Introduction to Glycemic Metrics

Continuous glucose monitoring (CGM) has transformed the way clinicians assess and manage diabetes. Unlike self-monitoring of blood glucose (SMBG), which captures a single point in time, or A1C, which reflects an average over about three months, CGM provides a continuous view of glucose trends. By showing fluctuations, variability, and episodes of hypo- and hyperglycemia, CGM data equips healthcare professionals with deeper insights that move beyond the limitations of one-time readings or long-term averages.

For healthcare professionals, understanding the core set of CGM-derived glycemic metrics is essential. These metrics form the foundation of modern diabetes management and are increasingly used in clinical practice, research, and diabetes self-management. 

Why Glycemic Metrics Matter

A1C has long been considered the standard measure of glycemic control. While useful, it has important limitations:

• It does not capture glucose variability or hypoglycemia.
• It can be inaccurate in conditions affecting hemoglobin (anemia, hemoglobinopathies, kidney disease).
• It offers limited guidance for day-to-day management.
• It is an average of the past 2-3 months.

CGM metrics address these limitations by providing a dynamic view of glucose control. Studies have shown that metrics like Time in Range (TIR) are strongly associated with risk of microvascular and macrovascular complications. By standardizing how these metrics are defined and reported, clinicians worldwide can apply them consistently to patient care.

The Core Glycemic Metrics

The International Consensus on CGM Metrics (2019) identified a standard group of metrics that should be part of every CGM interpretation for a recommended time period (14 days or more), and active CGM wear time (70% or more). 

1. Time in Range (TIR)

Definition: Percentage of time glucose is between 70 and 180 mg/dL.

Target: At least 70% of the day (about 17 hours) in range for most adults with type 1 or type 2 diabetes.

Clinical relevance: Strong predictor of long-term complication risk; easy for patients to understand.

Communication tip: “TIR shows how much of your day your glucose is in the zone where you feel your best physically and mentally”.

2. Time Below Range (TBR)

Definition: Percentage of time spent below 70 mg/dL, with a subset below 54 mg/dL.

Target: Less than 4% of the day under 70 mg/dL, and less than 1% under 54 mg/dL.

Clinical relevance: Measures hypoglycemia risk, which is one of the most dangerous aspects of diabetes care.

Communication tip: “Too much time below range can make you feel shaky and confused and put you at risk of not having glucose for your brain to function.  Our goal is to minimize this risk.”

3. Time Above Range (TAR)

Definition: Percentage of time spent above 180 mg/dL, with a subset above 250 mg/dL.

Target: Less than 25% of the day above 180 mg/dL and less than 5% above 250 mg/dL.

Clinical relevance: Reflects hyperglycemia burden and long-term risks.

Communication tip: “Too much time above range puts the body at risk of being exposed to high glucose levels that can damage body cells and organs.”

4. Mean Glucose

Definition: The average glucose value over the monitoring period.

Clinical relevance: Provides a quick summary of control and aligns with A1C but lacks detail about highs, lows, and variability.

Communication tip: “This gives us a simple overall picture, but it doesn’t show the full story.”

5. Glucose Management Indicator (GMI)

Definition: An estimate of A1C based on average CGM glucose.

Clinical relevance: Helps bridge CGM data with lab A1C. Discrepancies between the two may highlight variability or hemoglobin-related conditions.

Communication tip: “This is like a forecasted A1C based on your CGM data.”

6. Coefficient of Variation (CV)

Definition: A measure of glucose variability, calculated as standard deviation divided by mean glucose.

Target: 36% or less.

Clinical relevance: A strong predictor of hypoglycemia risk; provides more useful information than standard deviation alone.

Communication tip: “This tells us how steady your glucose is. Lower variability usually means fewer surprises.”

Quick Reference Table

Applying Metrics in Practice

Individualize targets. Targets vary by patient age, type of diabetes, comorbidities, and treatment goals. For example, older adults or those with hypoglycemia unawareness may require more relaxed TIR goals but stricter limits on TBR.

Use metrics to guide care.

• In primary care, TIR can support medication titration and lifestyle counseling.

• For diabetes educators, TIR and TAR provide a way to set achievable, motivating goals with patients.

• Pharmacists can monitor TAR and TBR when adjusting insulin and diabetes medication therapies.

Leverage technology. Reports such as the Ambulatory Glucose Profile (AGP) summarizes all metrics in a standardized, visual format. Most diabetes data platforms align with these standards.

Looking Ahead

While these core metrics are considered essential, newer composite measures such as the Glycemia Risk Index (GRI) are emerging to capture both hypo- and hyperglycemia risk in a single score. As CGM adoption expands, healthcare professionals should expect more tools that combine traditional metrics with predictive analytics to support patient care.

Key Takeaway

Understanding and applying the core glycemic metrics moves clinical care beyond A1C. By integrating TIR, TBR, TAR, mean glucose, GMI, and CV into routine practice, healthcare professionals can better assess safety, stability, and progress toward individualized goals. These metrics are practical, evidence-based, and central to modern diabetes management.

1. ^{Battelino T, Danne T, Bergenstal RM, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care. 2019;42(8):1593–1603. doi:10.2337/dci19-0028}

2. ^{American Diabetes Association Professional Practice Committee. 6. Glycemic Goals: Standards of Care in Diabetes—2024. Diabetes Care. 2024;47(Suppl. 1):S113–S124. doi:10.2337/dc24-S006}

3. ^{Bergenstal RM, Beck RW, Close KL, et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care. 2018;41(11):2275–2280. doi:10.2337/dc18-1581}

4. ^{Danne T, Nimri R, Battelino T, et al. International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care. 2017;40(12):1631–1640. doi:10.2337/dc17-1600}

5. ^{Rodbard D. Glycemic Variability: Measurement and Utility in Clinical Medicine and Research. Diabetes Technology & Therapeutics. 2018;20(S2):S25–S40. doi:10.1089/dia.2018.0092}

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