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Bellavia A, Melloni GEM, Park JG, Discacciati A, Murphy SA. Estimating and presenting hazard ratios and absolute risks from a Cox model with complex nonlinear interactions. Am J Epidemiol 2024; 193:1155-1160. [PMID: 38775274 DOI: 10.1093/aje/kwae037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 02/21/2024] [Accepted: 04/03/2024] [Indexed: 08/06/2024] Open
Abstract
Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification.
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Affiliation(s)
- Andrea Bellavia
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Giorgio E M Melloni
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Jeong-Gun Park
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Andrea Discacciati
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Sabina A Murphy
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
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Sun P, Guo X, Ding E, Li C, Ren H, Xu Y, Qian J, Deng F, Shi W, Dong H, Lin EZ, Guo P, Fang J, Zhang Q, Zhao W, Tong S, Lu X, Pollitt KJG, Shi X, Tang S. Association between Personal Abiotic Airborne Exposures and Body Composition Changes among Healthy Adults (60-69 Years Old): A Combined Exposome-Wide and Lipidome Mediation Approach from the China BAPE Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:77005. [PMID: 39028628 PMCID: PMC11259245 DOI: 10.1289/ehp13865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 05/25/2024] [Accepted: 06/24/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Evidence suggested that abiotic airborne exposures may be associated with changes in body composition. However, more evidence is needed to identify key pollutants linked to adverse health effects and their underlying biomolecular mechanisms, particularly in sensitive older adults. OBJECTIVES Our research aimed to systematically assess the relationship between abiotic airborne exposures and changes in body composition among healthy older adults, as well as the potential mediating mechanisms through the serum lipidome. METHODS From September 2018 to January 2019, we conducted a monthly survey among 76 healthy adults (60-69 years old) in the China Biomarkers of Air Pollutant Exposure (BAPE) study, measuring their personal exposures to 632 abiotic airborne pollutions using MicroPEM and the Fresh Air wristband, 18 body composition indicators from the InBody 770 device, and lipidomics from venous blood samples. We used an exposome-wide association study (ExWAS) and deletion/substitution/addition (DSA) model to unravel complex associations between exposure to contaminant mixtures and body composition, a Bayesian kernel machine regression (BKMR) model to assess the overall effect of key exposures on body composition, and mediation analysis to identify lipid intermediators. RESULTS The ExWAS and DSA model identified that 2,4,5-T methyl ester (2,4,5-TME), 9,10-Anthracenedione (ATQ), 4b,8-dimethyl-2-isopropylphenanthrene, and 4b,5,6,7,8,8a,9,10-octahydro-(DMIP) were associated with increased body fat mass (BFM), fat mass indicators (FMI), percent body fat (PBF), and visceral fat area (VFA) in healthy older adults [Bonferroni-Hochberg false discovery rate ( FD R BH ) < 0.05 ]. The BKMR model demonstrated a positive correlation between contaminants (anthracene, ATQ, copaene, di-epi-α -cedrene, and DMIP) with VFA. Mediation analysis revealed that phosphatidylcholine [PC, PC(16:1e/18:1), PC(16:2e/18:0)] and sphingolipid [SM, SM(d18:2/24:1)] mediated a significant portion, ranging from 12.27% to 26.03% (p-value < 0.05 ), of the observed increase in VFA. DISCUSSION Based on the evidence from multiple model results, ATQ and DMIP were statistically significantly associated with the increased VFA levels of healthy older adults, potentially regulated through lipid intermediators. These findings may have important implications for identifying potentially harmful environmental chemicals and developing targeted strategies for the control and prevention of chronic diseases in the future, particularly as the global population is rapidly aging. https://doi.org/10.1289/EHP13865.
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Affiliation(s)
- Peijie Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, China
| | - Xiaojie Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Enmin Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chenfeng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Occupational Health and Environment Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Huimin Ren
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, China
| | - Yibo Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China
| | - Jiankun Qian
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, China
| | - Fuchang Deng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wanying Shi
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Haoran Dong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Elizabeth Z. Lin
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Pengfei Guo
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qian Zhang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Wenhua Zhao
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Shilu Tong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Xiaobo Lu
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, China
| | - Krystal J. Godri Pollitt
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Song Tang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
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Wang F, Lin Y, Xu J, Wei F, Huang S, Wen S, Zhou H, Jiang Y, Wang H, Ling W, Li X, Yang X. Risk of papillary thyroid carcinoma and nodular goiter associated with exposure to semi-volatile organic compounds: A multi-pollutant assessment based on machine learning algorithms. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:169962. [PMID: 38219999 DOI: 10.1016/j.scitotenv.2024.169962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
BACKGROUND Exposure to semi-volatile organic compounds (SVOCs) may link to thyroid nodule risk, but studies of mixed-SVOCs exposure effects are lacking. Traditional analytical methods are inadequate for dealing with mixed exposures, while machine learning (ML) seems to be a good way to fill the gaps in the field of environmental epidemiology research. OBJECTIVES Different ML algorithms were used to explore the relationship between mixed-SVOCs exposure and thyroid nodule. METHODS A 1:1:1 age- and gender-matched case-control study was conducted in which 96 serum SVOCs were measured in 50 papillary thyroid carcinoma (PTC), 50 nodular goiters (NG), and 50 controls. Different ML techniques such as Random Forest, AdaBoost were selected based on their predictive power, and variables were selected based on their weights in the models. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were used to assess the mixed effects of the SVOCs exposure on thyroid nodule. RESULTS Forty-three of 96 SVOCs with detection rate >80 % were included in the analysis. ML algorithms showed a consistent selection of SVOCs associated with thyroid nodule. Fluazifop-butyl and fenpropathrin are positively associated with PTC and NG in single compound models (all P < 0.05). WQS model shows that exposure to mixed-SVOCs was associated with an increased risk of PTC and NG, with the mixture dominated by fenpropathrin, followed by fluazifop-butyl and propham. In the BKMR model, mixtures showed a significant positive association with thyroid nodule risk at high exposure levels, and fluazifop-butyl showed positive effects associated with PTC and NG. CONCLUSION This study confirms the feasibility of ML methods for variable selection in high-dimensional complex data and showed that mixed exposure to SVOCs was associated with increased risk of PTC and NG. The observed association was primarily driven by fluazifop-butyl and fenpropathrin. The findings warranted further investigation.
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Affiliation(s)
- Fei Wang
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Yuanxin Lin
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Jianing Xu
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China; School of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Fugui Wei
- Department of Head and Neck Surgery, The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Simei Huang
- School of Science, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Shifeng Wen
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Huijiao Zhou
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Yuwei Jiang
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Haoyu Wang
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Wenlong Ling
- Department of Thyroid Surgery, The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Xiangzhi Li
- Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China; Department of Public Health, School of Medicine, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Xiaobo Yang
- Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China.
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Ren X, Mi Z, Georgopoulos PG. Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024; 34:197-207. [PMID: 36725924 PMCID: PMC9889956 DOI: 10.1038/s41370-023-00518-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 12/29/2022] [Accepted: 01/06/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales. OBJECTIVE To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches. METHODS We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches. RESULTS We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models. SIGNIFICANCE The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.
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Affiliation(s)
- Xiang Ren
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ, 08854, USA
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ, 08854, USA
- Department of Environmental and Occupational Health and Justice, Rutgers School of Public Health, Piscataway, NJ, 08854, USA
| | - Zhongyuan Mi
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ, 08854, USA
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Panos G Georgopoulos
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ, 08854, USA.
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
- Department of Environmental and Occupational Health and Justice, Rutgers School of Public Health, Piscataway, NJ, 08854, USA.
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ, 08901, USA.
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YOU L, SUN G, YU D, LIU X, XU G. [New advances in exposomics-analysis methods and research paradigms based on chromatography-mass spectrometry]. Se Pu 2024; 42:109-119. [PMID: 38374591 PMCID: PMC10877474 DOI: 10.3724/sp.j.1123.2023.12001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Indexed: 02/21/2024] Open
Abstract
The occurrence and development of human diseases are influenced by both genetic and environmental factors. Research models that describe disease occurrence only from the perspective of genetics present certain limitations. In recent years, effects of environment factors on the occurrence and development of diseases have attracted extensive attentions. Exposomics focuses on the measurement of all exposure factors in an individual's life and how these factors are related to disease development. Exposomics provides new ideas to promote studies on the relationship between human health and environmental factors. Environmental exposures are characterized with different physical and chemical properties, as well as very low concentrations in vivo, which contribute great challenges in the comprehensive measurement of chemical residues in the human body. Chromatography-mass spectrometry-based technologies combine the high-efficiency separation ability of chromatography with the high resolution and sensitive detection characteristics of mass spectrometry; the combination of these techniques can achieve the high-coverage, high-throughput, and sensitive detection of environmental exposures, thus providing a powerful tool for measuring chemical exposures. Exposomics-analysis methods based on chromatography-mass spectrometry mainly include targeted quantitative analysis, suspect screening, and non-targeted screening. To explore the relationship between environmental exposure and the occurrence and development of diseases, researchers have developed research paradigms, including exposome wide association study, mixed-exposure study, exposomics and multi-omics (genome, transcriptome, proteome, metabolome)-association study, and so on. The emergence of these methods has brought about unprecedented developments in exposomics studies. In this manuscript, analytical methods based on chromatography-mass spectrometry, exposomics research paradigms, and their relevant prospects are reviewed.
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Chung MK, House JS, Akhtari FS, Makris KC, Langston MA, Islam KT, Holmes P, Chadeau-Hyam M, Smirnov AI, Du X, Thessen AE, Cui Y, Zhang K, Manrai AK, Motsinger-Reif A, Patel CJ. Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs). EXPOSOME 2024; 4:osae001. [PMID: 38344436 PMCID: PMC10857773 DOI: 10.1093/exposome/osae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/16/2023] [Accepted: 11/20/2023] [Indexed: 03/07/2024]
Abstract
This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.
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Affiliation(s)
- Ming Kei Chung
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Konstantinos C Makris
- Cyprus International Institute for Environmental and Public Health, School of Health Sciences, Cyprus University of Technology, Limassol, Cyprus
| | - Michael A Langston
- Department of Electrical Engineering and Computer Science, University of TN, Knoxville, TN, USA
| | - Khandaker Talat Islam
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern CA, Los Angeles, CA, USA
| | - Philip Holmes
- Department of Physics, Villanova University, Villanova, Philadelphia, USA
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Alex I Smirnov
- Department of Chemistry, NC State University, Raleigh, NC, USA
| | - Xiuxia Du
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of NC at Charlotte, Charlotte, NC, USA
| | - Anne E Thessen
- Department of Biomedical Informatics, University of CO Anschutz Medical Campus, Aurora, CO, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of NY, Rensselaer, NY, USA
| | - Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Ray M, Zhao S, Wang S, Bohl A, Romano PS. Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation. BMC Health Serv Res 2023; 23:1419. [PMID: 38102614 PMCID: PMC10722658 DOI: 10.1186/s12913-023-10423-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 12/03/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Risk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify covariates as main effects, but they often ignore interactions or use stratification to account for effect modification, despite limitations due to rare events and sparse data. Three Agency for Healthcare Research and Quality (AHRQ) hospital-level Quality Indicators currently use stratified models, but their variable performance and limited interpretability motivated the design of better models. METHODS We analysed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. We used hierarchical group lasso regularisation (HGLR) to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 (Pancreatic Resection Mortality Rate), IQI 11 (Abdominal Aortic Aneurysm Repair Mortality Rate), and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO). RESULTS HGLR identified clinically meaningful interactions for all models. Synergistic IQI 11 interactions, such as between hypertension and respiratory failure, suggest patients who merit special attention in perioperative care. Antagonistic IQI 11 interactions, such as between shock and chronic comorbidities, illustrate that naïve main effects models overestimate risk in key subpopulations. Interactions for PSI 14 suggest key subpopulations for whom the risk of wound dehiscence is similar between open and laparoscopic approaches, whereas laparoscopic approach is safer for other groups. Model performance was similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features. CONCLUSIONS In this application to high-profile, high-stakes risk-adjustment models, HGLR selected interactions that maintained or improved model performance in populations with heterogeneous risk, while identifying clinically important interactions. The HGLR package is scalable to handle a large number of covariates and their interactions and is customisable to use multiple CPU cores to reduce analysis time. The HGLR method will allow scholars to avoid creating stratified models on sparse data, improve model calibration, and reduce bias. Future work involves testing using other combinations of risk factors, such as vital signs and laboratory values. Our study focuses on a real-world problem of considerable importance to hospitals and policy-makers who must use RA models for statutorily mandated public reporting and payment programmes.
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Affiliation(s)
- Monika Ray
- Division of General Internal Medicine, School of Medicine, University of California, Davis, Sacramento, California, USA.
- Center for Healthcare Policy and Research, University of California, Davis, Sacramento, California, USA.
| | - Sharon Zhao
- Mathematica Inc., Princeton, New Jersey, USA
| | - Sheng Wang
- Mathematica Inc., Princeton, New Jersey, USA
| | - Alex Bohl
- Mathematica Inc., Princeton, New Jersey, USA
| | - Patrick S Romano
- Division of General Internal Medicine, School of Medicine, University of California, Davis, Sacramento, California, USA
- Center for Healthcare Policy and Research, University of California, Davis, Sacramento, California, USA
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Chen H, Wang M, Zhang C, Li J. A methodological study of exposome based on an open database: Association analysis between exposure to metal mixtures and hyperuricemia. CHEMOSPHERE 2023; 344:140318. [PMID: 37775054 DOI: 10.1016/j.chemosphere.2023.140318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND Exposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods. OBJECTS We aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data. METHODS Pre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA. RESULTS The prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02-1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13-1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA. CONCLUSIONS Heavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.
