2023 · The Journal of infectious diseases · paywalled
Association of Gut Microbiota With Objective Sleep Measures in Women With and Without Human Immunodeficiency Virus Infection: The IDOze Study
Zhang et al.
The finding, in our words
Seventeen gut microbial genera were associated with sleep continuity or timing in women. Enrichment of short-chain fatty acid-producing genera correlated with less fragmented sleep, while proinflammatory Acidaminococcus correlated with later sleep timing. Butyricimonas, a genus linked to better sleep continuity, was less abundant in women with HIV, suggesting microbiome-sleep relationships that could be explored through decentralised multi-omic sampling and wearable monitoring.
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 ↗
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 ↗
In a one-week virtual home clinic with 134 participants, 86 per cent returned saliva and 84 per cent returned stool, and most components were feasible and acceptable despite device and logistical challenges. This supports decentralised, patient-centric self-collection of non-blood biospecimens for research.
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 paper presents CGMacros, a publicly available dataset combining continuous glucose monitoring, activity tracking, food macronutrient data, blood analyses and gut microbiome profiles from 45 participants across healthy, pre-diabetic and type 2 diabetes groups over ten days of free-living monitoring. This multimodal dataset enables research into personalised nutrition and automated diet monitoring by linking glycaemic responses with dietary intake and physiological signals in a decentralised setting.