Abstract
Hemoglobin A1c (HbA1c) has long been considered a cornerstone of diabetes mellitus (DM) management, as both an indicator of average glycemia and a predictor of long-term complications among people with DM. However, HbA1c is subject to non-glycemic influences which confound interpretation and as a measure of average glycemia does not provide information regarding glucose trends or about the occurrence of hypoglycemia and/or hyperglycemia episodes. As such, solitary use of HbA1c, without accompanying glucose data, does not confer actionable information that can be harnessed to guide targeted therapy in many patients with DM. While conventional capillary blood glucose monitoring (BGM) sheds light on momentary glucose levels, in practical use the inherent infrequency of measurement precludes elucidation of glycemic trends or reliable detection of hypoglycemia or hyperglycemia episodes. In contrast, continuous glucose monitoring (CGM) data reveal glucose trends and potentially undetected hypo- and hyperglycemia patterns that can occur between discrete BGM measurements. The use of CGM has grown significantly over the past decades as an ever-expanding body of literature demonstrates a multitude of clinical benefits for people with DM. Continually improving CGM accuracy and ease of use have further fueled the widespread adoption of CGM. Furthermore, percent time in range correlates well with HbA1c, is accepted as a validated indicator of glycemia, and is associated with the risk of several DM complications. We explore the benefits and limitations of CGM use, the use of CGM in clinical practice, and the application of CGM to advanced diabetes technologies.
Introduction
For decades, hemoglobin A1c (HbA1c), an indicator of average glucose levels over the preceding approximately 3 months, has been widely recognized as a foundational indicator of glycemia and predictor of long-term vascular complications among people with diabetes mellitus (DM) (1, 2, 3). First developed in the 1960s, HbA1c has been included in the criteria for diabetes diagnosis per the American Diabetes Association (ADA) Standards of Medical Care in Diabetes since 2010 (4). As such, HbA1c has been a critical component of diabetes management and has many well-established benefits including widespread global availability, affordability, feasibility, and ease of measurement even among resource-limited communities. In an individual, an elevated HbA1c measurement can serve as a ‘call-to-action’ that therapeutic intervention must be undertaken. In addition, HbA1c has clinical utility as a metric of achievement of glycemic targets across populations. HbA1c is, however, an indirect marker of glycemia, signifying the amount of glucose bound to hemoglobin in red blood cells (5). Additionally, as newer technologies for measuring glycemia such as continuous glucose monitoring (CGM) have advanced and come of age, we have come to appreciate that the HbA1c assay is fraught with limitations which render it a less useful clinical measure for individualized management than previously appreciated.
A significant limitation of HbA1c is that it provides a single snapshot of average glycemia over months and as such does not provide actionable information regarding glucose patterns over the day, glucose variability, or the amount of time spent in the hyperglycemia, hypoglycemia, and target ranges or hypoglycemia (6). Importantly, the HbA1c is insensitive to the occurrence and frequency of hypoglycemia, which can be clinically relevant and dangerous to an individual even when present for short periods during the day or night (7). Additionally, since the HbA1c test is based upon intracellular glycation of hemoglobin molecules within red blood cells, any pathophysiological state that alters red blood cell lifespan can make HbA1c less reliable as a measure of glycemic control. Conditions that alter red blood cell lifespan and thus affect HbA1c include but are not limited to chronic kidney disease, chronic liver disease, iron deficiency, vitamin B12 deficiency, pregnancy, and erythropoietin administration (8). Additionally, some HbA1c assays are less reliable for patients with hemoglobinopathies including thalassemias and sickle cell variants (9). Even among patients without hemoglobinopathy or conditions known to alter red blood cell lifespan there appears to be interindividual variation in red blood cell lifespan which can result in differing HbA1c for the same average blood glucose (10). HbA1c levels may also vary with race/ethnicity independent of glucose levels, even when controlling for possible confounders such as socioeconomic status, treatment status, and healthcare utilization (11, 12). When HbA1c is elevated in the absence of artifactual interference or non-glycemic influences as mentioned earlier, it does indicate that action is needed; however, an elevated HbA1c alone cannot convey the granular information often needed to most effectively guide therapeutic modifications. Taken altogether, the role of HbA1c in clinical practice is limited by both its inability to reliably reflect average glycemia in some individuals as well as its inability to provide actionable data about glycemic patterns that can be harnessed to guide therapeutic modifications. A comparison between HbA1c and CGM is shown in Fig. 1. While traditional capillary blood glucose monitoring (BGM) can detect contemporaneous hypo- and hyperglycemia at the moment it is assessed, infrequent and intermittent BGM does not shed light on glycemic trends and is typically performed with insufficiency frequency to capture the hypo- or hyperglycemia episodes which often occur unrecognized in between sporadic BGM measurements.
Comparison of hemoglobin A1c and continuous glucose monitoring. CGM, continuous glucose monitoring; DM, diabetes mellitus; HbA1c, hemoglobin A1c.
Citation: Endocrine Connections 12, 7; 10.1530/EC-23-0085
In this review, we explore the benefits as well as limitations of CGM use, strategies for effective incorporation of CGM use and interpretation into real-world clinical practice, and the application of CGM technologies into integrated advanced DM technologies.
