Table of Contents

    Topic review

    Exposome and Asthma

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    Submitted by: Alicia Guillien

    Definition

    Asthma is a widespread respiratory disease caused by complex contribution from genetic, environmental and behavioral factors. For several decades, its sensitivity to environmental factors has been investigated in single exposure (or single family of exposures) studies, which might be a narrow approach to tackle the etiology of such a complex multifactorial disease. The emergence of the exposome concept, introduced by C. Wild (2005), offers an alternative to address exposure–health associations. 

    1. Introduction

    Asthma is a heterogeneous chronic respiratory disease characterized by an inflammation of the airways and which manifests by variable respiratory symptoms (wheeze, shortness of breath, chest tightness and/or cough) and variable expiratory airflow limitation [1]. Asthma affects approximately 300 million children and adults worldwide [2]. The prevalence of asthma has dramatically increased over the last decades [3]. The huge research efforts in identifying the causes of asthma led to the identification of genetic (such as the 17q21 ORMDL3/GSDML region for early childhood onset asthma), environmental (such as urban vs. rural area) and lifestyle risk factors (such as tobacco smoking). It also highlighted the complex etiology of this multifactorial disease, e.g., by the identification of specific windows of susceptibility and complex gene-by-environment interactions [4]. The exposome concept, introduced in the recent years to complement the genome for a better understanding of the development of complex diseases [5], offers new avenues in environmental epidemiology. In this review, the main objective was to present how the new methodological framework represented by the exposome has been applied to asthma research to date. After presenting an overview of the concept of exposome, we will review different statistical approaches to study the exposome–health associations. Finally, recent studies linking multiple families of exposures to asthma-related outcomes will be discussed.

    2. Exposome-Health Associations in Practice

    Exposome studies imply collection of a large number of exposures. This can be done relying on different methods of assessment (e.g., self-reported questionnaire, exposure biomarkers, geographic information system-based (GIS) models, personal sensors, …), for different time windows (pre-natal, early postnatal, during childhood, adolescence, adulthood), and, for external factors, with different locations (home, school, work) and spatial resolutions (e.g., urban indicators measured for various buffers (100, 300, 500 m)). From a methodological point of view, this large number of variables (possibly larger than the size of the study population) raises issues in terms of statistical power and false discovery rate [6]. Indeed, the multiplicity of tests implies the rise of the alpha risk, and methods developed to correct the p-value of an association for multiple hypothesis testing [7][8][9] lead to a decreased statistical power. Therefore, exposome–health association studies deserve a sufficient sample size to achieve adequate statistical power to detect associations of low to moderate associations sizes, as expected for most exposures [10][11]. Several other statistical challenges specifically linked to exposome studies have to be taken into account, such as the increased false discovery rate related to the high level of correlation between exposures and the difficulty to consider “mixture” effects [12][13]. Until now, no consensus establishing which statistical methods are to be used in exposome–health association studies has been reached [14]. However, some simulations have allowed the comparison of the performance of various methods in the exposome research context under some specific settings. For example, simulation studies compared i) the efficiency of various regression-based approaches in terms of false positive rate and sensitivity, with and without interactions between exposures [6][13]; ii) the performance of variable selection models in case-control studies [15]; iii) the performance of variables and function selection methods in the case of nonlinear effects of correlated exposures [14]; and iv) methods to correct for classical-type exposure measurement error [16]. Using the findings of these studies and a review of the literature, we summarized in Table 1 the strengths and weaknesses of the main statistical approaches used in exposome studies in the field of respiratory health.

    Table 1. Main statistical methods used in exposome-health association studies.

