Skip to content

Environmental Health Perspectives

Facebook Page EHP Twitter Feed Open Access icon  

Email this to someoneShare on FacebookTweet about this on TwitterShare on LinkedInShare on Google+Share on StumbleUpon

Exposure Science and the Exposome: An Opportunity for Coherence in the Environmental Health Sciences

[do action=”authors”]Paul J. Lioy, Stephen M. Rappaport[/do][do action=”affiliations”]Environmental and Occupational Health Sciences Institute, Robert Wood Johnson Medical School–UMDNJ and Rutgers University, Piscataway, New Jersey, School of Public Health, University of California, Berkeley, California, E-mail:[/do][do action=”citation-string”]Environ Health Perspect 119:a466-a467 (2011). [online 01 November 2011] [/do]


[do action=”notes”]The authors declare they have no actual or potential competing financial interests.[/do] [do action=”notes”] Paul J. Lioy is a professor of environmental and occupational medicine and director of exposure science at Robert Wood Johnson Medical School–UMDNJ and Deputy Director for Government Relations at the Environmental and Occupational Health Sciences Institute, Piscataway, New Jersey. He was the 1998 recipient of the Wesolowski Award for Lifetime Achievements in Exposure Science, and is a Distinguished Alumnus in Science, Mathematics and Engineering from the Graduate School of Rutgers University. Currently, he is an associate editor of Environmental Health Perspectives, and the vice chair of the National Research Council Committee on Exposure Science in the 21st Century. He is also a co-principal investigator on the Mount Sinai School of Medicine, National Children’s Study (NCS) New York–Northern New Jersey Study Center. [/do]

[do action=”notes”] Stephen Rappaport is a professor of environmental health at the School of Public Health, University of California, Berkeley, and directs the Berkeley Center for Exposure Biology, a multidisciplinary program to develop a new generation of biomarkers and biosensors for environmental epidemiology. He is a pioneer in the emerging field of exposure biology and a prominent advocate of the concept of the exposome for environmental health. Much of his current research involves the development and application of blood protein adducts as biomarkers of exposure to toxic chemicals arising from inhalation, ingestion, and endogenous processes. He was the 2010 recipient of the Weslowski Award for Lifetime Achievements in Exposure Science. [/do]

The field of exposure science began with qualitative observations and quantitative measurements of air contaminants to aid our understanding of exposure–disease relationships. In fact, some of the earliest writings that describe the essence of exposure science are found in Bernardino Ramazzini’s 1700 treatise on occupational diseases (Franco 1999). In the 1920s, exposure scientists collaborated with epidemiologists to investigate workplace exposures as sources of occupational diseases (Rappaport 2011). Between the 1950s and 1970, investigations expanded to include exposures to pollutants in ambient and indoor air and water (Rappaport 2011). Following establishment of U.S. governmental agencies in the 1970s to regulate exposures in the workplace (Occupational Safety and Health Administration) and the ambient environment [U.S. Environmental Protection Agency (EPA)], the paths of exposure scientists diverged into those investigating sources of pollutants in occupational settings and those investigating ambient sources of pollutants (Rappaport 2011). By the 1990s the two groups had essentially parted ways, and the term “exposure science” was associated with community and personal exposures to ambient pollutants (Lioy 2010; Ott 1990, 1995). Investigations of total personal exposure initially employed external measurements of chemicals that can enter the body by inhalation, ingestion, and dermal contact (1970s), and internal markers of exposure were added in the 1980s and 1990s (Centers for Disease Control and Prevention 2009; Hoffmann et al. 2000; Sexton et al. 1995; Wallace et al. 1985). In the 21st century, exposure science has increasingly embraced deterministic models to predict levels of diverse exposures based on categorical data (Cohen Hubal et al. 2010; Georgopoulos and Lioy 2006; Lioy 2010) and on measured levels of pollutants in biological fluids and tissues (Georgopoulos et al. 2009).

