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Framing Fetal and Early Life Exposome Within Epidemiology

  • Jessica E. Laine
  • Oliver Robinson
Chapter

Abstract

The time periods that influence fetal and early life development are identified in this chapter as key windows of susceptibility to exposures and critical developmental stages of preconception, and the prenatal, perinatal, and postnatal periods. We highlight in this chapter these key developmental windows that characterize the fetal and early life exposome, and present a review of studies that have identified fetal and early life external and internal domains of the exposome. We also present a discussion of issues in exposome study design, including choice of biological samples and statistical complexities, specific to the key developmental times of fetal and early life. While notable studies and consortia have been established to investigate the exposome during the times of fetal development and early life, we argue that future exposome research must expand to incorporate the preconception period, build upon the existing and large body of knowledge of reproductive and peri/pre-natal epidemiological methods and study design, and utilize methods of causal inference. Collectively, this will aid in strengthening both the internal and external validity of our studies, and in the identification of potential causal mechanisms underlying many preventable diseases. Such advancements will lead to better risk assessments and potential policy and medical interventions.

Keywords

Early-life exposome Pre-conception monitoring 

References

  1. Agay-Shay K, Martinez D, Valvi D, Garcia-Esteban R, Basagana X, Robinson O, Casas M, Sunyer J, Vrijheid M (2015) Exposure to endocrine-disrupting chemicals during pregnancy and weight at 7 years of age: a multi-pollutant approach. Environ Health Perspect 123(10):1030–1037.  https://doi.org/10.1289/ehp.1409049CrossRefGoogle Scholar
  2. Agha G, Hajj H, Rifas-Shiman SL, Just AC, Hivert MF, Burris HH, Lin X, Litonjua AA, Oken E, DeMeo DL, Gillman MW, Baccarelli AA (2016) Birth weight-for-gestational age is associated with DNA methylation at birth and in childhood. Clin Epigenetics 8:118.  https://doi.org/10.1186/s13148-016-0285-3CrossRefGoogle Scholar
  3. Andra SS, Austin C, Arora M (2016) The tooth exposome in children’s health research. Curr Opin Pediatr 28(2):221–227.  https://doi.org/10.1097/MOP.0000000000000327CrossRefGoogle Scholar
  4. ATSDR (2017) Agency for toxic substances and disease registry. Accessed Oct 2017. http://www.atsdr.cdc.gov
  5. Bailey KA, Laine J, Rager JE, Sebastian E, Olshan A, Smeester L, Drobná Z, Styblo M, Rubio-Andrade M, García-Vargas G, Fry RC (2014) Prenatal arsenic exposure and shifts in the newborn proteome: interindividual differences in tumor necrosis factor (TNF)-responsive signaling. Toxicol Sci 139(2):328–337.  https://doi.org/10.1093/toxsci/kfu053CrossRefGoogle Scholar
  6. Baird J, Hill CM, Kendrick T, Inskip HM, SWS Study Group (2009) Infant sleep disturbance is associated with preconceptional psychological distress: findings from the Southampton Women’s Survey. Sleep 32(4):566–568CrossRefGoogle Scholar
  7. Barker DJ (2004) The developmental origins of adult disease. J Am Coll Nutr 23(6 Suppl):588S–595SCrossRefGoogle Scholar
  8. Barr DB, Wang RY, Needham LL (2005) Biologic monitoring of exposure to environmental chemicals throughout the life stages: requirements and issues for consideration for the National Children’s Study. Environ Health Perspect 113(8):1083–1091CrossRefGoogle Scholar
  9. Ben-Shlomo Y, Kuh D (2002) A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol 31(2):285–293CrossRefGoogle Scholar
  10. Billionnet C, Sherrill D, Annesi-Maesano I (2012) Estimating the health effects of exposure to multi-pollutant mixture. Ann Epidemiol 22(2):126–141.  https://doi.org/10.1016/j.annepidem.2011.11.004CrossRefGoogle Scholar
  11. Braun JM, Kalkbrenner AE, Just AC, Yolton K, Calafat AM, Sjodin A, Hauser R, Webster GM, Chen A, Lanphear BP (2014) Gestational exposure to endocrine-disrupting chemicals and reciprocal social, repetitive, and stereotypic behaviors in 4- and 5-year-old children: the HOME study. Environ Health Perspect 122(5):513–520.  https://doi.org/10.1289/ehp.1307261CrossRefGoogle Scholar