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Affiliation(s)
- Haoran Chen
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China
| | - Min Wang
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China
| | - Chongyang Zhang
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China
| | - Jiao Li
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China.
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9
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Cavalier H, Trasande L, Porta M. Exposures to pesticides and risk of cancer: Evaluation of recent epidemiological evidence in humans and paths forward. Int J Cancer 2023; 152:879-912. [PMID: 36134639 PMCID: PMC9880902 DOI: 10.1002/ijc.34300] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 09/05/2022] [Accepted: 09/12/2022] [Indexed: 02/02/2023]
Abstract
Knowledge of the role in cancer etiology of environmental exposures as pesticides is a prerequisite for primary prevention. We review 63 epidemiological studies on exposure to pesticides and cancer risk in humans published from 2017 to 2021, with emphasis on new findings, methodological approaches, and gaps in the existing literature. While much of the recent evidence suggests causal relationships between pesticide exposure and cancer, the strongest evidence exists for acute myeloid leukemia (AML) and colorectal cancer (CRC), diseases in which the observed associations were consistent across several studies, including high-quality prospective studies and those using biomarkers for exposure assessment, with some observing dose-response relationships. Though high-quality studies have been published since the IARC monograph on organophosphate insecticides in 2017, there are still gaps in the literature on carcinogenic evidence in humans for a large number of pesticides. To further knowledge, we suggest leveraging new techniques and methods to increase sensitivity and precision of exposure assessment, incorporate multi-omics data, and investigate more thoroughly exposure to chemical mixtures. There is also a strong need for better and larger population-based cohort studies that include younger and nonoccupationally exposed individuals, particularly during developmental periods of susceptibility. Though the existing evidence has limitations, as always in science, there is sufficient evidence to implement policies and regulatory action that limit pesticide exposure in humans and, hence, further prevent a significant burden of cancers.
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Affiliation(s)
- Haleigh Cavalier
- Department of PediatricsNew York University (NYU) School of MedicineNew YorkNew YorkUSA
- Department of Environmental MedicineNew York University (NYU) School of MedicineNew YorkNew YorkUSA
- Department of Population HealthNew York University (NYU) School of MedicineNew YorkNew YorkUSA
| | - Leonardo Trasande
- Department of PediatricsNew York University (NYU) School of MedicineNew YorkNew YorkUSA
- Department of Environmental MedicineNew York University (NYU) School of MedicineNew YorkNew YorkUSA
- Department of Population HealthNew York University (NYU) School of MedicineNew YorkNew YorkUSA
- NYU School of Global Public HealthNew YorkNew YorkUSA
| | - Miquel Porta
- Department of PediatricsNew York University (NYU) School of MedicineNew YorkNew YorkUSA
- School of MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Hospital del Mar Medical Research Institute (IMIM PSMar PRBB)BarcelonaCataloniaSpain
- Department of Epidemiology, Gillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP)MadridSpain
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10
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Cano-Sancho G, Warembourg C, Güil N, Stratakis N, Lertxundi A, Irizar A, Llop S, Lopez-Espinosa MJ, Basagaña X, González JR, Coumoul X, Fernández-Barrés S, Antignac JP, Vrijheid M, Casas M. Nutritional Modulation of Associations between Prenatal Exposure to Persistent Organic Pollutants and Childhood Obesity: A Prospective Cohort Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:37011. [PMID: 36927187 PMCID: PMC10019508 DOI: 10.1289/ehp11258] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Prenatal exposure to persistent organic pollutants (POPs) may contribute to the development of childhood obesity and metabolic disorders. However, little is known about whether the maternal nutritional status during pregnancy can modulate these associations. OBJECTIVES The main objective was to characterize the joint associations and interactions between prenatal levels of POPs and nutrients on childhood obesity. METHODS We used data from to the Spanish INfancia y Medio Ambiente-Environment and Childhood (INMA) birth cohort, on POPs and nutritional biomarkers measured in maternal blood collected at the first trimester of pregnancy and child anthropometric measurements at 7 years of age. Six organochlorine compounds (OCs) [dichlorodiphenyldichloroethylene, hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH) and polychlorinated biphenyls 138, 153, 180] and four per- and polyfluoroalkyl substances (PFAS) were measured. Nutrients included vitamins (D, B12, and folate), polyunsaturated fatty acids (PUFAs), and dietary carotenoids. Two POPs-nutrients mixtures data sets were established: a) OCs, PFAS, vitamins, and carotenoids (n=660), and b) OCs, PUFAs, and vitamins (n=558). Joint associations of mixtures on obesity were characterized using Bayesian kernel machine regression (BKMR). Relative importance of biomarkers and two-way interactions were identified using gradient boosting machine, hierarchical group lasso regularization, and BKMR. Interactions were further characterized using multivariate regression models in the multiplicative and additive scale. RESULTS Forty percent of children had overweight or obesity. We observed a positive overall joint association of both POPs-nutrients mixtures on overweight/obesity risk, with HCB and vitamin B12 the biomarkers contributing the most. Recurrent interactions were found between HCB and vitamin B12 across screening models. Relative risk for a natural log increase of HCB was 1.31 (95% CI: 1.11, 1.54, pInteraction=0.02) in the tertile 2 of vitamin B12 and in the additive scale a relative excess risk due to interaction of 0.11 (95% CI: 0.02, 0.20) was found. Interaction between perfluorooctane sulfonate and β-cryptoxanthin suggested a protective effect of the antioxidant on overweight/obesity risk. CONCLUSION These results support that maternal nutritional status may modulate the effect of prenatal exposure to POPs on childhood overweight/obesity. These findings may help to develop a biological hypothesis for future toxicological studies and to better interpret inconsistent findings in epidemiological studies. https://doi.org/10.1289/EHP11258.
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Affiliation(s)
- German Cano-Sancho
- Laboratory for the Study of Residues and Contaminants in Foods (LABERCA), Oniris, Institut national de la recherche agronomique (INRAE), Nantes, France
| | - Charline Warembourg
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Institut de recherche en santé, environnement et travail (IRSET), Ecole des hautes études en santé publique (EHESP), Unité Mixte de Recherche (UMR) 1085 Institut national de la santé et de la recherche médicale (INSERM), Université de Rennes, Rennes, France
| | - Nuria Güil
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Nikos Stratakis
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
| | - Aitana Lertxundi
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Biodonostia, Unidad de Epidemiologia Ambiental y Desarrollo Infantil, San Sebastian, Gipuzkoa, Spain
- Facultad de Medicina, Universidad del País Vasco/Euskal Herriko Unibertsitatea, Leioa, Bizkaia, Spain
| | - Amaia Irizar
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Biodonostia, Unidad de Epidemiologia Ambiental y Desarrollo Infantil, San Sebastian, Gipuzkoa, Spain
- Facultad de Medicina, Universidad del País Vasco/Euskal Herriko Unibertsitatea, Leioa, Bizkaia, Spain
| | - Sabrina Llop
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region, Foundation for the Promotion of Health and Biomedical Research in the Valencian Community (FISABIO)–Public Health, FISABIO–Universitat Jaume I–Universitat de València, Valencia, Valencia, Spain
| | - Maria-Jose Lopez-Espinosa
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region, Foundation for the Promotion of Health and Biomedical Research in the Valencian Community (FISABIO)–Public Health, FISABIO–Universitat Jaume I–Universitat de València, Valencia, Valencia, Spain
- Faculty of Nursing and Chiropody, University of Valencia, Valencia, Valencia, Spain
| | - Xavier Basagaña
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Juan Ramon González
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Xavier Coumoul
- Institut national de la santé et de la recherche médicale (INSERM) UMR-S1124, Université de Paris, Paris, France
| | - Sílvia Fernández-Barrés
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Jean-Philippe Antignac
- Laboratory for the Study of Residues and Contaminants in Foods (LABERCA), Oniris, Institut national de la recherche agronomique (INRAE), Nantes, France
| | - Martine Vrijheid
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Maribel Casas
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
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11
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Rodriguez Martin L, Ottenbros I, Vogel N, Kolossa-Gehring M, Schmidt P, Řiháčková K, Juliá Molina M, Varea-Jiménez E, Govarts E, Pedraza-Diaz S, Lebret E, Vlaanderen J, Luijten M. Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study. TOXICS 2023; 11:toxics11030204. [PMID: 36976969 PMCID: PMC10058482 DOI: 10.3390/toxics11030204] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 01/28/2023] [Indexed: 06/01/2023]
Abstract
Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals to which people are exposed to on a daily basis. Human biomonitoring (HBM) approaches can provide inter alia information on the chemicals that are in our body at one point in time. Network analysis applied to such data may provide insight into real-life mixtures by visualizing chemical exposure patterns. The identification of groups of more densely correlated biomarkers, so-called "communities", within these networks highlights which combination of substances should be considered in terms of real-life mixtures to which a population is exposed. We applied network analyses to HBM datasets from Belgium, Czech Republic, Germany, and Spain, with the aim to explore its added value for exposure and risk assessment. The datasets varied in study population, study design, and chemicals analysed. Sensitivity analysis was performed to address the influence of different approaches to standardise for creatinine content of urine. Our approach demonstrates that network analysis applied to HBM data of highly varying origin provides useful information with regards to the existence of groups of biomarkers that are densely correlated. This information is relevant for regulatory risk assessment, as well as for the design of relevant mixture exposure experiments.
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Affiliation(s)
| | - Ilse Ottenbros
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands
- Center for Sustainability, Environment and Health, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
| | - Nina Vogel
- German Environment Agency (UBA), 14195 Berlin, Germany
| | | | | | - Katarína Řiháčková
- RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
| | - Miguel Juliá Molina
- National Centre for Environmental Health, Instituto de Salud Carlos III, 28220 Majadahonda, Spain
| | - Elena Varea-Jiménez
- National Centre for Environmental Health, Instituto de Salud Carlos III, 28220 Majadahonda, Spain
| | - Eva Govarts
- Health, Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium
| | - Susana Pedraza-Diaz
- National Centre for Environmental Health, Instituto de Salud Carlos III, 28220 Majadahonda, Spain
| | - Erik Lebret
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands
- Center for Sustainability, Environment and Health, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
| | - Jelle Vlaanderen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands
| | - Mirjam Luijten
- Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
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12
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Marín D, Orozco LY, Narváez DM, Ortiz-Trujillo IC, Molina FJ, Ramos CD, Rodriguez-Villamizar L, Bangdiwala SI, Morales O, Cuellar M, Hernández LJ, Henao EA, Lopera V, Corredor A, Toro MV, Groot H, Villamil-Osorio M, Muñoz DA, Hincapié RC, Amaya F, Oviedo AI, López L, Morales-Betancourt R, Marín-Ochoa BE, Sánchez-García OE, Marín JS, Abad JM, Toro JC, Pinzón E, Builes JJ, Rueda ZV. Characterization of the external exposome and its contribution to the clinical respiratory and early biological effects in children: The PROMESA cohort study protocol. PLoS One 2023; 18:e0278836. [PMID: 36662732 PMCID: PMC9858469 DOI: 10.1371/journal.pone.0278836] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 11/01/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Air pollution contains a mixture of different pollutants from multiple sources. However, the interaction of these pollutants with other environmental exposures, as well as their harmful effects on children under five in tropical countries, is not well known. OBJECTIVE This study aims to characterize the external exposome (ambient and indoor exposures) and its contribution to clinical respiratory and early biological effects in children. MATERIALS AND METHODS A cohort study will be conducted on children under five (n = 500) with a one-year follow-up. Enrolled children will be followed monthly (phone call) and at months 6 and 12 (in person) post-enrolment with upper and lower Acute Respiratory Infections (ARI) examinations, asthma development, asthma control, and genotoxic damage. The asthma diagnosis will be pediatric pulmonologist-based and a standardized protocol will be used. Exposure, effect, and susceptibility biomarkers will be measured on buccal cells samples. For environmental exposures PM2.5 will be sampled, and questionnaires, geographic information, dispersion models and Land Use Regression models for PM2.5 and NO2 will be used. Different statistical methods that include Bayesian and machine learning techniques will be used for the ambient and indoor exposures-and outcomes. This study was approved by the ethics committee at Universidad Pontificia Bolivariana. EXPECTED STUDY OUTCOMES/FINDINGS To estimate i) The toxic effect of particulate matter transcending the approach based on pollutant concentration levels; ii) The risk of developing an upper and lower ARI, based on different exposure windows; iii) A baseline of early biological damage in children under five, and describe its progression after a one-year follow-up; and iv) How physical and chemical PM2.5 characteristics influence toxicity and children's health.