In parallel with the above activities, during the 1980s and 1990s, molecular epidemiologists explored links between genetic and environmental factors and the resulting biochemical or biological indicators of possible ill health (biomarkers) measured in individual subjects (Bonassi and Au 2002). When completion of the human genome project in 2000 made it feasible to measure thousands of polymorphic genes in each subject, epidemiology increasingly focused on the genetic determinants of diseases (Hindorff et al. 2009). However, as results of these genome-wide association studies (GWAS) failed to explain most variability in human diseases (Manolio et al. 2009), interest in environmental factors reemerged. But there was no environmental analog of GWAS; that is, we had no way of characterizing the totality of a person’s environmental exposures. This prompted Christopher Wild to publish a commentary that defined the “exposome” as the environmental complement to the genome (Wild 2005). Recognizing that humans are exposed to health-impairing agents from both pollution and nonpollution sources and that these sources change during a lifetime, Wild indicated that “… the exposome encompasses life-course environmental exposures (including lifestyle factors) from the prenatal period onwards.” This is a powerful idea because it considers a person’s lifetime history of all exposures experienced from both external sources (e.g. pollution, radiation, and diet) and internal sources (e.g. inflammation, infection, and the microbiome) (Rappaport and Smith 2010). Thus, one can imagine a future in which individuals’ exposomes are contrasted between diseased and healthy populations for molecular epidemiology, or over different life stages as part of personalized medicine (Nicholson 2006). In either case, the goal would be to discover causes of ill health and to generate hypotheses regarding identification and elimination or reduction of harmful exposures.

If the exposome concept is to be useful to exposure science, methods will be needed to characterize individual exposomes and to investigate sources of exposome variability. Because exposures arise from diverse sources, Rappaport defined two generic approaches for characterizing exposomes (Rappaport 2011; Rappaport and Smith 2010). A “bottom-up” approach would focus on each category of external exposure—including air, water, diet, radiation, lifestyle, etc.—to quantify contaminant levels that would be summed over all categories to estimate individual exposomes. This approach is appealing to some exposure scientists because it focuses on the same external media that have long been investigated and leads logically to interventions for eliminating or reducing exposures. However, this bottom-up approach would require tremendous effort to evaluate the myriad of largely unknown analytes in various external media and would also miss important endogenous exposures. The alternative “top-down” approach would adopt untargeted omic methods to measure features of exposures in biological fluids, and thus finds appeal with exposure scientists who have used biomonitoring for assessing exposure levels, albeit on a chemical-by-chemical basis. This approach is more efficient because both exogenous and endogenous exposures would be represented by a single specimen of blood, for example, and would encourage contrasts of omic profiles between diseased and healthy populations in much the same manner as GWAS (Patel et al. 2010). Omic profiles would generate hypotheses to a) indentify particular exposures, b) develop specific biomarkers for high-throughput screens, and c) determine sources of external and internal exposure. Recent untargeted metabolomic studies have applied this top-down approach to identify hitherto unknown exposures associated with cardiovascular disease (Holmes et al. 2008; Wang et al. 2011).

When examined objectively, there is scientific value in both the bottom-up and top-down approaches for characterizing individual exposomes. The top-down approach offers appeal for discovering unknown causes of human disease (Rappaport 2011; Rappaport and Smith 2010), whereas the bottom-up approach encourages more comprehensive analyses of external exposures and methods for intervention and prevention (Lioy 2010). Indeed, we envision long-term strategies that embrace elements of both approaches for improving public health. Unfortunately, the differentiation between external (air, water, soil/dust, etc.) and internal (biological fluids) media has led to an apparent disconnect or competition between exposure scientists who focus on external monitoring and modeling and those who favor biomonitoring and omic methods. Indeed, we are encountering a view that can be summarized as “exposure science versus the exposome.” This is counterproductive because it potentially deprives exposure science of avenues for vastly diversifying its pool of relevant exposures and for strengthening the source-to-dose framework needed by the environmental health sciences. Rather than adopting defensive postures, we encourage exposure scientists to exploit the relative strengths of both monitoring approaches for assessing human exposures. Toward this end, the National Academy of Sciences will convene a workshop in December 2011 to better integrate the top-down and bottom-up approaches for characterizing individual exposomes (Emerging Technologies for Measuring Individual Exposomes, 8–9 December 2011, Washington, DC; information is available at

Other recent developments offer opportunities for exposure scientists to characterize individual exposomes. For example, the National Children’s Study (Landrigan et al. 2006) offers an evolving platform with which to link the top-down and bottom-up approaches. Because individual data and biospecimens will be collected during the first 21 years of life, this study will provide resources that can be used to evaluate the variability of exposome features during critical life stages. Moreover, the extensive questionnaire data, home samples, extant environmental data, and dietary histories of participants suggest avenues for modeling connections between the internal and external environments. Such prospective cohort studies will allow us to collect more and better exposure data with which to identify unknown health hazards and to develop appropriate preventive measures and regulations for recognized hazards. The exposome concept can play a key role in both endeavors.

