Identification of gut microbiome features associated with host metabolic health in a large population-based cohort
Keshet & Segal
The finding, in our words
This study of nearly 9000 individuals identified 145 bacterial pathways and over 87,000 gene families significantly associated with metabolic health measures derived from continuous glucose monitoring and imaging. The results highlight specific microbial functions, such as purine ribonucleosides degradation, that correlate with host metabolism and suggest potential targets for microbiome-based interventions.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
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 adults with pre-diabetes, a personalised postprandial-targeting diet produced larger shifts in gut microbiome composition and increased alpha-diversity compared with a Mediterranean diet, and specific microbial species partially mediated associations between dietary changes and improvements in HbA1c, HDL cholesterol and triglycerides, supporting microbiome-informed precision nutrition for cardiometabolic risk reduction.
The 10K longitudinal prospective cohort pairs deep multi-omic molecular profiling, including metabolomics, gut and oral microbiomes, and blood profiling, with two-week continuous glucose monitoring and home sleep apnea tracking. This broad dataset establishes baseline multimodal infrastructure to develop predictive models for disease progression across 10,000 individuals over 25 years of follow-up.
Shilo et al., European journal of epidemiology (paywalled) · source ↗
In adults with prediabetes and obesity, gut microbiome diversity correlated with body composition but not with continuous glucose monitoring-derived glycaemic variability. Dietary composition showed the strongest microbiome associations, suggesting that microbiome-informed nutrition strategies may be more relevant than glycaemic-driven approaches in this population.