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Affiliation(s)
- Diana Marín
- School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia
| | - Luz Yaneth Orozco
- School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia
- School of Engineering, Universidad de Antioquia, Medellín, Colombia
| | | | | | | | | | - Laura Rodriguez-Villamizar
- Department of Public Health, School of Health, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Shrikant I. Bangdiwala
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
- Statistics Department, Population Health Research Institute, McMaster University, Hamilton, Canada
| | - Olga Morales
- School of Medicine, Pediaciencias Group, Universidad de Antioquia, Noel Clinic, Medellín, Colombia
- Department of Pediatrics, Hospital San Vicente Fundación, Medellín, Colombia
| | - Martha Cuellar
- School of Medicine, Pediaciencias Group, Universidad de Antioquia, Noel Clinic, Medellín, Colombia
- Department of Pediatrics, SOMER Clinic, Medellín, Colombia
| | | | | | - Verónica Lopera
- Secretaría de Salud, Alcaldía de Medellín, Medellín, Colombia
| | - Andrea Corredor
- Department of Pediatrics, ONIROS Centro Especializado en Medicina Integral del Sueño, Bogotá, Colombia
| | - María Victoria Toro
- School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia
| | - Helena Groot
- Human Genetics Laboratory, Universidad de los Andes, Bogotá, Colombia
| | - Milena Villamil-Osorio
- Department of Pediatrics, Fundación Hospital Pediátrico la Misericordia, Bogotá, Colombia
| | | | | | - Ferney Amaya
- School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia
| | - Ana Isabel Oviedo
- School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia
| | - Lucelly López
- School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia
| | | | - Beatriz Elena Marín-Ochoa
- School of Social Communications and Journalism, Universidad Pontificia Bolivariana, Medellín, Colombia
| | | | | | | | | | - Eliana Pinzón
- Secretaria distrital de Salud, Alcaldia de Bogota, Bogota, Colombia
| | | | - Zulma Vanessa Rueda
- School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
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13
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Martenies SE, Hoskovec L, Wilson A, Moore BF, Starling AP, Allshouse WB, Adgate JL, Dabelea D, Magzamen S. Using non-parametric Bayes shrinkage to assess relationships between multiple environmental and social stressors and neonatal size and body composition in the Healthy Start cohort. Environ Health 2022; 21:111. [PMID: 36401268 PMCID: PMC9675112 DOI: 10.1186/s12940-022-00934-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/30/2022] [Indexed: 06/09/2023]
Abstract
BACKGROUND Both environmental and social factors have been linked to birth weight and adiposity at birth, but few studies consider the effects of exposure mixtures. Our objective was to identify which components of a mixture of neighborhood-level environmental and social exposures were driving associations with birth weight and adiposity at birth in the Healthy Start cohort. METHODS Exposures were assessed at the census tract level and included air pollution, built environment characteristics, and socioeconomic status. Prenatal exposures were assigned based on address at enrollment. Birth weight was measured at delivery and adiposity was measured using air displacement plethysmography within three days. We used non-parametric Bayes shrinkage (NPB) to identify exposures that were associated with our outcomes of interest. NPB models were compared to single-predictor linear regression. We also included generalized additive models (GAM) to assess nonlinear relationships. All regression models were adjusted for individual-level covariates, including maternal age, pre-pregnancy BMI, and smoking. RESULTS Results from NPB models showed most exposures were negatively associated with birth weight, though credible intervals were wide and generally contained zero. However, the NPB model identified an interaction between ozone and temperature on birth weight, and the GAM suggested potential non-linear relationships. For associations between ozone or temperature with birth weight, we observed effect modification by maternal race/ethnicity, where effects were stronger for mothers who identified as a race or ethnicity other than non-Hispanic White. No associations with adiposity at birth were observed. CONCLUSIONS NPB identified prenatal exposures to ozone and temperature as predictors of birth weight, and mothers who identify as a race or ethnicity other than non-Hispanic White might be disproportionately impacted. However, NPB models may have limited applicability when non-linear effects are present. Future work should consider a two-stage approach where NPB is used to reduce dimensionality and alternative approaches examine non-linear effects.
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Affiliation(s)
- Sheena E Martenies
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, 906 S Goodwin Ave, M/C 052, Urbana, IL, 61801, USA.
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USA.
| | - Lauren Hoskovec
- Department of Statistics, Colorado State University, Fort Collins, CO, USA
| | - Ander Wilson
- Department of Statistics, Colorado State University, Fort Collins, CO, USA
| | - Brianna F Moore
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Anne P Starling
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD Center), University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - William B Allshouse
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Campus, Aurora, CO, USA
| | - John L Adgate
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Campus, Aurora, CO, USA
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD Center), University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Sheryl Magzamen
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USA
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Ding E, Wang Y, Liu J, Tang S, Shi X. A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases. Hum Genomics 2022; 16:54. [DOI: 10.1186/s40246-022-00428-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/29/2022] [Indexed: 11/11/2022] Open
Abstract
AbstractAge-related diseases account for almost half of all diseases among adults worldwide, and their incidence is substantially affected by the exposome, which is the sum of all exogenous and endogenous environmental exposures and the human body’s response to these exposures throughout the entire lifespan. Herein, we perform a comprehensive review of the epidemiological literature to determine the key elements of the exposome that affect the development of age-related diseases and the roles of aging hallmarks in this process. We find that most exposure assessments in previous aging studies have used a reductionist approach, whereby the effect of only a single environmental factor or a specific class of environmental factors on the development of age-related diseases has been examined. As such, there is a lack of a holistic and unbiased understanding of the effect of multiple environmental factors on the development of age-related diseases. To address this, we propose several research strategies based on an exposomic framework that could advance our understanding—in particular, from a mechanistic perspective—of how environmental factors affect the development of age-related diseases. We discuss the statistical methods and other methods that have been used in exposome-wide association studies, with a particular focus on multiomics technologies. We also address future challenges and opportunities in the realm of multidisciplinary approaches and genome–exposome epidemiology. Furthermore, we provide perspectives on precise public health services for vulnerable populations, public communications, the integration of risk exposure information, and the bench-to-bedside translation of research on age-related diseases.
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15
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Genes, exposures, and interactions on preterm birth risk: an exploratory study in an Argentine population. J Community Genet 2022; 13:557-565. [PMID: 35976607 DOI: 10.1007/s12687-022-00605-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 08/12/2022] [Indexed: 10/15/2022] Open
Abstract
Preterm birth (PTB) is the main condition related to perinatal morbimortality worldwide. The aim of this study was to identify associations of spontaneous PTB with genetic variants, exposures, and interactions between and within them. We carried out a retrospective case-control study including parental sociodemographic and obstetric data, and fetal genetic variants. We sequenced the coding and flanking regions of five candidate genes from the placental blood cord of 69 preterm newborns and 61 at term newborns. We identify the characteristics with the greatest predictive power of PTB using penalized regressions, in which we include exposures (E), genetic variants (G), and two-way interactions. Few prenatal visits (< 5) was the main predictor of PTB from 26 G, 35 E, 299 G × G, 564 E × E, and 875 G × E evaluated terms. Within the fetal genetic characteristics, we observed associations of rs4845397 (KCNN3, allele T) variant; G × G interaction between rs12621551 (COL4A3, allele T) and rs73993878 (COL4A3, allele A), which showed sensitivity to anemia; and G × G interaction between rs11680670 (COL4A3, allele T) and rs2074351 (PON1, allele A), which showed sensitivity to vaginal discharge. The results of this exploratory study suggest that social disparities and metabolic pathways linked to uterine relaxation, inflammation/infections, and collagen metabolism would be involved in PTB etiology. Future studies with a larger sample size are necessary to confirm these findings and to analyze a greater number of exposures.
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16
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Pudjihartono N, Fadason T, Kempa-Liehr AW, O'Sullivan JM. A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction. FRONTIERS IN BIOINFORMATICS 2022; 2:927312. [PMID: 36304293 PMCID: PMC9580915 DOI: 10.3389/fbinf.2022.927312] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/03/2022] [Indexed: 01/14/2023] Open
Abstract
Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However, creating an accurate prediction model based on genotype data remains challenging due to the so-called “curse of dimensionality” (i.e., extensively larger number of features compared to the number of samples). Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most “informative” features and remove noisy “non-informative,” irrelevant and redundant features. In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases, focusing on the detection of relevant features (i.e., SNPs) for disease risk prediction.
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Affiliation(s)
| | - Tayaza Fadason
- Liggins Institute, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Andreas W. Kempa-Liehr
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
- *Correspondence: Andreas W. Kempa-Liehr, ; Justin M. O'Sullivan,
| | - Justin M. O'Sullivan
- Liggins Institute, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Australian Parkinson’s Mission, Garvan Institute of Medical Research, Sydney, NSW, Australia
- *Correspondence: Andreas W. Kempa-Liehr, ; Justin M. O'Sullivan,
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Miri M, Rezaei H, Momtaz SM, Najafi ML, Adli A, Pajohanfar NS, Abroudi M, Bazghandi MS, Razavi Z, Alonso L, Tonne C, Basagaña X, Nieuwenhuijsen MJ, Sunyer J, Nawrot TS, Dadvand P. Determinants of carbon load in airway macrophages in pregnant women. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 297:118765. [PMID: 34973383 DOI: 10.1016/j.envpol.2021.118765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/23/2021] [Accepted: 12/27/2021] [Indexed: 06/14/2023]
Abstract
The airway macrophages carbon loading (AMCL) has been suggested to be a biomarker of the long-term exposure to air pollution; however, to date no study has characterized AMCL for the pregnancy period. Therefore, this study aimed to assess the determinants of AMCL during pregnancy in Iran, a middle-income country. This study was based on a sample of 234 pregnant women with term and normal vaginal delivery who were residing in Sabzevar, Iran (2019). We characterized 35 potential determinants of personal exposure to air pollution for each participant, including six personal, nine indoor, and 20 home-outdoor factors. We applied Deletion/Substitution/Addition algorithm to identify the most relevant determinants that could predict AMCL levels. The median (IQR) of AMCL level was 0.12 (0.30) μm2 with a successful sputum induction in 82.9% (194) of participants. Ambient residential PM2.5 levels were positively associated with higher AMCL levels. On the other hand, increased residential distance to the traffic lights, squares and ring-roads, the duration of opening window per day, and opening window during cooking were inversely associated with AMCL levels. Our findings provide novel insights on the different personal, indoor, and outdoor determinants of personal exposure to air pollution during pregnancy in a middle-income country.
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Affiliation(s)
- Mohammad Miri
- Non-Communicable Diseases Research Center, Department of Environmental Health, School of Health, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Hossein Rezaei
- Student Research Committee, Department of Environmental Health, School of Health, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Seyed Mojtaba Momtaz
- Department of Environmental Health Engineering, Zarand School of Nursing, Kerman University of Medical Sciences, Kerman, Iran
| | - Moslem Lari Najafi
- Pharmaceutical Sciences and Cosmetic Products Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Abolfazl Adli
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Nasim Sadat Pajohanfar
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Mina Abroudi
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Malihe Sadat Bazghandi
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Zahra Razavi
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Lucia Alonso
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Cathryn Tonne
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Mark J Nieuwenhuijsen
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Tim S Nawrot
- Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium; Department of Public Health & Primary Care, Leuven University, Leuven, Belgium
| | - Payam Dadvand
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.
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Guxens M, Lubczynska MJ, Perez-Crespo L, Muetzel RL, El Marroun H, Basagana X, Hoek G, Tiemeier H. Associations of Air Pollution on the Brain in Children: A Brain Imaging Study. Res Rep Health Eff Inst 2022; 2022:1-61. [PMID: 36106707 PMCID: PMC9476146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Introduction Epidemiological studies are highlighting the negative effects of the exposure to air pollution on children's neurodevelopment. However, most studies assessed children's neurodevelopment using neuropsychological tests or questionnaires. Using magnetic resonance imaging (MRI) to precisely measure global and region-specific brain development would provide details of brain morphology and connectivity. This would help us understand the observed cognitive and behavioral changes related to air pollution exposure. Moreover, most studies assessed only a few air pollutants. This project investigates whether air pollution exposure to many pollutants during pregnancy and childhood is associated with the morphology and connectivity of the brain in school-age children and pre-adolescents. Methods We used data from the Generation R Study, a population-based birth cohort set up in Rotterdam, the Netherlands in 2002-2006 (n = 9,610). We used land-use regression (LUR) models to estimate the levels of 14 air pollutants at participant's homes during pregnancy and childhood: nitrogen oxides (NOx), nitrogen dioxide (NO2), particulate matter with aerodynamic diameter ≤10 μm (PM10) or ≤2.5 μm (PM2.5), PM between 10 μm and 2.5 μm (PMCOARSE), absorbance of the PM2.5 fraction - a measure of soot (PM2.5absorbance), the composition of PM2.5 such as polycyclic aromatic hydrocarbons (PAHs), organic carbon (OC), copper (Cu), iron (Fe), silicon (Si), zinc (Zn), and the oxidative potential of PM2.5 evaluated using two acellular methods: dithiothreitol (OPDTT) and electron spin resonance (OPESR). We performed MRI measurements of structural morphology (i.e., brain volumes, cortical thickness, and cortical surface area) using T1-weighted images in 6- to 10-year-old school-age children and 9- to 12-year-old pre-adolescents, structural connectivity (i.e., white matter microstructure) using diffusion tensor imaging (DTI) in pre-adolescents, and functional connectivity (i.e., connectivity score between brain areas) using resting-state functional MRI (rs-fMRI) in pre-adolescents. We assessed cognitive function using the Developmental Neuropsychological Assessment test (NEPSY-II) in school-age children. For each outcome, we ran regression analysis adjusted for several socioeconomic and lifestyle characteristics. We performed single-pollutant analyses followed by multipollutant analyses using the deletion/substitution/addition (DSA) approach. Results The project has air pollution and brain MRI data for 783 school-age children and 3,857 pre-adolescents. First, exposure to air pollution during pregnancy or childhood was not associated with global brain volumes (e.g., total brain, cortical gray matter, and cortical white matter) in school-age children or pre-adolescents. However, higher pregnancy or childhood exposure to several air pollutants was associated with a smaller corpus callosum and hippocampus, and a larger amygdala, nucleus accumbens, and cerebellum in pre-adolescents, but not in school-age children. Second, higher exposure to several air pollutants during pregnancy was associated with a thinner cortex in various regions of the brain in both school-age children and pre-adolescents. Higher exposure to air pollution during childhood was also associated with a thinner cortex in a single region in pre-adolescents. A thinner cortex in two regions mediated the association between higher exposure to air pollution during pregnancy and an impaired inhibitory control in school-age children. Third, higher exposure to air pollution during childhood was associated with smaller cortical surface areas in various regions of the brain except in a region where we observed a larger cortical surface area in pre-adolescents. In relation to brain structural connectivity, higher exposure to air pollution during pregnancy and childhood was associated with an alteration in white matter microstructure in pre-adolescents. In relation to brain functional connectivity, a higher exposure to air pollution, mainly during pregnancy and early childhood, was associated with a higher brain functional connectivity among several brain regions in pre-adolescents. Overall, we identified several air pollutants associated with brain structural morphology, structural connectivity, and functional connectivity, such as NOx, NO2, PM of various size fractions (i.e., PM10, PMCOARSE, and PM2.5), PM2.5absorbance, PAHs, OC, three elemental components of PM2.5 (i.e., Cu, Si, Zn), and the oxidative potential of PM2.5. Conclusions The results of this project suggest that exposure to air pollution during pregnancy and childhood play an adverse role in brain development. We observed this relationship even at levels of exposure that were below the European Union legislations. We acknowledge that identifying the independent effects of specific pollutants was particularly challenging. Most of our conclusions generally refer to traffic-related air pollutants. However, we did identify pollutants specifically originating from brake linings, tire wear, and tailpipe emissions from diesel combustion. The current direction toward innovative solutions for cleaner energy vehicles is a step in the right direction. However, our findings indicate that these measures might not be completely adequate to mitigate health problems attributable to traffic-related air pollution, as we also observed associations with markers of brake linings and tire wear.
