CGM

Clinical Background

Continuous Glucose Monitoring (CGM) systems represent a significant advancement in diabetes management, particularly for individuals with Type 1 and Type 2 diabetes. The technology is critical for continuously measuring interstitial glucose levels, allowing patients to maintain tighter glycemic control and reduce the risks associated with both hypo- and hyperglycemia. CGM functions through electrochemical sensors that provide real-time glucose readings, which is essential for patients who require frequent monitoring but may not have the ability to perform traditional blood glucose tests multiple times a day.

Traditional glucose monitoring methods include capillary blood glucose testing using blood glucose meters or assessing glycated hemoglobin (HbA1c) levels. While these methods have their place, they often fall short in offering a real-time overview of glucose fluctuations throughout the day (Gallieni et al. ). HbA1c is a retrospective measure with a significant time lag, typically reflecting median glucose levels over the previous 2-3 months, which complicates immediate care decisions (Gallieni et al. [39]). Notably, in the adolescent population, approximately 30% fail to achieve adequate glycemic targets, underscoring the need for more dynamic monitoring solutions( Brorsson et al. 2019.)

As evidenced by the research, CGM has been shown to enhance glycemic control in various settings, including adolescents starting continuous subcutaneous insulin infusion (CSII) for Type 1 diabetes. In a randomized controlled trial examining the Guided Self-Determination-Young (GSD-Y) education model, results indicated improved glycemic control (62 mmol/mol vs. 70 mmol/mol, p=0.009) among those using CGM compared to standard-care methods( Brorsson et al. 2019.)

Safety data indicate a strong preference for CGM over traditional methods, particularly concerning the management of diabetes throughout the day, including during critical periods like hemodialysis, where glucose fluctuations are pronounced (Gallieni et al. ). Studies suggest that CGM systems are reliable in detecting potentially dangerous glucose excursions, also highlighted by the average 24-hour glucose readings that show significant variability during dialysis (Gallieni et al. ]).

Another critical factor is the accuracy of CGM. The mean absolute relative difference (MARD) has been noted in assessing sensor accuracy, which is key for effective diabetes management (Gallieni et al. ). The finding that CGM systems provide superior accuracy in detecting glucose variability presents a rationale for their integration into standard diabetes care, especially for those with conditions like diabetic kidney disease that complicate traditional management strategies.

Continuous Glucose Monitoring Systems (CGMS) present a transformative approach to diabetes management by enabling real-time monitoring of interstitial glucose levels through a sensor implanted subcutaneously. This device generates current signals via electrochemical reactions involving glucose oxidase, allowing for the effective tracking of glucose fluctuations and aiding users in assessing the effectiveness of their diabetes management strategies (Di Molfetta et al. 2023.)

The conventional method of blood glucose monitoring using finger-stick tests, while beneficial for insulin-treated patients, is often insufficient for non-insulin-treated T2DM patients due to its drawbacks, such as the inability to detect asymptomatic hypoglycemia and requiring multiple blood samples (Aggarwal et al. 2022). Research suggests that CGMS can lower HbA1c levels by approximately 0.4% compared to self-monitored blood glucose (SMBG) (Aggarwal et al. 2022), reflecting its potential in enhancing glycemic control.

Alternative Options

While CGM offers significant advantages, alternative treatment options exist in the form of supplemental diabetes management strategies. For instance, traditional diabetes treatments like Multiple Daily Injections (MDI) of insulin or the use of insulin pumps provide different levels of flexibility in diabetes management. In addition, lifestyle modifications, including diet and exercise, form the foundation of diabetes management and can complement pharmacological therapies effectively.

In the landscape of diabetes management, newer methods leveraging advanced technologies continue to emerge. The Flash Glucose Monitoring (FGM) system, introduced as a novel alternative, allows for instantaneous access to glucose readings by scanning a sensor. It offers on-demand results while still providing insight into glucose trends over time, marking a shift towards more user-friendly technology (Gallieni et al. ). Both CGM and FGM are designed to support better management of glycemic variability, and as seen in recent studies, they hold substantial promise in improving health outcomes.

