Tackling the Complexity of the Exposome: Considerations from the Gunma University Initiative for Advanced Research (GIAR) Exposome Symposium
Abstract
:1. Introduction
2. What is the Exposome?
3. How to Perform an Exposome Study?
3.1. Study Design
3.2. Which Exposures to Measure
3.3. Sample Collection and Management
3.4. Exposome Data Analysis
4. Future Perspectives
5. Concluding Remarks
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Willett, W.C. Balancing life-style and genomics research for disease prevention. Science 2002, 296, 695–698. [Google Scholar] [CrossRef] [PubMed]
- Lim, S.S.; Vos, T.; Flaxman, A.D.; Danaei, G.; Shibuya, K.; Adair-Rohani, H.; Amann, M.; Anderson, H.R.; Andrews, K.G.; Aryee, M.; et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012, 380, 2224–2260. [Google Scholar] [CrossRef]
- Rappaport, S.M. Genetic Factors Are Not the Major Causes of Chronic Diseases. PLoS ONE 2016, 11, e0154387. [Google Scholar] [CrossRef]
- Rappaport, S.M.; Barupal, D.K.; Wishart, D.; Vineis, P.; Scalbert, A. The blood exposome and its role in discovering causes of disease. Environ. Health Perspect. 2014, 122, 769–774. [Google Scholar] [CrossRef]
- Wild, C.P. The exposome: From concept to utility. Int. J. Epidemiol. 2012, 41, 24–32. [Google Scholar] [CrossRef] [PubMed]
- Robinson, O.; Basagana, X.; Agier, L.; de Castro, M.; Hernandez-Ferrer, C.; Gonzalez, J.R.; Grimalt, J.O.; Nieuwenhuijsen, M.; Sunyer, J.; Slama, R.; et al. The Pregnancy Exposome: Multiple Environmental Exposures in the INMA-Sabadell Birth Cohort. Environ. Sci. Technol. 2015, 49, 10632–10641. [Google Scholar] [CrossRef] [Green Version]
- Turner, M.C.; Vineis, P.; Seleiro, E.; Dijmarescu, M.; Balshaw, D.; Bertollini, R.; Chadeau-Hyam, M.; Gant, T.; Gulliver, J.; Jeong, A.; et al. EXPOsOMICS: Final policy workshop and stakeholder consultation. BMC Public Health 2018, 18, 260. [Google Scholar] [CrossRef]
- Vineis, P.; Chadeau-Hyam, M.; Gmuender, H.; Gulliver, J.; Herceg, Z.; Kleinjans, J.; Kogevinas, M.; Kyrtopoulos, S.; Nieuwenhuijsen, M.; Phillips, D.H.; et al. The exposome in practice: Design of the EXPOsOMICS project. Int. J. Hyg. Environ. Health 2017, 220, 142–151. [Google Scholar] [CrossRef] [PubMed]
- Steckling, N.; Gotti, A.; Bose-O’Reilly, S.; Chapizanis, D.; Costopoulou, D.; De Vocht, F.; Gari, M.; Grimalt, J.O.; Heath, E.; Hiscock, R.; et al. Biomarkers of exposure in environment-wide association studies—Opportunities to decode the exposome using human biomonitoring data. Environ. Res. 2018, 164, 597–624. [Google Scholar] [CrossRef]
- Vrijheid, M.; Slama, R.; Robinson, O.; Chatzi, L.; Coen, M.; van den Hazel, P.; Thomsen, C.; Wright, J.; Athersuch, T.J.; Avellana, N.; et al. The human early-life exposome (HELIX): Project rationale and design. Environ. Health Perspect. 2014, 122, 535–544. [Google Scholar] [CrossRef]
- Balshaw, D.M.; Collman, G.W.; Gray, K.A.; Thompson, C.L. The Children’s Health Exposure Analysis Resource: Enabling research into the environmental influences on children’s health outcomes. Curr. Opin. Pediatr. 2017, 29, 385–389. [Google Scholar] [CrossRef] [PubMed]
