A wellness study of 108 individuals using personal, dense, dynamic data clouds
Open accessAbstract
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.
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Algorithms have been developed to automatically process and interpret wearables data to present evidence-based health insights such as detection of inflammation or prediction of cardiometabolic health based on activity habits.
“To address this data deluge, we and others have recently developed algorithms to automatically process and interpret wearables data in order to present evidence-based health insights such as detection of inflammation or prediction of cardiometabolic health based on activity habits [40-43].”
From Wearables and the Medical Revolution (2018) · cites this paper as [43]
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PyIOmica: longitudinal omics analysis and trend identification
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- Authors
- 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.
- Affiliations
- Institute for Systems Biology (US)
- University of Michigan (US)
- Corresponding author
- Price, Nathan D
- Hood, Leroy
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2017-07-17
- Volume
- 35
- Issue
- 8
- Pages
- 747-756
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC5568837
- OpenAlex
- W2734770034
- Cited by
- 427
- References
- 66
- Citations by year
- 2026: 22 · 2025: 24 · 2024: 28 · 2023: 43 · 2022: 79 · 2021: 45 · 2020: 58 · 2019: 57 · 2018: 60 · 2017: 10
- FWCI
- 27.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (RC2HG005805)
- National Cancer Institute (U24CA210967)
- National Institute of General Medical Sciences (P50GM076547)
- National Institute of Environmental Health Sciences (P30ES017885)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- biomarker discovery, polygenic risk score, polygenic risk, cardiometabolic disease, inflammatory bowel disease, microbiome, metabolome, proteome, whole genome sequencing, activity monitoring, behavioral coaching, disease transition, health monitoring
- MeSH terms
- Humans, Longitudinal Studies, Monitoring, Physiologic, Neoplasms, Nutritional Status, Models, Statistical, Biomarkers, Exercise, Databases, Factual, Computational Biology, Proteome, Genome-Wide Association Study, Metabolome, Microbiota
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