Revisiting the Relationships Between Measures of Glycemic Control and Hypoglycemia in Continuous Glucose Monitoring Data Sets

The Diabetes Control and Complications Trial (DCCT) study in 1993 (1) demonstrated a substantial reduction in microvascular complication risk in an intensely treated group of adults with type 1 diabetes, and later analysis confirmed a reduction in macrovascular risk (2). However, the intensively treated group had an approximate threefold increase in risk of severe hypoglycemia (SH), of which ∼60% was attributed to HbA_1c (3) with a strong inverse relationship between HbA_1c and SH.

Hypoglycemia is the main side effect of exogenous insulin, is associated with physical and psychological morbidity, and impacts on treatment outcomes (4,5). It can be clinically categorized into asymptomatic, symptomatic, and severe, with some discussion around which biochemical threshold defines hypoglycemia in type 1 diabetes (5). The International Hypoglycaemia Study Group has recently recommended a glucose threshold <3.0 mmol/L when reporting hypoglycemia in clinical trials. They also propose 3.9 mmol/L as a glucose alert value (6,7). Counterregulatory response can occur <3.3 mmol/L in healthy individuals (8), and cognitive dysfunction occurs at <2.8 mmol/L (9). Continuous glucose monitoring (CGM) has enabled more precise definition of hypoglycemia, requiring a minimum duration of 15 min below threshold with events separated by at least 30 min (10).

Since the DCCT, there have been therapeutic and technological advances in diabetes management that reduce hypoglycemia risk independently of glycemic control. The appearance of insulin analogs (11–13), improvements in insulin pump therapy (14), implementation of structured education (15), and multidisciplinary care by specialist teams (16) have all contributed to improvements in type 1 diabetes outcomes, potentially altering the relationships defined in the DCCT. Epidemiological analyses reinvestigating the relationship between SH and HbA_1c in pediatric populations have found weak or nonsignificant relationships (17–19). The 30-year follow-up data from the DCCT/Epidemiology of Diabetes Interventions and Complications (EDIC) cohort demonstrate a reduction in the frequency of SH over time, with the presence of a previous episode of SH the strongest predictor of future episodes of SH (20). Interestingly, the risk of SH in the intensive group was unchanged between the DCCT and EDIC studies above an HbA_1c of ∼8.5% and in the conventional group was higher in the EDIC cohort above a similar HbA_1c threshold, suggesting that the impact of advances in therapies on hypoglycemia has differentially addressed risk in those with lower HbA_1c values.

The JDRF CGM study was a 26-week randomized control trial evaluating the efficacy and safety of CGM in children and adults with type 1 diabetes published in 2008 (21–23). It was the first to clearly demonstrate that the continuous use of real-time CGM devices could be associated with improved glycemic control.

We hypothesize that the inverse relationship between HbA_1c and SH identified in the DCCT has evolved with advances in treatment and varies with threshold of hypoglycemia. We additionally hypothesize that unblinded CGM has an impact on the interaction between HbA_1c and hypoglycemia.

Data from the JDRF CGM study (21–23) were used in this analysis. The JDRF data set is freely accessible and was obtained from the Jaeb Center for Health Research (http://diabetes.jaeb.org/Dataset.aspx). All data were analyzed using Stata v14.2 (StataCorp, College Station, TX).

The JDRF CGM study was a 26-week randomized control trial evaluating the efficacy and safety of CGM in children and adults with type 1 diabetes. The study protocol and characteristics of the participants enrolled have been detailed elsewhere (21–23).

Briefly, a total of 448 people with type 1 diabetes treated by multiple-dose injection (MDI) or continuous subcutaneous insulin infusion (CSII) was randomized to CGM (intervention) or capillary glucose monitoring (control). Prior to randomization, all participants underwent 1 week of blinded CGM recording as a baseline for subsequent analysis. Blinded CGM was repeated in the control group at 13 and 26 weeks. HbA_1c was assessed at randomization, 13 weeks, and 26 weeks. HbA_1c values are reported as DCCT-aligned percentages.

Relationships between HbA_1c and time in hypoglycemia were assessed using baseline blinded data for all participants. A minimum of 6 days of recording was required to be included in the analysis. To assess the impact of CGM, the 26-week HbA_1c value and time below threshold for the last 6 weeks of the intervention period were used. Individuals were excluded if there were no CGM data at the beginning or end of this 6 week-analysis. The 26-week HbA_1c value and 26-week blinded CGM from the control group were additionally assessed. Again, individuals were excluded if <6 days of CGM data were available. Relationships were analyzed visually using quadratic regression plots, and a relationship between time spent below threshold (3.9, 3.3, 3.0, and 2.8 mmol/L) was assessed across duodeciles of HbA_1c. Glycemic variability (GV) measures were analyzed using MATLAB (MathWorks) to discriminate the subgroups with the highest risk of hypoglycemia (24). Episodes of hypoglycemia for CGM reports defined as time spent <3.0 mmol/L for 15 min separated by a minimum of 30 min were also analyzed.

