Personalized Prediction of Glycemic Responses to Food in Women with Gestational Diabetes: Gut Microbiota Matters

We aimed to develop a prediction model for postprandial glycemic response (PPGR) in pregnant women with gestational diabetes mellitus (GDM) and to explore the influence of gut microbial data on prediction accuracy. We enrolled 105 pregnant women (70 GDM and 35 healthy). Participants underwent continuous glucose monitoring (CGM) for 7 days and provided detailed food diaries. Stool samples were collected at 28.8 ± 3.6 gestational weeks, followed by 16S rRNA gene sequence analysis. We developed machine learning algorithms for predicting PPGR, incorporating CGM measurements, meal content, lifestyle factors, biochemical parameters, anthropometrics, and gut microbiota data. The accuracy of the models with and without gut microbiota were compared. PPGR prediction models were created based on 2,706 meals with measured PPGRs. The integration of microbiome data in models increased the explained variance in peak glycemic levels (GLUmax) from 34–42% and the explained variance in the incremental area under the glycemic curve 120 minutes after meal start (iAUC120) from 50–52%. The final model performed better than the model based solely on carbohydrate count in terms of correlation between predicted and measured PPGRs (r = 0.72 vs r = 0.51 for iAUC120 and r = 0.66 vs r = 0.35 for GLUmax). After summing the SHAP values of associated features, the microbiome emerged as the fourth most impactful parameter for GLUmax and iAUC120 prediction, following meal composition, CGM measurements, and meal context. Microbiome features rank among the top 5 most impactful parameters in predicting PPGR in women with GDM.