How to use this page. A 1-2 page desk reference for reading a CGM report in a non-diabetic / metabolic-health context. Click a pill in the header to jump to a section, or use Expand all to print. Every number is referenced - follow the number in square brackets to the source in section 6. Companion to Spencer's existing material on meal logging, sensor artefacts and practitioner workflow.
1. Priority read order — why SD first, not TIR
The hierarchy that surfaces early dysfunction
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In non-diabetic populations, mean glucose is usually unremarkable. Variability and fasting regulation are where early dysfunction shows up. Read in this order:
- Standard Deviation (SD) - narrow corridor vs wide swings around the mean.
- Fasting baseline - overnight profile and dawn rise.
- Delta peak - post-meal excursion above pre-meal baseline.
- Coefficient of variation (CV) - normalised variability signal.
- Average sensor glucose - last to deteriorate, useful as sanity check.
2. Non-diabetic reference values — Shah 2019 [1]
n = 153 healthy adults, ages 7-80, 10-day Dexcom G6
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| Metric | Non-diabetic reference | Early-signal zone |
|---|---|---|
| Mean glucose | 5.4–5.5 mmol/L (5.8 if >60 y) | Rising trend over months |
| TIR 3.9–7.8 mmol/L | 96% (IQR 93–98) | <90% sustained |
| TAR >7.8 mmol/L | 2.1% (~30 min/day) | >10% sustained |
| TBR <3.9 mmol/L | 1.1% (~15 min/day) | >3% sustained |
| Within-subject CV | 17 ± 3% | >25% |
| SD (derived from mean + CV) | ~0.9 mmol/L | >1.2 mmol/L |
| Post-meal delta peak | 1.5–2.5 mmol/L above pre-meal; peak <7.8 | Delta >3.5 or absolute peak >8.5 |
Honest confidence note. Shah's N is modest (153), single-device (Dexcom G6), and sensor accuracy is weakest in the 4–7 mmol/L range. Treat 95% TIR 3.9–7.8 as a defensible working reference, not a regulatory standard.
3. Methodology note — the Battelino / Shah distinction
The single most common error in NT-facing CGM material
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The single most common error in NT-facing CGM material is applying Battelino's diabetes targets to non-diabetic clients.
- Battelino 2019 [2] provides the metric definitions (TIR, TBR, TAR, GMI, CV) and the diabetes targets (TIR 3.9–10.0, CV <36%).
- Shah 2019 [1] provides the non-diabetic reference values.
Use Battelino for how to read the report. Use Shah for what normal looks like. Mixing the two hides the early-signal zone between 17% CV (Shah) and 36% CV (Battelino diabetes).
4. Referral triggers — GP investigation
Screening signals, not diagnoses
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| Signal | Threshold |
|---|---|
| Fasting glucose | ≥ 6.1 mmol/L (IFG) or ≥ 7.0 mmol/L on two occasions (diabetic range) |
| Any sustained reading | ≥ 11.1 mmol/L |
| 14-day mean with symptoms | ≥ 6.5 mmol/L |
| Persistent hypoglycaemia | TBR <3.9 mmol/L exceeding 3% with symptoms |
Scope of practice. CGM in non-diabetic coaching is a screening signal, not a diagnosis. These thresholds trigger a GP conversation - they do not replace one.
5. Lactate — the canary in the coalmine (teaser)
The future metric. Full framework in v1.1.
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A client can have pristine CGM metrics - TIR >95%, CV 17%, flat overnight - and still have a poor lactate profile. The pancreas is coping. The engine underneath is not.
- Fasting lactate can rise before fasting glucose in early metabolic dysfunction [3].
- Mitochondrial dysfunction is detectable in lean, insulin-resistant individuals before any glucose abnormality [4]: ~30% lower mitochondrial ATP synthesis, ~80% higher intramyocellular lipid, with normal fasting glucose, A1c and BMI.
GNL Grace is building the full lactate knowledge base and practical application now. v1.1 of this guide will add the operational lactate framework once the remaining five anchor papers land (Monnier 2017, Kelley 2002, Danne 2017, Zhang 2019, San-Millán & Brooks 2018).
6. References
5 primary sources
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[1] Shah VN, DuBose SN, Li Z, et al. Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study. J Clin Endocrinol Metab 2019;104(10):4356–4364. DOI: 10.1210/jc.2018-02763. PMID: 31127824.
[2] 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. PMID: 31177185.
[3] Broskey NT, Pories WJ, DeMaria EJ, et al. Fasting Plasma Lactate as a Possible Early Clinical Marker for Metabolic Disease Risk. Diabetes Metab Syndr 2024;18(2):102955. PMID: 38382370.
[4] Petersen KF, Dufour S, Befroy D, Garcia R, Shulman GI. Impaired Mitochondrial Activity in the Insulin-Resistant Offspring of Patients with Type 2 Diabetes. N Engl J Med 2004;350(7):664–671. PMID: 14960743.
[5] Guzzi J, Falter F, Kumar AB, Perrino AC. Mind the Gap: Wearable Lactate and Glucose Monitors for Hospitalized Patients. Cureus 2025;17(2):e78536. DOI: 10.7759/cureus.78536.