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

Nature Biotechnology volume 35, pages 747–756 (2017)Cite this article

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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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Figure 1: Types of longitudinal data collected.
Figure 2: Top 100 correlations per pair of data types.
Figure 3: Cardiometabolic community.
Figure 4: Cholesterol, serotonin, α-diversity, IBD, and bladder cancer communities.
Figure 5: Polygenic scores correlate with blood analytes.

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Acknowledgements

We would like to acknowledge significant contributions to this study from our 108 Pioneers, S. Kaplan, S. Mecca, S. Bell, G. Sorensen, C. Lewis, T. Kilgallon, M. Brunkow, S. Huang, C.-Y. Huang, D. Mauldin, S. Speck, M. Raff, J. Pizzorno, J. Guiltinan, R. Green, L. Smarr, E. Lazowska, C. Witwer, M. Flores, and many others who helped us on this wellness journey. This work was supported in part by the Robert Wood Johnson Foundation (L.H., N.D.P.), the M.J. Murdock Charitable Trust (L.H., N.D.P.), NIH grants 2P50GM076547 (L.H., N.D.P.), P30ES017885 (G.S.O.), U24CA2210967 (G.S.O.), RC2HG005805 (R.L.M.), and Arivale.

Author information

Author notes

  1. Daniel T McDonald

    Present address: Present address: University of California, San Diego, San Diego, California, USA.,

  2. Nathan D Price, Andrew T Magis and John C Earls: These authors contributed equally to this work.

  3. Nathan D Price and Leroy Hood: These authors jointly supervised this work.

Authors and Affiliations

  1. Institute for Systems Biology, Seattle, Washington, USA

    Nathan D Price, Gustavo Glusman, Roie Levy, Christopher Lausted, Daniel T McDonald, Ulrike Kusebauch, Christopher L Moss, Yong Zhou, Shizhen Qin, Robert L Moritz, Gilbert S Omenn, Jennifer C Lovejoy & Leroy Hood

  2. Arivale, Seattle, Washington, USA

    Nathan D Price, Andrew T Magis, John C Earls, Kristin Brogaard & Jennifer C Lovejoy

  3. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA

    Gilbert S Omenn

  4. Providence St. Joseph Health, Seattle, Washington, USA

    Leroy Hood

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Contributions

L.H. and N.D.P. conceived of and led the study. J.C.L. designed and managed the clinical and coaching aspects of the study. A.T.M. and J.C.E. performed most of the computational analyses. G.G. contributed many important ideas from the beginning of the study. G.G., R.L., and D.T.M. performed additional computational analysis. N.D.P., A.T.M., J.C.E., G.G., R.L., D.T.M., G.S.O., J.C.L., and L.H. analyzed data. C.L. generated the Olink proteomics data. U.K., C.L.M., Y.Z., S.Q., and R.L.M. generated the mass spectrometry proteomics data. K.B. managed most of the logistics of implementing the study. A.T.M., N.D.P., and L.H. were the primary writers of the paper, with contributions from all authors.

Corresponding authors

Correspondence to Nathan D Price or Leroy Hood.

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Competing interests

L.H. and N.D.P. are co-founders of Arivale and hold stock in the company. N.D.P. is on the Arivale board of directors; L.H. is chair and G.S.O. a member of Arivale's scientific advisory board. A.T.M., J.C.E., K.B., and J.C.L. are employees of Arivale and have stock options in the company, as do G.G. and G.S.O.

Integrated supplementary information

Supplementary Figure 1 Full inter-omic cross-sectional correlation network

Statistically-significant inter-omic cross-sectional Spearman correlations (padj<0.05) between all datasets collected in our cohort.

Supplementary Figure 2 Modularity vs. community analysis iteration

The maximum modularity observed in our inter-omic cross-sectional community analysis was 0.386 at iteration 61 of community pruning. There were 267 total iterations of community analysis.

Supplementary Figure 3 Recruitment, onboarding, and other important events in the P100

Flowchart of important events in the P100, including recruitment, onboarding, withdrawals, data collection, coaching calls, and events.

