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Annual Review of Biomedical Data Science

Volume 4, 2021

The Exposome in the Era of the Quantified Self

Abstract

Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.

Keyword(s): biosensors, exposome, multi-omics, quantified self, wearables
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/content/journals/10.1146/annurev-biodatasci-012721-122807
2021-07-20
2024-04-20
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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. Biomarkers Prev. 14:81847–50
     [Google Scholar]
  2. 2. 
    Wild CP. 2012. The exposome: from concept to utility. Int. J. Epidemiol. 41:124–32
     [Google Scholar]
  3. 3. 
    Haddad N, Andrianou XD, Makris KC. 2019. A scoping review on the characteristics of human exposome studies. Curr. Pollut. Rep. 5:4378–93
     [Google Scholar]
  4. 4. 
    Vermeulen R, Schymanski EL, Barabási A-L, Miller GW. 2020. The exposome and health: where chemistry meets biology. Science 367:6476392–96
     [Google Scholar]
  5. 5. 
    Jiang C, Wang X, Li X, Inlora J, Wang T et al. 2018. Dynamic human environmental exposome revealed by longitudinal personal monitoring. Cell 175:1277–91.e31
     [Google Scholar]
  6. 6. 
    Gao P. 2021. The exposome in the era of one health. Environ. Sci. Technol. 55:52790–99
     [Google Scholar]
  7. 7. 
    Jiang C, Zhang X, Gao P, Chen Q, Snyder M. 2021. Decoding personal biotic and abiotic airborne exposome. Nat. Protoc. 16:1129–51
     [Google Scholar]
  8. 8. 
    Li J, Li X, Zhang S, Snyder M. 2019. Gene-environment interaction in the era of precision medicine. Cell 177:138–44
     [Google Scholar]
  9. 9. 
    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 14:4434
     [Google Scholar]
  10. 10. 
    Wang G, Zhang R, Gomez ME, Yang L, Levy Zamora M et al. 2016. Persistent sulfate formation from London fog to Chinese haze. PNAS 113:4813630–35
     [Google Scholar]
  11. 11. 
    Downs SH, Schindler C, Liu L-JS, Keidel D, Bayer-Oglesby L et al. 2007. Reduced exposure to PM10 and attenuated age-related decline in lung function. N. Engl. J. Med. 357:232338–47
     [Google Scholar]
  12. 12. 
    McCreanor J, Cullinan P, Nieuwenhuijsen MJ, Stewart-Evans J, Malliarou E et al. 2007. Respiratory effects of exposure to diesel traffic in persons with asthma. N. Engl. J. Med. 357:232348–58
     [Google Scholar]
  13. 13. 
    Klitzman S, Freudenberg N. 2003. Implications of the World Trade Center attack for the public health and health care infrastructures. Am. J. Public Health 93:3400–6
     [Google Scholar]
  14. 14. 
    Pun VC, Manjourides J, Suh H. 2017. Association of ambient air pollution with depressive and anxiety symptoms in older adults: results from the NSHAP study. Environ. Health Perspect. 125:3342–48
     [Google Scholar]
  15. 15. 
    Zhao Y, Wang S, Lang L, Huang C, Ma W, Lin H. 2017. Ambient fine and coarse particulate matter pollution and respiratory morbidity in Dongguan, China. Environ. Pollut 222:126–31
     [Google Scholar]
  16. 16. 
    Zanobetti A, Dominici F, Wang Y, Schwartz JD. 2014. A national case-crossover analysis of the short-term effect of PM2.5 on hospitalizations and mortality in subjects with diabetes and neurological disorders. Environ. Health 13:38
     [Google Scholar]
  17. 17. 
    Li J, Cone JE, Kahn AR, Brackbill RM, Farfel MR et al. 2012. Association between World Trade Center exposure and excess cancer risk. JAMA 308:232479–88
     [Google Scholar]
  18. 18. 
    Schneider T, Vermeulen R, Brouwer DH, Cherrie JW, Kromhout H, Fogh CL. 1999. Conceptual model for assessment of dermal exposure. Occup. Environ. Med. 56:11765–73
     [Google Scholar]
  19. 19. 
    De Brouwere K, Buekers J, Cornelis C, Schlekat CE, Oller AR. 2012. Assessment of indirect human exposure to environmental sources of nickel: oral exposure and risk characterization for systemic effects. Sci. Total Environ. 419:25–36
     [Google Scholar]
  20. 20. 
    Veronesi B, Oortgiesen M, Carter JD, Devlin RB. 1999. Particulate matter initiates inflammatory cytokine release by activation of capsaicin and acid receptors in a human bronchial epithelial cell line. Toxicol. Appl. Pharmacol. 154:1106–15
     [Google Scholar]
  21. 21. 
