2026 · Journal of translational medicine · open access
Multiomics: the intersection of personalized nutrition in cardiometabolic diseases
Çelik et al.
The finding, in our words
This narrative review found that metabolomic markers enhance prediction of cardiometabolic risk beyond classic indicators, and that personalised nutrition plans based on microbiome and clinical characteristics improve glycaemic control measured by continuous glucose monitoring, HbA1c and triglycerides more than a standard Mediterranean diet. The integration of wearable-derived digital biomarkers with multiomic data could support patient-centric clinical applications for cardiometabolic disease management.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
The High-Definition Oncology feasibility study in 30 women with metastatic cancer demonstrated high adherence to biospecimen collection (97.4% plasma, 80.7% stool) and wearable monitoring (70–95% of days captured for activity, heart rate, sleep, and oxygen saturation). This establishes a viable framework for decentralised, multimodal data collection in oncology to support individualised treatment models.
Garma et al., JCO precision oncology (paywalled) · source ↗
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 ↗
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.
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.