ABSTRACT

It is recognized that traditional measures of glucose control (such as hemoglobin A1c [A1C]) provide little information regarding the need for day-to-day changes in therapies. While intermittent self-monitored blood glucose (SMBG) provides additional information with which to make treatment decisions, significant barriers to its use exist, such as inconvenience and lack of timely and regular feedback. Furthermore, important information regarding glucose trends may be missed. Continuous glucose monitoring (CGM) has become increasingly reliable and has demonstrated efficacy in terms of improving A1C, reducing hypoglycemia, and improving the time in target glucose range. Incremental progress continues to be made toward a fully functional artificial pancreas, of which CGM will play a vital role. As more and more data are presented to patients and providers, it has become increasingly paramount that the data are organized in a standardized way and that communication of data is streamlined using patients’ mobile devices where available and within the existing clinic infrastructure. Systems that provide immediate feedback to patients and decision support tools for patients and providers have demonstrated superior outcomes compared to routine SMBG alone. Alternate markers of glucose control may provide complementary information about glucose control and long-term prognosis. This chapter will review the latest evidence for use of professional and personal CGM, mobile glucose monitoring approaches, and biomarkers of glycemic control. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG.

INTRODUCTION

The current technology for monitoring of glucose levels has been well established since the 1980′s. This practice is beneficial to patients with diabetes from both a clinical and an economic standpoint when used optimally. Knowledge of the glucose levels that are measured can allow a patient to select an appropriate dose of insulin or implement dietary or other lifestyle changes to regulate their glucose levels. Expert groups provide recommendations for glucose targets, including A1C, self-monitored blood glucose (SMBG), and interstitial glucose (1,2). Although targets vary, expert groups recommend individualization based upon risk of hypoglycemia, polypharmacy, comorbidities, and other characteristics that may affect long-term benefit and individual patient characteristics. The ADA has expanded recommendations for assessing overall glucose levels to include the A1C or CGM metrics such as % Time in Range (TIR, the % of time spent 70-180 mg/dl), or the Glucose Management Indicator (GMI), which is an estimate of A1C that is derived from a 14-day CGM report for routine assessment of glucose levels (1).

The landscape of glucose monitoring technologies is expanding and rapidly changing. For a full review of glucose monitoring technologies, the reader is referred to one of many excellent reviews referenced throughout this chapter. Several trends are emerging in glucose monitoring and will be reviewed in more detail in this chapter:

• CGM: This practice is becoming more widely established as evidence supporting its use has accumulated. The data available through CGM can permit significantly more fine-tuned adjustments in insulin dosing and other therapies than spot testing from self-monitoring of blood glucose (SMBG) can provide. CGM technologies for automatic collection of data have spurred interest in noninvasive glucose monitoring as an additional tool for obtaining information about glucose levels.
• Closed loop control (CLC): Also known as an “artificial” or “bionic” pancreas, this technology links CGM with automatically controlled insulin delivery. The first steps toward CLC are now in use.
• Mobile Technology and Decision Support: In recent years, increasing connectivity between glucose monitoring technologies and mobile devices has facilitated ongoing improvements in self-care and communication of data.
• Alternate Markers of Glucose Control: Finally, the use of additional analytes besides glucose is still being established.

This chapter analyzes the technology, benefits, and problems with the use of intermittent SMBG and CGM, mobile technology and decision support, and alternate biomarkers of glycemic control.

CONTINUOUS GLUCOSE MONITORS

CGM measures glucose levels (typically interstitial glucose) continuously and updates the glucose level display every 5 minutes. Most CGMs consist of 1) a monitor to display the information (in some cases, this is the patient’s mobile device), 2) a sensor that is usually inserted into the subcutaneous tissue, and 3) a transmitter that transmits the sensor data to the monitor. Previously, all devices were approved for adjunctive use only due to limitations in accuracy; in this case patients must still perform fingerstick glucose monitoring in order to guide therapy and perform calibrations. However, in 2016, the FDA approved the use of the Dexcom G5 as the first CGM for stand-alone use. Newer technologies have eliminated the requirements for calibration of CGM with a fingerstick glucose. The accuracy of all commercially available CGMs is still the lowest in the hypoglycemic range, which is where the need for sensitivity and specificity is great in terms of serving as an alarm for hypoglycemia.