Benefits of CGM use
Fortunately, the advent and advancement of CGM technology have dramatically enhanced the quantity and quality of glucose information available to patients and clinicians. First approved by the United States Food and Drug Administration (FDA) in 1999 and commercially available since 2000, CGM systems are composed of a sensor that detects interstitial glucose levels, a transmitter that is attached to or secured near the sensor, and a receiver (either a handheld device or smartphone) that displays the information from the sensor and transmitter (13, 14). The receiver may update its display in real-time or on an intermittent basis when the sensor is scanned, depending on the system. Real-time CGM (rtCGM) systems include the transdermal devices Dexcom G6 and G7 (Dexcom Inc., San Diego, CA, USA), Guardian Sensor 3 (Medtronic, Northridge, CA, USA), and FreeStyle Libre 3 (Abbott Diabetes Care, Alameda, CA, USA), as well as the implantable device Eversense (Senseonics, Germantown, MD, USA). Intermittently scanned or flash CGM (isCGM) systems include the FreeStyle Libre 14 day system and FreeStyle Libre 2 (Abbott Diabetes Care) (15, 16). While early CGM models were limited by wide margins of measurement error and short sensor life, currently available systems have benefitted from substantial improvements in accuracy and sensor durability. As a result of this improved accuracy, with mean absolute relative difference (MARD) reduced to 10% or less, many of these systems are approved for non-adjunctive use and can be used to guide therapeutic decisions, making them reliable and powerful tools for diabetes management (17).
Contrary to the aforementioned limitations of HbA1c as a measure of glycemic control, CGM systems provide an abundance of information about individual glucose levels, trends, and glycemic variability. A retrospective review of this data allows clinicians to detect hyperglycemia and hypoglycemia; to assess the pattern, frequency, duration, and severity of these episodes; and to formulate therapeutic regimens specifically tailored to address the identified issues. Real-time viewing of glucose levels and notifications regarding direction and rate of change enables patients to promptly address any current or impending hyperglycemic or hypoglycemic events. In both scenarios, CGM data can reveal asymptomatic glycemic excursions that might otherwise go undiscovered and therefore untreated when relying on A1c and intermittent capillary BGM alone (18, 19, 20). Moreover, increased time spent in the standardized target range of 70–180 mg/dL (3.9–10 mmol/L) is associated with decreased risk and severity of microvascular (21, 22, 23, 24, 25) and macrovascular (26, 27) complications in patients with diabetes.
Since the inception of CGM, over two dozen trials enrolling over 3500 patients have been conducted, which collectively have demonstrated clinical benefit in a variety of patient populations, including patients with type 1 diabetes (T1D) and type 2 diabetes (T2D); patients on continuous subcutaneous insulin infusion (CSII) therapy and those utilizing multiple daily injections of insulin, basal insulin, oral antidiabetic drugs, or some combination thereof. Benefit has been found in both randomized control trials as well as observational studies and has been shown with both rtCGM and isCGM (28). rtCGM continuously records and transmits real-time numerical glucose data and trends to a receiver or smartphone. isCGM is able to record this same data; however, it is transmitted only when the user actively scans the sensor with the receiver or smartphone (29). Details of pertinent studies are outlined in Tables 1, 2 and 3.
Major studies evaluating clinical outcomes with CGM use in people with type 1 diabetes.
| Reference | Study design | Duration | Primary outcome | Number of participants | Mean age (years) | DM treatment type | rtCGM or isCGM | Improvement in HbA1c/TIR with CGM | Improvement in hypoglycemia/time below range with CGM |
|---|---|---|---|---|---|---|---|---|---|
| Deiss and colleagues (2006) (91) | RCT | 3 months | Change in HbA1c | 156 | Children and adults | CSII or MDI | rtCGM | Yes −1.0 ± 1.1% (CGM) vs −0.4 ± 1.0% (control) P = 0.003 |
No |
| JDRF CGM Study Group and colleagues (2008) (92) | RCT | 26 weeks | Change in HbA1c | 322 | Children and adults | CSII or MDI | rtCGM | Yes, for >25 years old −0.53%, P < 0.001 |
No |
| O’Connell and colleagues (2009) (93) | RCT | 3 months | Change in TIR | 55 | Children and adults | CSII | rtCGM | TIR: No HbA1c: Yes −0.43% P = 0.009 |
No |
| Battelino and colleagues (2012) (94) | RCT Crossover trial |
6-month arms | Change in HbA1c | 153 | Children and adults | CSII | rtCGM | Yes −0.43%, P < 0.001 |
Yes Time <3.9 mmol/L 19 (CGM on) vs 31 (CGM off) min/day, P = 0.009 |
