Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota.

Abstract

We developed a prediction model for postprandial glycemic response (PPGR) in pregnant women, including those with diet-treated gestational diabetes mellitus (GDM) and healthy women, and explored the role of gut microbiota in improving prediction accuracy. The study involved 105 pregnant women (77 with GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) for 7 days, provided food diaries, and gave stool samples for microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, and microbiota data (16S rRNA gene sequence analysis). Adding microbiome data increased the explained variance in peak glycemic levels (GLUmax) from 34 to 42% and in incremental area under the glycemic curve (iAUC120) from 50 to 52%. The final model showed better correlation with measured PPGRs than one based only on carbohydrate count (r = 0.72 vs. r = 0.51 for iAUC120). Although microbiome features were important, their contribution to model performance was modest.