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The Exposome: Molecules to Populations
- Megan M. Niedzwiecki1, Douglas I. Walker1,2,3, Roel Vermeulen4,5,6, Marc Chadeau-Hyam4,6, Dean P. Jones3, and Gary W. Miller2,7
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View Affiliations Hide AffiliationsAffiliations: 1Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; email: [email protected], [email protected] 2Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA 3Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University, Atlanta, Georgia 30322, USA; email: [email protected] 4Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands; email: [email protected] 5Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 Utrecht, Netherlands 6MRC/PHE Centre for Environmental Health, Department of Epidemiology and Public Health, Imperial College London, W2 1PG London, United Kingdom; email: [email protected] 7Current affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY 10032, USA; email: [email protected]
- Vol. 59:107-127 (Volume publication date January 2019) https://doi.org/10.1146/annurev-pharmtox-010818-021315
- First published as a Review in Advance on August 10, 2018
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Copyright © 2019 by Annual Reviews. All rights reserved
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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Literature Cited
- 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
- 2. Wild CP 2012. The exposome: from concept to utility. Int. J. Epidemiol. 41:24–32
- 3. Miller GW, Jones DP 2014. The nature of nurture: refining the definition of the exposome. Toxicol. Sci. 137:1–2
- 4. Miller GW 2014. The Exposome: A Primer Waltham, MA: Academic Press
- 5. Niedzwiecki MM, Miller GW 2017. The exposome paradigm in human health: lessons from the Emory Exposome Summer Course. Environ. Health Perspect. 125:064502
- 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
- 7. Rappaport SM 2016. Genetic factors are not the major causes of chronic diseases. PLOS ONE 11:e0154387
- 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
- 9. Brunekreef B 2013. Exposure science, the exposome, and public health. Environ. Mol. Mutagen. 54:596–98
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 25. O'Connell SG, Kincl LD, Anderson KA 2014. Silicone wristbands as personal passive samplers. Environ. Sci. Technol. 48:3327–35
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 36. Go YM, Jones DP 2016. Exposure memory and lung regeneration. Ann. Am. Thorac. Soc. 13:S452–61
- 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
- 38. Weinhold B 2006. Epigenetics: the science of change. Environ. Health Perspect. 114:A160–67
- 39. Albert R 2005. Scale-free networks in cell biology. J. Cell Sci. 118:4947–57
- 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
- 41. Jones DP 2016. Sequencing the exposome: a call to action. Toxicol. Rep. 3:29–45
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 50. Bessonneau V, Pawliszyn J, Rappaport SM 2017. The saliva exposome for monitoring of individuals’ health trajectories. Environ. Health Perspect. 125:077014
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 82. Elshal MF, McCoy JP 2006. Multiplex bead array assays: performance evaluation and comparison of sensitivity to ELISA. Methods 38:317–23
- 83. Tighe PJ, Ryder RR, Todd I, Fairclough LC 2015. ELISA in the multiplex era: potentials and pitfalls. Proteom. Clin. Appl. 9:406–22
- 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
- 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
- 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
- 87. Yates JR, Ruse CI, Nakorchevsky A 2009. Proteomics by mass spectrometry: approaches, advances, and applications. Annu. Rev. Biomed. Eng. 11:49–79
- 88. Rappaport SM, Li H, Grigoryan H, Funk WE, Williams ER 2012. Adductomics: characterizing exposures to reactive electrophiles. Toxicol. Lett. 213:83–90
- 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
- 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
- 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
- 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
- 93. Go YM, Jones DP 2014. Redox biology: interface of the exposome with the proteome, epigenome and genome. Redox Biol 2:358–60
- 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
- 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
- 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
- 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
- 98. Hou L, Zhang X, Wang D, Baccarelli A 2012. Environmental chemical exposures and human epigenetics. Int. J. Epidemiol. 41:79–105
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 109. Balding DJ 2006. A tutorial on statistical methods for population association studies. Nat. Rev. Genet. 7:781–91
- 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
- 111. Patterson N, Price AL, Reich D 2006. Population structure and eigenanalysis. PLOS Genet 2:e190
- 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
- 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
- 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
- 115. Tibshirani R 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- 116. Zou H, Hastie T 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
- 117. Zou H, Hastie T, Tibshirani R 2006. Sparse principal component analysis. J. Comput. Graph. Stat. 15:265–86
- 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
- 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
- 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
- 121. Hans C, Dobra A, West M 2007. Shotgun stochastic search for “large p” regression. J. Am. Stat. Assoc. 102:507–16
- 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
- 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
- 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
- 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
- 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
- 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
- 128. Gene Ontol. Consort. 2017. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res 45:D331–38
- 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
- 130. Huang DW, Sherman BT, Lempicki RA 2008. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4:44
- 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
- 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
- 133. Simon N, Friedman J, Hastie T, Tibshirani R 2013. A Sparse-Group Lasso. J. Comput. Graph. Stat. 22:231–45
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 143. Collins FS, Varmus H 2015. A new initiative on precision medicine. New Engl. J. Med. 372:793–95
- 144. Mirnezami R, Nicholson J, Darzi A 2012. Preparing for precision medicine. New Engl. J. Med. 366:489–91
- 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
- 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
- 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
- 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
- 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
- 150. O'Connell SG, Kincl LD, Anderson KA 2014. Silicone wristbands as personal passive samplers. Environ. Sci. Technol. 48:3327–35
- 151. Jones DP, Park Y, Ziegler TR 2012. Nutritional metabolomics: progress in addressing complexity in diet and health. Annu. Rev. Nutr. 32:183–202
- 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
- 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
- 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
- 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
- 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
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