| Tumminia and colleagues (2015) (95) | RCT Crossover trial |
6-month arms | Change in HbA1c | 20 | CSII: 31.3 MDI: 36.6 |
CSII or MDI | rtCGM | Yes, for those with CGM use >40% of the time 7.76 ± 0.4% (during CGM use) vs 8.54 ± 0.4% (baseline), P < 0.05 |
Yes, for patients on MDI during CGM period AUC <70 (mg/dL/day) 1.50 ± 2.4 (baseline) vs 0.49 ± 0.5 (end), P = 0.03 |
| Beck and colleagues (2017) (96) | RCT | 24 weeks | Change in HbA1c | 158 | 48 | MDI | rtCGM | Yes −0.6%, P < 0.001 |
Yes Median minutes <70 mg/dL, P = 0.002 |
| Lind and colleagues (2017) (97) | RCT Crossover trial |
26-week arms | Change in HbA1c | 161 | 43.7 | MDI | rtCGM | Yes −0.43%, P < 0.001 |
Numerically less time <70 mg/dL 2.79% (CGM) vs 4.79% (usual care) |
| Leelarathna and colleagues (2022) (98) | RCT | 24 weeks | Change in HbA1c | 156 | 44 | CSII or MDI | isCGM | Yes −0.5%, P < 0.001 |
Yes Time <70 mg/dL −3.0% (95% CI −4.5 to −1.4) |
| Nathanson and colleagues (2021) (99) | Retrospective Observational Study (Data from the Swedish National Diabetes Register) |
2-year observation period after initiation of isCGM | Change in HbA1c Rates of severe hypoglycemia |
isCGM users: 14,372 Control: 7691 |
isCGM: 47.88 Control: 48.75 |
CSII or MDI | isCGM | Yes −0.11%, P < 0.0001 Greatest in those with baseline HbA1c >8.5%: −0.23%, P = 0.0002 at 15–24 months post-index |
Yes Risk of severe hypoglycemia reduced by 21% for isCGM users compared to control, P = 0.0014 |
| Bolinder and colleagues (2016) (100) | RCT | 6 months | Change in TBR | 241 | CGM: 42 Control: 45 |
CSII or MDI | isCGM | HbA1c: No TIR: Yes, significantly increased in CGM group, P = 0.0006 |
Yes 38% reduction in time <70 mg/dL (−1.24 h/day, P < 0.0001) |
| Hermanns and colleagues (2014) (101) | RCT Crossover trial |
5-day arms | Change in TBR | 41 | 42 | CSII or MDI | rtCGM | Not studied | Yes Duration of hypoglycemia reduced by 56 min/day during rtCGM period (P < 0.01) |
| van Beers and colleagues (2016) (102) | RCT Crossover trial |
16-week arms | Change in TIR | 52 | 48.6 | CSII or MDI | rtCGM | TIR: +9.6% with CGM, P < 0.0001 |
Yes Duration of time <3.9 mmol/L −4.7%, P < 0.0001 |
| JDRF CGM Study Group and colleagues (2009) (103) | RCT | 26 weeks | Change in TBR Change in HbA1c Severe hypoglycemic events |
129 | Children and adults | CSII or MDI | rtCGM | HbA1c: −0.34%, P < 0.001 TIR: 1063 min/day (CGM) vs 949 min/day (control) at 26 weeks P = 0.003 |
No significant difference in median time per day <70 mg/dL (P = 0.16) Yes, for time <60 mg/dL (P = 0.05) No difference in severe hypoglycemic events |
| Riddlesworth and colleagues (2017) (104) | RCT | 24 weeks | Change in frequency of hypoglycemic events | 158 | CGM: 46 Control: 51 |
MDI | rtCGM | Not studied | Yes 30% decrease in median hypoglycemic event rate in CGM group, no change in control group (P = 0.03) |
| Battelino and colleagues (2011) (105) | RCT | 26 weeks | Time < 63 mg/dL | 120 | Children and adults | MDI | rtCGM | HbA1c: −0.27%, P = 0.008 TIR: Mean hours per day 17.6 (CGM) vs 16.0 (control), P = 0.009 |
Yes Significantly shorter duration of time <63 mg/dL, P = 0.03 |
| Heinemann and colleagues (2018) (106) (Impaired hypoglycemia awareness or severe hypoglycemia in past year) |
RCT | 26 weeks | Baseline-adjusted number of hypoglycemic events | 149 | Children and adults | MDI | rtCGM | No difference in A1c | Yes Decreased incidence of hypoglycemic events by 72% for patients in the rtCGM group, P < 0.0001 |
| Pratley and colleagues (2020) (31) (older adults >60 years) |
RCT | 26 weeks | Change in TBR | 203 | 68 (median age) | CSII and MDI | rtCGM | HbA1c: −0.3%, P < 0.001 TIR: +8.8%, P < 0.001 |
Yes Reduction in time <70 mg/dL of 27 min/day, P < 0.001 |
| Miller and colleagues (2022) (32) (Older Adults >60 years) |
Observational extension phase of WISDM RCT CGM cohort from RCT continued CGM BGM cohort from RCT initiated CGM |
26 weeks | Change in TBR | 194 | 68 (median age) | CSII and MDI | rtCGM | HbA1c: Yes. Sustained improvement compared to baseline at 52 weeks for CGM to CGM (P = 0.01) and BGM to CGM (P = 0.025) TIR: Yes. Sustained improvement compared to baseline at 52 weeks for CGM to CGM (P < 0.001) and BGM to CGM (P < 0.001) |
Yes CGM-CGM cohort: Median TBR decrease sustained at 52 weeks – 73 min/day at baseline, 40 min/day at 52 weeks, P < 0.001 |
BGM, blood glucose monitoring; CGM, continuous glucose monitoring; CSII, continuous subcutaneous insulin infusion; HbA1c, hemoglobin A1c; isCGM, intermittently scanned continuous glucose monitoring; MDI, multiple daily injections; RCT, randomized controlled trials; rtCGM, real-time continuous glucose monitoring; T1D, type 1 diabetes; T2D, type 2 diabetes; TAR, time above range; TBR, time below range; TIR, time in range.
