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

Nathan D. Price; Andrew T. Magis; John C. Earls; Gustavo Glusman; Roie Levy; Christopher Lausted; Daniel T. McDonald; Ulrike Kusebauch; Christopher L. Moss; Yong Zhou; Shizhen Qin; Robert L. Moritz; Kristin Brogaard; Gilbert S. Omenn; Jennifer C. Lovejoy; Leroy Hood

doi:10.1038/nbt.3870

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

Nathan D. Price, Andrew T. Magis, John C. Earls, Gustavo Glusman, Roie Levy, Christopher Lausted, Daniel T. McDonald, Ulrike Kusebauch, Christopher L. Moss, Yong Zhou, Shizhen Qin, Robert L. Moritz, Kristin Brogaard, Gilbert S. Omenn, Jennifer C. Lovejoy, Leroy Hood

Research output: Contribution to journal › Article › peer-review

170 Scopus citations

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.

Original language	English (US)
Pages (from-to)	747-756
Number of pages	10
Journal	Nature biotechnology
Volume	35
Issue number	8
DOIs	https://doi.org/10.1038/nbt.3870
State	Published - Aug 1 2017
Externally published	Yes

ASJC Scopus subject areas

Biotechnology
Bioengineering
Applied Microbiology and Biotechnology
Molecular Medicine
Biomedical Engineering

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Price, N. D., Magis, A. T., Earls, J. C., Glusman, G., Levy, R., Lausted, C., McDonald, D. T., Kusebauch, U., Moss, C. L., Zhou, Y., Qin, S., Moritz, R. L., Brogaard, K., Omenn, G. S., Lovejoy, J. C., & Hood, L. (2017). A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nature biotechnology, 35(8), 747-756. https://doi.org/10.1038/nbt.3870

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AU - Lausted, Christopher

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AU - Qin, Shizhen

AU - Moritz, Robert L.

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AU - Lovejoy, Jennifer C.

AU - Hood, Leroy

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

Abstract

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