ArticleNPJ biofilms and microbiomes2025
Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota.
Article in NPJ biofilms and microbiomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- The role of gut microbiome in aging-associated diseases: where do we stand now and how technology will transform the future.Gut microbes · 2026Review
- Quantifying the Contributions of Food, Glucose, Sleep, and Microbiome Data to Personalized Glycemic Response Prediction.Current developments in nutrition · 2026Article
- Effects of Non-Nutritive Artificial Sweeteners on Gut Microbiota and Host Metabolism and Health-Related Outcomes: A Review.Nutrients · 2026Review
- Advancing Personalized Medicine in Gestational Diabetes Mellitus Management Through Gut Microbiota Insights.International journal of molecular sciences · 2026Review
- Managing Gestational Diabetes Complexity with Continuous Glucose Monitoring: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Assisted Design of Plant-Protein Meat Analogues: Integrating Nutrition, Functionality, and Fibrillation-Based Texturization.Comprehensive reviews in food science and food safety · 2026Review
- Maternal Microbiome in Gestational Diabetes Mellitus: Mechanisms, Biomarkers, and Therapeutic Perspectives.Life (Basel, Switzerland) · 2026Review
- Gestational diabetes mellitus and maternal-infant microbiome axis: mechanistic insights and therapeutic interventions.Journal of translational medicine · 2026Review
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- The interplay between gut microbiota and gestational diabetes mellitus: mechanisms, characteristics, and intervention strategies.Frontiers in microbiology · 2026Review
- Early pregnancy is characterized by a significant decrease in the diversity of the oral microbiome and strong associations with lifestyle and conception method.Journal of oral microbiology · 2026Article
- Microbiota-Metabolome Alterations Suggest Pro-Inflammatory Phenotype in Congenital Central Hypoventilation Syndrome.International journal of microbiology · 2026Article
- Dual-center prospective validation of an early second-trimester predictive model integrating BMI trajectories and pathophysiological biomarkers for gestational diabetes risk stratification.Journal of diabetes investigation · 2026Article
- Chrononutrition in Gestational Diabetes: Toward Precision Timing in Maternal Care.Journal of personalized medicine · 2025Review
- Synergistic Effects of Bioactive Compounds on Human Adiposity Mechanisms of Fat Loss and Fat Accumulation.Food science & nutrition · 2025Review
- The Role of METS-IR in Early Screening for Gestational Diabetes Mellitus in Chinese Women: A Two-Center Prospective Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
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Authors and funding
23 authors.
Funding
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.
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