1932

Abstract

Derived from the term exposure, the exposome is an omic-scale characterization of the nongenetic drivers of health and disease. With the genome, it defines the phenome of an individual. The measurement of complex environmental factors that exert pressure on our health has not kept pace with genomics and historically has not provided a similar level of resolution. Emerging technologies make it possible to obtain detailed information on drugs, toxicants, pollutants, nutrients, and physical and psychological stressors on an omic scale. These forces can also be assessed at systems and network levels, providing a framework for advances in pharmacology and toxicology. The exposome paradigm can improve the analysis of drug interactions and detection of adverse effects of drugs and toxicants and provide data on biological responses to exposures. The comprehensive model can provide data at the individual level for precision medicine, group level for clinical trials, and population level for public health.

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2019-01-06
2024-10-16
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Literature Cited

  1. 1.  Wild CP 2005. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomark. Prev. 14:1847–50
    [Google Scholar]
  2. 2.  Wild CP 2012. The exposome: from concept to utility. Int. J. Epidemiol. 41:24–32
    [Google Scholar]
  3. 3.  Miller GW, Jones DP 2014. The nature of nurture: refining the definition of the exposome. Toxicol. Sci. 137:1–2
    [Google Scholar]
  4. 4.  Miller GW 2014. The Exposome: A Primer Waltham, MA: Academic Press
    [Google Scholar]
  5. 5.  Niedzwiecki MM, Miller GW 2017. The exposome paradigm in human health: lessons from the Emory Exposome Summer Course. Environ. Health Perspect. 125:064502
    [Google Scholar]
  6. 6.  Polderman TJ, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A et al. 2015. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47:702–9
    [Google Scholar]
  7. 7.  Rappaport SM 2016. Genetic factors are not the major causes of chronic diseases. PLOS ONE 11:e0154387
    [Google Scholar]
  8. 8.  Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:860–921
    [Google Scholar]
  9. 9.  Brunekreef B 2013. Exposure science, the exposome, and public health. Environ. Mol. Mutagen. 54:596–98
    [Google Scholar]
  10. 10.  Turner MC, Nieuwenhuijsen M, Anderson K, Balshaw D, Cui Y et al. 2017. Assessing the exposome with external measures: commentary on the state of the science and research recommendations. Annu. Rev. Public Health 38:215–39
    [Google Scholar]
  11. 11.  van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R et al. 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118:847–55
    [Google Scholar]
  12. 12.  Markevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P et al. 2017. Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environ. Res. 158:301–17
    [Google Scholar]
  13. 13.  Larkin A, Hystad P 2018. Evaluating street view exposure measures of visible green space for health research. J. Expo. Sci. Environ. Epidemiol. In press. https://doi.org/10.1038/s41370-018-0017-1
    [Crossref] [Google Scholar]
  14. 14.  Kloog I, Haim A, Stevens RG, Barchana M, Portnov BA 2008. Light at night co-distributes with incident breast but not lung cancer in the female population of Israel. Chronobiol. Int. 25:65–81
    [Google Scholar]
  15. 15.  Rybnikova NA, Haim A, Portnov BA 2016. Does artificial light-at-night exposure contribute to the worldwide obesity pandemic?. Int. J. Obes. 40:815–23
    [Google Scholar]
  16. 16.  Apte JS, Messier KP, Gani S, Brauer M, Kirchstetter TW et al. 2017. High-resolution air pollution mapping with Google Street View cars: exploiting big data. Environ. Sci. Technol. 51:6999–7008
    [Google Scholar]
  17. 17.  Pedersen M, Andersen ZJ, Stafoggia M, Weinmayr G, Galassi C et al. 2017. Ambient air pollution and primary liver cancer incidence in four European cohorts within the ESCAPE project. Environ. Res. 154:226–33
    [Google Scholar]
  18. 18.  Curto A, Donaire-Gonzalez D, Barrera-Gomez J, Marshall JD, Nieuwenhuijsen MJ et al. 2018. Performance of low-cost monitors to assess household air pollution. Environ. Res. 163:53–63
