{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T14:38:00Z","timestamp":1775313480794,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T00:00:00Z","timestamp":1573430400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T00:00:00Z","timestamp":1573430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000057","name":"U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35GM118110"],"award-info":[{"award-number":["R35GM118110"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.<\/jats:p>","DOI":"10.1038\/s41746-019-0185-y","type":"journal-article","created":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T06:03:16Z","timestamp":1573452196000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Real-time health monitoring through urine metabolomics"],"prefix":"10.1038","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5084-9035","authenticated-orcid":false,"given":"Ian J.","family":"Miller","sequence":"first","affiliation":[]},{"given":"Sean R.","family":"Peters","sequence":"additional","affiliation":[]},{"given":"Katherine A.","family":"Overmyer","sequence":"additional","affiliation":[]},{"given":"Brett R.","family":"Paulson","sequence":"additional","affiliation":[]},{"given":"Michael S.","family":"Westphall","sequence":"additional","affiliation":[]},{"given":"Joshua J.","family":"Coon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,11]]},"reference":[{"key":"185_CR1","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/nbt.3870","volume":"35","author":"ND Price","year":"2017","unstructured":"Price, N. D. et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol. 35, 747\u2013756 (2017).","journal-title":"Nat. Biotechnol."},{"key":"185_CR2","doi-asserted-by":"publisher","first-page":"283rv3","DOI":"10.1126\/scitranslmed.aaa3487","volume":"7","author":"SR Steinhubl","year":"2015","unstructured":"Steinhubl, S. R., Muse, E. D. & Topol, E. J. The emerging field of mobile health. Sci. Transl. Med. 7, 283rv3 (2015).","journal-title":"Sci. Transl. Med."},{"key":"185_CR3","doi-asserted-by":"publisher","first-page":"429","DOI":"10.2217\/pme-2018-0044","volume":"15","author":"J Dunn","year":"2018","unstructured":"Dunn, J., Runge, R. & Snyder, M. Wearables and the medical revolution. Per. Med. 15, 429\u2013448 (2018).","journal-title":"Per. Med."},{"key":"185_CR4","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1001\/jama.2016.17217","volume":"316","author":"AL Beam","year":"2016","unstructured":"Beam, A. L. & Kohane, I. S. Translating artificial intelligence into clinical care. JAMA 316, 2368\u20132369 (2016).","journal-title":"JAMA"},{"key":"185_CR5","unstructured":"ECG app and irregular heart rhythm notification available today on Apple Watch. Apple Newsroom. https:\/\/www.apple.com\/newsroom\/2018\/12\/ecg-app-and-irregular-heart-rhythm-notification-available-today-on-apple-watch\/. Accessed 17 May 2019."},{"key":"185_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.2001402","volume":"15","author":"X Li","year":"2017","unstructured":"Li, X. et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15, e2001402 (2017).","journal-title":"PLoS Biol."},{"key":"185_CR7","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1016\/j.cell.2012.02.009","volume":"148","author":"R Chen","year":"2012","unstructured":"Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293\u20131307 (2012).","journal-title":"Cell"},{"key":"185_CR8","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1038\/s41586-019-1236-x","volume":"569","author":"W Zhou","year":"2019","unstructured":"Zhou, W. et al. Longitudinal multi-omics of host\u2013microbe dynamics in prediabetes. Nature 569, 663\u2013671 (2019).","journal-title":"Nature"},{"key":"185_CR9","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1038\/s41591-019-0414-6","volume":"25","author":"SM Sch\u00fcssler-Fiorenza Rose","year":"2019","unstructured":"Sch\u00fcssler-Fiorenza Rose, S. M. et al. A longitudinal big data approach for precision health. Nat. Med. 25, 792\u2013804 (2019).","journal-title":"Nat. Med."},{"key":"185_CR10","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1089\/clinomi.01.03.07","volume":"1","author":"L Hood","year":"2014","unstructured":"Hood, L. & Price, N. D. Promoting wellness and demystifying disease: the 100K project. Clin. OMICs 1, 20\u201323 (2014).","journal-title":"Clin. OMICs"},{"key":"185_CR11","unstructured":"National Institutes of Health (NIH)\u2014All of us. https:\/\/allofus.nih.gov\/. Accessed 15 May 2019."