- Kawamoto, T.; Nitta, H.; Murata, K.; Toda, E.; Tsukamoto, N.; Hasegawa, M.; Yamagata, Z.; Kayama, F.; Kishi, R.; Ohya, Y.; et al. Rationale and study design of the Japan environment and children’s study (JECS). BMC Public Health 2014, 14, 25. [Google Scholar] [CrossRef] [PubMed]
- Rappaport, S.M. Implications of the exposome for exposure science. J. Expos. Sci. Environ. Epidemiol. 2011, 21, 5–9. [Google Scholar] [CrossRef] [PubMed]
- Wild, C.P. Complementing the genome with an “exposome”: The outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomark. Prev. 2005, 14, 1847–1850. [Google Scholar] [CrossRef] [PubMed]
- Vrijheid, M. The exposome: A new paradigm to study the impact of environment on health. Thorax 2014, 69, 876–878. [Google Scholar] [CrossRef] [PubMed]
- Ussar, S.; Griffin, N.W.; Bezy, O.; Fujisaka, S.; Vienberg, S.; Softic, S.; Deng, L.; Bry, L.; Gordon, J.I.; Kahn, C.R. Interactions between Gut Microbiota, Host Genetics and Diet Modulate the Predisposition to Obesity and Metabolic Syndrome. Cell Metab. 2015, 22, 516–530. [Google Scholar] [CrossRef] [Green Version]
- Singh, A.; Zapata, R.C.; Pezeshki, A.; Workentine, M.L.; Chelikani, P.K. Host genetics and diet composition interact to modulate gut microbiota and predisposition to metabolic syndrome in spontaneously hypertensive stroke-prone rats. FASEB J. 2019. [Google Scholar] [CrossRef]
- Wild, C.P.; Scalbert, A.; Herceg, Z. Measuring the exposome: A powerful basis for evaluating environmental exposures and cancer risk. Environ. Mol. Mutagenesis 2013, 54, 480–499. [Google Scholar] [CrossRef]
- Maitre, L.; de Bont, J.; Casas, M.; Robinson, O.; Aasvang, G.M.; Agier, L.; Andrusaityte, S.; Ballester, F.; Basagana, X.; Borras, E.; et al. Human Early Life Exposome (HELIX) study: A European population-based exposome cohort. BMJ Open 2018, 8, e021311. [Google Scholar] [CrossRef]
- Cooke, M.S.; Hu, C.W.; Chang, Y.J.; Chao, M.R. Urinary DNA adductomics—A novel approach for exposomics. Environ. Int. 2018, 121, 1033–1038. [Google Scholar] [CrossRef]
- Lu, S.S.; Grigoryan, H.; Edmands, W.M.; Hu, W.; Iavarone, A.T.; Hubbard, A.; Rothman, N.; Vermeulen, R.; Lan, Q.; Rappaport, S.M. Profiling the Serum Albumin Cys34 Adductome of Solid Fuel Users in Xuanwei and Fuyuan, China. Environ. Sci. Technol. 2017, 51, 46–57. [Google Scholar] [CrossRef] [PubMed]
- Subramanian, A.; Khatri, S.B. The Exposome and Asthma. Clin. Chest Med. 2019, 40, 107–123. [Google Scholar] [CrossRef] [PubMed]
- Shaffer, R.M.; Smith, M.N.; Faustman, E.M. Developing the Regulatory Utility of the Exposome: Mapping Exposures for Risk Assessment through Lifestage Exposome Snapshots (LEnS). Environ. Health Perspect. 2017, 125, 085003. [Google Scholar] [CrossRef] [PubMed]
- Jones, D.P. Sequencing the exposome: A call to action. Toxicol. Rep. 2016, 3, 29–45. [Google Scholar] [CrossRef] [PubMed]
- Chio, A.; Logroscino, G.; Traynor, B.J.; Collins, J.; Simeone, J.C.; Goldstein, L.A.; White, L.A. Global epidemiology of amyotrophic lateral sclerosis: A systematic review of the published literature. Neuroepidemiology 2013, 41, 118–130. [Google Scholar] [CrossRef] [PubMed]
