The Virtual Diabetes Control and Complications Trial #4: Relationship of HbA1c and Continuous Glucose Monitoring Metrics with Severe Hypoglycemic Events

Abstract

Objective:

To evaluate how continuous glucose monitoring (CGM)-derived metrics relate to severe hypoglycemia (SH) events in individuals with type 1 diabetes by utilizing a multistep machine-learning approach to generate virtual CGM profiles from glycemic data in the Diabetes Control and Complications Trial (DCCT).

Research Design and Methods:

Virtual CGM profiles were created for each DCCT participant using previously validated methods. HbA1c values and CGM metrics were analyzed as predictors of SH events within the subsequent 90 days using Poisson regression models. Sensitivity, specificity, and positive predictive value of time-below-range (TBR) <70 mg/dL for SH prediction were also assessed.

Results:

All CGM-derived measures, including TBR, level 2 hypoglycemia (glucose <54 mg/dL), time-in-range 70–180 mg/dL, time-in-tight-range 70–140 mg/dL, low blood glucose index, and coefficient of variation, were higher, while the mean HbA1c was lower for participants who experienced at least one SH event compared with participants who did not. Each 1% increase in TBR and each 0.5% increase in level 2 hypoglycemia were associated with rate ratios of 1.23 (95% CI, 1.20–1.27) and 1.36 (95% CI, 1.30–1.43) for SH events, respectively. A similar pattern was seen when assuming a 0.5 standard deviation change in these metrics. Despite this association, TBR threshold of >6% demonstrated only 13% positive predictive value for SH events.

Conclusion:

Hypoglycemia-focused CGM metrics reproduced by virtual CGM data from the DCCT were strongly associated with SH events, although the positive predictive value was low.