    Yoda Y, Tamura K, Shima M. 2017. Airborne endotoxin concentrations in indoor and outdoor particulate matter and their predictors in an urban city. Indoor Air 27:5955–64
     [Google Scholar]
  22. 22. 
    Lakey PSJ, Berkemeier T, Tong H, Arangio AM, Lucas K et al. 2016. Chemical exposure-response relationship between air pollutants and reactive oxygen species in the human respiratory tract. Sci. Rep. 6:32916
     [Google Scholar]
  23. 23. 
    Deng X, Zhang F, Wang L, Rui W, Long F et al. 2014. Airborne fine particulate matter induces multiple cell death pathways in human lung epithelial cells. Apoptosis 19:71099–112
     [Google Scholar]
  24. 24. 
    Pavilonis BT, Anthony TR, O'Shaughnessy PT, Humann MJ, Merchant JA et al. 2013. Indoor and outdoor particulate matter and endotoxin concentrations in an intensely agricultural county. J. Expo. Sci. Environ. Epidemiol. 23:3299–305
     [Google Scholar]
  25. 25. 
    Kreyling WG, Semmler-Behnke M, Takenaka S, Möller W. 2013. Differences in the biokinetics of inhaled nano- versus micrometer-sized particles. Acc. Chem. Res. 46:3714–22
     [Google Scholar]
  26. 26. 
    Elder A, Gelein R, Silva V, Feikert T, Opanashuk L et al. 2006. Translocation of inhaled ultrafine manganese oxide particles to the central nervous system. Environ. Health Perspect. 114:81172–78
     [Google Scholar]
  27. 27. 
    Hopkins LE, Patchin ES, Chiu P-L, Brandenberger C, Smiley-Jewell S, Pinkerton KE. 2014. Nose-to-brain transport of aerosolised quantum dots following acute exposure. Nanotoxicology 8:8885–93
     [Google Scholar]
  28. 28. 
    Gao P, da Silva E, Hou L, Denslow ND, Xiang P, Ma LQ. 2018. Human exposure to polycyclic aromatic hydrocarbons: metabolomics perspective. Environ. Int. 119:466–77
     [Google Scholar]
  29. 29. 
    Gao P, Li H, Wilson CP, Townsend TG, Xiang P et al. 2018. Source identification of PAHs in soils based on stable carbon isotopic signatures. Crit. Rev. Environ. Sci. Technol. 48:13–15923–48
     [Google Scholar]
  30. 30. 
    Xu J, Hu W, Liang D, Gao P. 2020. Photochemical impacts on the toxicity of PM2.5. Crit. Rev. Environ. Sci. Technol. https://doi.org/10.1080/10643389.2020.1816126
    [Crossref] [Google Scholar]
  31. 31. 
    Steinle S, Reis S, Sabel CE. 2013. Quantifying human exposure to air pollution—moving from static monitoring to spatio-temporally resolved personal exposure assessment. Sci. Total Environ. 443:184–93
     [Google Scholar]
  32. 32. 
    Holstius DM, Pillarisetti A, Smith KR, Seto E. 2014. Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmos. Meas. Tech. 7:41121–31
     [Google Scholar]
  33. 33. 
    Sloan CD, Philipp TJ, Bradshaw RK, Chronister S, Barber WB, Johnston JD. 2016. Applications of GPS-tracked personal and fixed-location PM2.5 continuous exposure monitoring. J. Air Waste Manag. Assoc. 66:153–65
     [Google Scholar]
  34. 34. 
    Levy Zamora M, Xiong F, Gentner D, Kerkez B, Kohrman-Glaser J, Koehler K 2019. Field and laboratory evaluations of the low-cost plantower particulate matter sensor. Environ. Sci. Technol. 53:2838–49
     [Google Scholar]
  35. 35. 
    Cavaliere A, Carotenuto F, Di Gennaro F, Gioli B, Gualtieri G et al. 2018. Development of low-cost air quality stations for next generation monitoring networks: calibration and validation of PM2.5 and PM10 sensors. Sensors 18:92843
     [Google Scholar]
  36. 36. 
    Tiele A, Esfahani S, Covington J. 2018. Design and development of a low-cost, portable monitoring device for indoor environment quality. J. Sensors 2018:5353816
     [Google Scholar]
  37. 37. 
    Fine GF, Cavanagh LM, Afonja A, Binions R. 2010. Metal oxide semi-conductor gas sensors in environmental monitoring. Sensors 10:65469–502
     [Google Scholar]
  38. 38. 
    Mead MI, Popoola OAM, Stewart GB, Landshoff P, Calleja M et al. 2013. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos. Environ. 70:186–203
     [Google Scholar]
  39. 39. 