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Affiliation(s)
- Monica Guxens
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, Madrid, Spain
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands
| | - Malgorzata J Lubczynska
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, Madrid, Spain
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands
| | - Laura Perez-Crespo
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, Madrid, Spain
| | - Ryan L Muetzel
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands
| | - Hanan El Marroun
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands
| | - Xavier Basagana
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, Madrid, Spain
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, the Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands
- Department of Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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19
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Preventing cognitive impairment by reducing air pollution. THE LANCET. HEALTHY LONGEVITY 2022; 3:e81-e82. [PMID: 36098325 DOI: 10.1016/s2666-7568(22)00006-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 01/11/2022] [Indexed: 12/24/2022] Open
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20
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Gautam Y, Johansson E, Mersha TB. Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm. J Pers Med 2022; 12:jpm12010066. [PMID: 35055381 PMCID: PMC8778153 DOI: 10.3390/jpm12010066] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 02/04/2023] Open
Abstract
Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.
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21
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Lin Y, Kong J, Tian T, Zhong X, Zhang S, Zhou H, Xiong Z, Zhao J, Huang Y, Liu M, Dong Y, Zheng J, Diao X, Wu J, Qin H, Hu Y, Wang X, Zhuang X, Liao X. Identification of Novel Phenotypes Correlated with CKD: A Phenotype-Wide Association Study. Int J Med Sci 2022; 19:1920-1928. [PMID: 36438912 PMCID: PMC9682501 DOI: 10.7150/ijms.63973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 08/18/2022] [Indexed: 11/24/2022] Open
Abstract
Background: A comprehensive understanding of phenotypes related to CKD will facilitate the identification and management of CKD. We aimed to panoramically test and validate associations between multiple phenotypes and CKD using a phenotype-wide association study (PheWAS). Methods: 15,815 subjects from cross-sectional cohorts of the National Health and Nutrition Examination Survey (1999-2006) were randomly 50:50 split into training and testing sets. CKD was defined as eGFR < 60 mL/min/1.73m2. We performed logistic regression analyses between each of 985 phenotypes with CKD in the training set (false discovery rate < 1%) and validated in the testing set (false discovery rate < 1% ). Random forest (RF) model, Nagelkerke's Pseudo-R2, and the area under the receiver operating characteristic (AUROC) were used to validate the identified phenotypes. Results: We identified 18 phenotypes significantly related to CKD, among which retinol, red cell distribution width (RDW), and C-peptide were less researched. The top 5 identified phenotypes were blood urea nitrogen (BUN), homocysteine (HCY), retinol, parathyroid hormone (PTH), and osmolality in RF importance ranking. Besides, BUN, HCY, PTH, retinol, and uric acid were the most important phenotypes based on Pseudo-R2. AUROC of the RF model was 0.951 (full model) and 0.914 (top 5 phenotypes). Conclusion: Our study demonstrated associations between multiple phenotypes with CKD from a holistic view, including 3 novel phenotypes: retinol, RDW, and C-peptide. Our findings provided valid evidence for the identification of novel biomarkers for CKD.
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Affiliation(s)
- Yifen Lin
- Cardiology department, First affiliated hospital of Sun Yat-Sen University.,NHC Key Laboratory of Assisted Circulation (Sun Yat-Sen University)
| | - Jianqiu Kong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital
| | - Ting Tian
- Department of Statistical Science, School of Mathematics, Southern China Center for Statistical Science, Sun Yat-Sen University
| | - Xiangbin Zhong
- Cardiology department, First affiliated hospital of Sun Yat-Sen University.,NHC Key Laboratory of Assisted Circulation (Sun Yat-Sen University)
| | - Shaozhao Zhang
- Cardiology department, First affiliated hospital of Sun Yat-Sen University.,NHC Key Laboratory of Assisted Circulation (Sun Yat-Sen University)
| | - Haojin Zhou
- Department of Statistical Science, School of Mathematics, Southern China Center for Statistical Science, Sun Yat-Sen University
| | - Zhenyu Xiong
- Cardiology department, First affiliated hospital of Sun Yat-Sen University.,NHC Key Laboratory of Assisted Circulation (Sun Yat-Sen University)
| | - Jiashu Zhao
- Department of Statistical Science, School of Mathematics, Southern China Center for Statistical Science, Sun Yat-Sen University
| | - Yiquan Huang
- Cardiology department, First affiliated hospital of Sun Yat-Sen University.,NHC Key Laboratory of Assisted Circulation (Sun Yat-Sen University)
| | - Menghui Liu
- Cardiology department, First affiliated hospital of Sun Yat-Sen University.,NHC Key Laboratory of Assisted Circulation (Sun Yat-Sen University)
| | - Yuehua Dong
- Department of Statistical Science, School of Mathematics, Southern China Center for Statistical Science, Sun Yat-Sen University
| | - Junjiong Zheng
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital
| | - Xiayao Diao
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital
| | - Jieyin Wu
- Department of Urology, the Third Affiliated Hospital of Sun Yat-Sen University
| | - Haide Qin
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital
| | - Yue Hu
- Department of Statistical Science, School of Mathematics, Southern China Center for Statistical Science, Sun Yat-Sen University
| | - Xueqin Wang
- Department of Statistical Science, School of Mathematics, Southern China Center for Statistical Science, Sun Yat-Sen University
| | - Xiaodong Zhuang
- Cardiology department, First affiliated hospital of Sun Yat-Sen University.,NHC Key Laboratory of Assisted Circulation (Sun Yat-Sen University)
| | - Xinxue Liao
- Cardiology department, First affiliated hospital of Sun Yat-Sen University.,NHC Key Laboratory of Assisted Circulation (Sun Yat-Sen University)
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22
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Ohanyan H, Portengen L, Huss A, Traini E, Beulens JWJ, Hoek G, Lakerveld J, Vermeulen R. Machine learning approaches to characterize the obesogenic urban exposome. ENVIRONMENT INTERNATIONAL 2022; 158:107015. [PMID: 34991269 DOI: 10.1016/j.envint.2021.107015] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously. OBJECTIVES We aimed to explore what environmental factors of the urban exposome are related to body mass index (BMI), and evaluated the consistency of findings across multiple statistical approaches. METHODS A cross-sectional analysis was conducted using baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study. BMI was obtained from self-reported height and weight. Geocoded exposures linked to individual home addresses (using 6-digit postcode) of 86 environmental factors were estimated, including air pollution, traffic noise, green-space, built environmental and neighborhood socio-demographic characteristics. Exposure-obesity associations were identified using the following approaches: sparse group Partial Least Squares, Bayesian Model Averaging, penalized regression using the Minimax Concave Penalty, Generalized Additive Model-based boosting Random Forest, Extreme Gradient Boosting, and Multiple Linear Regression, as the most conventional approach. The models were adjusted for individual socio-demographic variables. Environmental factors were ranked according to variable importance scores attributed by each approach and median ranks were calculated across these scores to identify the most consistent associations. RESULTS The most consistent environmental factors associated with BMI were the average neighborhood value of the homes, oxidative potential of particulate matter air pollution (OP), healthy food outlets in the neighborhood (5 km buffer), low-income neighborhoods, and one-person households in the neighborhood. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households and smaller amount of healthy food retailers. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households, smaller amounts of healthy food retailers and higher OP levels. Across the approaches, we observed consistent patterns of results based on model's capacity to incorporate linear or nonlinear associations. DISCUSSION The pluralistic analysis on environmental obesogens strengthens the existing evidence on the role of neighborhood socioeconomic position, urbanicity and air pollution.
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Affiliation(s)
- Haykanush Ohanyan
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, the Netherlands; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Utrecht, the Netherlands; Upstream Team, www.upstreamteam.nl. Amsterdam UMC, VU University Amsterdam, Amsterdam, Noord-Holland, the Netherlands.
| | - Lützen Portengen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Utrecht, the Netherlands
| | - Anke Huss
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Utrecht, the Netherlands
| | - Eugenio Traini
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Utrecht, the Netherlands
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, the Netherlands; Upstream Team, www.upstreamteam.nl. Amsterdam UMC, VU University Amsterdam, Amsterdam, Noord-Holland, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherland
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Utrecht, the Netherlands
| | - Jeroen Lakerveld
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, the Netherlands; Upstream Team, www.upstreamteam.nl. Amsterdam UMC, VU University Amsterdam, Amsterdam, Noord-Holland, the Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Utrecht, the Netherlands
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23
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Kane N. Revealing the racial and spatial disparity in pediatric asthma: A Kansas City case study. Soc Sci Med 2021; 292:114543. [PMID: 34802780 DOI: 10.1016/j.socscimed.2021.114543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 07/22/2021] [Accepted: 11/04/2021] [Indexed: 12/24/2022]
Abstract
Black and other socially disadvantaged children are disproportionately burdened by high rates of pediatric asthma. Intraurban variation in environmental risk factors and limited access to high-resolution health data make it difficult to identify vulnerable patients, communities, or the immediate exposures that may contribute to pediatric asthma exacerbation. This article presents a novel, interdisciplinary health disparities research and intervention strategy applied to the problem of pediatric asthma in Kansas City. First, address-level electronic health records from a major children's hospital in the Kansas City region are used to map the distribution of asthma encounters in 2012 at a high spatial resolution. Census tract Environmental Justice Screening Method (EJSM) indicators are then developed to scan for patterns in both the population health risks and vulnerabilities that may contribute to the burden of asthma in different communities. A Bayesian Profile Regression cluster analysis is used to systematically explore the complex relationships between census tract EJSM indicators and pediatric asthma incidence rates, helping to identify population characteristics and risk factors associated with both high and low rates of pediatric asthma throughout the region. The EJSM scanning exercise and BPR analysis demonstrate that each community faces a distinct set of risks and vulnerabilities that can contribute to the rate of acute pediatric asthma acute care encounters, providing targets for research and intervention. It is clear, however, that different forms of social disadvantage are driving high rates of pediatric asthma, which is closely tied to uneven development patterns and racial residential segregation. The results provide a starting point for designing place-based health disparities research and intervention strategies catered to the unique needs of vulnerable patients and communities; disparities-focused research and intervention strategies that leverage local knowledge and resources through community-based practices.
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Affiliation(s)
- Natalie Kane
- Children's Mercy Hospital, Kansas City, MO, USA.
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24
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Escriba-Montagut X, Basagaña X, Vrijheid M, Gonzalez JR. Software Application Profile: exposomeShiny—a toolbox for exposome data analysis. Int J Epidemiol 2021. [PMCID: PMC8855999 DOI: 10.1093/ije/dyab220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation Studying the role of the exposome in human health and its impact on different omic layers requires advanced statistical methods. Many of these methods are implemented in different R and Bioconductor packages, but their use may require strong expertise in R, in writing pipelines and in using new R classes which may not be familiar to non-advanced users. ExposomeShiny provides a bridge between researchers and most of the state-of-the-art exposome analysis methodologies, without the need of advanced programming skills. Implementation ExposomeShiny is a standalone web application implemented in R. It is available as source files and can be installed in any server or computer avoiding problems with data confidentiality. It is executed in RStudio which opens a browser window with the web application. General features The presented implementation allows the conduct of: (i) data pre-processing: normalization and missing imputation (including limit of detection); (ii) descriptive analysis; (iii) exposome principal component analysis (PCA) and hierarchical clustering; (iv) exposome-wide association studies (ExWAS) and variable selection ExWAS; (v) omic data integration by single association and multi-omic analyses; and (vi) post-exposome data analyses to gain biological insight for the exposures, genes or using the Comparative Toxicogenomics Database (CTD) and pathway analysis. Availability The exposomeShiny source code is freely available on Github at [https://github.com/isglobal-brge/exposomeShiny], Git tag v1.4. The software is also available as a Docker image [https://hub.docker.com/r/brgelab/exposome-shiny], tag v1.4. A user guide with information about the analysis methodologies as well as information on how to use exposomeShiny is freely hosted at [https://isglobal-brge.github.io/exposome_bookdown/].
