Characterization of Exposures of Urban and Rural Cohorts to Airborne PCBs
The AESOP Study (Airborne Exposures to Semi-volatile Organic Pollutants) seeks to answer key questions about what are the determinants of PCB exposures among children and their mothers, what are the exposure levels indoors and out, and how to best monitor exposures and metabolites.
Aim 1 is to maintain the urban and rural residential cohorts in East Chicago and Columbus Junction and gather demographic, residential, occupational, activity, diet history and baseline health data from subjects. Researchers will continue to enroll participants as our adolescents mature out of the cohort. Demographic, occupational, and activity questionnaires will be administered and extensive dietary health data will be ascertained from the NCI Dietary History Questionnaire (DHQ-II). Researchers will report back data in both communities as they become available.
Aim 2 is to collect air samples inside and outside at homes and schools and measure congener-specific concentrations of atmospheric PCBs. Air sampling will continue at homes and schools, indoors and outdoors in Columbus Junction.
Aim 3 is to collect blood annually from all subjects and measure PCB congeners and congener-specific metabolites in serum samples and report these values to participants. Researchers will continue to analyze collected blood specimens for PCBs, OH-PCBs and PCB sulfates as well as biomarkers of effect.
Aim 4 is to collect urine from all subjects and measure congener-specific PCB sulfate metabolites and evaluate the efficacy of urine as a biomarker for exposure to lower-chlorinated congeners.Urine collection has been ongoing and researchers have quantified creatinine levels in collected samples. Analytical methods for extraction and analysis of PCB-sulfates from urine are under development.
Aim 5 is to model exposures and body burdens for the atmospheric PCB Congeners from the East Chicago and Columbus Junction cohorts and compare modeled and measured data. The goal for 2019 is to have complete air, blood, urine, and dietary PCB data for year 9 of the AESOP Study from a set of dyads (mother-child participants). This will facilitate more sophisticated and inclusive models using new statistical tools to deal with left censoring to address this aim.
The AESOP Study has enrolled and followed 421 subjects and provided new insight into airborne exposures and resulting body burdens. It has changed prevailing views on how most Americans are exposed to PCBs. Researchers have demonstrated that subjects have substantial exposure to PCB congeners from inhalation in addition to ingestion and their blood shows enrichment with lower-chlorinated congeners. This has important implications for children's environmental health.
Jones, M.P., Linear regression with left-censored covariates and outcome using a pseudolikelihood approach. Environmetrics, 2018. 29(8): p. e2536. PMID: 30686916, PMCID: PMC6344928, DOI:10.1002/env.2536.
- Project Leader: Peter S. Thorne, PhD
Dr. Thorne is a Professor of Toxicology in the Department of Occupational and Environmental Health at the University of Iowa with a secondary appointment in the Department of Civil and Environmental Engineering. He is serving as a principal investigator for this study. He has worked successfully with the community advisory boards and schools in Columbus Junction and East Chicago, and will oversee the enrollment of the cohorts for the project.
- Keri C. Hornbuckle, PhD
Dr. Hornbuckle, ISRP Director and Project 4 Leader, will provide advice on fabrication and deployment of the passive monitors and high volume samplers, and will provide guidance in the analysis of PCB congener-specific data. Her experience in measuring and modeling PCBs in urban and rural settings will be drawn upon in the interpretation of exposure data.
- Michael Jones, PhD
Dr. Jones is a Professor of Biostatistics in the University of Iowa College of Public Health and has extensive experience developing and using statistical methods for the analysis of multiple PCB-congener measures, many of which fall below the limit of quantification resulting in left censored data.