Title |
A Pilot Characterization of the Human Chronobiome
|
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Published in |
Scientific Reports, December 2017
|
DOI | 10.1038/s41598-017-17362-6 |
Pubmed ID | |
Authors |
Carsten Skarke, Nicholas F. Lahens, Seth D. Rhoades, Amy Campbell, Kyle Bittinger, Aubrey Bailey, Christian Hoffmann, Randal S. Olson, Lihong Chen, Guangrui Yang, Thomas S. Price, Jason H. Moore, Frederic D. Bushman, Casey S. Greene, Gregory R. Grant, Aalim M. Weljie, Garret A. FitzGerald |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 30 | 35% |
United Kingdom | 11 | 13% |
Netherlands | 2 | 2% |
Germany | 2 | 2% |
India | 2 | 2% |
Ireland | 2 | 2% |
France | 1 | 1% |
Canada | 1 | 1% |
Sweden | 1 | 1% |
Other | 5 | 6% |
Unknown | 28 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 42 | 49% |
Scientists | 33 | 39% |
Practitioners (doctors, other healthcare professionals) | 9 | 11% |
Science communicators (journalists, bloggers, editors) | 1 | 1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 111 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 18 | 16% |
Researcher | 14 | 13% |
Student > Master | 12 | 11% |
Student > Bachelor | 11 | 10% |
Professor > Associate Professor | 8 | 7% |
Other | 20 | 18% |
Unknown | 28 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 20 | 18% |
Biochemistry, Genetics and Molecular Biology | 16 | 14% |
Agricultural and Biological Sciences | 9 | 8% |
Neuroscience | 7 | 6% |
Immunology and Microbiology | 5 | 5% |
Other | 17 | 15% |
Unknown | 37 | 33% |