CGM can provide both retrospective as well as real-time information to detect: 1) hypoglycemic and hyperglycemic excursions; 2) predict impending hypoglycemia; and 3) wide fluctuations in glucose levels, also known as glycemic variability. 24-hour telephone support is available for all FDA approved CGM devices. Use of CGM can help both the patient and their medical provider make fine tune adjustments to medication therapy and provide insight to the patient on behavioral changes to achieve glycemic control. Additionally, current efforts to link CGM measurement with automatically controlled insulin delivery, has progressed incrementally toward a fully functional artificial pancreas. Systems can be divided according to their intended use as professional CGM (which is a clinic-owned device and provides either retrospective or real-time glucose data) and personal CGM (which is patient-owned and provides real-time glucose data).

PROFESSIONAL CGM

Professional CGM describes CGM data that are obtained via healthcare provider owned equipment. It does not necessarily provide the glucose results in real time, but downloads the readings after they have been collected, similar to a 24-hour cardiac Holter monitor that provides information about cardiac rhythms after they have occurred. This allows the health care provider to obtain relatively unbiased glucose patterns during typical everyday life. The Endocrine Society recommendations state that professional CGM may be of benefit in adults with diabetes to detect nocturnal hypoglycemia, dawn phenomenon, postprandial hyperglycemia and to assist in management of diabetes therapies (3). Professional CGM is more readily reimbursed than personal CGM, but interpretation of both personal and professional CGM reports by qualified healthcare professionals may be reimbursed on a monthly basis.

Some personal CGM systems can be operated in a blinded fashion in order to provide professional glucose data. These systems will be discussed in more detail later (see “Personal [Real-time] Continuous Glucose Monitoring”). The first device for reading blood glucose levels continuously was a professional CGM that was approved by the FDA in June 1999. This device was the Continuous Glucose Monitor System (CGMS) manufactured by Medtronic MiniMed (Medtronic Diabetes, Northridge, CA) (4). Since then, newer models have shown improvements in accuracy and patient acceptance. In a meta-analysis of 22 articles, professional CGM resulted in a greater reduction in A1c (-0.28%, 95% CI -0.36% to -0.21%, P < 0.00001) as well as TIR (5.59%, 95% CI 0.12 to 11.06, P = 0.05) compared to usual practice (5).

Analysis of Retrospective Data

Data from all CGM devices can be studied retrospectively after downloading (8). It is recommended that diet, activity, symptom, and insulin data are collected during professional CGM to assist with interpretation, either via patient diary, direct entry of events into the device, or use of an accompanying app, depending on the system. Three time periods should be analyzed. These are:

• Overnight: Out-of-target overnight glucose levels can be modified by adjusting the basal insulin dose.
• Pre-prandial Period: Out-of-target pre-prandial glucose levels can be modified by adjusting the previous meal bolus, meal, or exercise pattern.
• Post-prandial period: Out-of-target postprandial glucose levels can be modified by adjusting the immediate meal bolus, meal, or exercise pattern.

In certain special situations, targets may need to be adjusted. Other important elements of a professional CGM analysis are shown in Table 1. An example of a patient who used CGM is presented in Figure 1. The CGM demonstrated high glucose levels from 6:00 PM to 11:00 PM post-supper and low glucose levels from 12:00 AM to 2AM. Recognition of these patterns allowed appropriately timed treatment interventions.

Table 1.

Elements of Professional Continuous Glucose Monitoring Analysis

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Overall Control Mean Glucose Glucose Variability (Standard Deviation, Coefficient of Variation) Daily Detail Diurnal Patterns: dawn phenomenon, overnight Meal effects Correction Exercise effects Other patterns (work days vs. weekend, menstrual cycles) Hypoglycemia Precipitating factors Corresponding meter glucose (recognition)

Figure 1.

CGM tracing of a patient whose glucose levels were high from 6:00 PM to 11:PM post-supper and low from 12:00 AM to 2AM.

Composite Metrics

As a measure of the quality of glycemia, the time in range (TIR), similar to the A1C is limited in its assessment of hypoglycemia. Multiple composite metrics have thus been reported (11). However, the use of multiple metrics increases complexity and is subject to issues with collinearity. The Glycemia Risk Index (GRI) is a composite metric that was developed using input from 330 clinical experts who analyzed 14-day tracings from 225 adults with diabetes (12). GRI more heavily weights very high or very low glucose values and correlates with clinician rankings more closely than TIR or time below range (%time < 70 mg/dl, TBR) alone.

Figure 2.

Ambulatory Glucose Profile for Insulin Pumps.

Green: lines above and below the shaded box (whiskers) show how many of the bolus dosages per hour were between 75 - 90% and between 10 - 25%.