    [Google Scholar]
  19. 19.  Kerckhoffs J, Hoek G, Vlaanderen J, van Nunen E, Messier K et al. 2017. Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring. Environ. Res. 159:500–8
    [Google Scholar]
  20. 20.  Hagemann R, Corsmeier U, Kottmeier C, Rinke R, Wieser A, Vogel B 2014. Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory ‘AERO-TRAM.’ Atmos. . Environ 94:341–52
    [Google Scholar]
  21. 21.  Hasenfratz D, Saukh O, Walser C, Hueglin C, Fierz M et al. 2015. Deriving high-resolution urban air pollution maps using mobile sensor nodes. Pervasive Mob. Comput. 16:Part B268–85
    [Google Scholar]
  22. 22.  Asimina S, Chapizanis D, Karakitsios S, Kontoroupis P, Asimakopoulos DN et al. 2018. Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies. Environ. Monit. Assess. 190:155
    [Google Scholar]
  23. 23.  Loh M, Sarigiannis D, Gotti A, Karakitsios S, Pronk A et al. 2017. How sensors might help define the external exposome. Int. J. Environ. Res. Public Health 14:434
    [Google Scholar]
  24. 24.  Nieuwenhuijsen MJ, Donaire-Gonzalez D, Foraster M, Martinez D, Cisneros A 2014. Using personal sensors to assess the exposome and acute health effects. Int. J. Environ. Res. Public Health 11:7805–19
    [Google Scholar]
  25. 25.  O'Connell SG, Kincl LD, Anderson KA 2014. Silicone wristbands as personal passive samplers. Environ. Sci. Technol. 48:3327–35
    [Google Scholar]
  26. 26.  Bergmann AJ, North PE, Vasquez L, Bello H, Del Carmen Gastanaga Ruiz M, Anderson KA 2017. Multi-class chemical exposure in rural Peru using silicone wristbands. J. Expo. Sci. Environ. Epidemiol. 27:560–68
    [Google Scholar]
  27. 27.  Donald CE, Scott RP, Blaustein KL, Halbleib ML, Sarr M et al. 2016. Silicone wristbands detect individuals’ pesticide exposures in West Africa. R. Soc. Open. Sci. 3:160433
    [Google Scholar]
  28. 28.  Hammel SC, Hoffman K, Webster TF, Anderson KA, Stapleton HM 2016. Measuring personal exposure to organophosphate flame retardants using silicone wristbands and hand wipes. Environ. Sci. Technol. 50:4483–91
    [Google Scholar]
  29. 29.  Kile ML, Scott RP, O'Connell SG, Lipscomb S, MacDonald M et al. 2016. Using silicone wristbands to evaluate preschool children's exposure to flame retardants. Environ. Res. 147:365–72
    [Google Scholar]
  30. 30.  Vineis P, Chadeau-Hyam M, Gmuender H, Gulliver J, Herceg Z et al. 2016. The exposome in practice: design of the EXPOsOMICS project. Int. J. Hyg. Environ. Health 220:142–51
    [Google Scholar]
  31. 31.  Turner MC, Vineis P, Seleiro E, Dijmarescu M, Balshaw D et al. 2018. EXPOsOMICS: final policy workshop and stakeholder consultation. BMC Public Health 18:260
    [Google Scholar]
  32. 32.  Murphy E, King EA 2016. Smartphone-based noise mapping: integrating sound level meter app data into the strategic noise mapping process. Sci. Total Environ. 562:852–59
    [Google Scholar]
  33. 33.  van Wel L, Huss A, Bachmann P, Zahner M, Kromhout H et al. 2017. Context-sensitive ecological momentary assessments; integrating real-time exposure measurements, data-analytics and health assessment using a smartphone application. Environ. Int. 103:8–12
    [Google Scholar]
  34. 34.  Smolders R, De Boever P 2014. Perspectives for environment and health research in Horizon 2020: dark ages or golden era?. Int. J. Hyg. Environ. Health 217:891–96
    [Google Scholar]
  35. 35. EARTO (Eur. Assoc. Res. Technol. Organ.). 2014. The TRL scale as a research & innovation policy tool, EARTO recommendations Rep., Eur. Assoc. Res. Technol. Organ Brussels, Belg: http://www.earto.eu/fileadmin/content/03_Publications/The_TRL_Scale_as_a_R_I_Policy_Tool_-_EARTO_Recommendations_-_Final.pdf
    [Google Scholar]
  36. 36.  Go YM, Jones DP 2016. Exposure memory and lung regeneration. Ann. Am. Thorac. Soc. 13:S452–61
    [Google Scholar]
  37. 37.  Jeanneret F, Boccard J, Badoud F, Sorg O, Tonoli D et al. 2014. Human urinary biomarkers of dioxin exposure: analysis by metabolomics and biologically driven data dimensionality reduction. Toxicol. Lett. 230:234–43
    [Google Scholar]
