A Machine Learning Framework to Quantify Postprandial Glucose Responses in Gestational Diabetes

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: Twenty-one participants provided analyzable data. Relative to self-reported mealtime, the ML algorithm’s predicted mealtimes had an absolute error of a median 30 (interquartile range [IQR]: 20–45) min. Comparing 1-h and 2-h 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. Conclusions: 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.