Major studies evaluating clinical outcomes with CGM use in people with type 2 diabetes.
| Study design | Duration | Primary outcome | Number of participants | Mean age (years) | DM treatment type | rtCGM or isCGM | Improvement in HbA1c/TIR with CGM | Improvement in hypoglycemia/TBR with CGM | |
|---|---|---|---|---|---|---|---|---|---|
| Beck and colleagues (2018) (107) | RCT | 24 weeks | Change in HbA1c | 158 | 60 | MDI | rtCGM | Yes −0.3%, P = 0.022 |
No difference between groups |
| Ehrhardt and colleagues (2011) (108) | RCT | 12 weeks (2 weeks on/1 week on cycles for intervention group) |
Change in HbA1c | 100 | CGM: 55.5 Control: 60.0 |
Mixed: diet/exercise or OAD or OAD + exenatide or basal insulin alone or in combination |
rtCGM | Yes Mean A1c decrease 1.0% for CGM vs 0.5% for control group (P = 0.006) |
Not studied |
| Haak and colleagues (2017) (109) | RCT | 6 months | Change in HbA1c | 224 | CGM: 59.0 Control: 59.5 |
CSII or MDI or prandial insulin alone | isCGM | No difference in A1c overall. Yes for <65 years old −0.33%, P = 0.0301 |
Yes Time <70 mg/dL reduced by 43% for CGM group, P = 0.0006 |
| Yoo and colleagues (2008) (110) | RCT | 3 months | Change in HbA1c | 65 | CGM: 54.6 Control: 57.5 |
OAD and/or insulin | rtCGM | Yes, P = 0.004 | No |
| Martens and colleagues (2021) (111) | RCT | 8 months | Change in HbA1c | 175 | 57 | Basal insulin ± OAD | rtCGM | HbA1c: −0.4%, P = 0.02 TIR: 15%, P < 0.001 |
Yes % Time <70 mg/dL −0.24%, P = 0.02 |
| Wright and colleagues (2021) (112) | Retrospective observational study using IBM Explorys Database | Mean follow-up period 159 days. | Change in HbA1c | 1034 | 51.6 | Basal insulin, OAD, or no medications | isCGM | Yes −1.5%, P < 0.001 |
Not studied |
| Elliott and colleagues (2021) (113) | Retrospective chart review at six diabetes centers in Canada | Mean follow-up period 123 days | Change in HbA1c | 91 | 64.3 | Basal insulin | isCGM | Yes −0.8%, P < 0.0001 |
Not studied |
| Bao and colleagues (2021) (34) | RCT | 8 months | Change in HbA1c | 175 | >65 years: 69 <65 years: 53 |
Basal insulin ± OAD | rtCGM | HbA1c: No TIR >65 years: +19%, P = 0.01 <65 years:+12%, P = −.003 |
No |
CGM, continuous glucose monitoring; CSII, continuous subcutaneous insulin infusion; HbA1c, hemoglobin A1c; isCGM, intermittently scanned continuous glucose monitoring; MDI, multiple daily injections; OAD, oral antidiabetic drugs; RCT, randomized controlled trials; rtCGM, real-time continuous glucose monitoring; T1D, type 1 diabetes; T2D, type 2 diabetes; TAR, time above range; TBR, time below range; TIR, time in range.
Major studies evaluating clinical outcomes with CGM use in people with type 1 or type 2 diabetes.
| Study design | Duration | Primary outcome | Number of participants | Mean age (years) | DM treatment type | rtCGM or isCGM | Improvement in HbA1c/TIR with CGM | Improvement in hypoglycemia/TBR with CGM | |
|---|---|---|---|---|---|---|---|---|---|
| Garg and colleagues (2006) ((114)) | RCT | 10 days | Differences in TAR, TIR, TBR | 91 T1D: 75 T2D: 16 |
44 | CSII or MDI | rtCGM | TIR: +26% h/day P < 0.0001 TAR: −23% h/day P < 0.0001 |
Yes −21% time <55 mg/dL, P < 0.0001 |
| New and colleagues (2015) (115) | RCT rtCGM with alarm vs rtCGM w/o alarm vs BGM |
100 days | Difference in time spent outside target range (outside 70–180 mg/dL) | 160 T1D: 126 T2D: 19 |
47 (median age) | CSII or MDI | rtCGM | No | Yes 1.0 h/day (CGM with alarm) vs 1.6 h/day (BGM), P = 0.030 |
| Deshmukh and colleagues (2020) (116) | Retrospective real-world analysis of data from a nationwide audit of isCGM use in routine clinical care | 7.5 months | Change in HbA1c | 10,370 | 38.0 | CSII or MDI | isCGM | Yes −5.2 mmol/mol, P < 0.0001 |
Yes Significant reduction in paramedic callouts post-isCGM (39) compared to pre-isCGM (275) |
| Ruedy and colleagues (2017) (33) | RCT | 24 weeks | Change in HbA1c | 116 T1D: 34 T2D: 82 |
67 (adults > 60 years) |
MDI | rtCGM | Yes −0.4%, P < 0.001 | No |
BGM, blood glucose monitoring; CGM, continuous glucose monitoring; CSII, continuous subcutaneous insulin infusion; HbA1c, hemoglobin A1c; isCGM, intermittently scanned continuous glucose monitoring; MDI, multiple daily injections; RCT, randomized controlled trials; rtCGM, real-time continuous glucose monitoring; T1D, type 1 diabetes; T2D, type 2 diabetes; TAR, time above range; TBR, time below range; TIR, time in range.