  38. 38.  Weinhold B 2006. Epigenetics: the science of change. Environ. Health Perspect. 114:A160–67
    [Google Scholar]
  39. 39.  Albert R 2005. Scale-free networks in cell biology. J. Cell Sci. 118:4947–57
    [Google Scholar]
  40. 40.  Uppal K, Walker DI, Liu K, Li S, Go YM, Jones DP 2016. Computational metabolomics: a framework for the million metabolome. Chem. Res. Toxicol. 29:1956–75
    [Google Scholar]
  41. 41.  Jones DP 2016. Sequencing the exposome: a call to action. Toxicol. Rep. 3:29–45
    [Google Scholar]
  42. 42.  Liu KH, Walker DI, Uppal K, Tran V, Rohrbeck P et al. 2016. High-resolution metabolomics assessment of military personnel: evaluating analytical strategies for chemical detection. J. Occup. Environ. Med. 58:S53–61
    [Google Scholar]
  43. 43.  Petrick L, Edmands W, Schiffman C, Grigoryan H, Perttula K et al. 2017. An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies. Metabolomics 13:27
    [Google Scholar]
  44. 44.  Park YH, Lee K, Soltow QA, Strobel FH, Brigham KL et al. 2012. High-performance metabolic profiling of plasma from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring. Toxicology 295:47–55
    [Google Scholar]
  45. 45.  Walker DI, Mallon CT, Hopke PK, Uppal K, Go YM et al. 2016. Deployment-associated exposure surveillance with high-resolution metabolomics. J. Occup. Environ. Med. 58:S12–21
    [Google Scholar]
  46. 46.  Bonvallot N, Tremblay-Franco M, Chevrier C, Canlet C, Debrauwer L et al. 2014. Potential input from metabolomics for exploring and understanding the links between environment and health. J. Toxicol. Environ. Health B 17:21–44
    [Google Scholar]
  47. 47.  Rager JE, Strynar MJ, Liang S, McMahen RL, Richard AM et al. 2016. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environ. Int. 88:269–80
    [Google Scholar]
  48. 48.  Jamin EL, Bonvallot N, Tremblay-Franco M, Cravedi JP, Chevrier C et al. 2014. Untargeted profiling of pesticide metabolites by LC-HRMS: an exposomics tool for human exposure evaluation. Anal. Bioanal. Chem. 406:1149–61
    [Google Scholar]
  49. 49.  Roca M, Leon N, Pastor A, Yusa V 2014. Comprehensive analytical strategy for biomonitoring of pesticides in urine by liquid chromatography–orbitrap high resolution mass spectrometry. J. Chromatogr. A 1374:66–76
    [Google Scholar]
  50. 50.  Bessonneau V, Pawliszyn J, Rappaport SM 2017. The saliva exposome for monitoring of individuals’ health trajectories. Environ. Health Perspect. 125:077014
    [Google Scholar]
  51. 51.  Bonvallot N, Tremblay-Franco M, Chevrier C, Canlet C, Warembourg C et al. 2013. Metabolomics tools for describing complex pesticide exposure in pregnant women in Brittany (France). PLOS ONE 8:e64433
    [Google Scholar]
  52. 52.  Houten SM, Chen J, Belpoggi F, Manservisi F, Sanchez-Guijo A et al. 2016. Changes in the metabolome in response to low-dose exposure to environmental chemicals used in personal care products during different windows of susceptibility. PLOS ONE 11:e0159919
    [Google Scholar]
  53. 53.  Wagner ND, Simpson AJ, Simpson MJ 2017. Metabolomic responses to sublethal contaminant exposure in neonate and adult Daphnia magna. Environ. Toxicol. . Chem 36:938–46
    [Google Scholar]
  54. 54.  Dong X, Zhang Y, Dong J, Zhao Y, Guo J et al. 2017. Urinary metabolomic profiling in rats exposed to dietary di(2-ethylhexyl) phthalate (DEHP) using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS). Environ. Sci. Pollut. Res. Int. 24:16659–72
    [Google Scholar]
  55. 55.  Warth B, Spangler S, Fang M, Johnson CH, Forsberg EM et al. 2017. Exposome-scale investigations guided by global metabolomics, pathway analysis, and cognitive computing. Anal. Chem. 89:11505–13
    [Google Scholar]
  56. 56.  Szabo DT, Pathmasiri W, Sumner S, Birnbaum LS 2017. Serum metabolomic profiles in neonatal mice following oral brominated flame retardant exposures to hexabromocyclododecane (HBCD) alpha, gamma, and commercial mixture. Environ. Health Perspect. 125:651–59
    [Google Scholar]
  57. 57.  Kakizuka S, Takeda T, Komiya Y, Koba A, Uchi H et al. 2015. Dioxin-produced alteration in the profiles of fecal and urinary metabolomes: a change in bile acids and its relevance to toxicity. Biol. Pharm. Bull. 38:1484–95