    Type of Analysis Examples of Methods Strengths Weaknesses Reference of the Method Use of the Method in Exposome or Asthma Field
    Single-exposure regression-based method Exposome-Wide Association Study (ExWAS)
    • Standardized method
    • High sensitivity to identify true predictors
    • Simple interpretation
    • Easy to summarize the results in a figure (e.g., volcano plot)
    • Interaction between exposures is not tested
    • Results do not account for confounding effect by co-exposures
    • High false discovery rate
    Patel et al., 2010 [17] Sbihi et al., 2017 [18]; North et al., 2017 [19]; Lepeule et al., 2018 [20]; Agier et al., 2019 [21]; Vrijheid et al., 2020 [22]; Agier et al., 2020 [23];
    Warembourg et al., 2019 [24];
    Nieuwenhuijsen et al., 2019 [25];
    Granum et al. [26]
    Multiple-exposures regression-based methods Deletion–Substitution–Addition (DSA) algorithm
    • All exposure variables are considered in a unique model with possibility to include interactions
    • The selected model is able to account for confounding effect by co-exposures
    • Low false discovery proportion to identify true predictors
    • Moderate sensitivity to identify true predictors
    • Instability
    • Time-consuming and thus not adapted for exposome of more than a few hundred variables
    Sinisi and van der Laan 2004 [27] Agier et al., 2019 [21]; Vrijheid et al., 2020 [22]; Agier et al., 2020 [23]; Warembourg et al., 2019 [24]; Nieuwenhuijsen et al., 2019 [25] Granum et al. [26]
    Elastic Net (ENET) and Least Absolute Shrinkage and Selection Operator (LASSO)
    • Able to deal with correlated variables
    • The selected model is able to account for confounding effect by co-exposures
    • Good prediction performance
    • Moderate sensitivity to identify true predictors
    • Instability
    Zou and Hastie 2005 [[28]; Tibshirani 1996 [29] Pries et al., 2019 [30]; Cowell et al., 2019 [31]
    Weighted Quantile Sum (WQS) regression
    • Able to deal with multicollinearity
    • The use of quantiles reduces the impact of outliers
    • Not able to consider categorical exposures
    • All exposures must be associated with the outcome in the same direction (i.e., all protective or all risks factors)
    Carrico et al., 2015 [32] -
    Supervised clustering approaches Latent Class Analysis (LCA)
    • Suitable for longitudinal data (Latent Transition Analysis [33])
    • Able to consider the outcome in a supervised approach
    • Not able to deal with continuous exposures
    • Model requires low correlation between variables
    • Interpretation of results may be difficult in case of large number of clusters
    • Limited dimension of the exposome (in relation to the sample size)
    Goodman et al., 1974 [34] Buck Louis et al., 2019 [35]; Harmouche-Karaki et al., 2019 [36]
    Bayesian Profile Regression (BPR)
    • Consider all exposure variables in a unique model
    • Able to determine the number of clusters minimizing the least-squared distance to the probability matrix
    • Able to deal with combined continuous and categorical variables
    • Computing time
    • Interpretation of results may be difficult in case of large number of clusters
    • Unstable method
    Molitor et al., 2020 [37] Berger et al., 2020 [38]; Belloni et al., 2020 [39]
    Analysis accounting for the hierarchical structure of the data Meet-in-the-Middle (MITM)
    • Considers the hierarchical layers in the exposome and the causal link between them to better document the causality in exposome–health associations
    • Needs an a priori selection of intermediate layers
    Chadeau-Hyam M et al., 2011 [40]. Vineis et al., 2020 [41]; Jeong et al., 2018 [42]; Cadiou et al., 2020 [43]
    Bayesian Kernel Machine Regression (BKMR)
    • Use of a smooth kernel function able to deal with non-monotonic exposure-outcome relationship
    • Able to deal with a priori knowledge about group of exposures
    • Able to deal with multicollinearity
    • Not able to deal with categorical outcomes
    • The hierarchical variable selection option can select only one variable per group
    Bobb et al., 2015 [44] Berger et al., 2020 [38]

    3. Conclusions

    Asthma is a widespread multifactorial disease, which deserves a comprehensive approach to better understand its etiology and development. Although most of previous studies in environmental epidemiology focused on a single exposure (or single exposure family), with the recent emergence of the exposome concept, several studies and European projects have started to assess the effect of multiple exposures on respiratory health. These studies are expected to contribute to a better understanding of the associations between the environment and health by using various holistic approaches. Although the first association studies between the exposome and asthma-related outcomes conducted so far mainly rely on the ExWAS method for successive single-exposure analysis and the DSA algorithm for multi-exposures analysis [21][23][24][25][26][45], further studies on larger sample size should attempt to apply more comprehensive statistical approaches, either able to account for the hierarchical structure of the multiple layers of the exposome or to account for the possible mixture effects in order to be more consistent with the complex structure of exposure data.