- Arora, M.; Austin, C.; Sarrafpour, B.; Hernandez-Avila, M.; Hu, H.; Wright, R.O.; Tellez-Rojo, M.M. Determining prenatal, early childhood and cumulative long-term lead exposure using micro-spatial deciduous dentine levels. PLoS ONE 2014, 9, e97805. [Google Scholar] [CrossRef] [PubMed]
- Velthorst, E.; Smith, L.; Bello, G.; Austin, C.; Gennings, C.; Modabbernia, A.; Franke, N.; Frangou, S.; Wright, R.; de Haan, L.; et al. New Research Strategy for Measuring Pre- and Postnatal Metal Dysregulation in Psychotic Disorders. Schizophr. Bull. 2017, 43, 1153–1157. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sorek-Hamer, M.; Just, A.C.; Kloog, I. Satellite remote sensing in epidemiological studies. Curr. Opin. Pediatr. 2016, 28, 228–234. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rosenfeld, A.; Dorman, M.; Schwartz, J.; Novack, V.; Just, A.C.; Kloog, I. Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel. Environ. Res. 2017, 159, 297–312. [Google Scholar] [CrossRef]
- Bose, S.; Ross, K.R.; Rosa, M.J.; Chiu, Y.M.; Just, A.; Kloog, I.; Wilson, A.; Thompson, J.; Svensson, K.; Rojo, M.M.T.; et al. Prenatal particulate air pollution exposure and sleep disruption in preschoolers: Windows of susceptibility. Environ. Int. 2019, 124, 329–335. [Google Scholar] [CrossRef] [PubMed]
- Manrai, A.K.; Ioannidis, J.P.A.; Patel, C.J. Signals Among Signals: Prioritizing Non-genetic Associations in Massive Datasets. Am. J. Epidemiol. 2019. [Google Scholar] [CrossRef] [PubMed]
- Le Goallec, A.; Patel, C.J. Age-dependent co-dependency structure of biomarkers in the general population of the United States. Aging 2019, 11, 1404–1426. [Google Scholar] [CrossRef] [PubMed]
- Lakhani, C.M.; Tierney, B.T.; Manrai, A.K.; Yang, J.; Visscher, P.M.; Patel, C.J. Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes. Nat. Genet. 2019, 51, 327–334. [Google Scholar] [CrossRef] [PubMed]
- O’Connell, S.G.; Kincl, L.D.; Anderson, K.A. Silicone wristbands as personal passive samplers. Environ. Sci. Technol. 2014, 48, 3327–3335. [Google Scholar] [CrossRef] [PubMed]
- Dixon, H.M.; Scott, R.P.; Holmes, D.; Calero, L.; Kincl, L.D.; Waters, K.M.; Camann, D.E.; Calafat, A.M.; Herbstman, J.B.; Anderson, K.A. Silicone wristbands compared with traditional polycyclic aromatic hydrocarbon exposure assessment methods. Anal. Bioanal. Chem. 2018, 410, 3059–3071. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jiang, C.; Wang, X.; Li, X.; Inlora, J.; Wang, T.; Liu, Q.; Snyder, M. Dynamic Human Environmental Exposome Revealed by Longitudinal Personal Monitoring. Cell 2018, 175, 277–291.e31. [Google Scholar] [CrossRef] [Green Version]
- Epel, E.S.; Blackburn, E.H.; Lin, J.; Dhabhar, F.S.; Adler, N.E.; Morrow, J.D.; Cawthon, R.M. Accelerated telomere shortening in response to life stress. Proc. Natl. Acad. Sci. USA 2004, 101, 17312–17315. [Google Scholar] [CrossRef] [Green Version]
- Rappaport, S.M. Biomarkers intersect with the exposome. Biomarkers 2012, 17, 483–489. [Google Scholar] [CrossRef]