    [Google Scholar]
  58. 58.  Zhang L, Hatzakis E, Nichols RG, Hao R, Correll J et al. 2015. Metabolomics reveals that aryl hydrocarbon receptor activation by environmental chemicals induces systemic metabolic dysfunction in mice. Environ. Sci. Technol. 49:8067–77
    [Google Scholar]
  59. 59.  Walker DI, Pennell KD, Uppal K, Xia X, Hopke PK et al. 2016. Pilot metabolome-wide association study of benzo(a)pyrene in serum from military personnel. J. Occup. Environ. Med. 58:S44–52
    [Google Scholar]
  60. 60.  Breitner S, Schneider A, Devlin RB, Ward-Caviness CK, Diaz-Sanchez D et al. 2016. Associations among plasma metabolite levels and short-term exposure to PM2.5 and ozone in a cardiac catheterization cohort. Environ. Int. 97:76–84
    [Google Scholar]
  61. 61.  Wang Z, Zheng Y, Zhao B, Zhang Y, Liu Z et al. 2015. Human metabolic responses to chronic environmental polycyclic aromatic hydrocarbon exposure by a metabolomic approach. J. Proteome Res. 14:2583–93
    [Google Scholar]
  62. 62.  Dudka I, Kossowska B, Senhadri H, Latajka R, Hajek J et al. 2014. Metabonomic analysis of serum of workers occupationally exposed to arsenic, cadmium and lead for biomarker research: a preliminary study. Environ. Int. 68:71–81
    [Google Scholar]
  63. 63.  Carrizo D, Chevallier OP, Woodside JV, Brennan SF, Cantwell MM et al. 2017. Untargeted metabolomic analysis of human serum samples associated with exposure levels of persistent organic pollutants indicate important perturbations in sphingolipids and glycerophospholipids levels. Chemosphere 168:731–38
    [Google Scholar]
  64. 64.  Pradhan SN, Das A, Meena R, Nanda RK, Rajamani P 2016. Biofluid metabotyping of occupationally exposed subjects to air pollution demonstrates high oxidative stress and deregulated amino acid metabolism. Sci. Rep. 6:35972
    [Google Scholar]
  65. 65.  Wang X, Liu L, Zhang W, Zhang J, Du X et al. 2017. Serum metabolome biomarkers associate low-level environmental perfluorinated compound exposure with oxidative/nitrosative stress in humans. Environ. Pollut. 229:168–76
    [Google Scholar]
  66. 66.  van Veldhoven K, Keski-Rahkonen P, Barupal DK, Villanueva CM, Font-Ribera L et al. 2018. Effects of exposure to water disinfection by-products in a swimming pool: a metabolome-wide association study. Environ. Int. 111:60–70
    [Google Scholar]
  67. 67.  Fischer ST, Lili LN, Li S, Tran VT, Stewart KB et al. 2017. Low-level maternal exposure to nicotine associates with significant metabolic perturbations in second-trimester amniotic fluid. Environ. Int. 107:227–34
    [Google Scholar]
  68. 68.  Chen CS, Yuan TH, Shie RH, Wu KY, Chan CC 2017. Linking sources to early effects by profiling urine metabolome of residents living near oil refineries and coal-fired power plants. Environ. Int. 102:87–96
    [Google Scholar]
  69. 69.  Salihovic S, Ganna A, Fall T, Broeckling CD, Prenni JE et al. 2015. The metabolic fingerprint of p, p′-DDE and HCB exposure in humans. Environ. Int. 88:60–66
    [Google Scholar]
  70. 70.  Vlaanderen JJ, Janssen NA, Hoek G, Keski-Rahkonen P, Barupal DK et al. 2017. The impact of ambient air pollution on the human blood metabolome. Environ. Res. 156:341–48
    [Google Scholar]
  71. 71.  Hamadeh HK, Bushel PR, Jayadev S, Martin K, DiSorbo O et al. 2002. Gene expression analysis reveals chemical-specific profiles. Toxicol. Sci. 67:219–31
    [Google Scholar]
  72. 72.  Wang TW, Vermeulen RC, Hu W, Liu G, Xiao X et al. 2015. Gene-expression profiling of buccal epithelium among non-smoking women exposed to household air pollution from smoky coal. Carcinogenesis 36:1494–501
    [Google Scholar]
  73. 73.  Chu JH, Hart JE, Chhabra D, Garshick E, Raby BA, Laden F 2016. Gene expression network analyses in response to air pollution exposures in the trucking industry. Environ. Health 15:101
    [Google Scholar]
  74. 74.  Fry RC, Navasumrit P, Valiathan C, Svensson JP, Hogan BJ et al. 2007. Activation of inflammation/NF-κB signaling in infants born to arsenic-exposed mothers. PLOS Genet 3:e207
    [Google Scholar]
  75. 75.  Spira A, Beane J, Shah V, Liu G, Schembri F et al. 2004. Effects of cigarette smoke on the human airway epithelial cell transcriptome. PNAS 101:10143–48