},{"key":"185_CR12","doi-asserted-by":"publisher","first-page":"565","DOI":"10.2217\/pme.13.57","volume":"10","author":"M Flores","year":"2013","unstructured":"Flores, M., Glusman, G., Brogaard, K., Price, N. D. & Hood, L. P4 medicine: how systems medicine will transform the healthcare sector and society. Per. Med. 10, 565\u2013576 (2013).","journal-title":"Per. Med."},{"key":"185_CR13","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1038\/nrd.2016.32","volume":"15","author":"DS Wishart","year":"2016","unstructured":"Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 15, 473\u2013484 (2016).","journal-title":"Nat. Rev. Drug Discov."},{"key":"185_CR14","doi-asserted-by":"publisher","first-page":"4752","DOI":"10.1039\/c1an15590c","volume":"136","author":"H Lv","year":"2011","unstructured":"Lv, H., Hung, C. S., Chaturvedi, K. S., Hooton, T. M. & Henderson, J. P. Development of an integrated metabolomic profiling approach for infectious diseases research. Analyst 136, 4752\u20134763 (2011).","journal-title":"Analyst"},{"key":"185_CR15","unstructured":"About chronic diseases | CDC. https:\/\/www.cdc.gov\/chronicdisease\/about\/index.htm. Accessed 29 March 2019 (2019)."},{"key":"185_CR16","doi-asserted-by":"publisher","first-page":"S48","DOI":"10.1038\/551S48a","volume":"551","author":"C Wald","year":"2017","unstructured":"Wald, C. Diagnostics: a flow of information. Nature 551, S48\u2013S50 (2017).","journal-title":"Nature"},{"key":"185_CR17","unstructured":"Your kidneys and how they work | NIDDK. National Institute of Diabetes and Digestive and Kidney Diseases. https:\/\/www.niddk.nih.gov\/health-information\/kidney-disease\/kidneys-how-they-work. Accessed 17 May 2019"},{"key":"185_CR18","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1002\/mas.21455","volume":"36","author":"MM Khamis","year":"2017","unstructured":"Khamis, M. M., Adamko, D. J. & El-Aneed, A. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom. Rev. 36, 115\u2013134 (2017).","journal-title":"Mass Spectrom. Rev."},{"key":"185_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0073076","volume":"8","author":"S Bouatra","year":"2013","unstructured":"Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013).","journal-title":"PLoS ONE"},{"key":"185_CR20","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1586\/14789450.2015.1094380","volume":"12","author":"J Wu","year":"2015","unstructured":"Wu, J. & Gao, Y. Physiological conditions can be reflected in human urine proteome and metabolome. Expert Rev. Proteom. 12, 623\u2013636 (2015).","journal-title":"Expert Rev. Proteom."},{"key":"185_CR21","doi-asserted-by":"publisher","first-page":"2155","DOI":"10.1021\/acs.analchem.8b04698","volume":"91","author":"I Bla\u017eenovi\u0107","year":"2019","unstructured":"Bla\u017eenovi\u0107, I. et al. Structure annotation of all mass spectra in untargeted metabolomics. Anal. Chem. 91, 2155\u20132162 (2019).","journal-title":"Anal. Chem."},{"key":"185_CR22","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1152\/physiolgenomics.00194.2006","volume":"29","author":"RM Salek","year":"2007","unstructured":"Salek, R. M. et al. A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol. Genom. 29, 99\u2013108 (2007).","journal-title":"Physiol. Genom."},{"key":"185_CR23","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1016\/j.clinbiochem.2013.05.049","volume":"46","author":"L Yu","year":"2013","unstructured":"Yu, L. et al. Analysis of urinary metabolites for breast cancer patients receiving chemotherapy by CE-MS coupled with on-line concentration. Clin. Biochem. 46, 1065\u20131073 (2013).","journal-title":"Clin. Biochem."},{"key":"185_CR24","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-016-0681-8","volume":"14","author":"A Alonso","year":"2016","unstructured":"Alonso, A. et al. Urine metabolome profiling of immune-mediated inflammatory diseases. BMC Med. 14, 133 (2016).","journal-title":"BMC Med."},{"key":"185_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/srep13888","volume":"5","author":"H Luan","year":"2015","unstructured":"Luan, H. et al. Comprehensive urinary metabolomic profiling and identification of potential noninvasive marker for idiopathic Parkinson\u2019s disease. Sci. Rep. 5, 13888 (2015).","journal-title":"Sci. Rep."