Current Diabetes Reviews - Volume 4, Issue 3, 2008

Diabetic complications can be reduced by achieving good metabolic control, which for insulin-requiring diabetic subjects, requires frequent self-monitoring of blood glucose (SMBG). However, SMBG represents only a snapshot of the glucose concentration and it does not provide trend information, nor does it reflect glycaemic fluctuations. In contrast, accurate and reliable devices sensing glucose on a (near)-continuous basis provide information about the direction, magnitude, duration, frequency of glycaemic fluctuations, and may facilitate specific therapeutic adjustments that need to be made to avoid hypoand hyperglycaemic excursions, thereby improving metabolic control. Particularly patients with brittle diabetes, hypoglycaemia unawareness, gastroparesis, pregnant women, or pump users, who are motivated to participate in their diabetes care and are technologically adept, may benefit from continuous glucose monitoring (CGM). In this special issue of Current Diabetes Reviews, leading researchers review the current evidence for CGM. This issue begins with a review of the indications, advantages, technical and clinical aspects of minimally invasive (needle-type glucose electrodes, and microdialysis-based systems), and non-invasive CGM sensors [1]. Next, an overview is given of fully implantable glucose monitoring systems (IGMS) (e.g. I.V. sensors for hospital use, e.g. ICU) [2]. Long-term use reducing impact of invasiveness due to implantation, less frequent calibration needs because of a more stable tissue environment around the sensor and potential easier inclusion in a closed-loop insulin delivery system are the expected benefits of IGMS. Although CGM systems may represent a breakthrough for glucose monitoring, the patient and the treating physician must be aware of the limitations of current CGM systems, that originate from physiological and technical aspects. Most of the systems monitor invasively glucose in the subcutaneous tissue. It is important to realize that there are discrepancies between blood and interstitial glucose concentration, which may affect the quality of the system calibration and thereby the accuracy of the data, as reviewed in the next article [3]. A clinically important task in diabetes management is the prevention of hypo/hyperglycemic events, as discussed in the next manuscript. By complex mathematical trend analysis, real-time CGM sensors can serve as a tool to predict impending glucose excursions for 20-30 minutes ahead, thereby providing alarm signals of hypo- and hyperglycaemic values warning the patient to take preventative actions [4]. The next two papers review criteria for evaluation of CGM accuracy and clinical performance. Clarke et al. describe the Continuous Glucose-Error Grid Analysis (CG-EGA), which reports point- and rate-accuracy for each of the relevant glycemic ranges; hypo-, eu-, and hyperglycemia [5]. Wentholt et al. discuss pros and cons of other methods to analyse accuracy, including regression analysis and correlation coefficient, relative difference measures, Bland Altman plot, ISO criteria, combined curve fitting, and epidemiological analyses. In this paper, recommendations for much needed head-to-head studies are given [6]. Next, three papers critically review the current clinical evidence of CGM sensors in type 1 diabetes and in diabetic pregnancy [7-9]. As written, only a few short-term randomized controlled trials using real-time CGM have provided us with evidence in favour of improved metabolic control, reductions in HbA1c, reductions in hypo- and hyperglycaemic episodes, and improved quality-of-life [7,8]. In a mini-review, the likely advantages of using CGM in pregnancy, aiming to improve a patient's overall glucose profile, thereby decreasing the risks of poor fetal outcomes, are described [9]. The next three papers review the importance of strict blood glucose control in critically ill patients [10-12]. Implementation of a strict glycemic control protocol, which is primordial to obtain normoglycemia, in the intensive care unit is feasible and cost-effective, but asks for careful consideration of some practical aspects, such as prevention of hypoglycaemia, training of nurses and selection of accurate blood glucose measurement tools. CGM devices and closed-loop systems are being developed and might be of great benefit to overcome these issues. Novel insights into post-stroke hyperglycaemia derived from CGM are also discussed in greater detail [12]. In a next article, Braithwaite et al. describe algorithms for intravenous insulin infusion [13]. Specific distinguishing algorithm design features and choice of parameters may be important to establish freedom from hypoglycemia, eliminate the need for administration of concentrated dextrose during euglycemia, control variability within the treatment course of individual patients, achieve adaptability to differing blood glucose targets, and minimize variability of glycemic control between treatment courses of different patients or patient populations.

