55-5.61, p = 0.001), MODD (OR 4.38, CI 1.48-12.93, p = 0.008), MAGE (OR 2.18, CI 1.47-3.24, p < 0.001). Multivariate logistic regression analysis showed that MAGE (OR 2.05, CI 1.36-3.09, p = 0.001) and BMI (OR 0.85, CI 0.73-0.99, p = 0.033) were significantly correlating with DPN. Glycemic variability, evaluated by MAGE, was the most significantly independent risk factor for DPN.

CONCLUSIONS

There was a close relationship between glycemic variability evaluated by MAGE and DPN in type 2 diabetes with well-controlled HbA1c.

Impact of macrophage deficiency and depletion on continuous glucose monitoring in vivo

Although it is assumed that macrophages (MQ) have a major negative impact on continuous glucose monitoring (CGM), surprisingly there is no data in the literature to directly support or refute the role of MQ or related foreign body giant cells in the bio-fouling of glucose sensors in vivo. As such, we developed the hypothesis that MQ are key in controlling glucose sensor performance and CGM in vivo and MQ deficiencies or depletion would enhance CGM. To test this hypothesis we determined the presence/distribution of MQ at the sensor tissue interface over a 28-day time period using F4/80 antibody and immunohistochemical analysis. We also evaluated the impact of spontaneous MQ deficiency (op/op mice) and induced-transgenic MQ depletions (Diphtheria Toxin Receptor (DTR) mice) on sensor function and CGM utilizing our murine CGM system. The results of these studies demonstrated: 1) a time dependent increase in MQ accumulation (F4/80 positive cells) at the sensor tissue interface; and 2) MQ deficient mice and MQ depleted C57BL/6 mice demonstrated improved sensor performance (MARD) when compared to normal mice (C57BL/6). These studies directly demonstrate the importance of MQ in sensor function and CGM in vivo.

Continuous glucose monitoring in acute coronary syndrome

BACKGROUND

Diabetes mellitus is an independent risk factor for cardiovascular disease.

OBJECTIVE

To compare the efficacy of devices for continuous glucose monitoring and capillary glucose monitoring in hospitalized patients with acute coronary syndrome using the following parameters: time to achieve normoglycemia, period of time in normoglycemia, and episodes of hypoglycemia.

METHODS

We performed a pilot, non-randomized, unblinded clinical trial that included 16 patients with acute coronary artery syndrome, a capillary or venous blood glucose ≥ 140 mg/dl, and treatment with a continuous infusion of fast acting human insulin. These patients were randomized into 2 groups: a conventional group, in which capillary measurement and recording as well as insulin adjustment were made every 4h, and an intervention group, in which measurement and recording as well as insulin adjustment were made every hour with a subcutaneous continuous monitoring system. Student's t-test was applied for mean differences and the X(2) test for qualitative variables.

RESULTS

We observed a statistically significant difference in the mean time for achieving normoglycemia, favoring the conventional group with a P = 0.02.

CONCLUSION

Continuous monitoring systems are as useful as capillary monitoring for achieving normoglycemia.

Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information

Several real-time short-term prediction methods, based on time-series modeling of past continuous glucose monitoring (CGM) sensor data have been proposed with the aim of allowing the patient, on the basis of predicted glucose concentration, to anticipate therapeutic decisions and improve therapy of type 1 diabetes. In this field, neural network (NN) approaches could improve prediction performance handling in their inputs additional information. In this contribution we propose a jump NN prediction algorithm (horizon 30 min) that exploits not only past CGM data but also ingested carbohydrates information. The NN is tuned on data of 10 type 1 diabetics and then assessed on 10 different subjects. Results show that predictions of glucose concentration are accurate and comparable to those obtained by a recently proposed NN approach (Zecchin et al. (2012) [26]) having higher structural and algorithmical complexity and requiring the patient to announce the meals. This strengthen the potential practical usefulness of the new jump NN approach.