  12. Braun JM, Messerlian C, Hauser R (2017) Fathers matter: why it’s time to consider the impact of paternal environmental exposures on children’s health. Curr Epidemiol Rep 4(1):46–55.  https://doi.org/10.1007/s40471-017-0098-8CrossRefGoogle Scholar
  13. Breton CV, Yao J, Millstein J, Gao L, Siegmund KD, Mack W, Whitfield-Maxwell L, Lurmann F, Hodis H, Avol E, Gilliland FD (2016) Prenatal air pollution exposures, DNA methyl transferase genotypes, and associations with newborn LINE1 and alu methylation and childhood blood pressure and carotid intima-media thickness in the Children’s Health Study. Environ Health Perspect 124(12):1905–1912.  https://doi.org/10.1289/ehp181CrossRefGoogle Scholar
  14. Buck Louis GM, Yeung E, Sundaram R, Laughon SK, Zhang C (2013) The exposome—exciting opportunities for discoveries in reproductive and perinatal epidemiology. Paediatr Perinat Epidemiol 27(3):229–236.  https://doi.org/10.1111/ppe.12040CrossRefGoogle Scholar
  15. Burris HH, Baccarelli AA, Byun HM, Cantoral A, Just AC, Pantic I, Solano-Gonzalez M, Svensson K, Tamayo y Ortiz M, Zhao Y, Wright RO, Tellez-Rojo MM (2015) Offspring DNA methylation of the aryl-hydrocarbon receptor repressor gene is associated with maternal BMI, gestational age, and birth weight. Epigenetics 10(10):913–921.  https://doi.org/10.1080/15592294.2015.1078963CrossRefGoogle Scholar
  16. Carrell DT, Hammoud SS (2010) The human sperm epigenome and its potential role in embryonic development. Mol Hum Reprod 16(1):37–47.  https://doi.org/10.1093/molehr/gap090CrossRefGoogle Scholar
  17. Chason RJ, Csokmay J, Segars JH, DeCherney AH, Armant DR (2011) Environmental and epigenetic effects upon preimplantation embryo metabolism and development. Trends Endocrinol Metab 22(10):412–420.  https://doi.org/10.1016/j.tem.2011.05.005CrossRefGoogle Scholar
  18. Cruickshank MN, Oshlack A, Theda C, Davis PG, Martino D, Sheehan P, Dai Y, Saffery R, Doyle LW, Craig JM (2013) Analysis of epigenetic changes in survivors of preterm birth reveals the effect of gestational age and evidence for a long term legacy. Genome Med 5(10):96.  https://doi.org/10.1186/gm500CrossRefGoogle Scholar
  19. Dadvand P, Ostro B, Figueras F, Foraster M, Basagana X, Valentin A, Martinez D, Beelen R, Cirach M, Hoek G, Jerrett M, Brunekreef B, Nieuwenhuijsen MJ (2014) Residential proximity to major roads and term low birth weight: the roles of air pollution, heat, noise, and road-adjacent trees. Epidemiology 25(4):518–525.  https://doi.org/10.1097/ede.0000000000000107CrossRefGoogle Scholar
  20. Day J, Savani S, Krempley BD, Nguyen M, Kitlinska JB (2016) Influence of paternal preconception exposures on their offspring: through epigenetics to phenotype. Am J Stem Cells 5(1):11–18Google Scholar
  21. Dennis KK, Auerbach SS, Balshaw DM, Cui Y, Fallin MD, Smith MT, Spira A, Sumner S, Miller GW (2016) The importance of the biological impact of exposure to the concept of the exposome. Environ Health Perspect 124(10):1504–1510.  https://doi.org/10.1289/EHP140CrossRefGoogle Scholar
  22. Dessì A, Atzori L, Noto A, Adriaan Visser GH, Gazzolo D, Zanardo V, Barberini L, Puddu M, Ottonello G, Atzei A, Magistris AD, Lussu M, Murgia F, Fanos V (2011) Metabolomics in newborns with intrauterine growth retardation (IUGR): urine reveals markers of metabolic syndrome. J Matern Fetal Neonatal Med 24(Suppl 2):35–39.  https://doi.org/10.3109/14767058.2011.605868CrossRefGoogle Scholar
  23. De Stavola BL, Daniel RM (2017) Commentary: Incorporating concepts and methods from causal inference into life course epidemiology. Int J Epidemiol 46(2):771.  https://doi.org/10.1093/ije/dyw367CrossRefGoogle Scholar
  24. Eidem HR, Ackerman WE, McGary KL, Abbot P, Rokas A (2015) Gestational tissue transcriptomics in term and preterm human pregnancies: a systematic review and meta-analysis. BMC Med Genet 8:27.  https://doi.org/10.1186/s12920-015-0099-8CrossRefGoogle Scholar
  25. Engel SM, Joubert BR, Wu MC, Olshan AF, Håberg SE, Ueland PM, Nystad W, Nilsen RM, Vollset SE, Peddada SD, London SJ (2014) Neonatal genome-wide methylation patterns in relation to birth weight in the Norwegian Mother and Child Cohort. Am J Epidemiol 179(7):834–842.  https://doi.org/10.1093/aje/kwt433CrossRefGoogle Scholar
  26. Fanos V, Atzori L, Makarenko K, Melis GB, Ferrazzi E (2013) Metabolomics application in maternal-fetal medicine. Biomed Res Int 2013:720514.  https://doi.org/10.1155/2013/720514CrossRefGoogle Scholar