Accurate and reliable devices sensing glucose on a (near)-continuous basis may facilitate specific therapeutic adjustments that need to be made to avoid hypo- and hyperglycaemic excursions, thereby improving metabolic control. Current continuous glucose monitoring (CGM) systems indicate the glucose level, the direction and magnitude of change of glucose levels, and can be used to assess glycaemic variability. In addition, real-time CGM sensors can serve as a tool to predict impending glucose excursions, thereby providing alarm signals of hypo- and hyperglycaemic values warning the patient to take preventative actions. Quality of life may also improve by using CGM via reducing the fear of hypoglycaemia. Particularly patients with brittle diabetes, hypoglycaemia unawareness, gastroparesis, pregnant women, or pump users, who are motivated to participate in their diabetes care and are technologically adept, may benefit from CGM. However, to successfully implement CGM in daily practice, these devices must be accurate and reliable, and one must be aware of the limitations of current CGM systems, that originate from physiological and technical aspects. Whether CGM succeeds in improving metabolic control, reducing hypoglycaemic episodes, and improving quality of life in the majority of patients remains to be proven. Should this be the case, real-time CGM may reduce chronic diabetic complications, and avoid hospitalisations, thereby reducing health care costs. In this article we will review indications, advantages, limitations, clinical and technical aspects of current minimallyinvasive and non-invasive CGM sensors.

Because of the limits of wearable needle-type or microdialysis-based enzymatic sensors in clinical use, fully implantable glucose monitoring systems (IGMS) represent a promising alternative. Long-term use reducing impact of invasiveness due to implantation, less frequent calibration needs because of a more stable tissue environment around the sensor and potential easier inclusion in a closed-loop insulin delivery system are the expected benefits of IGMS. First experiences with subcutaneous and intravenous IGMS have been recently collected in pilot studies. While no severe adverse events have been reported, biointerface issues have been responsible for the failures of IGMS. Tissue reactions around implanted subcutaneous devices and damages of intravenous sensors due to shearing forces of blood flow impaired IGMS function and longevity. In functioning systems, accuracy of glucose measurement reached satisfactory levels for average durations of about 120 days with subcutaneous IGMS and 259 days with intravenous sensors. Moreover, sensor information could help to improve time spent in normal glucose range when provided to patients wearing subcutaneous IGMS and allowed safe and effective closed-loop glucose control when intravenous sensors were connected to implanted pumps using intra-peritoneal insulin delivery. These data could open a favourable perspective for IGMS after improvement of biointerface conditions and if compatible with an affordable cost.

Over the past decade, several continuous glucose monitoring systems have been developed, representing remarkable technological achievements. Most of the systems monitor glucose invasively in the subcutaneous tissue. It is important to realize that there are discrepancies between blood and interstitial glucose concentration, which (1) may impact the quality of the system calibration and thereby the accuracy of the data, (2) may jeopardize the specificity and the sensitivity of hypoglycaemic alarms based on these systems and (3) must be considered in the design of closed-loop insulin delivery systems. The aim of this review is to make the point that the challenge of developing a continuous glucose monitoring system is not only technological, but must also take into account the physiology of glucose in alternate sites where it is sensed.

A clinically important task in diabetes management is the prevention of hypo/hyperglycemic events. The availability of continuous glucose monitoring (CGM) devices allow to develop new strategies, but new problems have also emerged. In this contribution, we discuss three major challenges which, in practical real time CGM applications, should be dealt with: filtering to enhance the signal-to-noise ratio, ahead-of-time prediction of glucose concentration, and generation of hypo/hyper-alerts. For all these challenges, some techniques, with a different degree of sophistication, have been proposed recently in the literature, but several issues remain open.

Continuous Glucose Sensors (CGS) generate rich and informative continuous data streams which have the potential to improve the glycemic condition of the patient with diabetes. Such data are critical to the development of closed loop systems for automated glycemic control. Thus the numerical and clinical accuracy of such must be assured. Although numerical point accuracy of these systems has been described using traditional statistics, there are no requirements, as of yet, for determining and reporting the rate (trend) accuracy of the data generated. In addition, little attention has been paid to the clinical accuracy. of these systems. Continuous Glucose-Error Grid Analysis (CG-EGA) is the only method currently available for assessing the clinical accuracy of such data and reporting this accuracy for each of the relevant glycemic ranges, - hypoglycemia, euglycemia, hyperglycemia. This manuscript reviews the development of the original Error Grid Analysis (EGA) and describes its inadequacies when used to determine point accuracy of CGS systems. The development of CG-EGA as a logical extension of EGA for use with CGS is described in detail and examples of how it can be used to describe the clinical accuracy of several CGS are shown. Information is presented on how to obtain assistance with the use of CG-EGA.

With more and more continuous glucose monitoring devices entering the market, the importance of adequate accuracy assessment grows. This review discusses pros and cons of Regression Analysis and Correlation Coefficient, Relative Difference measures, Bland Altman plot, ISO criteria, combined curve fitting, and epidemiological analyses, the latter including sensitivity, specificity and positive predictive value for hypoglycaemia. Finally, recommendations for much needed head-to-head studies are given. This paper is a revised and adapted version of How to assess and compare the accuracy of continuous glucose monitors?, Diabetes Technology and Therapeutics 2007, in press, published with permission of the editor.