    [Google Scholar]
  76. 76.  McHale CM, Zhang L, Lan Q, Li G, Hubbard AE et al. 2009. Changes in the peripheral blood transcriptome associated with occupational benzene exposure identified by cross-comparison on two microarray platforms. Genomics 93:343–49
    [Google Scholar]
  77. 77.  Jiang P, Hou Z, Bolin JM, Thomson JA, Stewart R 2017. RNA-Seq of human neural progenitor cells exposed to lead (Pb) reveals transcriptome dynamics, splicing alterations and disease risk associations. Toxicol. Sci. 159:251–65
    [Google Scholar]
  78. 78.  Tani H, Takeshita JI, Aoki H, Nakamura K, Abe R et al. 2017. Identification of RNA biomarkers for chemical safety screening in mouse embryonic stem cells using RNA deep sequencing analysis. PLOS ONE 12:e0182032
    [Google Scholar]
  79. 79.  Wang J, Wang X, Sheng N, Zhou X, Cui R et al. 2017. RNA-sequencing analysis reveals the hepatotoxic mechanism of perfluoroalkyl alternatives, HFPO2 and HFPO4, following exposure in mice. J. Appl. Toxicol. 37:436–44
    [Google Scholar]
  80. 80.  Huff M, da Silveira WA, Carnevali O, Renaud L, Hardiman G 2018. Systems analysis of the liver transcriptome in adult male zebrafish exposed to the plasticizer (2-ethylhexyl) phthalate (DEHP). Sci. Rep. 8:2118
    [Google Scholar]
  81. 81.  Grondin CJ, Davis AP, Wiegers TC, Wiegers JA, Mattingly CJ 2018. Accessing an expanded exposure science module at the Comparative Toxicogenomics Database. Environ. Health Perspect. 126:014501
    [Google Scholar]
  82. 82.  Elshal MF, McCoy JP 2006. Multiplex bead array assays: performance evaluation and comparison of sensitivity to ELISA. Methods 38:317–23
    [Google Scholar]
  83. 83.  Tighe PJ, Ryder RR, Todd I, Fairclough LC 2015. ELISA in the multiplex era: potentials and pitfalls. Proteom. Clin. Appl. 9:406–22
    [Google Scholar]
  84. 84.  Bassig BA, Dai Y, Vermeulen R, Ren D, Hu W et al. 2017. Occupational exposure to diesel engine exhaust and alterations in immune/inflammatory markers: a cross-sectional molecular epidemiology study in China. Carcinogenesis 38:1104–11
    [Google Scholar]
  85. 85.  Shiels MS, Shu XO, Chaturvedi AK, Gao YT, Xiang YB et al. 2017. A prospective study of immune and inflammation markers and risk of lung cancer among female never smokers in Shanghai. Carcinogenesis 38:1004–10
    [Google Scholar]
  86. 86.  Woeller CF, Thatcher TH, Van Twisk D, Pollock SJ, Croasdell A et al. 2016. Detection of serum microRNAs from Department of Defense Serum Repository: correlation with cotinine, cytokine, and polycyclic aromatic hydrocarbon levels. J. Occup. Environ. Med. 58:S62–71
    [Google Scholar]
  87. 87.  Yates JR, Ruse CI, Nakorchevsky A 2009. Proteomics by mass spectrometry: approaches, advances, and applications. Annu. Rev. Biomed. Eng. 11:49–79
    [Google Scholar]
  88. 88.  Rappaport SM, Li H, Grigoryan H, Funk WE, Williams ER 2012. Adductomics: characterizing exposures to reactive electrophiles. Toxicol. Lett. 213:83–90
    [Google Scholar]
  89. 89.  Grigoryan H, Edmands W, Lu SS, Yano Y, Regazzoni L et al. 2016. Adductomics pipeline for untargeted analysis of modifications to Cys34 of human serum albumin. Anal. Chem. 88:10504–12
    [Google Scholar]
  90. 90.  Liu S, Grigoryan H, Edmands WMB, Dagnino S, Sinharay R et al. 2018. Cys34 adductomes differ between patients with chronic lung or heart disease and healthy controls in central London. Environ. Sci. Technol. 52:2307–13
    [Google Scholar]
  91. 91.  Lu SS, Grigoryan H, Edmands WM, Hu W, Iavarone AT et al. 2017. Profiling the serum albumin Cys34 adductome of solid fuel users in Xuanwei and Fuyuan, China. Environ. Sci. Technol. 51:46–57
    [Google Scholar]
  92. 92.  Fernandez AF, Assenov Y, Martin-Subero JI, Balint B, Siebert R et al. 2012. A DNA methylation fingerprint of 1628 human samples. Genome Res 22:407–19
    [Google Scholar]
  93. 93.  Go YM, Jones DP 2014. Redox biology: interface of the exposome with the proteome, epigenome and genome. Redox Biol 2:358–60
    [Google Scholar]
  94. 94.  Salas LA, Bustamante M, Gonzalez JR, Gracia-Lavedan E, Moreno V et al. 2015. DNA methylation levels and long-term trihalomethane exposure in drinking water: an epigenome-wide association study. Epigenetics 10:650–61