  27. Felix JF, Joubert BR, Baccarelli AA, Sharp GC, Almqvist C, Annesi-Maesano I, Arshad H, Baiz N, Bakermans-Kranenburg MJ, Bakulski KM, Binder EB, Bouchard L, Breton CV, Brunekreef B, Brunst KJ, Burchard EG, Bustamante M, Chatzi L, Cheng Munthe-Kaas M, Corpeleijn E, Czamara D, Dabelea D, Davey Smith G, De Boever P, Duijts L, Dwyer T, Eng C, Eskenazi B, Everson TM, Falahi F, Fallin MD, Farchi S, Fernandez MF, Gao L, Gaunt TR, Ghantous A, Gillman MW, Gonseth S, Grote V, Gruzieva O, Haberg SE, Herceg Z, Hivert MF, Holland N, Holloway JW, Hoyo C, Hu D, Huang RC, Huen K, Jarvelin MR, Jima DD, Just AC, Karagas MR, Karlsson R, Karmaus W, Kechris KJ, Kere J, Kogevinas M, Koletzko B, Koppelman GH, Kupers LK, Ladd-Acosta C, Lahti J, Lambrechts N, Langie SAS, Lie RT, Liu AH, Magnus MC, Magnus P, Maguire RL, Marsit CJ, McArdle W, Melen E, Melton P, Murphy SK, Nawrot TS, Nistico L, Nohr EA, Nordlund B, Nystad W, Oh SS, Oken E, Page CM, Perron P, Pershagen G, Pizzi C, Plusquin M, Raikkonen K, Reese SE, Reischl E, Richiardi L, Ring S, Roy RP, Rzehak P, Schoeters G, Schwartz DA, Sebert S, Snieder H, Sorensen TIA, Starling AP, Sunyer J, Taylor JA, Tiemeier H, Ullemar V, Vafeiadi M, Van Ijzendoorn MH, Vonk JM, Vriens A, Vrijheid M, Wang P, Wiemels JL, Wilcox AJ, Wright RJ, Xu CJ, Xu Z, Yang IV, Yousefi P, Zhang H, Zhang W, Zhao S, Agha G, Relton CL, Jaddoe VWV, London SJ (2017) Cohort profile: pregnancy and childhood epigenetics (PACE) consortium. Int J Epidemiol 47(1):22–23u.  https://doi.org/10.1093/ije/dyx190CrossRefGoogle Scholar
  28. Forns J, Mandal S, Iszatt N, Polder A, Thomsen C, Lyche JL, Stigum H, Vermeulen R, Eggesbo M (2016) Novel application of statistical methods for analysis of multiple toxicants identifies DDT as a risk factor for early child behavioral problems. Environ Res 151:91–100.  https://doi.org/10.1016/j.envres.2016.07.014CrossRefGoogle Scholar
  29. Galea S (2017) Making epidemiology matter. Int J Epidemiol 46(4):1083–1085.  https://doi.org/10.1093/ije/dyx154CrossRefGoogle Scholar
  30. Gao F, McDaniel J, Chen EY, Rockwell HE, Drolet J, Vishnudas VK, Tolstikov V, Sarangarajan R, Narain NR, Kiebish MA (2017) Dynamic and temporal assessment of human dried blood spot MS/MS(ALL) shotgun lipidomics analysis. Nutr Metab (Lond) 14:28.  https://doi.org/10.1186/s12986-017-0182-6CrossRefGoogle Scholar
  31. Goldenberg RL, Culhane JF, Iams JD, Romero R (2008) Epidemiology and causes of preterm birth. Lancet 371(9606):75–84.  https://doi.org/10.1016/S0140-6736(08)60074-4CrossRefGoogle Scholar
  32. Golding J, Gregory S, Iles-Caven Y, Lingam R, Davis JM, Emmett P, Steer CD, Hibbeln JR (2014) Parental, prenatal, and neonatal associations with ball skills at age 8 using an exposome approach. J Child Neurol 29(10):1390–1398.  https://doi.org/10.1177/0883073814530501CrossRefGoogle Scholar
  33. Goodrich JM, Reddy P, Naidoo RN, Asharam K, Batterman S, Dolinoy DC (2016) Prenatal exposures and DNA methylation in newborns: a pilot study in Durban, South Africa. Environ Sci Process Impacts 18(7):908–917.  https://doi.org/10.1039/c6em00074fCrossRefGoogle Scholar
  34. Grevendonk L, Janssen BG, Vanpoucke C, Lefebvre W, Hoxha M, Bollati V, Nawrot TS (2016) Mitochondrial oxidative DNA damage and exposure to particulate air pollution in mother-newborn pairs. Environ Health 15:10.  https://doi.org/10.1186/s12940-016-0095-2CrossRefGoogle Scholar
  35. Gruzieva O, Xu CJ, Breton CV, Annesi-Maesano I, Anto JM, Auffray C, Ballereau S, Bellander T, Bousquet J, Bustamante M, Charles MA, de Kluizenaar Y, den Dekker HT, Duijts L, Felix JF, Gehring U, Guxens M, Jaddoe VV, Jankipersadsing SA, Merid SK, Kere J, Kumar A, Lemonnier N, Lepeule J, Nystad W, Page CM, Panasevich S, Postma D, Slama R, Sunyer J, Soderhall C, Yao J, London SJ, Pershagen G, Koppelman GH, Melen E (2017) Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect 125(1):104–110.  https://doi.org/10.1289/ehp36CrossRefGoogle Scholar
  36. Guxens M, Ballester F, Espada M, Fernandez MF, Grimalt JO, Ibarluzea J, Olea N, Rebagliato M, Tardon A, Torrent M, Vioque J, Vrijheid M, Sunyer J (2012) Cohort profile: the INMA—INfancia y medio ambiente—(environment and childhood) project. Int J Epidemiol 41(4):930–940.  https://doi.org/10.1093/ije/dyr054CrossRefGoogle Scholar
  37. Hellmuth C, Uhl O, Standl M, Demmelmair H, Heinrich J, Koletzko B, Thiering E (2017) Cord blood metabolome is highly associated with birth weight, but less predictive for later weight development. Obes Facts 10(2):85–100CrossRefGoogle Scholar
