Use of Exposomic Methods Incorporating Sensors in Environmental Epidemiology

Abstract

Purpose of Review

The exposome is a recently coined concept that comprises the totality of nongenetic factors that affect human health. It is recognized as a major conceptual advancement in environmental epidemiology, and there is increased demand for technologies that capture the spatial, temporal, and chemical variability of exposures across individuals (i.e., “exposomic sensors”). We review a selection of these tools, highlighting their strengths and limitations with regard to epidemiological research.

Recent Findings

Wearable passive samplers are emerging as promising exposomic sensors for individuals. In conjunction with targeted and untargeted assays, these sensors enable the measurement of complex multipollutant mixtures, which can include both known and previously unknown environmental contaminants. Because of their minimally burdensome and noninvasive nature, they are deployable among sensitive populations, such as seniors, pregnant women, and children. The integration of exposomic data captured by these sensors with other omic data (e.g., transcriptomic and metabolomic) presents exciting opportunities for investigating disease risk factors. For example, the linkage of exposomic sensor data with other omic data may indicate perturbation by multipollutant mixtures at multiple physiological levels, which would strengthen evidence of their effects and potentially indicate targets for interventions. However, there remain considerable theoretical and methodological challenges that must be overcome to realize the potential promise of omic integration.

Summary

Through continued investment and improvement in exposomic sensor technologies, it may be possible to refine their application and reduce their outstanding limitations to advance the fields of exposure science and epidemiology.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. 1.

    Vermeulen R, Schymanski EL, Barabási A-L, Miller GW. The exposome and health: where chemistry meets biology. Science. 2020;367(6476):392. https://doi.org/10.1126/science.aay3164.

  2. 2.

    Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev. 2005;14(8):1847. https://doi.org/10.1158/1055-9965.EPI-05-0456.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Rappaport SM, Smith MT. Environment and disease risks. Science. 2010;330(6003):460–1. https://doi.org/10.1126/science.1192603.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Vineis P, Chadeau-Hyam M, Gmuender H, Gulliver J, Herceg Z, Kleinjans J, et al. The exposome in practice: design of the EXPOsOMICS project. Int J Hyg Environ Health. 2017;220(2 Pt A):142–51. https://doi.org/10.1016/j.ijheh.2016.08.001.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Mulligan CC, Talaty N, Cooks RG. Desorption electrospray ionization with a portable mass spectrometer: in situ analysis of ambient surfaces. Chem Commun. 2006;16:1709–11. https://doi.org/10.1039/B517357D.

    Article  Google Scholar 

  6. 6.

    Bruno AM, Cleary SR, O'Leary AE, Gizzi MC, Mulligan CC. Balancing the utility and legality of implementing portable mass spectrometers coupled with ambient ionization in routine law enforcement activities. Anal Methods. 2017;9(34):5015–22. https://doi.org/10.1039/C7AY00972K.

    Article  Google Scholar 

  7. 7.

    Kogan VT, Gladkov GY, Viktorova OS. The ion-optical scheme of a portable mass spectrometer. Tech Phys. 2001;46(4):492–4. https://doi.org/10.1134/1.1365478.

    CAS  Article  Google Scholar 

  8. 8.

    Cheung K, Velasquez-Garcia LF, Akinwande AI. Chip-scale quadrupole mass filters for portable mass spectrometry. J Microelectromech Syst. 2010;19(3):469–83. https://doi.org/10.1109/JMEMS.2010.2046396.

    Article  Google Scholar 

  9. 9.

