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

Nature Biotechnology

Volumn 35, Issue 8, 2017, Pages 747-756

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

^a INSTITUTE FOR SYSTEMS BIOLOGY   (United States)

^b Arivale Inc   (United States)

^c UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^d Providence St Joseph Health   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMARKERS;

CLINICAL BIOMARKERS; CLINICAL TESTS; CORRELATION NETWORK; DAILY ACTIVITY; GENETIC RISKS; GENOME-WIDE ASSOCIATION STUDIES; INFLAMMATORY BOWEL DISEASE; WHOLE GENOME SEQUENCES;

GENES;

BIOLOGICAL MARKER; C PEPTIDE; CRESOL; CYSTINE; DOCOSAHEXAENOIC ACID; EICOSADIENOIC ACID; FIBROBLAST GROWTH FACTOR 21; GAMMA GLUTAMYLTYROSINE; GLUCOSE; GLUTAMIC ACID DERIVATIVE; GLUTAMINE DERIVATIVE; HEXANOIC ACID DERIVATIVE; HIGH DENSITY LIPOPROTEIN CHOLESTEROL; HYDROCINNAMIC ACID; ICOSAPENTAENOIC ACID; INHIBIN; INHIBIN BETA C CHAIN; INSULIN; LEPTIN; LEVOTHYROXINE; LOW DENSITY LIPOPROTEIN CHOLESTEROL; OMEGA 6 FATTY ACID; PHENYLACETIC ACID; PHENYLACETYLGLUTAMINE; PROTEOME; SEROTONIN; SERUM AMYLOID P; TRIACYLGLYCEROL; UNCLASSIFIED DRUG; UNINDEXED DRUG; UROBILIN;

ADULT; ARTICLE; ASIAN; BACTERIUM; BLADDER CANCER; CHOLESTEROL BLOOD LEVEL; CLINICAL LABORATORY; COMMUNITY STRUCTURE; CONTROLLED STUDY; COPY NUMBER VARIATION; CORIOBACTERIACEAE; DENSITY; DESULFOVIBRIONACEAE; FEMALE; GENETIC RISK; GENOME-WIDE ASSOCIATION STUDY; GLUCOSE BLOOD LEVEL; HEMOCHROMATOSIS; HOMEOSTASIS MODEL ASSESSMENT; HUMAN; HUMAN GENOME; INFLAMMATORY BOWEL DISEASE; MALE; MASS SPECTROMETRY; METABOLOME; MICROBIOME; MOGIBACTERIACEAE; NONHUMAN; PACIFIC ISLANDER; PRIORITY JOURNAL; QUANTITATIVE TRAIT; SEROTONIN BLOOD LEVEL; THROMBOCYTE ACTIVATION; VERRUCOMICROBIACEAE; WELLBEING; BIOLOGY; BLOOD; EXERCISE; FACTUAL DATABASE; GENETICS; LONGITUDINAL STUDY; METABOLISM; MICROFLORA; NEOPLASM; NUTRITIONAL STATUS; PHYSIOLOGIC MONITORING; PHYSIOLOGY; PROCEDURES; STATISTICAL MODEL;

BIOMARKERS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; EXERCISE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LONGITUDINAL STUDIES; METABOLOME; MICROBIOTA; MODELS, STATISTICAL; MONITORING, PHYSIOLOGIC; NEOPLASMS; NUTRITIONAL STATUS; PROTEOME;

EID: 85027179549     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.3870     Document Type: Article

References (67)

