Glycaemic Variability as a Digital Biomarker

Glycaemic variability describes the magnitude of glucose fluctuation around its average. It is most commonly reported as the coefficient of variation, the standard deviation divided by the mean and expressed as a percentage, which international consensus adopted specifically because it adjusts for the fact that higher average glucose naturally produces larger absolute swings.

Its role is to capture what the other continuous glucose metrics miss. Mean glucose describes the centre, time in range describes how much of the day sits within target, and neither distinguishes a steady day from a day of large oscillations that happen to average out. Consensus guidance recommends a coefficient of variation below thirty six percent as the threshold separating stable from unstable glucose, a value derived from where hypoglycaemia risk begins to rise sharply.

Variability is computed from the same continuous sensor stream as the other metrics, over the same recommended fourteen day window. The coefficient of variation is the consensus preferred metric, though the literature contains dozens of alternatives including standard deviation, mean amplitude of glycaemic excursions and various risk indices, each emphasising a different aspect of the fluctuation.

That proliferation is itself a limitation. Different metrics rank the same patients differently, and a study reporting variability without stating which metric it used has not reported enough to be interpreted. The coefficient of variation is the practical default because it is what consensus guidance names, it adjusts for mean level, and it comes with a threshold that can be acted on.

The primary use is hypoglycaemia risk. High variability is closely tied to time spent below range, since large swings from a given average are what carry a person into hypoglycaemia, and the consensus threshold exists for that reason. In trials, variability is reported alongside time in range and time below range as part of the standard metric set.

It is also used to compare treatment approaches that produce similar averages by different routes, where a regimen achieving the same mean glucose with lower variability is generally preferable. Because variability is closely linked to hypoglycaemia, it is reported alongside the Hypoglycemia Fear Survey, which captures the behavioural consequence: people who experience unpredictable lows change how they eat, dose and live in ways a glucose metric alone does not show.

The main problem is definitional. Dozens of variability metrics exist, they are not interchangeable, and cross study comparison is unreliable unless the same metric was used. Even the coefficient of variation is sensitive to the recording period and to how missing data was handled.

Sensor accuracy is lowest in the hypoglycaemic range, which contributes disproportionately to variability, so estimates carry more uncertainty than the headline number suggests. And while the association between high variability and hypoglycaemia is strong, evidence that reducing variability independently improves long term outcomes, beyond the effect of raising time in range, is less settled than the metric's popularity implies.

• Monnier L, et al. Toward defining the threshold between low and high glucose variability in diabetes. Diabetes Care. 2017. pubmed.ncbi.nlm.nih.gov
• Rodbard D. Glucose variability: a review of clinical applications and research developments. Diabetes Technol Ther. 2018. pubmed.ncbi.nlm.nih.gov
• Kovatchev B. Glycemic variability: risk factors, assessment, and control. J Diabetes Sci Technol. 2019. pubmed.ncbi.nlm.nih.gov
• Battelino T, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the International Consensus on Time in Range. Diabetes Care. 2019. pubmed.ncbi.nlm.nih.gov