Blue: shaded box shows 50% of the bolus dosages in the hour.

Orange: median (middle) dot.

Bolus Insulin Graph: Combines all bolus insulin doses into one graph to make a one-day (24-hour) picture. Each box on the graph covers 60 minutes of doses.

Insulin Profile Graph: Shows basal insulin pump settings over a 24-hour period.

Green: 10% of values are above (90% top line) and 10% are below (10% bottom line).

Blue: area between blue lines shows 50% of the glucose values.

Orange: median (middle) glucose line.

Glucose Profile: Daily glucose profiles are combined to make a one-day (24-hour) picture. Ideally, lines would stay within grey shaded area (target range).

Glucose Statistics: Metrics include mean glucose, estimated A1C, glucose ranges, coefficient of variation and standard deviation.

PERSONAL REAL-TIME CGM (RT-CGM) OR INTERMITTENTLY SCANNED CGM (IS-CGM)

RT-CGM devices not only display the current glucose every few minutes, but may also alert the patient for impending (projected alert) or actual (threshold alert) hyperglycemia or hypoglycemia or rate of change in glucose. By comparison, is-CGM requires patient interaction with the device to obtain readings but may still provide alerts for hypoglycemia or hyperglycemia. While few head to head studies are available, some studies suggest greater reduction in hypoglycemia and improvement in TIR with RT-CGM compared to is-CGM in persons with type 1 diabetes (13,14), even up to 24 months (15).

Over time, accuracy with RT-CGM and is-CGM has improved substantially (16,17,18). In fact, some devices, including the Dexcom and Freestyle Libre are approved for stand-alone use, meaning that under specified conditions, the device may be used to make treatment decisions without confirmatory blood glucose measure. However, the user will still experience a tradeoff between a high alarm sensitivity and specificity for detecting hypoglycemic events, particularly where glucose levels are changing rapidly (Figure 3). Current and recent glucose levels, trend information, and a visual alarm are all presented so that a patient can predict future low or high glucose excursions. Using this information will allow the patient to take actions to spend more time in the euglycemic range and less time in the hypoglycemic or hyperglycemic ranges. This potential decrease in glycemic variability will not necessarily be reflected in an improved A1C value, which reflects mean glycemic levels.

Figure 3.

Tradeoffs between emphasis on high sensitivity compared to emphasis on high specificity in a hypoglycemic alarm that is part of a continuous glucose monitor.

STEPS TOWARDS AN ARTIFICIAL PANCREAS

Until recently, RT-CGM technology has operated completely independently of insulin delivery. By combining continuous basal insulin delivery during fasting periods with discrete bolus doses of insulin at mealtimes, insulin delivery can be crafted to mimic the natural pattern of pancreatic insulin release. An artificial pancreas consists of: 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a signal to send the glucose level to the body surface for continuous display onto a monitor. Limitations to full implementation include sensor accuracy and lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulin sensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness (99). However, even incremental advances improve glucose control without increasing the complexity of decision-making on the part of the patient. These include:

• Low glucose (threshold) suspend: the insulin pump suspends when the glucose decreases below a pre-set value.
• Suspend before low: insulin pump suspends when hypoglycemia is predicted.
• Hybrid closed loop: insulin delivery increases or decreases based upon the sensor glucose value but meal boluses are still required.
• Closed loop control: fully closed loop delivery without the need for meal boluses
• Dual hormone systems: these are hybrid closed loop or closed loop control systems that utilize glucagon or other peptides (such as amylin) in an effort to more closely mimic the physiology of the endocrine pancreas.

The long-term safety, efficacy, cost, and cost-effectiveness of an artificial pancreas are still largely unknown at this time. However, the urgency of this technology is demonstrated by the #WeAreNotWaiting movement, which has given rise to home-grown, crowd-sourced, patient driven systems that utilize existing devices which are linked by open-source software, such as Open Artificial Pancreas System, and Loop. Recently Tidepool Loop received FDA approval (100). A retrospective observational study of patients with Type 1 diabetes demonstrated lower mean glucose, higher time in target range, and less time in hypoglycemia using Open Artificial Pancreas Systems (OpenAPS) compared to sensor augmented pump use alone (101). In general, open-source systems carry safety concerns, particularly among less tech-savvy patients, in the absence of regulatory approval (102). However, the healthcare provider can provide safety recommendations as well as a back-up plan in case of system failure (103). The reader is referred to one of several reviews as a detailed review is beyond the scope of this chapter (104,105).