Data were tested for normality and nonparametric tests were used accordingly. Descriptive data are reported as mean (SD) where normally distributed or as median and interquartile range (IQR) where skewed. Quadratic regression models were used to assess relationships between percentage time below threshold and HbA_1c for each threshold in each analysis group, and their curves were plotted. Nonparametric ANOVA was used to assess variance of time below threshold across the range of HbA_1c. Statistical tests were two-tailed, and the significance level was set at P < 0.05.

CGM data were available for 451 individuals at baseline, but 3 individuals were excluded due to missing HbA_1c values. Of the 448 individuals included in the baseline analysis (54.9% female, age 25.1 ± 15.8 years, and HbA_1c 7.44 ± 0.86%), 231 individuals were assigned to the CGM intervention group and 217 to the control group after the randomization. At 26 weeks, 6-week CGM data were available for 155 individuals in the CGM group after exclusion of 76 individuals due to incomplete data, and CGM data with a minimum of 6 days were available for 185 individuals in the control group after exclusion of 32 individuals due to incomplete data. The baseline clinical characteristics of the individuals included in the analyses at baseline and at 26 weeks are shown in Table 1.

Quadratic regression curves for the percentage time spent in hypoglycemia below four different biochemical thresholds (2.8, 3.0, 3.3, and 3.9 mmol/L) at different HbA_1c values are shown in Fig. 1. J-shaped relationships were observed for each threshold. The highest percentage time below threshold occurs at the lowest HbA_1c values. Each curve has an HbA_1c value that is associated with the lowest time in hypoglycemia for that threshold, indicated by the curve’s nadir point. HbA_1c values on either side of this point are associated with progressively increasing hypoglycemia, with the rate of change steeper with decreasing HbA_1c values compared with increasing HbA_1c values. The regression coefficients, coefficients of determination, and their associated P values were significant for all of the thresholds analyzed (P < 0.05 for all of them).

The 3.9 mmol/L curve has the steepest relationship, showing a greater rate of change for a given change in HbA_1c at both sides of the nadir, when compared with the 3.3, 3.0, and 2.8 mmol/L curves. J-shaped relationships were also present in the control group at 26 weeks, shown in Fig. 1C, confirming the relationships seen at baseline.

CGM changed the nature of the relationships observed in the baseline analysis, as shown in Fig. 1B. The overall relationship between time spent <3.9 mmol/L and HbA_1c remained significant; however, the slope was shallower at lower and higher HbA_1c values. The shapes of the 3.3, 3.0, and 2.8 mmol/L curves changed substantially with unblinded CGM, becoming flattened U-shaped curves with reduced variation in time spent below hypoglycemia threshold with change in HbA_1c. The regression curves for time <3.3, <3.0, and <2.8 mmol/L had low R² values and were not significant.

The mean percentage of sensor use in our intervention group was 72.5% (5.0 days/week). A total of 59.4% of the subjects used the sensor >70% of the total time during the 6-week CGM analyzed at the end of the follow-up. A total of 13.0% of subjects used the CGM <50% (3.5 days/week) of the time.

The percentage reduction in time spent in hypoglycemia in each HbA_1c duodecile has been also calculated using the curves from intervention and control groups at 26-week evaluation. Considering the 3.0 mmol/L as the hypoglycemia cutoff point (in line with the International Hypoglycaemia Study Group), the mean reduction in percentage time in hypoglycemia is 57.6%, with a maximum reduction of 75% of time in hypoglycemia between 6.5 and 7.5% of HbA_1c. With 3.9 mmol/L as the hypoglycemia cutoff point, the mean reduction in percentage time in hypoglycemia is 46.3% across the HbA_1c range of 6.0–9.5% and the maximum reduction of 43% achieved between HbA_1c of 6.0 and 7.5%.

At baseline, the HbA_1c values associated with the lowest time spent below hypoglycemia threshold were 8.1% (65 mmol/mol) for 2.8 mmol/L, 8.2% (66 mmol/mol) for 3.0 mmol/L, 8.4% (68 mmol/mol) for 3.3 mmol/L, and 8.6% (70 mmol/mol) for 3.9 mmol/L. The CGM nadir points for the 2.8, 3.0, and 3.3 mmol/L curves were reduced to 7.3% (56 mmol/mol), 7.4% (57 mmol/mol), and 7.7% (61 mmol/mol), respectively, whereas the nadir point for 3.9 mmol/L increased to 9.1% (76 mmol/mol).