Supplementary Figure 4 Genetic risk factors for hemochromatosis and ferritin levels

Boxplots for ferritin levels of male (A) and female (B) participants by round in the P100. Only one male in our study was homozygous for 282YY and was diagnosed with hemochromatosis after physician referral. Changes in ferritin levels are shown by the red arrows. A second male who was heterozygous for both risk factors (282YC/63DH) did not receive therapeutic phlebotomy, and ferritin levels increased. Six other males presented at baseline (round 1) with elevated ferritin levels but neither of these genetic risk factors; they were referred to their physician for monitoring. Four of the six were of self-reported Asian ancestry (orange dots).

Supplementary Figure 5 Population distribution of the 108 Pioneers (PC2 vs PC3)

PCA using a sample of 250,000 common SNPs. Translucent colored points represent the 2504 individuals in the Thousand Genomes Project, phase 3, color-coded by population. Black points represent the 108 Pioneers. AFR, EUR, SAS, EAS, and AMR represent the continental populations: African, European, South Asian, East Asian and Admixed Americans, respectively.

Supplementary Figure 6 Population distribution of the 108 Pioneers (PC1 vs PC2)

PCA using a sample of 250,000 common SNPs. Translucent colored points represent the 2504 individuals in the Thousand Genomes Project, phase 3, color-coded by population. Black points represent the 108 Pioneers. AFR, EUR, SAS, EAS, and AMR represent the continental populations: African, European, South Asian, East Asian and Admixed Americans, respectively.

Supplementary Figure 7 Dose-dependent effects of vitamin D supplementation

A common intervention for our participants was vitamin D supplementation. The Institute of Medicine has recommended a minimum 25-hydroxyvitamin D level of 20 ng/mL, while the Endocrine Society recommends a minimum level of 30 ng/mL. Shown here are the dose-dependent effects of supplementation on 25-hydroxyvitamin D levels from round 1 to round 2, with individuals taking less than 3000 IUs/day exhibiting relatively little gains in 25-hydroxyvitamin D levels. Individuals that were noncompliant with the recommendations (N=13) made no gains in 25-hydroxyvitamin D levels. A one-way ANOVA yields p=0.004 that a significant difference exists between one of the groups. Significant differences with noncompliant participants at the p<0.05 level are indicated with asterisks, as determined by Tukey’s range test. Individuals with low blood levels of 25-hydroxyvitamin D were recommended doses between 1000 and 5000 IU. If over time blood levels did not increase at the highest doses, individuals were referred to their physician for evaluation and, if appropriate, higher doses.

Supplementary Figure 8 Gut microbiome stability over nine months

Participant microbiomes tend to resemble themselves over time. Plotted in red is the unweighted UniFrac distance between consecutive microbiome samples for all participants. The blue box-and-whisker plots represent the distance distribution between each sample and all others in the same time points. In 97% of cases, an individual’s cross-timepoint distance is lower than the median inter-individual distance.

Supplementary Figure 9 Correlation across different vendors

Most clinical laboratory measurements were assayed by only one of the lab vendors (Quest or Genova) but certain measurements were measured by both due to overlaps in the standard analysis panels. Additionally, some analytes from the metabolomics and proteomics data were also measured by the clinical labs. This figure shows a comparison of these duplicated analytes. For example, triglycerides, total cholesterol, and fasting glucose show high correlation between clinical lab vendors, while LDL particle number is less correlated. The correlations represented in this figure have been removed from our correlation networks.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Tables 6–7, 9–13 (PDF 2297 kb)

Supplementary Dataset 1 (ZIP 214 kb)

Supplementary Dataset 2 (ZIP 169 kb)

Supplementary Code

Supplementary Code zip (ZIP 72 kb)

Supplementary Table 1

All analytes measured in the P100 (XLSX 317 kb)

Supplementary Table 2

Complete inter-omic correlation network for cross-sectional correlations (XLSX 247 kb)

Supplementary Table 3

Complete intra- and inter-omic correlation network for cross-sectional correlations (XLSX 1240 kb)

Supplementary Table 4

Complete inter-omic correlation network for delta correlations (XLSX 191 kb)

Supplementary Table 5

Complete intra- and inter-omic correlation network for delta correlations (XLSX 1834 kb)

Supplementary Table 8

Polygenic score quantitative traits tested in the P100 (XLSX 51 kb)

Supplementary Table 14

Age and sex adjustments for the correlation networks (XLSX 81 kb)

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Price, N., Magis, A., Earls, J. et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol 35, 747–756 (2017). https://doi.org/10.1038/nbt.3870

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