1. Hood, L. & Flores, M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N. Biotechnol. 29, 613-624 (2012).
2. Hood, L. & Friend, S.H. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat. Rev. Clin. Oncol. 8, 184-187 (2011).
3. Collins, F.S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793-795 (2015).
4. Yong, P.L., Saunders, R.S. & Olsen, L. The Healthcare Imperative: Lowering Costs and Improving Outcomes: Workshop Series Summary (National Academies Press, 2010).
5. David, L.A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014).
6. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293-1307 (2012).
7. Smarr, L. Quantifying your body: a how-to guide from a systems biology perspective. Biotechnol. J. 7, 980-991 (2012).
8. Hood, L. & Price, N.D. Promoting wellness and demystifying disease: The 100K project. Clinical OMICs 1, 20-23 (2014).
9. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001-D1006 (2014).
10. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289-300 (1995).
11. Girvan, M. & Newman, M.E.J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002).
12. Newman, M.E.J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103, 8577-8582 (2006).
13. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504 (2003).
14. Koh, K.K., Park, S.M. & Quon, M.J. Leptin and cardiovascular disease: response to therapeutic interventions. Circulation 117, 3238-3249 (2008).
15. Ridker, P.M. Clinical application of C-reactive protein for cardiovascular disease detection and prevention. Circulation 107, 363-369 (2003).
16. Woo, Y.C., Xu, A., Wang, Y. & Lam, K.S.L. Fibroblast growth factor 21 as an emerging metabolic regulator: clinical perspectives. Clin. Endocrinol. 78, 489-496 (2013).
17. Duong, T., Pommier, E.C. & Scheibel, A.B. Immunodetection of the amyloid P component in Alzheimer's disease. Acta Neuropathol. 78, 429-437 (1989).
18. Jenny, N.S., Arnold, A.M., Kuller, L.H., Tracy, R.P. & Psaty, B.M. Serum amyloid P and cardiovascular disease in older men and women: results from the Cardiovascular Health Study. Arterioscler. Thromb. Vasc. Biol. 27, 352-358 (2007).
19. Althaus, B.U., Staub, J.J., Ryff-De Lèche, A., Oberhänsli, A. & Stähelin, H.B. LDL/HDL-changes in subclinical hypothyroidism: possible risk factors for coronary heart disease. Clin. Endocrinol. 28, 157-163 (1988).
20. Jensen, L.J. et al. STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37, D412-D416 (2009).
21. Li, N., Wallén, N.H., Ladjevardi, M. & Hjemdahl, P. Effects of serotonin on platelet activation in whole blood. Blood Coagul. Fibrinolysis 8, 517-523 (1997).
22. Sauer, W.H., Berlin, J.A. & Kimmel, S.E. Selective serotonin reuptake inhibitors and myocardial infarction. Circulation 104, 1894-1898 (2001).
23. Li, M. et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl. Acad. Sci. USA 105, 2117-2122 (2008).
24. Poesen, R. et al. Microbiota-derived phenylacetylglutamine associates with overall mortality and cardiovascular disease in patients with CKD. J. Am. Soc. Nephrol. 27, 3479-3487 (2016).
25. Manichanh, C. et al. Reduced diversity of faecal microbiota in Crohn's disease revealed by a metagenomic approach. Gut 55, 205-211 (2006).
26. Jostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119-124 (2012).
27. Bradley, R., Fitzpatrick, A.L., Jenny, N.S., Lee, D.-H. & Jacobs, D.R. Jr. Associations between total serum GGT activity and metabolic risk: MESA. Biomark. Med. 7, 709-721 (2013).
28. Lim, J.-S., Lee, D.-H., Park, J.-Y., Jin, S.-H. & Jacobs, D.R. Jr. A strong interaction between serum gamma-glutamyltransferase and obesity on the risk of prevalent type 2 diabetes: results from the Third National Health and Nutrition Examination Survey. Clin. Chem. 53, 1092-1098 (2007).
29. Lang, R., Gundlach, A.L. & Kofler, B. The galanin peptide family: receptor pharmacology, pleiotropic biological actions, and implications in health and disease. Pharmacol. Ther. 115, 177-207 (2007).
30. Prentice, K.J. et al. The furan fatty acid metabolite CMPF is elevated in diabetes and induces β cell dysfunction. Cell Metab. 19, 653-666 (2014).
31. Zheng, J.S. et al. Serum metabolomics profiles in response to n-3 fatty acids in Chinese patients with type 2 diabetes: a double-blind randomised controlled trial. Sci. Rep. 6, 29522 (2016).
32. Guan, W. et al. Genome-wide association study of plasma N6 polyunsaturated fatty acids within the cohorts for heart and aging research in genomic epidemiology consortium. Circ Cardiovasc Genet 7, 321-331 (2014).
