Multi-omics microsampling for the profiling of lifestyle-associated changes in health.

Autor: Shen X; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Kellogg R; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Panyard DJ; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Bararpour N; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Castillo KE; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Lee-McMullen B; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Delfarah A; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Ubellacker J; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA., Ahadi S; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Rosenberg-Hasson Y; Human Immune Monitoring Center, Microbiology and Immunology, Stanford University Medical Center, Stanford, CA, USA., Ganz A; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Contrepois K; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Michael B; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Simms I; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Wang C; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA., Hornburg D; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA., Snyder MP; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.; Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.
Jazyk: angličtina
Zdroj: Nature biomedical engineering [Nat Biomed Eng] 2024 Jan; Vol. 8 (1), pp. 11-29. Date of Electronic Publication: 2023 Jan 19.
DOI: 10.1038/s41551-022-00999-8
Abstrakt: Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.
(© 2023. The Author(s).)
Databáze: MEDLINE
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