Recent advancements in the MedTech field have introduced new technologies that aim to improve glucose monitoring, particularly for patients with type 1 diabetes. One notable method includes the use of hybrid closed-loop systems, which integrate continuous glucose monitors with insulin delivery systems to automate insulin dosing based on real-time glucose levels. The t:slim X2 insulin pump featuring Control-IQ Technology stands out as it allows for frequent automated basal adjustments through data collected from a continuous glucose monitor, offering a more integrated approach to diabetes management (Wadwa et al. 2023.)

In a recent trial, the t:slim X2 system showed significant efficacy in maintaining glucose levels within the target range. Specifically, the closed-loop group experienced an increase from 56.7% to 69.3% of the time within the target glucose range of 70 to 180 mg per deciliter over 13 weeks, with a mean adjusted difference of 12.4 percentage points (P < 0.001). Additionally, a reduction in glycated hemoglobin levels and improved mean glucose levels were observed, showcasing its potential effectiveness (Wadwa et al. 2023.)

Recent studies have suggested that educational interventions, such as the GSD-Y model, enhance the ability to self-manage diabetes effectively among adolescents (Brorsson et al. 2019). Family involvement also plays a critical role in managing diabetes in adolescents, which can mitigate challenges related to adherence and psychosocial stressors.

In addition to CGM and hybrid closed-loop systems, traditional glucose monitoring methods, such as Self-Monitoring of Blood Glucose (SMBG), remain widely used, but they rely on patients taking their blood glucose measurements rather than providing continuous data (Heinemann et al. 2018). While SMBG allows for a more reactive approach to glucose management, it can lead to gaps in data that might result in critical events being missed.

Engaging in regular clinical assessments combining traditional methods with new CGM technologies can offer a more rounded approach to diabetes management, ensuring that patients benefit from both empirical medication adjustments and cutting-edge monitoring techniques.

Performance and Safety Endpoints

Claim or Benefit	Safety (S) and Performance (P) Objectives	Outcomes
Improved Glycemic Control;	(P) Time in target glucose range (P) Accuracy and reliability of glucose measurements	Time in Range (70-180 mg/dL): 38.5% (Villard et al. 2022) – 94.5% (Sivasubramanian et al. 2023) HbA1c reduction: 0.2-0.6% (Lin et al. 2021) MARD range: 6.6% (Tingsarat et al. 2022) – 23.72% (Gómez Medina et al. 2023); Overall MARD: 8.83% ± 4.03% (Yan et al., 2023) Agreement rate (20/20%): 89.71% – 91.82% (Meng et al. 2021; Yan et al. 2023) Clarke Error Grid zones A+B: 89.6% – 99.9% (Yan et al. 2023) Consensus Error Grid zones A+B: ≥99.82% (Meng et al. 2021)
Enhanced Safety in Glucose Monitoring;	(S) Hypoglycemia prevention and detection (S) Device-related adverse events (P) Device reliability	Hypoglycemia detection sensitivity: 43.48% (Meng et al. 2021) – 91.3% (Sivasubramanian et al. 2023)   Device-Related Adverse Events: Skin reactions frequency: 8.6% (Szadkowska et al. 2021) Sensor adhesion failure rate: 6.5% [6 of 92 sensors] (Szadkowska et al. 2021) Mild erythema/edema: 5.7% [16 of 280 readings] (Castorino et al. 2020) Bleeding events: 3.8% [3 of 78 participants] (Alva et al. 2023) Data loss events: 2.9% [20 events reported] (Villa-Tamayo et al. 2024) Sensor survival rate: >90.3% (Polsky et al. 2024) Data availability rate: >99.6% (Polsky et al. 2024)
Special Population Benefits;	(P) Accuracy and reliability of glucose measurements in pediatric and pregnant populations (S) Safety in vulnerable populations	Pediatric: MARD: 8.1-19.3% (Laffel et al. 2023, Park et al. 2023, Worth et al. 2022) Agreement rate (%20/20): 91.5-95.3% (Laffel et al. 2023) DKA incidence decreased from 4.8% (2013) to 1.0% (2022) (Bratke et al. 2024) Severe hypoglycemia decreased from 4.9% (2013) to 2.1% (2022) (Bratke et al. 2024) Hypoglycemia detection rate within 30-min window: 51% (Worth et al. 2022)   Pregnancy: MARD: 8.7-11.8% (Castorino et al. 2020, Polsky et al. 2024, Wyckoff et al. 2021) Agreement rate: 88.1% – 92.5% (Wyckoff et al. 2021, Polsky et al. 2024) Safety: No moderate/severe insertion site reactions or infections; No serious adverse events; Minor skin reactions only (Castorino et al. 2020, Polsky et al. 2024)