At baseline, the time below threshold hypoglycemia experienced at the National Institute for Health and Care Excellence target HbA_1c value of 6.5% (48 mmol/mol) was 8.6% for 3.9 mmol/L, 4.5% for 3.3 mmol/L, 3.0% for 3.0 mmol/L, and 2.5% for 2.8 mmol/L. With CGM, the percentage time below hypoglycemia threshold was 4.8% for 3.9 mmol/L, 1.8% for 3.3 mmol/L, 1.1% for 3.0 mmol/L, and 0.8% for 2.8 mmol/L.

The mean time spent below threshold for each HbA_1c duodecile is shown in Fig. 2. At baseline, Kruskal-Wallis analysis identified significant variances for time spent below threshold by HbA_1c for time <3.9 (P < 0.0001), <3.3 (P < 0.002), <3.0 (P < 0.010), and <2.8 mmol/L (P < 0.05). Significant variance was additionally identified in the control group for 3.9 mmol/L (P < 0.002) and 3.3 mmol/L (P < 0.02), but not for 3.0 mmol/L and 2.8 mmol/L (P = 0.09 and P = 0.06, respectively). However, in the CGM group, Kruskal-Wallis analysis only identified significant difference for the 3.9 mmol/L threshold (P < 0.01), with no variance of hypoglycemia across HbA_1c categories identified for the 3.3, 3.0, or 2.8 mmol/L thresholds (Fig. 2).

Measures of GV were analyzed for each HbA_1c duodecile. Kruskal-Wallis analysis identified significant variances by HbA_1c for SD and mean amplitude of glucose excursions (MAGE; P < 0.0001), with higher values of SD and MAGE occurring with higher duodeciles of HbA_1c. Significant variance was also described for low blood glucose index (LBGI), with the lowest duodeciles of HbA_1c being associated with a higher LBGI (P < 0.0001). However, the coefficient of variation (CV) showed no variance across HbA_1c duodeciles (P = 0.14).

A post hoc analysis identified three main groups defined by cut points of HbA_1c of <6.5% (48 mmol/mol), between 6.5% (48 mmol/mol) and 8.5% (69 mmol/mol), and >8.5% (69 mmol/mol) with significant differences in GV. The analysis was then repeated through the three main groups, and Kruskal-Wallis showed significant variances for SD (P < 0.0001), MAGE (P < 0.0001), LBGI (P < 0.0001), and high blood glucose index (HBGI; P < 0.0001). The percentage of CV showed no variance across the groups (P = 0.06). In addition, the number of episodes of hypoglycemia was calculated for the three HbA_1c groups. Significant variance was identified with an HbA_1c of <6.5% (48 mmol/mol) associated with a higher number of hypoglycemia episodes. The results are shown in Table 2.

A multiple linear regression analysis was performed to identify baseline independent factors associated with time <3.0 mmol/L or LBGI >2. The analysis was conducted through the whole cohort and in the group with an HbA_1c >8.5% (69 mmol/mol). Age, type 1 diabetes duration, HbA_1c, sex, BMI, and type of treatment at baseline (MDI or CSII) were included in the analysis. For time <3.0 mmol/L, the regression showed an R² of 0.114 with HbA_1c, and treatment modality significantly associated with the time under the threshold. In the analysis of the HbA_1c >8.5% group (69 mmol/mol), R² was 0.145. For this group, CSII treatment was associated with a reduction in time <3.0 mmol/L by 10% (P = 0.05). The analysis showed similar results when LBGI >2 was selected as the dependent variable. Again, higher HbA_1c and CSII treatment were significantly associated with lower LBGI across the cohort. For the group with an HbA_1c >8.5% (69 mmol/mol), the model showed an R² of 0.212, and treatment with CSII was independently associated with a reduction in LBGI of 0.2 (P < 0.05).

Using the largest available CGM data set, we have shown that the relationship between HbA_1c and hypoglycemia, defined at any threshold, is J-shaped, with excess time spent below threshold at lower HbA_1c values. Furthermore, we have demonstrated that the relationship between HbA_1c and hypoglycemia is weakened, and may even be abolished, by CGM and that CGM enables achievement of a lower HbA_1c and reduced hypoglycemia risk simultaneously. The increased hypoglycemia risk at higher HbA_1c values has been identified in other data sets (19) and is consistent with the persistent risk of SH above an HbA_1c of 8.5% (69 mmol/mol) in the DCCT/EDIC follow-up data (20).

