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Building an Early Life Exposome by Integrating Multiple Birth Cohorts: HELIX

  • Martine Vrijheid
  • Lea Maitre
Chapter

Abstract

The exposome has conceptually been described to comprise three overlapping domains: (1) a general external environment including factors such as the urban environment, climate factors, social capital, stress; (2) a specific external environment including specific contaminants, diet, physical activity, tobacco, and (3) an internal environment including internal biological factors such as metabolism, gut microflora, inflammation, and oxidative stress. Here, we aim to illustrate how these three domains and their interrelations may be studied in an epidemiological study design, using the HELIX (Human Early Life Exposome) project as an example. HELIX takes pregnancy and childhood periods (“early life”) as a starting point. In six existing birth cohort studies in Europe, HELIX estimated prenatal and postnatal exposures. Exposure models for the outdoor exposome (air pollutants, noise, meteorological factors, and natural and built environment characteristics) were developed for a total of 30,000 mother–child pairs. Exposure biomarkers (for persistent organic pollutants, metals, phthalate metabolites, phenolic compounds and organophosphate pesticides) and omics markers (metabolites, proteins, mRNA, miRNA, DNA methylation) were measured in a subset of 1200 children. Nested repeat-sampling panel studies (N = 150) collected data on variability in personal exposure to air pollution and built environment measures, in biomarkers for nonpersistent chemicals (phthalates and phenolic compounds) and in all omics techniques. Outcome examinations were carried out using common protocols in the six cohorts. We will discuss some first results of the HELIX project, including a description of the correlation structure of multiple exposure data.

Keywords

Early-life exposome Birth cohorts Pre-natal and post-natal exposures 

References

  1. Agier L, Portengen L, Chadeau-Hyam M, Basagana X, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, González JR, Nieuwenhuijsen MJ, Vineis P, Vrijheid M, Slama R, Vermeulen R (2016) A systematic comparison of linear regression-based statistical methods to assess exposome-health associations. Environ Health Perspect 124(12):1848–1856CrossRefGoogle Scholar
  2. Barouki R, Gluckman PD, Grandjean P, Hanson M, Heindel JJ (2012) Developmental origins of non-communicable disease: implications for research and public health. Environ Health 11:42CrossRefGoogle Scholar
  3. Billionnet C, Sherrill D, Annesi-Maesano I (2012) Estimating the health effects of exposure to multi-pollutant mixture. Ann Epidemiol 22:126–141CrossRefGoogle Scholar
  4. Chadeau-Hyam M, Athersuch TJ, Keun HC, De Iorio M, Ebbels TM, Jenab M, Sacerdote C, Bruce SJ, Holmes E, Vineis P (2011) Meeting-in-the-middle using metabolic profiling - a strategy for the identification of intermediate biomarkers in cohort studies. Biomarkers 16(1):83–88CrossRefGoogle Scholar
  5. Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L, Vineis P, Liquet B, Vermeulen RC (2013) Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers. Environ Mol Mutagen 54(7):542–557CrossRefGoogle Scholar
  6. Claus Henn B, Ettinger AS, Hopkins MR, Jim R, Amarasiriwardena C, Christiani DC, Coull BA, Bellinger DC, Wright RO (2016) Prenatal arsenic exposure and birth outcomes among a population residing near a mining-related superfund site. Environ Health Perspect 124(8):1308–1315CrossRefGoogle Scholar
  7. Godfrey KM, Gluckman PD, Hanson MA (2010) Developmental origins of metabolic disease: life course and intergenerational perspectives. Trends Endocrinol Metab 21:199–205CrossRefGoogle Scholar
  8. Heindel JJ, Balbus J, Birnbaum L, Brune-Drisse MN, Grandjean P, Gray K, Landrigan PJ, Sly PD, Suk W, Cory Slechta D, Thompson C, Hanson M (2015) Developmental origins of health and disease: integrating environmental influences. Endocrinology 156(10):3416–3421CrossRefGoogle Scholar
  9. Hsueh YM, Chen WJ, Lee CY, Chien SN, Shiue HS, Huang SR, Lin MI, Mu SC, Hsieh RL (2016) Association of arsenic methylation capacity with developmental delays and health status in children: a prospective case-control trial. Sci Rep 6:37287Google Scholar
  10. Robinson O, Vrijheid M (2015) The pregnancy exposome. Curr Environ Health Rep 2(2):204–213CrossRefGoogle Scholar
  11. Robinson O, Basagaña 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):10632–10641CrossRefGoogle Scholar
  12. Slama R, Vrijheid M (2015) Some challenges of studies aiming to relate the exposome to human health. Occup Environ Med 72(6):383–384CrossRefGoogle Scholar
  13. Sun Z, Tao Y, Li S, Ferguson KK, Meeker JD, Park SK et al (2013) Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environ Health 12:85.  https://doi.org/10.1186/1476-069X-12-85CrossRefGoogle Scholar
  14. Van den Bergh BR (2011) Developmental programming of early brain and behaviour development and mental health: a conceptual framework. Dev Med Child Neurol 53(Suppl 4):19–23CrossRefGoogle Scholar
  15. Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P et al (2014) The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect 122:535–544CrossRefGoogle Scholar
  16. 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):331–342CrossRefGoogle Scholar
  17. Wild CP (2012) The exposome: from concept to utility. Int J Epidemiol 41:24–32CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.ISGlobal, Institute for Global HealthBarcelonaSpain

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