    Wang C, Yin L, Zhang L, Xiang D, Gao R. 2010. Metal oxide gas sensors: sensitivity and influencing factors. Sensors 10:32088–106
     [Google Scholar]
  40. 40. 
    Lin C, Gillespie J, Schuder MD, Duberstein W, Beverland IJ, Heal MR. 2015. Evaluation and calibration of Aeroqual series 500 portable gas sensors for accurate measurement of ambient ozone and nitrogen dioxide. Atmos. Environ. 100:111–16
     [Google Scholar]
  41. 41. 
    Lin EZ, Esenther S, Mascelloni M, Irfan F, Godri Pollitt KJ 2020. The Fresh Air wristband: a wearable air pollutant sampler. Environ. Sci. Technol. Lett. 7:5308–14
     [Google Scholar]
  42. 42. 
    O'Connell SG, Kincl LD, Anderson KA. 2014. Silicone wristbands as personal passive samplers. Environ. Sci. Technol. 48:63327–35
     [Google Scholar]
  43. 43. 
    Dixon HM, Armstrong G, Barton M, Bergmann AJ, Bondy M et al. 2019. Discovery of common chemical exposures across three continents using silicone wristbands. R. Soc. Open Sci. 6:2181836
     [Google Scholar]
  44. 44. 
    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]
  45. 45. 
    Lipscomb ST, McClelland MM, MacDonald M, Cardenas A, Anderson KA, Kile ML. 2017. Cross-sectional study of social behaviors in preschool children and exposure to flame retardants. Environ. Health 16:23
     [Google Scholar]
  46. 46. 
    Prussin AJ, Marr LC. 2015. Sources of airborne microorganisms in the built environment. Microbiome 3:78
     [Google Scholar]
  47. 47. 
    Dai D, Prussin AJ 2nd, Marr LC, Vikesland PJ, Edwards MA, Pruden A. 2017. Factors shaping the human exposome in the built environment: opportunities for engineering control. Environ. Sci. Technol. 51:147759–74
     [Google Scholar]
  48. 48. 
    Ferguson RMW, Garcia-Alcega S, Coulon F, Dumbrell AJ, Whitby C, Colbeck I 2019. Bioaerosol biomonitoring: sampling optimization for molecular microbial ecology. Mol. Ecol. Resour. 19:3672–90
     [Google Scholar]
  49. 49. 
    Haig CW, Mackay WG, Walker JT, Williams C. 2016. Bioaerosol sampling: sampling mechanisms, bioefficiency and field studies. J. Hosp. Infect. 93:3242–55
     [Google Scholar]
  50. 50. 
    Mbareche H, Veillette M, Bilodeau GJ, Duchaine C. 2018. Bioaerosol sampler choice should consider efficiency and ability of samplers to cover microbial diversity. Appl. Environ. Microbiol. 84:23e01589–18
     [Google Scholar]
  51. 51. 
    Napoli C, Marcotrigiano V, Montagna MT. 2012. Air sampling procedures to evaluate microbial contamination: a comparison between active and passive methods in operating theatres. BMC Public Health 12:594
     [Google Scholar]
  52. 52. 
    King P, Pham LK, Waltz S, Sphar D, Yamamoto RT et al. 2016. Longitudinal metagenomic analysis of hospital air identifies clinically relevant microbes. PLOS ONE 11:8e0160124
     [Google Scholar]
  53. 53. 
    Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM et al. 2014. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345:62001048–52
     [Google Scholar]
  54. 54. 
    Volckens J, Quinn C, Leith D, Mehaffy J, Henry CS, Miller-Lionberg D. 2017. Development and evaluation of an ultrasonic personal aerosol sampler. Indoor Air 27:2409–16
     [Google Scholar]
  55. 55. 
    Wu Y-C, Shiledar A, Li Y-C, Wong J, Feng S et al. 2017. Air quality monitoring using mobile microscopy and machine learning. Light Sci. Appl. 6:9e17046
     [Google Scholar]
  56. 56. 
    Bautista-de los Santos QM, Schroeder JL, Sevillano-Rivera MC, Sungthong R, Ijaz UZ et al. 2016. Emerging investigators series: microbial communities in full-scale drinking water distribution systems—a meta-analysis. Environ. Sci. Water Res. Technol. 2:4631–44
     [Google Scholar]
  57. 57. 
    Gomez-Alvarez V, Pfaller S, Pressman JG, Wahman DG, Revetta RP. 2016. Resilience of microbial communities in a simulated drinking water distribution system subjected to disturbances: role of conditionally rare taxa and potential implications for antibiotic-resistant bacteria. Environ. Sci. Water Res. Technol. 2:4645–57
     [Google Scholar]
  58. 58. 
    de Oliveira LM, Das S, da Silva EB, Gao P, Gress J et al. 2018. Metal concentrations in traditional and herbal teas and their potential risks to human health. Sci. Total Environ. 633:649–57
     [Google Scholar]
  59. 59. 