In vivo evaluation of a chip based near infrared sensor for continuous glucose monitoring

In this paper we describe the concept and in vivo results of a minimally invasive, chip-based near infrared (NIR) sensor, combined with microdialysis, for continuous glucose monitoring. The sensor principle is based on difference absorption spectroscopy in selected wavelength bands of the near infrared spectrum (1300 nm, 1450 nm, and 1550 nm) in the 1st overtone band. In vitro measurements revealed a linear relationship between glucose concentration and the integrated difference spectroscopy signal with a coefficient of determination of 99% in the concentration range of 0-400mg/dl. The absolute error in this case is about 5mg/dl, corresponding to a relative error of about 5% for glucose concentrations larger than 50mg/dl and about 12% in the hypoglycemic range (<50mg/dl). In vivo measurements on 10 patients showed that the NIR-CGM sensor data reflects the blood reference values adequately, if a proper calibration and a signal drift correction is applied. The mean MARE (mean absolute relative error) value taken over all patient data is 13.8%. The best achieved MARE value is at 4.8%, whereas the worst lies at 25.8%, leading to a standard deviation of 5.5%.

Continuous glucose monitoring after kidney transplantation in non-diabetic patients: early hyperglycaemia is frequent and may herald post-transplantation diabetes mellitus and graft failure

OBJECTIVES

New onset of diabetes after transplantation (NODAT) is a known complication of renal transplantation, but early glycaemic status after transplantation has not been described prospectively. This study aimed to assess blood glucose (BG) levels immediately following kidney transplantation in non-diabetic subjects and to explore their relationship to later graft outcomes and NODAT occurrence.

PATIENTS AND METHODS

Over a 9-month period, 43 consecutive non-diabetic patients who received a kidney transplant were prospectively investigated. During the first 4 days after transplantation, fasting BG was measured and the 24-h BG profile assessed by continuous glucose monitoring (CGM). Capillary BG was measured on hospital admittance and at least four times a day for CGM calibration thereafter. All adverse events were recorded, and fasting BG and HbA1c were assessed at 3, 6 and 12 months and at the last visit to our centre.

RESULTS

Immediately following renal transplantation, capillary BG was 12.2 ± 3.8 mmol/L. On day 1 (D1), fasting BG was 9.9 ± 4.3 mmol/L and decreased to 6.0 ± 1.5 mmol/L on D3. The CGM-reported mean 24-h BG (mmol/L) was 10.2±2.4 on D1, 7.7 ± 1.3 on D2 and 7.5 ± 1.1 on D3. From D1 to D4, 43% of patients spent>12h/day with BG levels>7.7 mmol/L. While morbidity during the 3 months following transplantation appeared unrelated to BG, the first post-transplantation capillary BG measurement and fasting BG on D1 tended to be higher in patients who developed diabetes 3 months later. Tacrolimus treatment was associated with a higher incidence of dysglycaemia at 3 and 6 months. After a mean follow-up of 72 months, NODAT was frequently seen (18.6%), and was associated with tacrolimus medication (P<0.01) and a higher rate of renal transplantation failure (RR: 3.6, P<0.02).

CONCLUSION

Hyperglycaemia appears to be a nearly constant characteristic immediately following transplantation in non-diabetic kidney recipients. Higher BG values could identify patients at risk for later post-transplant diabetes and graft failure.

The incidence of severe hypoglycaemia in pregnant women with type 1 diabetes mellitus can be reduced with unchanged HbA1c levels and pregnancy outcomes in a routine care setting

AIMS

To investigate whether the incidence of severe hypoglycaemia in pregnant women with type 1 diabetes can be reduced without deteriorating HbA1c levels or pregnancy outcomes in a routine care setting.

METHODS

Two cohorts (2004-2006; n=108 and 2009-2011; n=104) were compared. In between the cohorts a focused intervention including education of caregivers and patients in preventing hypoglycaemia was implemented. Women were included at median 8 (range 5-13) weeks. Severe hypoglycaemia (requiring assistance from others) was prospectively reported in structured interviews.