  38. Horgan RP, Broadhurst DI, Walsh SK, Dunn WB, Brown M, Roberts CT, North RA, McCowan LM, Kell DB, Baker PN, Kenny LC (2011) Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy. J Proteome Res 10(8):3660–3673.  https://doi.org/10.1021/pr2002897CrossRefGoogle Scholar
  39. Hughes DA, Kircher M, He Z, Guo S, Fairbrother GL, Moreno CS, Khaitovich P, Stoneking M (2015) Evaluating intra- and inter-individual variation in the human placental transcriptome. Genome Biol 16(1):54.  https://doi.org/10.1186/s13059-015-0627-zCrossRefGoogle Scholar
  40. Iozzo P, Holmes M, Schmidt MV, Cirulli F, Guzzardi MA, Berry A, Balsevich G, Andreassi MG, Wesselink JJ, Liistro T, Gómez-Puertas P, Eriksson JG, Seckl J (2014) Developmental ORIgins of Healthy and Unhealthy AgeiNg: the role of maternal obesity—introduction to DORIAN. Obes Facts 7(2):130–151.  https://doi.org/10.1159/000362656CrossRefGoogle Scholar
  41. Ivorra C, García-Vicent C, Chaves FJ, Monleón D, Morales JM, Lurbe E (2012) Metabolomic profiling in blood from umbilical cords of low birth weight newborns. J Transl Med 10:142.  https://doi.org/10.1186/1479-5876-10-142CrossRefGoogle Scholar
  42. Janssen BG, Godderis L, Pieters N, Poels K, Kicinski M, Cuypers A, Fierens F, Penders J, Plusquin M, Gyselaers W, Nawrot TS (2013) Placental DNA hypomethylation in association with particulate air pollution in early life. Part Fibre Toxicol 10:22.  https://doi.org/10.1186/1743-8977-10-22CrossRefGoogle Scholar
  43. Janssen BG, Byun HM, Gyselaers W, Lefebvre W, Baccarelli AA, Nawrot TS (2015) Placental mitochondrial methylation and exposure to airborne particulate matter in the early life environment: an ENVIRONAGE birth cohort study. Epigenetics 10(6):536–544.  https://doi.org/10.1080/15592294.2015.1048412CrossRefGoogle Scholar
  44. Jeyabalan A (2013) Epidemiology of preeclampsia: impact of obesity. Nutr Rev 71(Suppl 1):S18–S25.  https://doi.org/10.1111/nure.12055CrossRefGoogle Scholar
  45. Joseph KS (2016) A consilience of inductions supports the extended fetuses-at-risk model. Paediatr Perinat Epidemiol 30(1):11–17.  https://doi.org/10.1111/ppe.12260CrossRefGoogle Scholar
  46. Joubert BR, Haberg SE, Nilsen RM, Wang X, Vollset SE, Murphy SK, Huang Z, Hoyo C, Midttun O, Cupul-Uicab LA, Ueland PM, Wu MC, Nystad W, Bell DA, Peddada SD, London SJ (2012) 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ Health Perspect 120(10):1425–1431.  https://doi.org/10.1289/ehp.1205412CrossRefGoogle Scholar
  47. Kershenbaum AD, Langston MA, Levine RS, Saxton AM, Oyana TJ, Kilbourne BJ, Rogers GL, Gittner LS, Baktash SH, Matthews-Juarez P, Juarez PD (2014) Exploration of preterm birth rates using the public health exposome database and computational analysis methods. Int J Environ Res Public Health 11(12):12346–12366.  https://doi.org/10.3390/ijerph111212346CrossRefGoogle Scholar
  48. King JC (2016) A summary of pathways or mechanisms linking preconception maternal nutrition with birth outcomes. J Nutr 146(7):1437S–1444S.  https://doi.org/10.3945/jn.115.223479CrossRefGoogle Scholar
  49. Kingsley SL, Eliot MN, Whitsel EA, Huang YT, Kelsey KT, Marsit CJ, Wellenius GA (2016) Maternal residential proximity to major roadways, birth weight, and placental DNA methylation. Environ Int 92-93:43–49.  https://doi.org/10.1016/j.envint.2016.03.020CrossRefGoogle Scholar
  50. Knight AK, Craig JM, Theda C, Baekvad-Hansen M, Bybjerg-Grauholm J, Hansen CS, Hollegaard MV, Hougaard DM, Mortensen PB, Weinsheimer SM, Werge TM, Brennan PA, Cubells JF, Newport DJ, Stowe ZN, Cheong JL, Dalach P, Doyle LW, Loke YJ, Baccarelli AA, Just AC, Wright RO, Tellez-Rojo MM, Svensson K, Trevisi L, Kennedy EM, Binder EB, Iurato S, Czamara D, Raikkonen K, Lahti JM, Pesonen AK, Kajantie E, Villa PM, Laivuori H, Hamalainen E, Park HJ, Bailey LB, Parets SE, Kilaru V, Menon R, Horvath S, Bush NR, LeWinn KZ, Tylavsky FA, Conneely KN, Smith AK (2016) An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol 17(1):206.  https://doi.org/10.1186/s13059-016-1068-zCrossRefGoogle Scholar
  51. Kumarathasan P, Vincent R, Das D, Mohottalage S, Blais E, Blank K, Karthikeyan S, Vuong NQ, Arbuckle TE, Fraser WD (2014) Applicability of a high-throughput shotgun plasma protein screening approach in understanding maternal biological pathways relevant to infant birth weight outcome. J Proteome 100:136–146.  https://doi.org/10.1016/j.jprot.2013.12.003CrossRefGoogle Scholar