},{"key":"185_CR26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0086223","volume":"9","author":"K Kim","year":"2014","unstructured":"Kim, K. et al. Mealtime, temporal, and daily variability of the human urinary and plasma metabolomes in a tightly controlled environment. PLoS ONE 9, e86223 (2014).","journal-title":"PLoS ONE"},{"key":"185_CR27","doi-asserted-by":"publisher","first-page":"8328","DOI":"10.1021\/acs.analchem.5b01503","volume":"87","author":"NW Kwiecien","year":"2015","unstructured":"Kwiecien, N. W. et al. High-resolution filtering for improved small molecule identification via GC\/MS. Anal. Chem. 87, 8328\u20138335 (2015).","journal-title":"Anal. Chem."},{"key":"185_CR28","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1038\/nbt.3683","volume":"34","author":"JA Stefely","year":"2016","unstructured":"Stefely, J. A. et al. Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling. Nat. Biotechnol. 34, 1191\u20131197 (2016).","journal-title":"Nat. Biotechnol."},{"key":"185_CR29","doi-asserted-by":"publisher","first-page":"2097","DOI":"10.1021\/es5002105","volume":"48","author":"EL Schymanski","year":"2014","unstructured":"Schymanski, E. L. et al. Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ. Sci. Technol. 48, 2097\u20132098 (2014).","journal-title":"Environ. Sci. Technol."},{"key":"185_CR30","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11306-007-0082-2","volume":"3","author":"LW Sumner","year":"2007","unstructured":"Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211\u2013221 (2007).","journal-title":"Metabolomics"},{"key":"185_CR31","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1016\/S0891-5849(01)00506-8","volume":"30","author":"AR Rechner","year":"2001","unstructured":"Rechner, A. R., Spencer, J. P., Kuhnle, G., Hahn, U. & Rice-Evans, C. A. Novel biomarkers of the metabolism of caffeic acid derivatives in vivo. Free Radic. Biol. Med. 30, 1213\u20131222 (2001).","journal-title":"Free Radic. Biol. Med."},{"key":"185_CR32","unstructured":"Health and economic costs of chronic disease | CDC. (2018). https:\/\/www.cdc.gov\/chronicdisease\/about\/costs\/index.htm. Accessed 15 Jan 2019."},{"key":"185_CR33","doi-asserted-by":"publisher","first-page":"456","DOI":"10.3389\/fpsyg.2017.00456","volume":"8","author":"JZ Bakdash","year":"2017","unstructured":"Bakdash, J. Z. & Marusich, L. R. Repeated measures correlation. Front. Psychol. 8, 456 (2017).","journal-title":"Front. Psychol."},{"key":"185_CR34","doi-asserted-by":"publisher","first-page":"606","DOI":"10.3389\/fpsyg.2012.00023","volume":"3","author":"CR Pernet","year":"2012","unstructured":"Pernet, C. R., Wilcox, R. & Rousselet, G. A. Robust correlation analyses: false positive and power validation using a new open source matlab toolbox. Front. Psychol. 3, 606 (2012).","journal-title":"Front. Psychol."},{"key":"185_CR35","doi-asserted-by":"publisher","first-page":"8615","DOI":"10.1021\/acs.jafc.5b03040","volume":"63","author":"SS Heinzmann","year":"2015","unstructured":"Heinzmann, S. S., Holmes, E., Kochhar, S., Nicholson, J. K. & Schmitt-Kopplin, P. 2-Furoylglycine as a candidate biomarker of coffee consumption. J. Agric. Food Chem. 63, 8615\u20138621 (2015).","journal-title":"J. Agric. Food Chem."},{"key":"185_CR36","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1039\/C4FO00042K","volume":"5","author":"IA Ludwig","year":"2014","unstructured":"Ludwig, I. A., Clifford, M. N., Lean, M. E. J., Ashihara, H. & Crozier, A. Coffee: biochemistry and potential impact on health. Food Funct. 5, 1695\u20131717 (2014).","journal-title":"Food Funct."},{"key":"185_CR37","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1093\/alcalc\/agn084","volume":"44","author":"A Helander","year":"2009","unstructured":"Helander, A., B\u00f6ttcher, M., Fehr, C., Dahmen, N. & Beck, O. Detection times for urinary ethyl glucuronide and ethyl sulfate in heavy drinkers during alcohol detoxification. Alcohol Alcoholism. 44, 55\u201361 (2009).","journal-title":"Alcohol Alcoholism."},{"key":"185_CR38","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1164\/ajrccm\/136.1.98","volume":"136","author":"LH Ketai","year":"1987","unstructured":"Ketai, L. H., Simon, R. H., Kreit, J. W. & Grum, C. M. Plasma hypoxanthine and exercise. Am. Rev. Respiratory Dis. 136, 98\u2013101 (1987).","journal-title":"Am. Rev. Respiratory Dis."