Impact of Hypoglycemia on Glucose Variability over Time for Individuals with Open-Source Automated Insulin Delivery Systems

Abstract

This study investigates glucose conditions preceding and following various hypoglycemia levels in individuals with type 1 diabetes using open-source automated insulin delivery (AID) systems. It also seeks to evaluate relationships between hypoglycemia and subsequent glycemic variability. Methods: Analysis of continuous glucose monitor (CGM) data from 122 adults with type 1 diabetes using open-source AID from the OpenAPS Data Commons was conducted. This study comprehensively analyzed the effects of hypoglycemia on glycemic variability, covering time periods before and after hypoglycemia. Results: Glucose variability normalization post-hypoglycemia can take up to 48 h, with severe hypoglycemia (41–50 mg/dL) linked to prolonged normalization. A cyclical pattern was observed where hypoglycemia predisposes individuals to further hypoglycemia, even with AID system use. A rise in glucose levels often precedes hypoglycemia, followed by an elevated mean time above range (TAR) post-hypoglycemia, indicating a ‘rebound’ effect. The experimental results are further validated on T1DEXI data (n = 222), originating from commercial AID systems. Different hypoglycemia categorization approaches did not show significant differences in glycemic variability outcomes. The level of hypoglycemia does influence the pattern of subsequent glucose fluctuations. Conclusion: Hypoglycemia, especially at lower levels, significantly impacts subsequent glycemic variability, even with the use of open-source AID systems. This should be studied further with a broader set of commercial AID systems to understand if these patterns are true of all types of AID systems. If these patterns occur in all types of AID systems, it underscores potential opportunities for enhancements in AID algorithms and highlights the importance of educating healthcare providers and people with diabetes about post-hypoglycemia glucose variability.

Keywords

• type 1 diabetes
• automated insulin delivery
• glycemic variability
• time series analysis
• CGM
• glucose