Individual variations in glycemic responses to carbohydrates and underlying metabolic physiology
Wu et al.
The finding, in our words
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.
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 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 ↗
The study demonstrates that frequent collection of 10 microlitres of capillary blood, combined with wearable sensor data, enables simultaneous measurement of thousands of metabolites, lipids, cytokines and proteins, revealing individualised metabolic and inflammatory responses to dietary interventions and molecular fluctuations linked to intra-day changes in heart rate, glucose, cortisol and physical activity, supporting dynamic health profiling outside the clinic.
Shen et al., Nature biomedical engineering · 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.
This narrative review finds that saliva is best suited to repeated neuroendocrine and metabolic measurements during exercise, sweat is most mature for electrolyte and sweat-rate monitoring with wearable compatibility, and exhaled breath offers access to volatile metabolism but remains instrumentally demanding. Each matrix requires matching to a defined clinical or exercise question with matrix-specific validation before translational use.