Metabolomics

Dataset Information

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A Pilot Characterization of the Human Chronobiome


ABSTRACT: 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.

INSTRUMENT(S): Xevo TQ MS (Waters)

SUBMITTER: Seth Rhoades 

PROVIDER: MTBLS656 | MetaboLights | 2018-05-16

REPOSITORIES: MetaboLights

Dataset's files

Source:
Action DRS
MTBLS656 Other
FILES Other
a_MTBLS656_plasma_mass_spectrometry.txt Txt
a_MTBLS656_saliva_mass_spectrometry.txt Txt
i_Investigation.txt Txt
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Publications


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 differe  ...[more]

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