    [Google Scholar]
  95. 95.  Lee KW, Richmond R, Hu P, French L, Shin J et al. 2015. Prenatal exposure to maternal cigarette smoking and DNA methylation: epigenome-wide association in a discovery sample of adolescents and replication in an independent cohort at birth through 17 years of age. Environ. Health Perspect. 123:193–99
    [Google Scholar]
  96. 96.  Bollati V, Baccarelli A, Hou L, Bonzini M, Fustinoni S et al. 2007. Changes in DNA methylation patterns in subjects exposed to low-dose benzene. Cancer Res 67:876–80
    [Google Scholar]
  97. 97.  Seow WJ, Kile ML, Baccarelli AA, Pan WC, Byun HM et al. 2014. Epigenome-wide DNA methylation changes with development of arsenic-induced skin lesions in Bangladesh: a case-control follow-up study. Environ. Mol. Mutagen. 55:449–56
    [Google Scholar]
  98. 98.  Hou L, Zhang X, Wang D, Baccarelli A 2012. Environmental chemical exposures and human epigenetics. Int. J. Epidemiol. 41:79–105
    [Google Scholar]
  99. 99.  Guida F, Sandanger TM, Castagne R, Campanella G, Polidoro S et al. 2015. Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation. Hum. Mol. Genet. 24:2349–59
    [Google Scholar]
  100. 100.  Everson TM, Punshon T, Jackson BP, Hao K, Lambertini L et al. 2018. Cadmium-associated differential methylation throughout the placental genome: epigenome-wide association study of two U.S. birth cohorts. Environ. Health Perspect. 126:017010
    [Google Scholar]
  101. 101.  Walker DI, Uppal K, Zhang L, Vermeulen R, Smith M et al. 2016. High-resolution metabolomics of occupational exposure to trichloroethylene. Int. J. Epidemiol. 45:1517–27
    [Google Scholar]
  102. 102.  Uppal K, Ma C, Go YM, Jones DP, Wren J 2018. xMWAS: a data-driven integration and differential network analysis tool. Bioinformatics 34:701–2
    [Google Scholar]
  103. 103.  Li S, Sullivan NL, Rouphael N, Yu T, Banton S et al. 2017. Metabolic phenotypes of response to vaccination in humans. Cell 169:862–77
    [Google Scholar]
  104. 104.  Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M et al. 2014. The human early-life exposome (HELIX): project rationale and design. Environ. Health Perspect. 122:535–44
    [Google Scholar]
  105. 105.  Vineis P, Chadeau-Hyam M, Gmuender H, Gulliver J, Herceg Z et al. 2017. The exposome in practice: design of the EXPOsOMICS project. Int. J. Hygiene Environ. Health 220:142–51
    [Google Scholar]
  106. 106.  Chadeau-Hyam M, Ebbels TM, Brown IJ, Chan Q, Stamler J et al. 2010. Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification. J. Proteome Res. 9:4620–27
    [Google Scholar]
  107. 107.  Robinson O, Basagana X, Agier L, de Castro M, Hernandez-Ferrer C et al. 2015. The pregnancy exposome: multiple environmental exposures in the INMA-Sabadell birth cohort. Environ. Sci. Technol. 49:10632–41
    [Google Scholar]
  108. 108.  Agier L, Portengen L, Chadeau-Hyam M, Basagana X, Giorgis-Allemand L et al. 2016. A systematic comparison of linear regression-based statistical methods to assess exposome-health associations. Environ. Health Perspect. 124:1848–56
    [Google Scholar]
  109. 109.  Balding DJ 2006. A tutorial on statistical methods for population association studies. Nat. Rev. Genet. 7:781–91
    [Google Scholar]
  110. 110.  Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L et al. 2013. Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers. Environ. Mol. Mutagen. 54:542–57
    [Google Scholar]
  111. 111.  Patterson N, Price AL, Reich D 2006. Population structure and eigenanalysis. PLOS Genet 2:e190
    [Google Scholar]
  112. 112.  Castagne R, Boulange CL, Karaman I, Campanella G, Santos Ferreira DL et al. 2017. Improving visualization and interpretation of metabolome-wide association studies: an application in a population-based cohort using untargeted 1H NMR metabolic profiling. J. Proteome Res. 16:3623–33
    [Google Scholar]
  113. 113.  Hoggart CJ, Clark TG, De Lorio M, Whittaker JC, Balding DJ 2008. Genome-wide significance for dense SNP and resequencing data. Genet. Epidemiol. 32:179–85
    [Google Scholar]
