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. 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

Nathan D Price  1   2 , Andrew T Magis  2 , John C Earls  2 , Gustavo Glusman  1 , Roie Levy  1 , Christopher Lausted  1 , Daniel T McDonald  1 , Ulrike Kusebauch  1 , Christopher L Moss  1 , Yong Zhou  1 , Shizhen Qin  1 , Robert L Moritz  1 , Kristin Brogaard  2 , Gilbert S Omenn  1   3 , Jennifer C Lovejoy  1   2 , Leroy Hood  1   4
Affiliations

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

Nathan D Price et al. Nat Biotechnol. 2017 Aug.

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.

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Conflict of interest statement

Competing Financial Interests Statement

LH and NDP are co-founders of Arivale and hold stock in the company. NDP is on the Arivale Board of Directors; LH is chair and GSO a member of Arivale’s Scientific Advisory Board. ATM, JCE, KB, and JCL are employees of Arivale and have stock options in the company, as do GG and GSO.

Figures

Figure 1
Figure 1. Types of longitudinal data collected
(A) Timeline of important events in the P100. (B) Schematic of the data collected every three months throughout the study.
Figure 2
Figure 2. Top 100 correlations per pair of data types
Subset of top statistically-significant Spearman inter-omic cross-sectional correlations between all datasets collected in our cohort. Each line represents one correlation that was significant after adjustment for multiple hypothesis testing using the method of Benjamini and Hochberg at padj<0.05. The mean of all three time points was used to compute the correlations between analytes. Up to 100 correlations per pair of data types are shown in this figure. See Supplementary Figure 1 for the complete inter-omic cross-sectional network.
Figure 3
Figure 3. Cardiometabolic community
All vertices and edges of the cardiometabolic community, with lines indicating significant (padj<0.05) correlations. Associations with FGF21 (red lines) and gamma-glutamyltyrosine (purple lines) are highlighted.
Figure 4
Figure 4. Cholesterol, serotonin, α-diversity, IBD, and bladder cancer communities
(A) Cholesterol community (B) Serotonin community (C) α-diversity community (D) The polygenic score for inflammatory bowel disease is negatively correlated with cystine (E) The polygenic score for bladder cancer is positively correlated with 5-acetylamino-6-formylamino-3-methyluracil (AFMU).
Figure 5
Figure 5. Polygenic scores correlate with blood analytes
Spearman correlations between polygenic scores (x-axis) and analyte measurements (y-axis) from our correlation network. The number of measurements used for each pairwise comparison, correlation coefficients, and adjusted p-values are indicated on each figure. Values have been age and/or sex adjusted as described in Online Methods. The line shown is a y~x regression line, and the shaded regions are 95% confidence intervals for the slope of the line.
See this image and copyright information in PMC

Comment in

  • Big data opens a window onto wellness.
    Butte AJ. Butte AJ. Nat Biotechnol. 2017 Aug 8;35(8):720-721. doi: 10.1038/nbt.3934. Nat Biotechnol. 2017. PMID: 28787419 Free PMC article. No abstract available.

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