Hsiao-Hsien Leon Hsu, Ander Wilson, Joel Schwartz, Itai Kloog, Robert O Wright, Brent A Coull, Rosalind J Wright
{"title":"产前环境空气污染物混合物暴露与学龄早期肺功能。","authors":"Hsiao-Hsien Leon Hsu, Ander Wilson, Joel Schwartz, Itai Kloog, Robert O Wright, Brent A Coull, Rosalind J Wright","doi":"10.1097/EE9.0000000000000249","DOIUrl":null,"url":null,"abstract":"<p><p>Research linking prenatal ambient air pollution with childhood lung function has largely considered one pollutant at a time. Real-life exposure is to mixtures of pollutants and their chemical components; not considering joint effects/effect modification by co-exposures contributes to misleading results.</p><p><strong>Methods: </strong>Analyses included 198 mother-child dyads recruited from two hospitals and affiliated community health centers in Boston, Massachusetts, USA. Daily prenatal pollutant exposures were estimated using satellite-based hybrid chemical-transport models, including nitrogen dioxide(NO<sub>2</sub>), ozone(O<sub>3</sub>), and fine particle constituents (elemental carbon [EC], organic carbon [OC], nitrate [NO<sub>3</sub> <sup>-</sup>], sulfate [SO<sub>4</sub> <sup>2-</sup>], and ammonium [NH<sub>4</sub> <sup>+</sup>]). Spirometry was performed at age 6.99 ± 0.89 years; forced expiratory volume in 1s (FEV<sub>1</sub>), forced vital capacity (FVC), and forced mid-expiratory flow (FEF<sub>25-75</sub>) z-scores accounted for age, sex, height, and race/ethnicity. We examined associations between weekly-averaged prenatal pollution mixture levels and outcomes using Bayesian Kernel Machine Regression-Distributed Lag Models (BKMR-DLMs) to identify susceptibility windows for each component and estimate a potentially complex mixture exposure-response relationship including nonlinear effects and interactions among exposures. We also performed linear regression models using time-weighted-mixture component levels derived by BKMR-DLMs adjusting for maternal age, education, perinatal smoking, and temperature.</p><p><strong>Results: </strong>Most mothers were Hispanic (63%) or Black (21%) with ≤12 years of education (67%). BKMR-DLMs identified a significant effect for O<sub>3</sub> exposure at 18-22 weeks gestation predicting lower FEV<sub>1</sub>/FVC. Linear regression identified significant associations for O<sub>3,</sub> NH<sub>4</sub> <sup>+</sup>, and OC with decreased FEV<sub>1</sub>/FVC, FEV<sub>1</sub>, and FEF<sub>25-75</sub>, respectively. There was no evidence of interactions among pollutants.</p><p><strong>Conclusions: </strong>In this multi-pollutant model, prenatal O<sub>3</sub>, OC, and NH<sub>4</sub> <sup>+</sup> were most strongly associated with reduced early childhood lung function.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/77/46/ee9-7-e249.PMC10097575.pdf","citationCount":"0","resultStr":"{\"title\":\"Prenatal Ambient Air Pollutant Mixture Exposure and Early School-age Lung Function.\",\"authors\":\"Hsiao-Hsien Leon Hsu, Ander Wilson, Joel Schwartz, Itai Kloog, Robert O Wright, Brent A Coull, Rosalind J Wright\",\"doi\":\"10.1097/EE9.0000000000000249\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Research linking prenatal ambient air pollution with childhood lung function has largely considered one pollutant at a time. Real-life exposure is to mixtures of pollutants and their chemical components; not considering joint effects/effect modification by co-exposures contributes to misleading results.