- Dennis, K.K.; Marder, E.; Balshaw, D.M.; Cui, Y.; Lynes, M.A.; Patti, G.J.; Rappaport, S.M.; Shaughnessy, D.T.; Vrijheid, M.; Barr, D.B. Biomonitoring in the Era of the Exposome. Environ. Health Perspect. 2017, 125, 502–510. [Google Scholar] [CrossRef]
- Bessonneau, V.; Pawliszyn, J.; Rappaport, S.M. The Saliva Exposome for Monitoring of Individuals’ Health Trajectories. Environ. Health Perspect. 2017, 125, 077014. [Google Scholar] [CrossRef]
- Andra, S.S.; Austin, C.; Arora, M. The tooth exposome in children’s health research. Curr. Opin. Pediatr. 2016, 28, 221–227. [Google Scholar] [CrossRef] [PubMed]
- Andra, S.S.; Austin, C.; Wright, R.O.; Arora, M. Reconstructing pre-natal and early childhood exposure to multi-class organic chemicals using teeth: Towards a retrospective temporal exposome. Environ. Int. 2015, 83, 137–145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Arora, M.; Austin, C. Teeth as a biomarker of past chemical exposure. Curr. Opin. Pediatr. 2013, 25, 261–267. [Google Scholar] [CrossRef] [PubMed]
- Arora, M.; Reichenberg, A.; Willfors, C.; Austin, C.; Gennings, C.; Berggren, S.; Lichtenstein, P.; Anckarsater, H.; Tammimies, K.; Bolte, S. Fetal and postnatal metal dysregulation in autism. Nat. Commun. 2017, 8, 15493. [Google Scholar] [CrossRef] [PubMed]
- Cuhadar, S.; Koseoglu, M.; Atay, A.; Dirican, A. The effect of storage time and freeze-thaw cycles on the stability of serum samples. Biochem. Med. 2013, 23, 70–77. [Google Scholar] [CrossRef]
- Wheelock, A.M.; Paulson, L.; Litton, J.E.; EuPA Biobank Initiative Group. The EuPA Biobank Initiative: Meeting the future challenges of biobanking in proteomics & systems medicine. J. Proteom. 2015, 127, 414–416. [Google Scholar] [CrossRef]
- Liu, X.; Hoene, M.; Yin, P.; Fritsche, L.; Plomgaard, P.; Hansen, J.S.; Nakas, C.T.; Niess, A.M.; Hudemann, J.; Haap, M.; et al. Quality Control of Serum and Plasma by Quantification of (4E,14Z)-Sphingadienine-C18-1-Phosphate Uncovers Common Preanalytical Errors During Handling of Whole Blood. Clin. Chem. 2018, 64, 810–819. [Google Scholar] [CrossRef] [Green Version]
- Patel, C.J. Analytic Complexity and Challenges in Identifying Mixtures of Exposures Associated with Phenotypes in the Exposome Era. Curr. Epidemiol. Rep. 2017, 4, 22–30. [Google Scholar] [CrossRef] [Green Version]
- Patel, C.J.; Bhattacharya, J.; Butte, A.J. An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS ONE 2010, 5, e10746. [Google Scholar] [CrossRef]
- Dunn, E.C.; Wiste, A.; Radmanesh, F.; Almli, L.M.; Gogarten, S.M.; Sofer, T.; Faul, J.D.; Kardia, S.L.; Smith, J.A.; Weir, D.R.; et al. Genome-Wide Association Study (Gwas) and Genome-Wide by Environment Interaction Study (Gweis) of Depressive Symptoms in African American and Hispanic/Latina Women. Depress. Anxiety 2016, 33, 265–280. [Google Scholar] [CrossRef]
- Gref, A.; Merid, S.K.; Gruzieva, O.; Ballereau, S.; Becker, A.; Bellander, T.; Bergstrom, A.; Bosse, Y.; Bottai, M.; Chan-Yeung, M.; et al. Genome-Wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up. Am. J. Respir. Crit. Care Med. 2017, 195, 1373–1383. [Google Scholar] [CrossRef] [PubMed]