    Li S, Gao P, Zhang J, Li Y, Peng B et al. 2012. Sequential dispersive liquid–liquid microextraction for the determination of aryloxyphenoxy-propionate herbicides in water. J. Sep. Sci. 35:233389–95
     [Google Scholar]
  60. 60. 
    Zhang J, Liang Z, Guo H, Gao P, Lu R et al. 2013. Ionic liquid-based totally organic solvent-free emulsification microextraction coupled with high performance liquid chromatography for the determination of three acaricides in fruit juice. Talanta 115:556–62
     [Google Scholar]
  61. 61. 
    Li S, Li T, Gao P, Lu R, Zhou W, Gao H. 2014. Slow-injection ultrasound-assisted emulsification–microextraction for determination of phthalate esters in water. J. Chromatogr. Sci. 52:91127–34
     [Google Scholar]
  62. 62. 
    Glasgow HB, Burkholder JM, Reed RE, Lewitus AJ, Kleinman JE. 2004. Real-time remote monitoring of water quality: a review of current applications, and advancements in sensor, telemetry, and computing technologies. J. Exp. Mar. Biol. Ecol. 300:1–2409–48
     [Google Scholar]
  63. 63. 
    FAO (Food Agric. Organ. U.N.) 2018. Soil pollution: a hidden reality Tech. Rep., Food Agric Organ. U.N., Rome:
  64. 64. 
    Guo Y, Laux SJ, Burdier M, Gao P, Ma LQ, Townsend TG. 2020. Polycyclic aromatic hydrocarbons in processed yard trash. Waste Manag. Res. 38:8825–30
     [Google Scholar]
  65. 65. 
    Su J, Gao P, Laux SJ, Ma LQ, Townsend TG. 2019. Contribution of asphalt products to total and bioaccessible polycyclic aromatic hydrocarbons. Int. J. Environ. Res. 13:3499–509
     [Google Scholar]
  66. 66. 
    Gao P, Liu Y, Wang Y, Liu X, Wang Z, Ma LQ 2019. Spatial and temporal changes of P and Ca distribution and fractionation in soil and sediment in a karst farmland-wetland system. Chemosphere 220:644–50
     [Google Scholar]
  67. 67. 
    Xu M, Gao P, Yang Z, Su L, Wu J et al. 2019. Biochar impacts on phosphorus cycling in rice ecosystem. Chemosphere 225:311–19
     [Google Scholar]
  68. 68. 
    Teng Y, Zhou Q, Gao P. 2019. Applications and challenges of elemental sulfur, nanosulfur, polymeric sulfur, sulfur composites, and plasmonic nanostructures. Crit. Rev. Environ. Sci. Technol. 49:242314–58
     [Google Scholar]
  69. 69. 
    da Silva EB, Gao P, Xu M, Guan D, Tang X, Ma LQ. 2020. Background concentrations of trace metals As, Ba, Cd, Co, Cu, Ni, Pb, Se, and Zn in 214 Florida urban soils: different cities and land uses. Environ. Pollut. 264:114737
     [Google Scholar]
  70. 70. 
    Xu M, da Silva EB, Gao P, Liao R, Wu J et al. 2020. Biochar impact on chromium accumulation by rice through Fe microbial-induced redox transformation. J. Hazard. Mater. 388:121807
     [Google Scholar]
  71. 71. 
    Li C, Zhang R, Li Y, Zhang S, Gao P et al. 2017. Relative bioavailability and bioaccessibility of PCBs in soils based on a mouse model and Tenax-improved physiologically-based extraction test. Chemosphere 186:709–15
     [Google Scholar]
  72. 72. 
    Zhang S, Li C, Li Y, Zhang R, Gao P et al. 2017. Bioaccessibility of PAHs in contaminated soils: comparison of five in vitro methods with Tenax as a sorption sink. Sci. Total Environ. 601–2:968–74
     [Google Scholar]
  73. 73. 
    Liu Y, Gao P, Su J, da Silva EB, de Oliveira LM et al. 2019. PAHs in urban soils of two Florida cities: background concentrations, distribution, and sources. Chemosphere 214:220–27
     [Google Scholar]
  74. 74. 
    Gao P, da Silva EB, Townsend T, Liu X, Ma LQ. 2019. Emerging PAHs in urban soils: concentrations, bioaccessibility, and spatial distribution. Sci. Total Environ. 670:800–5
     [Google Scholar]
  75. 75. 
