Continuous Glucose Monitoring for Non-Diabetics: What Your Blood Sugar Does All Day
CGMs were designed for people with diabetes who need real-time feedback to manage insulin. A growing number of healthy adults now wear them to see how food, exercise and sleep affect their glucose.
- Fasting glucose and HbA1c give a snapshot and a roughly three-month average; a CGM shows what happens in between, including rises after meals and overnight patterns.
- Even people with normal standard test results can have large swings: in one study, people classed as normoglycemic reached the prediabetic range 15% of the time and the diabetic range 2% of the time.3 Whether these swings harm long-term health in people without diabetes is not established.
- Responses to the same food vary widely from person to person: in an 800-person study, glucose responses to identical meals differed greatly between individuals.1
- Personalised diets based on predicted glucose responses lowered post-meal glucose in a randomized trial,1 but whether wearing a CGM improves long-term health in people without diabetes has not been shown.
- Interpret readings in context: glucose can rise during intense exercise, and healthy adults spend a median 96% of the day between 70 and 140 mg/dL.2
The idea of personalised nutrition was boosted by a 2015 study from Israel's Weizmann Institute. Researchers monitored glucose for a week in 800 people, measured responses to 46,898 meals, and found high variability in responses to identical meals. A machine-learning model combining blood tests, diet, body measurements, physical activity and gut microbiome data predicted individual responses, and a diet based on it lowered post-meal glucose in a blinded randomized trial.1
What a CGM Actually Measures
Modern CGMs, such as the Dexcom G7 and Abbott's FreeStyle Libre 3, use a small filament under the skin to measure glucose in the fluid between cells every few minutes and send readings to a phone app. Because they measure this fluid rather than blood, readings lag slightly behind blood glucose. In the study that set reference values for healthy people, participants wore a sensor for up to 10 days.2
What Healthy CGM Data Looks Like
Reference data come from a study of 153 healthy, non-obese people aged 7 to 80 who wore a blinded sensor. Average glucose was 98 to 99 mg/dL, or 104 mg/dL in those over 60; the median time between 70 and 140 mg/dL was 96%; glucose was above 140 mg/dL for a median 2.1% of the day (about 30 minutes) and below 70 mg/dL for 1.1% (about 15 minutes).2 Another study found that people classed as normoglycemic by standard tests still reached the prediabetic range 15% of the time, and that responses to the same standardised meals varied between individuals.3
The Most Valuable Insights from CGM
Individual responses. Because responses to the same food differ between people,1 a CGM can show which of your meals raise glucose the most.
Food order. Eating carbohydrate last can blunt the rise:4 in 16 people with type 2 diabetes, the peak rise in glucose was 54% lower when protein and vegetables came first and carbohydrate 10 minutes later than when carbohydrate came first.5 Walking after meals helps too: in 41 adults with type 2 diabetes, a 10-minute walk after each main meal lowered post-meal glucose by about 12% compared with one 30-minute daily walk, with the largest effect after the evening meal.6
Sleep. In young healthy adults, suppressing deep (slow-wave) sleep all night, without changing total sleep time, markedly reduced insulin sensitivity and glucose tolerance.7
Reference Values From Healthy People
| Measure | Healthy reference value |
|---|---|
| Average glucose | 98–99 mg/dL (104 mg/dL over age 60) |
| Time between 70 and 140 mg/dL | 96% of the day (median) |
| Time above 140 mg/dL | 2.1% of the day, about 30 minutes (median) |
| Time below 70 mg/dL | 1.1% of the day, about 15 minutes (median) |
| Within-person variability (coefficient of variation) | About 17% |
These are reference values from 153 healthy, non-obese people, not validated health targets.2
Put this research into practice: Biomarker Reference Tool
References
- 1Zeevi D, et al. "Personalized Nutrition by Prediction of Glycemic Responses." Cell. 2015;163(5):1079-1094. PubMed · DOI
- 2Shah VN, et al. "Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study." J Clin Endocrinol Metab. 2019;104(10):4356-4364. PubMed · DOI
- 3Hall H, et al. "Glucotypes reveal new patterns of glucose dysregulation." PLoS Biol. 2018;16(7):e2005143. PubMed · DOI
- 4Shukla AP, et al. "Food Order Has a Significant Impact on Postprandial Glucose and Insulin Levels." Diabetes Care. 2015;38(7):e98-9. PubMed · DOI
- 5Shukla AP, et al. "Carbohydrate-last meal pattern lowers postprandial glucose and insulin excursions in type 2 diabetes." BMJ Open Diabetes Res Care. 2017;5(1):e000440. PubMed · DOI
- 6Reynolds AN, et al. "Advice to walk after meals is more effective for lowering postprandial glycaemia in type 2 diabetes mellitus than advice that does not specify timing: a randomised crossover study." Diabetologia. 2016;59(12):2572-2578. PubMed · DOI
- 7Tasali E, et al. "Slow-wave sleep and the risk of type 2 diabetes in humans." Proc Natl Acad Sci U S A. 2008;105(3):1044-9. PubMed · DOI
