Background: 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 individuals with type 1 diabetes using open-source AID from the OpenAPS Data Commons was conducted. The 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 hours, with severe hypoglycemia (40-50 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-severe hypoglycemia, indicating a 'rebound' effect. 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 impact subsequent glycemic variability, even with use of 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.
MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes
Department of Genetics, Stanford University School of Medicine（斯坦福大学医学院遗传学系）；Replica Health；Boston Children’s Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Diabetes Research Institute, Mills-Peninsula Medical Center（米尔斯半岛医学中心糖尿病研究所）
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Department of Endocrinology, Mayo Clinic Arizona（梅奥诊所亚利桑那分部内分泌科）
A Real-Time Digital Twin for Type 1 Diabetes using Simulation-Based Inference
University of Bern, Switzerland（伯尔尼大学）；Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Switzerland（糖尿病、内分泌学、营养医学和代谢学部门，伯尔尼大学医院，伯尔尼大学，瑞士）；Diabetes Center Berne, Switzerland（伯恩糖尿病中心）；University of Tübingen, Germany（图宾根大学）
