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Big Data Challenges from an Integrative Exposome/Expotype Perspective

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Big Data, Big Challenges: A Healthcare Perspective

Part of the book series: Lecture Notes in Bioengineering ((LNBE))

Abstract

Most diseases result from the complex interplay between genetic and environmental factors. The exposome is a new concept that seeks to define biotechnical approaches to systematically measure a large subset of environmental exposures of an individual from conception to end of life and associate them with health and disease status. Biomedical informaticians have paid limited attention so far to developing methods to process and integrate data about the contribution of environmental factors to individual health. There is a need for new digital methods and resources that collect, store, annotate, analyze and present reliable and updated information about environmental factors affecting our health on both population and individual/patient scale. For instance, defining the concept of expotype, analogous to genotype and phenotype, could represent an opportunity to make progress in the characterization of human individual exposome data. This chapter presents eight challenges related to the processing of individual exposome (expotype) big data and how to integrate them with genomic and clinical data for biomedical research and clinical practice.

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References

  1. Martin-Sanchez F, Verspoor K (2014) Big data in medicine is driving big changes. Yearb Med Inform 15(9):14–20

    Google Scholar 

  2. Wild CP (2005) Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers 14(8):1847–1850

    Article  CAS  Google Scholar 

  3. Patel CJ, Ioannidis JP (2014) Studying the elusive environment in large scale. JAMA 311(21):2173–2174

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wild CP (2012) The exposome: from concept to utility. Int J Epidemiol 41(1):24–32

    Article  PubMed  Google Scholar 

  5. Martin Sanchez F, Gray K, Bellazzi R, Lopez-Campos G (2014) Exposome informatics: considerations for the design of future biomedical research information systems. JAMIA. 21(3):386–390

    PubMed  Google Scholar 

  6. Thomas DC, Lewinger JP, Murcray CE, et al (2012) Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome. Am J Epidemiol 175:203–207; discussion 208–209

    Article  PubMed  PubMed Central  Google Scholar 

  7. Manrai AK, Cui Y, Bushel PR, Hall M, Karakitsios S, Mattingly CJ, Ritchie M, Schmitt C, Sarigiannis DA, Thomas DC, Wishart D, Balshaw DM, Patel CJ (2017) Informatics and data analytics to support exposome-based discovery for public health. Annu Rev Public Health 20(38):279–294. https://doi.org/10.1146/annurev-publhealth-082516-012737 Epub 2016 Dec 23 PubMed PMID: 28068484

    Article  Google Scholar 

  8. Collins FS, Varmus H (2015) A new initiative on precision medicine. NEJM 372(9):793–795

    Article  CAS  PubMed  Google Scholar 

  9. Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T, Zeevi D, Costea PI, Godneva A, Kalka IN, Bar N, Shilo S, Lador D, Vila AV, Zmora N, Pevsner-Fischer M, Israeli D, Kosower N, Malka G, Wolf BC, Avnit-Sagi T, Lotan-Pompan M, Weinberger A, Halpern Z, Carmi S, Fu J, Wijmenga C, Zhernakova A, Elinav E, Segal E (2018) Environment dominates over host genetics in shaping human gut microbiota. Nature 555(7695):210–215

    Article  CAS  PubMed  Google Scholar 

  10. Favé MJ, Lamaze FC, Soave D, Hodgkinson A, Gauvin H, Bruat V, Grenier JC, Gbeha E, Skead K, Smargiassi A, Johnson M, Idaghdour Y, Awadalla P (2018) Gene-by-environment interactions in urban populations modulate risk phenotypes. Nat Commun 9(1):827

    Article  PubMed  PubMed Central  Google Scholar 

  11. Dennis KK, Marder E, Balshaw DM, Cui Y, Lynes MA, Patti GJ, Rappaport SM, Shaughnessy DT, Vrijheid M, Barr DB (2017) Biomonitoring in the Era of the Exposome. Environ Health Perspect 125(4):502–510

    Article  CAS  PubMed  Google Scholar 

  12. Ding YP, Ladeiro Y, Morilla I, Bouhnik Y, Marah A, Zaag H, Cazals-Hatem D, Seksik P, Daniel F, Hugot JP, Wainrib G, Tréton X, Ogier-Denis E (2017) Integrative network-based analysis of colonic detoxification gene expression in ulcerative colitis according to smoking status. J Crohns Colitis 11(4):474–484

    PubMed  Google Scholar 

  13. Jacquez GM, Sabel CE, Shi C (2015) Genetic GIScience: toward a place-based synthesis of the genome, exposome, and behavome. Ann Assoc Am Geogr 105(3):454–472

    Article  PubMed  PubMed Central  Google Scholar 

  14. Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) (2016) National health and nutrition examination survey data. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Hyattsville, MD [last visit 2017-0403]. Available from https://www.cdc.gov/nchs/nhanes/

  15. Gottlieb L, Tobey R, Cantor J, Hessler D, Adler NE (2016) Integrating social and medical data to improve population health: opportunities and barriers. Health Aff (Millwood) 35(11):2116–2123