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Affiliation(s)
| | - Xavier Basagaña
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Martine Vrijheid
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
| | - Juan R Gonzalez
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
- Corresponding author. Barcelona Biomedical Research Park (PRBB), Doctor Aiguader, 88. 08003 Barcelona, Spain. E-mail:
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Vrijheid M, Basagaña X, Gonzalez JR, Jaddoe VWV, Jensen G, Keun HC, McEachan RRC, Porcel J, Siroux V, Swertz MA, Thomsen C, Aasvang GM, Andrušaitytė S, Angeli K, Avraam D, Ballester F, Burton P, Bustamante M, Casas M, Chatzi L, Chevrier C, Cingotti N, Conti D, Crépet A, Dadvand P, Duijts L, van Enckevort E, Esplugues A, Fossati S, Garlantezec R, Gómez Roig MD, Grazuleviciene R, Gützkow KB, Guxens M, Haakma S, Hessel EVS, Hoyles L, Hyde E, Klanova J, van Klaveren JD, Kortenkamp A, Le Brusquet L, Leenen I, Lertxundi A, Lertxundi N, Lionis C, Llop S, Lopez-Espinosa MJ, Lyon-Caen S, Maitre L, Mason D, Mathy S, Mazarico E, Nawrot T, Nieuwenhuijsen M, Ortiz R, Pedersen M, Perelló J, Pérez-Cruz M, Philippat C, Piler P, Pizzi C, Quentin J, Richiardi L, Rodriguez A, Roumeliotaki T, Sabin Capote JM, Santiago L, Santos S, Siskos AP, Strandberg-Larsen K, Stratakis N, Sunyer J, Tenenhaus A, Vafeiadi M, Wilson RC, Wright J, Yang T, Slama R. Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview. Environ Epidemiol 2021; 5:e166. [PMID: 34934888 PMCID: PMC8683140 DOI: 10.1097/ee9.0000000000000166] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 06/28/2021] [Indexed: 11/26/2022] Open
Abstract
Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE's results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward.
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Affiliation(s)
- Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Corresponding Author. Address: ISGlobal, Institute for Global Health, C. Doctor Aiguader 88, 08003 Barcelona, Spain. E-mail: (M. Vrijheid)
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan R. Gonzalez
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Vincent W. V. Jaddoe
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Genon Jensen
- Health & Environment Alliance (HEAL), Brussels, Belgium
| | - Hector C. Keun
- Department of Surgery & Cancer and Department of Metabolism, Digestion & Reproduction, Imperial College London, London, United Kingdom
| | - Rosemary R. C. McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford,United Kingdom
| | - Joana Porcel
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Valerie Siroux
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
| | - Morris A. Swertz
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Cathrine Thomsen
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Gunn Marit Aasvang
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Sandra Andrušaitytė
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Karine Angeli
- French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Risk Assessment Department, Maisons-Alfort, France
| | - Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | - Ferran Ballester
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
- Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain
| | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Maribel Casas
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Cécile Chevrier
- University Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail)—UMR_S 1085, Rennes, France
| | | | - David Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Amélie Crépet
- French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Risk Assessment Department, Maisons-Alfort, France
| | - Payam Dadvand
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Liesbeth Duijts
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Esther van Enckevort
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Ana Esplugues
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
- Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain
| | - Serena Fossati
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Ronan Garlantezec
- CHU de Rennes, University Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail)—UMR_S 1085, Rennes, France
| | - María Dolores Gómez Roig
- Institut de Recerca Sant Joan de Déu (IR-SJD), Barcelona, Spain
- Maternal and Child Health and Development Network II (SAMID II), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- BCNatal—Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Sant Joan de Déu, Barcelona, Spain
| | | | - Kristine B. Gützkow
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Mònica Guxens
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Department of Child and Adolescence Psychiatry, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Sido Haakma
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Ellen V. S. Hessel
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Lesley Hoyles
- Department of Biosciences, Nottingham Trent University, Nottingham, United Kingdom
| | - Eleanor Hyde
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Jana Klanova
- RECETOX Centre, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jacob D. van Klaveren
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Andreas Kortenkamp
- Brunel University London, College of Health, Medicine and Life Sciences, Uxbridge, United Kingdom
| | - Laurent Le Brusquet
- University Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, France
| | - Ivonne Leenen
- Health & Environment Alliance (HEAL), Brussels, Belgium
| | - Aitana Lertxundi
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- University of Basque Country UPV/EHU, Basque Country, Bilbao, Spain
- Biodonostia, Research Health Institute, Donostia-San Sebastian, Spain
| | - Nerea Lertxundi
- University of Basque Country UPV/EHU, Basque Country, Bilbao, Spain
- Biodonostia, Research Health Institute, Donostia-San Sebastian, Spain
| | - Christos Lionis
- Department of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Sabrina Llop
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
| | - Maria-Jose Lopez-Espinosa
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
- Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain
| | - Sarah Lyon-Caen
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
| | - Lea Maitre
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford,United Kingdom
| | - Sandrine Mathy
- University Grenoble Alpes, CNRS, INRAE, Grenoble INP, GAEL, Grenoble, France
| | - Edurne Mazarico
- Institut de Recerca Sant Joan de Déu (IR-SJD), Barcelona, Spain
- Maternal and Child Health and Development Network II (SAMID II), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- BCNatal—Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Sant Joan de Déu, Barcelona, Spain
| | - Tim Nawrot
- Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium
- Centre for Health and Environment, Leuven University, Leuven, Belgium
| | - Mark Nieuwenhuijsen
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Rodney Ortiz
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Marie Pedersen
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | | | - Míriam Pérez-Cruz
- Institut de Recerca Sant Joan de Déu (IR-SJD), Barcelona, Spain
- Maternal and Child Health and Development Network II (SAMID II), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- BCNatal—Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Sant Joan de Déu, Barcelona, Spain
| | - Claire Philippat
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
| | - Pavel Piler
- RECETOX Centre, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Costanza Pizzi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Joane Quentin
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | | | - Theano Roumeliotaki
- Department of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | | | | | - Susana Santos
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Alexandros P. Siskos
- Department of Surgery & Cancer and Department of Metabolism, Digestion & Reproduction, Imperial College London, London, United Kingdom
| | | | - Nikos Stratakis
- ISGlobal, Barcelona, Spain
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Arthur Tenenhaus
- University Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, France
| | - Marina Vafeiadi
- Department of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Rebecca C. Wilson
- Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford,United Kingdom
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford,United Kingdom
| | - Remy Slama
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
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Jedynak P, Maitre L, Guxens M, Gützkow KB, Julvez J, López-Vicente M, Sunyer J, Casas M, Chatzi L, Gražulevičienė R, Kampouri M, McEachan R, Mon-Williams M, Tamayo I, Thomsen C, Urquiza J, Vafeiadi M, Wright J, Basagaña X, Vrijheid M, Philippat C. Prenatal exposure to a wide range of environmental chemicals and child behaviour between 3 and 7 years of age - An exposome-based approach in 5 European cohorts. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 763:144115. [PMID: 33422710 PMCID: PMC7840589 DOI: 10.1016/j.scitotenv.2020.144115] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/20/2020] [Accepted: 11/20/2020] [Indexed: 05/20/2023]
Abstract
BACKGROUND Studies looking at associations between environmental chemicals and child behaviour usually consider only one exposure or family of exposures. OBJECTIVE This study explores associations between prenatal exposure to a wide range of environmental chemicals and child behaviour. METHODS We studied 708 mother-child pairs from five European cohorts recruited in 2003-2009. We assessed 47 exposure biomarkers from eight chemical exposure families in maternal blood or urine collected during pregnancy. We used the Strengths and Difficulties Questionnaire (SDQ) to evaluate child behaviour between three and seven years of age. We assessed associations of SDQ scores with exposures using an adjusted least absolute shrinkage and selection operator (LASSO) considering all exposures simultaneously and an adjusted exposome-wide association study (ExWAS) considering each exposure independently. RESULTS LASSO selected only copper (Cu) as associated with externalizing behaviour. In the ExWAS, bisphenol A [BPA, incidence rate ratio (IRR): 1.06, 95% confidence interval (95%CI): 1.01;1.12] and mono-n-butyl phthalate (MnBP, IRR: 1.06, 95%CI: 1.00;1.13) were associated with greater risk of externalizing behaviour problems. Cu (IRR: 0.90, 95%CI: 0.82;0.98), perfluoroundecanoate (PFUnDA, IRR: 0.92, 95%CI: 0.84;0.99) and organochlorine compounds (OCs) were associated with lower risk of externalizing behaviour problems, however the associations with OCs were mainly seen among women with insufficient weight gain during pregnancy. Internalizing score worsen in association with exposure to diethyl thiophosphate (DETP, IRR: 1.11, 95%CI: 1.00;1.24) but the effect was driven by the smallest cohort. Internalizing score improved with increased concentration of perfluorooctane sulfonate (PFOS, IRR: 0.92, 95%CI: 0.85;1.00), however the association was driven by the two smallest cohorts with the lowest PFOS concentrations. DISCUSSION This study added evidence on deleterious effects of prenatal exposure to BPA and MnBP on child behaviour. Other associations should be interpreted cautiously since they were not consistent with previous studies or they have not been studied extensively.
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Affiliation(s)
- Paulina Jedynak
- University Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Grenoble, France.
| | - Léa Maitre
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Mónica Guxens
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre-Sophia Children's Hospital, Rotterdam, the Netherlands
| | | | - Jordi Julvez
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Hospital Universitari Sant Joan de Reus, Reus, Spain; ISGlobal, Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Mónica López-Vicente
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Social Medicine, University of Crete, Heraklion, Greece; Department of Genetics and Cell Biology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | | | - Mariza Kampouri
- Department of Social Medicine, University of Crete, Heraklion, Greece
| | - Rosie McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Mark Mon-Williams
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Ibon Tamayo
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - José Urquiza
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Marina Vafeiadi
- Department of Social Medicine, University of Crete, Heraklion, Greece
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Claire Philippat
- University Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Grenoble, France
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27
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Agier L, Basagaña X, Hernandez-Ferrer C, Maitre L, Tamayo Uria I, Urquiza J, Andrusaityte S, Casas M, de Castro M, Cequier E, Chatzi L, Donaire-Gonzalez D, Giorgis-Allemand L, Gonzalez JR, Grazuleviciene R, Gützkow KB, Haug LS, Sakhi AK, McEachan RRC, Meltzer HM, Nieuwenhuijsen M, Robinson O, Roumeliotaki T, Sunyer J, Thomsen C, Vafeiadi M, Valentin A, West J, Wright J, Siroux V, Vrijheid M, Slama R. Association between the pregnancy exposome and fetal growth. Int J Epidemiol 2021; 49:572-586. [PMID: 32167557 DOI: 10.1093/ije/dyaa017] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 01/31/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Several environmental contaminants were shown to possibly influence fetal growth, generally from single exposure family studies, which are prone to publication bias and confounding by co-exposures. The exposome paradigm offers perspectives to avoid selective reporting of findings and to control for confounding by co-exposures. We aimed to characterize associations of fetal growth with the pregnancy chemical and external exposomes. METHODS Within the Human Early-Life Exposome project, 131 prenatal exposures were assessed using biomarkers and environmental models in 1287 mother-child pairs from six European cohorts. We investigated their associations with fetal growth using a deletion-substitution-addition (DSA) algorithm considering all exposures simultaneously, and an exposome-wide association study (ExWAS) considering each exposure independently. We corrected for exposure measurement error and tested for exposure-exposure and sex-exposure interactions. RESULTS The DSA model identified lead blood level, which was associated with a 97 g birth weight decrease for each doubling in lead concentration. No exposure passed the multiple testing-corrected significance threshold of ExWAS; without multiple testing correction, this model was in favour of negative associations of lead, fine particulate matter concentration and absorbance with birth weight, and of a positive sex-specific association of parabens with birth weight in boys. No two-way interaction between exposure variables was identified. CONCLUSIONS This first large-scale exposome study of fetal growth simultaneously considered >100 environmental exposures. Compared with single exposure studies, our approach allowed making all tests (usually reported in successive publications) explicit. Lead exposure is still a health concern in Europe and parabens health effects warrant further investigation.
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Affiliation(s)
- Lydiane Agier
- Inserm, CNRS, University Grenoble Alpes, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, Grenoble, France
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Carles Hernandez-Ferrer
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Léa Maitre
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ibon Tamayo Uria
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Jose Urquiza
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Maribel Casas
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Montserrat de Castro
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David Donaire-Gonzalez
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology (EEPI), Utrecht University, Utrecht, The Netherlands.,Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
| | - Lise Giorgis-Allemand
- Inserm, CNRS, University Grenoble Alpes, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, Grenoble, France
| | - Juan R Gonzalez
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | | | - Line S Haug
- Norwegian Institute of Public Health, Oslo, Norway
| | | | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | | | - Mark Nieuwenhuijsen
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Oliver Robinson
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | | | - Jordi Sunyer
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | | | - Antonia Valentin
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Jane West
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Valérie Siroux
- Inserm, CNRS, University Grenoble Alpes, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, Grenoble, France
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Rémy Slama
- Inserm, CNRS, University Grenoble Alpes, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, Grenoble, France
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Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study. PLoS One 2021; 16:e0249236. [PMID: 33765068 PMCID: PMC7993848 DOI: 10.1371/journal.pone.0249236] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 03/13/2021] [Indexed: 11/26/2022] Open
Abstract
Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.
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Huhn S, Escher BI, Krauss M, Scholz S, Hackermüller J, Altenburger R. Unravelling the chemical exposome in cohort studies: routes explored and steps to become comprehensive. ENVIRONMENTAL SCIENCES EUROPE 2021; 33:17. [PMID: 33614387 PMCID: PMC7877320 DOI: 10.1186/s12302-020-00444-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 11/30/2020] [Indexed: 05/04/2023]
Abstract
Environmental factors contribute to the risk for adverse health outcomes against a background of genetic predisposition. Among these factors, chemical exposures may substantially contribute to disease risk and adverse outcomes. In fact, epidemiological cohort studies have established associations between exposure against individual chemicals and adverse health effects. Yet, in daily life individuals are exposed to complex mixtures in varying compositions. To capture the totality of environmental exposures the concept of the exposome has been developed. Here, we undertake an overview of major exposome projects, which pioneered the field of exposomics and explored the links between chemical exposure and health outcomes using cohort studies. We seek to reflect their achievements with regard to (i) capturing a comprehensive picture of the environmental chemical exposome, (ii) aggregating internal exposures using chemical and bioanalytical means of detection, and (iii) identifying associations that provide novel options for risk assessment and intervention. Various complementary approaches can be distinguished in addressing relevant exposure routes and it emerges that individual exposure histories may not easily be grouped. The number of chemicals for which human exposure can be detected is substantial and highlights the reality of mixture exposures. Yet, to a large extent it depends on targeted chemical analysis with the specific challenges to capture all relevant exposure routes and assess the chemical concentrations occurring in humans. The currently used approaches imply prior knowledge or hypotheses about relevant exposures. Typically, the number of chemicals considered in exposome projects is counted in dozens-in contrast to the several thousands of chemicals for which occurrence have been reported in human serum and urine. Furthermore, health outcomes are often still compared to single chemicals only. Moreover, explicit consideration of mixture effects and the interrelations between different outcomes to support causal relationships and identify risk drivers in complex mixtures remain underdeveloped and call for specifically designed exposome-cohort studies.
