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Nat Biotechnol. 2017 Aug;35(8):747-756. doi: 10.1038/nbt.3870. Epub 2017 Jul 17.

A wellness study of 108 individuals using personal, dense, dynamic data clouds.

Author information

1
Institute for Systems Biology, Seattle, Washington, USA.
2
Arivale, Seattle, Washington, USA.
3
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
4
Providence St. Joseph Health, Seattle, Washington, USA.

Abstract

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.

PMID:
28714965
PMCID:
PMC5568837
DOI:
10.1038/nbt.3870
[Indexed for MEDLINE]
Free PMC Article

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