Glycemic Variability Before and After Hypoglycemia Across Different Timeframes in Type 1 Diabetes with and Without Automated Insulin Delivery

Abstract

Background: Managing Type 1 diabetes (T1D) aims to optimize glucose within the target range while minimizing hyperglycemia and hypoglycemia, yet exercise complicates glycemic outcomes. Despite advances, evidence is limited on how exercise relates to glycemic variability (GV) and hypoglycemia in automated insulin delivery (AID) and non-AID users, including evidence on GV’s temporal course before and after hypoglycemia, especially following long episodes. Objective: We aimed to characterize −48 to +48 h CGM trajectories around hypoglycemia, compare commercial AID and non-AID users, and assess modifiers (exercise, episode duration/severity, gender). Methods: This study analyzes the Type 1 Diabetes and Exercise Initiative (T1DEXI) dataset, assessing GV, hypoglycemia, gender, and exercise interactions in AID (n = 222) and non-AID (n = 276) users. The study examined patterns of glycemic metrics, including time below range (TBR) and glycemic variability surrounding hypoglycemia events, focusing on the 48 h before and after these events. We further assessed the impact of different hypoglycemia levels (41–50 mg/dL, 51–60 mg/dL, and 61–70 mg/dL) on post-event glucose stability. Results: Glycemic variability increased before and after hypoglycemia for up to 48 h in both AID and non-AID users, with statistically significant differences in GV metrics. TBR elevation persisted across all groups, peaking around hypoglycemic episodes. Notably, females using AID achieved significantly improved glucose stability compared to non-AID females, which is a larger within-group difference than that observed in males. Individual-level AID analyses revealed that long-duration hypoglycemia episodes (>40 min) resulted in prolonged TBR elevation, suggesting a slower recovery period despite AID intervention. Conclusions: GV trends may aid in predicting hypoglycemia over extended time periods. Integrating GV patterns into AID systems could improve glucose stability and mitigate hypoglycemia cycles, especially with the possible evaluation of hypoglycemia duration. Future research should explore hormonal influences (e.g., menstrual cycle effects) and inter-individual variability for optimized individual diabetes management.