RESULTS

In the first vs. second cohort, severe hypoglycaemia during pregnancy occurred in 45% vs. 23%, p=0.0006, corresponding to incidences of 2.5 vs. 1.6 events/patient-year, p=0.04. Unconsciousness and/or convulsions occurred at 24% vs. 8% of events. Glucagon and/or glucose injections were given at 15% vs. 5% of events. At inclusion HbA1c was comparable between the cohorts while in the second cohort fewer women reported impaired hypoglycaemia awareness (56% vs. 36%, p=0.0006), insulin dose in women on multiple daily injections was lower (0.77 IU/kg (0.4-1.7) vs. 0.65 (0.2-1.4), p=0.0006) and more women were on insulin analogues (rapid-acting 44% vs. 97%, p<0.0001; long-acting 6% vs. 76%, p<0.0001) and insulin pumps (5% vs. 23%, p<0.0001). Pregnancy outcomes were similar in the two cohorts.

CONCLUSIONS

A 36% reduction in the incidence of severe hypoglycaemia in pregnancy with unchanged HbA1c levels and pregnancy outcomes was observed after implementation of focused intervention against severe hypoglycaemia in a routine care setting. Improved insulin treatment, increased health professional education and fewer women with impaired hypoglycaemia awareness may contribute.

Efficacy of α-glucosidase inhibitors combined with dipeptidyl-peptidase-4 inhibitor (alogliptin) for glucose fluctuation in patients with type 2 diabetes mellitus by continuous glucose monitoring

Aims/Introduction

The combination therapy of dipeptidyl‐peptidase (DPP)‐4 inhibitor and α‐glucosidase inhibitors (α‐GIs) is highly effective in suppressing postprandial hyperglycemia. The aim of the present study was to compare the effects of voglibose and miglitol on glucose fluctuation, when used in combination with DPP‐4 inhibitor by using continuous glucose monitoring (CGM).

Materials and Methods

In a randomized cross‐over study, 16 patients with type 2 diabetes who presented with postprandial hyperglycemia despite alogliptin (25 mg) were treated with voglibose (0.9 mg) or miglitol (150 mg). We measured standard deviation (SD); mean amplitude of glycemic excursions (MAGE), and mean, minimum and maximum glucose measured by CGM during three phases (alogliptin monotherapy, dual therapy of alogliptin and voglibose, and dual therapy of alogliptin and miglitol). The primary outcome measure was SD between α‐GIs.

Results

SD was significantly improved by the addition of either voglibose (18.9 ± 10.1) or miglitol (19.6 ± 8.2) to alogliptin monotherapy (36.2 ± 8.7). MAGE improved significantly with the addition of either voglibose (57.5 ± 26.1, P < 0.01) or miglitol (64.6 ± 26.2, P < 0.01) to alogliptin monotherapy (101.5 ± 21.5). There was no significant difference in glucose fluctuation between α‐GIs. There were no differences between two groups in mean (132.6 ± 21.4 and 138.7 ± 25.4) and maximum (184.3 ± 48.7 and 191.9 ± 38.3). The minimum glucose under alogliptin plus voglibose (94.9 ± 20.2) was significantly lower than that under alogliptin and miglitol (105.3 ± 21.0).

Conclusions

Glucose fluctuation was improved by the addition of voglibose or miglitol to alogliptin. Glucose fluctuations and postprandial hyperglycemia were similar between α‐GIs. This trial was registered with the University Hospital Medical Information Network (no. UMIN R000010028).

Glycemic variability in relation to oral disposition index in the subjects with different stages of glucose tolerance

BACKGROUND

Glucose variability could be an independent risk factor for diabetes complications in addition to average glucose. The deficiency in islet β cell secretion and insulin sensitivity, the two important pathophysiological mechanisms of diabetes, are responsible for glycemic disorders. The oral disposition index evaluated by product of insulin secretion and sensitivity is a useful marker of islet β cell function. The aim of the study is to investigate glycemic variability in relation to oral disposition index in the subjects across a range of glucose tolerance from the normal to overt type 2 diabetes.