  52. Laine JE, Fry RC (2016) A systems toxicology-based approach reveals biological pathways dysregulated by prenatal arsenic exposure. Ann Glob Health 82(1):189–196.  https://doi.org/10.1016/j.aogh.2016.01.015CrossRefGoogle Scholar
  53. Laine JE, Bailey KA, Olshan AF, Smeester L, Drobná Z, Stýblo M, Douillet C, García-Vargas G, Rubio-Andrade M, Pathmasiri W, McRitchie S, Sumner SJ, Fry RC (2017) Neonatal metabolomic profiles related to prenatal arsenic exposure. Environ Sci Technol 51(1):625–633.  https://doi.org/10.1021/acs.est.6b04374CrossRefGoogle Scholar
  54. Lassi ZS, Imam AM, Dean SV, Bhutta ZA (2014) Preconception care: caffeine, smoking, alcohol, drugs and other environmental chemical/radiation exposure. Reprod Health 11(Suppl 3):S6.  https://doi.org/10.1186/1742-4755-11-S3-S6CrossRefGoogle Scholar
  55. Le HQ, Batterman SA, Wirth JJ, Wahl RL, Hoggatt KJ, Sadeghnejad A, Hultin ML, Depa M (2012) Air pollutant exposure and preterm and term small-for-gestational-age births in Detroit, Michigan: long-term trends and associations. Environ Int 44:7–17.  https://doi.org/10.1016/j.envint.2012.01.003CrossRefGoogle Scholar
  56. Lenters V, Portengen L, Rignell-Hydbom A, Jonsson BA, Lindh CH, Piersma AH, Toft G, Bonde JP, Heederik D, Rylander L, Vermeulen R (2016) Prenatal phthalate, perfluoroalkyl acid, and organochlorine exposures and term birth weight in three birth cohorts: multi-pollutant models based on elastic net regression. Environ Health Perspect 124(3):365–372.  https://doi.org/10.1289/ehp.1408933CrossRefGoogle Scholar
  57. Lewis RM, Demmelmair H, Gaillard R, Godfrey KM, Hauguel-de Mouzon S, Huppertz B, Larque E, Saffery R, Symonds ME, Desoye G (2013) The placental exposome: placental determinants of fetal adiposity and postnatal body composition. Ann Nutr Metab 63(3):208–215.  https://doi.org/10.1159/000355222CrossRefGoogle Scholar
  58. Liu J, Morgan M, Hutchison K, Calhoun VD (2010) A study of the influence of sex on genome wide methylation. PLoS One 5(4):e10028.  https://doi.org/10.1371/journal.pone.0010028CrossRefGoogle Scholar
  59. Liu SH, Ulbricht CM, Chrysanthopoulou SA, Lapane KL (2016) Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies. BMC Res Notes 9:354.  https://doi.org/10.1186/s13104-016-2163-7CrossRefGoogle Scholar
  60. Maitre L, Fthenou E, Athersuch T, Coen M, Toledano MB, Holmes E, Kogevinas M, Chatzi L, Keun HC (2014) Urinary metabolic profiles in early pregnancy are associated with preterm birth and fetal growth restriction in the Rhea mother-child cohort study. BMC Med 12:110.  https://doi.org/10.1186/1741-7015-12-110CrossRefGoogle Scholar
  61. Maitre L, Villanueva CM, Lewis MR, Ibarluzea J, Santa-Marina L, Vrijheid M, Sunyer J, Coen M, Toledano MB (2016) Maternal urinary metabolic signatures of fetal growth and associated clinical and environmental factors in the INMA study. BMC Med 14(1):177.  https://doi.org/10.1186/s12916-016-0706-3CrossRefGoogle Scholar
  62. Marsit CJ (2015) Influence of environmental exposure on human epigenetic regulation. J Exp Biol 218(Pt 1):71–79.  https://doi.org/10.1242/jeb.106971CrossRefGoogle Scholar
  63. McCullough LE, Mendez MA, Miller EE, Murtha AP, Murphy SK, Hoyo C (2015) Associations between prenatal physical activity, birth weight, and DNA methylation at genomically imprinted domains in a multiethnic newborn cohort. Epigenetics 10(7):597–606.  https://doi.org/10.1080/15592294.2015.1045181CrossRefGoogle Scholar
  64. Mishra GD, Cooper R, Kuh D (2010) A life course approach to reproductive health: theory and methods. Maturitas 65(2):92–97.  https://doi.org/10.1016/j.maturitas.2009.12.009CrossRefGoogle Scholar
  65. Moussa HN, Alrais MA, Leon MG, Abbas EL, Sibai BM (2016) Obesity epidemic: impact from preconception to postpartum. Future Sci OA 2(3):FSO137.  https://doi.org/10.4155/fsoa-2016-0035CrossRefGoogle Scholar
  66. National Academies of Sciences, Engineering, and Medicine, Division on Earth and Life Studies, Board on Environmental Studies and Toxicology, Committee on Incorporating 21st Century Science into Risk-Based Evaluations (2017) Using 21st century science to improve risk-related evaluations. National Academies Press (US), Washington, DC.  https://doi.org/10.17226/24635CrossRefGoogle Scholar