    Anderson KA, Points GL, Donald CE, Dixon HM, Scott RP, Wilson G, et al. Preparation and performance features of wristband samplers and considerations for chemical exposure assessment. J Expo Sci Environ Epidemiol. 2017;27(6):551–9. https://doi.org/10.1038/jes.2017.9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Hammel SC, Hoffman K, Webster TF, Anderson KA, Stapleton HM. Measuring personal exposure to organophosphate flame retardants using silicone wristbands and hand wipes. Environ Sci Technol. 2016;50(8):4483–91. https://doi.org/10.1021/acs.est.6b00030.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Hammel SC, Phillips AL, Hoffman K, Stapleton HM. Evaluating the use of silicone wristbands to measure personal exposure to brominated flame retardants. Environ Sci Technol. 2018;52(20):11875–85. https://doi.org/10.1021/acs.est.8b03755.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Lin EZ, Esenther S, Mascelloni M, Irfan F, Godri Pollitt KJ. The Fresh Air wristband: a wearable air pollutant sampler. Environ Sci Technol Lett. 2020;7(5):308–14. https://doi.org/10.1021/acs.estlett.9b00800.

    CAS  Article  Google Scholar 

  13. 13.

    Harner T, Farrar NJ, Shoeib M, Jones KC, Gobas FAPC. Characterization of polymer-coated glass as a passive air sampler for persistent organic pollutants. Environ Sci Technol. 2003;37(11):2486–93. https://doi.org/10.1021/es0209215.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Melymuk L, Robson M, Helm PA, Diamond ML. Evaluation of passive air sampler calibrations: selection of sampling rates and implications for the measurement of persistent organic pollutants in air. Atmos Environ. 2011;45(10):1867–75. https://doi.org/10.1016/j.atmosenv.2011.01.011.

    CAS  Article  Google Scholar 

  15. 15.

    Okeme JO, Yang C, Abdollahi A, Dhal S, Harris SA, Jantunen LM, et al. Passive air sampling of flame retardants and plasticizers in Canadian homes using PDMS, XAD-coated PDMS and PUF samplers. Environ Pollut. 2018;239:109–17. https://doi.org/10.1016/j.envpol.2018.03.103.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Tromp PC, Beeltje H, Okeme JO, Vermeulen R, Pronk A, Diamond ML. Calibration of polydimethylsiloxane and polyurethane foam passive air samplers for measuring semi volatile organic compounds using a novel exposure chamber design. Chemosphere. 2019;227:435–43. https://doi.org/10.1016/j.chemosphere.2019.04.043.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Donald CE, Scott RP, Blaustein KL, Halbleib ML, Sarr M, Jepson PC, et al. Silicone wristbands detect individuals' pesticide exposures in West Africa. R Soc Open Sci. 2016;3(8):160433. https://doi.org/10.1098/rsos.160433.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Rohlman D, Donatuto J, Heidt M, Barton M, Campbell L, Anderson AK, et al. A case study describing a community-engaged approach for evaluating polycyclic aromatic hydrocarbon exposure in a native American community. Int J Environ Res Public Health. 2019;16(3). https://doi.org/10.3390/ijerph16030327.

  19. 19.

    Doherty BT, Pearce JL, Anderson KA, Karagas MR, Romano ME. Assessment of multipollutant exposures during pregnancy using silicone wristbands. Front Public Health. 2020;8:570.

    Article  Google Scholar 

  20. 20.

    Dixon HM, Armstrong G, Barton M, Bergmann AJ, Bondy M, Halbleib ML, et al. Discovery of common chemical exposures across three continents using silicone wristbands. R Soc Open Sci. 2019;6(2):181836. https://doi.org/10.1098/rsos.181836.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Kile ML, Scott RP, O'Connell SG, Lipscomb S, MacDonald M, McClelland M, et al. Using silicone wristbands to evaluate preschool children's exposure to flame retardants. Environ Res. 2016;147:365–72. https://doi.org/10.1016/j.envres.2016.02.034.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    O'Connell SG, Kincl LD, Anderson KA. Silicone wristbands as personal passive samplers. Environ Sci Technol. 2014;48(6):3327–35. https://doi.org/10.1021/es405022f.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Paulik LB, Hobbie KA, Rohlman D, Smith BW, Scott RP, Kincl L, et al. Environmental and individual PAH exposures near rural natural gas extraction. Environ Pollut. 2018;241:397–405. https://doi.org/10.1016/j.envpol.2018.05.010.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Donald CE, Scott RP, Wilson G, Hoffman PD, Anderson KA. Artificial turf: chemical flux and development of silicone wristband partitioning coefficients. Air Qual Atmos Health. 2019;12(5):597–611. https://doi.org/10.1007/s11869-019-00680-1.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Harley KG, Parra KL, Camacho J, Bradman A, Nolan JES, Lessard C, et al. Determinants of pesticide concentrations in silicone wristbands worn by Latina adolescent girls in a California farmworker community: the COSECHA youth participatory action study. Sci Total Environ. 2019;652:1022–9. https://doi.org/10.1016/j.scitotenv.2018.10.276.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Koelmel JP, Lin EZ, Nicholas A, Guo P, Zhou Y, Godri Pollitt KJ. Head, shoulders, knees, and toes: placement of wearable passive samplers alters exposure profiles observed. Environ Sci Technol. 2021. (In Press).

