Continuous Glucose Monitoring: A Practical Guide to Data Interpretation and Clinical Management

Dr Neeraj Manikath , claude.ai

Abstract

Continuous glucose monitoring (CGM) has revolutionized diabetes management by providing real-time glucose data and trends that enable more precise therapeutic interventions. This review provides practical guidance for internists on interpreting CGM metrics, implementing actionable management strategies, and navigating common clinical scenarios. We highlight evidence-based approaches alongside clinical pearls to optimize patient outcomes in both type 1 and type 2 diabetes.

Introduction

The landscape of diabetes management has undergone a paradigm shift with the widespread adoption of CGM technology. Unlike traditional self-monitoring of blood glucose (SMBG), which provides isolated snapshots, CGM offers a continuous narrative of glycemic excursions, revealing patterns invisible to conventional monitoring methods. For the practicing internist, proficiency in CGM data interpretation has become essential, as recent guidelines now recommend CGM for all insulin-treated patients and many with type 2 diabetes on intensive therapy.

Understanding CGM Metrics: Beyond the Hemoglobin A1c

Time in Range: The Primary Target

Time in Range (TIR), defined as the percentage of time spent between 70-180 mg/dL, has emerged as the cornerstone metric for CGM assessment. The landmark DEVOTE trial and subsequent consensus statements established that a TIR of ≥70% correlates with an HbA1c of approximately 7% and reduced microvascular complications.

Clinical Pearl: Each 5% increase in TIR translates to approximately 0.5% reduction in HbA1c. However, TIR provides information A1c cannot—the glycemic variability and time spent in hypoglycemia.

Target ranges vary by population:

• Non-pregnant adults: 70-180 mg/dL (>70% TIR)
• Pregnant women: 63-140 mg/dL (>70% TIR)
• Older adults/high-risk individuals: 70-180 mg/dL (>50% TIR)

Time Below Range: The Silent Threat

Time Below Range (TBR) encompasses both Level 1 (<70 mg/dL) and Level 2 (<54 mg/dL) hypoglycemia. The consensus target is <4% for Level 1 and <1% for Level 2 hypoglycemia.

Oyster: Nocturnal hypoglycemia often occurs asymptomatically. Always examine the 12 AM-6 AM window separately. Patients may report "sleeping well" while experiencing significant overnight lows that contribute to impaired awareness of hypoglycemia.

Hack: The "Rule of 58"—if >58 minutes per day is spent below 70 mg/dL (approximately 4% of 1440 minutes), immediate intervention is required regardless of the patient's symptoms or HbA1c level.

Glucose Management Indicator and Coefficient of Variation

The Glucose Management Indicator (GMI) estimates the expected HbA1c based on mean CGM glucose over 10-14 days. A discrepancy >0.5% between GMI and laboratory HbA1c suggests glycation abnormalities, hemoglobinopathies, or red cell turnover disorders.

Coefficient of Variation (CV) quantifies glycemic variability; a CV <36% indicates acceptable stability. High CV (>36%) with normal TIR suggests a "roller coaster" pattern requiring intervention even when average control appears adequate.

Clinical Pearl: CV = (Standard Deviation ÷ Mean Glucose) × 100. A rising CV despite stable mean glucose signals deteriorating glycemic stability and often precedes worsening TIR by several weeks.

Pattern Recognition and Intervention Strategies

The Dawn Phenomenon vs. Waning Insulin Effect

Morning hyperglycemia represents a diagnostic challenge. The dawn phenomenon shows glucose elevation starting at 4-5 AM even with stable overnight levels, while waning insulin demonstrates gradual rise throughout the night.

Management Hack: For dawn phenomenon in type 1 diabetes on pump therapy, program a 20% basal rate increase from 3-8 AM. For patients on basal insulin injections, switching from morning to bedtime administration or to newer ultra-long-acting insulins (degludec or glargine U-300) often resolves the issue.

Postprandial Excursions: Timing Matters

CGM reveals that many patients with acceptable HbA1c spend excessive time >180 mg/dL postprandially. The peak typically occurs 60-90 minutes after meal initiation.

Oyster: Patients often dose mealtime insulin at mealtime. Data consistently show that administering rapid-acting insulin 15-20 minutes before eating reduces postprandial excursions by 30-40 mg/dL. For ultra-rapid insulins (Fiasp, Lyumjev), 0-10 minutes pre-meal is optimal.

Management Strategy: For persistent postprandial spikes despite appropriate insulin:

1. Review carbohydrate content and glycemic index
2. Consider addition of GLP-1 receptor agonists (delay gastric emptying)
3. Evaluate for gastroparesis if erratic absorption patterns emerge
4. Add SGLT2 inhibitors for persistent elevations in type 2 diabetes

The "Compression Low" Artifact

Patients occasionally report sensor readings <40 mg/dL without symptoms, particularly during sleep. This usually represents sensor compression against the body, causing falsely low readings.

Hack: Instruct patients to look for sudden drops followed by rapid recovery without intervention. True hypoglycemia shows gradual decline and slower recovery. Always confirm suspected severe hypoglycemia with fingerstick before aggressive treatment.

Advanced Pattern Analysis

The "Brittle Diabetes" Illusion

High glycemic variability labeled as "brittle diabetes" often represents correctable behavioral or pharmacologic issues rather than true physiologic instability.