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Affiliation(s)
- Sebastian Huhn
- Helmholtz Centre for Environmental Research GmbH – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Pediatric Epidemiology, Department of Pediatrics, University of Leipzig Medical Center, Leipzig, Germany
| | - Beate I. Escher
- Helmholtz Centre for Environmental Research GmbH – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Environmental Toxicology, Center for Applied Geosciences, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Martin Krauss
- Helmholtz Centre for Environmental Research GmbH – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
| | - Stefan Scholz
- Helmholtz Centre for Environmental Research GmbH – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
| | - Jörg Hackermüller
- Helmholtz Centre for Environmental Research GmbH – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
| | - Rolf Altenburger
- Helmholtz Centre for Environmental Research GmbH – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
- Department of Bioanalytical Ecotoxicology, RWTH-Aachen University, Aachen, Germany
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Use of Exposomic Methods Incorporating Sensors in Environmental Epidemiology. Curr Environ Health Rep 2021; 8:34-41. [PMID: 33569731 DOI: 10.1007/s40572-021-00306-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/29/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE OF REVIEW The exposome is a recently coined concept that comprises the totality of nongenetic factors that affect human health. It is recognized as a major conceptual advancement in environmental epidemiology, and there is increased demand for technologies that capture the spatial, temporal, and chemical variability of exposures across individuals (i.e., "exposomic sensors"). We review a selection of these tools, highlighting their strengths and limitations with regard to epidemiological research. RECENT FINDINGS Wearable passive samplers are emerging as promising exposomic sensors for individuals. In conjunction with targeted and untargeted assays, these sensors enable the measurement of complex multipollutant mixtures, which can include both known and previously unknown environmental contaminants. Because of their minimally burdensome and noninvasive nature, they are deployable among sensitive populations, such as seniors, pregnant women, and children. The integration of exposomic data captured by these sensors with other omic data (e.g., transcriptomic and metabolomic) presents exciting opportunities for investigating disease risk factors. For example, the linkage of exposomic sensor data with other omic data may indicate perturbation by multipollutant mixtures at multiple physiological levels, which would strengthen evidence of their effects and potentially indicate targets for interventions. However, there remain considerable theoretical and methodological challenges that must be overcome to realize the potential promise of omic integration. Through continued investment and improvement in exposomic sensor technologies, it may be possible to refine their application and reduce their outstanding limitations to advance the fields of exposure science and epidemiology.
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Guillien A, Cadiou S, Slama R, Siroux V. The Exposome Approach to Decipher the Role of Multiple Environmental and Lifestyle Determinants in Asthma. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:1138. [PMID: 33525356 PMCID: PMC7908097 DOI: 10.3390/ijerph18031138] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/15/2021] [Accepted: 01/23/2021] [Indexed: 12/12/2022]
Abstract
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. After presenting an overview of the exposome concept, we discuss different statistical approaches used to study the exposome-health associations and review recent studies linking multiple families of exposures to asthma-related outcomes. The few studies published so far on the association between the exposome and asthma-related outcomes showed differences in terms of study design, population, exposome definition and statistical methods used, making their results difficult to compare. Regarding statistical methods, most studies applied successively univariate (Exposome-Wide Association Study (ExWAS)) and multivariate (adjusted for co-exposures) (e.g., Deletion-Substitution-Addition (DSA) algorithm) regression-based models. This latest approach makes it possible to assess associations between a large set of exposures and asthma outcomes. However, it cannot address complex interactions (i.e., of order ≥3) or mixture effects. Other approaches like cluster-based analyses, that lead to the identification of specific profiles of exposure at risk for the studied health-outcome, or mediation analyses, that allow the integration of information from intermediate biological layers, could offer a new avenue in the understanding of the environment-asthma association. European projects focusing on the exposome research have recently been launched and should provide new results to help fill the gap that currently exists in our understanding of the effect of environment on respiratory health.
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Affiliation(s)
- Alicia Guillien
- Inserm, CNRS, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB (Institute for Advanced Biosciences), University Grenoble Alpes, 38000 Grenoble, France; (S.C.); (R.S.); (V.S.)
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Bellavia A, Dickerson AS, Rotem RS, Hansen J, Gredal O, Weisskopf MG. Joint and interactive effects between health comorbidities and environmental exposures in predicting amyotrophic lateral sclerosis. Int J Hyg Environ Health 2021; 231:113655. [PMID: 33130429 PMCID: PMC7736520 DOI: 10.1016/j.ijheh.2020.113655] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 09/21/2020] [Accepted: 09/28/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Amyotrophic lateral sclerosis (ALS) is a rare yet devastating neurodegenerative condition. The mechanisms leading to ALS are most certainly complex and likely involve a joint contribution of several factors with possible synergistic or antagonistic interactions. To provide a better understanding of the association between non-genetic factors and ALS, we evaluated the joint exposure to multiple health and environmental factors linked with ALS in our previous studies, also screening for high-dimensional interactions. METHODS We used data from a nested case-control study within the Danish population, with 1086 ALS cases from 1982 to 2009, jointly investigating 4 hospital-based diagnoses - diabetes, obesity, physical/stress trauma, cardiovascular disease (CVD) during 1977-2009; and 4 environmental exposures - lead, formaldehyde, diesel exhaust, and solvents, assessed from individual occupational history. All covariates were evaluated as ever/never exposed, and we used targeted machine learning techniques to screen for important joint predictors and interactions. These were then evaluated in a final logistic regression model adjusting for potential confounders (age, SES, geography). All analyses were stratified by sex. RESULTS Among men, trauma and solvents were associated with higher odds of ALS (OR = 1.55, 95% CI: 1.08-2.23; OR = 1.49, 95% CI: 1.17-1.89, respectively), and presented a negative interaction (OR = 0.49, 95% CI: 0.30-0.80). A positive diesel/CVD interaction was observed (OR = 1.56, 95% CI: 0.94-2.60). Among women, solvents, trauma, lead, and CVD were associated with higher odds of ALS, and a negative lead/solvents interaction was documented (OR = 0.52, 95% CI: 0.42-0.63). CONCLUSIONS This study is one of the first attempts to evaluate joint and interactive effects of multiple risk factors on ALS, identifying potential synergistic and antagonistic mechanisms.
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Affiliation(s)
- Andrea Bellavia
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.
| | - Aisha S Dickerson
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA
| | - Ran S Rotem
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Johnni Hansen
- Danish Cancer Society, Institute of Cancer Epidemiology, Strandboulevarden 49, DK-2100, Copenhagen, Denmark
| | - Ole Gredal
- Danish Cancer Society, Institute of Cancer Epidemiology, Strandboulevarden 49, DK-2100, Copenhagen, Denmark
| | - Marc G Weisskopf
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
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Granum B, Oftedal B, Agier L, Siroux V, Bird P, Casas M, Warembourg C, Wright J, Chatzi L, de Castro M, Donaire D, Grazuleviciene R, Småstuen Haug L, Maitre L, Robinson O, Tamayo-Uria I, Urquiza J, Nieuwenhuijsen M, Slama R, Thomsen C, Vrijheid M. Multiple environmental exposures in early-life and allergy-related outcomes in childhood. ENVIRONMENT INTERNATIONAL 2020; 144:106038. [PMID: 32854059 PMCID: PMC8768577 DOI: 10.1016/j.envint.2020.106038] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 06/10/2020] [Accepted: 08/03/2020] [Indexed: 05/15/2023]
Abstract
INTRODUCTION Early onset and high prevalence of allergic diseases result in high individual and socio-economic burdens. Several studies provide evidence for possible effects of environmental factors on allergic diseases, but these are mainly single-exposure studies. The exposome provides a novel holistic approach by simultaneously studying a large set of exposures. The aim of the study was to evaluate the association between a broad range of prenatal and childhood environmental exposures and allergy-related outcomes in children. MATERIAL AND METHODS Analyses of associations between 90 prenatal and 107 childhood exposures and allergy-related outcomes (last 12 months: rhinitis and itchy rash; ever: doctor-diagnosed eczema and food allergy) in 6-11 years old children (n = 1270) from the European Human Early-Life Exposome cohort were performed. Initially, we used an exposome-wide association study (ExWAS) considering the exposures independently, followed by a deletion-substitution-addition selection (DSA) algorithm considering all exposures simultaneously. All the exposure variables selected in the DSA were included in a final multi-exposure model using binomial general linear model (GLM). RESULTS In ExWAS, no exposures were associated with the outcomes after correction for multiple comparison. In multi-exposure models for prenatal exposures, lower distance of residence to nearest road and higher di-iso-nonyl phthalate level were associated with increased risk of rhinitis, and particulate matter absorbance (PMabs) was associated with a decreased risk. Furthermore, traffic density on nearest road was associated with increased risk of itchy rash and diethyl phthalate with a reduced risk. DSA selected no associations of childhood exposures, or between prenatal exposures and eczema or food allergy. DISCUSSION This first comprehensive and systematic analysis of many environmental exposures suggests that prenatal exposure to traffic-related variables, PMabs and phthalates are associated with rhinitis and itchy rash.
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Affiliation(s)
- Berit Granum
- Norwegian Institute of Public Health, Oslo, Norway.
| | | | - Lydiane Agier
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Valerie Siroux
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Philippa Bird
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford UK
| | - Maribel Casas
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Charline Warembourg
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford UK
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA; Department of Social Medicine, University of Crete, Greece; Department of Genetics & Cell Biology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Montserrat de Castro
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - David Donaire
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Regina Grazuleviciene
- Department of Environmental Sciences, Vytauto Didziojo Universitetas, Kaunas, Lithuania
| | | | - Lea Maitre
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Oliver Robinson
- ISGlobal, Barcelona, Spain; MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | | | | | - Mark Nieuwenhuijsen
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Remy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | | | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
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Lazarevic N, Knibbs LD, Sly PD, Barnett AG. Performance of variable and function selection methods for estimating the nonlinear health effects of correlated chemical mixtures: A simulation study. Stat Med 2020; 39:3947-3967. [PMID: 32940933 DOI: 10.1002/sim.8701] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 03/29/2020] [Accepted: 06/29/2020] [Indexed: 01/18/2023]
Abstract
Statistical methods for identifying harmful chemicals in a correlated mixture often assume linearity in exposure-response relationships. Nonmonotonic relationships are increasingly recognized (eg, for endocrine-disrupting chemicals); however, the impact of nonmonotonicity on exposure selection has not been evaluated. In a simulation study, we assessed the performance of Bayesian kernel machine regression (BKMR), Bayesian additive regression trees (BART), Bayesian structured additive regression with spike-slab priors (BSTARSS), generalized additive models with double penalty (GAMDP) and thin plate shrinkage smoothers (GAMTS), multivariate adaptive regression splines (MARS), and lasso penalized regression. We simulated realistic exposure data based on pregnancy exposure to 17 phthalates and phenols in the US National Health and Nutrition Examination Survey using a multivariate copula. We simulated data sets of size N = 250 and compared methods across 32 scenarios, varying by model size and sparsity, signal-to-noise ratio, correlation structure, and exposure-response relationship shapes. We compared methods in terms of their sensitivity, specificity, and estimation accuracy. In most scenarios, BKMR, BSTARSS, GAMDP, and GAMTS achieved moderate to high sensitivity (0.52-0.98) and specificity (0.21-0.99). BART and MARS achieved high specificity (≥0.90), but low sensitivity in low signal-to-noise ratio scenarios (0.20-0.51). Lasso was highly sensitive (0.71-0.99), except for quadratic relationships (≤0.27). Penalized regression methods that assume linearity, such as lasso, may not be suitable for studies of environmental chemicals hypothesized to have nonmonotonic relationships with outcomes. Instead, BKMR, BSTARSS, GAMDP, and GAMTS are attractive methods for flexibly estimating the shapes of exposure-response relationships and selecting among correlated exposures.