    The entry is from 10.3390/ijerph18031138

    References

    1. Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention; National Heart, Lung, and Blood Institute, National Institutes of Health: Bethesda, MD, USA, 1995; updated 2012; NIH Publication no. 95-3659; Available online: http://www.ginasthma.org (accessed on 21 January 2021).
    2. James, S.L.; Abate, D.; Abate, K.H.; Abay, S.M.; Abbafati, C.; Abbasi, N.; Abbastabar, H.; Abd-Allah, F.; Abdela, J.; Abdelalim, A.; et al. Global, Regional, and National Incidence, Prevalence, and Years Lived with Disability for 354 Diseases and Injuries for 195 Countries and Territories, 1990–2017: A Systematic Analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392, 1789–1858.
    3. Lundbäck, B. Epidemiology of Rhinitis and Asthma. Clin. Exp. Allergy 1998, 28 (Suppl. 2), 3–10.
    4. Bouzigon, E.; Corda, E.; Aschard, H.; Dizier, M.-H.; Boland, A.; Bousquet, J.; Chateigner, N.; Gormand, F.; Just, J.; Le Moual, N.; et al. Effect of 17q21 Variants and Smoking Exposure in Early-Onset Asthma. N. Engl. J. Med. 2008, 359, 1985–1994.
    5. 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.
    6. Agier, L.; Portengen, L.; Chadeau-Hyam, M.; Basagaña, X.; Giorgis-Allemand, L.; Siroux, V.; Robinson, O.; Vlaanderen, J.; González, J.R.; Nieuwenhuijsen, M.J.; et al. A Systematic Comparison of Linear Regression-Based Statistical Methods to Assess Exposome-Health Associations. Environ. Health Perspect. 2016, 124, 1848–1856.
    7. Bonferroni, C. Teoria Statistica Delle Classi e Calcolo Delle Probabilita. Pubbl. R. Ist. Super Sci. Econ. Commer Firenze 1936, 8, 3–62.
    8. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 1995, 57, 289–300.
    9. Li, M.-X.; Yeung, J.M.Y.; Cherny, S.S.; Sham, P.C. Evaluating the Effective Numbers of Independent Tests and Significant P-Value Thresholds in Commercial Genotyping Arrays and Public Imputation Reference Datasets. Hum. Genet 2012, 131, 747–756.
    10. Santos, S.; Maitre, L.; Warembourg, C.; Agier, L.; Richiardi, L.; Basagaña, X.; Vrijheid, M. Applying the Exposome Concept in Birth Cohort Research: A Review of Statistical Approaches. Eur. J. Epidemiol. 2020, 35, 193–204.
    11. Chung, M.K.; Buck Louis, G.M.; Kannan, K.; Patel, C.J. Exposome-Wide Association Study of Semen Quality: Systematic Discovery of Endocrine Disrupting Chemical Biomarkers in Fertility Require Large Sample Sizes. Environ. Int. 2019, 125, 505–514.
    12. Tamayo-Uria, I.; Maitre, L.; Thomsen, C.; Nieuwenhuijsen, M.J.; Chatzi, L.; Siroux, V.; Aasvang, G.M.; Agier, L.; Andrusaityte, S.; Casas, M.; et al. The Early-Life Exposome: Description and Patterns in Six European Countries. Environ. Int. 2019, 123, 189–200.
    13. Barrera-Gómez, J.; Agier, L.; Portengen, L.; Chadeau-Hyam, M.; Giorgis-Allemand, L.; Siroux, V.; Robinson, O.; Vlaanderen, J.; González, J.R.; Nieuwenhuijsen, M.; et al. A Systematic Comparison of Statistical Methods to Detect Interactions in Exposome-Health Associations. Environ. Health 2017, 16, 74.
    14. Lazarevic, N.; Barnett, A.G.; Sly, P.D.; Knibbs, L.D. 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, 026001.
    15. Lenters, V.; Vermeulen, R.; Portengen, L. Performance of Variable Selection Methods for Assessing the Health Effects of Correlated Exposures in Case–Control Studies. Occup. Environ. Med. 2018, 75, 522–529.
    16. Agier, L.; Slama, R.; Basagaña, X. Relying on Repeated Biospecimens to Reduce the Effects of Classical-Type Exposure Measurement Error in Studies Linking the Exposome to Health. Environ. Res. 2020, 186, 109492.