    Gao P, Xu M, Liu Y, da Silva EB, Xiang P, Ma LQ. 2019. Emerging and legacy PAHs in urban soils of four small cities: concentrations, distribution, and sources. Sci. Total Environ. 685:463–70
     [Google Scholar]
  76. 76. 
    Li K, Gao P, Xiang P, Zhang X, Cui X, Ma LQ. 2017. Molecular mechanisms of PFOA-induced toxicity in animals and humans: implications for health risks. Environ. Int. 99:43–54
     [Google Scholar]
  77. 77. 
    Xiang P, Liu R-Y, Li C, Gao P, Cui X-Y, Ma LQ. 2017. Effects of organophosphorus flame retardant TDCPP on normal human corneal epithelial cells: implications for human health. Environ. Pollut. 230:22–30
     [Google Scholar]
  78. 78. 
    Xiang P, Jia Y, Wang K, Li M-Y, Qin Y-S et al. 2018. Water extract of indoor dust induces tight junction disruption in normal human corneal epithelial cells. Environ. Pollut. 243:Pt. A301–7
     [Google Scholar]
  79. 79. 
    Xiang P, He R-W, Liu R-Y, Li K, Gao P et al. 2018. Cellular responses of normal (HL-7702) and cancerous (HepG2) hepatic cells to dust extract exposure. Chemosphere 193:1189–97
     [Google Scholar]
  80. 80. 
    de Oliveira LM, Suchismita D, da Silva EB, Gao P, Vardanyan L et al. 2018. Interactive effects of chromate and arsenate on their uptake and speciation in Pteris ensiformis. Plant Soil 422:1–2515–26
     [Google Scholar]
  81. 81. 
    Eskola M, Elliott CT, Hajšlová J, Steiner D, Krska R. 2020. Towards a dietary-exposome assessment of chemicals in food: an update on the chronic health risks for the European consumer. Crit. Rev. Food Sci. Nutr. 60:111890–911
     [Google Scholar]
  82. 82. 
    Garvey M. 2019. Food pollution: a comprehensive review of chemical and biological sources of food contamination and impact on human health. Nutrire 44:1
     [Google Scholar]
  83. 83. 
    Jin Z, Berthiaume JM, Li Q, Henry F, Huang Z et al. 2014. Catabolism of (2E)-4-hydroxy-2-nonenal via ω- and ω-1-oxidation stimulated by ketogenic diet. J. Biol. Chem. 289:4632327–38
     [Google Scholar]
  84. 84. 
    Borofsky MS, Dauw CA, York N, Terry C, Lingeman JE. 2018. Accuracy of daily fluid intake measurements using a “smart” water bottle. Urolithiasis 46:4343–48
     [Google Scholar]
  85. 85. 
    Dimitratos SM, German JB, Schaefer SE. 2020. Wearable technology to quantify the nutritional intake of adults: validation study. JMIR mHealth uHealth 8:7e16405
     [Google Scholar]
  86. 86. 
    Picó C, Serra F, Rodríguez AM, Keijer J, Palou A. 2019. Biomarkers of nutrition and health: new tools for new approaches. Nutrients 11:51092
     [Google Scholar]
  87. 87. 
    Kim D, Cao Y, Mariappan D, Bono MS, Hart AJ, Marelli B. 2020. A microneedle technology for sampling and sensing bacteria in the food supply chain. Adv. Funct. Mater. 31:12005370
     [Google Scholar]
  88. 88. 
    Veleva BI, van Bezooijen RL, Chel VGM, Numans ME, Caljouw MAA. 2018. Effect of ultraviolet light on mood, depressive disorders and well-being. Photodermatol. Photoimmunol. Photomed. 34:5288–97
     [Google Scholar]
  89. 89. 
    Edström DW, Linder J, Wennersten G, Brismar K, Ros A-M. 2010. Phototherapy with ultraviolet radiation: a study of hormone parameters and psychological effects. J. Eur. Acad. Dermatol. Venereol. 24:4403–9
     [Google Scholar]
  90. 90. 
    Guy GP, Machlin SR, Ekwueme DU, Yabroff KR. 2015. Prevalence and costs of skin cancer treatment in the U.S., 2002–2006 and 2007–2011. Am. J. Prev. Med. 48:2183–87
     [Google Scholar]
  91. 91. 
    Araki H, Kim J, Zhang S, Banks A, Crawford KE et al. 2017. UV sensors: materials and device designs for an epidermal UV colorimetric dosimeter with near field communication capabilities. Adv. Funct. Mater. 27:21604465
     [Google Scholar]
  92. 92. 
    Taylor HR, West SK, Rosenthal FS, Muñoz B, Newland HS et al. 1988. Effect of ultraviolet radiation on cataract formation. N. Engl. J. Med. 319:221429–33
     [Google Scholar]
  93. 93. 
