Associations between fecal short-chain fatty acids and sleep continuity in older adults with insomnia symptoms
Magzal et al.
The finding, in our words
In older adults with insomnia symptoms, higher concentrations of faecal short-chain fatty acids correlated with poorer sleep efficiency and longer sleep onset latency measured by actigraphy. The findings suggest that stool metabolites combined with wearable sleep monitoring could help identify insomnia phenotypes remotely.
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 ↗
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 ↗
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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 ↗
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.
Çelik et al., Journal of translational medicine · source ↗