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A Pilot Characterization of the Human Chronobiome

Physiological function, disease expression and drug effects vary by time-of-day. Clock disruption in mice results in cardio-metabolic, immunological and neurological dysfunction; circadian misalignment using forced desynchrony increases cardiovascular risk factors in humans. Here we integrated data...

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Detalles Bibliográficos
Autores principales: Skarke, Carsten, Lahens, Nicholas F., Rhoades, Seth D., Campbell, Amy, Bittinger, Kyle, Bailey, Aubrey, Hoffmann, Christian, Olson, Randal S., Chen, Lihong, Yang, Guangrui, Price, Thomas S., Moore, Jason H., Bushman, Frederic D., Greene, Casey S., Grant, Gregory R., Weljie, Aalim M., FitzGerald, Garret A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719427/
https://www.ncbi.nlm.nih.gov/pubmed/29215023
http://dx.doi.org/10.1038/s41598-017-17362-6
Descripción
Sumario:Physiological function, disease expression and drug effects vary by time-of-day. Clock disruption in mice results in cardio-metabolic, immunological and neurological dysfunction; circadian misalignment using forced desynchrony increases cardiovascular risk factors in humans. Here we integrated data from remote sensors, physiological and multi-omics analyses to assess the feasibility of detecting time dependent signals - the chronobiome – despite the “noise” attributable to the behavioral differences of free-living human volunteers. The majority (62%) of sensor readouts showed time-specific variability including the expected variation in blood pressure, heart rate, and cortisol. While variance in the multi-omics is dominated by inter-individual differences, temporal patterns are evident in the metabolome (5.4% in plasma, 5.6% in saliva) and in several genera of the oral microbiome. This demonstrates, despite a small sample size and limited sampling, the feasibility of characterizing at scale the human chronobiome “in the wild”. Such reference data at scale are a prerequisite to detect and mechanistically interpret discordant data derived from patients with temporal patterns of disease expression, to develop time-specific therapeutic strategies and to refine existing treatments.