- Zeng, X.; Vonk, J.M.; van der Plaat, D.A.; Faiz, A.; Pare, P.D.; Joubert, P.; Nickle, D.; Brandsma, C.A.; Kromhout, H.; Vermeulen, R.; et al. Genome-wide interaction study of gene-by-occupational exposures on respiratory symptoms. Environ. Int. 2019, 122, 263–269. [Google Scholar] [CrossRef] [PubMed]
- Bentley, A.R.; Sung, Y.J.; Brown, M.R.; Winkler, T.W.; Kraja, A.T.; Ntalla, I.; Schwander, K.; Chasman, D.I.; Lim, E.; Deng, X.; et al. Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat. Genet. 2019, 51, 636–648. [Google Scholar] [CrossRef] [PubMed]
- Joubert, B.R.; Felix, J.F.; Yousefi, P.; Bakulski, K.M.; Just, A.C.; Breton, C.; Reese, S.E.; Markunas, C.A.; Richmond, R.C.; Xu, C.J.; et al. DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis. Am. J. Hum. Genet. 2016, 98, 680–696. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gruzieva, O.; Xu, C.J.; Breton, C.V.; Annesi-Maesano, I.; Anto, J.M.; Auffray, C.; Ballereau, S.; Bellander, T.; Bousquet, J.; Bustamante, M.; et al. Epigenome-Wide Meta-Analysis of Methylation in Children Related to Prenatal NO2 Air Pollution Exposure. Environ. Health Perspect. 2017, 125, 104–110. [Google Scholar] [CrossRef] [PubMed]
- Neveu, V.; Moussy, A.; Rouaix, H.; Wedekind, R.; Pon, A.; Knox, C.; Wishart, D.S.; Scalbert, A. Exposome-Explorer: A manually-curated database on biomarkers of exposure to dietary and environmental factors. Nucleic Acids Res. 2017, 45, D979–D984. [Google Scholar] [CrossRef] [PubMed]
- Faisandier, L.; Bonneterre, V.; De Gaudemaris, R.; Bicout, D.J. Occupational exposome: A network-based approach for characterizing Occupational Health Problems. J. Biomed. Inform. 2011, 44, 545–552. [Google Scholar] [CrossRef] [Green Version]
- Wishart, D.; Arndt, D.; Pon, A.; Sajed, T.; Guo, A.C.; Djoumbou, Y.; Knox, C.; Wilson, M.; Liang, Y.; Grant, J.; et al. T3DB: The toxic exposome database. Nucleic Acids Res. 2015, 43, D928–D934. [Google Scholar] [CrossRef]
- Warth, B.; Spangler, S.; Fang, M.; Johnson, C.H.; Forsberg, E.M.; Granados, A.; Martin, R.L.; Domingo-Almenara, X.; Huan, T.; Rinehart, D.; et al. Exposome-Scale Investigations Guided by Global Metabolomics, Pathway Analysis, and Cognitive Computing. Anal. Chem. 2017, 89, 11505–11513. [Google Scholar] [CrossRef]
- Walker, D.I.; Uppal, K.; Zhang, L.; Vermeulen, R.; Smith, M.; Hu, W.; Purdue, M.P.; Tang, X.; Reiss, B.; Kim, S.; et al. High-resolution metabolomics of occupational exposure to trichloroethylene. Int. J. Epidemiol. 2016, 45, 1517–1527. [Google Scholar] [CrossRef] [Green Version]
- Carlsten, C.; Blomberg, A.; Pui, M.; Sandstrom, T.; Wong, S.W.; Alexis, N.; Hirota, J. Diesel exhaust augments allergen-induced lower airway inflammation in allergic individuals: A controlled human exposure study. Thorax 2016, 71, 35–44. [Google Scholar] [CrossRef] [PubMed]
- Mookherjee, N.; Piyadasa, H.; Ryu, M.H.; Rider, C.F.; Ezzati, P.; Spicer, V.; Carlsten, C. Inhaled diesel exhaust alters the allergen-induced bronchial secretome in humans. Eur. Respir. J. 2018, 51. [Google Scholar] [CrossRef] [PubMed]
