1908-LB: A Machine-Learning Framework to Quantify Postprandial Glucose Responses in Gestational Diabetes

Objective: Continuous glucose monitoring (CGM) provides a novel approach to monitor postprandial glucose responses (PPGR) in persons with gestational diabetes mellitus (GDM). We sought to develop a machine learning (ML) framework to identify PPGRs automatically from CGM data to address challenges of...

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Bibliographic Details
Published inDiabetes (New York, N.Y.) Vol. 73; p. 1
Main Authors Barua, Souptik, Upadhyay, Dhairya A, Sangmo, Tenzin, Khan, Arsala, Berube, Lauren, Li, Ling-Jun, Williams, Shauna, Rosen, Todd, Rawal, Shristi
Format Journal Article
LanguageEnglish
Published New York American Diabetes Association 01.06.2024
Subjects
Algorithms
Diabetes mellitus
Gestational diabetes
Glucose
Glucose monitoring
Learning algorithms
Logging
Machine learning
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Summary:Objective: Continuous glucose monitoring (CGM) provides a novel approach to monitor postprandial glucose responses (PPGR) in persons with gestational diabetes mellitus (GDM). We sought to develop a machine learning (ML) framework to identify PPGRs automatically from CGM data to address challenges of manual meal logging. Methods: Adults 18+ years diagnosed with GDM were enrolled at 24-32 gestational weeks and wore a blinded Freestyle Libre CGM and logged meals for up to 14 days each over 3 visits. A random forest ML algorithm was trained to identify the first meal of the day from raw daily CGM profiles. Comparing self-recorded and ML-predicted PPGRs, the primary outcome was difference in start time of PPGR while the secondary outcomes were the ratio of the corresponding 2-hr and 3-hr area under the PPGR curves. Results: We analyzed data from 19 participants with CGM and self-recorded meal logs (35 ± 6 years, 47% Asian or Black/African-American, 37% Hispanic/Latino). The ML algorithm predicted start time of PPGRs to within a median 30 [19,45] minutes of self-logged meal time (Fig 1). The median ratio of the corresponding 2-hr PPGR AUCs was 1.0 [0.98,1.03] and 3-hr AUCs was 1.0 [1.00,1.01]. Conclusion: An ML algorithm showed promising performance in identifying PPGRs accurately from CGM data in persons with GDM, enabling a new automated approach to meal logging and analyzing postprandial glucose patterns.
ISSN:0012-1797
1939-327X
DOI:10.2337/db24-1908-LB