Attached Files

PDF Version


  1. Bonassi S, Au WW. 2002. Biomarkers in molecular epidemiology studies for health risk prediction. Mutat Res 511(1):73–86.
  2. Centers for Disease Control and Prevention 2009. Fourth National Report on Human Exposure to Environmental Chemicals. Atlanta, GA:National Center for Environmental Health, Centers for Disease Control and Prevention.
  3. Cohen Hubal EA, Richard AM, Shah I, Gallagher J, Kavlock R, Blancato J, et al. 2010. Exposure science and the U.S. EPA National Center for Computational Toxicology. J Expo Sci Environ Epidemiol 20(3):231–236.
  4. Franco G.. 1999. Ramazzini and workers’ health. Lancet 354(9181):858–861.
  5. Georgopoulos PG, Lioy PJ. 2006. From a theoretical framework of human exposure and dose assessment to computational system implementatino: the Modeling Environment for Total Risk Studies (MENTOR). J Toxicol Environ Health B Crit Rev 9(6):457–483.
  6. Georgopoulos PG, Sasso AF, Isukapalli SS, Lioy PJ, Vallero DA, Okino M, et al. 2009. Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities. J Expo Sci Environ Epidemiol 19(2):149–171.
  7. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. 2009. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106(23):9362–9367.
  8. Hoffmann K, Becker K, Friedrich C, Helm D, Krause C, Seifert B.. 2000. The German Environmental Survey 1990/1992 (GerES II): cadmium in blood, urine and hair of adults and children. J Expo Anal Environ Epidemiol 10(2):126–135.
  9. Holmes E, Loo RL, Stamler J, Bictash M, Yap IK, Chan Q, et al. 2008. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453(7193):396–400.
  10. Landrigan PJ, Trasande L, Thorpe LE, Gwynn C, Lioy PJ, D’Alton ME, et al. 2006. The National Children’s Study: a 21-year prospective study of 100,000 American children. Pediatrics 118(5):2173–2186.
  11. Lioy PJ. 2010. Exposure science: a view of the past and milestones for the future. Environ Health Perspect 118:1081–1090.
  12. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. 2009. Finding the missing heritability of complex diseases. Nature 461(7265):747–753.
  13. Nicholson JK. 2006. Global systems biology, personalized medicine and molecular epidemiology. Mol Syst Biol 2:52.; doi:10.1038/msb4100095 [3 October 2006]
  14. Ott WR. 1990. Total human exposure: basic concepts, EPA field studies, and future research needs. J Air Waste Manage Assoc 40(7):966–975.
  15. Ott WR. 1995. Human exposure assessment: the birth of a new science. J Expo Anal Environ Epidemiol 5(4):449–472.
  16. Patel CJ, Bhattacharya J, Butte AJ. 2010. An Environment-Wide Association study (EWAS) on type 2 diabetes mellitus. PLoS One 5(5):e10746.; doi:10.1371/journal.pone.0010746 [Online 20 May 2010]
  17. Rappaport SM. 2011. Implications of the exposome for exposure science. J Expo Sci Environ Epidemiol 21(1):5–9.
  18. Rappaport SM, Smith MT. 2010. Environment and disease risks. Science 330(6003):460–461.
  19. Sexton K, Kleffman DE, Callahan MA. 1995. An introduction to the National Human Exposure Assessment Survey (NHEXAS) and related phase I field studies. J Expo Anal Environ Epidemiol 5(3):229–232.
  20. Wallace LA, Pellizzari ED, Hartwell TD, Sparacino CM, Sheldon LS, Zelon H. 1985. Personal exposures, indoor-outdoor relationships, and breath levels of toxic air pollutants measured for 355 persons in New Jersey. Atmos Environ 19(10):1651–1661.
  21. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, et al. 2011. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472(7341):57–63.
  22. Wild CP. 2005. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev 14(8):1847–1850.

WP-Backgrounds Lite by InoPlugs Web Design and Juwelier Schönmann 1010 Wien