    17. Patel, C.J.; Bhattacharya, J.; Butte, A.J. An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus. PLoS ONE 2010, 5, e10746.
    18. Sbihi, H.; Koehoorn, M.; Tamburic, L.; Brauer, M. Asthma Trajectories in a Population-Based Birth Cohort. Impacts of Air Pollution and Greenness. Am. J. Respir. Crit. Care Med. 2017, 195, 607–613.
    19. North, M.L.; Brook, J.R.; Lee, E.Y.; Omana, V.; Daniel, N.M.; Steacy, L.M.; Evans, G.J.; Diamond, M.L.; Ellis, A.K. The Kingston Allergy Birth Cohort: Exploring Parentally Reported Respiratory Outcomes through the Lens of the Exposome. Ann. Allergy Asthma Immunol. 2017, 118, 465–473.
    20. Lepeule, J.; Litonjua, A.A.; Gasparrini, A.; Koutrakis, P.; Sparrow, D.; Vokonas, P.S.; Schwartz, J. Lung Function Association with Outdoor Temperature and Relative Humidity and Its Interaction with Air Pollution in the Elderly. Environ Res 2018, 165, 110–117.
    21. Agier, L.; Basagaña, X.; Maitre, L.; Granum, B.; Bird, P.K.; Casas, M.; Oftedal, B.; Wright, J.; Andrusaityte, S.; de Castro, M.; et al. Early-Life Exposome and Lung Function in Children in Europe: An Analysis of Data from the Longitudinal, Population-Based HELIX Cohort. Lancet Planet Health 2019, 3, e81–e92.
    22. Vrijheid, M.; Fossati, S.; Maitre, L.; Márquez, S.; Roumeliotaki, T.; Agier, L.; Andrusaityte, S.; Cadiou, S.; Casas, M.; de Castro, M.; et al. Early-Life Environmental Exposures and Childhood Obesity: An Exposome-Wide Approach. Environ. Health Perspect. 2020, 128, 067009.
    23. Agier, L.; Basagaña, X.; Hernandez-Ferrer, C.; Maitre, L.; Tamayo Uria, I.; Urquiza, J.; Andrusaityte, S.; Casas, M.; de Castro, M.; Cequier, E.; et al. Association between the Pregnancy Exposome and Fetal Growth. Int. J. Epidemiol. 2020, 49, 572–586.
    24. Warembourg, C.; Maitre, L.; Tamayo-Uria, I.; Fossati, S.; Roumeliotaki, T.; Aasvang, G.M.; Andrusaityte, S.; Casas, M.; Cequier, E.; Chatzi, L.; et al. Early-Life Environmental Exposures and Blood Pressure in Children. J. Am. Coll. Cardiol. 2019, 74, 1317–1328.
    25. Nieuwenhuijsen, M.J.; Agier, L.; Basagaña, X.; Urquiza, J.; Tamayo-Uria, I.; Giorgis-Allemand, L.; Robinson, O.; Siroux, V.; Maitre, L.; de Castro, M.; et al. Influence of the Urban Exposome on Birth Weight. Environ. Health Perspect. 2019, 127, 047007.
    26. Granum, B.; Oftedal, B.; Agier, L.; Siroux, V.; Bird, P.; Casas, M.; Warembourg, C.; Wright, J.; Chatzi, L.; de Castro, M.; et al. Multiple Environmental Exposures in Early-Life and Allergy-Related Outcomes in Childhood. Environ. Int. 2020, 144, 106038.
    27. Sinisi, S.E.; van der Laan, M.J. Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics. Stat Appl Genet Mol Biol 2004, 3, 1–38.
    28. Zou, H.; Hastie, T. Regularization and Variable Selection via the Elastic Net. J. R. Statist. Soc. B 2005, 67, 301–320.
    29. Tibshirani, R. Regression Shrinkage and Selection via the Lasso. J. R. Statist. Soc. B 1996, 58, 267–288.
    30. Pries, L.-K.; Lage-Castellanos, A.; Delespaul, P.; Kenis, G.; Luykx, J.J.; Lin, B.D.; Richards, A.L.; Akdede, B.; Binbay, T.; Altinyazar, V.; et al. Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study. Schizophr. Bull. 2019, 45, 960–965.
    31. Cowell, W.; Colicino, E.; Lee, A.G.; Bosquet Enlow, M.; Flom, J.D.; Berin, C.; Wright, R.O.; Wright, R.J. Data-Driven Discovery of Mid-Pregnancy Immune Markers Associated with Maternal Lifetime Stress: Results from an Urban Pre-Birth Cohort. Stress 2020, 23, 349–358.