In addition to these studies, the CONCEPTT trial demonstrated the benefits of CGM compared to BGM during pregnancy, including improvement in HbA1c (mean difference −0.19%, 95% CI −0.34 to −0.03, P = 0.0207), importantly without an associated significant increase in time in hypoglycemic range (3 vs 4% in CGM vs control, P = 0.10); decrease in large-for-gestational-age births; decrease in neonatal hypoglycemia; and decrease in neonatal intensive care admissions greater than 24 h in duration (30). CGM can also be of particular benefit to older adults, who may be more susceptible to episodes of hypoglycemia and hypoglycemia unawareness (31, 32, 33, 34, 35). While there can be challenges in technology use in this population related to cognitive or physical (visual, hearing, dexterity) impairments, there are also significant advantages, including caregiver access to glucose data through the CGM system share feature and reduction in glucose variability, hyperglycemia, hypoglycemia, and need for fingerstick measurements (35).
Lastly, there is evidence to suggest that the use of CGM is associated with a reduction in acute diabetes complications and diabetes-related healthcare utilization. One prospective trial comparing rtCGM to BGM found that patients randomized to CGM had significantly decreased total healthcare visits and emergency department visits over a 6-month period (36). The RELIEF study, a retrospective analysis from France, demonstrated that FreeStyle Libre initiation was associated with a decrease in acute diabetes complications, including diabetic ketoacidosis (DKA) and diabetes-related comas in both T1D and T2D, and hospitalizations for hyperglycemia and hypoglycemia in T2D (37). These findings suggest that not only can CGM improve clinical outcomes on the individual level but may also confer benefits in terms of healthcare utilization and expenditures from a health system perspective (38).
Taken together, a continually expanding literature has demonstrated a multitude of clinical benefits with CGM use, including A1c reduction, increased time in range (TIR), hypoglycemia reduction, as well as quality of life benefits, across subpopulations of people living with diabetes. Over the past two decades, CGM has evolved from nascent technology to a widely adopted component of standard diabetes care (1).
Limitations of CGM use
While CGM use is associated with a multitude of benefits, there are also limitations of use as well. While accuracy has improved with each generation of CGM devices, some potential for error is inherent in all systems. MARD is based on a comparison of CGM glucose readings with other matched glucose readings (which is most often capillary fingerstick), with a lower MARD indicating better performance (39). MARD <10% indicates high accuracy, and as such even the best devices currently on the market have some acceptable degree of error in measurement (39). Additionally, as CGM devices measure glucose concentration in the interstitial fluid, there is an inherent lag time of several minutes in comparison to blood glucose (40). This physiologic difference can lead to a delay in the detection of hypoglycemia; as a result, patients should perform capillary BGM when symptoms of hypoglycemia occur since BGM will indicate hypoglycemia contemporaneous with the onset of symptoms. Error also tends to be more common following the initiation of a new sensor, as each sensor requires a ‘warm-up’ period with sensor accuracy shown to improve after the first day of use (41). Various substances are also known to interfere with CGM readings, although the specific interferents vary by device. Older-generation Dexcom sensors as well as the presently available Medtronic Guardian 3 sensor have been noted to have falsely elevated glucose readings in the presence of acetaminophen use (42, https://www.medtronicdiabetes.com/sites/default/files/library/download-library/user-guides/Guardian%20Sensor%203%20User%20Guide%20-%20June-%202018.pdf). Other examples of interference with currently available CGM devices include ascorbic acid (Vitamin C) which can falsely elevate glucose readings on Freestyle Libre devices, and hydroxyurea which has been shown to falsely elevate glucose readings on Dexcom devices (43, https://www.freestyle.abbott/us-en/safety-information.html ). Another source of error is so-called ‘compression artifact,’ in which erroneous CGM readings are recorded when the wearer is in a position in which the CGM and nearby local tissue get compressed, often during sleep (44). Device compression can result in a falsely low reading, and when the device is linked to an alarm for severe hypoglycemia, the sleeping person may awaken with confusion regarding whether or not they are experiencing true hypoglycemia. Frequent alarms too can be a limitation – while serving a critical safety role to notify the person with diabetes about dangerous hypo- or hyperglycemia events, a qualitative survey of patients using CGM noted that some people find the alarms to be intrusive and annoying and that some CGM users then turn the alarms off, inactivating a major protective benefit of these devices (45). The frequency of alarms can be overly burdensome and when too frequent can lead to a state of ‘alarm fatigue’ which limits the patient’s overall ability to respond effectively to the alarms (46).