  27. 27.

    Eskenazi B, An S, Rauch SA, Coker ES, Maphula A, Obida M, et al. Prenatal exposure to DDT and pyrethroids for malaria control and child neurodevelopment: the VHEMBE cohort, South Africa. Environ Health Perspect. 2018;126(4):047004. https://doi.org/10.1289/EHP2129.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Murray J, Eskenazi B, Bornman R, Gaspar FW, Crause M, Obida M, et al. Exposure to DDT and hypertensive disorders of pregnancy among South African women from an indoor residual spraying region: the VHEMBE study. Environ Res. 2018;162:49–54. https://doi.org/10.1016/j.envres.2017.12.006.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Koelmel JP, Lin EZ, Guo P, Zhou J, He J, Chen A, et al. External exposomics in the China BAPE study: wristband samplers show individual and community wide exposure dynamics. Environ Pollut. 2021;270:116228.

  30. 30.

    Mahesh PA, Lokesh KS, Madhivanan P, Chaya SK, Jayaraj BS, Ganguly K, et al. The Mysuru stUdies of Determinants of Health in Rural Adults (MUDHRA), India. Epidemiol Health. 2018;40:e2018027–e. https://doi.org/10.4178/epih.e2018027.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Niedzwiecki MM, Walker DI, Vermeulen R, Chadeau-Hyam M, Jones DP, Miller GW. The exposome: molecules to populations. Annu Rev Pharmacol Toxicol. 2019;59(1):107–27. https://doi.org/10.1146/annurev-pharmtox-010818-021315.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L, Vineis P, et al. Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers. Environ Mol Mutagen. 2013;54(7):542–57. https://doi.org/10.1002/em.21797.

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Kalia V, Jones DP, Miller GW. Networks at the nexus of systems biology and the exposome. Curr Opin Toxicol. 2019;16:25–31. https://doi.org/10.1016/j.cotox.2019.03.008.

    Article  Google Scholar 

  34. 34.

    Yang X, Zhang M, Lu T, Chen S, Sun X, Guan Y, et al. Metabolomics study and meta-analysis on the association between maternal pesticide exposome and birth outcomes. Environ Res. 2020;182:109087. https://doi.org/10.1016/j.envres.2019.109087.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Maitre L, Robinson O, Martinez D, Toledano MB, Ibarluzea J, Marina LS, et al. Urine metabolic signatures of multiple environmental pollutants in pregnant women: an exposome approach. Environ Sci Technol. 2018;52(22):13469–80. https://doi.org/10.1021/acs.est.8b02215.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Walker DI, Uppal K, Zhang L, Vermeulen R, Smith M, Hu W, et al. High-resolution metabolomics of occupational exposure to trichloroethylene. Int J Epidemiol. 2016;45(5):1517–27. https://doi.org/10.1093/ije/dyw218.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Cai Y, Rosen Vollmar AK, Johnson CH. Analyzing metabolomics data for environmental health and exposome research. In: Li S, editor. Computational methods and data analysis for metabolomics. New York: Springer US; 2020. p. 447–67.