    32. Carrico, C.; Gennings, C.; Wheeler, D.C.; Factor-Litvak, P. Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. J. Agric. Biol. Environ. Stat. 2015, 20, 100–120.
    33. Lanza, S.T.; Patrick, M.E.; Maggs, J.L. Latent Transition Analysis: Benefits of a Latent Variable Approach to Modeling Transitions in Substance Use. J. Drug Issues 2010, 40, 93–120.
    34. Goodman, L.A. Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models. Biometrika 1974, 61, 215–231.
    35. Buck Louis, G.M.; Yeung, E.; Kannan, K.; Maisog, J.; Zhang, C.; Grantz, K.L.; Sundaram, R. Patterns and Variability of Endocrine-Disrupting Chemicals During Pregnancy: Implications for Understanding the Exposome of Normal Pregnancy. Epidemiology 2019, 30, S65–S75.
    36. Harmouche-Karaki, M.; Mahfouz, Y.; Salameh, P.; Matta, J.; Helou, K.; Narbonne, J.-F. Patterns of PCBs and OCPs Exposure in a Sample of Lebanese Adults: The Role of Diet and Physical Activity. Environ. Res. 2019, 179, 108789.
    37. Molitor, J.; Papathomas, M.; Jerrett, M.; Richardson, S. Bayesian Profile Regression with an Application to the National Survey of Children’s Health. Biostatistics 2010, 11, 484–498.
    38. Berger, K.; Coker, E.; Rauch, S.; Eskenazi, B.; Balmes, J.; Kogut, K.; Holland, N.; Calafat, A.M.; Harley, K. Prenatal Phthalate, Paraben, and Phenol Exposure and Childhood Allergic and Respiratory Outcomes: Evaluating Exposure to Chemical Mixtures. Sci. Total Environ. 2020, 725, 138418.
    39. Belloni, M.; Laurent, O.; Guihenneuc, C.; Ancelet, S. Bayesian Profile Regression to Deal With Multiple Highly Correlated Exposures and a Censored Survival Outcome. First Application in Ionizing Radiation Epidemiology. Front. Public Health 2020, 8, 557006.
    40. Chadeau-Hyam, M.; Athersuch, T.J.; Keun, H.C.; De Iorio, M.; Ebbels, T.M.D.; Jenab, M.; Sacerdote, C.; Bruce, S.J.; Holmes, E.; Vineis, P. Meeting-in-the-Middle Using Metabolic Profiling–a Strategy for the Identification of Intermediate Biomarkers in Cohort Studies. Biomarkers 2011, 16, 83–88.
    41. Vineis, P.; Demetriou, C.A.; Probst-Hensch, N. Long-Term Effects of Air Pollution: An Exposome Meet-in-the-Middle Approach. Int. J. Public Health 2020, 65, 125–127.
    42. Jeong, A.; Fiorito, G.; Keski-Rahkonen, P.; Imboden, M.; Kiss, A.; Robinot, N.; Gmuender, H.; Vlaanderen, J.; Vermeulen, R.; Kyrtopoulos, S.; et al. Perturbation of Metabolic Pathways Mediates the Association of Air Pollutants with Asthma and Cardiovascular Diseases. Environ. Int. 2018, 119, 334–345.
    43. Cadiou, S.; Bustamante, M.; Agier, L.; Andrusaityte, S.; Basagaña, X.; Carracedo, A.; Chatzi, L.; Grazuleviciene, R.; Gonzalez, J.R.; Gutzkow, K.B.; et al. Using Methylome Data to Inform Exposome-Health Association Studies: An Application to the Identification of Environmental Drivers of Child Body Mass Index. Environ. Int. 2020, 138, 105622.
    44. Bobb, J.F.; Valeri, L.; Claus Henn, B.; Christiani, D.C.; Wright, R.O.; Mazumdar, M.; Godleski, J.J.; Coull, B.A. Bayesian Kernel Machine Regression for Estimating the Health Effects of Multi-Pollutant Mixtures. Biostatistics 2015, 16, 493–508.
    45. Lubczyńska, M.J.; Muetzel, R.L.; El Marroun, H.; Basagaña, X.; Strak, M.; Denault, W.; Jaddoe, V.W.V.; Hillegers, M.; Vernooij, M.W.; Hoek, G.; et al. Exposure to Air Pollution during Pregnancy and Childhood, and White Matter Microstructure in Preadolescents. Environ. Health Perspect. 2020, 128, 027005.
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