33. Kang, T.-W. et al. Genome-wide association of serum bilirubin levels in Korean population. Hum. Mol. Genet. 19, 3672-3678 (2010).
34. Rothman, N. et al. A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci. Nat. Genet. 42, 978-984 (2010).
35. Okkels, H., Sigsgaard, T., Wolf, H. & Autrup, H. Arylamine N-acetyltransferase 1 (NAT1) and 2 (NAT2) polymorphisms in susceptibility to bladder cancer: the influence of smoking. Cancer Epidemiol. Biomarkers Prev. 6, 225-231 (1997).
36. Diabetes Prevention Program Research Group. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. The Lancet 374, 1677-1686 (2009).
37. Adams, P.C. et al. Hemochromatosis and iron-overload screening in a racially diverse population. N. Engl. J. Med. 352, 1769-1778 (2005).
38. Gaich, G. et al. The effects of LY2405319, an FGF21 analog, in obese human subjects with type 2 diabetes. Cell Metab. 18, 333-340 (2013).
39. Meier, C. et al. TSH-controlled L-thyroxine therapy reduces cholesterol levels and clinical symptoms in subclinical hypothyroidism: a double blind, placebo-controlled trial (Basel Thyroid Study). J. Clin. Endocrinol. Metab. 86, 4860-4866 (2001).
40. Ruttmann, E. et al. Gamma-glutamyltransferase as a risk factor for cardiovascular disease mortality: an epidemiological investigation in a cohort of 163, 944 Austrian adults. Circulation 112, 2130-2137 (2005).
41. Thompson, G.A. & Meister, A. Interrelationships between the binding sites for amino acids, dipeptides, and gamma-glutamyl donors in gamma-glutamyl transpeptidase. J. Biol. Chem. 252, 6792-6798 (1977).
42. Soga, T. et al. Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J. Hepatol. 55, 896-905 (2011).
43. Rogers, A.J. et al. Metabolomic derangements are associated with mortality in critically ill adult patients. PLoS One 9, e87538 (2014).
44. Sido, B. et al. Impairment of intestinal glutathione synthesis in patients with inflammatory bowel disease. Gut 42, 485-492 (1998).
45. Guo, L. et al. Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc. Natl. Acad. Sci. USA 112, E4901-E4910 (2015).
46. Hood, L. & Price, N.D. Demystifying disease, democratizing health care. Sci. Transl. Med. 6, 225ed5 (2014).
47. Tangpricha, V., Pearce, E.N., Chen, T.C. & Holick, M.F. Vitamin D insufficiency among free-living healthy young adults. Am. J. Med. 112, 659-662 (2002).
48. Micheel, C.M., Nass, S.J. & Omenn, G.S. Evolution of Translational Omics: Lessons Learned and the Path Forward (National Academies Press, 2012).
49. Glusman, G. et al. Identification of copy number variants in whole-genome data using Reference Coverage Profiles. Front. Genet. 6, 45 (2015).
50. Glusman, G., Caballero, J., Mauldin, D.E., Hood, L. & Roach, J.C. Kaviar: an accessible system for testing SNV novelty. Bioinformatics 27, 3216-3217 (2011).
51. Caporaso, J.G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621-1624 (2012).
52. Rideout, J.R. et al. Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences. PeerJ 2, e545 (2014).
53. Whittaker, R.H. Evolution and measurement of species diversity. Taxon 21, 213-251 (1972).
54. Caporaso, J.G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266-267 (2010).
55. Caporaso, J.G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335-336 (2010).
56. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610-618 (2012).
57. Lane, D.J. in Nucleic Acid Techniques in Bacterial Systematics (eds. Stackebrandt, E. & Goodfellow, M.) 115-175 (John Wiley and Sons, Chichester, UK, 1991).
58. Lozupone, C., Lladser, M.E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169-172 (2011).
59. Hamady, M., Lozupone, C. & Knight, R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 4, 17-27 (2010).
60. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228-8235 (2005).
61. Pérez, F. & Granger, B.E. IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9, 21-29 (2007).
62. Hunter, J.D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90-95 (2007).
63. Kusebauch, U. et al. Human SRMAtlas: A resource of targeted assays to quantify the complete human proteome. Cell 166, 766-778 (2016).
64. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966-968 (2010).
65. Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference (eds. van der Walt, S. & Millman, J.) 57-61 (SciPy, 2010).
66. Schult, D.A. & Swart, P. Exploring network structure, dynamics, and function using NetworkX. Proceedings of the 7th Python in Science Conference (eds. Varoquaux, G., Vaught, T. & Millman, J.) 11-15 (SciPy, 2008).
67. Green, R.C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565-574 (2013).