Can a CGM Actually Catch Prediabetes Before a Blood Test Would?
Whether a continuous glucose monitor can flag prediabetes on its own is a tested question, and the test results don't agree with each other.
This piece covers research directly comparing CGM-derived metrics against standard lab-based prediabetes classification. It does not cover CGM's role in already-diagnosed diabetes, or CGM's effect on eating and exercise behavior.
A continuous glucose monitor sold to someone without diabetes is often pitched as an early-warning system that can flag metabolic trouble before a standard blood test would. Research testing that claim directly has produced conflicting results: one look at CGM metrics added almost nothing beyond a standard blood marker when separating people with prediabetes from those without it, while a separate look at a different population found real, measurable differences between the two groups on several CGM metrics.
The pitch versus the test
Continuous glucose monitors marketed to people without diabetes lean on a specific promise: your glucose curve can reveal a metabolic problem quietly building before a yearly blood test catches it. It's an appealing idea, it turns a device you already wear into an early-detection tool rather than just a curiosity.
That promise has actually been tested directly, by comparing what a CGM records in people with normal glucose control against what it records in people already classified as prediabetic using standard lab markers. The two studies that ran this comparison didn't land on the same answer.
2 studies
- In 41 adults without diabetes classified as normoglycemic, at increased diabetes risk, or prediabetic based on HbA1c and a diabetes risk score, only mean interstitial glucose differed meaningfully across the three CGM-monitored groups, driven by higher values in the prediabetes group. Glycemic variability and time-in-range metrics did not differ significantly, and the study reported substantial overlap between groups.
- In 53 non-diabetic and 52 prediabetic Indian and South Asian adults monitored for 14 days, the study found significant between-group differences in mean glucose levels, time spent in a restricted target range, and a glycemic-variability measure based on standard deviation, differences that improved over the monitoring period.
Two studies, two populations, two answers
The Balogh study is the more skeptical of the two: beyond a higher average glucose reading, which a standard blood test already captures in a different form, the CGM-specific metrics people are usually told to pay attention to, glycemic variability and time in range, didn't reliably separate the prediabetes group from people at increased risk or with normal glucose control.
The Chaudhry study, run in a different population using a different CGM platform, found the opposite on several of the same kinds of metrics. Real, measurable separation between non-diabetic and prediabetic participants on mean glucose, time in range, and variability. What a CGM actually shows without diabetes may depend on who is wearing it as much as on the device itself, since the two studies used different populations, sample sizes, and CGM platforms rather than repeating the same test.
What neither study settles
Neither study, on its own, tells a wearer whether their own CGM data is meaningfully ahead of a standard blood test. The Balogh study was cross-sectional and comparatively small, 41 participants split across three groups, and it directly reports substantial overlap in the CGM measures between groups. That limits how confidently any single reading could be classified.
And the broader review literature on CGM outside of established diabetes treats this as an open question rather than a settled one. Evidence supporting CGM's use specifically in people with prediabetes or obesity, as opposed to those already on glucose-lowering treatment, has been described as limited and indirect.
The Balogh study included only 41 participants across three comparison groups and was cross-sectional, meaning it captured one snapshot rather than tracking whether CGM metrics predict who later develops diabetes; the Chaudhry study, though larger, was conducted specifically in an Indian and South Asian population and does not establish that its results generalize to other demographic groups.
Common questions
Can a CGM detect prediabetes before a blood test would?
The direct evidence is mixed. One cross-sectional study found CGM metrics added little beyond a standard glucose marker in separating prediabetes from normal glucose control, while a separate study in a different population found real differences on several CGM measures.
Why did these two studies reach different conclusions?
The studies used different populations, sample sizes, and CGM platforms rather than repeating the identical test, which the source material does not resolve into a single explanation for the disagreement.
Is CGM's use in prediabetes well established in the broader research?
According to a 2026 review, evidence supporting CGM adoption specifically in people with prediabetes or obesity, rather than those already receiving diabetes treatment, has been described as limited and indirect.
Does this mean CGM data is useless for people without diabetes?
This research addresses a narrower question, whether CGM can distinguish prediabetes from normal glucose control, not the full range of what a CGM might show a non-diabetic wearer about their own glucose patterns.
Sources
- Limited Discriminatory Capacity of Continuous Glucose Monitoring in Prediabetes and Increased Diabetes Risk: A Cross-Sectional Study.
- Metabolic health tracking using Ultrahuman M1 continuous glucose monitoring platform in non- and pre-diabetic Indians: a multi-armed observational study.
- Continuous Glucose Monitoring in Type 2 Diabetes and Beyond: A Review.