  67. North ML, Brook JR, Lee EY, Omana V, Daniel NM, Steacy LM, Evans GJ, Diamond ML, Ellis AK (2017) The Kingston Allergy Birth Cohort: exploring parentally reported respiratory outcomes through the lens of the exposome. Ann Allergy Asthma Immunol 118(4):465–473.  https://doi.org/10.1016/j.anai.2017.01.002CrossRefGoogle Scholar
  68. Oyana TJ, Matthews-Juarez P, Cormier SA, Xu X, Juarez PD (2015) Using an external exposome framework to examine pregnancy-related morbidities and mortalities: implications for health disparities research. Int J Environ Res Public Health 13(1):ijerph13010013.  https://doi.org/10.3390/ijerph13010013CrossRefGoogle Scholar
  69. Patel CJ (2017) Analytic complexity and challenges in identifying mixtures of exposures associated with phenotypes in the exposome era. Curr Epidemiol Rep 4(1):22–30.  https://doi.org/10.1007/s40471-017-0100-5CrossRefGoogle Scholar
  70. Patel CJ, Ioannidis JP (2014) Placing epidemiological results in the context of multiplicity and typical correlations of exposures. J Epidemiol Community Health 68(11):1096–1100.  https://doi.org/10.1136/jech-2014-204195CrossRefGoogle Scholar
  71. Patel CJ, Manrai AK (2015) Development of exposome correlation globes to map out environment-wide associations. Pac Symp Biocomput 2015:231–242Google Scholar
  72. Pearl J (2001) Direct and indirect effects. In: Proceedings of the 17th conference in uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc, San Francisco, pp 411–420Google Scholar
  73. Rager JE, Bailey KA, Smeester L, Miller SK, Parker JS, Laine JE, Drobná Z, Currier J, Douillet C, Olshan AF, Rubio-Andrade M, Stýblo M, García-Vargas G, Fry RC (2014) Prenatal arsenic exposure and the epigenome: altered microRNAs associated with innate and adaptive immune signaling in newborn cord blood. Environ Mol Mutagen 55(3):196–208.  https://doi.org/10.1002/em.21842CrossRefGoogle Scholar
  74. Rangel M, dos Santos JC, Ortiz PH, Hirata M, Jasiulionis MG, Araujo RC, Ierardi DF, Franco Mdo C (2014) Modification of epigenetic patterns in low birth weight children: importance of hypomethylation of the ACE gene promoter. PLoS One 9(8):e106138.  https://doi.org/10.1371/journal.pone.0106138CrossRefGoogle Scholar
  75. Rappaport SM (2012) Biomarkers intersect with the exposome. Biomarkers 17(6):483–489.  https://doi.org/10.3109/1354750X.2012.691553CrossRefGoogle Scholar
  76. Rappaport SM, Smith MT (2010) Epidemiology. Environment and disease risks. Science 330(6003):460–461.  https://doi.org/10.1126/science.1192603CrossRefGoogle Scholar
  77. Robins JM, Greenland S (1992) Identifiability and exchangeability for direct and indirect effects. Epidemiology 3:143–155CrossRefGoogle Scholar
  78. Robinson O, Vrijheid M (2015) The pregnancy exposome. Curr Environ Health Rep 2(2):204–213.  https://doi.org/10.1007/s40572-015-0043-2CrossRefGoogle Scholar
  79. Robinson O, Basagana X, Agier L, de Castro M, Hernandez-Ferrer C, Gonzalez JR, Grimalt JO, Nieuwenhuijsen M, Sunyer J, Slama R, Vrijheid M (2015) The pregnancy exposome: multiple environmental exposures in the INMA-Sabadell Birth Cohort. Environ Sci Technol 49(17):10632–10641.  https://doi.org/10.1021/acs.est.5b01782CrossRefGoogle Scholar
  80. Robinson O, Tamayo O, de Castro M, Valentin A, Giorgis-Allemand L, Hjertager Krog N, et al (2018) The urban exposome during pregnancy and its socio-economic determinants. Environ Health Perspect (in press)Google Scholar
  81. Robledo CA, Yeung E, Mendola P, Sundaram R, Maisog J, Sweeney AM, Barr DB, Louis GM (2015) Preconception maternal and paternal exposure to persistent organic pollutants and birth size: the LIFE study. Environ Health Perspect 123(1):88–94.  https://doi.org/10.1289/ehp.1308016CrossRefGoogle Scholar
  82. Rojas D, Rager JE, Smeester L, Bailey KA, Drobná Z, Rubio-Andrade M, Stýblo M, García-Vargas G, Fry RC (2015) Prenatal arsenic exposure and the epigenome: identifying sites of 5-methylcytosine alterations that predict functional changes in gene expression in newborn cord blood and subsequent birth outcomes. Toxicol Sci 143(1):97–106.  https://doi.org/10.1093/toxsci/kfu210CrossRefGoogle Scholar
  83. Romero R, Kusanovic JP, Gotsch F, Erez O, Vaisbuch E, Mazaki-Tovi S, Moser A, Tam S, Leszyk J, Master SR, Juhasz P, Pacora P, Ogge G, Gomez R, Yoon BH, Yeo L, Hassan SS, Rogers WT (2010a) Isobaric labeling and tandem mass spectrometry: a novel approach for profiling and quantifying proteins differentially expressed in amniotic fluid in preterm labor with and without intra-amniotic infection/inflammation. J Matern Fetal Neonatal Med 23(4):261–280.  https://doi.org/10.3109/14767050903067386CrossRefGoogle Scholar