- Brook, J.R.; Setton, E.M.; Seed, E.; Shooshtari, M.; Doiron, D.; CANUE—The Canadian Urban Environmental Health Research Consortium. The Canadian Urban Environmental Health Research Consortium—A protocol for building a national environmental exposure data platform for integrated analyses of urban form and health. BMC Public Health 2018, 18, 114. [Google Scholar] [CrossRef] [PubMed]
- David, A.; Lange, A.; Abdul-Sada, A.; Tyler, C.R.; Hill, E.M. Disruption of the Prostaglandin Metabolome and Characterization of the Pharmaceutical Exposome in Fish Exposed to Wastewater Treatment Works Effluent As Revealed by Nanoflow-Nanospray Mass Spectrometry-Based Metabolomics. Environ. Sci. Technol. 2017, 51, 616–624. [Google Scholar] [CrossRef] [PubMed]
Exposure Group | Exposure |
---|---|
External | |
- Meteorology | Climate change, temperature, humidity, wind, atmospheric pressure |
- Outdoor exposures | NO2, SO2, CO, O3, VOCs, PM, radiation, UV, traffic, pollen |
- Built environment | Population density, building density, facilities, green space, walkability, neighborhood safety, accessibility to resources (e.g., hospitals, bus stations), noise |
- Home environment | VOCs, PM, NO2, CO, aldehydes, metals, plasticizers, dust, pets, pests, allergen (e.g., house dust mites), mold, fungi, microbes, endotoxin |
- Personal behavior | Diets, physical activity, tobacco smoke, alcohol, drugs, sleep, sex, cosmetics |
- Social economic factors | Social factors, education, economy, psychological and mental stress |
- Food and water contaminants | Fertilizers, metals, pesticides, plasticizers, DBPs, PCBs, flame retardants, PFASs |
- Medications | Medicines, surgeries |
- Occupational exposures | Chemicals, dust, metals, virus, animal proteins, plants, heat/cold stress |
Internal | |
Primary external exposures and associated metabolites, epigenetic (e.g., methylations, histone modifications), microbiome/metabolome/proteome/transcriptome/genome changes, etc. |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, P.; Arora, M.; Chaleckis, R.; Isobe, T.; Jain, M.; Meister, I.; Melén, E.; Perzanowski, M.; Torta, F.; Wenk, M.R.; et al. Tackling the Complexity of the Exposome: Considerations from the Gunma University Initiative for Advanced Research (GIAR) Exposome Symposium. Metabolites 2019, 9, 106. https://doi.org/10.3390/metabo9060106
Zhang P, Arora M, Chaleckis R, Isobe T, Jain M, Meister I, Melén E, Perzanowski M, Torta F, Wenk MR, et al. Tackling the Complexity of the Exposome: Considerations from the Gunma University Initiative for Advanced Research (GIAR) Exposome Symposium. Metabolites. 2019; 9(6):106. https://doi.org/10.3390/metabo9060106
Chicago/Turabian StyleZhang, Pei, Manish Arora, Romanas Chaleckis, Tomohiko Isobe, Mohit Jain, Isabel Meister, Erik Melén, Matthew Perzanowski, Federico Torta, Markus R. Wenk, and et al. 2019. "Tackling the Complexity of the Exposome: Considerations from the Gunma University Initiative for Advanced Research (GIAR) Exposome Symposium" Metabolites 9, no. 6: 106. https://doi.org/10.3390/metabo9060106