Key result
Machine learning identifies CGM postprandial glucose responses within ~30 minutes of self-reported mealtimes.
Why the study?
Does a random forest machine learning algorithm accurately identify postprandial glucose responses from continuous glucose monitoring data compared to self-reported mealtimes in pregnant adults with gestational diabetes?
Observational (n=21)
Does a random forest machine learning algorithm accurately identify postprandial glucose responses from continuous glucose monitoring data compared to self-reported mealtimes in pregnant adults with gestational diabetes?
A random forest machine learning algorithm can automatically and accurately identify postprandial glucose responses from continuous glucose monitoring data in pregnant adults with 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: 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.
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Barua et al. (2025) conducted an observational in Gestational diabetes mellitus (GDM) or impaired glucose tolerance (IGT) (n=21). Random forest machine learning algorithm vs. Self-reported mealtimes was evaluated on Absolute error of predicted mealtimes and difference in 1-hour and 2-hour postprandial glucose response values. A random forest machine learning algorithm identified postprandial glucose responses from CGM data with a median absolute error of 30 minutes compared to self-reported mealtimes.
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