    Negelspach DC, Kaladchibachi S, Fernandez F. 2018. The circadian activity rhythm is reset by nanowatt pulses of ultraviolet light. Proc. R. Soc. B 285: 1884.20181288
     [Google Scholar]
  94. 94. 
    Agar NS, Halliday GM, Barnetson RS, Ananthaswamy HN, Wheeler M, Jones AM 2004. The basal layer in human squamous tumors harbors more UVA than UVB fingerprint mutations: a role for UVA in human skin carcinogenesis. PNAS 101:144954–59
     [Google Scholar]
  95. 95. 
    Fitzpatrick TB. 1988. The validity and practicality of sun-reactive skin types I through VI. Arch. Dermatol. 124:6869–71
     [Google Scholar]
  96. 96. 
    Heo SY, Kim J, Gutruf P, Banks A, Wei P et al. 2018. Wireless, battery-free, flexible, miniaturized dosimeters monitor exposure to solar radiation and to light for phototherapy. Sci. Transl. Med. 10:470eaau1643
     [Google Scholar]
  97. 97. 
    Eriksson C, Bodin T, Selander J. 2017. Burden of disease from road traffic and railway noise—a quantification of healthy life years lost in Sweden. Scand. J. Work Environ. Health 43:6519–25
     [Google Scholar]
  98. 98. 
    Tobollik M, Hintzsche M, Wothge J, Myck T, Plass D. 2019. Burden of disease due to traffic noise in Germany. Int. J. Environ. Res. Public Health 16:132304
     [Google Scholar]
  99. 99. 
    Rojas-Rueda D, Vrijheid M, Robinson O, Gunn Marit A, Gražulevičienė R et al. 2019. Environmental burden of childhood disease in Europe. Int. J. Environ. Res. Public Health 16:61084
     [Google Scholar]
  100. 100. 
    Ma J, Li C, Kwan M-P, Kou L, Chai Y. 2020. Assessing personal noise exposure and its relationship with mental health in Beijing based on individuals’ space-time behavior. Environ. Int. 139:105737
     [Google Scholar]
  101. 101. 
    Kardous CA, Shaw PB. 2014. Evaluation of smartphone sound measurement applications. J. Acoust. Soc. Am. 135:4EL186–92
     [Google Scholar]
  102. 102. 
    Murphy E, King EA 2016. Testing the accuracy of smartphones and sound level meter applications for measuring environmental noise. Appl. Acoust. 106:16–22
     [Google Scholar]
  103. 103. 
    Kardous CA, Shaw PB. 2016. Evaluation of smartphone sound measurement applications (apps) using external microphones—a follow-up study. J. Acoust. Soc. Am. 140:4EL327–33
     [Google Scholar]
  104. 104. 
    Serpanos YC, Renne B, Schoepflin JR, Davis D 2018. The accuracy of smartphone sound level meter applications with and without calibration. Am. J. Speech Lang. Pathol. 27:41319–28
     [Google Scholar]
  105. 105. 
    McLennon T, Patel S, Behar A, Abdoli-Eramaki M. 2019. Evaluation of smartphone sound level meter applications as a reliable tool for noise monitoring. J. Occup. Environ. Hyg. 16:9620–27
     [Google Scholar]
  106. 106. 
    Colomina M-T, Sánchez-Santed F, Conejo NM, Collado P, Salvador A et al. 2018. The psychoexposome: a holistic perspective beyond health and disease. Psicothema 30:15–7
     [Google Scholar]
  107. 107. 
    Tost H, Champagne FA, Meyer-Lindenberg A. 2015. Environmental influence in the brain, human welfare and mental health. Nat. Neurosci. 18:104121–31
     [Google Scholar]
  108. 108. 
    Schneiderman N, Ironson G, Siegel SD. 2005. Stress and health: psychological, behavioral, and biological determinants. Annu. Rev. Clin. Psychol. 1:607–28
     [Google Scholar]
  109. 109. 
    Regier DA, Kuhl EA, Kupfer DJ. 2013. The DSM-5: classification and criteria changes. World Psychiatry 12:292–98
     [Google Scholar]
  110. 110. 
    Kamdar MR, Wu MJ. 2016. PRISM: a data-driven platform for monitoring mental health. Pac. Symp. Biocomput. 21:333–44
     [Google Scholar]
  111. 111. 
    Abdullah S, Choudhury T. 2018. Sensing technologies for monitoring serious mental illnesses. IEEE Multimed 25:161–75
     [Google Scholar]
  112. 112. 
    Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N et al. 2018. Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1:18
     [Google Scholar]
  113. 113. 
