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Predicting personal PAH exposure using high dimensional questionnaire and wristband data

Journal of Exposure Science & Environmental Epidemiology (2024)Cite this article

Abstract

Background

Polycyclic aromatic hydrocarbons (PAHs) are a class of pervasive environmental pollutants with a variety of known health effects. While significant work has been completed to estimate personal exposure to PAHs, less has been done to identify sources of these exposures. Comprehensive characterization of reported sources of personal PAH exposure is a critical step to more easily identify individuals at risk of high levels of exposure and for developing targeted interventions based on source of exposure.

Objective

In this study, we leverage data from a New York (NY)-based birth cohort to identify personal characteristics or behaviors associated with personal PAH exposure and develop models for the prediction of PAH exposure.

Methods

We quantified 61 PAHs measured using silicone wristband samplers in association with 75 questionnaire variables from 177 pregnant individuals. We evaluated univariate associations between each compound and questionnaire variable, conducted regression tree analysis for each PAH compound and completed a principal component analysis of for each participant’s entire PAH exposure profile to determine the predictors of PAH levels.

Results

Regression tree analyses of individual compounds and exposure mixture identified income, time spent outdoors, maternal age, country of birth, transportation type, and season as the variables most frequently predictive of exposure.

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Fig. 1: Heat map of the statistical significance of Spearman correlations between PAH compounds and questionnaire variables.
Fig. 2: Heat map of the Gini index of variable importance for each of the 23 PAH for which the model was sufficiently predictive (∆RMSEOverall > 1.8).
Fig. 3: Heat map of ∆RMSELOO after removal of questionnaire variable from regression tree analysis for each of the 23 PAH for which the model was sufficiently predictive (∆RMSEOverall > 1.8).

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Data availability

Data are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the participants in this study. At Oregon State University, we thank Michael Barton, Carolyn Poutasse, Kaley Adams, Peter Hoffman, Kyle Messier, Clarisa Caballero-Ignacio, Samantha Samon, Lane Tidwell, Richard Evoy, Caoilinn Haggerty, Ty Bryde, Teresa Valdez, Kaci Graber, Jessica Scotten, Ian Moran, Christine Ghetu, and other members of the Food Safety and Environmental Stewardship Laboratory. At Columbia, we thank the Fair Start study team, including the laboratory and data cores who supported this research. Pacific Northwest National Laboratory is a multi-program laboratory operated by Battelle for the U.S. Department of Energy under contract DEAC05-76RL01830.

Funding

Research reported in this publication was supported by the National Institute of Health (NIH) under award number UH3OD023290 and the National Institute of Environmental Health Sciences (NIEHS) under award numbers 1R21ES024718, 4R33ES024718, P30ES030287, and P42ES016465. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or NIEHS. SMMcL was funded by the NIH T32 ES007322; TRANSFORM TL-1 Fellowship 5TL1TR001875-07; MASS-A&WMA APERG throughout this project.

Author information

Author notes

  1. These authors contributed equally: Sarah M. McLarnan, Lisa M. Bramer.

Authors and Affiliations

  1. Department of Environmental Health Sciences, Columbia University, Columbia Center for Children’s Environmental Health, Mailman School of Public Health, New York City, NY, USA

    Sarah M. McLarnan, Lehyla Calero, Darrell Holmes, Elizabeth A. Gibson, Haleigh M. Cavalier & Julie B. Herbstman

  2. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Lisa M. Bramer & Katrina M. Waters

  3. Environmental and Molecular Toxicology, Food Safety and Environmental Stewardship Program, Oregon State University, Corvallis, OR, USA

    Holly M. Dixon, Richard P. Scott, Katrina M. Waters & Kim A. Anderson

  4. Oregon State University, College of Public Health and Human Sciences, Corvallis, OR, USA

    Diana Rohlman & Laurel Kincl

  5. Division of Clinical Immunology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA

    Rachel L. Miller

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Contributions

Funding for this project was acquired by KAA and JBH. Project design was done by SMMcL, LMB, HMD, Elizabeth AG, KAA, and JBH. LMB and EAG completed the formal analyses. LC and DH managed cohort data acquisition. RPS provided quantification of wristband values. The manuscript was written by SMMcL with support from LMB and HMD. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Sarah M. McLarnan.

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Competing interests

KAA and DR, authors of this research, disclose a financial interest in MyExposome, Inc., which is marketing products related to the research being reported. The terms of this arrangement have been reviewed and approved by Oregon State University in accordance with its policy on research conflicts of interest. The authors have no other relevant financial or non-financial interests to disclose.

Ethical approval

Approval for research on human subjects was obtained from the Columbia University Institutional Review Board (AAAK6753).

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McLarnan, S.M., Bramer, L.M., Dixon, H.M. et al. Predicting personal PAH exposure using high dimensional questionnaire and wristband data. J Expo Sci Environ Epidemiol (2024). https://doi.org/10.1038/s41370-023-00617-y

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