Furthermore, the wealth of data generated by CGM devices can be overwhelming and has even led some patients to completely discontinue CGM use (47). This information overload can be particularly challenging for patients with limited health literacy and/or numeracy, both of which have been associated with poorer diabetes-related outcomes (48, 49). The minute-to-minute data generated by CGM can also lead to excessively frequent insulin doses in an attempt to rapidly decrease glucose levels. As the commonly used rapid-acting prandial insulin formulations have an onset of action of 15–30 min and a duration of action of several hours, multiple insulin doses given in close proximity to each other for perceived persistent hyperglycemia before the insulin dose has taken full effect will lead to ‘stacking’ of insulin doses and eventual hypoglycemia (50). Providers, especially those without significant experience or a systematic approach to navigating CGM data, are similarly susceptible to feeling inundated by the plethora of data and outputs generated by CGM devices and their corresponding software applications (17). The effective review of CGM data within the confines of a time-limited clinic visit remains a barrier for many providers and likely will remain so as CGM use expands with more uptake and utilization in the primary care space, where a majority of diabetes care is delivered (51). Adoption of a simplified and systematic approach to CGM interpretation may lessen this obstacle for primary care providers, for whom the interpretation of CGM data may appear overwhelming and present a limiting barrier to the implementation of CGM-guided therapeutic changes (52). It should also be noted that CGM data does not seamlessly integrate into the electronic health record at most sites, which can make it difficult for providers to easily access patient glucose data from within the electronic medical record (53).
CGM use and adherence can also be limited by adhesion issues. A survey of users who had discontinued CGM after previous use noted that the top-cited reasons for discontinuation included skin-related issues such as discomfort, issues related to device insertion, and problems with adhesives holding the sensor on the skin effectively (54). Even with various supplemental adhesive products available on the market challenges related to skin rashes and irritation associated with CGM use remain problematic for some patients (55). Furthermore, from a psychosocial standpoint, the presence of a medical device on one’s body and the perceived associated stigma can limit successful CGM use (56). It is also recommended that CGM be removed for any computed tomography or magnetic resonance imaging testing due to limited safety data; this means that the device must be replaced after each imaging study, which can be costly for patients who require multiple studies (https://www.dexcom.com/faqs/use-dexcom-g6-during-mri-ct-scan-or-diathermy). Air travel may also be challenging as it is unclear if Advanced Imaging Technology body scanners and x-ray machines damage CGM devices; as a result, it is generally recommended that CGM users request alternative security screening measures at the airport (https://www.dexcom.com/en-us/dexcom-airport-and-travel-guide-flying-dexcom-cgm).
Lastly, optimal CGM utilization is limited by barriers related to cost, access, and equity. There exists a major disparity in CGM adoption with significantly higher utilization by non-Hispanic White individuals relative to Hispanic and non-Hispanic Black individuals, and gaps as such exist in multiple age cohorts ranging from young adults to Medicare beneficiaries (57, 58). From an access standpoint, commercial insurance eligibility criteria for CGM (which for some plans is not even readily available to patients) can sometimes be restrictive and onerous resulting in overall limited coverage for patients with T2D (59). In the public insurance realm, Medicare coverage previously had very onerous requirements, most notably a requirement for documentation of four daily blood glucose tests as part of its coverage requirements until July 2021. In the most recent 2023 guideline, Medicare has expanded CGM coverage to any patient onbeyond acquiring a CGM device, reliable internpoglycemia (https://www.cms.gov/medicare-coverage-database/view/article.aspx?articleid=52464&ver=49&contractorName=all&sortBy=updated&bc=13). Medicaid coverage for CGM is inconsistent among states and also fraught with barriers and restrictions in many states; of note, the ADA reported in 2021 that Medicaid beneficiaries on insulin were two to five times less likely to use CGM compared to individuals on commercial health insurance (https://diabetes.org/sites/default/files/2021-10/ADA%20CGM%20Utilization%20White%20Paper.pdf). Provider implicit bias also appears to play a role in driving these inequities, as assessments have demonstrated provider bias in recommending diabetes technology based on both patient race/ethnicity as well as patient insurance status (60). Lastly, beyond acquiring a CGM device, reliable internet access and a smartphone with Bluetooth capabilities are required to fully integrate CGM devices and to transmit glucose data to one’s provider. While access to these technologies has increased rapidly, there remains a digital divide in regards to home internet and smartphone adoption by lower-income individuals, which in turn affects their ability to optimally use CGM even when access to CGM is secured in the first place (61).
Use of CGM in clinical practice
As previously discussed, the abundance of information generated by CGM systems can be overwhelming, and therefore a limitation, from a practical standpoint. In fact, it was noted as recently as 2019 that despite the profound advancements in CGM technology, implementation in routine clinical practice remained relatively low. This was felt to be in part related to a dearth of clearly defined and agreed-upon glycemic targets for clinical use. As such, in early 2019, the Advanced Technologies and Treatments for Diabetes Congress assembled an international panel of experts to develop a consensus regarding standardized CGM metrics and associated targets for use in clinical care (62). The use of these standardized metrics helps to streamline the interpretation of CGM data in clinical practice. The ten standardized metrics and (when applicable) their targets are as follows (62):
Number of days CGM is worn and percentage of time CGM is active: The recommendation of 14 days of CGM wear and 70% time CGM active is based on trials by the JDRF CGM Study group investigating the optimal duration and frequency of CGM to reflect long-term glycemic control, which found that 14 days of CGM data provided a reliable approximation of glucose metrics for a 3-month period (63, 64). It is worth noting, however, that a follow-up study reported that while 14 days of CGM data is adequate for approximation of TIR, other glucose metrics including time below range and glycemic variability may require a longer duration of CGM wear for reliability (65). Additionally, it must be noted that the adequacy of 70% time CGM active is dependent on glucose data loss occurring in random, shorter gaps rather than data gaps of longer duration, as can occur with isCGM if the device is not scanned frequently enough (66).