    Li X, Dunn J, Salins D, Zhou G, Zhou W et al. 2017. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLOS Biol 15:1e2001402
     [Google Scholar]
  114. 114. 
    Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R et al. 2018. Glucotypes reveal new patterns of glucose dysregulation. PLOS Biol 16:7e2005143
     [Google Scholar]
  115. 115. 
    Mishra T, Wang M, Metwally AA, Bogu GK, Brooks AW et al. 2020. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat. Biomed. Eng. 4:1208–20
     [Google Scholar]
  116. 116. 
    Barupal DK, Fiehn O. 2019. Generating the Blood Exposome Database using a comprehensive text mining and database fusion approach. Environ. Health Perspect. 127:997008
     [Google Scholar]
  117. 117. 
    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. 122:8769–74
     [Google Scholar]
  118. 118. 
    Jing J, Gao Y. 2018. Urine biomarkers in the early stages of diseases: current status and perspective. Discov. Med. 25:13657–65
     [Google Scholar]
  119. 119. 
    O'Lenick CR, Pleil JD, Stiegel MA, Sobus JR, Wallace MAG. 2019. Detection and analysis of endogenous polar volatile organic compounds (PVOCs) in urine for human exposome research. Biomarkers 24:3240–48
     [Google Scholar]
  120. 120. 
    Maitre L, Robinson O, Martinez D, Toledano MB, Ibarluzea J et al. 2018. Urine metabolic signatures of multiple environmental pollutants in pregnant women: an exposome approach. Environ. Sci. Technol. 52:2213469–80
     [Google Scholar]
  121. 121. 
    Tang Q, Jin G, Wang G, Liu T, Liu X et al. 2020. Current sampling methods for gut microbiota: a call for more precise devices. Front. Cell. Infect. Microbiol. 10:151
     [Google Scholar]
  122. 122. 
    Wu W-K, Chen C-C, Panyod S, Chen R-A, Wu M-S et al. 2019. Optimization of fecal sample processing for microbiome study—the journey from bathroom to bench. J. Formos. Med. Assoc. 118:2545–55
     [Google Scholar]
  123. 123. 
    Park S-M, Won DD, Lee BJ, Escobedo D, Esteva A et al. 2020. A mountable toilet system for personalized health monitoring via the analysis of excreta. Nat. Biomed. Eng. 4:6624–35
     [Google Scholar]
  124. 124. 
    Mena-Bravo A, Luque de Castro MD. 2014. Sweat: a sample with limited present applications and promising future in metabolomics. J. Pharm. Biomed. Anal. 90:139–47
     [Google Scholar]
  125. 125. 
    Bandodkar AJ, Jeang WJ, Ghaffari R, Rogers JA. 2019. Wearable sensors for biochemical sweat analysis. Annu. Rev. Anal. Chem. 12:1–22
     [Google Scholar]
  126. 126. 
    Ray TR, Choi J, Bandodkar AJ, Krishnan S, Gutruf P et al. 2019. Bio-integrated wearable systems: a comprehensive review. Chem. Rev. 119:85461–533
     [Google Scholar]
  127. 127. 
    Oh SY, Hong SY, Jeong YR, Yun J, Park H et al. 2018. Skin-attachable, stretchable electrochemical sweat sensor for glucose and pH detection. ACS Appl. Mater. Interfaces 10:1613729–40
     [Google Scholar]
  128. 128. 
    Sivakumar M, Madhu R, Chen S-M, Veeramani V, Manikandan A et al. 2016. Low-temperature chemical synthesis of CoWO4 nanospheres for sensitive nonenzymatic glucose sensor. J. Phys. Chem. C 120:3017024–28
     [Google Scholar]
  129. 129. 
    Emaminejad S, Gao W, Wu E, Davies ZA, Yin Yin Nyein H et al. 2017. Autonomous sweat extraction and analysis applied to cystic fibrosis and glucose monitoring using a fully integrated wearable platform. PNAS 114:184625–30
     [Google Scholar]
  130. 130. 
    Gao W, Emaminejad S, Nyein HYY, Challa S, Chen K et al. 2016. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 529:7587509–14
     [Google Scholar]
  131. 131. 
    Parlak O, Keene ST, Marais A, Curto VF, Salleo A. 2018. Molecularly selective nanoporous membrane-based wearable organic electrochemical device for noninvasive cortisol sensing. Sci. Adv. 4:7eaar2904
     [Google Scholar]
  132. 132. 
    Torrente-Rodríguez RM, Tu J, Yang Y, Min J, Wang M et al. 2020. Investigation of cortisol dynamics in human sweat using a graphene-based wireless mHealth system. Matter 2:4921–37
     [Google Scholar]
  133. 133. 