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Affiliation(s)
- Nina Lazarevic
- School of Public Health, Faculty of Medicine, The University of Queensland, Herston, Queensland, Australia
| | - Luke D Knibbs
- School of Public Health, Faculty of Medicine, The University of Queensland, Herston, Queensland, Australia
| | - Peter D Sly
- Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, South Brisbane, Queensland, Australia
| | - Adrian G Barnett
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Kelvin Grove, Queensland, Australia
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Vrijheid M, Fossati S, Maitre L, Márquez S, Roumeliotaki T, Agier L, Andrusaityte S, Cadiou S, Casas M, de Castro M, Dedele A, Donaire-Gonzalez D, Grazuleviciene R, Haug LS, McEachan R, Meltzer HM, Papadopouplou E, Robinson O, Sakhi AK, Siroux V, Sunyer J, Schwarze PE, Tamayo-Uria I, Urquiza J, Vafeiadi M, Valentin A, Warembourg C, Wright J, Nieuwenhuijsen MJ, Thomsen C, Basagaña X, Slama R, Chatzi L. Early-Life Environmental Exposures and Childhood Obesity: An Exposome-Wide Approach. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:67009. [PMID: 32579081 PMCID: PMC7313401 DOI: 10.1289/ehp5975] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 05/14/2020] [Accepted: 05/21/2020] [Indexed: 05/20/2023]
Abstract
BACKGROUND Chemical and nonchemical environmental exposures are increasingly suspected to influence the development of obesity, especially during early life, but studies mostly consider single exposure groups. OBJECTIVES Our study aimed to systematically assess the association between a wide array of early-life environmental exposures and childhood obesity, using an exposome-wide approach. METHODS The HELIX (Human Early Life Exposome) study measured child body mass index (BMI), waist circumference, skinfold thickness, and body fat mass in 1,301 children from six European birth cohorts age 6-11 y. We estimated 77 prenatal exposures and 96 childhood exposures (cross-sectionally), including indoor and outdoor air pollutants, built environment, green spaces, tobacco smoking, and biomarkers of chemical pollutants (persistent organic pollutants, metals, phthalates, phenols, and pesticides). We used an exposure-wide association study (ExWAS) to screen all exposure-outcome associations independently and used the deletion-substitution-addition (DSA) variable selection algorithm to build a final multiexposure model. RESULTS The prevalence of overweight and obesity combined was 28.8%. Maternal smoking was the only prenatal exposure variable associated with higher child BMI (z-score increase of 0.28, 95% confidence interval: 0.09, 0.48, for active vs. no smoking). For childhood exposures, the multiexposure model identified particulate and nitrogen dioxide air pollution inside the home, urine cotinine levels indicative of secondhand smoke exposure, and residence in more densely populated areas and in areas with fewer facilities to be associated with increased child BMI. Child blood levels of copper and cesium were associated with higher BMI, and levels of organochlorine pollutants, cobalt, and molybdenum were associated with lower BMI. Similar results were found for the other adiposity outcomes. DISCUSSION This first comprehensive and systematic analysis of many suspected environmental obesogens strengthens evidence for an association of smoking, air pollution exposure, and characteristics of the built environment with childhood obesity risk. Cross-sectional biomarker results may suffer from reverse causality bias, whereby obesity status influenced the biomarker concentration. https://doi.org/10.1289/EHP5975.
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Affiliation(s)
- Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Serena Fossati
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Léa Maitre
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Sandra Márquez
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Theano Roumeliotaki
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Lydiane Agier
- Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, INSERM, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Solène Cadiou
- Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, INSERM, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France
| | - Maribel Casas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Montserrat de Castro
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Audrius Dedele
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - David Donaire-Gonzalez
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
| | | | - Line S Haug
- Norwegian Institute of Public Health, Oslo, Norway
| | - Rosemary McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | | | | | - Oliver Robinson
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | | | - Valerie Siroux
- Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, INSERM, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | | | - Ibon Tamayo-Uria
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
- Division of Immunology and Immunotherapy, CIMA, Universidad de Navarra, and Instituto de Investigación Sanitaria de Navarra (IdISNA), Pamplona, Spain
| | - Jose Urquiza
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Antonia Valentin
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Charline Warembourg
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Mark J Nieuwenhuijsen
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | | | - Xavier Basagaña
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Rémy Slama
- Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, INSERM, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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Matta K, Vigneau E, Cariou V, Mouret D, Ploteau S, Le Bizec B, Antignac JP, Cano-Sancho G. Associations between persistent organic pollutants and endometriosis: A multipollutant assessment using machine learning algorithms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 260:114066. [PMID: 32041029 DOI: 10.1016/j.envpol.2020.114066] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/17/2019] [Accepted: 01/23/2020] [Indexed: 06/10/2023]
Abstract
Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.
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Affiliation(s)
| | | | | | | | - Stéphane Ploteau
- Service de Gynécologie-obstétrique, CIC FEA, Hôpital Mère Enfant, CHU Hôtel Dieu, Nantes, France
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Applying the exposome concept in birth cohort research: a review of statistical approaches. Eur J Epidemiol 2020; 35:193-204. [PMID: 32221742 PMCID: PMC7154018 DOI: 10.1007/s10654-020-00625-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 03/17/2020] [Indexed: 12/30/2022]
Abstract
The exposome represents the totality of life course environmental exposures (including lifestyle and other non-genetic factors), from the prenatal period onwards. This holistic concept of exposure provides a new framework to advance the understanding of complex and multifactorial diseases. Prospective pregnancy and birth cohort studies provide a unique opportunity for exposome research as they are able to capture, from prenatal life onwards, both the external (including lifestyle, chemical, social and wider community-level exposures) and the internal (including inflammation, metabolism, epigenetics, and gut microbiota) domains of the exposome. In this paper, we describe the steps required for applying an exposome approach, describe the main strengths and limitations of different statistical approaches and discuss their challenges, with the aim to provide guidance for methodological choices in the analysis of exposome data in birth cohort studies. An exposome approach implies selecting, pre-processing, describing and analyzing a large set of exposures. Several statistical methods are currently available to assess exposome-health associations, which differ in terms of research question that can be answered, of balance between sensitivity and false discovery proportion, and between computational complexity and simplicity (parsimony). Assessing the association between many exposures and health still raises many exposure assessment issues and statistical challenges. The exposome favors a holistic approach of environmental influences on health, which is likely to allow a more complete understanding of disease etiology.
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38
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Xue J, Lai Y, Liu CW, Ru H. Towards Mass Spectrometry-Based Chemical Exposome: Current Approaches, Challenges, and Future Directions. TOXICS 2019; 7:toxics7030041. [PMID: 31426576 PMCID: PMC6789759 DOI: 10.3390/toxics7030041] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 08/12/2019] [Accepted: 08/14/2019] [Indexed: 12/13/2022]
Abstract
The proposal of the “exposome” concept represents a shift of the research paradigm in studying exposure-disease relationships from an isolated and partial way to a systematic and agnostic approach. Nevertheless, exposome implementation is facing a variety of challenges including measurement techniques and data analysis. Here we focus on the chemical exposome, which refers to the mixtures of chemical pollutants people are exposed to from embryo onwards. We review the current chemical exposome measurement approaches with a focus on those based on the mass spectrometry. We further explore the strategies in implementing the concept of chemical exposome and discuss the available chemical exposome studies. Early progresses in the chemical exposome research are outlined, and major challenges are highlighted. In conclusion, efforts towards chemical exposome have only uncovered the tip of the iceberg, and further advancement in measurement techniques, computational tools, high-throughput data analysis, and standardization may allow more exciting discoveries concerning the role of exposome in human health and disease.
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Affiliation(s)
- Jingchuan Xue
- Center for Environmental Health and Susceptibility, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yunjia Lai
- Center for Environmental Health and Susceptibility, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chih-Wei Liu
- Center for Environmental Health and Susceptibility, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongyu Ru
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC 27607, USA.
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39
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Tamayo-Uria I, Maitre L, Thomsen C, Nieuwenhuijsen MJ, Chatzi L, Siroux V, Aasvang GM, Agier L, Andrusaityte S, Casas M, de Castro M, Dedele A, Haug LS, Heude B, Grazuleviciene R, Gutzkow KB, Krog NH, Mason D, McEachan RRC, Meltzer HM, Petraviciene I, Robinson O, Roumeliotaki T, Sakhi AK, Urquiza J, Vafeiadi M, Waiblinger D, Warembourg C, Wright J, Slama R, Vrijheid M, Basagaña X. The early-life exposome: Description and patterns in six European countries. ENVIRONMENT INTERNATIONAL 2019; 123:189-200. [PMID: 30530161 DOI: 10.1016/jenvint.2018.11.067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 11/26/2018] [Accepted: 11/26/2018] [Indexed: 05/27/2023]
Abstract
Characterization of the "exposome", the set of all environmental factors that one is exposed to from conception onwards, has been advocated to better understand the role of environmental factors on chronic diseases. Here, we aimed to describe the early-life exposome. Specifically, we focused on the correlations between multiple environmental exposures, their patterns and their variability across European regions and across time (pregnancy and childhood periods). We relied on the Human Early-Life Exposome (HELIX) project, in which 87 environmental exposures during pregnancy and 122 during the childhood period (grouped in 19 exposure groups) were assessed in 1301 pregnant mothers and their children at 6-11 years in 6 European birth cohorts. Some correlations between exposures in the same exposure group reached high values above 0.8. The median correlation within exposure groups was >0.3 for many exposure groups, reaching 0.69 for water disinfection by products in pregnancy and 0.67 for the meteorological group in childhood. Median correlations between different exposure groups rarely reached 0.3. Some correlations were driven by cohort-level associations (e.g. air pollution and chemicals). Ten principal components explained 45% and 39% of the total variance in the pregnancy and childhood exposome, respectively, while 65 and 90 components were required to explain 95% of the exposome variability. Correlations between maternal (pregnancy) and childhood exposures were high (>0.6) for most exposures modeled at the residential address (e.g. air pollution), but were much lower and even close to zero for some chemical exposures. In conclusion, the early life exposome was high dimensional, meaning that it cannot easily be measured by or reduced to fewer components. Correlations between exposures from different exposure groups were much lower than within exposure groups, which have important implications for co-exposure confounding in multiple exposure studies. Also, we observed the early life exposome to be variable over time and to vary by cohort, so measurements at one time point or one place will not capture its complexities.
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Affiliation(s)
- Ibon Tamayo-Uria
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain; Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Léa Maitre
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | | | - Mark J Nieuwenhuijsen
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Valérie Siroux
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | | | - Lydiane Agier
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Maribel Casas
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Montserrat de Castro
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Audrius Dedele
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Line S Haug
- Norwegian Institute of Public Health, Oslo, Norway
| | - Barbara Heude
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center (CRESS), Team 'Early Origin of the child's Health and Development' (ORCHAD), Villejuif, France; Paris Descartes University, Paris, France
| | | | | | - Norun H Krog
- Norwegian Institute of Public Health, Oslo, Norway
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | | | - Inga Petraviciene
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Oliver Robinson
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain; MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Theano Roumeliotaki
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | | | - Jose Urquiza
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Dagmar Waiblinger
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Charline Warembourg
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Rémy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.
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40
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Tamayo-Uria I, Maitre L, Thomsen C, Nieuwenhuijsen MJ, Chatzi L, Siroux V, Aasvang GM, Agier L, Andrusaityte S, Casas M, de Castro M, Dedele A, Haug LS, Heude B, Grazuleviciene R, Gutzkow KB, Krog NH, Mason D, McEachan RRC, Meltzer HM, Petraviciene I, Robinson O, Roumeliotaki T, Sakhi AK, Urquiza J, Vafeiadi M, Waiblinger D, Warembourg C, Wright J, Slama R, Vrijheid M, Basagaña X. The early-life exposome: Description and patterns in six European countries. ENVIRONMENT INTERNATIONAL 2019; 123:189-200. [PMID: 30530161 PMCID: PMC9946269 DOI: 10.1016/j.envint.2018.11.067] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 11/26/2018] [Accepted: 11/26/2018] [Indexed: 05/20/2023]
Abstract
Characterization of the "exposome", the set of all environmental factors that one is exposed to from conception onwards, has been advocated to better understand the role of environmental factors on chronic diseases. Here, we aimed to describe the early-life exposome. Specifically, we focused on the correlations between multiple environmental exposures, their patterns and their variability across European regions and across time (pregnancy and childhood periods). We relied on the Human Early-Life Exposome (HELIX) project, in which 87 environmental exposures during pregnancy and 122 during the childhood period (grouped in 19 exposure groups) were assessed in 1301 pregnant mothers and their children at 6-11 years in 6 European birth cohorts. Some correlations between exposures in the same exposure group reached high values above 0.8. The median correlation within exposure groups was >0.3 for many exposure groups, reaching 0.69 for water disinfection by products in pregnancy and 0.67 for the meteorological group in childhood. Median correlations between different exposure groups rarely reached 0.3. Some correlations were driven by cohort-level associations (e.g. air pollution and chemicals). Ten principal components explained 45% and 39% of the total variance in the pregnancy and childhood exposome, respectively, while 65 and 90 components were required to explain 95% of the exposome variability. Correlations between maternal (pregnancy) and childhood exposures were high (>0.6) for most exposures modeled at the residential address (e.g. air pollution), but were much lower and even close to zero for some chemical exposures. In conclusion, the early life exposome was high dimensional, meaning that it cannot easily be measured by or reduced to fewer components. Correlations between exposures from different exposure groups were much lower than within exposure groups, which have important implications for co-exposure confounding in multiple exposure studies. Also, we observed the early life exposome to be variable over time and to vary by cohort, so measurements at one time point or one place will not capture its complexities.
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Affiliation(s)
- Ibon Tamayo-Uria
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain; Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Léa Maitre
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | | | - Mark J Nieuwenhuijsen
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Valérie Siroux
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | | | - Lydiane Agier
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Maribel Casas
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Montserrat de Castro
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Audrius Dedele
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Line S Haug
- Norwegian Institute of Public Health, Oslo, Norway
| | - Barbara Heude
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center (CRESS), Team 'Early Origin of the child's Health and Development' (ORCHAD), Villejuif, France; Paris Descartes University, Paris, France
| | | | | | - Norun H Krog
- Norwegian Institute of Public Health, Oslo, Norway
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | | | - Inga Petraviciene
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Oliver Robinson
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain; MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Theano Roumeliotaki
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | | | - Jose Urquiza
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Dagmar Waiblinger
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Charline Warembourg
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Rémy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.
