Perspective > Medscape Diabetes & Endocrinology
Elizabeth Selvin, PhD, MPH
DISCLOSURES March 20, 2024
Continuous glucose monitoring (CGM) has revolutionized diabetes care, especially for patients with type 1 diabetes. CGM is also increasingly being used for patients with type 2 diabetes as well as managing diabetes during pregnancy. As the adoption of this technology grows, so does the debate around how best to report and interpret the complicated data generated by CGM systems.
The glucose management indicator (GMI), which estimates A1c values from CGM mean glucose, was originally proposed to simplify CGM data. Though CGM has merits, it also has limitations, which is why we recommend abandoning the use of GMI. Instead, we should rely on CGM mean glucose, alongside laboratory A1c testing, to manage diabetes.
The Emergence of GMI
For over three decades, laboratory-measured A1c has been the cornerstone of diabetes management. A1c is an indicator of chronic hyperglycemia, reflecting the nonenzymatic glycation of hemoglobin over the past 2-3 months. Randomized clinical trials have demonstrated that lowering A1c reduces the risk for major diabetes complications. Modern A1c assays are well standardized, traceable to reference methods, and demonstrate excellent accuracy and precision.
Investigators from the A1c-Derived Average Glucose study and the Diabetes Control and Complications Trial led early efforts to establish the mathematical association between mean glucose and A1c. However, these studies were conducted prior to the availability of modern CGM technology, when collecting frequent glucose measurements over long periods was difficult.
In 2017, a new equation was proposed for translating CGM mean glucose into an estimated A1c value, initially referred to as eA1c. This equation was incorporated into CGM summary reports, providing patients and clinicians a familiar “A1c-like” value. In response to concerns raised by the US Food and Drug Administration (FDA) that people could confuse eA1C with laboratory A1c, it was later renamed the glucose management indicator, or GMI.
Despite its widespread use, many studies have shown significant discordance between GMI and laboratory A1c. This discordance is observed in diverse populations, including in persons with type 1 and type 2 diabetes and different comorbidities, including chronic kidney disease. GMI values can also vary significantly when comparing different brands of CGM sensors, even when worn simultaneously on the same person.
Case Study: Discordance Between GMI and Measured A1c
Mr H is a 50-year-old man with a long-standing (18 year) history of type 1 diabetes and coronary artery disease (myocardial infarction in 2009). He is treated with basal insulin (insulin glargine) and fast-acting insulin. His A1c laboratory test results at his past three visits (3-6 months apart) were 6.9%, 6.8%, and 6.7%, all below the usual treatment target of 7%.
On his Freestyle Libre CGM, at each of those visits, his GMI values were 7.5%, 7.3%, and 7.3%, with corresponding CGM mean glucose levels of 173 mg/dL, 165 mg/dL, and 154 mg/dL. Mr H initially expressed concern that he was not reaching his glycemic goals because his GMI values were all above 7%. He wanted to know why his laboratory A1c test results differed from the GMI, and he was worried that something was wrong with the laboratory A1c. Later, he read a blog post on a diabetes web forum that said that the GMI was “meaningless,” which made him confused about how he should use the GMI data from his sensor.
Understanding Discordance: CGM vs Laboratory A1c
As seen with this patient, discordance between GMI and A1c can cause confusion. The reason for the discordance between Mr H’s laboratory A1c and GMI is almost certainly the imperfect performance of the GMI equation. Mr H’s CGM mean glucose values were consistent with his well-controlled A1c and showed similar improvements over time.
A1c assays are well-calibrated, standardized, and accuracy is monitored; CGM sensors lack traceability to a reference method and have looser standards for accuracy. The accuracy of CGM technology is also influenced by sensor calibration, glucose lag time, and patient-related factors. Even if we were to derive new, better equations to estimate A1c from CGM glucose, these two values will always have a degree of expected discordance.
A New Approach
Given the persistent discordance between GMI and A1c, it may be time to shift our focus. Instead of relying on GMI as a surrogate for A1c, we should emphasize the use of CGM mean glucose alongside laboratory A1c testing. Patients and clinicians should be aware that CGM mean glucose values typically correlate well with laboratory A1c but will not line up perfectly.
The real strengths of CGM systems are the detailed data on glucose patterns, trends, and real-time feedback to patients. CGM systems are especially helpful for reducing hypoglycemia. CGM mean glucose is just one metric among many, but it is a helpful summary measure.
CGM mean glucose is not currently a standard clinical target. To enhance diabetes care, clinical guidelines should include tables with side-by-side equivalents of mean glucose and laboratory A1c, derived from contemporary, diverse populations wearing the latest CGM sensors. These equivalent values should be rigorously validated in external populations. Such values will help patients and clinicians become familiar with typical CGM mean glucose values and could be a stepping stone toward using CGM mean glucose as a treatment target. Other studies should focus on establishing the cost-effectiveness of CGM, particularly for patients who are at lower risk for hypoglycemia. And we need rigorous studies linking CGM metrics, including CGM mean glucose, to long-term complications with head-to-head comparisons to A1c.
GMI has outlasted its usefulness. GMI is frequently discordant with laboratory A1c, has the potential to confuse patients and clinicians, and relying on it could delay or decrease laboratory A1c testing and lead to inappropriate treatment decisions. A1c and CGM systems provide complementary information. Embracing CGM mean glucose, time in range, and other CGM metrics while retaining regular laboratory A1c testing can optimize glucose control and prevent complications in patients with diabetes. As CGM technology improves, our approach to diabetes management and the metrics we use should advance alongside it.
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