    Article  Google Scholar 

  16. Swan M (2012) Health 2050: the realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. J Pers Med 2(3):93–118

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kiossoglou P, Borda A, Gray K, Martin-Sanchez F, Verspoor K, Lopez-Campos G (2017) Characterising the scope of exposome research: a generalisable approach. Stud Health Technol Inform 245:457–461

    PubMed  Google Scholar 

  18. Cui Y, Balshaw DM, Kwok RK, Thompson CL, Collman GW, Birnbaum LS (2016) The exposome: embracing the complexity for discovery in environmental health. Environ Health Perspect 124(8):A137–A140

    Article  PubMed  PubMed Central  Google Scholar 

  19. Smith MT, Zhang L, McHale CM, Skibola CF, Rappaport SM (2011) Benzene: the exposome and future investigations of leukemia etiology. Chem Biol Interact 192(1–2):155–159

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Goldfarb DS (2016) The exposome for kidney stones. Urolithiasis 44(1):3–7

    Article  PubMed  Google Scholar 

  21. Rappaport SM, Barupal DK, Wishart D, Vineis P, Scalbert A (2014) The blood exposome and its role in discovering causes of disease. Environ Health Perspect 122(8):769–774

    Article  PubMed  PubMed Central  Google Scholar 

  22. Donald CE, Scott RP, Blaustein KL, Halbleib ML, Sarr M, Jepson PC et al (2016) Silicone wristbands detect individuals’ pesticide exposures in West Africa. R Soc Open Sci 3(8):160433

    Article  PubMed  PubMed Central  Google Scholar 

  23. Faisandier L, Bonneterre V, De Gaudemaris R, Bicout DJ (2011) Occupational exposome: a network-based approach for characterizing occupational health problems. J Biomed Inform 44(4):545–552

    Article  PubMed  Google Scholar 

  24. Martin-Sanchez FJ, Lopez-Campos GH (2016) The new role of biomedical informatics in the age of digital medicine. Methods Inf Med 55(5):392–402

    Article  PubMed  Google Scholar 

  25. Sarigiannis DA (2017) Assessing the impact of hazardous waste on children’s health: the exposome paradigm. Environ Res 158:531–541

    Article  CAS  PubMed  Google Scholar 

  26. Fan JW, Li J, Lussier YA (2017) Semantic modeling for exposomics with exploratory evaluation in clinical context. J Healthc Eng 2017:3818302

    PubMed  PubMed Central  Google Scholar 

  27. Rattray NJW, Deziel NC, Wallach JD, Khan SA, Vasiliou V, Ioannidis JPA, Johnson CH (2018) Beyond genomics: understanding exposotypes through metabolomics. Hum Genomics 12(1):4

    Article  PubMed  PubMed Central  Google Scholar 

  28. Institute of Medicine (2014) Capturing social and behavioral domains and measures in electronic health records: phase 2. The National Academies Press, Washington, DC. https://doi.org/10.17226/18951

  29. Casey JA, Schwartz BS, Stewart WF, Adler NE (2016) Using electronic health records for population health research: a review of methods and applications. Annu Rev Public Health 37:61–81

    Article  PubMed  Google Scholar 

  30. Biro S, Williamson T, Leggett JA, Barber D, Morkem R, Moore K, Belanger P, Mosley B, Janssen I (2016) Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity. BMC Med Inform Decis Mak 11(16):32

    Article  Google Scholar 

  31. Wang Y, Chen ES, Pakhomov S, Lindemann E, Melton GB (2016) investigating longitudinal tobacco use information from social history and clinical notes in the electronic health record. In: AMIA annual symposiym proceedings, pp 1209–1218

    Google Scholar 

  32. Maranhão PA, Bacelar-Silva GM, Ferreira DNG, Calhau C, Vieira-Marques P, Cruz-Correia RJ (2018) Nutrigenomic information in the openEHR data set. Appl Clin Inform 9(1):221–231

    Article  PubMed  PubMed Central  Google Scholar 

  33. Boland MR, Parhi P, Li L, Miotto R, Carroll R, Iqbal U, Nguyen PA, Schuemie M, You SC, Smith D, Mooney S, Ryan P, Li YJ, Park RW, Denny J, Dudley JT, Hripcsak G, Gentine P, Tatonetti NP (2017) Uncovering exposures responsible for birth season—disease effects: a global study. J Am Med Inform Assoc. https://doi.org/10.1093/jamia/ocx105. [Epub ahead of print]

    Article  Google Scholar 

  34. Agier L, Portengen L, Chadeau-Hyam M, Basagaña X, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, González JR, Nieuwenhuijsen MJ, Vineis P, Vrijheid M, Slama R, Vermeulen R (2016) A systematic comparison of linear regression-based statistical methods to assess exposome-health associations. Environ Health Perspect 124(12):1848–1856

