We found that the GMI compares favourably to measured HbA1c (Fig. 1) [10, 11]. Poor correlation can occur in routine clinical practice if significant glycaemic change occurs for the 2 weeks before GMI calculation, as the sensors base the calculation on that timeframe. In our study, we based the GMI calculation on 100 days of data, which might have better approximated the laboratory measurement of HbA1c.

For any given GMI value, the daily “glucose change” values can vary considerably (Fig. 3). Furthermore, greater daily glucose variation can be partly attributed to younger age and shorter duration of diabetes. In our sample, Libre monitoring showed that younger individuals and those with a shorter duration of T1D had greater glucose variability (Fig. 3), which is concerning. GMI fluctuations at a particular time as summated are less in the first half of the day vs. the second half.

Early optimisation of glucose control significantly reduces the risk of microvascular complications [12, 13]. Greater daily glucose fluctuation, occurring between peaks and troughs, is linked to an increased occurrence of hypoglycaemic episodes [14, 15]. Although greater glycaemic variation is known in adolescents [13, 16], our population focused on adults over 18 years of age, so hormonal or psychological aspects of development would not be the cause. Structured education can reduce glycaemic variability and it may be that younger people with shorter duration of diabetes have yet to take part in such a programme. Although it may be assumed that younger people are more active and that blood glucose responses to physical activity may be highly variable, recent studies suggest that overall, physical activity levels are not associated with measures of glucose variability [17,18,19].

Glycaemic variability metrics fall into two general categories: The first is metrics of amplitude and the second is time-dependency [20]. These are representative of the duration of events. The metric we employed is from the first category and does not quantify the absolute time spent out of normoglycaemia. Time in range will quantify duration but not give an estimation of amplitude [21]. For this, standard reports usually give a CV of < 36%, which is considered to identify those at lesser risk of hypoglycaemia [22]. However, a problem with using the CV is that when the mean is large, even a moderate amount of variability (measured by the standard deviation), may seem small relative to the magnitude of the mean [23]. In Fig. 4, we show that glycaemic variability (AGF) is greatest in the second half of the day and then declines toward midnight with the degree of GMI for summated glucose levels over 18 months being greater for those with higher GMI (by tertile of GMI).

Above a GMI of 54 mmol/mol, the percentage of values > 18 mmol/l rose exponentially (Fig. 2b), but even at values of 54 mmol/mol, some individuals showed high variation in glucose levels from one reading to the next. Furthermore, those in the highest tertile of % > 18 mmol/l had peaks in the % above 18 mmol/l at both clock times 15:00 and 22:00. This emphasizes the limitations of HbA1c (approximated by GMI) with regard to identifying glucose variability. In such individuals, a change in clinical approach might be needed, even though HbA1c is at, or close to, target.

We also show that the summated GMI over time was at its lowest for all GMI categories (tertiles of GMI) at just before midday (Fig. 4) and that there was much less variation in summated GMI over time for those in the lowest versus the highest tertile of GMI. Meal consumption is positively associated with glucose variability [24] and was likely having an effect. Newer, rapid-acting insulin analogues may result in reduced early postprandial hyperglycaemia and less hypoglycaemia several hours after the meal [25, 26]. However, the effectiveness is related to rates of insulin absorption. As well as determining glycaemic variability, the use of either CGM or flash glucose monitoring can also help to reduce hypoglycaemia and improve glycaemic variability [27,28,29].

As there are no established critical clinical levels to be applied for age and duration with the condition, splitting the cohort into halves by age and duration gave samples of equivalent size and allowed more confident comparison between the two groups. The use of terciles to highlight and quantify the impact of age and duration showed that younger individuals in the early years of living with the condition were at higher risk of increases in both GMI and AGF.

It is important to point out that the link between the three chosen metrics GMI, AGF, and % ACT was weak with R2 between them < 0.2, suggesting that these metrics are relatively independent of each other and may each be capturing different aspects of the physiology of glucose handling in people with T1D.

This study utilized data from intermittent scanning CGM (flash glucose monitoring), which required participants to swipe a reader over the sensor. This might introduce bias, as there may be a greater propensity to swipe at extremes of glucose. However, the sensor records the glucose reading each minute and stores a glucose reading every 15 min for the previous 8 h [30]. These data are then captured after each swipe. Another limitation is that we have not been able to include systematic data on insulin dosing or carbohydrate intake. This will be the subject of a future study. It should also be pointed out that DEXCOM® and other continuous glucose monitoring systems are available. However, LIBRE was the technology most widely used by our patients at the time of this study.