Extrapolation of Continuous Glucose Monitoring System Performance from Adults to Children and Pregnant WomenPhysiological Basis of Glucose Measurement via CGM

CGM systems typically consist of a small sensor with an electrode that is inserted into the subcutaneous tissue, a transmitter that sends readings from the sensor to a receiver at regular intervals, and a receiver or a compatible smart device that displays the glucose level and trends. The sensor utilizes an enzymatic reaction, often involving glucose oxidase, to detect glucose molecules in the interstitial fluid, generating an electrical current proportional to the glucose concentration. This electrical signal is then converted into a glucose reading in milligrams per deciliter (mg/dL) or millimoles per liter (mmol/L) by an algorithm within the receiver or connected device.

It is well established that there is a physiological lag time between changes in blood glucose and the corresponding changes in interstitial fluid glucose.[1] This delay, which typically ranges from 5 to 20 minutes, is due to the time required for glucose to diffuse from the blood into the interstitial space. Newer CGM models may exhibit shorter lag times.[2] The lag time can also be influenced by the rate at which blood glucose levels are changing, with more rapid changes potentially leading to a more pronounced lag.

The accuracy of CGM systems is typically evaluated using metrics such as the Mean Absolute Relative Difference (MARD) and Clarke Error Grid analysis. MARD represents the average percentage difference between the glucose values measured by the CGM and a reference method (e.g., a laboratory blood glucose test), with lower values indicating higher accuracy. The Clarke Error Grid analysis is a method used to assess the clinical accuracy of glucose monitoring devices by categorizing the difference between the device readings and reference values into five zones (A, B, C, D, and E) based on their potential impact on clinical decisions. Zones A and B are generally considered clinically acceptable, indicating that the device readings are sufficiently accurate for diabetes management.[3]

Physiological Comparison: Adults vs. Children (Age 3+)

Similarities in Glucose Measurement via CGM:

In both adults and children, CGM technology relies on the fundamental principle of measuring glucose concentration within the interstitial fluid. The core mechanism of the sensor, involving an electrochemical reaction to detect glucose, remains consistent across all age groups. Furthermore, a correlation between blood glucose and interstitial fluid glucose levels exists in both populations, providing the physiological basis for CGM to estimate blood glucose.

Differences in Physiology and Glucose Metabolism:

Children exhibit several physiological differences compared to adults that can affect glucose metabolism. They have higher metabolic rates and increased glucose requirements relative to their body size.[4] This heightened metabolic demand, coupled with limited glycogen stores, makes children more susceptible to rapid fluctuations in glucose levels and a higher risk of hypoglycemia.⁴ The growth hormone (GH)/IGF-1 axis, which plays a significant role in glucose homeostasis, is also more active during childhood.[5] Insulin sensitivity typically varies with age and pubertal status in children. Prepubertal children generally demonstrate higher insulin sensitivity compared to adults and adolescents, while the onset of puberty is associated with increased insulin resistance.⁵ Moreover, the rate of cerebral metabolism is significantly higher in children, necessitating a greater supply of glucose to the developing brain.⁴ It has also been suggested that children may have a less mature glucose regulatory system compared to adults, particularly during physical activity.⁵ Finally, it is important to note that normal blood glucose reference ranges are age-dependent, with children having different target ranges than adults.[6]

Impact on CGM Measurement:

The higher metabolic rates and increased glucose requirements in children could potentially lead to more rapid and pronounced changes in glucose levels compared to adults. This might influence the lag time between blood and interstitial glucose, although studies suggest that the lag time in adolescents might even be slightly shorter than in adults.[7] While children have increased subcutaneous tissue compared to adults⁴, this difference in tissue composition has not been shown to fundamentally alter the electrochemical reaction within the CGM sensor or the correlation between interstitial fluid glucose and blood glucose, provided that proper sensor insertion techniques are utilized. The key factor in determining the validity of extrapolating CGM performance from adults to children is the actual accuracy of the device.

