Diabetes technology and obesity medicine · 2025
A Machine Learning Framework to Quantify Postprandial Glucose Responses in Gestational Diabetes.
СтатьяНазвание и аннотация: Перевод в очереди
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Barua Souptik, Sangmo Tenzin, Upadhyay Dhairya, Mucahit Esra, Martinez Vanessa, Berube Lauren, Penfield Christina, Heo Hye, Li Ling-Jun, Williams Shauna, Rosen Todd, Rawal Shristi
Аннотация
OBJECTIVE: To develop a machine learning (ML) framework to identify postprandial glucose responses (PPGR) automatically from continuous glucose monitoring (CGM) data in pregnant adults with gestational diabetes mellitus (GDM).
METHODS: Pregnant adults diagnosed with GDM or impaired glucose tolerance (IGT) wore blinded CGMs and logged mealtimes for up to three 14-day time periods after enrollment. A random forest ML algorithm was applied to identify morning PPGRs from daily CGM profiles, and its performance compared against PPGRs derived using self-reported mealtimes.
RESULTS: 21 participants provided analyzable data. Relative to self-reported mealtime, the ML algorithm's predicted mealtimes had an absolute error of a median 30 [IQR: 20,45] minutes. Comparing 1-hour and 2-hour PPGR values from the CGM using self-reported and ML-predicted mealtimes showed a median difference of 8.7 [IQR: 0,22.7] mg/dL and 3.3 [IQR: 0,13.2] mg/dL respectively for the two timepoints.
CONCLUSION: A random forest ML algorithm accurately identified PPGRs from CGM data in persons with GDM, enabling an automated and convenient approach to monitoring postprandial dysglycemia in this population.
METHODS: Pregnant adults diagnosed with GDM or impaired glucose tolerance (IGT) wore blinded CGMs and logged mealtimes for up to three 14-day time periods after enrollment. A random forest ML algorithm was applied to identify morning PPGRs from daily CGM profiles, and its performance compared against PPGRs derived using self-reported mealtimes.
RESULTS: 21 participants provided analyzable data. Relative to self-reported mealtime, the ML algorithm's predicted mealtimes had an absolute error of a median 30 [IQR: 20,45] minutes. Comparing 1-hour and 2-hour PPGR values from the CGM using self-reported and ML-predicted mealtimes showed a median difference of 8.7 [IQR: 0,22.7] mg/dL and 3.3 [IQR: 0,13.2] mg/dL respectively for the two timepoints.
CONCLUSION: A random forest ML algorithm accurately identified PPGRs from CGM data in persons with GDM, enabling an automated and convenient approach to monitoring postprandial dysglycemia in this population.