  38. 38.

    Csala A, Zwinderman AH. Multivariate statistical methods for high-dimensional multiset omics data analysis. In: Husi H, editor. Computational biology. Brisbane: Codon Publications; 2019.

    Google Scholar 

  39. 39.

    Meng C, Zeleznik OA, Thallinger GG, Kuster B, Gholami AM, Culhane AC. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief Bioinform. 2016;17(4):628–41. https://doi.org/10.1093/bib/bbv108.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Palermo G, Piraino P, Zucht H-D. Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data. Adv Appl Bioinforma Chem. 2009;2:57–70. https://doi.org/10.2147/aabc.s3619.

    CAS  Article  PubMed Central  Google Scholar 

  41. 41.

    Saccenti E, Hoefsloot HCJ, Smilde AK, Westerhuis JA, Hendriks MMWB. Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics. 2014;10(3):361–74. https://doi.org/10.1007/s11306-013-0598-6.

    CAS  Article  Google Scholar 

  42. 42.

    Lubin JH, Colt JS, Camann D, Davis S, Cerhan JR, Severson RK, et al. Epidemiologic evaluation of measurement data in the presence of detection limits. Environ Health Perspect. 2004;112(17):1691–6. https://doi.org/10.1289/ehp.7199.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Baccarelli A, Pfeiffer R, Consonni D, Pesatori AC, Bonzini M, Patterson DG, et al. Handling of dioxin measurement data in the presence of non-detectable values: overview of available methods and their application in the Seveso chloracne study. Chemosphere. 2005;60(7):898–906. https://doi.org/10.1016/j.chemosphere.2005.01.055.

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Johnstone IM, Titterington DM. Statistical challenges of high-dimensional data. Philos Trans A Math Phys Eng Sci. 2009;367(1906):4237–53. https://doi.org/10.1098/rsta.2009.0159.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Mahieu NG, Patti GJ. Systems-level annotation of a metabolomics data set reduces 25 000 features to fewer than 1000 unique metabolites. Anal Chem. 2017;89(19):10397–406. https://doi.org/10.1021/acs.analchem.7b02380.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Mahieu NG, Huang X, Chen Y Jr, Patti GJ. Credentialing features: a platform to benchmark and optimize untargeted metabolomic methods. Anal Chem. 2014;86(19):9583–9. https://doi.org/10.1021/ac503092d.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    de Jong FA, Beecher C. Addressing the current bottlenecks of metabolomics: Isotopic Ratio Outlier Analysis™, an isotopic-labeling technique for accurate biochemical profiling. Bioanalysis. 2012;4(18):2303–14. https://doi.org/10.4155/bio.12.202.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Patterson RE, Kirpich AS, Koelmel JP, Kalavalapalli S, Morse AM, Cusi K, et al. Improved experimental data processing for UHPLC–HRMS/MS lipidomics applied to nonalcoholic fatty liver disease. Metabolomics. 2017;13(11):142. https://doi.org/10.1007/s11306-017-1280-1.

    CAS  Article  Google Scholar 

  49. 49.

    Flikka K, Martens L, Vandekerckhove J, Gevaert K, Eidhammer I. Improving the reliability and throughput of mass spectrometry-based proteomics by spectrum quality filtering. Proteomics. 2006;6(7):2086–94. https://doi.org/10.1002/pmic.200500309.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Braun JM, Gennings C, Hauser R, Webster TF. What can epidemiological studies tell us about the impact of chemical mixtures on human health? Environ Health Perspect. 2016;124(1):A6–9. https://doi.org/10.1289/ehp.1510569.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Hamra GB, Buckley JP. Environmental exposure mixtures: questions and methods to address them. Curr Epidemiol Rep. 2018;5(2):160–5. https://doi.org/10.1007/s40471-018-0145-0.

