A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota
Guizar-Heredia et al.
The finding, in our words
The review summarises evidence that continuous glucose monitoring data, when combined with gut microbiota, genetic and physiological variables through machine learning, can predict individual postprandial glycemic responses to foods. This enables personalised nutrition recommendations for preventing metabolic syndrome and type 2 diabetes, advancing decentralised dietary management.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
Continuous glucose monitoring of 55 participants showed considerable interindividual variation in postprandial glycaemic responses to carbohydrate meals, with rice producing the highest overall responses. Potato-spikers were more insulin resistant with lower beta cell function, grape-spikers were more insulin sensitive, and mitigators were less effective in insulin-resistant individuals. Multi-omics profiling revealed signatures linking glycaemic responses to triglycerides, metabolites and microbiome pathways.
The Human Phenotype Project enrolled 28,000 participants in a deep-phenotyping cohort that combines blood and microbiome sampling with continuous glucose and sleep monitoring. An AI model trained on dietary and glucose data predicted disease onset more accurately than existing methods, showing how integrating continuous digital signals with multi-omic data can support personalised risk assessment for decentralised diagnostics.
Reicher et al., Nature medicine (paywalled) · source ↗
The Guangzhou Nutrition and Health Study integrates 14-day real-time continuous glucose monitoring with multi-omics data, including serum and faecal metabolomes, proteomes, and gut microbiome sequencing, to explore biomarkers and mechanisms of metabolic disease.
In pregnancies affected by type 1 diabetes, higher maternal time above range on continuous glucose monitoring was associated with altered lipid metabolites in maternal and cord blood, and distinct metabolite profiles were seen for large for gestational age, neonatal hypoglycaemia and offspring hyperinsulinism, suggesting that optimising diet and insulin from the first trimester could improve outcomes.
Post-menopausal women showed higher fasting glucose, HbA1c and inflammation, along with poorer glycaemic variability and time in range, compared to pre-menopausal women. These findings highlight the value of continuous glucose monitoring in decentralised diagnostics for identifying metabolic risks in mid-life women.