The prevalence of type 1 diabetes continues to increase worldwide at a rate higher than previously projected, while the number of patients achieving American Diabetes Association (ADA) glycated hemoglobin (A1c) goals remains suboptimal. There are numerous barriers to patients achieving A1c targets including increased frequency of severe hypoglycemia associated with lowering plasma glucose as measured by lower A1c values. Continuous glucose monitoring (CGM) was first approved for retrospective analysis and now has advanced to the next step in diabetes management with the approval of real-time glucose sensing. Real-time CGM, in short term studies, has been shown to decrease A1c values, improve glucose variability (GV), and minimize the time and number of hypoglycemic events in patients with type 1 diabetes. These products are approved for adjunctive use to self-monitoring of blood glucose (SMBG), but future long-term studies are needed to document their safety, efficacy, ability to replace SMBG as a tool of monitoring, and ultimately utility into closed-loop insulin delivery systems. New algorithms will need to be developed that account for rapid changes in the glucose values, so that accuracy of the sensor data can be maintained. In addition, for better clinical care and usage, algorithms also need to be developed for both patients and the providers to guide them for their ongoing diabetes care.

Maintaining near-normal glycaemia in all patients with diabetes mellitus (DM) has become a standard and a well accepted recommendation. Unfortunately, most people with DM do not achieve this clinical goal because of marked glycaemic fluctuations and hypoglycaemia. Real-time continuous glucose monitoring (RT-CGM) has been introduced recently into clinical practice offering more knowledge about current glucose concentration and trend and enabling people with DM to intervene and prevent unwanted glucose excursions by acting upon real-time and predictive alarms. Several RT-CGM devices proved to be sufficiently accurate and feasible for routine use. Observational reports with The Guardian and Paradigm RT by Medtronic, the STS by DexCom, FreeStyle Navigator by Abbott and GlucoDay by Menarini established initial clinical benefit. Five randomised controlled trials (RCT) demonstrated significantly improved glucose variability or metabolic control, one of them showing a statistically significant and clinically meaningful decrease of HbA1c with a 3 months use of the Guardian RT (Medtronic, Northridge, CA). The great potential of RT-CGM devices to improve daily glucose control and quality of life in people with DM can only be developed further through RCTs, clarifying in more details the optimal clinical use and the most beneficial indications for this novel technique.

The effects of diabetes in pregnancy were first noticed in the beginning of the 19th 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.

Two randomised controlled trials have shown that maintenance of blood glucose levels below 110 mg/dl with intensive insulin therapy reduces mortality and morbidity of surgical and medical critically ill patients. An absolute reduction in the risk of death of 3-4 % is expected in intention-to-treat analysis, but the survival benefit increases when treatment is continued for at least a few days. Future studies set up to confirm the survival benefit and assign it as statistically significant in an intention-to-treat medical patient population should be adequately powered with inclusion of at least 5000 patients. For the observed benefits of intensive insulin therapy strict maintenance of normoglycaemia is primordial, whereas glycaemia- independent actions of insulin have minor, organ-specific impact. Pathophysiological mechanisms underlying the clinical effects are currently being unravelled further and might help to find new strategies for further improving outcome. Implementation of a strict glycemic control protocol in the intensive care unit is feasible and cost-effective, but asks for careful consideration of some practical aspects, such as prevention of hypoglycaemia, training of nurses and selection of accurate blood glucose measurement tools. Continuous blood glucose monitoring devices and closed-loop systems are under development and might be of great benefit to overcome these issues.

Stress hyperglycemia recently became a major therapeutic target in the Intensive Care Unit (ICU) since it occurs in most critically ill patients and is associated with adverse outcome, including increased mortality. Intensive insulin therapy to achieve normoglycemia may reduce mortality, morbidity and the length of ICU and in-hospital stay. However, obtaining normoglycemia requires extensive efforts from the medical staff, including frequent glucose monitoring and adjustment of insulin dose. Current insulin titration is based upon discrete glucose measurements, which may miss fast changes in glycemia and which does not give a full picture of overall glycemic control. Recent evidence suggests that continuous monitoring of glucose levels may help to signal glycemic excursions and eventually to optimize insulin titration in the ICU. In this review we will summarise monitoring and treatment strategies to achieve normoglycemia in the ICU, with special emphasis on the possible advantages of continuous glucose monitoring.

