2024 · Diabetology & metabolic syndrome · open access
Metabolomics analysis of serum and urine in type 1 diabetes patients with different time in range derived from continuous glucose monitoring
Ma et al.
The finding, in our words
Continuous glucose monitoring-derived time-in-range correlated with distinct serum and urine metabolite profiles in type 1 diabetes patients. Lower time-in-range associated with reduced mevalonolactone and thromboxane B3, implicating tryptophan, vitamin B6 and purine pathways in glycaemic control and complication risk.
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.
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 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 ↗
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.