</p><p><strong>Methods: </strong>Analyses included 198 mother-child dyads recruited from two hospitals and affiliated community health centers in Boston, Massachusetts, USA. Daily prenatal pollutant exposures were estimated using satellite-based hybrid chemical-transport models, including nitrogen dioxide(NO<sub>2</sub>), ozone(O<sub>3</sub>), and fine particle constituents (elemental carbon [EC], organic carbon [OC], nitrate [NO<sub>3</sub> <sup>-</sup>], sulfate [SO<sub>4</sub> <sup>2-</sup>], and ammonium [NH<sub>4</sub> <sup>+</sup>]). Spirometry was performed at age 6.99 ± 0.89 years; forced expiratory volume in 1s (FEV<sub>1</sub>), forced vital capacity (FVC), and forced mid-expiratory flow (FEF<sub>25-75</sub>) z-scores accounted for age, sex, height, and race/ethnicity. We examined associations between weekly-averaged prenatal pollution mixture levels and outcomes using Bayesian Kernel Machine Regression-Distributed Lag Models (BKMR-DLMs) to identify susceptibility windows for each component and estimate a potentially complex mixture exposure-response relationship including nonlinear effects and interactions among exposures. We also performed linear regression models using time-weighted-mixture component levels derived by BKMR-DLMs adjusting for maternal age, education, perinatal smoking, and temperature.</p><p><strong>Results: </strong>Most mothers were Hispanic (63%) or Black (21%) with ≤12 years of education (67%). BKMR-DLMs identified a significant effect for O<sub>3</sub> exposure at 18-22 weeks gestation predicting lower FEV<sub>1</sub>/FVC. Linear regression identified significant associations for O<sub>3,</sub> NH<sub>4</sub> <sup>+</sup>, and OC with decreased FEV<sub>1</sub>/FVC, FEV<sub>1</sub>, and FEF<sub>25-75</sub>, respectively. There was no evidence of interactions among pollutants.</p><p><strong>Conclusions: </strong>In this multi-pollutant model, prenatal O<sub>3</sub>, OC, and NH<sub>4</sub> <sup>+</sup> were most strongly associated with reduced early childhood lung function.</p>\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2023-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/77/46/ee9-7-e249.PMC10097575.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1097/EE9.0000000000000249\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/4/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/EE9.0000000000000249","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/4/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Prenatal Ambient Air Pollutant Mixture Exposure and Early School-age Lung Function.
Research linking prenatal ambient air pollution with childhood lung function has largely considered one pollutant at a time. Real-life exposure is to mixtures of pollutants and their chemical components; not considering joint effects/effect modification by co-exposures contributes to misleading results.
Methods: Analyses included 198 mother-child dyads recruited from two hospitals and affiliated community health centers in Boston, Massachusetts, USA. Daily prenatal pollutant exposures were estimated using satellite-based hybrid chemical-transport models, including nitrogen dioxide(NO2), ozone(O3), and fine particle constituents (elemental carbon [EC], organic carbon [OC], nitrate [NO3-], sulfate [SO42-], and ammonium [NH4+]). Spirometry was performed at age 6.99 ± 0.89 years; forced expiratory volume in 1s (FEV1), forced vital capacity (FVC), and forced mid-expiratory flow (FEF25-75) z-scores accounted for age, sex, height, and race/ethnicity. We examined associations between weekly-averaged prenatal pollution mixture levels and outcomes using Bayesian Kernel Machine Regression-Distributed Lag Models (BKMR-DLMs) to identify susceptibility windows for each component and estimate a potentially complex mixture exposure-response relationship including nonlinear effects and interactions among exposures. We also performed linear regression models using time-weighted-mixture component levels derived by BKMR-DLMs adjusting for maternal age, education, perinatal smoking, and temperature.
Results: Most mothers were Hispanic (63%) or Black (21%) with ≤12 years of education (67%). BKMR-DLMs identified a significant effect for O3 exposure at 18-22 weeks gestation predicting lower FEV1/FVC. Linear regression identified significant associations for O3, NH4+, and OC with decreased FEV1/FVC, FEV1, and FEF25-75, respectively. There was no evidence of interactions among pollutants.
Conclusions: In this multi-pollutant model, prenatal O3, OC, and NH4+ were most strongly associated with reduced early childhood lung function.