  114. 114.  Wold S, Ruhe A, Wold H, Dunn WJ 1984. The collinearity problem in linear-regression—the partial least-squares (PLS) approach to generalized inverses. SIAM J. Sci. Stat. Comput. 5:735–43
    [Google Scholar]
  115. 115.  Tibshirani R 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
    [Google Scholar]
  116. 116.  Zou H, Hastie T 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
    [Google Scholar]
  117. 117.  Zou H, Hastie T, Tibshirani R 2006. Sparse principal component analysis. J. Comput. Graph. Stat. 15:265–86
    [Google Scholar]
  118. 118.  Chun H, Keles S 2010. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J. R. Stat. Soc. Ser. B 72:3–25
    [Google Scholar]
  119. 119.  Bottolo L, Chadeau-Hyam M, Hastie DI, Langley SR, Petretto E et al. 2011. ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration. Bioinformatics 27:587–88
    [Google Scholar]
  120. 120.  Guan YT, Stephens M 2011. Bayesian variable selection regression for genome-wide association studies and other large-scale problems. Ann. Appl. Stat. 5:1780–815
    [Google Scholar]
  121. 121.  Hans C, Dobra A, West M 2007. Shotgun stochastic search for “large p” regression. J. Am. Stat. Assoc. 102:507–16
    [Google Scholar]
  122. 122.  Liquet B, Bottolo L, Campanella G, Richardson S, Chadeau-Hyam M 2016. R2GUESS: a graphics processing unit-based R package for Bayesian variable selection regression of multivariate responses. J. Stat. Softw. 69:1–32
    [Google Scholar]
  123. 123.  Billionnet C, Sherrill D, Annesi-Maesano I, GERIE Study 2012. Estimating the health effects of exposure to multi-pollutant mixture. Ann. Epidemiol. 22:126–41
    [Google Scholar]
  124. 124.  Patel CJ 2017. Analytic complexity and challenges in identifying mixtures of exposures associated with phenotypes in the exposome era. Curr. Epidemiol. Rep. 4:22–30
    [Google Scholar]
  125. 125.  Sun Z, Tao Y, Li S, Ferguson KK, Meeker JD et al. 2013. Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environ. Health 12:85
    [Google Scholar]
  126. 126.  Braun JM, Gennings C, Hauser R, Webster TF 2016. What can epidemiological studies tell us about the impact of chemical mixtures on human health?. Environ. Health Perspect. 124:A6–9
    [Google Scholar]
  127. 127.  Jain P, Vineis P, Liquet B, Vlaanderen J, Bodinier B et al. 2017. A multivariate approach to investigate the combined biological effects of multiple exposures J. Epidemiol. . Community Health 72:564–71
    [Google Scholar]
  128. 128. Gene Ontol. Consort. 2017. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res 45:D331–38
    [Google Scholar]
  129. 129.  Ashburner M, Ball CA, Blake JA, Botstein D, Butler H et al. 2000. Gene Ontology: tool for the unification of biology. Nat. Genet. 25:25
    [Google Scholar]
  130. 130.  Huang DW, Sherman BT, Lempicki RA 2008. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4:44
    [Google Scholar]
  131. 131.  Li S, Park Y, Duraisingham S, Strobel FH, Khan N et al. 2013. Predicting Network Activity from High Throughput Metabolomics. PLOS Comput. Biol. 9:e1003123
    [Google Scholar]
  132. 132.  Guida F, Sandanger TM, Castagne R, Campanella G, Polidoro S et al. 2015. Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation. Hum. Mol. Genet. 24:2349–59
    [Google Scholar]
  133. 133.  Simon N, Friedman J, Hastie T, Tibshirani R 2013. A Sparse-Group Lasso. J. Comput. Graph. Stat. 22:231–45
    [Google Scholar]
  134. 134.  Liquet B, Lafaye de Micheaux P, Hejblum B, Thiebaut R 2016. Group and sparse group partial least square approaches applied in genomics context. Bioinformatics 32:35–42
    [Google Scholar]
  135. 135.  Salamanca BV, Ebbels TM, Iorio MD 2014. Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure. Stat. Appl. Genet. Mol. Biol. 13:191–201
    [Google Scholar]
  136. 136.  Valcarcel B, Ebbels TM, Kangas AJ, Soininen P, Elliot P et al. 2014. Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity. J. R. Soc. Interface 11:20130908
    [Google Scholar]
  137. 137.  Valcarcel B, Wurtz P, Seich al Basatena NK, Tukiainen T, Kangas AJ et al. 2011. A differential network approach to exploring differences between biological states: an application to prediabetes. PLOS ONE 6:e24702