Closed Loop Systems (CLC)

Additional steps toward closed loop control (CLC) insulin delivery require algorithmic insulin adjustments, which arguably present additional safety concerns. Overnight CLC insulin delivery is relatively straightforward, whereas post-meal control and exercise effects remain the most challenging of events to manage. Until recently, most randomized studies have been small and reported only short-term outcomes, often in controlled settings.

ILET BIONIC PANCREAS

The iLet Bionic pancreas was approved by the FDA in 2023. This insulin pump is initiated using the patient’s body weight and requires meal announcements (designated as small, medium, or large) but not formal carb counting and thus represents an incremental step toward a fully closed loop insulin pump. In A 13-week multi-center randomized study of 219 participants with type 1 diabetes demonstrated a greater reduction in A1c with the iLet bionic pancreas (-0.5%, [95% CI -0.6 to -0.3; P<0.001) but no difference in hypoglycemia compared to standard care (132).

OTHER SYSTEMS

Systems have utilized single hormone (rapid acting insulin only) or dual hormone (both fast- acting insulin analog and glucagon to imitate normal physiology) as directed by a computer algorithm (Figure 4) (133). At this time, there are insufficient data demonstrating the superiority of one system or algorithm compared to others. The three most common algorithms are:

• Model Predictive Control (MPC): predicts future glucose levels and adjusts insulin delivery in response.
• Proportional Integral Derivative (PID): calculates the deviation of glucose from target to determine insulin delivery.
• Fuzzy Logic (FL): mimics insulin dosing based upon clinical expertise.

A meta-analysis of 40 randomized studies (35 studies using insulin alone and 9 dual hormone studies) including 1027 participants with type 1 diabetes demonstrated a significant increase in % time in target range (70-180 mg/dl, weighted mean difference 9.6%, 95% CI 7.5-12%), as well as less time in hypoglycemia or hyperglycemia, regardless of type of system (134). In another meta-analysis of 24 studies and 585 participants (7 studies using dual-hormone therapy and 20 studies of insulin only) reported greater improvement in time in target with artificial pancreas systems (12.6%, 95% CI 9.0-16.2, p<0.0001), and greater improvement with dual hormone compared to single hormone systems (135). Another meta-analysis of studies with at least 8 weeks duration confirmed these findings (136). A systematic review and meta-analysis of 25 studies in 504 children demonstrated superior %TIR with CLC and bi-hormonal systems vs. single hormone systems (137).

Figure 4.

Dual hormone Closed Loop Control system.

MINIMALLY INVASIVE AND NON-INVASIVE GLUCOSE MONITORS

Continuous hypoglycemia detection systems using current sensing technology must be either implanted (fully or partially, either subcutaneously or into a blood vessel). Implantation is more secure, but may be associated with biocompatibility problems or local irritation. Less invasive methods may be categorized as minimally invasive or noninvasive. Minimally invasive techniques extract fluid (tears or interstitial fluid) while noninvasive technologies do not.

Minimally invasive methods include electrical, nanotechnology, and optical approaches while noninvasive techniques rely on some form of radiation without the need to access bodily fluids. Noninvasive methods frequently incorporate electric, thermal, optical, or nanotechnology methods for detection. Many noninvasive devices under development are aimed for non- continuous monitoring as they often require controlled surroundings including factors such as light, motion and temperature.

• Optical approaches utilize reflective, absorptive, or refractive properties of infrared and optical bands of the light spectrum to detect glucose. Pure optical methods under development utilize Raman and Near infra-red spectroscopy.
• Thermal methods detect glucose via the far-infrared band of the spectrum and provide noninvasive approaches for glucose monitoring.
• Electric methods use electromagnetic radiation, currents, or ultrasound approaches to detect dielectric properties of glucose. Reverse iontophoresis has been employed with early minimally invasive approaches while bioimpedance spectroscopy has been used in recent noninvasive approaches.
• Nanotechnologies aim to miniaturize existing technologies, including fluorescence and surface plasmon resonance (SPR) approaches (138).

Few devices (other than interstitial CGMs discussed above) have demonstrated high levels of accuracy recommended by expert groups, though several have been approved by CE or FDA (139).