Mean glucose
Glucose management indicator (GMI): Due to the observed discrepancy between CGM mean glucose and lab-measured HbA1c (67, 68), a calculated metric called the Glucose Management Indicator (GMI) has been developed. GMI is calculated via a formula that was developed and validated from the regression line of a plot with glucose concentration along the x-axis and simultaneously measured HbA1c on the y-axis (GMI (%) = 3.31 + 0.02392 × (mean glucose in mg/dL)) (69, https://www.jaeb.org/gmi/). While GMI has proven a useful and easily conceptualized CGM-derived metric of overall glycemia in the management of patients with diabetes, it should be noted the linear relationship that defines GMI may be less accurate in some settings, particularly when mean glucose is low or near-normal (70). A greater discordance between the two values was recently reported among people who do not have diabetes (70).
Glycemic variability (%CV): Glycemic variability is most simply described as the degree to which an individual’s blood glucose concentration fluctuates. The metric of %CV is calculated as (s.d. of glucose/mean glucose) × 100 and encompasses the duration, frequency, and amplitude of shifts in blood glucose between low and high levels (6). A target of %CV ≤36% has been shown to help distinguish between stable and unstable glycemic control (71). Greater degree of glycemic variability has been associated with increased risk of adverse cardiovascular outcomes (72).
-
Time above range (TAR) >250 mg/dL (Level 2 Hyperglycemia): target <5%
-
Time above range (TAR) >180 mg/dL (Level 1 Hyperglycemia): target <25%
-
Time in range (TIR) 70–180 mg/dL (In range): target >70%
-
Time below range (TBR) <70 mg/dL (Level1 Hypoglycemia): target <4%
-
Time below range (TBR) <54 mg/dL (Level 2 Hypoglycemia): target <1%
The TIR metrics were developed in order to provide glucose data that is more actionable than the HbA1c. TIR (the proportion of time a person’s glucose level is in the target range of 70–180 mg/dL (3.9–10 mmol/L)) is a measure of glycemic control that is easy to understand (1), with a TIR of 70% corresponding to HbA1c of ~7.0% and a 10% increase in TIR correlating with an approximate 0.6–0.8% decrease in HbA1c (73, 74). The target ranges listed earlier are the standard for patients with T1D and T2D but can be adjusted to allow more conservative targets among older or high-risk adults, as detailed in Table 4 (62). It is worth noting, however, that the currently accepted target ranges for CGM metrics are based on limited data. As examples, recommended %CV was based on a study which demonstrated the stability of glycemia when <36% among people with T2D (71), and other metrics including a target of <4% for TBR <70 are not borne out of robust supporting data.
Time In range targets for adults with T1D or T2D.
| Metric | Adults > 25 years old with T1D or T2D | Older or high-risk adults with T1D and T2D |
|---|---|---|
| % readings (time per day) | ||
| TAR – Level 2 >250 mg/dL (>13.9 mmol/L) | <5% (<1 h, 12 min) | <10% (<2 h, 24 min) |
| TAR – Level 1 >180 mg/dL (>10.0 mmol/L) | <25% (<6 h) | <50% (<12 h) |
| TIR 70 – 180 mg/dL (3.9–10.0 mmol/L) | >70% (>16 h, 48 min) | >50% (>12 h) |
| TBR – Level 1 <70 mg/dL (<3.9 mmol/L) | <4% (<1 h) | <1% (<15 min) |
| TBR – Level 2 <54 mg/dL(<3.0 mmol/L) | <1% (<15 min) | 0% |
T1D, type 1 diabetes; T2D, type 2 diabetes; TAR, time above range; TBR, time below range; TIR, time in range.
Beyond the established core CGM metrics, there is also a recommended standardized template for data presentation, the ambulatory glucose profile (AGP). Created by Mazze et al. (75) and further refined by an expert panel (76), the AGP incorporates the data from the core metrics, as well as a composite visual display of 14 days of glucose data, into a single-paged report for review in the clinic (62). There are currently numerous platforms available to clinicians for reviewing CGM data, including the AGP. These include Dexcom Clarity (Dexcom Inc.), Abbott LibreView (Abbott Diabetes Care), Medtronic CareLink (Medtronic), Senseonics Eversense Pro Data Management System (Senseonics), Glooko (Mountain View, CA, USA), and TidePool (TidePool, Palo Alto, CA, USA). An example AGP is shown in Fig. 2.
Ambulatory glucose profile (AGP) example.
Citation: Endocrine Connections 12, 7; 10.1530/EC-23-0085
Additionally, guidance has been developed to facilitate streamlined CGM data interpretation. A systematic, stepwise approach has been described, which promotes focused and efficient extraction of salient data to inform appropriate changes to patients’ treatment regimens. In order to prevent clinicians from becoming overwhelmed by the volume of available data, this approach recommends a focused review of (i) % time CGM is active to ensure adequate data is available for analysis, (ii) CGM metrics to identify the nature of the problem (i.e. hyperglycemia, hypoglycemia, or both), (iii) AGP to identify patterns and localize to the problem, and (iv) daily glucose trends to inform therapy modifications (52).