    [Google Scholar]
  138. 138.  Barban N, Billari FC 2012. Classifying life course trajectories: a comparison of latent class and sequence analysis. J. R. Stat. Soc. C 61:765–84
    [Google Scholar]
  139. 139.  Chadeau-Hyam M, Tubert-Bitter P, Guihenneuc-Jouyaux C, Campanella G, Richardson S et al. 2014. Dynamics of the risk of smoking-induced lung cancer: a compartmental hidden Markov model for longitudinal analysis. Epidemiology 25:28–34
    [Google Scholar]
  140. 140.  Michely JA, Meyer MR, Maurer HH 2018. Power of Orbitrap-based LC-high resolution-MS/MS for comprehensive drug testing in urine with or without conjugate cleavage or using dried urine spots after on-spot cleavage in comparison to established LC–MSn or GC–MS procedures. Drug Testing Anal 10:158–63
    [Google Scholar]
  141. 141.  Leist M, Ghallab A, Graepel R, Marchan R, Hassan R et al. 2017. Adverse outcome pathways: opportunities, limitations and open questions. Arch. Toxicol. 91:3477–505
    [Google Scholar]
  142. 142.  Nymark P, Rieswijk L, Ehrhart F, Jeliazkova N, Tsiliki G et al. 2017. A data fusion pipeline for generating and enriching adverse outcome pathway descriptions. Toxicol. Sci. 162:264–75
    [Google Scholar]
  143. 143.  Collins FS, Varmus H 2015. A new initiative on precision medicine. New Engl. J. Med. 372:793–95
    [Google Scholar]
  144. 144.  Mirnezami R, Nicholson J, Darzi A 2012. Preparing for precision medicine. New Engl. J. Med. 366:489–91
    [Google Scholar]
  145. 145.  Wambaugh JF, Wang A, Dionisio KL, Frame A, Egeghy P et al. 2014. High throughput heuristics for prioritizing human exposure to environmental chemicals. Environ. Sci. Technol. 48:12760–67
    [Google Scholar]
  146. 146.  Lane KJ, Levy JI, Scammell MK, Patton AP, Durant JL et al. 2015. Effect of time-activity adjustment on exposure assessment for traffic-related ultrafine particles. J. Exposure Sci. Environ. Epidemiol. 25:506–16
    [Google Scholar]
  147. 147.  Menni C, Metrustry SJ, Mohney RP, Beevers S, Barratt B et al. 2015. Circulating levels of antioxidant vitamins correlate with better lung function and reduced exposure to ambient pollution. Am. J. Respir. Crit. Care Med. 191:1203–7
    [Google Scholar]
  148. 148.  Chadeau-Hyam M, Athersuch TJ, Keun HC, De Iorio M, Ebbels TM et al. 2011. Meeting-in-the-middle using metabolic profiling—a strategy for the identification of intermediate biomarkers in cohort studies. Biomarkers 16:83–88
    [Google Scholar]
  149. 149.  Lan Q, Zhang L, Tang X, Shen M, Smith MT et al. 2010. Occupational exposure to trichloroethylene is associated with a decline in lymphocyte subsets and soluble CD27 and CD30 markers. Carcinogenesis 31:1592–96
    [Google Scholar]
  150. 150.  O'Connell SG, Kincl LD, Anderson KA 2014. Silicone wristbands as personal passive samplers. Environ. Sci. Technol. 48:3327–35
    [Google Scholar]
  151. 151.  Jones DP, Park Y, Ziegler TR 2012. Nutritional metabolomics: progress in addressing complexity in diet and health. Annu. Rev. Nutr. 32:183–202
    [Google Scholar]
  152. 152.  Go YM, Uppal K, Walker DI, Tran V, Dury L et al. 2014. Mitochondrial metabolomics using high-resolution Fourier-transform mass spectrometry. Methods Mol. Biol. 1198:43–73
    [Google Scholar]
  153. 153.  Go YM, Walker DI, Liang Y, Uppal K, Soltow QA et al. 2015. Reference standardization for mass spectrometry and high-resolution metabolomics applications to exposome research. Toxicol. Sci. 148:531–43
    [Google Scholar]
  154. 154.  Uppal K, Soltow QA, Strobel FH, Pittard WS, Gernert KM et al. 2013. xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data. BMC Bioinform 14:15
    [Google Scholar]
  155. 155.  Go YM, Walker DI, Soltow QA, Uppal K, Wachtman LM et al. 2014. Metabolome-wide association study of phenylalanine in plasma of common marmosets. Amino Acids 47:589–601
    [Google Scholar]
  156. 156.  Blicharz T, Gong P, Bunner BM, Chu LL, Leonard KM et al. 2018. Microneedle-based device for the one-step painless collection of capillary blood samples. Nat. Biomed. Eng. 2:151–57
    [Google Scholar]
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