1
|
Esu CO, Pyo J, Cho K. Machine learning-derived dose-response relationships considering interactions in mixtures: Applications to the oxidative potential of particulate matter. JOURNAL OF HAZARDOUS MATERIALS 2024; 475:134864. [PMID: 38876025 DOI: 10.1016/j.jhazmat.2024.134864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/03/2024] [Accepted: 06/07/2024] [Indexed: 06/16/2024]
Abstract
Conventional environmental health research is primarily focused on isolated chemical exposures, neglecting the complex interactions between multiple pollutants that may synergistically or antagonistically influence toxicity, thereby posing unexpected health risks. In this study, we address this knowledge gap by introducing an explainable machine learning (ML) approach with Feature Localized Intercept Transformed-Shapley Additive Explanations (FLIT-SHAP) designed to extract the dose-response relationships of specific pollutants in mixtures. In contrast to traditional SHAP, FLIT-SHAP can localize the global intercept to elucidate mixture effects, which is crucial for understanding the oxidative potential (OP) of ambient particulate matter (PM). Assessing multi-pollutant OP using FLIT-SHAP revealed both synergistic (55-63 %) and antagonistic (25-42 %) effects in laboratory-controlled OP data, but an antagonistic (33-66 %; lower OP) effect in ambient PM. Notably, the FLIT-SHAP approach demonstrated higher prediction accuracy (R2 = 0.99) compared to the additive model (R2 = 0.89) when evaluated against real-world PM samples. Quinones, such as phenanthrenequinone, play a more significant role in PM2.5 than previously recognized. Through this study, we highlighted the potential of FLIT-SHAP to enhance toxicity predictions and aid decision-making in the field of environmental health.
Collapse
Affiliation(s)
- Charles O Esu
- Department of Environmental Engineering, Pusan National University, Republic of Korea
| | - JongCheol Pyo
- Department of Environmental Engineering, Pusan National University, Republic of Korea
| | - Kuk Cho
- Department of Environmental Engineering, Pusan National University, Republic of Korea; Institute of Environmental Studies, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, Republic of Korea.
| |
Collapse
|
2
|
Amiri S, Li YC, Buchwald D, Pandey G. Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:171102. [PMID: 38387571 PMCID: PMC10939716 DOI: 10.1016/j.scitotenv.2024.171102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/16/2024] [Accepted: 02/18/2024] [Indexed: 02/24/2024]
Abstract
Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017-2019), and the last five years (2014-2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.
Collapse
Affiliation(s)
- Solmaz Amiri
- Institute for Research and Education to Advance Community Health (IREACH), Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA.
| | - Yan-Chak Li
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dedra Buchwald
- Institute for Research and Education to Advance Community Health (IREACH), Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
3
|
Bunyavanich S, Becker PM, Altman MC, Lasky-Su J, Ober C, Zengler K, Berdyshev E, Bonneau R, Chatila T, Chatterjee N, Chung KF, Cutcliffe C, Davidson W, Dong G, Fang G, Fulkerson P, Himes BE, Liang L, Mathias RA, Ogino S, Petrosino J, Price ND, Schadt E, Schofield J, Seibold MA, Steen H, Wheatley L, Zhang H, Togias A, Hasegawa K. Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop. J Allergy Clin Immunol 2024; 153:954-968. [PMID: 38295882 PMCID: PMC10999353 DOI: 10.1016/j.jaci.2024.01.014] [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: 12/13/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 02/29/2024]
Abstract
Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.
Collapse
Affiliation(s)
| | - Patrice M Becker
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | | | - Jessica Lasky-Su
- Brigham & Women's Hospital and Harvard Medical School, Boston, Mass
| | | | | | | | | | - Talal Chatila
- Boston Children's Hospital and Harvard Medical School, Boston, Mass
| | | | | | | | - Wendy Davidson
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Gang Dong
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Gang Fang
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - Patricia Fulkerson
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | | | - Liming Liang
- Harvard T. H. Chan School of Public Health, Boston, Mass
| | | | - Shuji Ogino
- Brigham & Women's Hospital and Harvard Medical School, Boston, Mass; Harvard T. H. Chan School of Public Health, Boston, Mass; Broad Institute of MIT and Harvard, Boston, Mass
| | | | | | - Eric Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Max A Seibold
- National Jewish Health, Denver, Colo; University of Colorado School of Medicine, Aurora, Colo
| | - Hanno Steen
- Boston Children's Hospital and Harvard Medical School, Boston, Mass
| | - Lisa Wheatley
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Hongmei Zhang
- School of Public Health, University of Memphis, Memphis, Tenn
| | - Alkis Togias
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Kohei Hasegawa
- Massachusetts General Hospital and Harvard Medical School, Boston, Mass
| |
Collapse
|
4
|
You L, Kou J, Wang M, Ji G, Li X, Su C, Zheng F, Zhang M, Wang Y, Chen T, Li T, Zhou L, Shi X, Zhao C, Liu X, Mei S, Xu G. An exposome atlas of serum reveals the risk of chronic diseases in the Chinese population. Nat Commun 2024; 15:2268. [PMID: 38480749 PMCID: PMC10937660 DOI: 10.1038/s41467-024-46595-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: 03/01/2023] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
Although adverse environmental exposures are considered a major cause of chronic diseases, current studies provide limited information on real-world chemical exposures and related risks. For this study, we collected serum samples from 5696 healthy people and patients, including those with 12 chronic diseases, in China and completed serum biomonitoring including 267 chemicals via gas and liquid chromatography-tandem mass spectrometry. Seventy-four highly frequently detected exposures were used for exposure characterization and risk analysis. The results show that region is the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances are associated with multiple chronic diseases, and some of them exceed safe ranges. Multi-exposure models reveal significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provides a comprehensive human serum exposome atlas and disease risk information, which can guide subsequent in-depth cause-and-effect studies between environmental exposures and human health.
