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<p>Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in <i>G6PC1</i>, which is essential for proper glucose storage and metabolism. With the advent of continuous glucose monitoring systems, development of algorithms to analyze and predict glucose levels has gained considerable attention, with the aim of preemptively managing fluctuations before they become problematic. However, there is a lack of research focusing specifically on patients with GSD. Therefore, this study aimed to forecast glucose levels in patients with GSD using state-of-the-art deep-learning (DL) algorithms. This retrospective study utilized blood glucose data from patients with GSD who were either hospitalized or managed at Yonsei University Wonju Severance Christian Hospital, Korea, between August 2020 and February 2024. In this study, three state-of-the-art DL models for time-series forecasting were employed: PatchTST, LTSF N-Linear, and TS Mixer. First, the models were used to predict the patients’ Glucose levels for the next hour. Second, a binary classification task was performed to assess whether hypoglycemia could be predicted alongside direct glucose levels. Consequently, this is the first study to demonstrate the capability of forecasting glucose levels in patients with GSD using continuous glucose-monitoring data and DL models. Our model provides patients with GSD with a more accessible tool for managing glucose levels. This study has a broader effect, potentially serving as a foundation for improving the care of patients with rare diseases using DL-based solutions.</p>

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A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月15日

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  • Ji Seung Ryu,
  • Jang Hoon Ru,
  • Yunkoo Kang,
  • Sejung Yang

摘要

Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. With the advent of continuous glucose monitoring systems, development of algorithms to analyze and predict glucose levels has gained considerable attention, with the aim of preemptively managing fluctuations before they become problematic. However, there is a lack of research focusing specifically on patients with GSD. Therefore, this study aimed to forecast glucose levels in patients with GSD using state-of-the-art deep-learning (DL) algorithms. This retrospective study utilized blood glucose data from patients with GSD who were either hospitalized or managed at Yonsei University Wonju Severance Christian Hospital, Korea, between August 2020 and February 2024. In this study, three state-of-the-art DL models for time-series forecasting were employed: PatchTST, LTSF N-Linear, and TS Mixer. First, the models were used to predict the patients’ Glucose levels for the next hour. Second, a binary classification task was performed to assess whether hypoglycemia could be predicted alongside direct glucose levels. Consequently, this is the first study to demonstrate the capability of forecasting glucose levels in patients with GSD using continuous glucose-monitoring data and DL models. Our model provides patients with GSD with a more accessible tool for managing glucose levels. This study has a broader effect, potentially serving as a foundation for improving the care of patients with rare diseases using DL-based solutions.