Ja Hyeong Kim1, Qi Yan2, Karan Uppal3, Xin Cui4, Chenxiao Ling5, Douglas I Walker6, Julia E Heck7, Ondine S von Ehrenstein8, Dean P Jones9, Beate Ritz10. 1. Department of Pediatrics, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, 44033, South Korea. Electronic address: jahyungk@uuh.ulsan.kr. 2. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA. Electronic address: qyan@ucla.edu. 3. Computational Systems Medicine & Metabolomics Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University, Atlanta, GA, 30322, USA. Electronic address: kuppal2@emory.edu. 4. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA; Perinatal Epidemiology and Health Outcomes Research Unit, Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, 94304, USA; California Perinatal Quality Care Collaborative, Palo Alto, CA, 94305, USA. Electronic address: xincuix@stanford.edu. 5. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA. Electronic address: lingcx@ucla.edu. 6. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. Electronic address: douglas.walker@mssm.edu. 7. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA. Electronic address: jeheck@ucla.edu. 8. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA; Department of Community Health Sciences, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA. Electronic address: ovehren@ucla.edu. 9. Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, and Critical Care Medicine, School of Medicine, Emory University, Atlanta, GA, 30322, USA. Electronic address: dpjones@emory.edu. 10. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA; Department of Community Health Sciences, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA; Department of Neurology, Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA. Electronic address: britz@ucla.edu.
Abstract
BACKGROUND: Previously, numerous epidemiologic studies reported an association between autism spectrum disorder (ASD) and exposure to air pollution during pregnancy. However, there have been no metabolomics studies investigating the impact of pregnancy pollution exposure to ASD risk in offspring. OBJECTIVES: To identify differences in maternal metabolism that may reflect a biological response to exposure to high air pollution in pregnancies of offspring who later did or did not develop ASD. METHODS: We obtained stored mid-pregnancy serum from 214 mothers who lived in California's Central Valley and experienced the highest levels of air pollution during early pregnancy. We estimated each woman's average traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 μm) during the first trimester using the California Line Source Dispersion Model, version 4 (CALINE4). By utilizing liquid chromatography-high resolution mass spectrometry, we identified the metabolic profiles of maternal serum for 116 mothers with offspring who later developed ASD and 98 control mothers. Partial least squares discriminant analysis (PLS-DA) was employed to select metabolic features associated with air pollution exposure or autism risk in offspring. We also conducted extensive pathway enrichment analysis to elucidate potential ASD-related changes in the metabolome of pregnant women. RESULTS: We extracted 4022 and 4945 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for potential confounders, we identified 167 and 222 discriminative features (HILIC and C18, respectively). Pathway enrichment analysis to discriminate metabolic features associated with ASD risk indicated various metabolic pathway perturbations linked to the tricarboxylic acid (TCA) cycle and mitochondrial function, including carnitine shuttle, amino acid metabolism, bile acid metabolism, and vitamin A metabolism. CONCLUSION: Using high resolution metabolomics, we identified several metabolic pathways disturbed in mothers with ASD offspring among women experiencing high exposure to traffic-related air pollution during pregnancy that were associated with mitochondrial dysfunction. These findings provide us with a better understanding of metabolic disturbances involved in the development of ASD under adverse environmental conditions.
BACKGROUND: Previously, numerous epidemiologic studies reported an association between autism spectrum disorder (ASD) and exposure to air pollution during pregnancy. However, there have been no metabolomics studies investigating the impact of pregnancy pollution exposure to ASD risk in offspring. OBJECTIVES: To identify differences in maternal metabolism that may reflect a biological response to exposure to high air pollution in pregnancies of offspring who later did or did not develop ASD. METHODS: We obtained stored mid-pregnancy serum from 214 mothers who lived in California's Central Valley and experienced the highest levels of air pollution during early pregnancy. We estimated each woman's average traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 μm) during the first trimester using the California Line Source Dispersion Model, version 4 (CALINE4). By utilizing liquid chromatography-high resolution mass spectrometry, we identified the metabolic profiles of maternal serum for 116 mothers with offspring who later developed ASD and 98 control mothers. Partial least squares discriminant analysis (PLS-DA) was employed to select metabolic features associated with air pollution exposure or autism risk in offspring. We also conducted extensive pathway enrichment analysis to elucidate potential ASD-related changes in the metabolome of pregnant women. RESULTS: We extracted 4022 and 4945 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for potential confounders, we identified 167 and 222 discriminative features (HILIC and C18, respectively). Pathway enrichment analysis to discriminate metabolic features associated with ASD risk indicated various metabolic pathway perturbations linked to the tricarboxylic acid (TCA) cycle and mitochondrial function, including carnitine shuttle, amino acid metabolism, bile acid metabolism, and vitamin A metabolism. CONCLUSION: Using high resolution metabolomics, we identified several metabolic pathways disturbed in mothers with ASD offspring among women experiencing high exposure to traffic-related air pollution during pregnancy that were associated with mitochondrial dysfunction. These findings provide us with a better understanding of metabolic disturbances involved in the development of ASD under adverse environmental conditions.
Authors: Qi Yan; Zeyan Liew; Karan Uppal; Xin Cui; Chenxiao Ling; Julia E Heck; Ondine S von Ehrenstein; Jun Wu; Douglas I Walker; Dean P Jones; Beate Ritz Journal: Environ Int Date: 2019-06-20 Impact factor: 9.621
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Authors: Deborah L Christensen; Kim Van Naarden Braun; Jon Baio; Deborah Bilder; Jane Charles; John N Constantino; Julie Daniels; Maureen S Durkin; Robert T Fitzgerald; Margaret Kurzius-Spencer; Li-Ching Lee; Sydney Pettygrove; Cordelia Robinson; Eldon Schulz; Chris Wells; Martha S Wingate; Walter Zahorodny; Marshalyn Yeargin-Allsopp Journal: MMWR Surveill Summ Date: 2018-11-16
Authors: Anna V Golubeva; Susan A Joyce; Gerard Moloney; Aurelijus Burokas; Eoin Sherwin; Silvia Arboleya; Ian Flynn; Dmitry Khochanskiy; Angela Moya-Pérez; Veronica Peterson; Kieran Rea; Kiera Murphy; Olga Makarova; Sergey Buravkov; Niall P Hyland; Catherine Stanton; Gerard Clarke; Cormac G M Gahan; Timothy G Dinan; John F Cryan Journal: EBioMedicine Date: 2017-09-21 Impact factor: 8.143