Collapse
Affiliation(s)
- Lei You
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
| | - Jing Kou
- State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, # 13 Hangkong Road, Wuhan, Hubei, 430030, China
| | - Mengdie Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
- School of Public Health, China Medical University, No. 77 Puhe Road, Shenbei New District, Shenyang, 110122, China
| | - Guoqin Ji
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
- School of Life Science, China Medical University, No. 77 Puhe Road, Shenbei New District, Shenyang, 110122, China
| | - Xiang Li
- State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, # 13 Hangkong Road, Wuhan, Hubei, 430030, China
| | - Chang Su
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Fujian Zheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
| | - Mingye Zhang
- State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, # 13 Hangkong Road, Wuhan, Hubei, 430030, China
| | - Yuting Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
| | - Tiantian Chen
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
| | - Ting Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
| | - Lina Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
| | - Xianzhe Shi
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
| | - Chunxia Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China.
| | - Surong Mei
- State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, # 13 Hangkong Road, Wuhan, Hubei, 430030, China.
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China.
| |
Collapse
|
5
|
Midya V, Alcala CS, Rechtman E, Gregory JK, Kannan K, Hertz-Picciotto I, Teitelbaum SL, Gennings C, Rosa MJ, Valvi D. Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18139-18150. [PMID: 37595051 PMCID: PMC10666542 DOI: 10.1021/acs.est.3c00848] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 08/10/2023] [Accepted: 08/10/2023] [Indexed: 08/20/2023]
Abstract
A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, investigating the effect of interactions among these ECs can be challenging. We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression and a machine-learning method termed Signed iterative Random Forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multiordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs no). WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical-chemical interactions associated with ASD.
Collapse
Affiliation(s)
- Vishal Midya
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Cecilia Sara Alcala
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Elza Rechtman
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Jill K. Gregory
- Instructional
Technology Group,Icahn School of Medicine
at Mount Sinai, New York, New York 10029, United States
| | - Kurunthachalam Kannan
- Department
of Pediatrics and Department of Environmental Medicine, New York University School of Medicine, New York, New York 10016, United States
| | - Irva Hertz-Picciotto
- Department
of Public Health Sciences, School of Medicine, University of California at Davis, Davis, California 95616, United States
- UC
Davis MIND (Medical Investigations of Neurodevelopmental Disorders)
Institute, University of California at Davis, Sacramento, California 95817, United States
| | - Susan L. Teitelbaum
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Chris Gennings
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Maria J. Rosa
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Damaskini Valvi
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| |
Collapse
|
6
|
Shamji MH, Ollert M, Adcock IM, Bennett O, Favaro A, Sarama R, Riggioni C, Annesi-Maesano I, Custovic A, Fontanella S, Traidl-Hoffmann C, Nadeau K, Cecchi L, Zemelka-Wiacek M, Akdis CA, Jutel M, Agache I. EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics. Allergy 2023; 78:1742-1757. [PMID: 36740916 DOI: 10.1111/all.15667] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/17/2023] [Accepted: 02/01/2023] [Indexed: 02/07/2023]
Abstract
Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.
