The effect of probiotic administration on metabolomics and glucose metabolism in CF patients
Gur et al.
The finding, in our words
Probiotic administration for four months in 23 cystic fibrosis patients altered urine and stool metabolite profiles and gave a modest reduction in insulin resistance among non-diabetic participants, though continuous glucose monitoring detected no significant glycaemic change. The approach shows how decentralised metabolomic sampling and CGM can together explore microbiome-metabolism relationships in CF.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
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.
A scoping review of 52 studies found most were high-quality RCTs using CGM, metabolomics and microbiome analysis in endurance athletes, yet evidence linking these biomarkers to performance, recovery or long-term health remains insufficient. This gap between biomarker discovery and clinical validation must be closed before these decentralised tools can guide precision nutrition.
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 ↗
In n-of-1 dietary trials, a personal gut microbial carb-sensitivity score derived from stool metagenomics correlated with glycaemic phenotypes during high-carbohydrate but not low-carbohydrate intake, and this association was validated in an independent cohort, supporting precision nutrition approaches that integrate stool microbiome signatures with continuous glucose monitoring for individualised dietary advice.