METHODS

75-g oral glucose tolerance test (OGTT) was performed in total 220 subjects: 47 with normal glucose regulation (NGR), 52 with impaired glucose metabolism (IGM, 8 with isolated impaired fasting glucose [IFG], 18 with isolated impaired glucose tolerance [IGT] and 26 with combined IFG and IGT), 61 screen-diagnosed diabetes by isolated 2-h glucose (DM2h) and 60 newly diagnosed diabetes by both fasting and 2-h glucose (DM). Insulin sensitivity index (Matsuda index, ISI), insulin secretion index (ΔI30/ΔG30), and integrated β cell function measured by the oral disposition index (ΔI30/ΔG30 multiplied by the ISI) were derived from OGTT. All subjects were monitored using the continuous glucose monitoring system for consecutive 72 hours. The multiple parameters of glycemic variability included the standard deviation of blood glucose (SD), mean of blood glucose (MBG), high blood glucose index (HBGI), continuous overlapping net glycemic action calculated every 1 h (CONGA1), mean of daily differences (MODD) and mean amplitude of glycemic excursions (MAGE).

RESULTS

From the NGR to IGM to DM2h to DM group, the respective values of SD (mean ± SD) (0.9 ± 0.3, 1.5 ± 0.5, 1.9 ± 0.6 and 2.2 ± 0.6 mmol/), MBG (5.9 ± 0.5, 6.7 ± 0.7, 7.7 ± 1.0 and 8.7 ± 1.5 mmol/L), HGBI [median(Q1-Q3)][0.8(0.2-1.2), 2.0(1.2-3.7), 3.8(2.4-5.6) and 6.4(3.2-9.5)], CONGA1 (1.0 ± 0.2, 1.3 ± 0.2, 1.5 ± 0.3 and 1.8 ± 0.4 mmol/L), MODD (0.9 ± 0.3, 1.4 ± 0.4, 1.8 ± 0.7 and 2.1 ± 0.7 mmol/L) and MAGE (2.1 ± 0.6, 3.3 ± 1.0, 4.3 ± 1.4 and 4.8 ± 1.6 mmol/L) were all increased progressively (all p < 0.05), while their oral disposition indices [745(546-947), 362(271-475), 203(134-274) and 91(70-139)] were decreased progressively (p < 0.05). In addition, SD, MBG, HGBI, CONGA1, MODD and MAGE were all negatively associated with the oral disposition index in each group (all p < 0.05) and in the entire data set (r = -0.66, -0.66, -0.72, -0.59, -0.61 and -0.65, respectively, p < 0.05).

CONCLUSIONS

Increased glycemic variability parameters are consistently associated with decreased oral disposition index in subjects across the range of glucose tolerance from the NGR to IGM to DM2h to DM group.

Continuous glucose monitoring

Continuous glucose monitoring (CGM) technology with its recent development in the real-time feedback has got the potential to revolutionize diabetes care in the near future in the arena of the rapeutic interventions and flexibility in variations in lifestyle or dietary intake. CGM has made the attainment of near-normal blood glucose concentrations, a practical goal for most patients with diabetes.

A MEMS affinity glucose sensor using a biocompatible glucose-responsive polymer

We present a MEMS affinity sensor that can potentially allow long-term continuous monitoring of glucose in subcutaneous tissue for diabetes management. The sensing principle is based on detection of viscosity changes due to affinity binding between glucose and poly(acrylamide-ran-3-acrylamidophenylboronic acid) (PAA-ran-PAAPBA), a biocompatible, glucose-specific polymer. The device uses a magnetically driven vibrating microcantilever as a sensing element, which is fabricated from Parylene and situated in a microchamber. A solution of PAA-ran-PAAPBA fills the microchamber, which is separated from the surroundings by a semi-permeable membrane. Glucose permeates through the membrane and binds reversibly to the phenylboronic acid moiety of the polymer. This results in a viscosity change of the sensing solution, which is obtained by measuring the damped cantilever vibration using an optical lever setup, allowing determination of the glucose concentration. Experimental results demonstrate that the device is capable of detecting glucose at physiologically relevant concentrations from 27 mg/dL to 324 mg/dL. The glucose response time constant of the sensor is approximately 3 min, which can be further improved with device design optimization. Excellent reversibility and stability are observed in sensor responses, as highly desired for long-term, stable continuous glucose monitoring.