From our data, two groups of people are identified at increased risk of hypoglycemia: those with tight glycemic control manifested by a lower HbA_1c and reduced glucose variability assessed by SD and MAGE and people with higher HbA_1c values and increased glucose variability with higher SD, MAGE, and HBGI values. We hypothesize that this second group is more labile and an important group to recognize, as it is not consistently recognized that people with higher HbA_1c values may be at risk for hypoglycemia. This may inhibit access to treatments like CGM that we have shown can address hypoglycemia risk.

Additionally, we have shown that achieving an HbA_1c value between 8.1 and 8.6% may be associated with the lowest hypoglycemia risk, and therefore, this may be a safe initial objective for those individuals with a higher HbA_1c and a history of severe or challenging hypoglycemia.

Associations between HbA_1c and some measures of GV have previously been described in the JDRF data set (25). Furthermore, as demonstrated in the DCCT/EDIC cohort (20), insulin pump use in the JDRF data set was associated with a lower risk of hypoglycemia across the whole range of HbA_1c. As 87% of all of the patients included in the JDRF analysis were on a pump, our results cannot be extrapolated to a population solely treated with MDI. Similar analysis using MDI populations would be of interest.

Rates of SH in the JDRF study were low and cannot be assessed by HbA_1c, as overall rates were low, and direct comparison with SH in the DCCT and EDIC cohorts is not possible. However, CGM has allowed more detailed description of hypoglycemia at differing thresholds and with intervention. Percentage time below hypoglycemic thresholds is a valid measure of hypoglycemia exposure that reduces counterregulatory responses in a dose-response relationship (26), increasing the risk of SH.

CGM abolished the relationship between HbA_1c and hypoglycemia <3.3, <3.0, and <2.8 mmol/L, both in the quadratic regression curves and in ANOVA. The relationship between HbA_1c and time spent <3.9 mmol/L remained after 26 weeks of CGM, suggesting that mild biochemical hypoglycemia is likely to remain more frequent at the extremes of HbA_1c but that it is not more likely to progress to more significant hypoglycemia and may therefore be managed appropriately. This finding has implications for the selection of hypoglycemia thresholds in clinical practice in which, with the support of CGM, 3.9 mmol/L may be considered a safe threshold for action.

The decoupling of the relationship between HbA_1c and hypoglycemia that is seen with CGM between the thresholds of 3.9 and 3.3 mmol/L may be explained by the alerts and alarms for hypoglycemia and glucose rate of change. These may be personalized and commonly set between 3.5 and 4 mmol/L, a level that may not prevent glucose values <3.9 mmol/L but may reduce the risk of glucose concentrations <3.3 mmol/L.

Our results further support the role of CGM in enabling and empowering patients to intensify glycemic control without increasing the risk of hypoglycemia. For people achieving the National Institute for Health and Care Excellence target HbA_1c of 6.5% (48 mmol/mol) in the JDRF study, at baseline, 64 min/day was spent with glucose <3.3 mmol/L and 36 min/day <2.8 mmol/L. With CGM, these times fell to 26 and 12 min, respectively.

A potential limitation to our study is that the data were derived from early generation CGM devices that are less accurate than contemporary devices, especially in the hypoglycemia range.

We present data that suggest that the relationship between HbA_1c and time spent in hypoglycemia in adults and children with type 1 diabetes is J-shaped. Lower HbA_1c values remain associated with increased hypoglycemia risk, although the magnitude of risk depends on the biochemical hypoglycemia threshold. Higher HbA_1c values are additionally associated with increased hypoglycemia, which may be attributable to greater glucose variability. This has the potential to change the paradigm when deciding suitability of candidates for CGM in terms of clinical benefit. Hypoglycemia risk is minimized at an HbA_1c value of ∼8.1–8.6% (65–70 mmol/mol) and continuous use of CGM (5 days/week) may reduce the percentage time spent in hypoglycemia, changing the relationship between HbA_1c and hypoglycemia for time <3.3 mmol/L while enabling optimization of HbA_1c.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. M.G. and N.O. directed and conducted the statistical analysis, interpreted the data, and wrote the manuscript. A.J.T. conducted statistical analysis, interpreted the results, and wrote the manuscript. M.R. and I.C. contributed to interpreting the results and reviewing the manuscript. V.M. conducted the statistical analysis and contributed to interpreting the results. M.G. and N.O. are the guarantors of this work and, as such, had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.