Application of CGM to advanced technologies
While CGM use alone has demonstrated benefits in improving patient outcomes, CGM devices have become interconnected with various apps and other diabetes technology devices including smart insulin pens and insulin pumps. Each major CGM device on the market has its own cloud-based centralized management app that allows for the aggregation of data and transmission for sharing. Data from CGM devices can also interface directly with other mobile health apps and devices. For example, Dexcom G6 can integrate with other fitness tracking apps including Apple Health and Google Fit as well as Garmin smartwatches and cycling computers (https://www.dexcom.com/en-us/partnerships/lifestyle-devices). There also appears to be a role in connecting CGM to so-called ‘digiceuticals’ or digital therapeutics. One example of this is Bluestar, an FDA-approved digital diabetes management platform which integrates directly with Dexcom G6 (https://www.businesswire.com/news/home/20210405005126/en/Welldoc-and-Dexcom-Expand-Strategic-Partnership-to-Integrate-Platforms-and-Offer-Integrated-Type-2-Diabetes-Management-Solution). Additionally, CGM data can be directly integrated with smart connected insulin pens and smart pen caps to assist with insulin dosing guidance and support; examples of these include the Medtronic InPen with the Dexcom G6 and Guardian Connect CGM, the Eli Lilly Tempo pen and pen cap with Dexcom G6, and Bigfoot Medical’s Unity system which connects smart pen cap devices with the Freestyle Libre 2 (77, https://www.medtronicdiabetes.com/products/cgm-and-smart-pen, https://www.bigfootbiomedical.com/bigfoot-unity, https://www.lillytempo.com/?gclid=EAIaIQobChMIjNii6d_N_QIVZOTjBx1jDAD6EAAYASAAEgJbmvD_BwE).
An evolving advancement in integrated CGM use is the continually expanding relationship between CGM and CSII. These integrated technologies have been termed automated insulin delivery (AID) and have advanced rapidly in recent decades (78). The initial integrated relationship between the two diabetes technologies was referred to as sensor-augmented pump therapy in which the CGM sensor transmits glucose readings to the pump without any resultant changes in insulin delivery in response to CGM data (79). The next major technology advancement was termed low glucose suspend (LGS) in which the pump suspends insulin delivery up to 2 h if the CGM device detects hypoglycemia at a pre-determined threshold; this feature was shown in trials to reduce severe hypoglycemia (80). Subsequently, predictive low glucose suspend (PLGS) pump algorithms were developed, in which CGM data trends were utilized to calculate and predict impending hypoglycemia to enable suspension of insulin delivery prior to hypoglycemia onset. PLGS was demonstrated to reduce hypoglycemia without rebound hyperglycemia (81). The most recent and significant advancement in this integrative relationship between CGM and CSII was the development of hybrid AID (also known as hybrid closed-loop) therapy, in which the pump uses an automated algorithm to adjust basal insulin (and in some cases, provide automated correction boluses) based on sensor-derived glucose with a goal of maintaining blood glucose within a set target range (82). The first hybrid AID system to gain FDA clearance was the Medtronic MiniMed 670G with Guardian 3 CGM in 2016 (and later the MiniMed 770G in 2020); subsequently, the Tandem t:slim X2 pump with Control-IQ hybrid AID technology with Dexcom G6 was FDA cleared in 2019 and the Insulet Omnipod 5 with Dexcom G6 was the first tubeless hybrid AID system cleared by the FDA in 2022 (82, 83). These hybrid AID systems have revolutionized insulin delivery through meaningful, real-time utilization of CGM glucose data and have been shown in pivotal trials to significantly improve TIR and reduce hypoglycemia in both adult and pediatric patients with T1D (84, 85, 86).
Future technologies currently in development include dual-hormone pumps using insulin plus either glucagon or pramlintide as well as closed-loop pumps such as the ‘bionic pancreas,’ a full AID system which would completely automate insulin delivery and only require meal announcements and input of meal size rather than requiring the user to input counted carbohydrates for bolus dosing (87, 88, 89). All of these proposed future technologies for insulin delivery closely intertwine with CGM and necessitate accurate and reliable CGM data to safely guide their algorithms. As AID technologies evolve, their role in additional populations with individualized needs and targets, such as pregnant women, will likely continue to grow (90).
Conclusion
CGM has come to play an integral role in diabetes management, and CGM data capture the amount and timing of hyper- and hypoglycemia as well as glucose variability in ways that conventional HbA1c measurements cannot. CGM has been demonstrated in multiple studies to improve glycemic outcomes and quality of life in both T1D and T2D in a wide variety of populations. While there remain limitations and barriers to effective CGM access and utilization both for patients and providers, CGM has revolutionized the delivery of diabetes care via direct integration with advanced diabetes technologies ranging from connected apps and fitness devices to smart insulin pens and direct automation with insulin pumps in hybrid closed-loop systems. CGM has become a standard for optimal diabetes management and will be an essential component of the next generation of advanced diabetes technologies going forward including dual-hormone pumps and fully closed-loop systems.
Declaration of interest
JF, KC, and EDS have no relevant financial or non-financial interests to disclose. GA has received research support to Northwestern University from Emmes, Fractyl Health, Insulet, Tandem Diabetes and Welldoc. GA has served as consultant for Bayer Healthcare, Dexcom, Eli-Lilly and Insulet.
Funding
The authors did not receive support from any organization for the submitted work.
Author contribution statement
All authors contributed to the discussion, and wrote, reviewed, and revised the manuscript. All authors approved the final manuscript. EDS is the guarantor of this work and, as such, takes responsibility for the integrity and accuracy of the work.
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