Glucose360: An Open-Source Python Platform with Event-Based Integration for Continuous Glucose Monitoring Data Analysis

Continuous glucose monitoring (CGM) devices provide real-time actionable data on blood glucose levels, making them essential tools for effective glucose management. Integrating blood glucose data with food log data is crucial for understanding how dietary choices impact glucose levels. Despite their...

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Bibliographic Details
Published in:Diabetes technology & therapeutics
Main Authors: Ehlert, Ben, Aron, Dhruv, Perelman, Dalia, Wu, Yue, Snyder, Michael P
Format: Journal Article
Language:English
Published: United States 03.09.2025
Subjects:
glycemic variability
glucose lifestyle management
open-source software
glucose data analysis
continuous glucose monitoring
Python package
ISSN:1557-8593, 1557-8593
Online Access:Get more information
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Summary:Continuous glucose monitoring (CGM) devices provide real-time actionable data on blood glucose levels, making them essential tools for effective glucose management. Integrating blood glucose data with food log data is crucial for understanding how dietary choices impact glucose levels. Despite their utility, many CGM applications lack integration with other external services, such as food trackers, and do not generate useful glycemic variability (GV) metrics or advanced visualizations. Existing solutions vary in functionality: some are proprietary, many require additional user programming or custom preprocessing to meet diverse research needs, and few have created solutions to connect CGM data with external services. Recent reviews highlight gaps such as insufficient postprandial analytics, absence of composite indices, and inadequate tools for nontechnical users. Glucose360 and commonly used alternative CGM applications and tools were compared by calculating GV metrics on 60 participant datasets and by contrasting their general applications for research workflows. To address limitations, we developed Glucose360, featuring (1) an open-source python framework for event-based CGM data integration and analysis; (2) automated calculation of glucose metrics specific for meals and exercise events and other short-interval events; and (3) a user-friendly web application, designed for users with minimal programming experience and accessible at vurhd2.shinyapps.io/glucose360/. Overall, Glucose360 provides a holistic analysis pipeline that is useful for both individuals and researchers to track and analyze CGM data. The source code for Glucose360 can be found at github.com/vurhd2/Glucose360.
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ISSN:1557-8593
1557-8593
DOI:10.1177/15209156251374711