  84. Romero R, Mazaki-Tovi S, Vaisbuch E, Kusanovic JP, Chaiworapongsa T, Gomez R, Nien JK, Yoon BH, Mazor M, Luo J, Banks D, Ryals J, Beecher C (2010b) Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery. J Matern Fetal Neonatal Med 23(12):1344–1359.  https://doi.org/10.3109/14767058.2010.482618CrossRefGoogle Scholar
  85. Rosofsky A, Janulewicz P, Thayer KA, McClean M, Wise LA, Calafat AM, Mikkelsen EM, Taylor KW, Hatch EE (2017) Exposure to multiple chemicals in a cohort of reproductive-aged Danish women. Environ Res 154:73–85.  https://doi.org/10.1016/j.envres.2016.12.011CrossRefGoogle Scholar
  86. Rossnerova A, Tulupova E, Tabashidze N, Schmuczerova J, Dostal M, Rossner P Jr, Gmuender H, Sram RJ (2013) Factors affecting the 27K DNA methylation pattern in asthmatic and healthy children from locations with various environments. Mutat Res 741-742:18–26.  https://doi.org/10.1016/j.mrfmmm.2013.02.003CrossRefGoogle Scholar
  87. Rothman KJ, Greenland S, Lash TL (2008) Modern epidemiology, 3rd edn. Lippincott, Williams & Wilkins, Philadelphia, PAGoogle Scholar
  88. Saenen ND, Vrijens K, Janssen BG, Roels HA, Neven KY, Vanden Berghe W, Gyselaers W, Vanpoucke C, Lefebvre W, De Boever P, Nawrot TS (2017) Lower placental leptin promoter methylation in association with fine particulate matter air pollution during pregnancy and placental nitrosative stress at birth in the ENVIRONAGE cohort. Environ Health Perspect 125(2):262–268.  https://doi.org/10.1289/ehp38CrossRefGoogle Scholar
  89. Shaffer RM, Smith MN, Faustman EM (2017) Developing the regulatory utility of the exposome: mapping exposures for risk assessment through lifestage exposome snapshots (LEnS). Environ Health Perspect 125(8):085003.  https://doi.org/10.1289/EHP1250CrossRefGoogle Scholar
  90. Shaughnessy DT, McAllister K, Worth L, Haugen AC, Meyer JN, Domann FE, Van Houten B, Mostoslavsky R, Bultman SJ, Baccarelli AA, Begley TJ, Sobol RW, Hirschey MD, Ideker T, Santos JH, Copeland WC, Tice RR, Balshaw DM, Tyson FL (2014) Mitochondria, energetics, epigenetics, and cellular responses to stress. Environ Health Perspect 122(12):1271–1278.  https://doi.org/10.1289/ehp.1408418CrossRefGoogle Scholar
  91. Simpkin AJ, Suderman M, Gaunt TR, Lyttleton O, McArdle WL, Ring SM, Tilling K, Davey Smith G, Relton CL (2015) Longitudinal analysis of DNA methylation associated with birth weight and gestational age. Hum Mol Genet 24(13):3752–3763.  https://doi.org/10.1093/hmg/ddv119CrossRefGoogle Scholar
  92. Sõber S, Reiman M, Kikas T, Rull K, Inno R, Vaas P, Teesalu P, Marti JM, Mattila P, Laan M (2015) Extensive shift in placental transcriptome profile in preeclampsia and placental origin of adverse pregnancy outcomes. Sci Rep 5:13336.  https://doi.org/10.1038/srep13336CrossRefGoogle Scholar
  93. Steer CD, Bolton P, Golding J (2015) Preconception and prenatal environmental factors associated with communication impairments in 9 year old children using an exposome-wide approach. PLoS One 10(3):e0118701.  https://doi.org/10.1371/journal.pone.0118701CrossRefGoogle Scholar
  94. Swartz MD, Cai Y, Chan W, Symanski E, Mitchell LE, Danysh HE, Langlois PH, Lupo PJ (2015) Air toxics and birth defects: a Bayesian hierarchical approach to evaluate multiple pollutants and spina bifida. Environ Health 14:16.  https://doi.org/10.1186/1476-069x-14-16CrossRefGoogle Scholar
  95. Tea I, Gall GL, Küster A, Guignard N, Alexandre-Gouabau MC, Darmaun D, Robins RJ (2012) 1H-NMR-based metabolic profiling of maternal and umbilical cord blood indicates altered materno-foetal nutrient exchange in preterm infants. PLoS One 7:6–9.  https://doi.org/10.1371/journal.pone.0029947CrossRefGoogle Scholar
  96. Toivonen KI, Oinonen KA, Duchene KM (2017) Preconception health behaviours: a scoping review. Prev Med 96:1–15.  https://doi.org/10.1016/j.ypmed.2016.11.022CrossRefGoogle Scholar
  97. Vafeiadi M, Vrijheid M, Fthenou E, Chalkiadaki G, Rantakokko P, Kiviranta H, Kyrtopoulos SA, Chatzi L, Kogevinas M (2014) Persistent organic pollutants exposure during pregnancy, maternal gestational weight gain, and birth outcomes in the mother-child cohort in Crete, Greece (RHEA study). Environ Int 64:116–123.  https://doi.org/10.1016/j.envint.2013.12.015CrossRefGoogle Scholar