Evidence of CGM Accuracy in Children (Age 3+)

Numerous studies have investigated the accuracy of CGM systems in children aged 3 years and older, providing data on MARD values and Clarke Error Grid analyses. For instance, a retrospective review of Dexcom G6 accuracy in hospitalized children (ages 2-18 years) reported a MARD of 15.9%, with 96.6% of the values falling within the clinically acceptable zones A and B of the Clarke Error Grid.³ Another study evaluating the Dexcom G7 system in children and adolescents (ages 2-17 years) found overall MARD values ranging from 8.1% to 9.3%, with a high percentage (91.5% to 98.7%) of readings in Zones A and B.[8] These findings indicate good accuracy across different age ranges within the pediatric population.

Studies comparing various CGM systems have also demonstrated acceptable accuracy in children. A study at a diabetes summer camp comparing FreeStyle Libre, Dexcom G6, and Medtronic Enlite in children aged 9-14 years reported MARD values of 13.3%, 10.3%, and 8.5%, respectively, with over 98% of readings for each system falling within the clinically benign Zones A and B of the Parkes Error Grid, a similar metric to the Clarke Error Grid.[9] These results suggest that different CGM technologies exhibit comparable accuracy in the pediatric population.

While some studies might report slightly higher MARD values in children, the overall accuracy, as indicated by MARD generally below 15% and a high percentage of readings in the clinically acceptable zones of the error grids, suggests that the performance of CGM technology is largely maintained in children aged 3 and older.³ The physiological differences between adults and children, while present, do not appear to fundamentally invalidate the ability of CGM technology to provide clinically reliable glucose measurements in this younger population.

Physiological Comparison: Adults vs. Pregnant Women

Similarities in Glucose Measurement via CGM:

Similar to the comparison between adults and children, the fundamental principle of CGM technology remains consistent in pregnant women. The systems measure glucose in the interstitial fluid using the same electrochemical methods employed in non-pregnant adults.[10]

Differences in Physiology and Glucose Metabolism During Pregnancy:

Pregnancy induces a range of significant physiological and metabolic adaptations to support fetal growth and development. One of the most notable changes is the development of insulin resistance, particularly during the second and third trimesters.⁸ This insulin resistance is primarily driven by increased levels of placental hormones such as human placental lactogen (hPL) and human placental growth hormone (hPGH), as well as increased levels of progesterone, cortisol, and prolactin.[11] To compensate for this reduced insulin sensitivity, maternal insulin secretion increases.⁹ Interestingly, fasting blood glucose levels tend to be lower in pregnant women compared to non-pregnant adults, while postprandial glucose levels may be elevated.[12] Hepatic glucose production also increases during pregnancy to meet the growing demands of the fetus.¹⁰ Furthermore, pregnancy is characterized by increases in blood volume and fluid shifts, which could potentially influence interstitial fluid dynamics.⁸ Overall, pregnancy induces a dynamic state of altered insulin sensitivity and glucose tolerance.⁸ Due to the critical importance of maintaining appropriate glucose levels for fetal health, glycemic targets are generally lower and tighter during pregnancy compared to non-pregnant adults.[13]

Impact on CGM Measurement:

The increased insulin resistance and altered glucose metabolism inherent to pregnancy could theoretically affect the correlation between blood and interstitial fluid glucose. Changes in blood volume and fluid shifts might also influence the subcutaneous tissue environment where the CGM sensor is situated.⁸ Therefore, it is essential to examine the evidence specifically regarding the accuracy of CGM technology in pregnant women to determine if these physiological changes significantly impact its performance.

Evidence of CGM Accuracy in Pregnant Women

Numerous studies have evaluated the accuracy of CGM systems in pregnant women, including those with various types of diabetes. A study assessing the Dexcom G7 system in pregnant women with type 1, type 2, and gestational diabetes reported an overall MARD of 9.5%, with 99.8% of paired values in the clinically acceptable Zones A and B of the Consensus Error Grid.[14] Another investigation of the Dexcom G6 system in pregnant women with type 1, type 2, or gestational diabetes found an overall MARD of 10.3%, with 92.5% of CGM values within ±20%/20 mg/dL of reference values.[15] These results indicate a high level of accuracy for Dexcom CGM systems in pregnant women.