Hyperglycaemia following acute stroke is both common and prolonged, regardless of diabetes status. A substantial body of evidence, derived from animal and human literature, has demonstrated that post-stroke hyperglycaemia has a deleterious effect upon clinical and radiological stroke outcomes. Whether intensive glycaemic manipulation positively influences the fate of ischaemic tissue remains to be shown. This article provides an overview of the prevalence, aetiology, and mechanisms of tissue injury arising as a result of post-stroke hyperglycaemia, as well as exploring the evidence from glucose-lowering treatment trials to date. Additionally, novel insights into post-stroke hyperglycaemia derived from continuous glucose monitoring are discussed. Stroke is a leading cause of death worldwide and the commonest cause of long-term disability amongst adults. Increasing evidence suggests that disordered physiological variables following acute ischaemic stroke adversely affect outcomes. Of these, post-stroke hyperglycaemia (PSH) is the most frequently recognised abnormality and is documented in up to 50% of patients at the time of stroke presentation [1]. Importantly, a significant proportion of hyperglycaemic acute stroke patients (∼50%) have undiagnosed disorders of glucose metabolism, including diabetes [2,3]. Animal and human data have repeatedly demonstrated that PSH negatively impacts upon the fate of ischaemic brain tissue, with greater infarct growth, higher mortality and more severe disability being consistent findings amongst hyperglycaemic stroke subjects. For these reasons, PSH represents an attractive physiological target for acute stroke therapies with potential application across broad time windows, stroke subtypes and stroke severity. In addition to providing an overview of the adverse effects of hyperglycaemia following acute ischaemic stroke, this article aims to summarise the evidence from current glucose-lowering treatment trials as well as exploring continuous glucose monitoring and the implications for future glycaemic manipulation.

This review aims to classify algorithms for intravenous insulin infusion according to design. Essential input data include the current blood glucose (BGcurrent), the previous blood glucose (BGprevious), the test time of BGcurrent (test timecurrent), the test time of BGprevious (test timeprevious), and the previous insulin infusion rate (IRprevious). Output data consist of the next insulin infusion rate (IRnext) and next test time. The classification differentiates between “IR” and “MR” algorithm types, both defined as a rule for assigning an insulin infusion rate (IR), having a glycemic target. Both types are capable of assigning the IR for the next iteration of the algorithm (IRnext) as an increasing function of BGcurrent, IRprevious, and rate-of-change of BG with respect to time, each treated as an independent variable. Algorithms of the IR type directly seek to define IRnext as an incremental adjustment to IRprevious. At test timecurrent, under an IR algorithm the differences in values of IRnext that might be assigned depending upon the value of BGcurrent are not necessarily continuously dependent upon, proportionate to, or commensurate with either the IRprevious or the rate-of-change of BG. Algorithms of the MR type create a family of IR functions of BG differing according to maintenance rate (MR), each being an iso-MR curve. The change of IRnext with respect to BGcurrent is a strictly increasing function of MR. At test timecurrent, algorithms of the MR type use IRprevious and the rate-of-change of BG to define the MR, multiplier, or column assignment, which will be used for patient assignment to the right iso-MR curve and as precedent for IRnext. Bolus insulin therapy is especially effective when used in proportion to carbohydrate load to cover anticipated incremental transitory enteral or parenteral carbohydrate exposure. Specific distinguishing algorithm design features and choice of parameters may be important to establish freedom from hypoglycemia, eliminate the need for administration of concentrated dextrose during euglycemia, control variability within the treatment course of individual patients, achieve adaptability to differing blood glucose targets, and minimize variability of glycemic control between treatment courses of different patients or patient populations. Areas for future work include the reduction of nursing burden, the development of a theory that will account for lag time of interstitial monitoring and pharmacodynamic delay of insulin action, and management strategies for the narrow euglycemic range. It is hoped that hypoglycemia and variability of control will become negligible problems, and that fear of hypoglycemia no longer will deflect investigators and caregivers from providing optimal glycemic management.

Improvements in accuracy of real-time continuous glucose monitoring facilitate the development of closed-loop systems consisting of a continuous glucose monitor, a control algorithm, and an insulin pump. Closedloop systems can be divided according to the way meal delivery is handled as “fully closed-loop” or “closed-loop” with meal announcement systems. Depending on the subcutaneous (sc) or intravenous (iv) body interface, three major types of closed-loop systems are recognised, (i) sc sensing and sc delivery system, (ii) the iv sensing and intraperitoneal delivery system, and (iii) the iv glucose sensing and iv insulin delivery system. Given the current research focus, this review centres on the sc-sc closed-loop approach, which has the greatest potential for a near-future commercial exploitation as recognised by the JDRF Artificial Pancreas Project. Other approaches utilising intraperitoneal or intravenous sensing/delivery are also discussed. Closed-loop systems may revolutionise diabetes management but their introduction is likely to be gradual starting from simpler applications such as hypoglycaemia prevention or overnight glucose control progressing to more complex approaches such as 24/7 glucose control. The most important question is what is achievable with existing technologies and when the first generation of closed-loop systems will find its way into clinical practice.