MOBILE TECHNOLOGY AND DECISION SUPPORT

It has become increasingly clear that the isolated use of glucose monitoring technologies without a specific plan to address the data provides minimal benefit, particularly among patients with type 2 diabetes or who are not using insulin (140). In order for glucose monitoring to provide the most benefit, patients and providers must be able to easily obtain and communicate the data. Data must be organized in such a way that patterns can be identified, and patients must receive feedback at the point of care. The widespread use of mobile devices provides opportunities for data collection, analysis, and communication of results with health care providers as well as facilitates digital or remote clinical models of care (141). Finally, as healthcare providers are inundated with more data and spend increasing amounts of time using electronic medical records, it has also become paramount that devices and or reports from the devices communicate or interface with these systems (142).

Hurdles to wider implementation of mobile technology include the lack of usability (both for patients, as well as providers who may be expected to review and act upon reports), safety, efficacy (including long-term adherence), and cost-effectiveness studies (143). The lack of data is in part due to the rapidly changing technology itself, which renders the technology obsolete by the time a vigorous clinical trial is conducted and published. The fee for service model is a major barrier to adapting many glucose monitoring technologies, which often require frequent feedback and treatment adjustments, efforts that are not reimbursed without an actual office visit. Finally, cyber security is a big concern for all medical devices, especially for devices that are controlled by a smartphone (144).

BIOMARKERS OF GLYCEMIC CONTROL

1,5-Anhyroglucitol

The aforementioned biomarkers for measuring glycemic control, (A1C, fructosamine, and glycated albumin) only reflect mean levels of glycemia. These measures can fail to portray hyperglycemic excursions if they are balanced by hypoglycemic excursions. Plasma 1,5- anhydroglucitol (1,5-AG) is a naturally occurring dietary monosaccharide, with a structure similar to that of glucose (Figure 5). This analyte has been proposed as a marker for postprandial hyperglycemia (213). An automated laboratory grade assay named Glycomark is approved in the U.S. for measuring 1,5-AG as a short-term marker for glycemic control. A similar laboratory assay has been used in Japan. During normoglycemia, 1,5-AG is maintained at constant steady-state levels because of a large body pool compared with the amount of intake and because this substance is metabolically inert. Normally, 1,5-AG is filtered and completely reabsorbed by the renal tubules. During acute hyperglycemia when the blood glucose levels exceed 180 mg/dl, which is the renal threshold for spilling glucose into the urine, serum 1,5-AG falls. This fall occurs due to competitive inhibition of renal tubular reabsorption by filtered glucose. The greater the amount of glucose in renal filtrate (due to hyperglycemia), the less 1, 5-AG is reabsorbed by the kidneys. The 1,5-AG levels respond sensitively and rapidly to rises in serum glucose and a fall in the serum level of this analyte can indicate transient elevations of serum glucose occurring over as short a period as a few days. Measurement of 1,5-AG can be useful in assessing the prior 1-2 weeks for: 1) the degree of postprandial hyperglycemia; and 2) the mean short-term level of glycemia. This assay might prove useful in assessing the extent of glycemic variability that is present in an individual with a close-to-normal A1C level, but who is suspected to be alternating between frequent periods of hyperglycemia and hypoglycemia. In such a patient, the 1,5-AG level would be low, which would indicate frequent periods of hyperglycemia, whereas in a patient with little glycemic variability, the 1,5-AG levels would not be particularly depressed because of a lack of frequent hyperglycemic periods. In the ARIC study, 1,5-AG was associated with severe hypoglycemia after adjustment for other variables, an observation which is consistent with the role of 1,5-AG in reflecting glycemic variability, a known risk factor for hypoglycemia (214).

Longitudinal data from the ARIC study showed that 1,5-AG was associated with ESRD over a 19-year follow-up period, but the relationship was no longer significant after adjusting for glucose control with other markers (215). Among participants with diabetes and A1C <7%, each 5 mcg/mL decrease in 1,5-AG was associated with an increase in dementia risk by 16%, and at A1C >7%, there was also a significant association over a median 21-year follow-up period (216). There was also an association of 1,5-AG and cardiovascular outcomes in ARIC, which persisted, though were attenuated after adjusting for A1C (217). Therefore, it is not yet clear whether 1,5-AG, as a measure of glucose excursions, provides incremental value beyond A1C for predicting long-term complications.

Figure 5.

Structure of glucose (left) and 1,5-anhydroglucitol (right).

CONCLUSIONS

Many new types of technology are increasingly being developed and applied to fight diabetes and its complications. New technologies will improve the lives of people with diabetes by measuring glucose and other biomarkers of glycemic control and linking glucose levels with insulin delivery to improve the lives of people with diabetes.

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