    Kim J, Campbell AS, de Ávila BE-F, Wang J 2019. Wearable biosensors for healthcare monitoring. Nat. Biotechnol. 37:4389–406
     [Google Scholar]
  134. 134. 
    Seshadri DR, Li RT, Voos JE, Rowbottom JR, Alfes CM et al. 2019. Wearable sensors for monitoring the physiological and biochemical profile of the athlete. NPJ Digit. Med. 2:172
     [Google Scholar]
  135. 135. 
    Kim J, Valdés-Ramírez G, Bandodkar AJ, Jia W, Martinez AG et al. 2014. Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites. Analyst 139:71632–36
     [Google Scholar]
  136. 136. 
    Tékus E, Kaj M, Szabó E, Szénási NL, Kerepesi I et al. 2012. Comparison of blood and saliva lactate level after maximum intensity exercise. Acta Biol. Hung. 63:Suppl. 189–98
     [Google Scholar]
  137. 137. 
    Kim J, Imani S, de Araujo WR, Warchall J, Valdés-Ramírez G et al. 2015. Wearable salivary uric acid mouthguard biosensor with integrated wireless electronics. Biosens. Bioelectron. 74:1061–68
     [Google Scholar]
  138. 138. 
    Yan Q, Peng B, Su G, Cohan BE, Major TC, Meyerhoff ME. 2011. Measurement of tear glucose levels with amperometric glucose biosensor/capillary tube configuration. Anal. Chem. 83:218341–46
     [Google Scholar]
  139. 139. 
    Yao H, Liao Y, Lingley AR, Afanasiev A, Lähdesmäki I et al. 2012. A contact lens with integrated telecommunication circuit and sensors for wireless and continuous tear glucose monitoring. J. Micromech. Microeng. 22:7075007
     [Google Scholar]
  140. 140. 
    Thomas N, Lähdesmäki I, Parviz BA. 2012. A contact lens with an integrated lactate sensor. Sens. Actuators B 162:1128–34
     [Google Scholar]
  141. 141. 
    Kim J, Kim M, Lee M-S, Kim K, Ji S et al. 2017. Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics. Nat. Commun. 8:14997
     [Google Scholar]
  142. 142. 
    Park J, Kim J, Kim S-Y, Cheong WH, Jang J et al. 2018. Soft, smart contact lenses with integrations of wireless circuits, glucose sensors, and displays. Sci. Adv. 4:eaap9841
     [Google Scholar]
  143. 143. 
    Barbosa F Jr., Tanus-Santos JE, Gerlach RF, Parsons PJ 2005. A critical review of biomarkers used for monitoring human exposure to lead: advantages, limitations, and future needs. Environ. Health Perspect. 113:121669–74
     [Google Scholar]
  144. 144. 
    Andra SS, Austin C, Arora M. 2016. The tooth exposome in childrenʼs health research. Curr. Opin. Pediatr. 28:2221–27
     [Google Scholar]
  145. 145. 
    Poh M-Z, Swenson NC, Picard RW. 2010. A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. IEEE Trans. Biomed. Eng. 57:51243–52
     [Google Scholar]
  146. 146. 
    Swan M. 2013. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1:285–99
     [Google Scholar]
  147. 147. 
    Swan M. 2009. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 6:2492–525
     [Google Scholar]
  148. 148. 
    Dewey FE, Grove ME, Pan C, Goldstein BA, Bernstein JA et al. 2014. Clinical interpretation and implications of whole-genome sequencing. JAMA 311:101035–45
     [Google Scholar]
  149. 149. 
    Clifton L, Clifton DA, Pimentel MAF, Watkinson PJ, Tarassenko L. 2014. Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors. IEEE J. Biomed. Health Inf. 18:3722–30
     [Google Scholar]
  150. 150. 
    Jameson JL, Longo DL. 2015. Precision medicine—personalized, problematic, and promising. Obstet. Gynecol. Surv. 70:10612–14
     [Google Scholar]
  151. 151. 
    Patel CJ 2019. Exposome-wide association studies: a data-driven approach for searching for exposures associated with phenotype. Unraveling the Exposome S Dagnino, A Macherone 315–36 Cham, Switz: Springer Int.
     [Google Scholar]
  152. 152. 
    Martin-Sanchez F 2019. Big data challenges from an integrative exposome/expotype perspective. Big Data, Big Challenges: A Healthcare Perspective M Househ, AW Kushniruk, EM Borycki 127–41 Cham, Switz: Springer Int.
     [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-012721-122807
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The Exposome in the Era of the Quantified Self
Annual Review of Biomedical Data Science 4, 255 (2021); https://doi.org/10.1146/annurev-biodatasci-012721-122807
/content/journals/10.1146/annurev-biodatasci-012721-122807
/content/journals/10.1146/annurev-biodatasci-012721-122807
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