Calculators for CGM Users
Eight tools built around continuous glucose monitor data. Estimate A1C from your average, predict per-meal glucose response, count net carbs for insulin dosing, and track the underlying insulin sensitivity that CGM patterns reflect.
The eight tools
A1C from Average Glucose
Estimate your next lab HbA1c from 14–30 days of CGM averages. The same Nathan ADAG math your CGM uses to compute GMI.
Open calculator →A1C ↔ eAG Converter
The forward direction — translate a lab A1C into the average glucose that matches it. Useful for reconciling lab A1C with CGM GMI.
Open calculator →Glucose Unit Converter
mg/dL ↔ mmol/L conversion for reading international guidelines, pump settings, and research papers from outside the US.
Open calculator →Glycemic Load Calculator
Predict post-meal glucose response before eating. Per-meal GL ≤20 typically keeps post-prandial glucose under 140 mg/dL for most adults.
Open calculator →Net Carbs Calculator
For insulin dosing (T1D) and carb-restricted T2D — full fiber subtraction, 100% erythritol, 50% other sugar alcohols.
Open calculator →HOMA-IR Calculator
Underlying insulin resistance from fasting glucose + insulin. CGM patterns are downstream of HOMA-IR; tracking both gives the full picture.
Open calculator →Fiber Intake Calculator
Viscous soluble fiber (oat beta-glucan, psyllium) at 3g+ per meal reliably reduces post-prandial glucose by 20–30%.
Open calculator →Cholesterol Ratios
TG/HDL ratio is the most sensitive single marker for whether glycemic control is improving insulin sensitivity — faster signal than waiting for HbA1c.
Open calculator →How CGM data fits with the lab A1C
Continuous glucose monitors (Dexcom G7, Libre 3, Medtronic Guardian) compute "GMI" — Glucose Management Indicator — using essentially the same Nathan 2008 ADAG formula as lab A1C estimation, but applied to 14+ days of CGM data rather than the ~120 days of red blood cell glycation history a lab A1C reflects. The 2018 Bergenstal consensus paper in Diabetes Care renamed the metric from "estimated A1C" to GMI specifically because individual A1C-GMI gaps were clinically meaningful and shouldn\'t be ignored.
The practical implication: use both. CGM data gives you fast-feedback metabolic information — within days you know if a diet change is moving your average glucose. Lab A1C gives you the longer-term standardised measurement that endocrinologists, insurance, and clinical trials use. The two should track in the same direction; a persistent gap is worth discussing with your care team.
The five CGM metrics that matter
- Time in Range (TIR): percentage of readings 70–180 mg/dL. ≥70% is the 2019 Battelino consensus target.
- Average glucose: drives the estimated A1C / GMI calculation.
- Glucose Management Indicator (GMI): A1C equivalent of your CGM data.
- Coefficient of Variation (CV): measures glucose variability. Target <36% — lower values indicate more stable glucose patterns.
- Time Above Range / Time Below Range: percentage above 180 (hyperglycemia) and below 70 (hypoglycemia). Both should be minimised.
The 2019 Battelino consensus established these as the standard CGM metrics for clinical and research use. Most CGM apps display all five automatically. The Glycemic Load and Net Carbs calculators above help you predict and prevent the spikes that hurt TIR and drive Time Above Range higher.
Practical workflow for non-diabetic CGM users
A growing number of non-diabetic adults wear CGMs for metabolic health insight (Levels, Nutrisense, Veri, Stelo, January AI). For this population:
- Establish baseline. 14 days of normal eating gives your typical glucose patterns. Use the A1C-from-average-glucose calculator to predict your "lab A1C equivalent."
- Test interventions. Try eating vegetables and protein before carbs (Shukla 2015 = 29% lower spike). Add 10g viscous fiber pre-meal. Add 1 Tbsp vinegar to dressings (Östman 2005 = 30% reduction).
- Compare meals. The Glycemic Load Calculator predicts which meals will spike; CGM data confirms or contradicts. Most people discover personal "trigger foods" their nutrition app doesn\'t flag.
- Watch the trend. Day-to-day variation is noise; a week-over-week trend in average glucose is signal. Use the rolling average view in your CGM app.
Related
FAQ
- Why does my CGM GMI differ from my lab A1C?
- GMI (Glucose Management Indicator) uses essentially the same Nathan 2008 ADAG formula as lab A1C estimation but applied to 14+ days of CGM data rather than the ~120 days of red blood cell history a lab A1C reflects. The 2018 Bergenstal et al. consensus paper renamed the metric from "estimated A1C" to GMI specifically because individual GMI-A1C gaps were clinically meaningful — about 50% of T1D and T2D adults have gaps over 0.3% and about 10% have gaps over 0.7%. Both numbers can be correct; they're measuring different averaging windows and individual glycation kinetics differ.
- How long does my CGM data need to cover for an accurate A1C estimate?
- Minimum 14 days of CGM wear time with at least 70% data capture, per the 2019 International Consensus on Time in Range (Battelino et al., Diabetes Care). The 14-day window correlates ~0.84 with subsequent 90-day A1C in stable subjects. 30 days is better; 90 days approaches the full A1C measurement period. After major treatment changes (new medication, significant diet shift), the CGM-based estimate will be ahead of the lagging lab A1C until ~6 weeks after the change stabilises.
- What's "Time in Range" and how does it compare to A1C?
- Time in Range (TIR) measures the percentage of CGM readings between 70–180 mg/dL (3.9–10.0 mmol/L) — the consensus target window for most adults with diabetes. The 2019 Battelino consensus established TIR ≥70% as the standard target, with each 10% improvement in TIR corresponding to approximately 0.5% reduction in A1C. TIR captures glucose variability that A1C cannot — two people with identical 7.0% A1Cs can have radically different TIR depending on spike patterns. For CGM users, TIR is the more actionable daily metric; A1C remains the standard for cross-population comparisons and clinical trials.
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