Studies on FreeStyle Libre systems in pregnant women have also shown acceptable accuracy. A study comparing FreeStyle Libre with self-monitored blood glucose (SMBG) in pregnant women with diabetes demonstrated that 88.1% of sensor results were within Zone A and 99.8% within Zones A and B of the Consensus Error Grid, with an overall MARD of 11.8%.[16] These findings suggest that FreeStyle Libre provides clinically reliable glucose measurements in this population as well.

The MARD values reported in these studies involving pregnant women (generally below 12%).[17] Furthermore, Clarke Error Grid analyses consistently show a high percentage of readings in the clinically acceptable Zones A and B (typically above 97%).[18] This body of evidence indicates that despite the significant physiological changes that occur during pregnancy, CGM technology maintains clinically acceptable accuracy and reliability.

Consensus Guidelines and Recommendations

Major diabetes organizations provide recommendations that support the use of CGM technology in both children and pregnant women. The American Diabetes Association (ADA) recommends that CGM should be offered to children with type 1 and type 2 diabetes who are on insulin therapy.[19] The ADA also recommends CGM use in pregnancies complicated by type 1 diabetes and suggests that it may be beneficial for managing other types of diabetes during pregnancy as well.[20] Similarly, the Juvenile Diabetes Research Foundation (JDRF) supports the use of CGM in children with type 1 diabetes to improve glycemic control and reduce the risk of hypoglycemia.[21] The Endocrine Society also recommends the use of CGM in pregnant women with type 1 diabetes to enhance glycemic management and reduce the risk of neonatal complications.²⁰ These endorsements from leading medical organizations highlight the recognized clinical utility and reliability of CGM in these specific populations, suggesting that fundamental physiological differences do not preclude accurate glucose measurement.

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[1] Continuous Glucose Monitoring (CGM): What It Is – Cleveland Clinic

[2] What are the differences between interstitial fluid (ISF) and blood glucose (BG) readings?

[3] Continuous Glucose Monitor Accuracy for Diabetes Management in Hospitalized Children

[4] Anatomical and Physiological Differences – Children’s Health Queensland

[5] Glucose Metabolism in Children With Growth Hormone Deficiency – Frontiers

[6] Glucose: Reference Range, Interpretation, Collection and Panels

[7] A Comparison of Time Delay in Three Continuous Glucose Monitors for Adolescents and Adults | Request PDF – ResearchGate

[8] Accuracy of a Seventh-Generation Continuous Glucose Monitoring System in Children and Adolescents With Type 1 Diabetes – ResearchGate

[9] Performance of three different continuous glucose monitoring systems in children with type 1 diabetes during a diabetes summer camp

[10] Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus

[11] Unveiling Gestational Diabetes: An Overview of Pathophysiology and Management – MDPI

[12] Nutrition and Metabolic Adaptations in Physiological and Complicated Pregnancy: Focus on Obesity and Gestational Diabetes – PMC – PubMed Central

[13] Gestational diabetes – Treatment – NHS

[14] Performance of the Dexcom G7 CGM System in Pregnant Women with Diabetes

[15] Accuracy of Continuous Glucose Monitoring in Pregnancy During Inpatient Acute Glycemic Variability in Women with Type 1 Diabetes Mellitus

[16] Accuracy, User Acceptability, and Safety Evaluation for the FreeStyle Libre Flash Glucose Monitoring System When Used by Pregnant Women with Diabetes – White Rose Research Online

[17] Accuracy of Continuous Glucose Monitoring in Pregnancy During Inpatient Acute Glycemic Variability in Women with Type 1 Diabetes Mellitus | Request PDF – ResearchGate

[18] Evaluation of the performance and usability of a novel continuous glucose monitoring system | springermedizin.de

[19] Guidance for the Use of Continuous Glucose Monitoring in School Setting – American Diabetes Association

[20] 15. Management of Diabetes in Pregnancy: Standards of Care in Diabetes—2024

[21] Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA) – PubMed

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Literature Search Period: 2020 – 2024 | Search Time: Nov. 2024 – May. 2025 | Next review/update: Nov. 2025