  98. Valero De Bernabé J, Soriano T, Albaladejo R, Juarranz M, Calle ME, Martínez D, Domínguez-Rojas V (2004) Risk factors for low birth weight: a review. Eur J Obstet Gynecol Reprod Biol 116(1):3–15.  https://doi.org/10.1016/j.ejogrb.2004.03.007CrossRefGoogle Scholar
  99. VanderWeele T (2015) Explanation in causal inference: methods for mediation and interaction, 1st edn. Oxford University Press, New York, NYGoogle Scholar
  100. Vineis P, Chadeau-Hyam M, Gmuender H, Gulliver J, Herceg Z, Kleinjans J, Kogevinas M, Kyrtopoulos S, Nieuwenhuijsen M, Phillips DH, Probst-Hensch N, Scalbert A, Vermeulen R, Wild CP (2016) The exposome in practice: design of the EXPOsOMICS project. Int J Hyg Environ Health 220(2 Pt A):142–151.  https://doi.org/10.1016/j.ijheh.2016.08.001CrossRefGoogle Scholar
  101. Volberg V, Yousefi P, Huen K, Harley K, Eskenazi B, Holland N (2017) CpG methylation across the adipogenic PPARgamma gene and its relationship with birthweight and child BMI at 9 years. BMC Med Genet 18(1):7.  https://doi.org/10.1186/s12881-016-0365-4CrossRefGoogle Scholar
  102. Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, Basagana X, Brochot C, Bucchini L, Bustamante M, Carracedo A, Casas M, Estivill X, Fairley L, van Gent D, Gonzalez JR, Granum B, Grazuleviciene R, Gutzkow KB, Julvez J, Keun HC, Kogevinas M, McEachan RR, Meltzer HM, Sabido E, Schwarze PE, Siroux V, Sunyer J, Want EJ, Zeman F, Nieuwenhuijsen MJ (2014) The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect 122(6):535–544.  https://doi.org/10.1289/ehp.1307204CrossRefGoogle Scholar
  103. Vrijheid M, Casas M, Gascon M, Valvi D, Nieuwenhuijsen M (2016) Environmental pollutants and child health-a review of recent concerns. Int J Hyg Environ Health 219(4–5):331–342.  https://doi.org/10.1016/j.ijheh.2016.05.001CrossRefGoogle Scholar
  104. Wang F, Shi Z, Wang P, You W, Liang G (2013) Comparative proteome profile of human placenta from normal and preeclamptic pregnancies. PLoS One 8(10):e78025.  https://doi.org/10.1371/journal.pone.0078025CrossRefGoogle Scholar
  105. Warren J, Fuentes M, Herring A, Langlois P (2012) Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure. Biometrics 68(4):1157–1167.  https://doi.org/10.1111/j.1541-0420.2012.01774.xCrossRefGoogle Scholar
  106. Wilcox AJ (2010) Fertility and pregnancy: an epidemiologic perspective. Oxford University Press, OxfordGoogle Scholar
  107. Wilcox AJ, Weinberg CR, Basso O (2011) On the pitfalls of adjusting for gestational age at birth. Am J Epidemiol 174(9):1062–1068.  https://doi.org/10.1093/aje/kwr230CrossRefGoogle Scholar
  108. Wild CP (2005) Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev 14(8):1847–1850.  https://doi.org/10.1158/1055-9965.epi-05-0456CrossRefGoogle Scholar
  109. Wild CP (2012) The exposome: from concept to utility. Int J Epidemiol 41(1):24–32.  https://doi.org/10.1093/ije/dyr236CrossRefGoogle Scholar
  110. Woodruff TJ, Zota AR, Schwartz JM (2011) Environmental chemicals in pregnant women in the United States: NHANES 2003–2004. Environ Health Perspect 119(6):878–885.  https://doi.org/10.1289/ehp.1002727CrossRefGoogle Scholar
  111. Wright ML, Starkweather AR, York TP (2016) Mechanisms of the maternal exposome and implications for health outcomes. ANS Adv Nurs Sci 39(2):E17–E30.  https://doi.org/10.1097/ANS.0000000000000110CrossRefGoogle Scholar
  112. Yoon M, Nong A, Clewell HJ, Taylor MD, Dorman DC, Andersen ME (2009) Evaluating placental transfer and tissue concentrations of manganese in the pregnant rat and fetuses after inhalation exposures with a PBPK model. Toxicol Sci 112(1):44–58.  https://doi.org/10.1093/toxsci/kfp198CrossRefGoogle Scholar
  113. Yorifuji T, Debes F, Weihe P, Grandjean P (2011) Prenatal exposure to lead and cognitive deficit in 7- and 14-year-old children in the presence of concomitant exposure to similar molar concentration of methylmercury. Neurotoxicol Teratol 33(2):205–211.  https://doi.org/10.1016/j.ntt.2010.09.004CrossRefGoogle Scholar
  114. Zhang Y, Wang Q, Wang H, Duan E (2017) Uterine fluid in pregnancy: a biological and clinical outlook. Trends Mol Med 23(7):604–614.  https://doi.org/10.1016/j.molmed.2017.05.002CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and HealthSchool of Public Health, Imperial CollegeLondonUK

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