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41
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Lazarevic N, Barnett AG, Sly PD, Knibbs LD. Statistical Methodology in Studies of Prenatal Exposure to Mixtures of Endocrine-Disrupting Chemicals: A Review of Existing Approaches and New Alternatives. ENVIRONMENTAL HEALTH PERSPECTIVES 2019; 127:26001. [PMID: 30720337 PMCID: PMC6752940 DOI: 10.1289/ehp2207] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 01/09/2019] [Accepted: 01/10/2019] [Indexed: 05/19/2023]
Abstract
BACKGROUND Prenatal exposures to endocrine-disrupting chemicals (EDCs) during critical developmental windows have been implicated in the etiologies of a wide array of adverse perinatal and pediatric outcomes. Epidemiological studies have concentrated on the health effects of individual chemicals, despite the understanding that EDCs act together via common mechanisms, that pregnant women are exposed to multiple EDCs simultaneously, and that substantial toxicological evidence of adverse developmental effects has been documented. There is a move toward multipollutant models in environmental epidemiology; however, there is no current consensus on appropriate statistical methods. OBJECTIVES We aimed to review the statistical methods used in these studies, to identify additional applicable methods, and to determine the strengths and weaknesses of each method for addressing the salient statistical and epidemiological challenges. METHODS We searched Embase, MEDLINE, and Web of Science for epidemiological studies of endocrine-sensitive outcomes in the children of mothers exposed to EDC mixtures during pregnancy and identified alternative statistical methods from the wider literature. DISCUSSION We identified 74 studies and analyzed the methods used to estimate mixture health effects, identify important mixture components, account for nonmonotonicity in exposure–response relationships, assess interactions, and identify windows of exposure susceptibility. We identified both frequentist and Bayesian methods that are robust to multicollinearity, performing shrinkage, variable selection, dimension reduction, statistical learning, or smoothing, including methods that were not used by the studies included in our review. CONCLUSIONS Compelling motivation exists for analyzing EDCs as mixtures, yet many studies make simplifying assumptions about EDC additivity, relative potency, and linearity, or overlook the potential for bias due to asymmetries in chemical persistence. We discuss the potential impacts of these choices and suggest alternative methods to improve analyses of prenatal exposure to EDC mixtures. https://doi.org/10.1289/EHP2207.
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Affiliation(s)
- Nina Lazarevic
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Adrian G Barnett
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Peter D Sly
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Luke D Knibbs
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Air Quality & Health Research and Evaluation, Glebe, New South Wales, Australia
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42
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Agier L, Basagaña X, Maitre L, Granum B, Bird PK, Casas M, Oftedal B, Wright J, Andrusaityte S, de Castro M, Cequier E, Chatzi L, Donaire-Gonzalez D, Grazuleviciene R, Haug LS, Sakhi AK, Leventakou V, McEachan R, Nieuwenhuijsen M, Petraviciene I, Robinson O, Roumeliotaki T, Sunyer J, Tamayo-Uria I, Thomsen C, Urquiza J, Valentin A, Slama R, Vrijheid M, Siroux V. 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. [PMID: 30737192 DOI: 10.1016/s2542-5196(19)30010-5] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/21/2018] [Accepted: 01/04/2019] [Indexed: 05/20/2023]
Abstract
BACKGROUND Several single-exposure studies have documented possible effects of environmental factors on lung function, but none has relied on an exposome approach. We aimed to evaluate the association between a broad range of prenatal and postnatal lifestyle and environmental exposures and lung function in children. METHODS In this analysis, we used data from 1033 mother-child pairs from the European Human Early-Life Exposome (HELIX) cohort (consisting of six existing longitudinal birth cohorts in France, Greece, Lithuania, Norway, Spain, and the UK of children born between 2003 and 2009) for whom a valid spirometry test was recorded for the child. 85 prenatal and 125 postnatal exposures relating to outdoor, indoor, chemical, and lifestyle factors were assessed, and lung function was measured by spirometry in children at age 6-12 years. Two agnostic linear regression methods, a deletion-substitution-addition (DSA) algorithm considering all exposures simultaneously, and an exposome-wide association study (ExWAS) considering exposures independently, were applied to test the association with forced expiratory volume in 1 s percent predicted values (FEV1%). We tested for two-way interaction between exposures and corrected for confounding by co-exposures. FINDINGS In the 1033 children (median age 8·1 years, IQR 6·5-9·0), mean FEV1% was 98·8% (SD 13·2). In the ExWAS, prenatal perfluorononanoate (p=0·034) and perfluorooctanoate (p=0·030) exposures were associated with lower FEV1%, and inverse distance to nearest road during pregnancy (p=0·030) was associated with higher FEV1%. Nine postnatal exposures were associated with lower FEV1%: copper (p=0·041), ethyl-paraben (p=0·029), five phthalate metabolites (mono-2-ethyl 5-carboxypentyl phthalate [p=0·016], mono-2-ethyl-5-hydroxyhexyl phthalate [p=0·023], mono-2-ethyl-5-oxohexyl phthalate [p=0·0085], mono-4-methyl-7-oxooctyl phthalate [p=0·040], and the sum of di-ethylhexyl phthalate metabolites [p=0·014]), house crowding (p=0·015), and facility density around schools (p=0·027). However, no exposure passed the significance threshold when corrected for multiple testing in ExWAS, and none was selected with the DSA algorithm, including when testing for exposure interactions. INTERPRETATION Our systematic exposome approach identified several environmental exposures, mainly chemicals, that might be associated with lung function. Reducing exposure to these ubiquitous chemicals could help to prevent the development of chronic respiratory disease. FUNDING European Community's Seventh Framework Programme (HELIX project).
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Affiliation(s)
- Lydiane Agier
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Inserm, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Lea Maitre
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Berit Granum
- Norwegian Institute of Public Health, Oslo, Norway
| | - Philippa K Bird
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Maribel Casas
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Montserrat de Castro
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Social Medicine, University of Crete, Heraklion, Greece; Department of Genetics and Cell Biology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - David Donaire-Gonzalez
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Line S Haug
- Norwegian Institute of Public Health, Oslo, Norway
| | | | - Vasiliki Leventakou
- Department of Genetics and Cell Biology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Rosemary McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Mark Nieuwenhuijsen
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Inga Petraviciene
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Oliver Robinson
- ISGlobal, Barcelona, Spain; MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Theano Roumeliotaki
- Department of Genetics and Cell Biology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ibon Tamayo-Uria
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Jose Urquiza
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Antonia Valentin
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Rémy Slama
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Inserm, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Valérie Siroux
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Inserm, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France.
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43
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Argacha JF, Mizukami T, Bourdrel T, Bind MA. Ecology of the cardiovascular system: Part II - A focus on non-air related pollutants. Trends Cardiovasc Med 2018; 29:274-282. [PMID: 30224235 DOI: 10.1016/j.tcm.2018.09.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 09/03/2018] [Accepted: 09/03/2018] [Indexed: 12/22/2022]
Abstract
An integrated exposomic view of the relation between environment and cardiovascular health should consider the effects of both air and non-air related environmental stressors. Cardiovascular impacts of ambient air temperature, indoor and outdoor air pollution were recently reviewed. We aim, in this second part, to address the cardiovascular effects of noise, food pollutants, radiation, and some other emerging environmental factors. Road traffic noise exposure is associated with increased risk of premature arteriosclerosis, coronary artery disease, and stroke. Numerous studies report an increased prevalence of hypertension in people exposed to noise, especially while sleeping. Sleep disturbances generated by nocturnal noise are followed by a neuroendocrine stress response. Some oxidative and inflammatory endothelial reactions are observed during experimental session of noise exposure. Moreover, throughout the alimentation, the cardiovascular system is exposed to persistent organic pollutants (POPs) as dioxins or pesticides, and plastic associated chemicals (PACs), such as bisphenol A. Epidemiological studies show positive associations of exposures to POPs and PACs with diabetes, arteriosclerosis and cardiovascular disease incidence. POPs and PACS share some abilities to interact with nuclear receptors activating different pathways leading to oxidative stress, insulin resistance and angiotensin potentiation. Regarding radiation, survivors of nuclear explosion have an excess risk of cardiovascular disease. Dose-effect relationships remain debated, but an increased cardiovascular risk at low dose of radiation exposure may be of concern. Some emerging environmental factors like electromagnetic fields, greenspace and light exposure may also require further attention. Non-air related environmental stressors also play an important role in the burden of cardiovascular disease. Specific methodologies should be developed to assess the interactions between air and non-air related pollutants.
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Affiliation(s)
- J F Argacha
- Cardiology Department, Universitair Ziekenhuis Brussel, VUB, Belgium.
| | - T Mizukami
- Cardiology Department, Universitair Ziekenhuis Brussel, VUB, Belgium
| | - T Bourdrel
- Radiology Department, Imaging Medical Center Etoile-Neudorf, Strasbourg, France
| | - M-A Bind
- Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
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44
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Bobb JF, Claus Henn B, Valeri L, Coull BA. Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. Environ Health 2018; 17:67. [PMID: 30126431 PMCID: PMC6102907 DOI: 10.1186/s12940-018-0413-y] [Citation(s) in RCA: 561] [Impact Index Per Article: 93.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 08/10/2018] [Indexed: 05/17/2023]
Abstract
BACKGROUND Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables. METHODS This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code. RESULTS Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability. CONCLUSIONS This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health.
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Affiliation(s)
- Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave #1600, Seattle, WA, 98101, USA.
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Birgit Claus Henn
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
| | - Linda Valeri
- Psychiatric Biostatistics Laboratory, McLean Hospital, Belmont, MA, USA
| | - Brent A Coull
- Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
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45
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Riggs DW, Yeager RA, Bhatnagar A. Defining the Human Envirome: An Omics Approach for Assessing the Environmental Risk of Cardiovascular Disease. Circ Res 2018; 122:1259-1275. [PMID: 29700071 PMCID: PMC6398443 DOI: 10.1161/circresaha.117.311230] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Both genetic and environmental factors contribute to the development of cardiovascular disease, but in comparison with genetics, environmental factors have received less attention. Evaluation of environmental determinants of cardiovascular disease is limited by the lack of comprehensive omics approaches for integrating multiple environmental exposures. Hence, to understand the effects of the environment as a whole (envirome), it is important to delineate specific domains of the environment and to assess how, individually and collectively; these domains affect cardiovascular health. In this review, we present a hierarchical model of the envirome; defined by 3 consecutively nested domains, consisting of natural, social, and personal environments. Extensive evidence suggests that features of the natural environment such as sunlight, altitude, diurnal rhythms, vegetation, and biodiversity affect cardiovascular health. However, the effects of the natural environment are moderated by the social environment comprised of built environments, agricultural and industrial activities, pollutants and contaminants, as well as culture, economic activities, and social networks that affect health by influencing access to healthcare, social cohesion, and socioeconomic status. From resources available within society, individuals create personal environments, characterized by private income, wealth and education, and populated by behavioral and lifestyle choices relating to nutrition, physical activity, sleep, the use of recreational drugs, and smoking. An understanding of the interactions between different domains of the envirome and their integrated effects on cardiovascular health could lead to the development of new prevention strategies and deeper insights into etiologic processes that contribute to cardiovascular disease risk and susceptibility.
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Affiliation(s)
- Daniel W Riggs
- From the Diabetes and Obesity Center (D.W.R., R.A.Y., A.B.)
| | - Ray A Yeager
- From the Diabetes and Obesity Center (D.W.R., R.A.Y., A.B.)
| | - Aruni Bhatnagar
- From the Diabetes and Obesity Center (D.W.R., R.A.Y., A.B.)
- Institute of Molecular Cardiology (A.B.), University of Louisville, KY
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46
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Jain P, Vineis P, Liquet B, Vlaanderen J, Bodinier B, van Veldhoven K, Kogevinas M, Athersuch TJ, Font-Ribera L, Villanueva CM, Vermeulen R, Chadeau-Hyam M. A multivariate approach to investigate the combined biological effects of multiple exposures. J Epidemiol Community Health 2018; 72:564-571. [PMID: 29563153 PMCID: PMC6031275 DOI: 10.1136/jech-2017-210061] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 02/17/2018] [Accepted: 02/19/2018] [Indexed: 12/18/2022]
Abstract
Epidemiological studies provide evidence that environmental exposures may affect health through complex mixtures. Formal investigation of the effect of exposure mixtures is usually achieved by modelling interactions, which relies on strong assumptions relating to the identity and the number of the exposures involved in such interactions, and on the order and parametric form of these interactions. These hypotheses become difficult to formulate and justify in an exposome context, where influential exposures are numerous and heterogeneous. To capture both the complexity of the exposome and its possibly pleiotropic effects, models handling multivariate predictors and responses, such as partial least squares (PLS) algorithms, can prove useful. As an illustrative example, we applied PLS models to data from a study investigating the inflammatory response (blood concentration of 13 immune markers) to the exposure to four disinfection by-products (one brominated and three chlorinated compounds), while swimming in a pool. To accommodate the multiple observations per participant (n=60; before and after the swim), we adopted a multilevel extension of PLS algorithms, including sparse PLS models shrinking loadings coefficients of unimportant predictors (exposures) and/or responses (protein levels). Despite the strong correlation among co-occurring exposures, our approach identified a subset of exposures (n=3/4) affecting the exhaled levels of 8 (out of 13) immune markers. PLS algorithms can easily scale to high-dimensional exposures and responses, and prove useful for exposome research to identify sparse sets of exposures jointly affecting a set of (selected) biological markers. Our descriptive work may guide these extensions for higher dimensional data.
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Affiliation(s)
- Pooja Jain
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK.,Molecular and Genetic Epidemiology Unit, Italian Institute for Genomic Medicine (IIGM), Turin, Italy
| | - Benoît Liquet
- UMR CNRS 5142, Laboratoire de Mathématiques et de leurs Applications, Université de Pau et des Pays de l'Adour, Anglet, France.,School of Mathematics, ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Jelle Vlaanderen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands
| | - Barbara Bodinier
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Karin van Veldhoven
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Manolis Kogevinas
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Toby J Athersuch
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK.,Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Laia Font-Ribera
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Cristina M Villanueva
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Roel Vermeulen
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK.,Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK.,Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands
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