Systematic Approach:

1. Review insulin doses—overtreating highs creates rebound lows
2. Assess adherence—missed doses create saw-tooth patterns
3. Evaluate carbohydrate intake consistency
4. Screen for eating disorders (common in young adults with type 1 diabetes)
5. Consider medication interactions (beta-blockers mask hypoglycemia symptoms)

Pearl: The "15-15 rule" for hypoglycemia treatment (15g carbohydrate, recheck in 15 minutes) is gold standard, yet CGM data reveal most patients consume 30-50g during lows, causing predictable hyperglycemic rebounds at 2-3 hours.

Exercise-Related Patterns

CGM unmasks exercise physiology: anaerobic activity may initially raise glucose (catecholamine surge), while aerobic exercise typically lowers glucose during and for 6-24 hours post-activity.

Management Hack: The "temp basal" feature on insulin pumps reducing basal rates by 50-80% starting 60-90 minutes before aerobic exercise prevents exercise-induced hypoglycemia. For injection users, reducing pre-exercise bolus by 25-50% achieves similar effects.

Special Populations and Scenarios

Type 2 Diabetes and Non-Insulin Therapies

Recent evidence supports CGM benefit in type 2 diabetes, particularly for those on SGLT2 inhibitors or sulfonylureas. The MOBILE study demonstrated significant HbA1c reduction with CGM in non-insulin-treated type 2 diabetes.

Oyster: Metformin's full effect on postprandial glucose may take 4-6 weeks. CGM allows real-time feedback during titration, improving adherence and outcomes.

Hospitalized Patients and Critical Illness

While CGM accuracy decreases during rapid glucose fluctuations and vasoconstriction, newer systems show promise in hospital settings. Current guidelines recommend confirmatory point-of-care testing for all treatment decisions.

Pearl: CGM trend arrows provide valuable directional information in subcutaneous insulin protocols. A single arrow (→) suggests glucose changing 1-2 mg/dL/min; double arrows (↑↑) indicate >2 mg/dL/min changes.

Pregnancy and Preconception

Pregnancy demands tighter targets: 63-140 mg/dL with >70% TIR and <4% below 63 mg/dL. The CONCEPTT trial demonstrated that CGM use in type 1 diabetes pregnancy reduced neonatal hypoglycemia and large-for-gestational-age births.

Management Hack: First-trimester insulin requirements often decrease (20-30%), while second and third trimester show progressive increases (up to 50-100% total daily dose). CGM facilitates rapid dose adjustments without waiting for HbA1c results.

Practical Implementation Strategies

The First CGM Review Visit

Structured Approach:

1. Download and review Ambulatory Glucose Profile (AGP) report
2. Assess data sufficiency (minimum 70% of 14 days)
3. Review TIR, TBR, TAR (time above range) percentages
4. Examine overnight patterns (highest intervention yield)
5. Identify problematic meal patterns
6. Address hypoglycemia before hyperglycemia

Communication Hack: Use colored zones (green for TIR, red for lows, yellow for highs) when explaining data. Patients respond better to visual representations than numerical percentages.

Setting Realistic Expectations

Pearl: Achieving 100% TIR is neither possible nor necessary. Emphasize progress over perfection. A patient moving from 45% to 65% TIR has achieved significant clinical benefit even without reaching the 70% goal.

Alert Fatigue Prevention

Excessive CGM alarms lead to alert fatigue and reduced system use. Start conservatively:

• High alert: 250-300 mg/dL initially
• Low alert: 70 mg/dL (or 80 mg/dL if hypoglycemia unawareness)
• Urgent low: 55 mg/dL
• Rate-of-change alerts: use sparingly

Hack: Silence predictive alerts initially and add back selectively based on problematic patterns identified in downloads.

Emerging Technologies and Future Directions

Integration of CGM with automated insulin delivery (AID) systems represents the treatment frontier. The "hybrid closed-loop" systems adjust basal insulin automatically based on CGM data, with studies showing TIR improvements to 70-80% and significant hypoglycemia reduction.

Smart insulin pens with dose capture and CGM integration eliminate the "stacking" problem where patients administer correction doses too frequently. These systems calculate insulin-on-board and recommend appropriate dosing.

Conclusion

CGM has transformed diabetes management from reactive to proactive care. Mastery of CGM data interpretation enables internists to make precise, evidence-based therapeutic adjustments that improve both glycemic control and quality of life. The key principles—prioritize TBR reduction over TIR optimization, recognize actionable patterns, and implement structured review processes—ensure that this powerful technology translates into meaningful clinical benefit.

The future promises even greater integration of CGM data with artificial intelligence algorithms and decision support tools. However, the fundamental skill of pattern recognition and thoughtful clinical interpretation will remain central to optimizing diabetes outcomes.

Key References

1. Battelino T, et al. Clinical targets for continuous glucose monitoring data interpretation. Diabetes Care. 2019;42(8):1593-1603.

2. Beck RW, et al. Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections. JAMA. 2017;317(4):371-378.

3. Aleppo G, et al. MOBILE study: Flash CGM in type 2 diabetes inadequately controlled with non-insulin therapy. Diabetes Care. 2022;45(12):2655-2663.

4. Feig DS, et al. Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT). Lancet. 2017;390(10110):2347-2359.

5. Danne T, et al. International consensus on use of CGM. Diabetes Care. 2017;40(12):1631-1640.

6. Bergenstal RM, et al. Glucose management indicator (GMI): A new term for estimating A1C from CGM data. Diabetes Care. 2018;41(11):2275-2280.

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Disclosure: This review represents current evidence-based practice. Clinicians should always individualize treatment based on patient-specific factors and emerging evidence.