},{"key":"185_CR39","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1111\/j.1748-1716.1991.tb09157.x","volume":"142","author":"K Sahlin","year":"1991","unstructured":"Sahlin, K., Ekberg, K. & Cizinsky, S. Changes in plasma hypoxanthine and free radical markers during exercise in man. Acta Physiol. Scand. 142, 275\u2013281 (1991).","journal-title":"Acta Physiol. Scand."},{"key":"185_CR40","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.ejphar.2004.09.054","volume":"504","author":"K Shinomiya","year":"2004","unstructured":"Shinomiya, K. et al. Effects of chlorogenic acid and its metabolites on the sleep\u2013wakefulness cycle in rats. Eur. J. Pharmacol. 504, 185\u2013189 (2004).","journal-title":"Eur. J. Pharmacol."},{"key":"185_CR41","doi-asserted-by":"publisher","first-page":"6995","DOI":"10.1021\/ac0708588","volume":"79","author":"CM Slupsky","year":"2007","unstructured":"Slupsky, C. M. et al. Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Anal. Chem. 79, 6995\u20137004 (2007).","journal-title":"Anal. Chem."},{"key":"185_CR42","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1038\/nn.3648","volume":"17","author":"E Aarts","year":"2014","unstructured":"Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C. V. & van der Sluis, S. A solution to dependency: using multilevel analysis to accommodate nested data. Nat. Neurosci. 17, 491\u2013496 (2014).","journal-title":"Nat. Neurosci."},{"key":"185_CR43","doi-asserted-by":"publisher","DOI":"10.1002\/bmc.3864","volume":"31","author":"H Mizuno","year":"2017","unstructured":"Mizuno, H. et al. The great importance of normalization of LC\u2013MS data for highly-accurate non-targeted metabolomics. Biomed. Chromatogr. 31, e3864 (2017).","journal-title":"Biomed. Chromatogr."},{"key":"185_CR44","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1002\/jms.768","volume":"40","author":"BJ Shortt","year":"2005","unstructured":"Shortt, B. J., Darrach, M. R., Holland, P. M. & Chutjian, A. Miniaturized system of a gas chromatograph coupled with a Paul ion trap mass spectrometer. J. Mass Spectrom. 40, 36\u201342 (2005).","journal-title":"J. Mass Spectrom."},{"key":"185_CR45","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/s13361-014-1026-5","volume":"26","author":"C-H Chen","year":"2015","unstructured":"Chen, C.-H. et al. Design of portable mass spectrometers with handheld probes: aspects of the sampling and miniature pumping systems. J. Am. Soc. Mass Spectrom. 26, 240\u2013247 (2015).","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"185_CR46","doi-asserted-by":"publisher","unstructured":"Snyder, M. & Zhou, W. Big data and health. The Lancet Digital Health (2019). https:\/\/doi.org\/10.1016\/s2589-7500(19)30109-8.","DOI":"10.1016\/s2589-7500(19)30109-8"},{"key":"185_CR47","doi-asserted-by":"crossref","unstructured":"Wickham, H. ggplot2: elegant Graphics for Data Analysis (Springer, 2016).","DOI":"10.1007\/978-3-319-24277-4"},{"key":"185_CR48","doi-asserted-by":"crossref","unstructured":"Wilkins, D. Treemapify: Draw treemaps in \u2018ggplot2\u2019. Online: CRAN. R-project. org\/package=treemapify. (2017). Accessed 28 March 2018.","DOI":"10.32614\/CRAN.package.treemapify"},{"key":"185_CR49","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"185_CR50","doi-asserted-by":"publisher","first-page":"331","DOI":"10.21105\/joss.01026","volume":"3","author":"R Vallat","year":"2018","unstructured":"Vallat, R. Pingouin: statistics in Python. J. Open Source Softw. 3, 331 (2018).","journal-title":"J. Open Source Softw."},{"key":"185_CR51","doi-asserted-by":"crossref","unstructured":"Miller, I. J. et al. Real time health monitoring through urine metabolomics. (2019). Preprint at https:\/\/www.biorxiv.org\/content\/10.1101\/681742v1.","DOI":"10.1101\/681742"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0185-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0185-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0185-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T14:04:16Z","timestamp":1722002656000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0185-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,11]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["185"],"URL":"https:\/\/doi.org\/10.1038\/s41746-019-0185-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/681742","asserted-by":"object"}]},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,11]]},"assertion":[{"value":"25 June 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"109"}}