Collapse
Affiliation(s)
- Mohamed H Shamji
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), Esch-sur-Alzette, Luxembourg
- Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, Odense, Denmark
| | - Ian M Adcock
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | | | | | - Roudin Sarama
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | - Carmen Riggioni
- Pediatric Allergy and Clinical Immunology Service, Institut de Reserca Sant Joan de Deú, Barcelona, Spain
| | - Isabella Annesi-Maesano
- Research Director and Deputy DIrector of Institut Desbrest of Epidemiology and Public Health (IDESP) French NIH (INSERM) and University of Montpellier, Montpellier, France
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | - Claudia Traidl-Hoffmann
- Environmental Medicine Faculty of Medicine University of Augsburg, Augsburg, Germany
- CK-CARE, Christine Kühne Center for Allergy Research and Education, Davos, Switzerland
| | - Kari Nadeau
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, California, USA
| | - Lorenzo Cecchi
- SOS Allergology and Clinical Immunology, USL Toscana Centro, Prato, Italy
| | | | - Cezmi A Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Marek Jutel
- Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland
- ALL-MED Medical Research Institute, Wroclaw, Poland
| | - Ioana Agache
- Faculty of Medicine, Transylvania University, Brasov, Romania
| |
Collapse
|
7
|
Querdibitty CD, Campbell J, Wetherill MS, Salvatore AL. Geographic and social economic disparities in the risk of exposure to ambient air respiratory toxicants at Oklahoma licensed early care and education facilities. ENVIRONMENTAL RESEARCH 2023; 218:114975. [PMID: 36462693 DOI: 10.1016/j.envres.2022.114975] [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: 10/02/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Early life exposures to hazardous air pollutants has been associated with adverse asthma-related outcomes. Neighborhood-level social and economic factors play an essential role in the distribution of hazardous air pollutants and children spend a substantial amount of time at early care and education (ECE) facilities. While the indoor air quality of these facilities has been described, particularly for criteria air pollutants such as volatile organic compounds and particulate matter, little is known about the ambient air quality of ECE facilities. OBJECTIVES We conducted a cross-sectional study to estimate the ambient air quality of Oklahoma licensed ECE facilities and to explore associations between ambient air quality and select geographic predictors. METHODS We estimated ambient air quality using the total respiratory hazard quotient from the National Air Toxics Assessment according to the geographical location of licensed Oklahoma ECE facilities (N = 3184). We then determined whether urban and rural ECE facilities' air respiratory toxicant exposure risk differed by ECE facilities' neighborhood-level social and economic inequities including: 1) racial-ethnic minority community, 2) neighborhood socioeconomic status, and 3) residential segregation. RESULTS Urban ECE facilities in Hispanic segregated counties were five times more likely to be at risk of high air respiratory exposure, adjusted for integrated urban counties (p < 0.0001, 95% CI [3.824, 7.699]). Rural ECE facilities in African American segregated counties were nine times more likely to be at risk of high air respiratory toxicant exposure, adjusted for integrated rural counties (p < 0.0001, 95% CI [5.641, 15.928]). CONCLUSION We found geographically and socially disparate patterns of higher exposures to ambient air respiratory toxicants at Oklahoma ECE facilities. Safer siting policies and interventions are needed to mitigate air respiratory toxicant exposures, which may help to reduce asthma control disparities and improve respiratory health outcomes in Oklahoma ECE facilities.
Collapse
Affiliation(s)
- Cassandra D Querdibitty
- Department of Health Promotion Sciences, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Oklahoma Area Tribal Epidemiology Center, Southern Plains Tribal Health Board, Oklahoma City, OK, USA.
| | - Janis Campbell
- Department of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Marianna S Wetherill
- Department of Health Promotion Sciences, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Alicia L Salvatore
- Department of Health Promotion Sciences, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Institute for Research on Equity and Community Health (iREACH), Christiana Care, Wilmington, DE, USA; Department of Human Development and Family Sciences, University of Delaware, Newark, DE, USA.
| |
Collapse
|
8
|
Melén E, Koppelman GH, Vicedo-Cabrera AM, Andersen ZJ, Bunyavanich S. Allergies to food and airborne allergens in children and adolescents: role of epigenetics in a changing environment. THE LANCET. CHILD & ADOLESCENT HEALTH 2022; 6:810-819. [PMID: 35985346 DOI: 10.1016/s2352-4642(22)00215-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
Allergic diseases affect millions of children and adolescents worldwide. In this Review, we focus on allergies to food and airborne allergens and provide examples of prevalence trends during a time when climate change is of increasing concern. Profound environmental changes have affected natural systems in terms of biodiversity loss, air pollution, and climate. We discuss the potential links between these changes and allergic diseases in children, and the clinical implications. Several exposures of relevance for allergic disease also correlate with epigenetic changes such as DNA methylation. We propose that epigenetics could be a promising tool by which exposures and hazards related to a changing environment can be captured. Epigenetics might also provide promising biomarkers and help to elucidate the mechanisms related to allergic disease initiation and progress.
Collapse
Affiliation(s)
- Erik Melén
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology and Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, Beatrix Children's Hospital, University of Groningen, Groningen, Netherlands
| | - Ana Maria Vicedo-Cabrera
- Institute of Social and Preventive Medicine and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | | | - Supinda Bunyavanich
- Division of Allergy and Immunology, Department of Pediatrics, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|