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Taylor KW, Joubert BR, Braun JM, Dilworth C, Gennings C, Hauser R, et al. Statistical approaches for assessing health effects of environmental chemical mixtures in epidemiology: lessons from an innovative workshop. Environ Health Perspect. 2016;124(12):A227–A9. https://doi.org/10.1289/EHP547.

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Weisskopf MG, Seals RM, Webster TF. Bias amplification in epidemiologic analysis of exposure to mixtures. Environ Health Perspect. 2018;126(4):047003. https://doi.org/10.1289/EHP2450.

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Lazarevic N, Barnett AG, Sly PD, Knibbs LD. Statistical methodology in studies of prenatal exposure to mixtures of endocrine-disrupting chemicals: a review of existing approaches and new alternatives. Environ Health Perspect. 2019;127(2):26001. https://doi.org/10.1289/EHP2207.

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Barrera-Gómez J, Agier L, Portengen L, Chadeau-Hyam M, Giorgis-Allemand L, Siroux V, et al. A systematic comparison of statistical methods to detect interactions in exposome-health associations. Environ Health. 2017;16(1):74. https://doi.org/10.1186/s12940-017-0277-6.

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Stafoggia M, Breitner S, Hampel R, Basagaña X. Statistical approaches to address multi-pollutant mixtures and multiple exposures: the state of the science. Curr Environ Health Rep. 2017;4(4):481–90. https://doi.org/10.1007/s40572-017-0162-z.

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Morgenstern H, Thomas D. Principles of study design in environmental epidemiology. Environ Health Perspect. 1993;101 Suppl 4(Suppl 4):23–38. https://doi.org/10.1289/ehp.93101s423.

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Romano ME, Kalloo G, Etzel T, Braun JM. Seasonal variation in exposure to endocrine-disrupting chemicals. Epidemiology. 2017;28(5):e42–e3. https://doi.org/10.1097/EDE.0000000000000696.

    Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Calafat AM, Ye X, Silva MJ, Kuklenyik Z, Needham LL. Human exposure assessment to environmental chemicals using biomonitoring. Int J Androl. 2006;29(1):166–71. https://doi.org/10.1111/j.1365-2605.2005.00570.x.

    CAS  Article  PubMed  Google Scholar 

  60. 60.

    Klaassen CD. Casarett & Doull's toxicology: the basic science of poisons. Ninth ed. McGraw-Hill Education: New York; 2019.

    Google Scholar 

  61. 61.

    Bois FY, Jamei M, Clewell HJ. PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals. Toxicology. 2010;278(3):256–67. https://doi.org/10.1016/j.tox.2010.06.007.

    CAS  Article  PubMed  Google Scholar 

  62. 62.

    Weisskopf MG, Webster TF. Trade-offs of personal versus more proxy exposure measures in environmental epidemiology. Epidemiology. 2017;28(5):635–43. https://doi.org/10.1097/EDE.0000000000000686.

    Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Ramirez-Andreotta MD, Brody JG, Lothrop N, Loh M, Beamer PI, Brown P. Reporting back environmental exposure data and free choice learning. Environ Health. 2016;15(1):2. https://doi.org/10.1186/s12940-015-0080-1.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Tomsho KS, Schollaert C, Aguilar T, Bongiovanni R, Alvarez M, Scammell MK, et al. A mixed methods evaluation of sharing air pollution results with study participants via report-back communication. Int J Environ Res Public Health. 2019;16(21):4183. https://doi.org/10.3390/ijerph16214183.

    Article  PubMed Central  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Krystal J. Godri Pollitt.

Ethics declarations

Conflict of Interest

The authors report no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Susceptibility Factors in Environmental Health

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Doherty, B.T., Koelmel, J.P., Lin, E.Z. et al. Use of Exposomic Methods Incorporating Sensors in Environmental Epidemiology. Curr Envir Health Rpt (2021). https://doi.org/10.1007/s40572-021-00306-8

Download citation

Keywords

  • Exposome
  • Environmental health
  • Contaminants
  • Wristbands
  • Wearable sensors
  • Exposure assessment