    Article  PubMed  PubMed Central  Google Scholar 

  35. Barrera-Gómez J, Agier L, Portengen L, Chadeau-Hyam M, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, González JR, Nieuwenhuijsen M, Vineis P, Vrijheid M, Vermeulen R, Slama R, Basagaña X (2017) A systematic comparison of statistical methods to detect interactions in exposome-health associations. Environ Health 16(1):74

    Article  PubMed  PubMed Central  Google Scholar 

  36. Patel CJ, Chen R, Kodama K et al (2013) Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus. Hum Genet 132:495–508

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. McGinnis DP, Brownstein JS, Patel CJ (2016) Environment-wide association study of blood pressure in the national health and nutrition examination survey (1999–2012). Sci Rep 26(6):30373

    Article  Google Scholar 

  38. Patel CJ (2017) Analytic complexity and challenges in identifying mixtures of exposures associated with phenotypes in the exposome era. Curr Epidemiol Rep 4(1):22–30

    Article  PubMed  PubMed Central  Google Scholar 

  39. Patel CJ, Manrai AK (2015) Development of exposome correlation globes to map out environment-wide associations. Pac Symp Biocomput 231–242

    Google Scholar 

  40. Lopez-Campos G, Bellazzi R, Martin-Sanchez F (2013) INDIV-3D. A new model for individual data integration and visualisation using spatial coordinates. Stud Health Technol Inform 190:172–174

    Google Scholar 

  41. National Academies of Sciences, Engineering, and Medicine (2017) Measuring personal environmental exposures. In: Proceedings of a workshop—in brief. The National Academies Press, Washington, DC. https://doi.org/10.17226/24711

    Google Scholar 

  42. Dagliati A, Marinoni A, Cerra C, Decata P, Chiovato L, Gamba P, Bellazzi R (2015) Integration of administrative, clinical, and environmental data to support the management of type 2 diabetes mellitus: from satellites to clinical care. J Diabetes Sci Technol 10(1):19–26

    Article  PubMed  PubMed Central  Google Scholar 

  43. Antman EM, Loscalzo J (2016) Precision medicine in cardiology. Nat Rev Cardiol 13(10):591–602

    Article  PubMed  Google Scholar 

  44. Rappaport SM (2016) Genetic factors are not the major causes of chronic diseases. PLoS One 11(4):e0154387

    Article  PubMed  PubMed Central  Google Scholar 

  45. Galli SJ (2016) Toward precision medicine and health: opportunities and challenges in allergic diseases. J Allergy Clin Immunol 137(5):1289–1300

    Article  PubMed  PubMed Central  Google Scholar 

  46. Agustí A, Bafadhel M, Beasley R, Bel EH, Faner R, Gibson PG, Louis R, McDonald VM, Sterk PJ, Thomas M, Vogelmeier C, Pavord ID (2017) On behalf of all participants in the seminar. Precision medicine in airway diseases: moving to clinical practice. Eur Respir J 50(4)

    Article  PubMed  Google Scholar 

  47. Lopez-Campos G, Merolli M, Martin-Sanchez F (2017) Biomedical informatics and the digital component of the exposome. Stud Health Technol Inform 245:496–500

    PubMed  Google Scholar 

  48. Measuring national well-being: insights into children’s mental health and well-being (2015) ONS. Accessed March 23, 2018. https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/articles/measuringnationalwellbeing/2015-10-20

  49. Cantor MN, Thorpe L (2018) Integrating data on social determinants of health into electronic health records. Health Aff (Millwood) 37(4):585–590

    Article  Google Scholar 

  50. Dennis KK, Jones DP (2016) The exposome: a new frontier for education. Am Biol Teach 78(7):542–548

    Article  PubMed  PubMed Central  Google Scholar 

  51. Niedzwiecki MM, Miller GW (2017) The exposome paradigm in human health: lessons from the emory exposome summer course. Environ Health Perspect 125(6):064502

    Article  PubMed  PubMed Central  Google Scholar 

  52. Johnson CH, Athersuch TJ, Collman GW, Dhungana S, Grant DF, Jones DP, Patel CJ, Vasiliou V (2017) Yale school of public health symposium on lifetime exposures and human health: the exposome; summary and future reflections. Hum Genomics 11(1):32

    Article  PubMed  PubMed Central  Google Scholar 

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Author information

Authors and Affiliations

  1. Instituto de Salud Carlos III, Madrid, Spain

    Fernando Martin-Sanchez

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  1. Fernando Martin-Sanchez
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Correspondence to Fernando Martin-Sanchez .

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Editors and Affiliations

  1. Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

    Mowafa Househ

  2. School of Health Information Sciences, University of Victoria, Victoria, BC, Canada

    Andre W. Kushniruk

  3. School of Health Information Sciences, University of Victoria, Victoria, BC, Canada

    Elizabeth M. Borycki

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Martin-Sanchez, F. (2019). Big Data Challenges from an Integrative Exposome/Expotype Perspective. In: Househ, M., Kushniruk, A., Borycki, E. (eds) Big Data, Big Challenges: A Healthcare Perspective. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-06109-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-06109-8_11

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  • Online ISBN: 978-3-030-06109-8

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